[
    {
        "id": 0,
        "statement": "haroldo be mention as a brazil scorer for 2 different game",
        "label": 1,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "df['brazil scorers'].apply(lambda x: 'haroldo' in x).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1,
        "statement": "4 of the 5 game be for the south american championship",
        "label": 1,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "(df['competition'].value_counts()['south american championship'] == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 2,
        "statement": "friedenreich be mention as a brazil scorer for 2 different game",
        "label": 1,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "df['brazil scorers'].str.contains('friedenreich').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3,
        "statement": "there be 2 different game where the highest score be 3 goal",
        "label": 1,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "len(df[df['score'].str.extract(r'^(\\d+) - \\d+$')[0].astype(int) == 3]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 4,
        "statement": "4 of the 5 game be play in may 1919",
        "label": 1,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "(df['date'].str.contains('may') & df['date'].str.contains('1919')).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 5,
        "statement": "neco be mention as a brazil scorer for 2 different game",
        "label": 0,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "df[df['brazil scorers'].str.contains('neco')]['date'].nunique() != 2",
        "pandas_eval": "False"
    },
    {
        "id": 6,
        "statement": "all 5 of the game be for the south american championship",
        "label": 0,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "df['competition'].eq('south american championship').all()",
        "pandas_eval": "False"
    },
    {
        "id": 7,
        "statement": "friedenreich be mention as a brazil scorer for 4 different game",
        "label": 0,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "df['brazil scorers'].apply(lambda x: 'friedenreich' in x).sum() == 4",
        "pandas_eval": "False"
    },
    {
        "id": 8,
        "statement": "there be 2 different game where the lowest score be 3 goal",
        "label": 0,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "len(df[df['score'].str.extract(r'(\\d+) - \\d+')[0].astype(int) >= 3]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 9,
        "statement": "2 of the 5 game be play in may 1919",
        "label": 0,
        "table_caption": "1919 in brazilian football",
        "table_text": "date#result#score#brazil scorers#competition\nmay 11 , 1919#w#6 - 0#friedenreich (3) , neco (2) , haroldo#south american championship\nmay 18 , 1919#w#3 - 1#heitor , am\u00edlcar , millon#south american championship\nmay 26 , 1919#d#2 - 2#neco (2)#south american championship\nmay 29 , 1919#w#1 - 0#friedenreich#south american championship\njune 1 , 1919#d#3 - 3#haroldo , arlindo (2)#ta\u00e7a roberto cherry\n",
        "pandas_code": "len(df[df['date'].str.contains('may 1919')]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 10,
        "statement": "racing like a pro be the most viewed episode",
        "label": 1,
        "table_caption": "one tree hill (season 5)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code#us viewers (millions)\n89#1#4 years , 6 months , 2 days#greg prange#mark schwahn#january 8 , 2008#3t6801#3.36\n90#2#racing like a pro#paul johansson#mark schwahn#january 8 , 2008#3t6802#3.57\n91#3#my way home is through you#david jackson#john a norris#january 15 , 2008#3t6803#2.72\n92#4#it 's alright , ma (i'm only bleeding)#janice cooke#adele lim#january 22 , 2008#3t6804#3.04\n93#5#i forgot to remember to forget#liz friedlander#terrence coli#january 29 , 2008#3t6805#2.79\n94#6#don't dream it 's over#thomas j wright#mark schwahn#february 5 , 2008#3t6806#2.86\n95#7#in da club#greg prange#mike herro and david strauss#february 12 , 2008#3t6807#3.16\n96#8#please please please let me get what i want#paul johansson#mike daniels#february 19 , 2008#3t6808#2.85\n97#9#for tonight you 're only here to know#joe davola#mark schwahn#february 26 , 2008#3t6809#3.18\n98#10#running to stand still#clark mathis#william h brown#march 4 , 2008#3t6810#2.93\n99#11#you 're gonna need someone on your side#michael j leone#zachary haynes#march 11 , 2008#3t6811#2.50\n100#12#hundred#les butler#mark schwahn#march 18 , 2008#3t6812#3.00\n101#13#echoes , silence , patience , and grace#greg prange#mark schwahn#april 14 , 2008#3t6813#2.80\n102#14#what do you go home to#liz friedlander#mark schwahn#april 21 , 2008#3t6814#2.92\n103#15#life is short#paul johansson#eliza delson#april 28 , 2008#3t6815#2.57\n104#16#cryin' won't help you now#greg prange#william h brown#may 5 , 2008#3t6816#2.29\n105#17#hate is safer than love#stuart gillard#mark schwahn#may 12 , 2008#3t6817#2.72\n",
        "pandas_code": "df.loc[df['title'] == 'racing like a pro', 'us viewers (millions)'].max() == df['us viewers (millions)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 11,
        "statement": "4 year , 6 month , 2 day be the oldest episode in season",
        "label": 1,
        "table_caption": "one tree hill (season 5)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code#us viewers (millions)\n89#1#4 years , 6 months , 2 days#greg prange#mark schwahn#january 8 , 2008#3t6801#3.36\n90#2#racing like a pro#paul johansson#mark schwahn#january 8 , 2008#3t6802#3.57\n91#3#my way home is through you#david jackson#john a norris#january 15 , 2008#3t6803#2.72\n92#4#it 's alright , ma (i'm only bleeding)#janice cooke#adele lim#january 22 , 2008#3t6804#3.04\n93#5#i forgot to remember to forget#liz friedlander#terrence coli#january 29 , 2008#3t6805#2.79\n94#6#don't dream it 's over#thomas j wright#mark schwahn#february 5 , 2008#3t6806#2.86\n95#7#in da club#greg prange#mike herro and david strauss#february 12 , 2008#3t6807#3.16\n96#8#please please please let me get what i want#paul johansson#mike daniels#february 19 , 2008#3t6808#2.85\n97#9#for tonight you 're only here to know#joe davola#mark schwahn#february 26 , 2008#3t6809#3.18\n98#10#running to stand still#clark mathis#william h brown#march 4 , 2008#3t6810#2.93\n99#11#you 're gonna need someone on your side#michael j leone#zachary haynes#march 11 , 2008#3t6811#2.50\n100#12#hundred#les butler#mark schwahn#march 18 , 2008#3t6812#3.00\n101#13#echoes , silence , patience , and grace#greg prange#mark schwahn#april 14 , 2008#3t6813#2.80\n102#14#what do you go home to#liz friedlander#mark schwahn#april 21 , 2008#3t6814#2.92\n103#15#life is short#paul johansson#eliza delson#april 28 , 2008#3t6815#2.57\n104#16#cryin' won't help you now#greg prange#william h brown#may 5 , 2008#3t6816#2.29\n105#17#hate is safer than love#stuart gillard#mark schwahn#may 12 , 2008#3t6817#2.72\n",
        "pandas_code": "df[df['no in season'] == 1]['title'].iloc[0] == '4 years , 6 months , 2 days'",
        "pandas_eval": "True"
    },
    {
        "id": 12,
        "statement": "cryin' won't help you now be the least viewed episode",
        "label": 1,
        "table_caption": "one tree hill (season 5)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code#us viewers (millions)\n89#1#4 years , 6 months , 2 days#greg prange#mark schwahn#january 8 , 2008#3t6801#3.36\n90#2#racing like a pro#paul johansson#mark schwahn#january 8 , 2008#3t6802#3.57\n91#3#my way home is through you#david jackson#john a norris#january 15 , 2008#3t6803#2.72\n92#4#it 's alright , ma (i'm only bleeding)#janice cooke#adele lim#january 22 , 2008#3t6804#3.04\n93#5#i forgot to remember to forget#liz friedlander#terrence coli#january 29 , 2008#3t6805#2.79\n94#6#don't dream it 's over#thomas j wright#mark schwahn#february 5 , 2008#3t6806#2.86\n95#7#in da club#greg prange#mike herro and david strauss#february 12 , 2008#3t6807#3.16\n96#8#please please please let me get what i want#paul johansson#mike daniels#february 19 , 2008#3t6808#2.85\n97#9#for tonight you 're only here to know#joe davola#mark schwahn#february 26 , 2008#3t6809#3.18\n98#10#running to stand still#clark mathis#william h brown#march 4 , 2008#3t6810#2.93\n99#11#you 're gonna need someone on your side#michael j leone#zachary haynes#march 11 , 2008#3t6811#2.50\n100#12#hundred#les butler#mark schwahn#march 18 , 2008#3t6812#3.00\n101#13#echoes , silence , patience , and grace#greg prange#mark schwahn#april 14 , 2008#3t6813#2.80\n102#14#what do you go home to#liz friedlander#mark schwahn#april 21 , 2008#3t6814#2.92\n103#15#life is short#paul johansson#eliza delson#april 28 , 2008#3t6815#2.57\n104#16#cryin' won't help you now#greg prange#william h brown#may 5 , 2008#3t6816#2.29\n105#17#hate is safer than love#stuart gillard#mark schwahn#may 12 , 2008#3t6817#2.72\n",
        "pandas_code": "df.loc[df['title'] == \"cryin' won't help you now\", 'us viewers (millions)'].iloc[0] == df['us viewers (millions)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 13,
        "statement": "greg prange direct 4 episode",
        "label": 1,
        "table_caption": "one tree hill (season 5)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code#us viewers (millions)\n89#1#4 years , 6 months , 2 days#greg prange#mark schwahn#january 8 , 2008#3t6801#3.36\n90#2#racing like a pro#paul johansson#mark schwahn#january 8 , 2008#3t6802#3.57\n91#3#my way home is through you#david jackson#john a norris#january 15 , 2008#3t6803#2.72\n92#4#it 's alright , ma (i'm only bleeding)#janice cooke#adele lim#january 22 , 2008#3t6804#3.04\n93#5#i forgot to remember to forget#liz friedlander#terrence coli#january 29 , 2008#3t6805#2.79\n94#6#don't dream it 's over#thomas j wright#mark schwahn#february 5 , 2008#3t6806#2.86\n95#7#in da club#greg prange#mike herro and david strauss#february 12 , 2008#3t6807#3.16\n96#8#please please please let me get what i want#paul johansson#mike daniels#february 19 , 2008#3t6808#2.85\n97#9#for tonight you 're only here to know#joe davola#mark schwahn#february 26 , 2008#3t6809#3.18\n98#10#running to stand still#clark mathis#william h brown#march 4 , 2008#3t6810#2.93\n99#11#you 're gonna need someone on your side#michael j leone#zachary haynes#march 11 , 2008#3t6811#2.50\n100#12#hundred#les butler#mark schwahn#march 18 , 2008#3t6812#3.00\n101#13#echoes , silence , patience , and grace#greg prange#mark schwahn#april 14 , 2008#3t6813#2.80\n102#14#what do you go home to#liz friedlander#mark schwahn#april 21 , 2008#3t6814#2.92\n103#15#life is short#paul johansson#eliza delson#april 28 , 2008#3t6815#2.57\n104#16#cryin' won't help you now#greg prange#william h brown#may 5 , 2008#3t6816#2.29\n105#17#hate is safer than love#stuart gillard#mark schwahn#may 12 , 2008#3t6817#2.72\n",
        "pandas_code": "len(df[df['directed by'] == 'greg prange']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 14,
        "statement": "mark schwahn write 8 episode",
        "label": 1,
        "table_caption": "one tree hill (season 5)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code#us viewers (millions)\n89#1#4 years , 6 months , 2 days#greg prange#mark schwahn#january 8 , 2008#3t6801#3.36\n90#2#racing like a pro#paul johansson#mark schwahn#january 8 , 2008#3t6802#3.57\n91#3#my way home is through you#david jackson#john a norris#january 15 , 2008#3t6803#2.72\n92#4#it 's alright , ma (i'm only bleeding)#janice cooke#adele lim#january 22 , 2008#3t6804#3.04\n93#5#i forgot to remember to forget#liz friedlander#terrence coli#january 29 , 2008#3t6805#2.79\n94#6#don't dream it 's over#thomas j wright#mark schwahn#february 5 , 2008#3t6806#2.86\n95#7#in da club#greg prange#mike herro and david strauss#february 12 , 2008#3t6807#3.16\n96#8#please please please let me get what i want#paul johansson#mike daniels#february 19 , 2008#3t6808#2.85\n97#9#for tonight you 're only here to know#joe davola#mark schwahn#february 26 , 2008#3t6809#3.18\n98#10#running to stand still#clark mathis#william h brown#march 4 , 2008#3t6810#2.93\n99#11#you 're gonna need someone on your side#michael j leone#zachary haynes#march 11 , 2008#3t6811#2.50\n100#12#hundred#les butler#mark schwahn#march 18 , 2008#3t6812#3.00\n101#13#echoes , silence , patience , and grace#greg prange#mark schwahn#april 14 , 2008#3t6813#2.80\n102#14#what do you go home to#liz friedlander#mark schwahn#april 21 , 2008#3t6814#2.92\n103#15#life is short#paul johansson#eliza delson#april 28 , 2008#3t6815#2.57\n104#16#cryin' won't help you now#greg prange#william h brown#may 5 , 2008#3t6816#2.29\n105#17#hate is safer than love#stuart gillard#mark schwahn#may 12 , 2008#3t6817#2.72\n",
        "pandas_code": "(df['written by'] == 'mark schwahn').sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 15,
        "statement": "in 1957 formula one season , jean behra win more than any other driver , 4 time",
        "label": 1,
        "table_caption": "1957 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nxi gran premio ciudad de buenos aires#buenos aires#27 january#juan manuel fangio#maserati#report\nvii gran premio di siracusa#syracuse#7 april#peter collins#lancia - ferrari#report\nxvii pau grand prix#pau#22 april#jean behra#maserati#report\nv glover trophy#goodwood#22 april#stuart lewis - evans#connaught - alta#report\nx gran premio di napoli#posillipo#28 april#peter collins#lancia - ferrari#report\nxxiii grand prix de reims#reims - gueux#14 july#luigi musso#lancia - ferrari#report\nv grand prix de caen#caen#28 july#jean behra#brm#report\nix brdc international trophy#silverstone#14 september#jean behra#brm#report\nv gran premio di modena#modena#22 september#jean behra#maserati#report\n",
        "pandas_code": "df['winning driver'].value_counts().max() == 4 and df['winning driver'].value_counts().idxmax() == 'jean behra'",
        "pandas_eval": "True"
    },
    {
        "id": 16,
        "statement": "in 1957 formula one season lancia - ferrari be use 3 time be the most used",
        "label": 1,
        "table_caption": "1957 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nxi gran premio ciudad de buenos aires#buenos aires#27 january#juan manuel fangio#maserati#report\nvii gran premio di siracusa#syracuse#7 april#peter collins#lancia - ferrari#report\nxvii pau grand prix#pau#22 april#jean behra#maserati#report\nv glover trophy#goodwood#22 april#stuart lewis - evans#connaught - alta#report\nx gran premio di napoli#posillipo#28 april#peter collins#lancia - ferrari#report\nxxiii grand prix de reims#reims - gueux#14 july#luigi musso#lancia - ferrari#report\nv grand prix de caen#caen#28 july#jean behra#brm#report\nix brdc international trophy#silverstone#14 september#jean behra#brm#report\nv gran premio di modena#modena#22 september#jean behra#maserati#report\n",
        "pandas_code": "df['constructor'].value_counts().max() == df['constructor'].value_counts()['lancia - ferrari']",
        "pandas_eval": "True"
    },
    {
        "id": 17,
        "statement": "1957 formula one season , april 22 be the only day feature 2 race on the same day in the series",
        "label": 1,
        "table_caption": "1957 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nxi gran premio ciudad de buenos aires#buenos aires#27 january#juan manuel fangio#maserati#report\nvii gran premio di siracusa#syracuse#7 april#peter collins#lancia - ferrari#report\nxvii pau grand prix#pau#22 april#jean behra#maserati#report\nv glover trophy#goodwood#22 april#stuart lewis - evans#connaught - alta#report\nx gran premio di napoli#posillipo#28 april#peter collins#lancia - ferrari#report\nxxiii grand prix de reims#reims - gueux#14 july#luigi musso#lancia - ferrari#report\nv grand prix de caen#caen#28 july#jean behra#brm#report\nix brdc international trophy#silverstone#14 september#jean behra#brm#report\nv gran premio di modena#modena#22 september#jean behra#maserati#report\n",
        "pandas_code": "len(df[df['date'] == '22 april']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 18,
        "statement": "1957 formula one season jean behra be the only one to use the same constructor 2 race in a row",
        "label": 1,
        "table_caption": "1957 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nxi gran premio ciudad de buenos aires#buenos aires#27 january#juan manuel fangio#maserati#report\nvii gran premio di siracusa#syracuse#7 april#peter collins#lancia - ferrari#report\nxvii pau grand prix#pau#22 april#jean behra#maserati#report\nv glover trophy#goodwood#22 april#stuart lewis - evans#connaught - alta#report\nx gran premio di napoli#posillipo#28 april#peter collins#lancia - ferrari#report\nxxiii grand prix de reims#reims - gueux#14 july#luigi musso#lancia - ferrari#report\nv grand prix de caen#caen#28 july#jean behra#brm#report\nix brdc international trophy#silverstone#14 september#jean behra#brm#report\nv gran premio di modena#modena#22 september#jean behra#maserati#report\n",
        "pandas_code": "((df['constructor'].eq('brm') & df['winning driver'].eq('jean behra')).shift().fillna(False) & (df['constructor'].eq('brm') & df['winning driver'].eq('jean behra'))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 20,
        "statement": "the n be from 861 - 869",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#package / option\n861#telemarket#italy#italian#televendita#4:3#no#no ( fta )\n862#noello sat#italy#italian#televendita#4:3#no#no ( fta )\n863#elite shopping tv#italy#italian#televendita#4:3#no#no ( fta )\n864#juwelo#italy#italian#televendita#4:3#no#no ( fta )\n865#dipr\u00e8 tv#italy#italian#arte#4:3#no#no ( fta )\n866#telemarket for you#italy#italian#televendita#4:3#no#no ( fta )\n867#la sorgente sat 1#italy#italian#televendita#4:3#no#no ( fta )\n868#la sorgente sat 2#italy#italian#televendita#4:3#no#no ( fta )\n869#la sorgente sat 3#italy#italian#televendita#4:3#no#no ( fta )\n",
        "pandas_code": "all(df['n degree'].between(861, 869))",
        "pandas_eval": "True"
    },
    {
        "id": 21,
        "statement": "italy be the only country and italian be the only language on the table",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#package / option\n861#telemarket#italy#italian#televendita#4:3#no#no ( fta )\n862#noello sat#italy#italian#televendita#4:3#no#no ( fta )\n863#elite shopping tv#italy#italian#televendita#4:3#no#no ( fta )\n864#juwelo#italy#italian#televendita#4:3#no#no ( fta )\n865#dipr\u00e8 tv#italy#italian#arte#4:3#no#no ( fta )\n866#telemarket for you#italy#italian#televendita#4:3#no#no ( fta )\n867#la sorgente sat 1#italy#italian#televendita#4:3#no#no ( fta )\n868#la sorgente sat 2#italy#italian#televendita#4:3#no#no ( fta )\n869#la sorgente sat 3#italy#italian#televendita#4:3#no#no ( fta )\n",
        "pandas_code": "(df['country'].unique() == ['italy']).all() and (df['language'].unique() == ['italian']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 22,
        "statement": "there be only 1 television service with a content of arte",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#package / option\n861#telemarket#italy#italian#televendita#4:3#no#no ( fta )\n862#noello sat#italy#italian#televendita#4:3#no#no ( fta )\n863#elite shopping tv#italy#italian#televendita#4:3#no#no ( fta )\n864#juwelo#italy#italian#televendita#4:3#no#no ( fta )\n865#dipr\u00e8 tv#italy#italian#arte#4:3#no#no ( fta )\n866#telemarket for you#italy#italian#televendita#4:3#no#no ( fta )\n867#la sorgente sat 1#italy#italian#televendita#4:3#no#no ( fta )\n868#la sorgente sat 2#italy#italian#televendita#4:3#no#no ( fta )\n869#la sorgente sat 3#italy#italian#televendita#4:3#no#no ( fta )\n",
        "pandas_code": "df[df['content'] == 'arte'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 23,
        "statement": "4:3 be the dar and the hdtv be a no for all television in italy",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#package / option\n861#telemarket#italy#italian#televendita#4:3#no#no ( fta )\n862#noello sat#italy#italian#televendita#4:3#no#no ( fta )\n863#elite shopping tv#italy#italian#televendita#4:3#no#no ( fta )\n864#juwelo#italy#italian#televendita#4:3#no#no ( fta )\n865#dipr\u00e8 tv#italy#italian#arte#4:3#no#no ( fta )\n866#telemarket for you#italy#italian#televendita#4:3#no#no ( fta )\n867#la sorgente sat 1#italy#italian#televendita#4:3#no#no ( fta )\n868#la sorgente sat 2#italy#italian#televendita#4:3#no#no ( fta )\n869#la sorgente sat 3#italy#italian#televendita#4:3#no#no ( fta )\n",
        "pandas_code": "all((df['dar'] == '4:3') & (df['hdtv'] == 'no') & (df['country'] == 'italy'))",
        "pandas_eval": "True"
    },
    {
        "id": 24,
        "statement": "all but 1 have a content of televendita",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#package / option\n861#telemarket#italy#italian#televendita#4:3#no#no ( fta )\n862#noello sat#italy#italian#televendita#4:3#no#no ( fta )\n863#elite shopping tv#italy#italian#televendita#4:3#no#no ( fta )\n864#juwelo#italy#italian#televendita#4:3#no#no ( fta )\n865#dipr\u00e8 tv#italy#italian#arte#4:3#no#no ( fta )\n866#telemarket for you#italy#italian#televendita#4:3#no#no ( fta )\n867#la sorgente sat 1#italy#italian#televendita#4:3#no#no ( fta )\n868#la sorgente sat 2#italy#italian#televendita#4:3#no#no ( fta )\n869#la sorgente sat 3#italy#italian#televendita#4:3#no#no ( fta )\n",
        "pandas_code": "df['content'].value_counts().get('televendita', 0) == len(df) - 1",
        "pandas_eval": "True"
    },
    {
        "id": 25,
        "statement": "most of the player be american",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#no#nationality#position#years for grizzlies#school / club team\nmahmoud abdul - rauf#1#united states#point guard#2000 - 2001#lsu\nshareef abdur - rahim#3#united states#forward#1996 - 2001#california\ntony allen#9#united states#guard#2010present#oklahoma state\nashraf amaya#18#united states#small forward#1995 - 1996#southern illinois\nnick anderson#5#united states#guard / forward#2001 - 2002#illinois\ngreg anthony#2#united states#point guard#1995 - 1997#unlv\nrobert archibald#21#scotland#forward / center#2002 - 2003#illinois\ngilbert arenas#10#united states#guard#2012#arizona\ndarrell arthur#00#united states#forward#2009 - 2013#kansas\nchucky atkins#32 (3 in '06 - '07)#united states#point guard#2006 - 2007#south florida\nisaac austin#9#united states#center#2000 - 2002#arizona state\n",
        "pandas_code": "df['nationality'].value_counts(normalize=True)['united states'] > 0.5",
        "pandas_eval": "True"
    },
    {
        "id": 27,
        "statement": "3 player come from illinois",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#no#nationality#position#years for grizzlies#school / club team\nmahmoud abdul - rauf#1#united states#point guard#2000 - 2001#lsu\nshareef abdur - rahim#3#united states#forward#1996 - 2001#california\ntony allen#9#united states#guard#2010present#oklahoma state\nashraf amaya#18#united states#small forward#1995 - 1996#southern illinois\nnick anderson#5#united states#guard / forward#2001 - 2002#illinois\ngreg anthony#2#united states#point guard#1995 - 1997#unlv\nrobert archibald#21#scotland#forward / center#2002 - 2003#illinois\ngilbert arenas#10#united states#guard#2012#arizona\ndarrell arthur#00#united states#forward#2009 - 2013#kansas\nchucky atkins#32 (3 in '06 - '07)#united states#point guard#2006 - 2007#south florida\nisaac austin#9#united states#center#2000 - 2002#arizona state\n",
        "pandas_code": "len(df[df['school / club team'] == 'illinois']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 28,
        "statement": "all of the player either be american or study in america",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#no#nationality#position#years for grizzlies#school / club team\nmahmoud abdul - rauf#1#united states#point guard#2000 - 2001#lsu\nshareef abdur - rahim#3#united states#forward#1996 - 2001#california\ntony allen#9#united states#guard#2010present#oklahoma state\nashraf amaya#18#united states#small forward#1995 - 1996#southern illinois\nnick anderson#5#united states#guard / forward#2001 - 2002#illinois\ngreg anthony#2#united states#point guard#1995 - 1997#unlv\nrobert archibald#21#scotland#forward / center#2002 - 2003#illinois\ngilbert arenas#10#united states#guard#2012#arizona\ndarrell arthur#00#united states#forward#2009 - 2013#kansas\nchucky atkins#32 (3 in '06 - '07)#united states#point guard#2006 - 2007#south florida\nisaac austin#9#united states#center#2000 - 2002#arizona state\n",
        "pandas_code": "all((df['nationality'] == 'united states') | df['school / club team'].isin(['lsu', 'california', 'oklahoma state', 'southern illinois', 'illinois', 'unlv', 'arizona', 'kansas', 'south florida', 'arizona state']))",
        "pandas_eval": "True"
    },
    {
        "id": 29,
        "statement": "tony allen come after isaac austin leave",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#no#nationality#position#years for grizzlies#school / club team\nmahmoud abdul - rauf#1#united states#point guard#2000 - 2001#lsu\nshareef abdur - rahim#3#united states#forward#1996 - 2001#california\ntony allen#9#united states#guard#2010present#oklahoma state\nashraf amaya#18#united states#small forward#1995 - 1996#southern illinois\nnick anderson#5#united states#guard / forward#2001 - 2002#illinois\ngreg anthony#2#united states#point guard#1995 - 1997#unlv\nrobert archibald#21#scotland#forward / center#2002 - 2003#illinois\ngilbert arenas#10#united states#guard#2012#arizona\ndarrell arthur#00#united states#forward#2009 - 2013#kansas\nchucky atkins#32 (3 in '06 - '07)#united states#point guard#2006 - 2007#south florida\nisaac austin#9#united states#center#2000 - 2002#arizona state\n",
        "pandas_code": "df[df['player'] == 'tony allen']['years for grizzlies'].values[0].split('present')[0] > df[df['player'] == 'isaac austin']['years for grizzlies'].values[0].split(' - ')[1]",
        "pandas_eval": "True"
    },
    {
        "id": 30,
        "statement": "the highest number of win be during the 2009 , 2012 , and 2010 season",
        "label": 1,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df[df['season'].isin(['2009', '2012', '2010'])]['wins'].astype(str).replace('did not compete', '0').astype(int).max() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 31,
        "statement": "there be 3 season that have no tie",
        "label": 1,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df[df['ties'] == '0'].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 32,
        "statement": "there be two more win in the 2002 season , than t here be in the 2001 season",
        "label": 1,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "int(df[df['season'] == '2002']['wins'].iloc[0]) - int(df[df['season'] == '2001']['wins'].iloc[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 33,
        "statement": "the 2011 season do not have as many win as the 2012 season",
        "label": 1,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df[df['season'] == '2011']['wins'].values[0] < df[df['season'] == '2012']['wins'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 34,
        "statement": "the most common division be the bafl division two south east",
        "label": 1,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df['division'].value_counts().idxmax() == 'bafl division two south east'",
        "pandas_eval": "True"
    },
    {
        "id": 35,
        "statement": "the lowest number of win be during the 2009 , 2012 , and 2010 season",
        "label": 0,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df.loc[df['season'].isin(['2009', '2010', '2012']), 'wins'].replace('did not compete', '0').astype(int).min() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 36,
        "statement": "there be only one season that have no tie",
        "label": 0,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df[df['ties'] == '0'].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 37,
        "statement": "there be two more loss in the 2002 season , than there be in the 2001 season",
        "label": 0,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "(df[df['season'] == '2002']['wins'].astype(str).replace('did not compete', '0').astype(int).values[0] + df[df['season'] == '2002']['ties'].astype(str).replace('did not compete', '0').astype(int).values[0]) - (df[df['season'] == '2001']['wins'].astype(str).replace('did not compete', '0').astype(int).values[0] + df[df['season'] == '2001']['ties'].astype(str).replace('did not compete', '0').astype(int).values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 38,
        "statement": "the 2011 season have as many win as the 2012 season",
        "label": 0,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "(df[df['season'] == '2011']['wins'].values[0] == df[df['season'] == '2012']['wins'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 39,
        "statement": "the most rare division be the bafi division two south east",
        "label": 0,
        "table_caption": "east kent mavericks",
        "table_text": "season#division#wins#ties#final position\n2001#british senior flag league , southern#3#1#2 / 4\n2002#british senior flag league , nine - man league#5#3#2 / 7\n2003 to 2005#did not compete#did not compete#did not compete#did not compete\n2006#bafl division two south#0#0#4 / 4\n2007#bafl division two south east#5#0#3 / 6\n2008#bafl division two south east#6#0#3 / 5\n2009#bafl division two south east#8#1#1 / 4\n2010#bafl division one south east#8#1#1 / 4\n2011#bafl division one south east#2#6#\n2012#bafl division one south and central#8#2#8 / 2\n",
        "pandas_code": "df['division'].value_counts().idxmin() == 'bafl division two south east'",
        "pandas_eval": "False"
    },
    {
        "id": 40,
        "statement": "40% of the 5th congress die in office",
        "label": 1,
        "table_caption": "5th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nrhode island at - large#elisha potter ( f )#resigned sometime in 1797#thomas tillinghast ( f )#seated november 13 , 1797\nsouth carolina 1st#william l smith ( f )#resigned july 10 , 1797#thomas pinckney ( f )#seated november 23 , 1797\nmassachusetts 11th#theophilus bradbury ( f )#resigned july 24 , 1797#bailey bartlett ( f )#seated november 27 , 1797\nnew hampshire at - large#jeremiah smith ( f )#resigned july 26 , 1797#peleg sprague ( f )#seated december 15 , 1797\nconnecticut at - large#james davenport ( f )#died august 3 , 1797#william edmond ( f )#seated november 13 , 1797\npennsylvania 5th#george ege ( f )#resigned sometime in october , 1797#joseph hiester ( dr )#seated december 1 , 1797\npennsylvania 4th#samuel sitgreaves ( f )#resigned sometime in 1798#robert brown ( dr )#seated december 4 , 1798\nnorth carolina 10th#nathan bryan ( dr )#died june 4 , 1798#richard dobbs spaight ( dr )#seated december 10 , 1798\npennsylvania 1st#john swanwick ( dr )#died august 1 , 1798#robert waln ( f )#seated december 3 , 1798\nconnecticut at - large#joshua coit ( f )#died september 5 , 1798#jonathan brace ( f )#seated december 3 , 1798\n",
        "pandas_code": "(df['reason for change'].str.contains('died').sum() / len(df)) >= 0.4",
        "pandas_eval": "True"
    },
    {
        "id": 41,
        "statement": "60% of the 5th congress resign",
        "label": 1,
        "table_caption": "5th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nrhode island at - large#elisha potter ( f )#resigned sometime in 1797#thomas tillinghast ( f )#seated november 13 , 1797\nsouth carolina 1st#william l smith ( f )#resigned july 10 , 1797#thomas pinckney ( f )#seated november 23 , 1797\nmassachusetts 11th#theophilus bradbury ( f )#resigned july 24 , 1797#bailey bartlett ( f )#seated november 27 , 1797\nnew hampshire at - large#jeremiah smith ( f )#resigned july 26 , 1797#peleg sprague ( f )#seated december 15 , 1797\nconnecticut at - large#james davenport ( f )#died august 3 , 1797#william edmond ( f )#seated november 13 , 1797\npennsylvania 5th#george ege ( f )#resigned sometime in october , 1797#joseph hiester ( dr )#seated december 1 , 1797\npennsylvania 4th#samuel sitgreaves ( f )#resigned sometime in 1798#robert brown ( dr )#seated december 4 , 1798\nnorth carolina 10th#nathan bryan ( dr )#died june 4 , 1798#richard dobbs spaight ( dr )#seated december 10 , 1798\npennsylvania 1st#john swanwick ( dr )#died august 1 , 1798#robert waln ( f )#seated december 3 , 1798\nconnecticut at - large#joshua coit ( f )#died september 5 , 1798#jonathan brace ( f )#seated december 3 , 1798\n",
        "pandas_code": "(df['reason for change'].str.contains('resigned').sum() / len(df)) >= 0.6",
        "pandas_eval": "True"
    },
    {
        "id": 42,
        "statement": "john swanick die before joshua colt",
        "label": 1,
        "table_caption": "5th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nrhode island at - large#elisha potter ( f )#resigned sometime in 1797#thomas tillinghast ( f )#seated november 13 , 1797\nsouth carolina 1st#william l smith ( f )#resigned july 10 , 1797#thomas pinckney ( f )#seated november 23 , 1797\nmassachusetts 11th#theophilus bradbury ( f )#resigned july 24 , 1797#bailey bartlett ( f )#seated november 27 , 1797\nnew hampshire at - large#jeremiah smith ( f )#resigned july 26 , 1797#peleg sprague ( f )#seated december 15 , 1797\nconnecticut at - large#james davenport ( f )#died august 3 , 1797#william edmond ( f )#seated november 13 , 1797\npennsylvania 5th#george ege ( f )#resigned sometime in october , 1797#joseph hiester ( dr )#seated december 1 , 1797\npennsylvania 4th#samuel sitgreaves ( f )#resigned sometime in 1798#robert brown ( dr )#seated december 4 , 1798\nnorth carolina 10th#nathan bryan ( dr )#died june 4 , 1798#richard dobbs spaight ( dr )#seated december 10 , 1798\npennsylvania 1st#john swanwick ( dr )#died august 1 , 1798#robert waln ( f )#seated december 3 , 1798\nconnecticut at - large#joshua coit ( f )#died september 5 , 1798#jonathan brace ( f )#seated december 3 , 1798\n",
        "pandas_code": "df[df['vacator'] == 'john swanwick ( dr )']['reason for change'].values[0].split('died ')[1] < df[df['vacator'] == 'joshua coit ( f )']['reason for change'].values[0].split('died ')[1]",
        "pandas_eval": "True"
    },
    {
        "id": 43,
        "statement": "joshua colt die after john swanick",
        "label": 1,
        "table_caption": "5th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nrhode island at - large#elisha potter ( f )#resigned sometime in 1797#thomas tillinghast ( f )#seated november 13 , 1797\nsouth carolina 1st#william l smith ( f )#resigned july 10 , 1797#thomas pinckney ( f )#seated november 23 , 1797\nmassachusetts 11th#theophilus bradbury ( f )#resigned july 24 , 1797#bailey bartlett ( f )#seated november 27 , 1797\nnew hampshire at - large#jeremiah smith ( f )#resigned july 26 , 1797#peleg sprague ( f )#seated december 15 , 1797\nconnecticut at - large#james davenport ( f )#died august 3 , 1797#william edmond ( f )#seated november 13 , 1797\npennsylvania 5th#george ege ( f )#resigned sometime in october , 1797#joseph hiester ( dr )#seated december 1 , 1797\npennsylvania 4th#samuel sitgreaves ( f )#resigned sometime in 1798#robert brown ( dr )#seated december 4 , 1798\nnorth carolina 10th#nathan bryan ( dr )#died june 4 , 1798#richard dobbs spaight ( dr )#seated december 10 , 1798\npennsylvania 1st#john swanwick ( dr )#died august 1 , 1798#robert waln ( f )#seated december 3 , 1798\nconnecticut at - large#joshua coit ( f )#died september 5 , 1798#jonathan brace ( f )#seated december 3 , 1798\n",
        "pandas_code": "(df[df['vacator'] == 'joshua coit ( f )']['reason for change'].values[0].split(' ')[1] > df[df['vacator'] == 'john swanwick ( dr )']['reason for change'].values[0].split(' ')[1])",
        "pandas_eval": "True"
    },
    {
        "id": 45,
        "statement": "the language be italian originale more often than italian",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#ppv#package / option\n300 - 315#sky cinema active#italy#italian#locandine e trailer#16:9#no#no#sky cinema\n301#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n301#sky cinema 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n302#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n302#sky cinema + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n303#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n303#sky cinema + 24 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n304#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n304#sky cinema hits hd#italy#italian originale#cinema#16:9#no#no#sky cinema + sky hd\n306#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n306#sky cinema family hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n308#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n308#sky cinema passion hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n310#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n310#sky cinema comedy hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n312#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n312#sky cinema max hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n313#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n313#sky cinema max + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n314#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n314#sky cinema cult hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n315#sky cinema classics#italy#italian originale#cinema#16:9#no#no#sky cinema\n315#sky cinema classics hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n321#sky 3d#italy#italian originale#tematico#16:9#yes#no#tutti i pacchetti + sky hd\n331#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n331#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n332#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n332#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n333#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n333#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n334#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n334#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n336#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n336#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n338#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n338#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n340#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n340#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n342#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n342#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n343#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n343#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n344#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n344#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n345#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n",
        "pandas_code": "(df['language'].value_counts()['italian originale'] > df['language'].value_counts()['italian'])",
        "pandas_eval": "True"
    },
    {
        "id": 46,
        "statement": "16:9 appear as the dar more often than 4:3",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#ppv#package / option\n300 - 315#sky cinema active#italy#italian#locandine e trailer#16:9#no#no#sky cinema\n301#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n301#sky cinema 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n302#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n302#sky cinema + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n303#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n303#sky cinema + 24 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n304#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n304#sky cinema hits hd#italy#italian originale#cinema#16:9#no#no#sky cinema + sky hd\n306#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n306#sky cinema family hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n308#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n308#sky cinema passion hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n310#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n310#sky cinema comedy hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n312#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n312#sky cinema max hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n313#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n313#sky cinema max + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n314#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n314#sky cinema cult hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n315#sky cinema classics#italy#italian originale#cinema#16:9#no#no#sky cinema\n315#sky cinema classics hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n321#sky 3d#italy#italian originale#tematico#16:9#yes#no#tutti i pacchetti + sky hd\n331#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n331#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n332#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n332#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n333#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n333#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n334#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n334#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n336#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n336#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n338#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n338#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n340#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n340#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n342#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n342#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n343#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n343#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n344#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n344#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n345#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n",
        "pandas_code": "(df['dar'].value_counts()['16:9'] > df['dar'].value_counts()['4:3'])",
        "pandas_eval": "True"
    },
    {
        "id": 47,
        "statement": "hdtv be as yes only 11 time",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#ppv#package / option\n300 - 315#sky cinema active#italy#italian#locandine e trailer#16:9#no#no#sky cinema\n301#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n301#sky cinema 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n302#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n302#sky cinema + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n303#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n303#sky cinema + 24 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n304#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n304#sky cinema hits hd#italy#italian originale#cinema#16:9#no#no#sky cinema + sky hd\n306#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n306#sky cinema family hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n308#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n308#sky cinema passion hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n310#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n310#sky cinema comedy hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n312#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n312#sky cinema max hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n313#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n313#sky cinema max + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n314#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n314#sky cinema cult hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n315#sky cinema classics#italy#italian originale#cinema#16:9#no#no#sky cinema\n315#sky cinema classics hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n321#sky 3d#italy#italian originale#tematico#16:9#yes#no#tutti i pacchetti + sky hd\n331#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n331#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n332#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n332#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n333#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n333#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n334#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n334#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n336#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n336#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n338#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n338#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n340#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n340#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n342#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n342#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n343#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n343#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n344#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n344#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n345#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n",
        "pandas_code": "(df['hdtv'] == 'yes').sum() == 11",
        "pandas_eval": "True"
    },
    {
        "id": 48,
        "statement": "qualsiasi tranne sky hd do not become a package / option until 331",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#ppv#package / option\n300 - 315#sky cinema active#italy#italian#locandine e trailer#16:9#no#no#sky cinema\n301#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n301#sky cinema 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n302#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n302#sky cinema + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n303#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n303#sky cinema + 24 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n304#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n304#sky cinema hits hd#italy#italian originale#cinema#16:9#no#no#sky cinema + sky hd\n306#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n306#sky cinema family hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n308#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n308#sky cinema passion hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n310#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n310#sky cinema comedy hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n312#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n312#sky cinema max hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n313#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n313#sky cinema max + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n314#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n314#sky cinema cult hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n315#sky cinema classics#italy#italian originale#cinema#16:9#no#no#sky cinema\n315#sky cinema classics hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n321#sky 3d#italy#italian originale#tematico#16:9#yes#no#tutti i pacchetti + sky hd\n331#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n331#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n332#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n332#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n333#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n333#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n334#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n334#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n336#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n336#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n338#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n338#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n340#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n340#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n342#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n342#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n343#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n343#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n344#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n344#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n345#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n",
        "pandas_code": "df[(df['n degree'].str.split('-').str[0].str.strip().astype(int) < 331) & (df['package / option'] == 'qualsiasi tranne sky hd')].empty",
        "pandas_eval": "True"
    },
    {
        "id": 49,
        "statement": "sky cinema active be the only 1 with locandine e trailer",
        "label": 1,
        "table_caption": "television in italy",
        "table_text": "n degree#television service#country#language#content#dar#hdtv#ppv#package / option\n300 - 315#sky cinema active#italy#italian#locandine e trailer#16:9#no#no#sky cinema\n301#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n301#sky cinema 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n302#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n302#sky cinema + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n303#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n303#sky cinema + 24 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n304#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n304#sky cinema hits hd#italy#italian originale#cinema#16:9#no#no#sky cinema + sky hd\n306#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n306#sky cinema family hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n308#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n308#sky cinema passion hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n310#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n310#sky cinema comedy hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n312#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n312#sky cinema max hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n313#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n313#sky cinema max + 1 hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n314#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n314#sky cinema cult hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n315#sky cinema classics#italy#italian originale#cinema#16:9#no#no#sky cinema\n315#sky cinema classics hd#italy#italian originale#cinema#16:9#yes#no#sky cinema + sky hd\n321#sky 3d#italy#italian originale#tematico#16:9#yes#no#tutti i pacchetti + sky hd\n331#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n331#sky cinema 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n332#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n332#sky cinema + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n333#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n333#sky cinema + 24#italy#italian originale#cinema#16:9#no#no#sky cinema\n334#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n334#sky cinema hits#italy#italian originale#cinema#16:9#no#no#sky cinema\n336#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n336#sky cinema family#italy#italian originale#cinema#16:9#no#no#sky cinema\n338#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n338#sky cinema passion#italy#italian originale#cinema#16:9#no#no#sky cinema\n340#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n340#sky cinema comedy#italy#italian originale#cinema#16:9#no#no#sky cinema\n342#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n342#sky cinema max#italy#italian originale#cinema#16:9#no#no#sky cinema\n343#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n343#sky cinema max + 1#italy#italian originale#cinema#16:9#no#no#sky cinema\n344#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n344#sky cinema cult#italy#italian originale#cinema#16:9#no#no#sky cinema\n345#cartello promozionale sky hd#italy#italian#promozionale#4:3#no#no#qualsiasi tranne sky hd\n",
        "pandas_code": "df[(df['television service'] == 'sky cinema active') & (df['content'] == 'locandine e trailer')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 50,
        "statement": "delyth thomas direct the first 8 episode of the season",
        "label": 1,
        "table_caption": "list of the story of tracy beaker episodes",
        "table_text": "#original title#directed by#written by#original airdate#production code\n3 - 1#leavin' on a jet plane#delyth thomas#mary morris#12 february 2004#3.1\n3 - 2#supernatural shelley#delyth thomas#laura summers#12 february 2004#3.2\n3 - 3#jackie#delyth thomas#gary parker#19 february 2004#3.3\n3 - 4#free louise#delyth thomas#laura summers#19 february 2004#3.4\n3 - 5#the bygraves#delyth thomas#gary parker#26 february 2004#3.5\n3 - 6#down with school#delyth thomas#laura summers#26 february 2004#3.6\n3 - 7#crashed and thrashed#delyth thomas#mary morris#5 march 2004#3.7\n3 - 8#no - one quite like grandpa#delyth thomas#othniel smith#6 march 2004#3.8\n3 - 9#down to earth#joss agnew#dan anthony#12 march 2004#3.9\n3 - 10#nothing happens here#joss agnew#mary morris#12 march 2004#3.10\n3 - 11#the beaker club#joss agnew#simon nicholson#19 march 2004#3.11\n3 - 12#power cut#joss agnew#abigail abben mensah#19 march 2004#3.12\n3 - 13#just desserts#joss agnew#othniel smith#26 march 2004#3.13\n3 - 14#mind your own business#joss agnew#holly lyons#26 march 2004#3.14\n3 - 15#i am not in love#joss agnew#holly lyons#2 april 2004#3.15\n3 - 16#time capsule#joss agnew#marvin close#2 april 2004#3.16\n3 - 17#football trial#joss agnew#andy walker#9 april 2004#3.17\n3 - 18#we are family#joss agnew#tracy brabin#9 april 2004#3.18\n3 - 19#be prepared#laurence wilson#laura summers#16 april 2004#3.19\n3 - 20#sufia the silent#laurence wilson#ariane sherine#16 april 2004#3.20\n3 - 21#babies suck!#laurence wilson#mary morris#23 april 2004#3.21\n3 - 22#exploited#laurence wilson#marvin close#23 april 2004#3.22\n3 - 23#be quiet#laurence wilson#dan anthony#7 may 2004#3.23\n3 - 24#dad trouble#laurence wilson#gary parker#30 april 2004#3.24\n3 - 25#the big race#laurence wilson#gary parker#30 april 2004#3.25\n",
        "pandas_code": "all(df['directed by'].iloc[:8] == 'delyth thomas')",
        "pandas_eval": "True"
    },
    {
        "id": 51,
        "statement": "josh agnew direct 10 of the episode in the season",
        "label": 1,
        "table_caption": "list of the story of tracy beaker episodes",
        "table_text": "#original title#directed by#written by#original airdate#production code\n3 - 1#leavin' on a jet plane#delyth thomas#mary morris#12 february 2004#3.1\n3 - 2#supernatural shelley#delyth thomas#laura summers#12 february 2004#3.2\n3 - 3#jackie#delyth thomas#gary parker#19 february 2004#3.3\n3 - 4#free louise#delyth thomas#laura summers#19 february 2004#3.4\n3 - 5#the bygraves#delyth thomas#gary parker#26 february 2004#3.5\n3 - 6#down with school#delyth thomas#laura summers#26 february 2004#3.6\n3 - 7#crashed and thrashed#delyth thomas#mary morris#5 march 2004#3.7\n3 - 8#no - one quite like grandpa#delyth thomas#othniel smith#6 march 2004#3.8\n3 - 9#down to earth#joss agnew#dan anthony#12 march 2004#3.9\n3 - 10#nothing happens here#joss agnew#mary morris#12 march 2004#3.10\n3 - 11#the beaker club#joss agnew#simon nicholson#19 march 2004#3.11\n3 - 12#power cut#joss agnew#abigail abben mensah#19 march 2004#3.12\n3 - 13#just desserts#joss agnew#othniel smith#26 march 2004#3.13\n3 - 14#mind your own business#joss agnew#holly lyons#26 march 2004#3.14\n3 - 15#i am not in love#joss agnew#holly lyons#2 april 2004#3.15\n3 - 16#time capsule#joss agnew#marvin close#2 april 2004#3.16\n3 - 17#football trial#joss agnew#andy walker#9 april 2004#3.17\n3 - 18#we are family#joss agnew#tracy brabin#9 april 2004#3.18\n3 - 19#be prepared#laurence wilson#laura summers#16 april 2004#3.19\n3 - 20#sufia the silent#laurence wilson#ariane sherine#16 april 2004#3.20\n3 - 21#babies suck!#laurence wilson#mary morris#23 april 2004#3.21\n3 - 22#exploited#laurence wilson#marvin close#23 april 2004#3.22\n3 - 23#be quiet#laurence wilson#dan anthony#7 may 2004#3.23\n3 - 24#dad trouble#laurence wilson#gary parker#30 april 2004#3.24\n3 - 25#the big race#laurence wilson#gary parker#30 april 2004#3.25\n",
        "pandas_code": "(df['directed by'].value_counts()['joss agnew'] == 10)",
        "pandas_eval": "True"
    },
    {
        "id": 52,
        "statement": "laurence wilson direct 7 of the episode in the season",
        "label": 1,
        "table_caption": "list of the story of tracy beaker episodes",
        "table_text": "#original title#directed by#written by#original airdate#production code\n3 - 1#leavin' on a jet plane#delyth thomas#mary morris#12 february 2004#3.1\n3 - 2#supernatural shelley#delyth thomas#laura summers#12 february 2004#3.2\n3 - 3#jackie#delyth thomas#gary parker#19 february 2004#3.3\n3 - 4#free louise#delyth thomas#laura summers#19 february 2004#3.4\n3 - 5#the bygraves#delyth thomas#gary parker#26 february 2004#3.5\n3 - 6#down with school#delyth thomas#laura summers#26 february 2004#3.6\n3 - 7#crashed and thrashed#delyth thomas#mary morris#5 march 2004#3.7\n3 - 8#no - one quite like grandpa#delyth thomas#othniel smith#6 march 2004#3.8\n3 - 9#down to earth#joss agnew#dan anthony#12 march 2004#3.9\n3 - 10#nothing happens here#joss agnew#mary morris#12 march 2004#3.10\n3 - 11#the beaker club#joss agnew#simon nicholson#19 march 2004#3.11\n3 - 12#power cut#joss agnew#abigail abben mensah#19 march 2004#3.12\n3 - 13#just desserts#joss agnew#othniel smith#26 march 2004#3.13\n3 - 14#mind your own business#joss agnew#holly lyons#26 march 2004#3.14\n3 - 15#i am not in love#joss agnew#holly lyons#2 april 2004#3.15\n3 - 16#time capsule#joss agnew#marvin close#2 april 2004#3.16\n3 - 17#football trial#joss agnew#andy walker#9 april 2004#3.17\n3 - 18#we are family#joss agnew#tracy brabin#9 april 2004#3.18\n3 - 19#be prepared#laurence wilson#laura summers#16 april 2004#3.19\n3 - 20#sufia the silent#laurence wilson#ariane sherine#16 april 2004#3.20\n3 - 21#babies suck!#laurence wilson#mary morris#23 april 2004#3.21\n3 - 22#exploited#laurence wilson#marvin close#23 april 2004#3.22\n3 - 23#be quiet#laurence wilson#dan anthony#7 may 2004#3.23\n3 - 24#dad trouble#laurence wilson#gary parker#30 april 2004#3.24\n3 - 25#the big race#laurence wilson#gary parker#30 april 2004#3.25\n",
        "pandas_code": "(df['directed by'] == 'laurence wilson').sum() == 7",
        "pandas_eval": "True"
    },
    {
        "id": 53,
        "statement": "6 episode in the season air in february 2004",
        "label": 1,
        "table_caption": "list of the story of tracy beaker episodes",
        "table_text": "#original title#directed by#written by#original airdate#production code\n3 - 1#leavin' on a jet plane#delyth thomas#mary morris#12 february 2004#3.1\n3 - 2#supernatural shelley#delyth thomas#laura summers#12 february 2004#3.2\n3 - 3#jackie#delyth thomas#gary parker#19 february 2004#3.3\n3 - 4#free louise#delyth thomas#laura summers#19 february 2004#3.4\n3 - 5#the bygraves#delyth thomas#gary parker#26 february 2004#3.5\n3 - 6#down with school#delyth thomas#laura summers#26 february 2004#3.6\n3 - 7#crashed and thrashed#delyth thomas#mary morris#5 march 2004#3.7\n3 - 8#no - one quite like grandpa#delyth thomas#othniel smith#6 march 2004#3.8\n3 - 9#down to earth#joss agnew#dan anthony#12 march 2004#3.9\n3 - 10#nothing happens here#joss agnew#mary morris#12 march 2004#3.10\n3 - 11#the beaker club#joss agnew#simon nicholson#19 march 2004#3.11\n3 - 12#power cut#joss agnew#abigail abben mensah#19 march 2004#3.12\n3 - 13#just desserts#joss agnew#othniel smith#26 march 2004#3.13\n3 - 14#mind your own business#joss agnew#holly lyons#26 march 2004#3.14\n3 - 15#i am not in love#joss agnew#holly lyons#2 april 2004#3.15\n3 - 16#time capsule#joss agnew#marvin close#2 april 2004#3.16\n3 - 17#football trial#joss agnew#andy walker#9 april 2004#3.17\n3 - 18#we are family#joss agnew#tracy brabin#9 april 2004#3.18\n3 - 19#be prepared#laurence wilson#laura summers#16 april 2004#3.19\n3 - 20#sufia the silent#laurence wilson#ariane sherine#16 april 2004#3.20\n3 - 21#babies suck!#laurence wilson#mary morris#23 april 2004#3.21\n3 - 22#exploited#laurence wilson#marvin close#23 april 2004#3.22\n3 - 23#be quiet#laurence wilson#dan anthony#7 may 2004#3.23\n3 - 24#dad trouble#laurence wilson#gary parker#30 april 2004#3.24\n3 - 25#the big race#laurence wilson#gary parker#30 april 2004#3.25\n",
        "pandas_code": "len(df[df['original airdate'].str.contains('february 2004')]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 54,
        "statement": "only one episode in the season air in may 2004",
        "label": 1,
        "table_caption": "list of the story of tracy beaker episodes",
        "table_text": "#original title#directed by#written by#original airdate#production code\n3 - 1#leavin' on a jet plane#delyth thomas#mary morris#12 february 2004#3.1\n3 - 2#supernatural shelley#delyth thomas#laura summers#12 february 2004#3.2\n3 - 3#jackie#delyth thomas#gary parker#19 february 2004#3.3\n3 - 4#free louise#delyth thomas#laura summers#19 february 2004#3.4\n3 - 5#the bygraves#delyth thomas#gary parker#26 february 2004#3.5\n3 - 6#down with school#delyth thomas#laura summers#26 february 2004#3.6\n3 - 7#crashed and thrashed#delyth thomas#mary morris#5 march 2004#3.7\n3 - 8#no - one quite like grandpa#delyth thomas#othniel smith#6 march 2004#3.8\n3 - 9#down to earth#joss agnew#dan anthony#12 march 2004#3.9\n3 - 10#nothing happens here#joss agnew#mary morris#12 march 2004#3.10\n3 - 11#the beaker club#joss agnew#simon nicholson#19 march 2004#3.11\n3 - 12#power cut#joss agnew#abigail abben mensah#19 march 2004#3.12\n3 - 13#just desserts#joss agnew#othniel smith#26 march 2004#3.13\n3 - 14#mind your own business#joss agnew#holly lyons#26 march 2004#3.14\n3 - 15#i am not in love#joss agnew#holly lyons#2 april 2004#3.15\n3 - 16#time capsule#joss agnew#marvin close#2 april 2004#3.16\n3 - 17#football trial#joss agnew#andy walker#9 april 2004#3.17\n3 - 18#we are family#joss agnew#tracy brabin#9 april 2004#3.18\n3 - 19#be prepared#laurence wilson#laura summers#16 april 2004#3.19\n3 - 20#sufia the silent#laurence wilson#ariane sherine#16 april 2004#3.20\n3 - 21#babies suck!#laurence wilson#mary morris#23 april 2004#3.21\n3 - 22#exploited#laurence wilson#marvin close#23 april 2004#3.22\n3 - 23#be quiet#laurence wilson#dan anthony#7 may 2004#3.23\n3 - 24#dad trouble#laurence wilson#gary parker#30 april 2004#3.24\n3 - 25#the big race#laurence wilson#gary parker#30 april 2004#3.25\n",
        "pandas_code": "df[df['original airdate'].str.contains('may 2004')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 55,
        "statement": "only bas van erp and robin ammerlaan eric stuurman do not do single event",
        "label": 1,
        "table_caption": "netherlands at the 2008 summer paralympics",
        "table_text": "athlete#class#event#round of 64#round of 32#round of 16#quarterfinals#semifinals#final / bronze medal match\nrobin ammerlaan#open#singles#reid ( gbr ) w 6 - 3 , 6 - 0#rajakaruna ( sri ) w 6 - 0 , 6 - 0#welch ( gbr ) w 6 - 2 , 6 - 3#saida ( jpn ) w 6 - 4 , 6 - 2#vink ( ned ) w 7 - 5 , 4 - 6 , 7 - 6 (6)#kunieda ( jpn ) l 3 - 6 , 0 - 6\nbas van erp#open#mixed quad singles#n / a#n / a#raffaele ( ita ) w 6 - 1 , 6 - 1#taylor ( usa ) l 6 - 1 , 5 - 7 , 6 - 7 (1)#did not advance#did not advance\nmaikel scheffers#open#singles#farkas ( hun ) w 6 - 3 , 6 - 4#lee ( kor ) w 6 - 2 , 6 - 1#fujimoto ( jpn ) w 6 - 1 , 6 - 1#jeremiasz ( fra ) w 6 - 3 , 6 - 3#kunieda ( jpn ) l 1 - 6 , 1 - 6#vink ( ned ) w 6 - 3 , 6 - 1\neric stuurman#open#singles#shi ( chn ) w 6 - 4 , 6 - 4#kunieda ( jpn ) l 2 - 6 , 1 - 6#did not advance#did not advance#did not advance#did not advance\nronald vink#open#singles#moran ( usa ) w 6 - 0 , 6 - 1#tur ( esp ) w 6 - 1 , 6 - 0#legner ( aut ) w 6 - 4 , 6 - 3#houdet ( fra ) w 6 - 2 , 6 - 1#ammerlaan ( ned ) l 5 - 7 , 6 - 4 , 6 - 7 (6)#scheffers ( ned ) l 3 - 6 , 1 - 6\nrobin ammerlaan eric stuurman#open#doubles#n / a#-#felix - gergely ( svk ) w 6 - 2 , 6 - 2#olsson - wikstrom ( swe ) l 6 - 7 (5) , 4 - 6#did not advance#did not advance\n",
        "pandas_code": "df[~df['athlete'].isin(['bas van erp', 'robin ammerlaan eric stuurman'])]['event'].eq('singles').all()",
        "pandas_eval": "True"
    },
    {
        "id": 56,
        "statement": "2 player do not have information for round of 32",
        "label": 1,
        "table_caption": "netherlands at the 2008 summer paralympics",
        "table_text": "athlete#class#event#round of 64#round of 32#round of 16#quarterfinals#semifinals#final / bronze medal match\nrobin ammerlaan#open#singles#reid ( gbr ) w 6 - 3 , 6 - 0#rajakaruna ( sri ) w 6 - 0 , 6 - 0#welch ( gbr ) w 6 - 2 , 6 - 3#saida ( jpn ) w 6 - 4 , 6 - 2#vink ( ned ) w 7 - 5 , 4 - 6 , 7 - 6 (6)#kunieda ( jpn ) l 3 - 6 , 0 - 6\nbas van erp#open#mixed quad singles#n / a#n / a#raffaele ( ita ) w 6 - 1 , 6 - 1#taylor ( usa ) l 6 - 1 , 5 - 7 , 6 - 7 (1)#did not advance#did not advance\nmaikel scheffers#open#singles#farkas ( hun ) w 6 - 3 , 6 - 4#lee ( kor ) w 6 - 2 , 6 - 1#fujimoto ( jpn ) w 6 - 1 , 6 - 1#jeremiasz ( fra ) w 6 - 3 , 6 - 3#kunieda ( jpn ) l 1 - 6 , 1 - 6#vink ( ned ) w 6 - 3 , 6 - 1\neric stuurman#open#singles#shi ( chn ) w 6 - 4 , 6 - 4#kunieda ( jpn ) l 2 - 6 , 1 - 6#did not advance#did not advance#did not advance#did not advance\nronald vink#open#singles#moran ( usa ) w 6 - 0 , 6 - 1#tur ( esp ) w 6 - 1 , 6 - 0#legner ( aut ) w 6 - 4 , 6 - 3#houdet ( fra ) w 6 - 2 , 6 - 1#ammerlaan ( ned ) l 5 - 7 , 6 - 4 , 6 - 7 (6)#scheffers ( ned ) l 3 - 6 , 1 - 6\nrobin ammerlaan eric stuurman#open#doubles#n / a#-#felix - gergely ( svk ) w 6 - 2 , 6 - 2#olsson - wikstrom ( swe ) l 6 - 7 (5) , 4 - 6#did not advance#did not advance\n",
        "pandas_code": "((df['round of 32'] == 'n / a') | (df['round of 32'] == '-')).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 57,
        "statement": "ultimately 3 athlete did not advance to the final / bronze medal match",
        "label": 1,
        "table_caption": "netherlands at the 2008 summer paralympics",
        "table_text": "athlete#class#event#round of 64#round of 32#round of 16#quarterfinals#semifinals#final / bronze medal match\nrobin ammerlaan#open#singles#reid ( gbr ) w 6 - 3 , 6 - 0#rajakaruna ( sri ) w 6 - 0 , 6 - 0#welch ( gbr ) w 6 - 2 , 6 - 3#saida ( jpn ) w 6 - 4 , 6 - 2#vink ( ned ) w 7 - 5 , 4 - 6 , 7 - 6 (6)#kunieda ( jpn ) l 3 - 6 , 0 - 6\nbas van erp#open#mixed quad singles#n / a#n / a#raffaele ( ita ) w 6 - 1 , 6 - 1#taylor ( usa ) l 6 - 1 , 5 - 7 , 6 - 7 (1)#did not advance#did not advance\nmaikel scheffers#open#singles#farkas ( hun ) w 6 - 3 , 6 - 4#lee ( kor ) w 6 - 2 , 6 - 1#fujimoto ( jpn ) w 6 - 1 , 6 - 1#jeremiasz ( fra ) w 6 - 3 , 6 - 3#kunieda ( jpn ) l 1 - 6 , 1 - 6#vink ( ned ) w 6 - 3 , 6 - 1\neric stuurman#open#singles#shi ( chn ) w 6 - 4 , 6 - 4#kunieda ( jpn ) l 2 - 6 , 1 - 6#did not advance#did not advance#did not advance#did not advance\nronald vink#open#singles#moran ( usa ) w 6 - 0 , 6 - 1#tur ( esp ) w 6 - 1 , 6 - 0#legner ( aut ) w 6 - 4 , 6 - 3#houdet ( fra ) w 6 - 2 , 6 - 1#ammerlaan ( ned ) l 5 - 7 , 6 - 4 , 6 - 7 (6)#scheffers ( ned ) l 3 - 6 , 1 - 6\nrobin ammerlaan eric stuurman#open#doubles#n / a#-#felix - gergely ( svk ) w 6 - 2 , 6 - 2#olsson - wikstrom ( swe ) l 6 - 7 (5) , 4 - 6#did not advance#did not advance\n",
        "pandas_code": "df['final / bronze medal match'].str.contains('did not advance').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 59,
        "statement": "4 athlete have record win in the round of 64",
        "label": 1,
        "table_caption": "netherlands at the 2008 summer paralympics",
        "table_text": "athlete#class#event#round of 64#round of 32#round of 16#quarterfinals#semifinals#final / bronze medal match\nrobin ammerlaan#open#singles#reid ( gbr ) w 6 - 3 , 6 - 0#rajakaruna ( sri ) w 6 - 0 , 6 - 0#welch ( gbr ) w 6 - 2 , 6 - 3#saida ( jpn ) w 6 - 4 , 6 - 2#vink ( ned ) w 7 - 5 , 4 - 6 , 7 - 6 (6)#kunieda ( jpn ) l 3 - 6 , 0 - 6\nbas van erp#open#mixed quad singles#n / a#n / a#raffaele ( ita ) w 6 - 1 , 6 - 1#taylor ( usa ) l 6 - 1 , 5 - 7 , 6 - 7 (1)#did not advance#did not advance\nmaikel scheffers#open#singles#farkas ( hun ) w 6 - 3 , 6 - 4#lee ( kor ) w 6 - 2 , 6 - 1#fujimoto ( jpn ) w 6 - 1 , 6 - 1#jeremiasz ( fra ) w 6 - 3 , 6 - 3#kunieda ( jpn ) l 1 - 6 , 1 - 6#vink ( ned ) w 6 - 3 , 6 - 1\neric stuurman#open#singles#shi ( chn ) w 6 - 4 , 6 - 4#kunieda ( jpn ) l 2 - 6 , 1 - 6#did not advance#did not advance#did not advance#did not advance\nronald vink#open#singles#moran ( usa ) w 6 - 0 , 6 - 1#tur ( esp ) w 6 - 1 , 6 - 0#legner ( aut ) w 6 - 4 , 6 - 3#houdet ( fra ) w 6 - 2 , 6 - 1#ammerlaan ( ned ) l 5 - 7 , 6 - 4 , 6 - 7 (6)#scheffers ( ned ) l 3 - 6 , 1 - 6\nrobin ammerlaan eric stuurman#open#doubles#n / a#-#felix - gergely ( svk ) w 6 - 2 , 6 - 2#olsson - wikstrom ( swe ) l 6 - 7 (5) , 4 - 6#did not advance#did not advance\n",
        "pandas_code": "len(df[df['round of 64'].str.contains('w', case=False, na=False)]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 60,
        "statement": "in the 1982 , the washington redskins beat the new orleans saint 27 to 10 , with their win score match a win against the new york giant earlier in the season",
        "label": 1,
        "table_caption": "1982 washington redskins season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 12 , 1982#philadelphia eagles#w 37 - 34 (ot)#veterans stadium#1 - 0#68885\n2#september 19 , 1982#tampa bay buccaneers#w 21 - 13#tampa stadium#2 - 0#66187\n10#november 21 , 1982#new york giants#w 27 - 17#giants stadium#3 - 0#70766\n11#november 28 , 1982#philadelphia eagles#w 13 - 9#rfk stadium#4 - 0#48313\n12#december 5 , 1982#dallas cowboys#l 10 - 24#rfk stadium#4 - 1#54633\n13#december 12 , 1982#st louis cardinals#w 12 - 7#busch memorial stadium#5 - 1#35308\n14#december 19 , 1982#new york giants#w 15 - 14#rfk stadium#6 - 1#50030\n15#december 26 , 1982#new orleans saints#w 27 - 10#louisiana superdome#7 - 1#48667\n",
        "pandas_code": "any((df['opponent'] == 'new orleans saints') & (df['result'] == 'w 27 - 10') & any((df['opponent'] == 'new york giants') & (df['result'].str.startswith('w 27'))))",
        "pandas_eval": "True"
    },
    {
        "id": 61,
        "statement": "in 1982 , the redskin lose to the dallas cowboy , score only 10 total point against the cowboys' score of 24",
        "label": 1,
        "table_caption": "1982 washington redskins season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 12 , 1982#philadelphia eagles#w 37 - 34 (ot)#veterans stadium#1 - 0#68885\n2#september 19 , 1982#tampa bay buccaneers#w 21 - 13#tampa stadium#2 - 0#66187\n10#november 21 , 1982#new york giants#w 27 - 17#giants stadium#3 - 0#70766\n11#november 28 , 1982#philadelphia eagles#w 13 - 9#rfk stadium#4 - 0#48313\n12#december 5 , 1982#dallas cowboys#l 10 - 24#rfk stadium#4 - 1#54633\n13#december 12 , 1982#st louis cardinals#w 12 - 7#busch memorial stadium#5 - 1#35308\n14#december 19 , 1982#new york giants#w 15 - 14#rfk stadium#6 - 1#50030\n15#december 26 , 1982#new orleans saints#w 27 - 10#louisiana superdome#7 - 1#48667\n",
        "pandas_code": "df[(df['opponent'] == 'dallas cowboys') & (df['result'] == 'l 10 - 24')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 62,
        "statement": "in 1982 , the only losing game play by the redskin be against the dallas cowboy , who win with 14 more point than the redskin",
        "label": 1,
        "table_caption": "1982 washington redskins season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 12 , 1982#philadelphia eagles#w 37 - 34 (ot)#veterans stadium#1 - 0#68885\n2#september 19 , 1982#tampa bay buccaneers#w 21 - 13#tampa stadium#2 - 0#66187\n10#november 21 , 1982#new york giants#w 27 - 17#giants stadium#3 - 0#70766\n11#november 28 , 1982#philadelphia eagles#w 13 - 9#rfk stadium#4 - 0#48313\n12#december 5 , 1982#dallas cowboys#l 10 - 24#rfk stadium#4 - 1#54633\n13#december 12 , 1982#st louis cardinals#w 12 - 7#busch memorial stadium#5 - 1#35308\n14#december 19 , 1982#new york giants#w 15 - 14#rfk stadium#6 - 1#50030\n15#december 26 , 1982#new orleans saints#w 27 - 10#louisiana superdome#7 - 1#48667\n",
        "pandas_code": "df[(df['opponent'] == 'dallas cowboys') & (df['result'].str.startswith('l')) & (df['result'].str.extract(r'(\\d+) - (\\d+)').astype(int).diff(axis=1)[1] == 14)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 63,
        "statement": "in 1982 , the redskin win most of the game in which the team play , and in 2 game -- 1 against the giant and the other against the saint , the redskin win the game with 27 point",
        "label": 1,
        "table_caption": "1982 washington redskins season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 12 , 1982#philadelphia eagles#w 37 - 34 (ot)#veterans stadium#1 - 0#68885\n2#september 19 , 1982#tampa bay buccaneers#w 21 - 13#tampa stadium#2 - 0#66187\n10#november 21 , 1982#new york giants#w 27 - 17#giants stadium#3 - 0#70766\n11#november 28 , 1982#philadelphia eagles#w 13 - 9#rfk stadium#4 - 0#48313\n12#december 5 , 1982#dallas cowboys#l 10 - 24#rfk stadium#4 - 1#54633\n13#december 12 , 1982#st louis cardinals#w 12 - 7#busch memorial stadium#5 - 1#35308\n14#december 19 , 1982#new york giants#w 15 - 14#rfk stadium#6 - 1#50030\n15#december 26 , 1982#new orleans saints#w 27 - 10#louisiana superdome#7 - 1#48667\n",
        "pandas_code": "all(df[df['opponent'].isin(['new york giants', 'new orleans saints']) & df['result'].str.startswith('w')]['result'].str.extract(r'(\\\\d+) - \\\\d+').dropna()[0].astype(int) == 27) and (df['result'].str.startswith('w').sum() / len(df) > 0.5)",
        "pandas_eval": "True"
    },
    {
        "id": 64,
        "statement": "in 1982 season , the lowest score earn by the redskin be that of just 10 point in a game against the cowboy at rfk stadium",
        "label": 1,
        "table_caption": "1982 washington redskins season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 12 , 1982#philadelphia eagles#w 37 - 34 (ot)#veterans stadium#1 - 0#68885\n2#september 19 , 1982#tampa bay buccaneers#w 21 - 13#tampa stadium#2 - 0#66187\n10#november 21 , 1982#new york giants#w 27 - 17#giants stadium#3 - 0#70766\n11#november 28 , 1982#philadelphia eagles#w 13 - 9#rfk stadium#4 - 0#48313\n12#december 5 , 1982#dallas cowboys#l 10 - 24#rfk stadium#4 - 1#54633\n13#december 12 , 1982#st louis cardinals#w 12 - 7#busch memorial stadium#5 - 1#35308\n14#december 19 , 1982#new york giants#w 15 - 14#rfk stadium#6 - 1#50030\n15#december 26 , 1982#new orleans saints#w 27 - 10#louisiana superdome#7 - 1#48667\n",
        "pandas_code": "df[(df['opponent'] == 'dallas cowboys') & (df['game site'] == 'rfk stadium') & (df['result'].str.extract(r'(\\d+) - \\d+')[0].astype(int) == 10)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 65,
        "statement": "dayton , oh be the market as often as louisville , key",
        "label": 1,
        "table_caption": "main line broadcasting",
        "table_text": "dma#market#station#frequency#branding#format\n53#louisville , ky#wgzb - fm#96.5#b96.5#urban\n53#louisville , ky#wdjx - fm#99.7#99.7 djx#contemporary hit radio\n53#louisville , ky#wmjm - fm#101.3#magic 101.3#urban ac\n53#louisville , ky#wxma - fm#102.3#102.3 the max#hot ac\n53#louisville , ky#wesi#105.1#easy rock 105.1#soft adult contemporary\n56#richmond - petersburg , va#wlfv - fm#93.1#93.1 the wolf#southern country\n56#richmond - petersburg , va#wwlb - fm#98.9#98.9 liberty#variety hits\n56#richmond - petersburg , va#warv - fm#100.3#big oldies 107.3#oldies\n56#richmond - petersburg , va#wbbt - fm#107.3#big oldies 107.3#oldies\n60#dayton , oh#wrou - fm#92.1#92.1 wrou#urban ac\n60#dayton , oh#wgtz - fm#92.9#fly 92.9#variety hits\n60#dayton , oh#wcli - fm#101.5#click 101.5#modern hit music\n60#dayton , oh#wdht - fm#102.9#hot 102.9#rhythmic contemporary\n60#dayton , oh#wing - am#1410#espn 1410#sports\n166#hagerstown , md - chambersburg , pa#wqcm - fm#94.3#94.3 wqcm#rock\n166#hagerstown , md - chambersburg , pa#wikz - fm#95.1#mix 95.1#adult contemporary\n166#hagerstown , md - chambersburg , pa#wdld - fm#96.7#wild 96.7#rhythmic contemporary hit radio\n166#hagerstown , md - chambersburg , pa#wcha - am#800#true oldies 96.3#oldies\n166#hagerstown , md - chambersburg , pa#whag - am#1410#true oldies 96.3#oldies\n",
        "pandas_code": "df[df['market'] == 'dayton , oh'].shape[0] == df[df['market'] == 'louisville , ky'].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 66,
        "statement": "urban ac be format fewer time than oldie",
        "label": 1,
        "table_caption": "main line broadcasting",
        "table_text": "dma#market#station#frequency#branding#format\n53#louisville , ky#wgzb - fm#96.5#b96.5#urban\n53#louisville , ky#wdjx - fm#99.7#99.7 djx#contemporary hit radio\n53#louisville , ky#wmjm - fm#101.3#magic 101.3#urban ac\n53#louisville , ky#wxma - fm#102.3#102.3 the max#hot ac\n53#louisville , ky#wesi#105.1#easy rock 105.1#soft adult contemporary\n56#richmond - petersburg , va#wlfv - fm#93.1#93.1 the wolf#southern country\n56#richmond - petersburg , va#wwlb - fm#98.9#98.9 liberty#variety hits\n56#richmond - petersburg , va#warv - fm#100.3#big oldies 107.3#oldies\n56#richmond - petersburg , va#wbbt - fm#107.3#big oldies 107.3#oldies\n60#dayton , oh#wrou - fm#92.1#92.1 wrou#urban ac\n60#dayton , oh#wgtz - fm#92.9#fly 92.9#variety hits\n60#dayton , oh#wcli - fm#101.5#click 101.5#modern hit music\n60#dayton , oh#wdht - fm#102.9#hot 102.9#rhythmic contemporary\n60#dayton , oh#wing - am#1410#espn 1410#sports\n166#hagerstown , md - chambersburg , pa#wqcm - fm#94.3#94.3 wqcm#rock\n166#hagerstown , md - chambersburg , pa#wikz - fm#95.1#mix 95.1#adult contemporary\n166#hagerstown , md - chambersburg , pa#wdld - fm#96.7#wild 96.7#rhythmic contemporary hit radio\n166#hagerstown , md - chambersburg , pa#wcha - am#800#true oldies 96.3#oldies\n166#hagerstown , md - chambersburg , pa#whag - am#1410#true oldies 96.3#oldies\n",
        "pandas_code": "(df['format'].value_counts()['urban ac'] < df['format'].value_counts()['oldies'])",
        "pandas_eval": "True"
    },
    {
        "id": 67,
        "statement": "sport be only the format 1 time",
        "label": 1,
        "table_caption": "main line broadcasting",
        "table_text": "dma#market#station#frequency#branding#format\n53#louisville , ky#wgzb - fm#96.5#b96.5#urban\n53#louisville , ky#wdjx - fm#99.7#99.7 djx#contemporary hit radio\n53#louisville , ky#wmjm - fm#101.3#magic 101.3#urban ac\n53#louisville , ky#wxma - fm#102.3#102.3 the max#hot ac\n53#louisville , ky#wesi#105.1#easy rock 105.1#soft adult contemporary\n56#richmond - petersburg , va#wlfv - fm#93.1#93.1 the wolf#southern country\n56#richmond - petersburg , va#wwlb - fm#98.9#98.9 liberty#variety hits\n56#richmond - petersburg , va#warv - fm#100.3#big oldies 107.3#oldies\n56#richmond - petersburg , va#wbbt - fm#107.3#big oldies 107.3#oldies\n60#dayton , oh#wrou - fm#92.1#92.1 wrou#urban ac\n60#dayton , oh#wgtz - fm#92.9#fly 92.9#variety hits\n60#dayton , oh#wcli - fm#101.5#click 101.5#modern hit music\n60#dayton , oh#wdht - fm#102.9#hot 102.9#rhythmic contemporary\n60#dayton , oh#wing - am#1410#espn 1410#sports\n166#hagerstown , md - chambersburg , pa#wqcm - fm#94.3#94.3 wqcm#rock\n166#hagerstown , md - chambersburg , pa#wikz - fm#95.1#mix 95.1#adult contemporary\n166#hagerstown , md - chambersburg , pa#wdld - fm#96.7#wild 96.7#rhythmic contemporary hit radio\n166#hagerstown , md - chambersburg , pa#wcha - am#800#true oldies 96.3#oldies\n166#hagerstown , md - chambersburg , pa#whag - am#1410#true oldies 96.3#oldies\n",
        "pandas_code": "df[df['format'] == 'sports'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 68,
        "statement": "big oldie 107.3 be the brand 2 time in a row",
        "label": 1,
        "table_caption": "main line broadcasting",
        "table_text": "dma#market#station#frequency#branding#format\n53#louisville , ky#wgzb - fm#96.5#b96.5#urban\n53#louisville , ky#wdjx - fm#99.7#99.7 djx#contemporary hit radio\n53#louisville , ky#wmjm - fm#101.3#magic 101.3#urban ac\n53#louisville , ky#wxma - fm#102.3#102.3 the max#hot ac\n53#louisville , ky#wesi#105.1#easy rock 105.1#soft adult contemporary\n56#richmond - petersburg , va#wlfv - fm#93.1#93.1 the wolf#southern country\n56#richmond - petersburg , va#wwlb - fm#98.9#98.9 liberty#variety hits\n56#richmond - petersburg , va#warv - fm#100.3#big oldies 107.3#oldies\n56#richmond - petersburg , va#wbbt - fm#107.3#big oldies 107.3#oldies\n60#dayton , oh#wrou - fm#92.1#92.1 wrou#urban ac\n60#dayton , oh#wgtz - fm#92.9#fly 92.9#variety hits\n60#dayton , oh#wcli - fm#101.5#click 101.5#modern hit music\n60#dayton , oh#wdht - fm#102.9#hot 102.9#rhythmic contemporary\n60#dayton , oh#wing - am#1410#espn 1410#sports\n166#hagerstown , md - chambersburg , pa#wqcm - fm#94.3#94.3 wqcm#rock\n166#hagerstown , md - chambersburg , pa#wikz - fm#95.1#mix 95.1#adult contemporary\n166#hagerstown , md - chambersburg , pa#wdld - fm#96.7#wild 96.7#rhythmic contemporary hit radio\n166#hagerstown , md - chambersburg , pa#wcha - am#800#true oldies 96.3#oldies\n166#hagerstown , md - chambersburg , pa#whag - am#1410#true oldies 96.3#oldies\n",
        "pandas_code": "(df['branding'].str.contains('big oldies 107.3', case=False, na=False).sum() >= 2)",
        "pandas_eval": "True"
    },
    {
        "id": 69,
        "statement": "richmond - petersburg , va be the market fewer time than other market",
        "label": 1,
        "table_caption": "main line broadcasting",
        "table_text": "dma#market#station#frequency#branding#format\n53#louisville , ky#wgzb - fm#96.5#b96.5#urban\n53#louisville , ky#wdjx - fm#99.7#99.7 djx#contemporary hit radio\n53#louisville , ky#wmjm - fm#101.3#magic 101.3#urban ac\n53#louisville , ky#wxma - fm#102.3#102.3 the max#hot ac\n53#louisville , ky#wesi#105.1#easy rock 105.1#soft adult contemporary\n56#richmond - petersburg , va#wlfv - fm#93.1#93.1 the wolf#southern country\n56#richmond - petersburg , va#wwlb - fm#98.9#98.9 liberty#variety hits\n56#richmond - petersburg , va#warv - fm#100.3#big oldies 107.3#oldies\n56#richmond - petersburg , va#wbbt - fm#107.3#big oldies 107.3#oldies\n60#dayton , oh#wrou - fm#92.1#92.1 wrou#urban ac\n60#dayton , oh#wgtz - fm#92.9#fly 92.9#variety hits\n60#dayton , oh#wcli - fm#101.5#click 101.5#modern hit music\n60#dayton , oh#wdht - fm#102.9#hot 102.9#rhythmic contemporary\n60#dayton , oh#wing - am#1410#espn 1410#sports\n166#hagerstown , md - chambersburg , pa#wqcm - fm#94.3#94.3 wqcm#rock\n166#hagerstown , md - chambersburg , pa#wikz - fm#95.1#mix 95.1#adult contemporary\n166#hagerstown , md - chambersburg , pa#wdld - fm#96.7#wild 96.7#rhythmic contemporary hit radio\n166#hagerstown , md - chambersburg , pa#wcha - am#800#true oldies 96.3#oldies\n166#hagerstown , md - chambersburg , pa#whag - am#1410#true oldies 96.3#oldies\n",
        "pandas_code": "df['market'].value_counts().idxmax() != 'richmond - petersburg , va'",
        "pandas_eval": "True"
    },
    {
        "id": 70,
        "statement": "rom\u00e1n chalbaud be the director of the first film in 1978",
        "label": 1,
        "table_caption": "list of venezuelan submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#original title#film title used in nomination#director#result\n1978 (50th)#el pez que fuma#el pez que fuma#rom\u00e1n chalbaud#not nominated\n1984 (57th)#la casa de agua#the house of water#jacobo penzo#not nominated\n1985 (58th)#oriana#oriana#fina torres#not nominated\n1991 (64th)#jeric\u00f3#jericho#luis alberto lamata#not nominated\n1994 (67th)#golpes a mi puerta#knocks at my door#alejandro saderman#not nominated\n1995 (68th)#sicario#sicario#jos\u00e9 ram\u00f3n novoa#not nominated\n1997 (70th)#una vida y dos mandados#one life and two trails#alberto aruelo#not nominated\n1998 (71st)#rizo#loop#julio sosa#not nominated\n1999 (72nd)#huelepega : ley de la calle#huelepega#elia schneider#not nominated\n2000 (73rd)#oro diablo#oro diablo#jos\u00e9 ram\u00f3n novoa#not nominated\n2001 (74th)#una casa con vista al mar#a house with a view of the sea#alberto aruelo#not nominated\n2002 (75th)#la pluma del arc\u00e1ngel#the archangel 's feather#luis manzo#not nominated\n2003 (76th)#sangrador#sangrador#leonardo henriquez#not nominated\n2004 (77th)#punto y raya#punto y raya#elia schneider#not nominated\n2005 (78th)#1888: el extraordinario viaje de santa isabel#1888: the extraordinary voyage of the santa isabel#alfredo anzola#disqualified\n2006 (79th)#maroa : una ni\u00f1a de la calle#maroa#solveig hoogesteijn#not nominated\n2007 (80th)#postales de leningrado#postcards from leningrad#mariana rondon#not nominated\n2008 (81st)#el tinte de la fama#the color of fame#alberto bellame#not nominated\n2009 (82nd)#libertador morales , el justiciero#libertador morales , el justiciero#efterpi charalambidis#not nominated\n2010 (83rd)#hermano#hermano#marcel rasquin#not nominated\n2011 (84th)#the rumble of the stones#el rumor de las piedras#alejandro bellame#not nominated\n2012 (85th)#rock , paper , scissors#piedra , papel o tijera#hern\u00e1n jabes#not nominated\n",
        "pandas_code": "df[df['year (ceremony)'].str.startswith('1978')]['director'].iloc[0] == 'rom\u00e1n chalbaud'",
        "pandas_eval": "True"
    },
    {
        "id": 71,
        "statement": "el pez que fuma be 6 year earlier than la casa de agua",
        "label": 1,
        "table_caption": "list of venezuelan submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#original title#film title used in nomination#director#result\n1978 (50th)#el pez que fuma#el pez que fuma#rom\u00e1n chalbaud#not nominated\n1984 (57th)#la casa de agua#the house of water#jacobo penzo#not nominated\n1985 (58th)#oriana#oriana#fina torres#not nominated\n1991 (64th)#jeric\u00f3#jericho#luis alberto lamata#not nominated\n1994 (67th)#golpes a mi puerta#knocks at my door#alejandro saderman#not nominated\n1995 (68th)#sicario#sicario#jos\u00e9 ram\u00f3n novoa#not nominated\n1997 (70th)#una vida y dos mandados#one life and two trails#alberto aruelo#not nominated\n1998 (71st)#rizo#loop#julio sosa#not nominated\n1999 (72nd)#huelepega : ley de la calle#huelepega#elia schneider#not nominated\n2000 (73rd)#oro diablo#oro diablo#jos\u00e9 ram\u00f3n novoa#not nominated\n2001 (74th)#una casa con vista al mar#a house with a view of the sea#alberto aruelo#not nominated\n2002 (75th)#la pluma del arc\u00e1ngel#the archangel 's feather#luis manzo#not nominated\n2003 (76th)#sangrador#sangrador#leonardo henriquez#not nominated\n2004 (77th)#punto y raya#punto y raya#elia schneider#not nominated\n2005 (78th)#1888: el extraordinario viaje de santa isabel#1888: the extraordinary voyage of the santa isabel#alfredo anzola#disqualified\n2006 (79th)#maroa : una ni\u00f1a de la calle#maroa#solveig hoogesteijn#not nominated\n2007 (80th)#postales de leningrado#postcards from leningrad#mariana rondon#not nominated\n2008 (81st)#el tinte de la fama#the color of fame#alberto bellame#not nominated\n2009 (82nd)#libertador morales , el justiciero#libertador morales , el justiciero#efterpi charalambidis#not nominated\n2010 (83rd)#hermano#hermano#marcel rasquin#not nominated\n2011 (84th)#the rumble of the stones#el rumor de las piedras#alejandro bellame#not nominated\n2012 (85th)#rock , paper , scissors#piedra , papel o tijera#hern\u00e1n jabes#not nominated\n",
        "pandas_code": "(df[df['original title'] == 'el pez que fuma']['year (ceremony)'].values[0].split(' ')[0] == str(int(df[df['original title'] == 'la casa de agua']['year (ceremony)'].values[0].split(' ')[0]) - 6))",
        "pandas_eval": "True"
    },
    {
        "id": 72,
        "statement": "alberto aruelo direct 2 film",
        "label": 1,
        "table_caption": "list of venezuelan submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#original title#film title used in nomination#director#result\n1978 (50th)#el pez que fuma#el pez que fuma#rom\u00e1n chalbaud#not nominated\n1984 (57th)#la casa de agua#the house of water#jacobo penzo#not nominated\n1985 (58th)#oriana#oriana#fina torres#not nominated\n1991 (64th)#jeric\u00f3#jericho#luis alberto lamata#not nominated\n1994 (67th)#golpes a mi puerta#knocks at my door#alejandro saderman#not nominated\n1995 (68th)#sicario#sicario#jos\u00e9 ram\u00f3n novoa#not nominated\n1997 (70th)#una vida y dos mandados#one life and two trails#alberto aruelo#not nominated\n1998 (71st)#rizo#loop#julio sosa#not nominated\n1999 (72nd)#huelepega : ley de la calle#huelepega#elia schneider#not nominated\n2000 (73rd)#oro diablo#oro diablo#jos\u00e9 ram\u00f3n novoa#not nominated\n2001 (74th)#una casa con vista al mar#a house with a view of the sea#alberto aruelo#not nominated\n2002 (75th)#la pluma del arc\u00e1ngel#the archangel 's feather#luis manzo#not nominated\n2003 (76th)#sangrador#sangrador#leonardo henriquez#not nominated\n2004 (77th)#punto y raya#punto y raya#elia schneider#not nominated\n2005 (78th)#1888: el extraordinario viaje de santa isabel#1888: the extraordinary voyage of the santa isabel#alfredo anzola#disqualified\n2006 (79th)#maroa : una ni\u00f1a de la calle#maroa#solveig hoogesteijn#not nominated\n2007 (80th)#postales de leningrado#postcards from leningrad#mariana rondon#not nominated\n2008 (81st)#el tinte de la fama#the color of fame#alberto bellame#not nominated\n2009 (82nd)#libertador morales , el justiciero#libertador morales , el justiciero#efterpi charalambidis#not nominated\n2010 (83rd)#hermano#hermano#marcel rasquin#not nominated\n2011 (84th)#the rumble of the stones#el rumor de las piedras#alejandro bellame#not nominated\n2012 (85th)#rock , paper , scissors#piedra , papel o tijera#hern\u00e1n jabes#not nominated\n",
        "pandas_code": "(df['director'] == 'alberto aruelo').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 73,
        "statement": "everyone 's result , except 1 be not nominate",
        "label": 1,
        "table_caption": "list of venezuelan submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#original title#film title used in nomination#director#result\n1978 (50th)#el pez que fuma#el pez que fuma#rom\u00e1n chalbaud#not nominated\n1984 (57th)#la casa de agua#the house of water#jacobo penzo#not nominated\n1985 (58th)#oriana#oriana#fina torres#not nominated\n1991 (64th)#jeric\u00f3#jericho#luis alberto lamata#not nominated\n1994 (67th)#golpes a mi puerta#knocks at my door#alejandro saderman#not nominated\n1995 (68th)#sicario#sicario#jos\u00e9 ram\u00f3n novoa#not nominated\n1997 (70th)#una vida y dos mandados#one life and two trails#alberto aruelo#not nominated\n1998 (71st)#rizo#loop#julio sosa#not nominated\n1999 (72nd)#huelepega : ley de la calle#huelepega#elia schneider#not nominated\n2000 (73rd)#oro diablo#oro diablo#jos\u00e9 ram\u00f3n novoa#not nominated\n2001 (74th)#una casa con vista al mar#a house with a view of the sea#alberto aruelo#not nominated\n2002 (75th)#la pluma del arc\u00e1ngel#the archangel 's feather#luis manzo#not nominated\n2003 (76th)#sangrador#sangrador#leonardo henriquez#not nominated\n2004 (77th)#punto y raya#punto y raya#elia schneider#not nominated\n2005 (78th)#1888: el extraordinario viaje de santa isabel#1888: the extraordinary voyage of the santa isabel#alfredo anzola#disqualified\n2006 (79th)#maroa : una ni\u00f1a de la calle#maroa#solveig hoogesteijn#not nominated\n2007 (80th)#postales de leningrado#postcards from leningrad#mariana rondon#not nominated\n2008 (81st)#el tinte de la fama#the color of fame#alberto bellame#not nominated\n2009 (82nd)#libertador morales , el justiciero#libertador morales , el justiciero#efterpi charalambidis#not nominated\n2010 (83rd)#hermano#hermano#marcel rasquin#not nominated\n2011 (84th)#the rumble of the stones#el rumor de las piedras#alejandro bellame#not nominated\n2012 (85th)#rock , paper , scissors#piedra , papel o tijera#hern\u00e1n jabes#not nominated\n",
        "pandas_code": "df[df['result'] != 'disqualified']['result'].eq('not nominated').all()",
        "pandas_eval": "True"
    },
    {
        "id": 74,
        "statement": "the earliest year (ceremony) be 2012 (85th)",
        "label": 1,
        "table_caption": "list of venezuelan submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#original title#film title used in nomination#director#result\n1978 (50th)#el pez que fuma#el pez que fuma#rom\u00e1n chalbaud#not nominated\n1984 (57th)#la casa de agua#the house of water#jacobo penzo#not nominated\n1985 (58th)#oriana#oriana#fina torres#not nominated\n1991 (64th)#jeric\u00f3#jericho#luis alberto lamata#not nominated\n1994 (67th)#golpes a mi puerta#knocks at my door#alejandro saderman#not nominated\n1995 (68th)#sicario#sicario#jos\u00e9 ram\u00f3n novoa#not nominated\n1997 (70th)#una vida y dos mandados#one life and two trails#alberto aruelo#not nominated\n1998 (71st)#rizo#loop#julio sosa#not nominated\n1999 (72nd)#huelepega : ley de la calle#huelepega#elia schneider#not nominated\n2000 (73rd)#oro diablo#oro diablo#jos\u00e9 ram\u00f3n novoa#not nominated\n2001 (74th)#una casa con vista al mar#a house with a view of the sea#alberto aruelo#not nominated\n2002 (75th)#la pluma del arc\u00e1ngel#the archangel 's feather#luis manzo#not nominated\n2003 (76th)#sangrador#sangrador#leonardo henriquez#not nominated\n2004 (77th)#punto y raya#punto y raya#elia schneider#not nominated\n2005 (78th)#1888: el extraordinario viaje de santa isabel#1888: the extraordinary voyage of the santa isabel#alfredo anzola#disqualified\n2006 (79th)#maroa : una ni\u00f1a de la calle#maroa#solveig hoogesteijn#not nominated\n2007 (80th)#postales de leningrado#postcards from leningrad#mariana rondon#not nominated\n2008 (81st)#el tinte de la fama#the color of fame#alberto bellame#not nominated\n2009 (82nd)#libertador morales , el justiciero#libertador morales , el justiciero#efterpi charalambidis#not nominated\n2010 (83rd)#hermano#hermano#marcel rasquin#not nominated\n2011 (84th)#the rumble of the stones#el rumor de las piedras#alejandro bellame#not nominated\n2012 (85th)#rock , paper , scissors#piedra , papel o tijera#hern\u00e1n jabes#not nominated\n",
        "pandas_code": "df['year (ceremony)'].min() != '2012 (85th)'",
        "pandas_eval": "True"
    },
    {
        "id": 75,
        "statement": "scient (guangzhou) infant nutrition have the most sample fail",
        "label": 1,
        "table_caption": "official test failures of the 2008 chinese milk scandal",
        "table_text": "producer#product#samples taken#samples failed#melamine content (mg / kg)\nshijiazhuang sanlu group#\u4e09\u9e7f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#11#11#2563\nshanghai panda dairy#\u718a\u8c93\u53ef\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#5#3#619\nqingdao shengyuan dairy#\u8056\u5143\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#17#8#150\nshanxi gu cheng dairy#\u53e4\u57ce\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#13#4#141.6\njiangxi guangming yingxiong dairy#\u82f1\u96c4\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#98.6\nbaoji huimin dairy#\u60e0\u6c11\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#79.17\ninner mongolia mengniu dairy#\u8499\u725b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#28#3#68.2\ntorador dairy industry (tianjin)#\u53ef\u6dc7\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#67.94\nguangdong yashili group#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#30#8#53.4\nhunan peiyi dairy#\u5357\u5c71\u500d\u76ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#53.4\nheilongjiang qilin dairy#\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c892\u6bb5\u57fa\u7c89#1#1#31.74\nshanxi yashili dairy#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#4#2#26.3\nshenzhen jinbishi milk#\u91d1\u5fc5\u6c0f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#18\nscient (guangzhou) infant nutrition#\u65bd\u6069\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#20#14#17\nguangzhou jinding dairy products factory#\u91d1\u9f0e\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#16.2\ninner mongolia yili industrial group#\u4f0a\u5229\u724c\u5152\u7ae5\u914d\u65b9\u4e73\u7c89#35#1#12\nyantai ausmeadow nutriment#\u6fb3\u7f8e\u591a\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#16#6#10.7\nqingdao suncare nutritional technology#\u611b\u53ef\u4e01\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#4.8\nxi'an baiyue dairy#\u5fa1\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#3.73\nyantai leilei dairy#\u78ca\u78ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#3#1.2\nshanghai baoanli dairy#\u5bf6\u5b89\u529b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#0.21\n",
        "pandas_code": "df.loc[df['producer'] == 'scient (guangzhou) infant nutrition', 'samples failed'].values[0] == df['samples failed'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 76,
        "statement": "no producer have 0 sample fail",
        "label": 1,
        "table_caption": "official test failures of the 2008 chinese milk scandal",
        "table_text": "producer#product#samples taken#samples failed#melamine content (mg / kg)\nshijiazhuang sanlu group#\u4e09\u9e7f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#11#11#2563\nshanghai panda dairy#\u718a\u8c93\u53ef\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#5#3#619\nqingdao shengyuan dairy#\u8056\u5143\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#17#8#150\nshanxi gu cheng dairy#\u53e4\u57ce\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#13#4#141.6\njiangxi guangming yingxiong dairy#\u82f1\u96c4\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#98.6\nbaoji huimin dairy#\u60e0\u6c11\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#79.17\ninner mongolia mengniu dairy#\u8499\u725b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#28#3#68.2\ntorador dairy industry (tianjin)#\u53ef\u6dc7\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#67.94\nguangdong yashili group#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#30#8#53.4\nhunan peiyi dairy#\u5357\u5c71\u500d\u76ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#53.4\nheilongjiang qilin dairy#\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c892\u6bb5\u57fa\u7c89#1#1#31.74\nshanxi yashili dairy#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#4#2#26.3\nshenzhen jinbishi milk#\u91d1\u5fc5\u6c0f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#18\nscient (guangzhou) infant nutrition#\u65bd\u6069\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#20#14#17\nguangzhou jinding dairy products factory#\u91d1\u9f0e\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#16.2\ninner mongolia yili industrial group#\u4f0a\u5229\u724c\u5152\u7ae5\u914d\u65b9\u4e73\u7c89#35#1#12\nyantai ausmeadow nutriment#\u6fb3\u7f8e\u591a\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#16#6#10.7\nqingdao suncare nutritional technology#\u611b\u53ef\u4e01\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#4.8\nxi'an baiyue dairy#\u5fa1\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#3.73\nyantai leilei dairy#\u78ca\u78ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#3#1.2\nshanghai baoanli dairy#\u5bf6\u5b89\u529b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#0.21\n",
        "pandas_code": "all(df['samples failed'] != 0)",
        "pandas_eval": "True"
    },
    {
        "id": 77,
        "statement": "yantai leilei dairy have more sample fail than xi'an baiyue dairy",
        "label": 1,
        "table_caption": "official test failures of the 2008 chinese milk scandal",
        "table_text": "producer#product#samples taken#samples failed#melamine content (mg / kg)\nshijiazhuang sanlu group#\u4e09\u9e7f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#11#11#2563\nshanghai panda dairy#\u718a\u8c93\u53ef\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#5#3#619\nqingdao shengyuan dairy#\u8056\u5143\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#17#8#150\nshanxi gu cheng dairy#\u53e4\u57ce\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#13#4#141.6\njiangxi guangming yingxiong dairy#\u82f1\u96c4\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#98.6\nbaoji huimin dairy#\u60e0\u6c11\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#79.17\ninner mongolia mengniu dairy#\u8499\u725b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#28#3#68.2\ntorador dairy industry (tianjin)#\u53ef\u6dc7\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#67.94\nguangdong yashili group#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#30#8#53.4\nhunan peiyi dairy#\u5357\u5c71\u500d\u76ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#53.4\nheilongjiang qilin dairy#\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c892\u6bb5\u57fa\u7c89#1#1#31.74\nshanxi yashili dairy#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#4#2#26.3\nshenzhen jinbishi milk#\u91d1\u5fc5\u6c0f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#18\nscient (guangzhou) infant nutrition#\u65bd\u6069\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#20#14#17\nguangzhou jinding dairy products factory#\u91d1\u9f0e\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#16.2\ninner mongolia yili industrial group#\u4f0a\u5229\u724c\u5152\u7ae5\u914d\u65b9\u4e73\u7c89#35#1#12\nyantai ausmeadow nutriment#\u6fb3\u7f8e\u591a\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#16#6#10.7\nqingdao suncare nutritional technology#\u611b\u53ef\u4e01\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#4.8\nxi'an baiyue dairy#\u5fa1\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#3.73\nyantai leilei dairy#\u78ca\u78ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#3#1.2\nshanghai baoanli dairy#\u5bf6\u5b89\u529b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#0.21\n",
        "pandas_code": "df[df['producer'] == 'yantai leilei dairy']['samples failed'].values[0] > df[df['producer'] == \"xi'an baiyue dairy\"]['samples failed'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 78,
        "statement": "shijiazhuang sanlu group have the most melamine content (mg / kg) content",
        "label": 1,
        "table_caption": "official test failures of the 2008 chinese milk scandal",
        "table_text": "producer#product#samples taken#samples failed#melamine content (mg / kg)\nshijiazhuang sanlu group#\u4e09\u9e7f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#11#11#2563\nshanghai panda dairy#\u718a\u8c93\u53ef\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#5#3#619\nqingdao shengyuan dairy#\u8056\u5143\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#17#8#150\nshanxi gu cheng dairy#\u53e4\u57ce\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#13#4#141.6\njiangxi guangming yingxiong dairy#\u82f1\u96c4\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#98.6\nbaoji huimin dairy#\u60e0\u6c11\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#79.17\ninner mongolia mengniu dairy#\u8499\u725b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#28#3#68.2\ntorador dairy industry (tianjin)#\u53ef\u6dc7\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#67.94\nguangdong yashili group#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#30#8#53.4\nhunan peiyi dairy#\u5357\u5c71\u500d\u76ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#53.4\nheilongjiang qilin dairy#\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c892\u6bb5\u57fa\u7c89#1#1#31.74\nshanxi yashili dairy#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#4#2#26.3\nshenzhen jinbishi milk#\u91d1\u5fc5\u6c0f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#18\nscient (guangzhou) infant nutrition#\u65bd\u6069\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#20#14#17\nguangzhou jinding dairy products factory#\u91d1\u9f0e\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#16.2\ninner mongolia yili industrial group#\u4f0a\u5229\u724c\u5152\u7ae5\u914d\u65b9\u4e73\u7c89#35#1#12\nyantai ausmeadow nutriment#\u6fb3\u7f8e\u591a\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#16#6#10.7\nqingdao suncare nutritional technology#\u611b\u53ef\u4e01\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#4.8\nxi'an baiyue dairy#\u5fa1\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#3.73\nyantai leilei dairy#\u78ca\u78ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#3#1.2\nshanghai baoanli dairy#\u5bf6\u5b89\u529b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#0.21\n",
        "pandas_code": "df.loc[df['melamine content (mg / kg)'].idxmax(), 'producer'] == 'shijiazhuang sanlu group'",
        "pandas_eval": "True"
    },
    {
        "id": 79,
        "statement": "every producer than only have 1 sample take have that sample fail",
        "label": 1,
        "table_caption": "official test failures of the 2008 chinese milk scandal",
        "table_text": "producer#product#samples taken#samples failed#melamine content (mg / kg)\nshijiazhuang sanlu group#\u4e09\u9e7f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#11#11#2563\nshanghai panda dairy#\u718a\u8c93\u53ef\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#5#3#619\nqingdao shengyuan dairy#\u8056\u5143\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#17#8#150\nshanxi gu cheng dairy#\u53e4\u57ce\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#13#4#141.6\njiangxi guangming yingxiong dairy#\u82f1\u96c4\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#98.6\nbaoji huimin dairy#\u60e0\u6c11\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#79.17\ninner mongolia mengniu dairy#\u8499\u725b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#28#3#68.2\ntorador dairy industry (tianjin)#\u53ef\u6dc7\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#67.94\nguangdong yashili group#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#30#8#53.4\nhunan peiyi dairy#\u5357\u5c71\u500d\u76ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#53.4\nheilongjiang qilin dairy#\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c892\u6bb5\u57fa\u7c89#1#1#31.74\nshanxi yashili dairy#\u96c5\u58eb\u5229\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#4#2#26.3\nshenzhen jinbishi milk#\u91d1\u5fc5\u6c0f\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#2#2#18\nscient (guangzhou) infant nutrition#\u65bd\u6069\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#20#14#17\nguangzhou jinding dairy products factory#\u91d1\u9f0e\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#16.2\ninner mongolia yili industrial group#\u4f0a\u5229\u724c\u5152\u7ae5\u914d\u65b9\u4e73\u7c89#35#1#12\nyantai ausmeadow nutriment#\u6fb3\u7f8e\u591a\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#16#6#10.7\nqingdao suncare nutritional technology#\u611b\u53ef\u4e01\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#4.8\nxi'an baiyue dairy#\u5fa1\u5bf6\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#1#3.73\nyantai leilei dairy#\u78ca\u78ca\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#3#3#1.2\nshanghai baoanli dairy#\u5bf6\u5b89\u529b\u724c\u5b30\u5e7c\u5152\u914d\u65b9\u4e73\u7c89#1#1#0.21\n",
        "pandas_code": "all(df[df['samples taken'] == 1]['samples failed'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 80,
        "statement": "5 player from legia warsaw appear in ekstraklasa",
        "label": 1,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "len(df[df['ekstraklasa'] == 1]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 81,
        "statement": "3 of the 5 legia warsaw player who have appear in the uefa cup be midfielders",
        "label": 1,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "(df[df['uefa cup'] > 0]['position'].value_counts()['midfielder'] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 82,
        "statement": "all 5 player from legia who appear in 2 uefa cup match also appear in 1 ekstraklasa",
        "label": 1,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "all(df[(df['uefa cup'] == 2)]['ekstraklasa'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 83,
        "statement": "maciej iwa\u0144ski be the only 2008 - 09 legia warsaw forward to have play in the uefa cup",
        "label": 1,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "((df['player'] == 'maciej iwa\u0144ski') & (df['position'] == 'forward') & (df['uefa cup'] > 0)).any() & ~(df[(df['position'] == 'forward') & (df['uefa cup'] > 0) & (df['player'] != 'maciej iwa\u0144ski')].any().any())",
        "pandas_eval": "True"
    },
    {
        "id": 84,
        "statement": "no 2008 - 09 legia warsaw player participate in the polish cup",
        "label": 1,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "(df['polish cup'] == 0).all()",
        "pandas_eval": "True"
    },
    {
        "id": 85,
        "statement": "there be more score in the ekstraklasa than in the uefa cup",
        "label": 0,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "df['ekstraklasa'].sum() > df['uefa cup'].sum()",
        "pandas_eval": "False"
    },
    {
        "id": 86,
        "statement": "there be more forward position than midfielder in the uefa cup",
        "label": 0,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "(df[df['position'] == 'forward']['uefa cup'].sum() > df[df['position'] == 'midfielder']['uefa cup'].sum())",
        "pandas_eval": "False"
    },
    {
        "id": 87,
        "statement": "not all player from the legia appear in ekstraklasa but all appear in uefa cup",
        "label": 0,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "not df['ekstraklasa'].all() and df['uefa cup'].all()",
        "pandas_eval": "False"
    },
    {
        "id": 88,
        "statement": "maciej iwa\u0144ski of serbia be only only forward position that play in the 2008 - 09 uefa cup",
        "label": 0,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 89,
        "statement": "there be more player that participate in the polish cup than of the uefa cup",
        "label": 0,
        "table_caption": "legia warsaw season 2008 - 09",
        "table_text": "player#position#ekstraklasa#polish cup#uefa cup#total\nj\u00e1n mucha#goalkeeper#1#0#2#3\nroger#midfielder#1#0#2#3\nmaciej iwa\u0144ski#forward#1#0#2#3\nmiroslav radovi\u0107#midfielder#1#0#2#3\nwojciech szala#midfielder#1#0#2#3\n",
        "pandas_code": "(df['polish cup'].sum() > df['uefa cup'].sum())",
        "pandas_eval": "False"
    },
    {
        "id": 90,
        "statement": "the majority of the episode be direct by gerry cohen",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n132#1#magnificent seven#gerry cohen#arthur silver#september 13 , 1992#7.01\n133#2#t - r - a - something - something spells tramp#gerry cohen#ron leavitt & ellen l fogle#september 20 , 1992#7.02\n134#3#every bundy has a birthday#gerry cohen#richard gurman#september 27 , 1992#7.03\n135#4#al on the rocks#gerry cohen#andrew smith#october 4 , 1992#7.04\n136#5#what i did for love#gerry cohen#ellen l fogle#october 11 , 1992#7.05\n137#6#frat chance#gerry cohen#larry jacobson#october 25 , 1992#7.06\n138#7#the chicago wine party#gerry cohen#stacie lipp#november 1 , 1992#7.07\n139#8#kelly doesn't live here anymore#amanda bearse#gabrielle topping#november 8 , 1992#7.08\n140#9#rock of ages#gerry cohen#al aidekman#november 15 , 1992#7.09\n141#10#death of a shoe salesman#gerry cohen#stacie lipp#november 22 , 1992#7.10\n142#11#old college try#gerry cohen#dianne burroughs , joey gutierrez & p sharon#december 13 , 1992#7.11\n143#12#christmas#gerry cohen#ellen l fogle#december 20 , 1992#7.12\n144#13#the wedding show#gerry cohen#arthur silver#january 10 , 1993#7.13\n145#14#it doesn't get any better than this#sam w orender#michael g moye#january 24 , 1993#7.14\n146#15#heels on wheels#gerry cohen#stacie lipp#february 7 , 1993#7.15\n147#16#mr empty pants#gerry cohen#george tricker#february 14 , 1993#7.16\n148#17#you can't miss#amanda bearse#joel valentincic & scott zimbler#february 21 , 1993#7.17\n149#18#peggy and the pirates#gerry cohen#richard gurman#february 28 , 1993#7.18\n150#19#go for the old#gerry cohen#stacie lipp#march 14 , 1993#7.19\n151#20#un - alful entry#amanda bearse#larry jacobson#march 28 , 1993#7.20\n152#21#movie show#gerry cohen#ellen l fogle#april 11 , 1993#7.21\n153#22#'til death do us part#gerry cohen#stacie lipp#april 25 , 1993#7.22\n154#23#tis time to smell the roses#gerry cohen#kevin curran#may 2 , 1993#7.23\n155#24#old insurance dodge#gerry cohen#larry jacobson#may 9 , 1993#7.24\n156#25#wedding repercussions#gerry cohen#arthur silver#may 16 , 1993#7.25\n",
        "pandas_code": "df['directed by'].value_counts().idxmax() == 'gerry cohen'",
        "pandas_eval": "True"
    },
    {
        "id": 91,
        "statement": "5 episode be written by stacie lipp",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n132#1#magnificent seven#gerry cohen#arthur silver#september 13 , 1992#7.01\n133#2#t - r - a - something - something spells tramp#gerry cohen#ron leavitt & ellen l fogle#september 20 , 1992#7.02\n134#3#every bundy has a birthday#gerry cohen#richard gurman#september 27 , 1992#7.03\n135#4#al on the rocks#gerry cohen#andrew smith#october 4 , 1992#7.04\n136#5#what i did for love#gerry cohen#ellen l fogle#october 11 , 1992#7.05\n137#6#frat chance#gerry cohen#larry jacobson#october 25 , 1992#7.06\n138#7#the chicago wine party#gerry cohen#stacie lipp#november 1 , 1992#7.07\n139#8#kelly doesn't live here anymore#amanda bearse#gabrielle topping#november 8 , 1992#7.08\n140#9#rock of ages#gerry cohen#al aidekman#november 15 , 1992#7.09\n141#10#death of a shoe salesman#gerry cohen#stacie lipp#november 22 , 1992#7.10\n142#11#old college try#gerry cohen#dianne burroughs , joey gutierrez & p sharon#december 13 , 1992#7.11\n143#12#christmas#gerry cohen#ellen l fogle#december 20 , 1992#7.12\n144#13#the wedding show#gerry cohen#arthur silver#january 10 , 1993#7.13\n145#14#it doesn't get any better than this#sam w orender#michael g moye#january 24 , 1993#7.14\n146#15#heels on wheels#gerry cohen#stacie lipp#february 7 , 1993#7.15\n147#16#mr empty pants#gerry cohen#george tricker#february 14 , 1993#7.16\n148#17#you can't miss#amanda bearse#joel valentincic & scott zimbler#february 21 , 1993#7.17\n149#18#peggy and the pirates#gerry cohen#richard gurman#february 28 , 1993#7.18\n150#19#go for the old#gerry cohen#stacie lipp#march 14 , 1993#7.19\n151#20#un - alful entry#amanda bearse#larry jacobson#march 28 , 1993#7.20\n152#21#movie show#gerry cohen#ellen l fogle#april 11 , 1993#7.21\n153#22#'til death do us part#gerry cohen#stacie lipp#april 25 , 1993#7.22\n154#23#tis time to smell the roses#gerry cohen#kevin curran#may 2 , 1993#7.23\n155#24#old insurance dodge#gerry cohen#larry jacobson#may 9 , 1993#7.24\n156#25#wedding repercussions#gerry cohen#arthur silver#may 16 , 1993#7.25\n",
        "pandas_code": "(df['written by'].value_counts()['stacie lipp'] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 92,
        "statement": "old college try be the episode that be written by the most people",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n132#1#magnificent seven#gerry cohen#arthur silver#september 13 , 1992#7.01\n133#2#t - r - a - something - something spells tramp#gerry cohen#ron leavitt & ellen l fogle#september 20 , 1992#7.02\n134#3#every bundy has a birthday#gerry cohen#richard gurman#september 27 , 1992#7.03\n135#4#al on the rocks#gerry cohen#andrew smith#october 4 , 1992#7.04\n136#5#what i did for love#gerry cohen#ellen l fogle#october 11 , 1992#7.05\n137#6#frat chance#gerry cohen#larry jacobson#october 25 , 1992#7.06\n138#7#the chicago wine party#gerry cohen#stacie lipp#november 1 , 1992#7.07\n139#8#kelly doesn't live here anymore#amanda bearse#gabrielle topping#november 8 , 1992#7.08\n140#9#rock of ages#gerry cohen#al aidekman#november 15 , 1992#7.09\n141#10#death of a shoe salesman#gerry cohen#stacie lipp#november 22 , 1992#7.10\n142#11#old college try#gerry cohen#dianne burroughs , joey gutierrez & p sharon#december 13 , 1992#7.11\n143#12#christmas#gerry cohen#ellen l fogle#december 20 , 1992#7.12\n144#13#the wedding show#gerry cohen#arthur silver#january 10 , 1993#7.13\n145#14#it doesn't get any better than this#sam w orender#michael g moye#january 24 , 1993#7.14\n146#15#heels on wheels#gerry cohen#stacie lipp#february 7 , 1993#7.15\n147#16#mr empty pants#gerry cohen#george tricker#february 14 , 1993#7.16\n148#17#you can't miss#amanda bearse#joel valentincic & scott zimbler#february 21 , 1993#7.17\n149#18#peggy and the pirates#gerry cohen#richard gurman#february 28 , 1993#7.18\n150#19#go for the old#gerry cohen#stacie lipp#march 14 , 1993#7.19\n151#20#un - alful entry#amanda bearse#larry jacobson#march 28 , 1993#7.20\n152#21#movie show#gerry cohen#ellen l fogle#april 11 , 1993#7.21\n153#22#'til death do us part#gerry cohen#stacie lipp#april 25 , 1993#7.22\n154#23#tis time to smell the roses#gerry cohen#kevin curran#may 2 , 1993#7.23\n155#24#old insurance dodge#gerry cohen#larry jacobson#may 9 , 1993#7.24\n156#25#wedding repercussions#gerry cohen#arthur silver#may 16 , 1993#7.25\n",
        "pandas_code": "df[df['title'] == 'old college try']['written by'].str.count(',').max() == df['written by'].str.count(',').max()",
        "pandas_eval": "True"
    },
    {
        "id": 93,
        "statement": "4 episode air in both november february",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n132#1#magnificent seven#gerry cohen#arthur silver#september 13 , 1992#7.01\n133#2#t - r - a - something - something spells tramp#gerry cohen#ron leavitt & ellen l fogle#september 20 , 1992#7.02\n134#3#every bundy has a birthday#gerry cohen#richard gurman#september 27 , 1992#7.03\n135#4#al on the rocks#gerry cohen#andrew smith#october 4 , 1992#7.04\n136#5#what i did for love#gerry cohen#ellen l fogle#october 11 , 1992#7.05\n137#6#frat chance#gerry cohen#larry jacobson#october 25 , 1992#7.06\n138#7#the chicago wine party#gerry cohen#stacie lipp#november 1 , 1992#7.07\n139#8#kelly doesn't live here anymore#amanda bearse#gabrielle topping#november 8 , 1992#7.08\n140#9#rock of ages#gerry cohen#al aidekman#november 15 , 1992#7.09\n141#10#death of a shoe salesman#gerry cohen#stacie lipp#november 22 , 1992#7.10\n142#11#old college try#gerry cohen#dianne burroughs , joey gutierrez & p sharon#december 13 , 1992#7.11\n143#12#christmas#gerry cohen#ellen l fogle#december 20 , 1992#7.12\n144#13#the wedding show#gerry cohen#arthur silver#january 10 , 1993#7.13\n145#14#it doesn't get any better than this#sam w orender#michael g moye#january 24 , 1993#7.14\n146#15#heels on wheels#gerry cohen#stacie lipp#february 7 , 1993#7.15\n147#16#mr empty pants#gerry cohen#george tricker#february 14 , 1993#7.16\n148#17#you can't miss#amanda bearse#joel valentincic & scott zimbler#february 21 , 1993#7.17\n149#18#peggy and the pirates#gerry cohen#richard gurman#february 28 , 1993#7.18\n150#19#go for the old#gerry cohen#stacie lipp#march 14 , 1993#7.19\n151#20#un - alful entry#amanda bearse#larry jacobson#march 28 , 1993#7.20\n152#21#movie show#gerry cohen#ellen l fogle#april 11 , 1993#7.21\n153#22#'til death do us part#gerry cohen#stacie lipp#april 25 , 1993#7.22\n154#23#tis time to smell the roses#gerry cohen#kevin curran#may 2 , 1993#7.23\n155#24#old insurance dodge#gerry cohen#larry jacobson#may 9 , 1993#7.24\n156#25#wedding repercussions#gerry cohen#arthur silver#may 16 , 1993#7.25\n",
        "pandas_code": "len(df[df['original air date'].str.contains('november|february', case=False, regex=True)]) >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 94,
        "statement": "amanda bearse direct more episode than sam w orender",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n132#1#magnificent seven#gerry cohen#arthur silver#september 13 , 1992#7.01\n133#2#t - r - a - something - something spells tramp#gerry cohen#ron leavitt & ellen l fogle#september 20 , 1992#7.02\n134#3#every bundy has a birthday#gerry cohen#richard gurman#september 27 , 1992#7.03\n135#4#al on the rocks#gerry cohen#andrew smith#october 4 , 1992#7.04\n136#5#what i did for love#gerry cohen#ellen l fogle#october 11 , 1992#7.05\n137#6#frat chance#gerry cohen#larry jacobson#october 25 , 1992#7.06\n138#7#the chicago wine party#gerry cohen#stacie lipp#november 1 , 1992#7.07\n139#8#kelly doesn't live here anymore#amanda bearse#gabrielle topping#november 8 , 1992#7.08\n140#9#rock of ages#gerry cohen#al aidekman#november 15 , 1992#7.09\n141#10#death of a shoe salesman#gerry cohen#stacie lipp#november 22 , 1992#7.10\n142#11#old college try#gerry cohen#dianne burroughs , joey gutierrez & p sharon#december 13 , 1992#7.11\n143#12#christmas#gerry cohen#ellen l fogle#december 20 , 1992#7.12\n144#13#the wedding show#gerry cohen#arthur silver#january 10 , 1993#7.13\n145#14#it doesn't get any better than this#sam w orender#michael g moye#january 24 , 1993#7.14\n146#15#heels on wheels#gerry cohen#stacie lipp#february 7 , 1993#7.15\n147#16#mr empty pants#gerry cohen#george tricker#february 14 , 1993#7.16\n148#17#you can't miss#amanda bearse#joel valentincic & scott zimbler#february 21 , 1993#7.17\n149#18#peggy and the pirates#gerry cohen#richard gurman#february 28 , 1993#7.18\n150#19#go for the old#gerry cohen#stacie lipp#march 14 , 1993#7.19\n151#20#un - alful entry#amanda bearse#larry jacobson#march 28 , 1993#7.20\n152#21#movie show#gerry cohen#ellen l fogle#april 11 , 1993#7.21\n153#22#'til death do us part#gerry cohen#stacie lipp#april 25 , 1993#7.22\n154#23#tis time to smell the roses#gerry cohen#kevin curran#may 2 , 1993#7.23\n155#24#old insurance dodge#gerry cohen#larry jacobson#may 9 , 1993#7.24\n156#25#wedding repercussions#gerry cohen#arthur silver#may 16 , 1993#7.25\n",
        "pandas_code": "(df['directed by'].value_counts()['amanda bearse'] > df['directed by'].value_counts()['sam w orender'])",
        "pandas_eval": "True"
    },
    {
        "id": 95,
        "statement": "the boston patriot have 2 player pick in 1963",
        "label": 1,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "(df[(df['afl team'] == 'boston patriots') & (df['year'] == 1963)]['player name'].nunique() >= 2)",
        "pandas_eval": "True"
    },
    {
        "id": 96,
        "statement": "jarrell williams and billy moody have the position of hb when they be pick",
        "label": 1,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "all(df[df['player name'].isin(['jarrell williams', 'billy moody'])]['position'] == 'hb')",
        "pandas_eval": "True"
    },
    {
        "id": 97,
        "statement": "danny brabham and lance alworth have the lowest score in overall",
        "label": 1,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "df[df['player name'].isin(['danny brabham', 'lance alworth'])]['overall'].min() == df['overall'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 98,
        "statement": "3 player be pick 8 for 3 year in a row",
        "label": 1,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "((df['pick'] == 8) & (df['year'].isin([1962, 1963, 1964]))).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 99,
        "statement": "1 player be select in round 1 , while 3 be select in round 2",
        "label": 1,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "(df[df['round'] == 1].shape[0] == 1) & (df[df['round'] == 2].shape[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 100,
        "statement": "the boston patriot do not have any repeat player in the draft of 1963",
        "label": 0,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "len(df[(df['year'] == 1963) & (df['afl team'] == 'boston patriots')]['player name'].unique()) == len(df[(df['year'] == 1963) & (df['afl team'] == 'boston patriots')])",
        "pandas_eval": "False"
    },
    {
        "id": 101,
        "statement": "billy moody and jarrell williams be the number 1 pick",
        "label": 0,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "((df['player name'] == 'billy moody') & (df['pick'] == 1)).any() and ((df['player name'] == 'jarrell williams') & (df['pick'] == 1)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 102,
        "statement": "danny brabham and lance alworth be on the same team",
        "label": 0,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "df[df['player name'].isin(['danny brabham', 'lance alworth'])].groupby('afl team').size().max() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 103,
        "statement": "8 player have the same 4 pick 3 year in a row",
        "label": 0,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "len(df[(df['pick'] == 4) & (df['year'].isin([1961, 1962, 1963]))]) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 104,
        "statement": "all 4 player be select in round 1",
        "label": 0,
        "table_caption": "list of arkansas razorbacks in the nfl draft",
        "table_text": "year#round#pick#overall#player name#position#afl team\n1961#12#2#90#wayne harris#c#boston patriots\n1961#17#6#134#jarrell williams#hb#dallas texans\n1962#2#1#9#lance alworth#wr#oakland raiders\n1962#29#8#232#paul dudley#hb#san diego chargers\n1962#31#4#244#jim collier#te#buffalo bills\n1963#1#6#6#danny brabham#lb#houston oilers\n1963#13#4#100#dave adams#g#boston patriots\n1963#23#8#184#dave adams#g#kansas city chiefs\n1963#27#7#215#dave adams#g#boston patriots\n1963#16#6#126#wes bryant#t#boston patriots\n1963#23#5#181#billy moody#hb#denver broncos\n1963#25#2#194#jerry mazzanti#de#san diego chargers\n1963#27#8#216#bill clay#e#kansas city chiefs\n1964#19#2#146#jerry lamb#e#kansas city chiefs\n1964#26#7#207#gordon quest#qb#oakland raiders\n1965#2#8#16#ronnie caveness#lb#kansas city chiefs\n1966#2#8#17#jim lindsey#rb#buffalo bills\n1966#4#8#33#bobby burnett#rb#buffalo bills\n1966#17#1#146#mike bender#g#miami dolphins\n1966#10#9#91#bobby crockett#wr#buffalo bills\n",
        "pandas_code": "len(df[df['round'] == 1]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 105,
        "statement": "tulane have the only match list to result in a tie",
        "label": 1,
        "table_caption": "1966 miami hurricanes football team",
        "table_text": "game#date#opponent#result#hurricanes points#opponents#record\n1#sept 17#colorado#win#24#3#1 - 0 - 0\n2#sept 24#florida state#loss#20#23#1 - 1 - 0\n3#oct 1#louisiana state#loss#8#10#1 - 2 - 0\n4#oct 14#georgia#win#7#6#2 - 2 - 0\n5#oct 21#indiana#win#14#7#3 - 2 - 0\n6#oct 28#southern california#win#10#7#4 - 2 - 0\n7#nov 5#tulane#tie#10#10#4 - 2 - 1\n8#nov 11#pittsburgh#win#38#14#5 - 2 - 1\n9#nov 18#iowa#win#44#0#6 - 2 - 1\n",
        "pandas_code": "(df[df['opponent'] == 'tulane']['result'] == 'tie').all()",
        "pandas_eval": "True"
    },
    {
        "id": 106,
        "statement": "the hurricane win less than 10 point against louisiana state and georgia",
        "label": 1,
        "table_caption": "1966 miami hurricanes football team",
        "table_text": "game#date#opponent#result#hurricanes points#opponents#record\n1#sept 17#colorado#win#24#3#1 - 0 - 0\n2#sept 24#florida state#loss#20#23#1 - 1 - 0\n3#oct 1#louisiana state#loss#8#10#1 - 2 - 0\n4#oct 14#georgia#win#7#6#2 - 2 - 0\n5#oct 21#indiana#win#14#7#3 - 2 - 0\n6#oct 28#southern california#win#10#7#4 - 2 - 0\n7#nov 5#tulane#tie#10#10#4 - 2 - 1\n8#nov 11#pittsburgh#win#38#14#5 - 2 - 1\n9#nov 18#iowa#win#44#0#6 - 2 - 1\n",
        "pandas_code": "((df['opponent'] == 'louisiana state') & (df['hurricanes points'] < 10)).any() & ((df['opponent'] == 'georgia') & (df['hurricanes points'] < 10)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 107,
        "statement": "florida state be the only opponent list to win more than 20 point",
        "label": 1,
        "table_caption": "1966 miami hurricanes football team",
        "table_text": "game#date#opponent#result#hurricanes points#opponents#record\n1#sept 17#colorado#win#24#3#1 - 0 - 0\n2#sept 24#florida state#loss#20#23#1 - 1 - 0\n3#oct 1#louisiana state#loss#8#10#1 - 2 - 0\n4#oct 14#georgia#win#7#6#2 - 2 - 0\n5#oct 21#indiana#win#14#7#3 - 2 - 0\n6#oct 28#southern california#win#10#7#4 - 2 - 0\n7#nov 5#tulane#tie#10#10#4 - 2 - 1\n8#nov 11#pittsburgh#win#38#14#5 - 2 - 1\n9#nov 18#iowa#win#44#0#6 - 2 - 1\n",
        "pandas_code": "(df[df['opponent'] == 'florida state']['opponents'].iloc[0] > 20) and (df[df['opponents'] > 20]['opponent'].nunique() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 108,
        "statement": "iowa be the only opponent list to not win any point",
        "label": 1,
        "table_caption": "1966 miami hurricanes football team",
        "table_text": "game#date#opponent#result#hurricanes points#opponents#record\n1#sept 17#colorado#win#24#3#1 - 0 - 0\n2#sept 24#florida state#loss#20#23#1 - 1 - 0\n3#oct 1#louisiana state#loss#8#10#1 - 2 - 0\n4#oct 14#georgia#win#7#6#2 - 2 - 0\n5#oct 21#indiana#win#14#7#3 - 2 - 0\n6#oct 28#southern california#win#10#7#4 - 2 - 0\n7#nov 5#tulane#tie#10#10#4 - 2 - 1\n8#nov 11#pittsburgh#win#38#14#5 - 2 - 1\n9#nov 18#iowa#win#44#0#6 - 2 - 1\n",
        "pandas_code": "df[df['opponent'] == 'iowa']['opponents'].eq(0).all()",
        "pandas_eval": "True"
    },
    {
        "id": 109,
        "statement": "the hurricane play against tulane , pittsburgh , and iowa in november",
        "label": 1,
        "table_caption": "1966 miami hurricanes football team",
        "table_text": "game#date#opponent#result#hurricanes points#opponents#record\n1#sept 17#colorado#win#24#3#1 - 0 - 0\n2#sept 24#florida state#loss#20#23#1 - 1 - 0\n3#oct 1#louisiana state#loss#8#10#1 - 2 - 0\n4#oct 14#georgia#win#7#6#2 - 2 - 0\n5#oct 21#indiana#win#14#7#3 - 2 - 0\n6#oct 28#southern california#win#10#7#4 - 2 - 0\n7#nov 5#tulane#tie#10#10#4 - 2 - 1\n8#nov 11#pittsburgh#win#38#14#5 - 2 - 1\n9#nov 18#iowa#win#44#0#6 - 2 - 1\n",
        "pandas_code": "all(df[df['date'].str.contains('nov')]['opponent'].isin(['tulane', 'pittsburgh', 'iowa']))",
        "pandas_eval": "True"
    },
    {
        "id": 111,
        "statement": "rainer ohlhauser have both the highest amount of goal and the second highest amount of goal in consecutive season",
        "label": 1,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df['top goalscorer'].iloc[-2:] == 'rainer ohlhauser').all() and (df['goals'].iloc[-2] < df['goals'].iloc[-1])",
        "pandas_eval": "True"
    },
    {
        "id": 112,
        "statement": "heinz letti be the top goalscorer in both the 1952 - 53 season and the 1954 - 55 season",
        "label": 1,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "all(df[(df['season'].isin(['1952 - 53', '1954 - 55']))]['top goalscorer'] == 'heinz lettl')",
        "pandas_eval": "True"
    },
    {
        "id": 113,
        "statement": "seemann be rank 1 higher in the 1950 - 51 season than in the 1951 - 52 season",
        "label": 1,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df.loc[df['season'] == '1950 - 51', 'rank'].values[0] == '9') & (df.loc[df['season'] == '1951 - 52', 'rank'].values[0] == '8')",
        "pandas_eval": "True"
    },
    {
        "id": 114,
        "statement": "peter grosser have 18 goal , while klaus kuhnert have 20 goal",
        "label": 1,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df[df['top goalscorer'] == 'peter grosser']['goals'].values[0] == 18) & (df[df['top goalscorer'] == 'klaus kuhnert']['goals'].values[0] == 20)",
        "pandas_eval": "True"
    },
    {
        "id": 115,
        "statement": "bayern munich have the highest rank of 10 during the 1954 - 55 season",
        "label": 0,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df.loc[df['season'] == '1954 - 55', 'rank'].values[0] == '10')",
        "pandas_eval": "False"
    },
    {
        "id": 116,
        "statement": "rainer ohlhauser have both the highest amount of goal and the second highest amount of goal in consecutive season , 1959 - 60 and 1960 - 61",
        "label": 0,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df.loc[df['season'].isin(['1959 - 60', '1960 - 61']), 'top goalscorer'].eq('rainer ohlhauser').all()) and (df.loc[df['season'] == '1959 - 60', 'goals'].iloc[0] > df.loc[df['season'] == '1960 - 61', 'goals'].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 117,
        "statement": "heinz letti be the top goalscorer in the 1952 - 53 season , 1953 - 54 season and the 1954 - 55 season",
        "label": 0,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df.loc[df['season'] == '1952 - 53', 'top goalscorer'].values[0] == 'heinz lettl') & (df.loc[df['season'] == '1953 - 54', 'top goalscorer'].values[0] == 'heinz lettl') & (df.loc[df['season'] == '1954 - 55', 'top goalscorer'].values[0] == 'heinz lettl')",
        "pandas_eval": "False"
    },
    {
        "id": 118,
        "statement": "seemann be rank 1 higher in the 1950 - 51 season than in the 1951 - 52 season and 1 lower in the 1952 - 53 season",
        "label": 0,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df.loc[df['season'] == '1950 - 51', 'rank'].values[0] == str(int(df.loc[df['season'] == '1951 - 52', 'rank'].values[0]) + 1)) & (df.loc[df['season'] == '1950 - 51', 'rank'].values[0] == str(int(df.loc[df['season'] == '1952 - 53', 'rank'].values[0]) - 1))",
        "pandas_eval": "False"
    },
    {
        "id": 119,
        "statement": "peter grosser have 18 goal , while klaus kuhnert have 20 more goal than him",
        "label": 0,
        "table_caption": "list of fc bayern munich seasons",
        "table_text": "season#division#rank#avgatt#top goalscorer#goals\n1945 - 46#ol#6#unknown#holzm\u00fcller#17\n1946 - 47#ol#11#13 316#franz bachl#12\n1947 - 48#ol#4#21 474#h\u00e4delt#17\n1948 - 49#ol#3#20 133#h\u00e4delt scholz#13\n1949 - 50#ol#13#16 067#franz bachl scholz#10\n1950 - 51#ol#9#16 824#seemann#15\n1951 - 52#ol#8#16 067#seemann#12\n1952 - 53#ol#7#15 933#heinz lettl#13\n1953 - 54#ol#9#17 867#sch\u00e4dlich#8\n1954 - 55#ol#16 \u2193#15 600#heinz lettl#11\n1955 - 56#ol2#2 \u2191#13 000#velhorn#19\n1956 - 57#ol#10#20 867#erich hahn#11\n1957 - 58#ol#7#21 000#sommerlatt#16\n1958 - 59#ol#4#22 800#klaus kuhnert#20\n1959 - 60#ol#5#17 800#peter grosser#18\n1960 - 61#ol#8#14 667#wagenbauer#13\n1961 - 62#ol#3#16 400#rainer ohlhauser#23\n1962 - 63#ol#3#19 067#rainer ohlhauser#24\n",
        "pandas_code": "(df[df['top goalscorer'] == 'peter grosser']['goals'].values[0] == 18) & (df[df['top goalscorer'] == 'klaus kuhnert']['goals'].values[0] == 38)",
        "pandas_eval": "False"
    },
    {
        "id": 120,
        "statement": "jordan fransoo be select in round 7 overall 186 as a defenseman and he be from canada have play for brandon wheat king (whl)",
        "label": 1,
        "table_caption": "list of ottawa senators draft picks",
        "table_text": "round#overall#player#position#nationality#club team\n1#6#mika zibanejad#centre#sweden#djurg\u00e5rdens if hockey (sel)\n1#21 (from nashville)#stefan noesen#right wing#united states#plymouth whalers (ohl)\n1#24 (from detroit)#matthew puempel#left wing#canada#peterborough petes (ohl)\n2#61 (from boston)#shane prince#left wing#united states#ottawa 67 's (ohl)\n4#96#jean - gabriel pageau#centre#canada#gatineau olympiques (qmjhl)\n5#126#fredrik claesson#defense#sweden#djurg\u00e5rdens if hockey (sel)\n6#156#darren kramer#centre#canada#spokane chiefs (whl)\n6#171 (from phoenix)#max mccormick#left wing#united states#sioux city musketeers (ushl)\n7#186#jordan fransoo#defense#canada#brandon wheat kings (whl)\n",
        "pandas_code": "((df['player'] == 'jordan fransoo') & (df['round'] == 7) & (df['overall'] == '186') & (df['position'] == 'defense') & (df['nationality'] == 'canada') & (df['club team'] == 'brandon wheat kings (whl)')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 121,
        "statement": "the player that play for plymouth whaler (ohl) be stefan noesen who be choose in round 1 , 21 overall (form nashville) and play the position right wing",
        "label": 1,
        "table_caption": "list of ottawa senators draft picks",
        "table_text": "round#overall#player#position#nationality#club team\n1#6#mika zibanejad#centre#sweden#djurg\u00e5rdens if hockey (sel)\n1#21 (from nashville)#stefan noesen#right wing#united states#plymouth whalers (ohl)\n1#24 (from detroit)#matthew puempel#left wing#canada#peterborough petes (ohl)\n2#61 (from boston)#shane prince#left wing#united states#ottawa 67 's (ohl)\n4#96#jean - gabriel pageau#centre#canada#gatineau olympiques (qmjhl)\n5#126#fredrik claesson#defense#sweden#djurg\u00e5rdens if hockey (sel)\n6#156#darren kramer#centre#canada#spokane chiefs (whl)\n6#171 (from phoenix)#max mccormick#left wing#united states#sioux city musketeers (ushl)\n7#186#jordan fransoo#defense#canada#brandon wheat kings (whl)\n",
        "pandas_code": "df[(df['player'] == 'stefan noesen') & (df['club team'] == 'plymouth whalers (ohl)') & (df['round'] == 1) & (df['overall'] == '21 (from nashville)') & (df['position'] == 'right wing')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 122,
        "statement": "there be 2 player player defense and they be jordan fransoo and fredrik claesson and be from canada and sweden",
        "label": 1,
        "table_caption": "list of ottawa senators draft picks",
        "table_text": "round#overall#player#position#nationality#club team\n1#6#mika zibanejad#centre#sweden#djurg\u00e5rdens if hockey (sel)\n1#21 (from nashville)#stefan noesen#right wing#united states#plymouth whalers (ohl)\n1#24 (from detroit)#matthew puempel#left wing#canada#peterborough petes (ohl)\n2#61 (from boston)#shane prince#left wing#united states#ottawa 67 's (ohl)\n4#96#jean - gabriel pageau#centre#canada#gatineau olympiques (qmjhl)\n5#126#fredrik claesson#defense#sweden#djurg\u00e5rdens if hockey (sel)\n6#156#darren kramer#centre#canada#spokane chiefs (whl)\n6#171 (from phoenix)#max mccormick#left wing#united states#sioux city musketeers (ushl)\n7#186#jordan fransoo#defense#canada#brandon wheat kings (whl)\n",
        "pandas_code": "df[(df['position'] == 'defense') & (df['player'].isin(['jordan fransoo', 'fredrik claesson'])) & (df['nationality'].isin(['canada', 'sweden']))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 123,
        "statement": "max mccormick be choose in round 6 , 171 overall (from phoenix) and he play the left wing position be from the united state",
        "label": 1,
        "table_caption": "list of ottawa senators draft picks",
        "table_text": "round#overall#player#position#nationality#club team\n1#6#mika zibanejad#centre#sweden#djurg\u00e5rdens if hockey (sel)\n1#21 (from nashville)#stefan noesen#right wing#united states#plymouth whalers (ohl)\n1#24 (from detroit)#matthew puempel#left wing#canada#peterborough petes (ohl)\n2#61 (from boston)#shane prince#left wing#united states#ottawa 67 's (ohl)\n4#96#jean - gabriel pageau#centre#canada#gatineau olympiques (qmjhl)\n5#126#fredrik claesson#defense#sweden#djurg\u00e5rdens if hockey (sel)\n6#156#darren kramer#centre#canada#spokane chiefs (whl)\n6#171 (from phoenix)#max mccormick#left wing#united states#sioux city musketeers (ushl)\n7#186#jordan fransoo#defense#canada#brandon wheat kings (whl)\n",
        "pandas_code": "df[(df['player'] == 'max mccormick') & (df['round'] == 6) & (df['overall'] == '171 (from phoenix)') & (df['position'] == 'left wing') & (df['nationality'] == 'united states')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 124,
        "statement": "the player from gatineau olympiques (qmjhl) be jean - gabriel pageau who play centre and be choose in the 4th round , 96 overall",
        "label": 1,
        "table_caption": "list of ottawa senators draft picks",
        "table_text": "round#overall#player#position#nationality#club team\n1#6#mika zibanejad#centre#sweden#djurg\u00e5rdens if hockey (sel)\n1#21 (from nashville)#stefan noesen#right wing#united states#plymouth whalers (ohl)\n1#24 (from detroit)#matthew puempel#left wing#canada#peterborough petes (ohl)\n2#61 (from boston)#shane prince#left wing#united states#ottawa 67 's (ohl)\n4#96#jean - gabriel pageau#centre#canada#gatineau olympiques (qmjhl)\n5#126#fredrik claesson#defense#sweden#djurg\u00e5rdens if hockey (sel)\n6#156#darren kramer#centre#canada#spokane chiefs (whl)\n6#171 (from phoenix)#max mccormick#left wing#united states#sioux city musketeers (ushl)\n7#186#jordan fransoo#defense#canada#brandon wheat kings (whl)\n",
        "pandas_code": "df[(df['club team'] == 'gatineau olympiques (qmjhl)') & (df['player'] == 'jean - gabriel pageau') & (df['position'] == 'centre') & (df['round'] == 4) & (df['overall'] == '96')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 125,
        "statement": "the highest 1st prize ( ) win be 135000 , in north carolina",
        "label": 1,
        "table_caption": "1988 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 17#mony senior tournament of champions#california#100000#dave hill (2)#211 ( - 5)#30000\nfeb 14#general foods pga seniors' championship#florida#350000#gary player (8)#284 ( - 4)#63000\nfeb 21#gte suncoast classic#florida#300000#dale douglass (5)#210 ( - 6)#45000\nfeb 28#aetna challenge#florida#300000#gary player (9)#207 ( - 9)#45000\nmar 6#vintage chrysler invitational#california#320000#orville moody (5)#263 ( - 25)#48000\nmar 13#gte classic#california#275000#harold henning (2)#214 ( - 2)#41250\nmar 20#the pointe / del e webb arizona classic#arizona#225000#al geiberger (4)#199 ( - 17)#33750\napr 17#doug sanders kingwood celebrity classic#texas#250000#chi chi rodriguez (11)#208 ( - 8)#37500\nmay 8#vantage at the dominion#texas#250000#billy casper (7)#205 ( - 11)#37500\nmay 15#united hospitals classic#pennsylvania#225000#bruce crampton (12)#205 ( - 5)#33750\nmay 22#nynex / golf digest commemorative#new york#300000#bob charles (4)#196 ( - 14)#45000\nmay 29#sunwest bank charley pride senior golf classic#new mexico#250000#bob charles (5)#206 ( - 10)#41250\njun 5#senior players reunion pro - am#texas#250000#orville moody (6)#206 ( - 10)#37500\njun 12#mazda senior tournament players championship#florida#400000#billy casper (8)#278 ( - 10)#60000\njun 19#northville long island classic#new york#350000#don bies (1)#202 ( - 14)#52500\njun 26#southwestern bell classic#oklahoma#250000#gary player (10)#203 ( - 13)#37500\njul 4#rancho murieta senior gold rush#california#350000#bob charles (6)#207 ( - 9)#52500\njul 10#gte northwest classic#washington#300000#bruce crampton (13)#207 ( - 9)#45000\njul 17#showdown classic#utah#350000#miller barber (21)#207 ( - 9)#52500\njul 23#newport cup#rhode island#250000#walt zembriski (1)#132 ( - 12)#37500\njul 31#digital seniors classic#massachusetts#300000#chi chi rodriguez (12)#202 ( - 14)#45000\naug 8#us senior open#illinois#325000#gary player (11)#288 (e)#65000\naug 14#mony syracuse senior classic#new york#250000#dave hill (3)#200 ( - 16)#37500\naug 21#greater grand rapids open#michigan#250000#orville moody (7)#203 ( - 10)#37500\naug 28#vantage presents bank one senior golf classic#kentucky#250000#bob charles (7)#200 ( - 10)#37500\nsep 10#gte north classic#indiana#350000#gary player (12)#201 ( - 15)#52500\nsep 18#crestar classic#virginia#325000#arnold palmer (10)#203 ( - 13)#48750\nsep 25#painewebber invitational#north carolina#300000#dave hill (4)#206 ( - 10)#45000\noct 2#pepsi senior challenge#georgia#300000#bob charles (8)#139 ( - 5)#45000\noct 9#vantage championship#north carolina#1000000#walt zembriski (2)#278 ( - 10)#135000\noct 30#general tire las vegas classic#nevada#250000#larry mowry (3)#204 ( - 12)#37500\nnov 13#fairfield barnett classic#florida#225000#miller barber (22)#197 ( - 19)#33750\nnov 20#gus machado senior classic#florida#300000#lee elder (8)#202 ( - 11)#45000\n",
        "pandas_code": "(df.loc[df['1st prize'].idxmax(), 'location'] == 'north carolina') and (df['1st prize'].max() == 135000)",
        "pandas_eval": "True"
    },
    {
        "id": 126,
        "statement": "gu machado senior classic win more in 1st prize ( ) than fairfield barnett classic",
        "label": 1,
        "table_caption": "1988 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 17#mony senior tournament of champions#california#100000#dave hill (2)#211 ( - 5)#30000\nfeb 14#general foods pga seniors' championship#florida#350000#gary player (8)#284 ( - 4)#63000\nfeb 21#gte suncoast classic#florida#300000#dale douglass (5)#210 ( - 6)#45000\nfeb 28#aetna challenge#florida#300000#gary player (9)#207 ( - 9)#45000\nmar 6#vintage chrysler invitational#california#320000#orville moody (5)#263 ( - 25)#48000\nmar 13#gte classic#california#275000#harold henning (2)#214 ( - 2)#41250\nmar 20#the pointe / del e webb arizona classic#arizona#225000#al geiberger (4)#199 ( - 17)#33750\napr 17#doug sanders kingwood celebrity classic#texas#250000#chi chi rodriguez (11)#208 ( - 8)#37500\nmay 8#vantage at the dominion#texas#250000#billy casper (7)#205 ( - 11)#37500\nmay 15#united hospitals classic#pennsylvania#225000#bruce crampton (12)#205 ( - 5)#33750\nmay 22#nynex / golf digest commemorative#new york#300000#bob charles (4)#196 ( - 14)#45000\nmay 29#sunwest bank charley pride senior golf classic#new mexico#250000#bob charles (5)#206 ( - 10)#41250\njun 5#senior players reunion pro - am#texas#250000#orville moody (6)#206 ( - 10)#37500\njun 12#mazda senior tournament players championship#florida#400000#billy casper (8)#278 ( - 10)#60000\njun 19#northville long island classic#new york#350000#don bies (1)#202 ( - 14)#52500\njun 26#southwestern bell classic#oklahoma#250000#gary player (10)#203 ( - 13)#37500\njul 4#rancho murieta senior gold rush#california#350000#bob charles (6)#207 ( - 9)#52500\njul 10#gte northwest classic#washington#300000#bruce crampton (13)#207 ( - 9)#45000\njul 17#showdown classic#utah#350000#miller barber (21)#207 ( - 9)#52500\njul 23#newport cup#rhode island#250000#walt zembriski (1)#132 ( - 12)#37500\njul 31#digital seniors classic#massachusetts#300000#chi chi rodriguez (12)#202 ( - 14)#45000\naug 8#us senior open#illinois#325000#gary player (11)#288 (e)#65000\naug 14#mony syracuse senior classic#new york#250000#dave hill (3)#200 ( - 16)#37500\naug 21#greater grand rapids open#michigan#250000#orville moody (7)#203 ( - 10)#37500\naug 28#vantage presents bank one senior golf classic#kentucky#250000#bob charles (7)#200 ( - 10)#37500\nsep 10#gte north classic#indiana#350000#gary player (12)#201 ( - 15)#52500\nsep 18#crestar classic#virginia#325000#arnold palmer (10)#203 ( - 13)#48750\nsep 25#painewebber invitational#north carolina#300000#dave hill (4)#206 ( - 10)#45000\noct 2#pepsi senior challenge#georgia#300000#bob charles (8)#139 ( - 5)#45000\noct 9#vantage championship#north carolina#1000000#walt zembriski (2)#278 ( - 10)#135000\noct 30#general tire las vegas classic#nevada#250000#larry mowry (3)#204 ( - 12)#37500\nnov 13#fairfield barnett classic#florida#225000#miller barber (22)#197 ( - 19)#33750\nnov 20#gus machado senior classic#florida#300000#lee elder (8)#202 ( - 11)#45000\n",
        "pandas_code": "df[df['tournament'] == 'gus machado senior classic']['1st prize'].values[0] > df[df['tournament'] == 'fairfield barnett classic']['1st prize'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 127,
        "statement": "florida be the location 6 time during the tour",
        "label": 1,
        "table_caption": "1988 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 17#mony senior tournament of champions#california#100000#dave hill (2)#211 ( - 5)#30000\nfeb 14#general foods pga seniors' championship#florida#350000#gary player (8)#284 ( - 4)#63000\nfeb 21#gte suncoast classic#florida#300000#dale douglass (5)#210 ( - 6)#45000\nfeb 28#aetna challenge#florida#300000#gary player (9)#207 ( - 9)#45000\nmar 6#vintage chrysler invitational#california#320000#orville moody (5)#263 ( - 25)#48000\nmar 13#gte classic#california#275000#harold henning (2)#214 ( - 2)#41250\nmar 20#the pointe / del e webb arizona classic#arizona#225000#al geiberger (4)#199 ( - 17)#33750\napr 17#doug sanders kingwood celebrity classic#texas#250000#chi chi rodriguez (11)#208 ( - 8)#37500\nmay 8#vantage at the dominion#texas#250000#billy casper (7)#205 ( - 11)#37500\nmay 15#united hospitals classic#pennsylvania#225000#bruce crampton (12)#205 ( - 5)#33750\nmay 22#nynex / golf digest commemorative#new york#300000#bob charles (4)#196 ( - 14)#45000\nmay 29#sunwest bank charley pride senior golf classic#new mexico#250000#bob charles (5)#206 ( - 10)#41250\njun 5#senior players reunion pro - am#texas#250000#orville moody (6)#206 ( - 10)#37500\njun 12#mazda senior tournament players championship#florida#400000#billy casper (8)#278 ( - 10)#60000\njun 19#northville long island classic#new york#350000#don bies (1)#202 ( - 14)#52500\njun 26#southwestern bell classic#oklahoma#250000#gary player (10)#203 ( - 13)#37500\njul 4#rancho murieta senior gold rush#california#350000#bob charles (6)#207 ( - 9)#52500\njul 10#gte northwest classic#washington#300000#bruce crampton (13)#207 ( - 9)#45000\njul 17#showdown classic#utah#350000#miller barber (21)#207 ( - 9)#52500\njul 23#newport cup#rhode island#250000#walt zembriski (1)#132 ( - 12)#37500\njul 31#digital seniors classic#massachusetts#300000#chi chi rodriguez (12)#202 ( - 14)#45000\naug 8#us senior open#illinois#325000#gary player (11)#288 (e)#65000\naug 14#mony syracuse senior classic#new york#250000#dave hill (3)#200 ( - 16)#37500\naug 21#greater grand rapids open#michigan#250000#orville moody (7)#203 ( - 10)#37500\naug 28#vantage presents bank one senior golf classic#kentucky#250000#bob charles (7)#200 ( - 10)#37500\nsep 10#gte north classic#indiana#350000#gary player (12)#201 ( - 15)#52500\nsep 18#crestar classic#virginia#325000#arnold palmer (10)#203 ( - 13)#48750\nsep 25#painewebber invitational#north carolina#300000#dave hill (4)#206 ( - 10)#45000\noct 2#pepsi senior challenge#georgia#300000#bob charles (8)#139 ( - 5)#45000\noct 9#vantage championship#north carolina#1000000#walt zembriski (2)#278 ( - 10)#135000\noct 30#general tire las vegas classic#nevada#250000#larry mowry (3)#204 ( - 12)#37500\nnov 13#fairfield barnett classic#florida#225000#miller barber (22)#197 ( - 19)#33750\nnov 20#gus machado senior classic#florida#300000#lee elder (8)#202 ( - 11)#45000\n",
        "pandas_code": "(df['location'] == 'florida').sum() == 6",
        "pandas_eval": "True"
    },
    {
        "id": 128,
        "statement": "288 (e) be the highest score of the pga tour",
        "label": 1,
        "table_caption": "1988 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 17#mony senior tournament of champions#california#100000#dave hill (2)#211 ( - 5)#30000\nfeb 14#general foods pga seniors' championship#florida#350000#gary player (8)#284 ( - 4)#63000\nfeb 21#gte suncoast classic#florida#300000#dale douglass (5)#210 ( - 6)#45000\nfeb 28#aetna challenge#florida#300000#gary player (9)#207 ( - 9)#45000\nmar 6#vintage chrysler invitational#california#320000#orville moody (5)#263 ( - 25)#48000\nmar 13#gte classic#california#275000#harold henning (2)#214 ( - 2)#41250\nmar 20#the pointe / del e webb arizona classic#arizona#225000#al geiberger (4)#199 ( - 17)#33750\napr 17#doug sanders kingwood celebrity classic#texas#250000#chi chi rodriguez (11)#208 ( - 8)#37500\nmay 8#vantage at the dominion#texas#250000#billy casper (7)#205 ( - 11)#37500\nmay 15#united hospitals classic#pennsylvania#225000#bruce crampton (12)#205 ( - 5)#33750\nmay 22#nynex / golf digest commemorative#new york#300000#bob charles (4)#196 ( - 14)#45000\nmay 29#sunwest bank charley pride senior golf classic#new mexico#250000#bob charles (5)#206 ( - 10)#41250\njun 5#senior players reunion pro - am#texas#250000#orville moody (6)#206 ( - 10)#37500\njun 12#mazda senior tournament players championship#florida#400000#billy casper (8)#278 ( - 10)#60000\njun 19#northville long island classic#new york#350000#don bies (1)#202 ( - 14)#52500\njun 26#southwestern bell classic#oklahoma#250000#gary player (10)#203 ( - 13)#37500\njul 4#rancho murieta senior gold rush#california#350000#bob charles (6)#207 ( - 9)#52500\njul 10#gte northwest classic#washington#300000#bruce crampton (13)#207 ( - 9)#45000\njul 17#showdown classic#utah#350000#miller barber (21)#207 ( - 9)#52500\njul 23#newport cup#rhode island#250000#walt zembriski (1)#132 ( - 12)#37500\njul 31#digital seniors classic#massachusetts#300000#chi chi rodriguez (12)#202 ( - 14)#45000\naug 8#us senior open#illinois#325000#gary player (11)#288 (e)#65000\naug 14#mony syracuse senior classic#new york#250000#dave hill (3)#200 ( - 16)#37500\naug 21#greater grand rapids open#michigan#250000#orville moody (7)#203 ( - 10)#37500\naug 28#vantage presents bank one senior golf classic#kentucky#250000#bob charles (7)#200 ( - 10)#37500\nsep 10#gte north classic#indiana#350000#gary player (12)#201 ( - 15)#52500\nsep 18#crestar classic#virginia#325000#arnold palmer (10)#203 ( - 13)#48750\nsep 25#painewebber invitational#north carolina#300000#dave hill (4)#206 ( - 10)#45000\noct 2#pepsi senior challenge#georgia#300000#bob charles (8)#139 ( - 5)#45000\noct 9#vantage championship#north carolina#1000000#walt zembriski (2)#278 ( - 10)#135000\noct 30#general tire las vegas classic#nevada#250000#larry mowry (3)#204 ( - 12)#37500\nnov 13#fairfield barnett classic#florida#225000#miller barber (22)#197 ( - 19)#33750\nnov 20#gus machado senior classic#florida#300000#lee elder (8)#202 ( - 11)#45000\n",
        "pandas_code": "df['score'].str.extract(r'(\\d+)')[0].astype(int).max() == 288",
        "pandas_eval": "True"
    },
    {
        "id": 129,
        "statement": "there be only 1 tournament that have a location of pennsylvania",
        "label": 1,
        "table_caption": "1988 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 17#mony senior tournament of champions#california#100000#dave hill (2)#211 ( - 5)#30000\nfeb 14#general foods pga seniors' championship#florida#350000#gary player (8)#284 ( - 4)#63000\nfeb 21#gte suncoast classic#florida#300000#dale douglass (5)#210 ( - 6)#45000\nfeb 28#aetna challenge#florida#300000#gary player (9)#207 ( - 9)#45000\nmar 6#vintage chrysler invitational#california#320000#orville moody (5)#263 ( - 25)#48000\nmar 13#gte classic#california#275000#harold henning (2)#214 ( - 2)#41250\nmar 20#the pointe / del e webb arizona classic#arizona#225000#al geiberger (4)#199 ( - 17)#33750\napr 17#doug sanders kingwood celebrity classic#texas#250000#chi chi rodriguez (11)#208 ( - 8)#37500\nmay 8#vantage at the dominion#texas#250000#billy casper (7)#205 ( - 11)#37500\nmay 15#united hospitals classic#pennsylvania#225000#bruce crampton (12)#205 ( - 5)#33750\nmay 22#nynex / golf digest commemorative#new york#300000#bob charles (4)#196 ( - 14)#45000\nmay 29#sunwest bank charley pride senior golf classic#new mexico#250000#bob charles (5)#206 ( - 10)#41250\njun 5#senior players reunion pro - am#texas#250000#orville moody (6)#206 ( - 10)#37500\njun 12#mazda senior tournament players championship#florida#400000#billy casper (8)#278 ( - 10)#60000\njun 19#northville long island classic#new york#350000#don bies (1)#202 ( - 14)#52500\njun 26#southwestern bell classic#oklahoma#250000#gary player (10)#203 ( - 13)#37500\njul 4#rancho murieta senior gold rush#california#350000#bob charles (6)#207 ( - 9)#52500\njul 10#gte northwest classic#washington#300000#bruce crampton (13)#207 ( - 9)#45000\njul 17#showdown classic#utah#350000#miller barber (21)#207 ( - 9)#52500\njul 23#newport cup#rhode island#250000#walt zembriski (1)#132 ( - 12)#37500\njul 31#digital seniors classic#massachusetts#300000#chi chi rodriguez (12)#202 ( - 14)#45000\naug 8#us senior open#illinois#325000#gary player (11)#288 (e)#65000\naug 14#mony syracuse senior classic#new york#250000#dave hill (3)#200 ( - 16)#37500\naug 21#greater grand rapids open#michigan#250000#orville moody (7)#203 ( - 10)#37500\naug 28#vantage presents bank one senior golf classic#kentucky#250000#bob charles (7)#200 ( - 10)#37500\nsep 10#gte north classic#indiana#350000#gary player (12)#201 ( - 15)#52500\nsep 18#crestar classic#virginia#325000#arnold palmer (10)#203 ( - 13)#48750\nsep 25#painewebber invitational#north carolina#300000#dave hill (4)#206 ( - 10)#45000\noct 2#pepsi senior challenge#georgia#300000#bob charles (8)#139 ( - 5)#45000\noct 9#vantage championship#north carolina#1000000#walt zembriski (2)#278 ( - 10)#135000\noct 30#general tire las vegas classic#nevada#250000#larry mowry (3)#204 ( - 12)#37500\nnov 13#fairfield barnett classic#florida#225000#miller barber (22)#197 ( - 19)#33750\nnov 20#gus machado senior classic#florida#300000#lee elder (8)#202 ( - 11)#45000\n",
        "pandas_code": "len(df[df['location'] == 'pennsylvania']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 130,
        "statement": "in the master tournament horton smith win 2 time , get top 3 time",
        "label": 1,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['wins'].values[0] == 2) & (df[df['tournament'] == 'masters tournament']['top - 5'].values[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 131,
        "statement": "horton smith didn't win at all in the us open",
        "label": 1,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "df[df['tournament'] == 'us open']['wins'].values[0] == 0",
        "pandas_eval": "True"
    },
    {
        "id": 132,
        "statement": "at the pga championship horton smith didn't win , but get top - 5 7 time",
        "label": 1,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'pga championship']['wins'].values[0] == 0) & (df[df['tournament'] == 'pga championship']['top - 5'].values[0] == 7)",
        "pandas_eval": "True"
    },
    {
        "id": 133,
        "statement": "horton smith win a total of 2 time , get top 10 19 time",
        "label": 1,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'totals']['wins'].values[0] == 2) & (df[df['tournament'] == 'totals']['top - 10'].values[0] == 19)",
        "pandas_eval": "True"
    },
    {
        "id": 134,
        "statement": "in total , horton smith make 59 cut at 72 event",
        "label": 1,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "df[df['tournament'] == 'totals']['cuts made'].values[0] == 59 and df[df['tournament'] == 'totals']['events'].values[0] == 72",
        "pandas_eval": "True"
    },
    {
        "id": 135,
        "statement": "in the master tournament horton smith win 1 time , get top 4 time",
        "label": 0,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['wins'].values[0] == 1) & (df[df['tournament'] == 'masters tournament']['top - 5'].values[0] == 4)",
        "pandas_eval": "False"
    },
    {
        "id": 136,
        "statement": "horton smith win at all in the us open",
        "label": 0,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "df[df['tournament'] == 'us open']['wins'].values[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 137,
        "statement": "at the pga championship horton smith win , get top - 5 7 time",
        "label": 0,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'pga championship']['wins'].values[0] == 1) & (df[df['tournament'] == 'pga championship']['top - 5'].values[0] == 7)",
        "pandas_eval": "False"
    },
    {
        "id": 138,
        "statement": "horton smith win a total of 3 time , get top 10 19 time",
        "label": 0,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "(df[df['tournament'] == 'totals']['wins'].values[0] == 3) & (df[df['tournament'] == 'totals']['top - 10'].values[0] == 19)",
        "pandas_eval": "False"
    },
    {
        "id": 139,
        "statement": "in total , horton smith make 72 cut at 59 event",
        "label": 0,
        "table_caption": "horton smith",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#3#3#11#27#20\nus open#0#2#4#12#23#17\nthe open championship#0#1#2#5#5#5\npga championship#0#7#10#14#17#17\ntotals#2#13#19#42#72#59\n",
        "pandas_code": "df[df['tournament'] == 'totals']['cuts made'].values[0] == 72 and df[df['tournament'] == 'totals']['events'].values[0] == 59",
        "pandas_eval": "False"
    },
    {
        "id": 140,
        "statement": "denizlisport have 3 manager 's between 1 november 2008 and 20 february 2009",
        "label": 1,
        "table_caption": "2008 - 09 s\u00fcper lig",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment\nkonyaspor#ra\u015fit \u00e7etiner#sacked#17 september 2008#giray bulak#24 september 2008\nkocaelispor#engin ipeko\u011flu#sacked#25 september 2008#y\u0131lmaz vural#28 september 2008\nbe\u015fikta\u015f#ertu\u011frul sa\u011flam#resigned#7 october 2008#mustafa denizli#9 october 2008\nankarag\u00fcc\u00fc#hakan kutlu#sacked#20 october 2008#\u00fcnal karaman#24 october 2008\nantalyaspor#jozef jarabinsk\u00fd#sacked#28 october 2008#mehmet \u00f6zdilek#28 october 2008\nhacettepe#osman \u00f6zdemir#resigned#2 november 2008#erdo\u011fan ar\u0131ca#3 november 2008\ndenizlispor#ali yal\u00e7\u0131n#resigned#2 november 2008#\u00fcmit kay\u0131han#10 november 2008\ngen\u00e7lerbirli\u011fi#mesut bakkal#resigned#3 november 2008#samet aybaba#5 november 2008\nbursaspor#samet aybaba#resigned#4 november 2008#g\u00fcven\u00e7 kurtar#4 november 2008\nankarag\u00fcc\u00fc#\u00fcnal karaman#resigned#8 december 2008#hakan kutlu#2 january 2009\nbursaspor#g\u00fcven\u00e7 kurtar#resigned#23 december 2008#ertu\u011frul sa\u011flam#2 january 2009\nkocaelispor#y\u0131lmaz vural#resigned#29 december 2008#erhan alt\u0131n#17 january 2009\ndenizlispor#\u00fcmit kay\u0131han#sacked#5 february 2009#mesut bakkal#6 february 2009\ngalatasaray#michael skibbe#sacked#23 february 2009#b\u00fclent korkmaz#23 february 2009\nhacettepe#erdo\u011fan ar\u0131ca#resigned#2 march 2009#erg\u00fcn penbe#2 march 2009\ngaziantepspor#nurullah sa\u011flam#resigned#9 march 2009#jos\u00e9 couceiro#6 april 2009\n",
        "pandas_code": "len(df[(df['team'] == 'denizlispor') & (pd.to_datetime(df['date of vacancy']) >= pd.to_datetime('1 november 2008')) & (pd.to_datetime(df['date of vacancy']) <= pd.to_datetime('20 february 2009'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 141,
        "statement": "g\u00fcven\u00e7 kurtar last less than 2 month with team bursaspor",
        "label": 1,
        "table_caption": "2008 - 09 s\u00fcper lig",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment\nkonyaspor#ra\u015fit \u00e7etiner#sacked#17 september 2008#giray bulak#24 september 2008\nkocaelispor#engin ipeko\u011flu#sacked#25 september 2008#y\u0131lmaz vural#28 september 2008\nbe\u015fikta\u015f#ertu\u011frul sa\u011flam#resigned#7 october 2008#mustafa denizli#9 october 2008\nankarag\u00fcc\u00fc#hakan kutlu#sacked#20 october 2008#\u00fcnal karaman#24 october 2008\nantalyaspor#jozef jarabinsk\u00fd#sacked#28 october 2008#mehmet \u00f6zdilek#28 october 2008\nhacettepe#osman \u00f6zdemir#resigned#2 november 2008#erdo\u011fan ar\u0131ca#3 november 2008\ndenizlispor#ali yal\u00e7\u0131n#resigned#2 november 2008#\u00fcmit kay\u0131han#10 november 2008\ngen\u00e7lerbirli\u011fi#mesut bakkal#resigned#3 november 2008#samet aybaba#5 november 2008\nbursaspor#samet aybaba#resigned#4 november 2008#g\u00fcven\u00e7 kurtar#4 november 2008\nankarag\u00fcc\u00fc#\u00fcnal karaman#resigned#8 december 2008#hakan kutlu#2 january 2009\nbursaspor#g\u00fcven\u00e7 kurtar#resigned#23 december 2008#ertu\u011frul sa\u011flam#2 january 2009\nkocaelispor#y\u0131lmaz vural#resigned#29 december 2008#erhan alt\u0131n#17 january 2009\ndenizlispor#\u00fcmit kay\u0131han#sacked#5 february 2009#mesut bakkal#6 february 2009\ngalatasaray#michael skibbe#sacked#23 february 2009#b\u00fclent korkmaz#23 february 2009\nhacettepe#erdo\u011fan ar\u0131ca#resigned#2 march 2009#erg\u00fcn penbe#2 march 2009\ngaziantepspor#nurullah sa\u011flam#resigned#9 march 2009#jos\u00e9 couceiro#6 april 2009\n",
        "pandas_code": "(pd.to_datetime(df[(df['outgoing manager'] == 'g\u00fcven\u00e7 kurtar') & (df['team'] == 'bursaspor')]['date of vacancy'].iloc[0]) - pd.to_datetime(df[(df['replaced by'] == 'g\u00fcven\u00e7 kurtar') & (df['team'] == 'bursaspor')]['date of appointment'].iloc[0])).days < 60",
        "pandas_eval": "True"
    },
    {
        "id": 143,
        "statement": "the percentage of manager sack be 37.5%",
        "label": 1,
        "table_caption": "2008 - 09 s\u00fcper lig",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment\nkonyaspor#ra\u015fit \u00e7etiner#sacked#17 september 2008#giray bulak#24 september 2008\nkocaelispor#engin ipeko\u011flu#sacked#25 september 2008#y\u0131lmaz vural#28 september 2008\nbe\u015fikta\u015f#ertu\u011frul sa\u011flam#resigned#7 october 2008#mustafa denizli#9 october 2008\nankarag\u00fcc\u00fc#hakan kutlu#sacked#20 october 2008#\u00fcnal karaman#24 october 2008\nantalyaspor#jozef jarabinsk\u00fd#sacked#28 october 2008#mehmet \u00f6zdilek#28 october 2008\nhacettepe#osman \u00f6zdemir#resigned#2 november 2008#erdo\u011fan ar\u0131ca#3 november 2008\ndenizlispor#ali yal\u00e7\u0131n#resigned#2 november 2008#\u00fcmit kay\u0131han#10 november 2008\ngen\u00e7lerbirli\u011fi#mesut bakkal#resigned#3 november 2008#samet aybaba#5 november 2008\nbursaspor#samet aybaba#resigned#4 november 2008#g\u00fcven\u00e7 kurtar#4 november 2008\nankarag\u00fcc\u00fc#\u00fcnal karaman#resigned#8 december 2008#hakan kutlu#2 january 2009\nbursaspor#g\u00fcven\u00e7 kurtar#resigned#23 december 2008#ertu\u011frul sa\u011flam#2 january 2009\nkocaelispor#y\u0131lmaz vural#resigned#29 december 2008#erhan alt\u0131n#17 january 2009\ndenizlispor#\u00fcmit kay\u0131han#sacked#5 february 2009#mesut bakkal#6 february 2009\ngalatasaray#michael skibbe#sacked#23 february 2009#b\u00fclent korkmaz#23 february 2009\nhacettepe#erdo\u011fan ar\u0131ca#resigned#2 march 2009#erg\u00fcn penbe#2 march 2009\ngaziantepspor#nurullah sa\u011flam#resigned#9 march 2009#jos\u00e9 couceiro#6 april 2009\n",
        "pandas_code": "(df['manner of departure'].value_counts(normalize=True)['sacked'] * 100 == 37.5)",
        "pandas_eval": "True"
    },
    {
        "id": 144,
        "statement": "the percentage of manager that resign be 62.5%",
        "label": 1,
        "table_caption": "2008 - 09 s\u00fcper lig",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment\nkonyaspor#ra\u015fit \u00e7etiner#sacked#17 september 2008#giray bulak#24 september 2008\nkocaelispor#engin ipeko\u011flu#sacked#25 september 2008#y\u0131lmaz vural#28 september 2008\nbe\u015fikta\u015f#ertu\u011frul sa\u011flam#resigned#7 october 2008#mustafa denizli#9 october 2008\nankarag\u00fcc\u00fc#hakan kutlu#sacked#20 october 2008#\u00fcnal karaman#24 october 2008\nantalyaspor#jozef jarabinsk\u00fd#sacked#28 october 2008#mehmet \u00f6zdilek#28 october 2008\nhacettepe#osman \u00f6zdemir#resigned#2 november 2008#erdo\u011fan ar\u0131ca#3 november 2008\ndenizlispor#ali yal\u00e7\u0131n#resigned#2 november 2008#\u00fcmit kay\u0131han#10 november 2008\ngen\u00e7lerbirli\u011fi#mesut bakkal#resigned#3 november 2008#samet aybaba#5 november 2008\nbursaspor#samet aybaba#resigned#4 november 2008#g\u00fcven\u00e7 kurtar#4 november 2008\nankarag\u00fcc\u00fc#\u00fcnal karaman#resigned#8 december 2008#hakan kutlu#2 january 2009\nbursaspor#g\u00fcven\u00e7 kurtar#resigned#23 december 2008#ertu\u011frul sa\u011flam#2 january 2009\nkocaelispor#y\u0131lmaz vural#resigned#29 december 2008#erhan alt\u0131n#17 january 2009\ndenizlispor#\u00fcmit kay\u0131han#sacked#5 february 2009#mesut bakkal#6 february 2009\ngalatasaray#michael skibbe#sacked#23 february 2009#b\u00fclent korkmaz#23 february 2009\nhacettepe#erdo\u011fan ar\u0131ca#resigned#2 march 2009#erg\u00fcn penbe#2 march 2009\ngaziantepspor#nurullah sa\u011flam#resigned#9 march 2009#jos\u00e9 couceiro#6 april 2009\n",
        "pandas_code": "(df[df['manner of departure'] == 'resigned'].shape[0] / df.shape[0]) == 0.625",
        "pandas_eval": "True"
    },
    {
        "id": 145,
        "statement": "kodiak island be the largest island by size in alaska",
        "label": 1,
        "table_caption": "list of islands of the united states by area",
        "table_text": "rank#islands name#area (sqmi)#area (km 2 )#location#population (2000)\n1#hawai i (big island)#4028#10433#hawaii#148677\n2#kodiak island#3588#9293#alaska#13913\n3#puerto rico#3515#9104#puerto rico#3808610\n4#prince of wales island#2577#6675#alaska#6000 \u2248\n5#chichagof island#2080#5388#alaska#1342\n6#st lawrence island#1983#5135#alaska#1292\n7#admiralty island#1684#4362#alaska#650\n8#nunivak island#1625#4209#alaska#210\n9#unimak island#1590#4119#alaska#64\n10#baranof island#1570#4065#alaska#8532\n11#long island#1401#3629#new york#7448618\n12#revillagigedo island#1145#2965#alaska#13950\n13#kupreanof island#1086#2813#alaska#785\n14#unalaska island#1051#2722#alaska#1759\n15#nelson island#843#2183#alaska#1065\n16#kuiu island#758#1962#alaska#10\n17#maui#727#1883#hawaii#117644\n18#afognak#698#1809#alaska#169\n19#umnak#692#1793#alaska#39\n20#o ahu#597#1545#hawaii#876151\n21#kaua i#552#1430#hawaii#58303\n22#atka island#410#1061#alaska#95\n23#attu island#346#896#alaska#20\n24#etolin island#336#870#alaska#15\n25#adak island#280#725#alaska#316\n26#montague island#279#722#alaska#0\n27#moloka i#260#673#hawaii#7404\n",
        "pandas_code": "df[(df['islands name'] == 'kodiak island') & (df['location'] == 'alaska')]['area (sqmi)'].max() == df[df['location'] == 'alaska']['area (sqmi)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 146,
        "statement": "puerto rico be larger in area than long island",
        "label": 1,
        "table_caption": "list of islands of the united states by area",
        "table_text": "rank#islands name#area (sqmi)#area (km 2 )#location#population (2000)\n1#hawai i (big island)#4028#10433#hawaii#148677\n2#kodiak island#3588#9293#alaska#13913\n3#puerto rico#3515#9104#puerto rico#3808610\n4#prince of wales island#2577#6675#alaska#6000 \u2248\n5#chichagof island#2080#5388#alaska#1342\n6#st lawrence island#1983#5135#alaska#1292\n7#admiralty island#1684#4362#alaska#650\n8#nunivak island#1625#4209#alaska#210\n9#unimak island#1590#4119#alaska#64\n10#baranof island#1570#4065#alaska#8532\n11#long island#1401#3629#new york#7448618\n12#revillagigedo island#1145#2965#alaska#13950\n13#kupreanof island#1086#2813#alaska#785\n14#unalaska island#1051#2722#alaska#1759\n15#nelson island#843#2183#alaska#1065\n16#kuiu island#758#1962#alaska#10\n17#maui#727#1883#hawaii#117644\n18#afognak#698#1809#alaska#169\n19#umnak#692#1793#alaska#39\n20#o ahu#597#1545#hawaii#876151\n21#kaua i#552#1430#hawaii#58303\n22#atka island#410#1061#alaska#95\n23#attu island#346#896#alaska#20\n24#etolin island#336#870#alaska#15\n25#adak island#280#725#alaska#316\n26#montague island#279#722#alaska#0\n27#moloka i#260#673#hawaii#7404\n",
        "pandas_code": "df[df['islands name'] == 'puerto rico']['area (sqmi)'].values[0] > df[df['islands name'] == 'long island']['area (sqmi)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 147,
        "statement": "long island have the highest population of all the island on the list",
        "label": 1,
        "table_caption": "list of islands of the united states by area",
        "table_text": "rank#islands name#area (sqmi)#area (km 2 )#location#population (2000)\n1#hawai i (big island)#4028#10433#hawaii#148677\n2#kodiak island#3588#9293#alaska#13913\n3#puerto rico#3515#9104#puerto rico#3808610\n4#prince of wales island#2577#6675#alaska#6000 \u2248\n5#chichagof island#2080#5388#alaska#1342\n6#st lawrence island#1983#5135#alaska#1292\n7#admiralty island#1684#4362#alaska#650\n8#nunivak island#1625#4209#alaska#210\n9#unimak island#1590#4119#alaska#64\n10#baranof island#1570#4065#alaska#8532\n11#long island#1401#3629#new york#7448618\n12#revillagigedo island#1145#2965#alaska#13950\n13#kupreanof island#1086#2813#alaska#785\n14#unalaska island#1051#2722#alaska#1759\n15#nelson island#843#2183#alaska#1065\n16#kuiu island#758#1962#alaska#10\n17#maui#727#1883#hawaii#117644\n18#afognak#698#1809#alaska#169\n19#umnak#692#1793#alaska#39\n20#o ahu#597#1545#hawaii#876151\n21#kaua i#552#1430#hawaii#58303\n22#atka island#410#1061#alaska#95\n23#attu island#346#896#alaska#20\n24#etolin island#336#870#alaska#15\n25#adak island#280#725#alaska#316\n26#montague island#279#722#alaska#0\n27#moloka i#260#673#hawaii#7404\n",
        "pandas_code": "df.loc[df['population (2000)'].replace('[^0-9]', '', regex=True).astype(int).idxmax(), 'islands name'] == 'long island'",
        "pandas_eval": "True"
    },
    {
        "id": 148,
        "statement": "montague island , which have zero population , be larger than moloka'i",
        "label": 1,
        "table_caption": "list of islands of the united states by area",
        "table_text": "rank#islands name#area (sqmi)#area (km 2 )#location#population (2000)\n1#hawai i (big island)#4028#10433#hawaii#148677\n2#kodiak island#3588#9293#alaska#13913\n3#puerto rico#3515#9104#puerto rico#3808610\n4#prince of wales island#2577#6675#alaska#6000 \u2248\n5#chichagof island#2080#5388#alaska#1342\n6#st lawrence island#1983#5135#alaska#1292\n7#admiralty island#1684#4362#alaska#650\n8#nunivak island#1625#4209#alaska#210\n9#unimak island#1590#4119#alaska#64\n10#baranof island#1570#4065#alaska#8532\n11#long island#1401#3629#new york#7448618\n12#revillagigedo island#1145#2965#alaska#13950\n13#kupreanof island#1086#2813#alaska#785\n14#unalaska island#1051#2722#alaska#1759\n15#nelson island#843#2183#alaska#1065\n16#kuiu island#758#1962#alaska#10\n17#maui#727#1883#hawaii#117644\n18#afognak#698#1809#alaska#169\n19#umnak#692#1793#alaska#39\n20#o ahu#597#1545#hawaii#876151\n21#kaua i#552#1430#hawaii#58303\n22#atka island#410#1061#alaska#95\n23#attu island#346#896#alaska#20\n24#etolin island#336#870#alaska#15\n25#adak island#280#725#alaska#316\n26#montague island#279#722#alaska#0\n27#moloka i#260#673#hawaii#7404\n",
        "pandas_code": "(df[df['islands name'] == 'montague island']['area (sqmi)'].values[0] > df[df['islands name'] == 'moloka i']['area (sqmi)'].values[0]) & (df[df['islands name'] == 'montague island']['population (2000)'].values[0] == '0')",
        "pandas_eval": "True"
    },
    {
        "id": 149,
        "statement": "umnak and afognak have a difference of 6 square mile",
        "label": 1,
        "table_caption": "list of islands of the united states by area",
        "table_text": "rank#islands name#area (sqmi)#area (km 2 )#location#population (2000)\n1#hawai i (big island)#4028#10433#hawaii#148677\n2#kodiak island#3588#9293#alaska#13913\n3#puerto rico#3515#9104#puerto rico#3808610\n4#prince of wales island#2577#6675#alaska#6000 \u2248\n5#chichagof island#2080#5388#alaska#1342\n6#st lawrence island#1983#5135#alaska#1292\n7#admiralty island#1684#4362#alaska#650\n8#nunivak island#1625#4209#alaska#210\n9#unimak island#1590#4119#alaska#64\n10#baranof island#1570#4065#alaska#8532\n11#long island#1401#3629#new york#7448618\n12#revillagigedo island#1145#2965#alaska#13950\n13#kupreanof island#1086#2813#alaska#785\n14#unalaska island#1051#2722#alaska#1759\n15#nelson island#843#2183#alaska#1065\n16#kuiu island#758#1962#alaska#10\n17#maui#727#1883#hawaii#117644\n18#afognak#698#1809#alaska#169\n19#umnak#692#1793#alaska#39\n20#o ahu#597#1545#hawaii#876151\n21#kaua i#552#1430#hawaii#58303\n22#atka island#410#1061#alaska#95\n23#attu island#346#896#alaska#20\n24#etolin island#336#870#alaska#15\n25#adak island#280#725#alaska#316\n26#montague island#279#722#alaska#0\n27#moloka i#260#673#hawaii#7404\n",
        "pandas_code": "abs(df[df['islands name'].isin(['umnak', 'afognak'])]['area (sqmi)'].diff().iloc[-1]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 150,
        "statement": "billy jones be the only player to play for 2 separate length of year",
        "label": 1,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "len(df[df['years'].str.count('-') == 2]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 151,
        "statement": "bob mcroberts , geoff vowden , bob latchford , and eddy brown all have total goal of less than 100",
        "label": 1,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "all(df[df['name'].isin(['bob mcroberts', 'geoff vowden', 'bob latchford', 'eddy brown'])]['total'].str.extract(r'(\\d+)')[0].astype(int) < 100)",
        "pandas_eval": "True"
    },
    {
        "id": 152,
        "statement": "bob mcroberts and fred wheldon both start their year prior to 1900",
        "label": 1,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "all(df[df['name'].isin(['bob mcroberts', 'fred wheldon'])]['years'].str.split(' - ').apply(lambda x: int(x[0]) < 1900))",
        "pandas_eval": "True"
    },
    {
        "id": 153,
        "statement": "there be 4 birmingham city fc player who have 0 (0) league cup and other b",
        "label": 1,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "(df[['league cup', 'other b']].apply(lambda x: x == '0 (0)').all(axis=1).sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 154,
        "statement": "joe bradford have the highest total , but no league cup for birmingham city fc",
        "label": 1,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "(int(df.loc[df['name'] == 'joe bradford', 'total'].values[0].split(' ')[0]) == max(df['total'].apply(lambda x: int(x.split(' ')[0])))) and (df.loc[df['name'] == 'joe bradford', 'league cup'].values[0] == '0 (0)')",
        "pandas_eval": "True"
    },
    {
        "id": 155,
        "statement": "billy jones only play from 1901 - 1909",
        "label": 0,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "df[df['name'] == 'billy jones']['years'].iloc[0] == '1901 - 1909'",
        "pandas_eval": "False"
    },
    {
        "id": 156,
        "statement": "geoff vowden , eddy brown , bob latchford , and bob mcroberts all score more than 100 goal",
        "label": 0,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "all(df[df['name'].isin(['geoff vowden', 'eddy brown', 'bob latchford', 'bob mcroberts'])]['total'].str.extract(r'(\\d+)').astype(int).squeeze() > 100)",
        "pandas_eval": "False"
    },
    {
        "id": 157,
        "statement": "fred wheldon start play in the 20th century",
        "label": 0,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "df[df['name'] == 'fred wheldon']['years'].str.split(' - ').str[0].astype(int).ge(1900).any()",
        "pandas_eval": "False"
    },
    {
        "id": 158,
        "statement": "there be 6 birmingham city fc player who have 0 league cup",
        "label": 0,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "len(df[df['league cup'].str.startswith('0')]) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 159,
        "statement": "joe bradford have the highest total and 3 league cup for birmingham city fc",
        "label": 0,
        "table_caption": "list of birmingham city f.c. records and statistics",
        "table_text": "name#years#league a#fa cup#league cup#other b#total\njoe bradford#1920 - 1935#249 (414)#18 (31)#0 (0)#0 (0)#267 (445)\ntrevor francis#1970 - 1979#119 (280)#6 (20)#4 (19)#4 (10)#133 (329)\npeter murphy#1952 - 1960#107 (245)#16 (24)#0 (0)#4 (9)#127 (278)\nfred wheldon#1890 - 1896#99 (155)#12 (13)#0 (0)#5 (7)#116 (175)\ngeorge briggs#1924 - 1933#98 (298)#9 (26)#0 (0)#0 (0)#107 (324)\nbilly jones#1901 - 1909 1912 - 1913#99 (236)#3 (17)#0 (0)#0 (0)#102 (253)\ngeoff vowden#1964 - 1970#79 (221)#8 (16)#7 (16)#0 (0)#94 (253)\neddy brown#1954 - 1958#74 (158)#13 (18)#0 (0)#3 (9)#90 (185)\nbob latchford#1969 - 1974#68 (160)#6 (12)#6 (16)#4 (6)#84 (193)\nbob mcroberts#1898 - 1905#70 (173)#12 (14)#0 (0)#0 (0)#82 (187)\n",
        "pandas_code": "df.loc[df['name'] == 'joe bradford', 'total'].values[0] == df['total'].max() and df.loc[df['name'] == 'joe bradford', 'league cup'].values[0] == '3 (3)'",
        "pandas_eval": "False"
    },
    {
        "id": 160,
        "statement": "william j jefferson be the only democratic candidate elect in 1996",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#robert livingston#republican#1977#re - elected#robert livingston (r) (unopposed)\nlouisiana 2#william j jefferson#democratic#1990#re - elected#william j jefferson (d) (unopposed)\nlouisiana 3#billy tauzin#republican#1980#re - elected#billy tauzin (r) (unopposed)\nlouisiana 4#jim mccrery#republican#1988#re - elected#jim mccrery (r) 71.38% paul chachere (d) 28.62%\nlouisiana 6#richard baker#republican#1986#re - elected#richard baker (r) 69.30% steve myers (d) 30.70%\n",
        "pandas_code": "df[(df['party'] == 'democratic') & (df['incumbent'] == 'william j jefferson') & (df['result'] == 're - elected')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 161,
        "statement": "billy tauzin ran unopposed in 1996",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#robert livingston#republican#1977#re - elected#robert livingston (r) (unopposed)\nlouisiana 2#william j jefferson#democratic#1990#re - elected#william j jefferson (d) (unopposed)\nlouisiana 3#billy tauzin#republican#1980#re - elected#billy tauzin (r) (unopposed)\nlouisiana 4#jim mccrery#republican#1988#re - elected#jim mccrery (r) 71.38% paul chachere (d) 28.62%\nlouisiana 6#richard baker#republican#1986#re - elected#richard baker (r) 69.30% steve myers (d) 30.70%\n",
        "pandas_code": "df[df['incumbent'] == 'billy tauzin']['candidates'].str.contains('unopposed').all()",
        "pandas_eval": "True"
    },
    {
        "id": 162,
        "statement": "richard baker be louisana 's 6th district incumbent",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#robert livingston#republican#1977#re - elected#robert livingston (r) (unopposed)\nlouisiana 2#william j jefferson#democratic#1990#re - elected#william j jefferson (d) (unopposed)\nlouisiana 3#billy tauzin#republican#1980#re - elected#billy tauzin (r) (unopposed)\nlouisiana 4#jim mccrery#republican#1988#re - elected#jim mccrery (r) 71.38% paul chachere (d) 28.62%\nlouisiana 6#richard baker#republican#1986#re - elected#richard baker (r) 69.30% steve myers (d) 30.70%\n",
        "pandas_code": "df[df['district'] == 'louisiana 6']['incumbent'].iloc[0] == 'richard baker'",
        "pandas_eval": "True"
    },
    {
        "id": 163,
        "statement": "jim mccrerey be first elect in 1988",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#robert livingston#republican#1977#re - elected#robert livingston (r) (unopposed)\nlouisiana 2#william j jefferson#democratic#1990#re - elected#william j jefferson (d) (unopposed)\nlouisiana 3#billy tauzin#republican#1980#re - elected#billy tauzin (r) (unopposed)\nlouisiana 4#jim mccrery#republican#1988#re - elected#jim mccrery (r) 71.38% paul chachere (d) 28.62%\nlouisiana 6#richard baker#republican#1986#re - elected#richard baker (r) 69.30% steve myers (d) 30.70%\n",
        "pandas_code": "df[df['incumbent'] == 'jim mccrery']['first elected'].iloc[0] == 1988",
        "pandas_eval": "True"
    },
    {
        "id": 164,
        "statement": "in 1977 , robert livingston be elect to louisana 's 1st district",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#robert livingston#republican#1977#re - elected#robert livingston (r) (unopposed)\nlouisiana 2#william j jefferson#democratic#1990#re - elected#william j jefferson (d) (unopposed)\nlouisiana 3#billy tauzin#republican#1980#re - elected#billy tauzin (r) (unopposed)\nlouisiana 4#jim mccrery#republican#1988#re - elected#jim mccrery (r) 71.38% paul chachere (d) 28.62%\nlouisiana 6#richard baker#republican#1986#re - elected#richard baker (r) 69.30% steve myers (d) 30.70%\n",
        "pandas_code": "df[(df['district'] == 'louisiana 1') & (df['incumbent'] == 'robert livingston') & (df['first elected'] == 1977)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 165,
        "statement": "during the 2009 st helen rlfc season the friendly competition result in 2 win",
        "label": 1,
        "table_caption": "2009 st helens rlfc season",
        "table_text": "competition#round#opponent#result#score#home / away#venue#attendance#date\nfriendly#n / a#widnes vikings#loss#26 - 28#home#gpw recruitment stadium#4128#27 / 12 / 2008\nfriendly#n / a#wakefield trinity wildcats#win#26 - 22#home#gpw recruitment stadium#3549#18 / 01 / 2009\nfriendly#n / a#widnes vikings#win#54 - 6#away#stobart stadium halton#2562#01 / 02 / 2009\nsuper league xiv#1#warrington wolves#win#26 - 14#home#gpw recruitment stadium#17009#13 / 02 / 2009\nsuper league xiv#2#huddersfield giants#win#6 - 23#away#galpharm stadium#11338#22 / 02 / 2009\nsuper league xiv#3#hull kingston rovers#loss#19 - 20#home#gpw recruitment stadium#11830#27 / 02 / 2009\nsuper league xiv#4#crusaders#win#0 - 4#away#brewery field#6351#07 / 03 / 2009\nsuper league xiv#5#salford city reds#win#38 - 12#home#gpw recruitment stadium#9723#13 / 03 / 2009\nsuper league xiv#6#leeds rhinos#win#26 - 18#home#gpw recruitment stadium#13966#20 / 03 / 2009\nsuper league xiv#7#wakefield trinity wildcats#win#18 - 42#away#belle vue#6038#29 / 03 / 2009\nchallenge cup 2009#4#leeds rhinos#win#18 - 22#away#headingley stadium#17689#05 / 04 / 2009\nsuper league xiv#8#wigan warriors#win#12 - 19#away#jjb stadium#22232#09 / 04 / 2009\nsuper league xiv#9#hull#win#44 - 22#home#gpw recruitment stadium#13684#13 / 04 / 2009\nsuper league xiv#10#castleford tigers#win#22 - 68#away#the jungle#8003#19 / 04 / 2009\nsuper league xiv#11#bradford bulls#loss#30 - 34#home#gpw recruitment stadium#11039#24 / 04 / 2009\nsuper league xiv#12#wigan warriors#loss#38 - 18#neutral#murrayfield stadium#29687#02 / 05 / 2009\nchallenge cup 2009#5#catalans dragons#win#42 - 8#home#gpw recruitment stadium#7176#10 / 05 / 2009\nsuper league xiv#13#catalans dragons#win#28 - 32#away#stade gilbert brutus#9065#16 / 05 / 2009\nsuper league xiv#14#harlequins rl#win#22 - 12#home#gpw recruitment stadium#9359#22 / 05 / 2009\nchallenge cup 2009#qf#gateshead thunder#win#6 - 66#away#gateshead international stadium#4325#30 / 05 / 2009\nsuper league xiv#15#hull fc#win#6 - 30#away#kc stadium#12009#05 / 06 / 2009\nsuper league xiv#16#castleford tigers#win#50 - 10#home#gpw recruitment stadium#9680#12 / 06 / 2009\nsuper league xiv#17#bradford bulls#win#18 - 44#away#grattan stadium#10599#21 / 06 / 2009\nsuper league xiv#18#celtic crusaders#win#30 - 0#home#gpw recruitment stadium#8684#26 / 06 / 2009\nsuper league xiv#19#salford city reds#loss#20 - 10#away#the willows#4808#03 / 07 / 2009\nsuper league xiv#20#warrington wolves#win#26 - 40#away#halliwell jones stadium#12075#11 / 07 / 2009\nsuper league xiv#21#wakefield trinity wildcats#loss#20 - 22#home#gpw recruitment stadium#8651#17 / 07 / 2009\nsuper league xiv#22#harlequins rl#win#24 - 42#away#twickenham stoop stadium#4258#25 / 07 / 2009\nsuper league xiv#23#wigan warriors#win#10 - 6#home#gpw recruitment stadium#15563#31 / 07 / 2009\nchallenge cup 2009#sf#huddersfield giants#loss#14 - 24#neutral#halliwell jones stadium#10638#09 / 08 / 2009\nsuper league xiv#24#hull kingston rovers#loss#26 - 10#away#new craven park#8976#16 / 08 / 2009\nsuper league xiv#25#huddersfield giants#win#12 - 10#home#gpw recruitment stadium#8708#21 / 08 / 2009\nsuper league xiv#26#leeds rhinos#loss#18 - 10#away#headingley carnegie#19997#04 / 09 / 2009\nsuper league xiv#27#catalans dragons#loss#12 - 24#home#gpw recruitment stadium#8268#11 / 09 / 2009\nsuper league xiv play - offs#qualifier#huddersfield giants#win#15 - 2#home#gpw recruitment stadium#6157#19 / 09 / 2009\nsuper league xiv play - offs#semi - final#wigan warriors#win#14 - 10#home#gpw recruitment stadium#13087#10 / 10 / 2009\n",
        "pandas_code": "(df[df['competition'] == 'friendly']['result'].value_counts()['win'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 166,
        "statement": "during the 2009 st helen rlfc season the super league xiv highest round be 27",
        "label": 1,
        "table_caption": "2009 st helens rlfc season",
        "table_text": "competition#round#opponent#result#score#home / away#venue#attendance#date\nfriendly#n / a#widnes vikings#loss#26 - 28#home#gpw recruitment stadium#4128#27 / 12 / 2008\nfriendly#n / a#wakefield trinity wildcats#win#26 - 22#home#gpw recruitment stadium#3549#18 / 01 / 2009\nfriendly#n / a#widnes vikings#win#54 - 6#away#stobart stadium halton#2562#01 / 02 / 2009\nsuper league xiv#1#warrington wolves#win#26 - 14#home#gpw recruitment stadium#17009#13 / 02 / 2009\nsuper league xiv#2#huddersfield giants#win#6 - 23#away#galpharm stadium#11338#22 / 02 / 2009\nsuper league xiv#3#hull kingston rovers#loss#19 - 20#home#gpw recruitment stadium#11830#27 / 02 / 2009\nsuper league xiv#4#crusaders#win#0 - 4#away#brewery field#6351#07 / 03 / 2009\nsuper league xiv#5#salford city reds#win#38 - 12#home#gpw recruitment stadium#9723#13 / 03 / 2009\nsuper league xiv#6#leeds rhinos#win#26 - 18#home#gpw recruitment stadium#13966#20 / 03 / 2009\nsuper league xiv#7#wakefield trinity wildcats#win#18 - 42#away#belle vue#6038#29 / 03 / 2009\nchallenge cup 2009#4#leeds rhinos#win#18 - 22#away#headingley stadium#17689#05 / 04 / 2009\nsuper league xiv#8#wigan warriors#win#12 - 19#away#jjb stadium#22232#09 / 04 / 2009\nsuper league xiv#9#hull#win#44 - 22#home#gpw recruitment stadium#13684#13 / 04 / 2009\nsuper league xiv#10#castleford tigers#win#22 - 68#away#the jungle#8003#19 / 04 / 2009\nsuper league xiv#11#bradford bulls#loss#30 - 34#home#gpw recruitment stadium#11039#24 / 04 / 2009\nsuper league xiv#12#wigan warriors#loss#38 - 18#neutral#murrayfield stadium#29687#02 / 05 / 2009\nchallenge cup 2009#5#catalans dragons#win#42 - 8#home#gpw recruitment stadium#7176#10 / 05 / 2009\nsuper league xiv#13#catalans dragons#win#28 - 32#away#stade gilbert brutus#9065#16 / 05 / 2009\nsuper league xiv#14#harlequins rl#win#22 - 12#home#gpw recruitment stadium#9359#22 / 05 / 2009\nchallenge cup 2009#qf#gateshead thunder#win#6 - 66#away#gateshead international stadium#4325#30 / 05 / 2009\nsuper league xiv#15#hull fc#win#6 - 30#away#kc stadium#12009#05 / 06 / 2009\nsuper league xiv#16#castleford tigers#win#50 - 10#home#gpw recruitment stadium#9680#12 / 06 / 2009\nsuper league xiv#17#bradford bulls#win#18 - 44#away#grattan stadium#10599#21 / 06 / 2009\nsuper league xiv#18#celtic crusaders#win#30 - 0#home#gpw recruitment stadium#8684#26 / 06 / 2009\nsuper league xiv#19#salford city reds#loss#20 - 10#away#the willows#4808#03 / 07 / 2009\nsuper league xiv#20#warrington wolves#win#26 - 40#away#halliwell jones stadium#12075#11 / 07 / 2009\nsuper league xiv#21#wakefield trinity wildcats#loss#20 - 22#home#gpw recruitment stadium#8651#17 / 07 / 2009\nsuper league xiv#22#harlequins rl#win#24 - 42#away#twickenham stoop stadium#4258#25 / 07 / 2009\nsuper league xiv#23#wigan warriors#win#10 - 6#home#gpw recruitment stadium#15563#31 / 07 / 2009\nchallenge cup 2009#sf#huddersfield giants#loss#14 - 24#neutral#halliwell jones stadium#10638#09 / 08 / 2009\nsuper league xiv#24#hull kingston rovers#loss#26 - 10#away#new craven park#8976#16 / 08 / 2009\nsuper league xiv#25#huddersfield giants#win#12 - 10#home#gpw recruitment stadium#8708#21 / 08 / 2009\nsuper league xiv#26#leeds rhinos#loss#18 - 10#away#headingley carnegie#19997#04 / 09 / 2009\nsuper league xiv#27#catalans dragons#loss#12 - 24#home#gpw recruitment stadium#8268#11 / 09 / 2009\nsuper league xiv play - offs#qualifier#huddersfield giants#win#15 - 2#home#gpw recruitment stadium#6157#19 / 09 / 2009\nsuper league xiv play - offs#semi - final#wigan warriors#win#14 - 10#home#gpw recruitment stadium#13087#10 / 10 / 2009\n",
        "pandas_code": "df[(df['competition'] == 'super league xiv') & (df['round'].str.isnumeric())]['round'].astype(int).max() == 27",
        "pandas_eval": "True"
    },
    {
        "id": 167,
        "statement": "during the 2009 st helen rlfc season there be 18 home game",
        "label": 1,
        "table_caption": "2009 st helens rlfc season",
        "table_text": "competition#round#opponent#result#score#home / away#venue#attendance#date\nfriendly#n / a#widnes vikings#loss#26 - 28#home#gpw recruitment stadium#4128#27 / 12 / 2008\nfriendly#n / a#wakefield trinity wildcats#win#26 - 22#home#gpw recruitment stadium#3549#18 / 01 / 2009\nfriendly#n / a#widnes vikings#win#54 - 6#away#stobart stadium halton#2562#01 / 02 / 2009\nsuper league xiv#1#warrington wolves#win#26 - 14#home#gpw recruitment stadium#17009#13 / 02 / 2009\nsuper league xiv#2#huddersfield giants#win#6 - 23#away#galpharm stadium#11338#22 / 02 / 2009\nsuper league xiv#3#hull kingston rovers#loss#19 - 20#home#gpw recruitment stadium#11830#27 / 02 / 2009\nsuper league xiv#4#crusaders#win#0 - 4#away#brewery field#6351#07 / 03 / 2009\nsuper league xiv#5#salford city reds#win#38 - 12#home#gpw recruitment stadium#9723#13 / 03 / 2009\nsuper league xiv#6#leeds rhinos#win#26 - 18#home#gpw recruitment stadium#13966#20 / 03 / 2009\nsuper league xiv#7#wakefield trinity wildcats#win#18 - 42#away#belle vue#6038#29 / 03 / 2009\nchallenge cup 2009#4#leeds rhinos#win#18 - 22#away#headingley stadium#17689#05 / 04 / 2009\nsuper league xiv#8#wigan warriors#win#12 - 19#away#jjb stadium#22232#09 / 04 / 2009\nsuper league xiv#9#hull#win#44 - 22#home#gpw recruitment stadium#13684#13 / 04 / 2009\nsuper league xiv#10#castleford tigers#win#22 - 68#away#the jungle#8003#19 / 04 / 2009\nsuper league xiv#11#bradford bulls#loss#30 - 34#home#gpw recruitment stadium#11039#24 / 04 / 2009\nsuper league xiv#12#wigan warriors#loss#38 - 18#neutral#murrayfield stadium#29687#02 / 05 / 2009\nchallenge cup 2009#5#catalans dragons#win#42 - 8#home#gpw recruitment stadium#7176#10 / 05 / 2009\nsuper league xiv#13#catalans dragons#win#28 - 32#away#stade gilbert brutus#9065#16 / 05 / 2009\nsuper league xiv#14#harlequins rl#win#22 - 12#home#gpw recruitment stadium#9359#22 / 05 / 2009\nchallenge cup 2009#qf#gateshead thunder#win#6 - 66#away#gateshead international stadium#4325#30 / 05 / 2009\nsuper league xiv#15#hull fc#win#6 - 30#away#kc stadium#12009#05 / 06 / 2009\nsuper league xiv#16#castleford tigers#win#50 - 10#home#gpw recruitment stadium#9680#12 / 06 / 2009\nsuper league xiv#17#bradford bulls#win#18 - 44#away#grattan stadium#10599#21 / 06 / 2009\nsuper league xiv#18#celtic crusaders#win#30 - 0#home#gpw recruitment stadium#8684#26 / 06 / 2009\nsuper league xiv#19#salford city reds#loss#20 - 10#away#the willows#4808#03 / 07 / 2009\nsuper league xiv#20#warrington wolves#win#26 - 40#away#halliwell jones stadium#12075#11 / 07 / 2009\nsuper league xiv#21#wakefield trinity wildcats#loss#20 - 22#home#gpw recruitment stadium#8651#17 / 07 / 2009\nsuper league xiv#22#harlequins rl#win#24 - 42#away#twickenham stoop stadium#4258#25 / 07 / 2009\nsuper league xiv#23#wigan warriors#win#10 - 6#home#gpw recruitment stadium#15563#31 / 07 / 2009\nchallenge cup 2009#sf#huddersfield giants#loss#14 - 24#neutral#halliwell jones stadium#10638#09 / 08 / 2009\nsuper league xiv#24#hull kingston rovers#loss#26 - 10#away#new craven park#8976#16 / 08 / 2009\nsuper league xiv#25#huddersfield giants#win#12 - 10#home#gpw recruitment stadium#8708#21 / 08 / 2009\nsuper league xiv#26#leeds rhinos#loss#18 - 10#away#headingley carnegie#19997#04 / 09 / 2009\nsuper league xiv#27#catalans dragons#loss#12 - 24#home#gpw recruitment stadium#8268#11 / 09 / 2009\nsuper league xiv play - offs#qualifier#huddersfield giants#win#15 - 2#home#gpw recruitment stadium#6157#19 / 09 / 2009\nsuper league xiv play - offs#semi - final#wigan warriors#win#14 - 10#home#gpw recruitment stadium#13087#10 / 10 / 2009\n",
        "pandas_code": "(df['home / away'] == 'home').sum() == 18",
        "pandas_eval": "True"
    },
    {
        "id": 168,
        "statement": "during the 2009 st helen rlfc season there be 16 away game",
        "label": 1,
        "table_caption": "2009 st helens rlfc season",
        "table_text": "competition#round#opponent#result#score#home / away#venue#attendance#date\nfriendly#n / a#widnes vikings#loss#26 - 28#home#gpw recruitment stadium#4128#27 / 12 / 2008\nfriendly#n / a#wakefield trinity wildcats#win#26 - 22#home#gpw recruitment stadium#3549#18 / 01 / 2009\nfriendly#n / a#widnes vikings#win#54 - 6#away#stobart stadium halton#2562#01 / 02 / 2009\nsuper league xiv#1#warrington wolves#win#26 - 14#home#gpw recruitment stadium#17009#13 / 02 / 2009\nsuper league xiv#2#huddersfield giants#win#6 - 23#away#galpharm stadium#11338#22 / 02 / 2009\nsuper league xiv#3#hull kingston rovers#loss#19 - 20#home#gpw recruitment stadium#11830#27 / 02 / 2009\nsuper league xiv#4#crusaders#win#0 - 4#away#brewery field#6351#07 / 03 / 2009\nsuper league xiv#5#salford city reds#win#38 - 12#home#gpw recruitment stadium#9723#13 / 03 / 2009\nsuper league xiv#6#leeds rhinos#win#26 - 18#home#gpw recruitment stadium#13966#20 / 03 / 2009\nsuper league xiv#7#wakefield trinity wildcats#win#18 - 42#away#belle vue#6038#29 / 03 / 2009\nchallenge cup 2009#4#leeds rhinos#win#18 - 22#away#headingley stadium#17689#05 / 04 / 2009\nsuper league xiv#8#wigan warriors#win#12 - 19#away#jjb stadium#22232#09 / 04 / 2009\nsuper league xiv#9#hull#win#44 - 22#home#gpw recruitment stadium#13684#13 / 04 / 2009\nsuper league xiv#10#castleford tigers#win#22 - 68#away#the jungle#8003#19 / 04 / 2009\nsuper league xiv#11#bradford bulls#loss#30 - 34#home#gpw recruitment stadium#11039#24 / 04 / 2009\nsuper league xiv#12#wigan warriors#loss#38 - 18#neutral#murrayfield stadium#29687#02 / 05 / 2009\nchallenge cup 2009#5#catalans dragons#win#42 - 8#home#gpw recruitment stadium#7176#10 / 05 / 2009\nsuper league xiv#13#catalans dragons#win#28 - 32#away#stade gilbert brutus#9065#16 / 05 / 2009\nsuper league xiv#14#harlequins rl#win#22 - 12#home#gpw recruitment stadium#9359#22 / 05 / 2009\nchallenge cup 2009#qf#gateshead thunder#win#6 - 66#away#gateshead international stadium#4325#30 / 05 / 2009\nsuper league xiv#15#hull fc#win#6 - 30#away#kc stadium#12009#05 / 06 / 2009\nsuper league xiv#16#castleford tigers#win#50 - 10#home#gpw recruitment stadium#9680#12 / 06 / 2009\nsuper league xiv#17#bradford bulls#win#18 - 44#away#grattan stadium#10599#21 / 06 / 2009\nsuper league xiv#18#celtic crusaders#win#30 - 0#home#gpw recruitment stadium#8684#26 / 06 / 2009\nsuper league xiv#19#salford city reds#loss#20 - 10#away#the willows#4808#03 / 07 / 2009\nsuper league xiv#20#warrington wolves#win#26 - 40#away#halliwell jones stadium#12075#11 / 07 / 2009\nsuper league xiv#21#wakefield trinity wildcats#loss#20 - 22#home#gpw recruitment stadium#8651#17 / 07 / 2009\nsuper league xiv#22#harlequins rl#win#24 - 42#away#twickenham stoop stadium#4258#25 / 07 / 2009\nsuper league xiv#23#wigan warriors#win#10 - 6#home#gpw recruitment stadium#15563#31 / 07 / 2009\nchallenge cup 2009#sf#huddersfield giants#loss#14 - 24#neutral#halliwell jones stadium#10638#09 / 08 / 2009\nsuper league xiv#24#hull kingston rovers#loss#26 - 10#away#new craven park#8976#16 / 08 / 2009\nsuper league xiv#25#huddersfield giants#win#12 - 10#home#gpw recruitment stadium#8708#21 / 08 / 2009\nsuper league xiv#26#leeds rhinos#loss#18 - 10#away#headingley carnegie#19997#04 / 09 / 2009\nsuper league xiv#27#catalans dragons#loss#12 - 24#home#gpw recruitment stadium#8268#11 / 09 / 2009\nsuper league xiv play - offs#qualifier#huddersfield giants#win#15 - 2#home#gpw recruitment stadium#6157#19 / 09 / 2009\nsuper league xiv play - offs#semi - final#wigan warriors#win#14 - 10#home#gpw recruitment stadium#13087#10 / 10 / 2009\n",
        "pandas_code": "len(df[df['home / away'] == 'away']) == 16",
        "pandas_eval": "True"
    },
    {
        "id": 169,
        "statement": "during the 2009 st helen rlfc season the challenge cup 2009 competition have 1 loss",
        "label": 1,
        "table_caption": "2009 st helens rlfc season",
        "table_text": "competition#round#opponent#result#score#home / away#venue#attendance#date\nfriendly#n / a#widnes vikings#loss#26 - 28#home#gpw recruitment stadium#4128#27 / 12 / 2008\nfriendly#n / a#wakefield trinity wildcats#win#26 - 22#home#gpw recruitment stadium#3549#18 / 01 / 2009\nfriendly#n / a#widnes vikings#win#54 - 6#away#stobart stadium halton#2562#01 / 02 / 2009\nsuper league xiv#1#warrington wolves#win#26 - 14#home#gpw recruitment stadium#17009#13 / 02 / 2009\nsuper league xiv#2#huddersfield giants#win#6 - 23#away#galpharm stadium#11338#22 / 02 / 2009\nsuper league xiv#3#hull kingston rovers#loss#19 - 20#home#gpw recruitment stadium#11830#27 / 02 / 2009\nsuper league xiv#4#crusaders#win#0 - 4#away#brewery field#6351#07 / 03 / 2009\nsuper league xiv#5#salford city reds#win#38 - 12#home#gpw recruitment stadium#9723#13 / 03 / 2009\nsuper league xiv#6#leeds rhinos#win#26 - 18#home#gpw recruitment stadium#13966#20 / 03 / 2009\nsuper league xiv#7#wakefield trinity wildcats#win#18 - 42#away#belle vue#6038#29 / 03 / 2009\nchallenge cup 2009#4#leeds rhinos#win#18 - 22#away#headingley stadium#17689#05 / 04 / 2009\nsuper league xiv#8#wigan warriors#win#12 - 19#away#jjb stadium#22232#09 / 04 / 2009\nsuper league xiv#9#hull#win#44 - 22#home#gpw recruitment stadium#13684#13 / 04 / 2009\nsuper league xiv#10#castleford tigers#win#22 - 68#away#the jungle#8003#19 / 04 / 2009\nsuper league xiv#11#bradford bulls#loss#30 - 34#home#gpw recruitment stadium#11039#24 / 04 / 2009\nsuper league xiv#12#wigan warriors#loss#38 - 18#neutral#murrayfield stadium#29687#02 / 05 / 2009\nchallenge cup 2009#5#catalans dragons#win#42 - 8#home#gpw recruitment stadium#7176#10 / 05 / 2009\nsuper league xiv#13#catalans dragons#win#28 - 32#away#stade gilbert brutus#9065#16 / 05 / 2009\nsuper league xiv#14#harlequins rl#win#22 - 12#home#gpw recruitment stadium#9359#22 / 05 / 2009\nchallenge cup 2009#qf#gateshead thunder#win#6 - 66#away#gateshead international stadium#4325#30 / 05 / 2009\nsuper league xiv#15#hull fc#win#6 - 30#away#kc stadium#12009#05 / 06 / 2009\nsuper league xiv#16#castleford tigers#win#50 - 10#home#gpw recruitment stadium#9680#12 / 06 / 2009\nsuper league xiv#17#bradford bulls#win#18 - 44#away#grattan stadium#10599#21 / 06 / 2009\nsuper league xiv#18#celtic crusaders#win#30 - 0#home#gpw recruitment stadium#8684#26 / 06 / 2009\nsuper league xiv#19#salford city reds#loss#20 - 10#away#the willows#4808#03 / 07 / 2009\nsuper league xiv#20#warrington wolves#win#26 - 40#away#halliwell jones stadium#12075#11 / 07 / 2009\nsuper league xiv#21#wakefield trinity wildcats#loss#20 - 22#home#gpw recruitment stadium#8651#17 / 07 / 2009\nsuper league xiv#22#harlequins rl#win#24 - 42#away#twickenham stoop stadium#4258#25 / 07 / 2009\nsuper league xiv#23#wigan warriors#win#10 - 6#home#gpw recruitment stadium#15563#31 / 07 / 2009\nchallenge cup 2009#sf#huddersfield giants#loss#14 - 24#neutral#halliwell jones stadium#10638#09 / 08 / 2009\nsuper league xiv#24#hull kingston rovers#loss#26 - 10#away#new craven park#8976#16 / 08 / 2009\nsuper league xiv#25#huddersfield giants#win#12 - 10#home#gpw recruitment stadium#8708#21 / 08 / 2009\nsuper league xiv#26#leeds rhinos#loss#18 - 10#away#headingley carnegie#19997#04 / 09 / 2009\nsuper league xiv#27#catalans dragons#loss#12 - 24#home#gpw recruitment stadium#8268#11 / 09 / 2009\nsuper league xiv play - offs#qualifier#huddersfield giants#win#15 - 2#home#gpw recruitment stadium#6157#19 / 09 / 2009\nsuper league xiv play - offs#semi - final#wigan warriors#win#14 - 10#home#gpw recruitment stadium#13087#10 / 10 / 2009\n",
        "pandas_code": "(df[df['competition'] == 'challenge cup 2009']['result'] == 'loss').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 170,
        "statement": "both the m3000 and t2000 have a max memory of 64 gb",
        "label": 1,
        "table_caption": "sparc enterprise",
        "table_text": "model#ru#max processors#processor frequency#max memory#max disk capacity#ga date\nm3000#2#1 sparc64 vii or vii +#2.52 , 2.75 ghz (vii) or 2.86 ghz (vii + )#64 gb#4 2.5 sas#october 2008 (vii) , april 2011 (vii + )\nt1000#1#1 ultrasparc t1#1.0 ghz#32 gb#one 3.5 sata or two 2.5 sas#march 2006\nt2000#2#1 ultrasparc t1#1.0 , 1.2 , 1.4 ghz#64 gb#up to four 2.5 sas#december 2005\nt5120#1#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#november 2007\nt5140#1#2 ultrasparc t2 +#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#april 2008\nt5220#2#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to sixteen 2.5 sas#november 2007\nt5240#2#2 ultrasparc t2 +#1.2 , 1.4 ghz#256 gb#up to sixteen 2.5 sas#april 2008\n",
        "pandas_code": "(df.loc[df['model'].isin(['m3000', 't2000']), 'max memory'] == '64 gb').all()",
        "pandas_eval": "True"
    },
    {
        "id": 171,
        "statement": "the t1000 be the only model with a max memory of 32 gb",
        "label": 1,
        "table_caption": "sparc enterprise",
        "table_text": "model#ru#max processors#processor frequency#max memory#max disk capacity#ga date\nm3000#2#1 sparc64 vii or vii +#2.52 , 2.75 ghz (vii) or 2.86 ghz (vii + )#64 gb#4 2.5 sas#october 2008 (vii) , april 2011 (vii + )\nt1000#1#1 ultrasparc t1#1.0 ghz#32 gb#one 3.5 sata or two 2.5 sas#march 2006\nt2000#2#1 ultrasparc t1#1.0 , 1.2 , 1.4 ghz#64 gb#up to four 2.5 sas#december 2005\nt5120#1#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#november 2007\nt5140#1#2 ultrasparc t2 +#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#april 2008\nt5220#2#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to sixteen 2.5 sas#november 2007\nt5240#2#2 ultrasparc t2 +#1.2 , 1.4 ghz#256 gb#up to sixteen 2.5 sas#april 2008\n",
        "pandas_code": "(df[df['max memory'] == '32 gb']['model'].unique() == ['t1000']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 172,
        "statement": "the m3000 vii , t5140 , and t5240 be all release in 2008",
        "label": 1,
        "table_caption": "sparc enterprise",
        "table_text": "model#ru#max processors#processor frequency#max memory#max disk capacity#ga date\nm3000#2#1 sparc64 vii or vii +#2.52 , 2.75 ghz (vii) or 2.86 ghz (vii + )#64 gb#4 2.5 sas#october 2008 (vii) , april 2011 (vii + )\nt1000#1#1 ultrasparc t1#1.0 ghz#32 gb#one 3.5 sata or two 2.5 sas#march 2006\nt2000#2#1 ultrasparc t1#1.0 , 1.2 , 1.4 ghz#64 gb#up to four 2.5 sas#december 2005\nt5120#1#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#november 2007\nt5140#1#2 ultrasparc t2 +#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#april 2008\nt5220#2#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to sixteen 2.5 sas#november 2007\nt5240#2#2 ultrasparc t2 +#1.2 , 1.4 ghz#256 gb#up to sixteen 2.5 sas#april 2008\n",
        "pandas_code": "all(df[df['model'].isin(['m3000', 't5140', 't5240'])]['ga date'].str.contains('2008'))",
        "pandas_eval": "True"
    },
    {
        "id": 173,
        "statement": "two of the model list have a max disk capacity of up to eight 2.5 sa",
        "label": 1,
        "table_caption": "sparc enterprise",
        "table_text": "model#ru#max processors#processor frequency#max memory#max disk capacity#ga date\nm3000#2#1 sparc64 vii or vii +#2.52 , 2.75 ghz (vii) or 2.86 ghz (vii + )#64 gb#4 2.5 sas#october 2008 (vii) , april 2011 (vii + )\nt1000#1#1 ultrasparc t1#1.0 ghz#32 gb#one 3.5 sata or two 2.5 sas#march 2006\nt2000#2#1 ultrasparc t1#1.0 , 1.2 , 1.4 ghz#64 gb#up to four 2.5 sas#december 2005\nt5120#1#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#november 2007\nt5140#1#2 ultrasparc t2 +#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#april 2008\nt5220#2#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to sixteen 2.5 sas#november 2007\nt5240#2#2 ultrasparc t2 +#1.2 , 1.4 ghz#256 gb#up to sixteen 2.5 sas#april 2008\n",
        "pandas_code": "(df['max disk capacity'] == 'up to eight 2.5 sas').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 174,
        "statement": "the m3000 , t2000 , t5220 , and t5240 all have 2 ru",
        "label": 1,
        "table_caption": "sparc enterprise",
        "table_text": "model#ru#max processors#processor frequency#max memory#max disk capacity#ga date\nm3000#2#1 sparc64 vii or vii +#2.52 , 2.75 ghz (vii) or 2.86 ghz (vii + )#64 gb#4 2.5 sas#october 2008 (vii) , april 2011 (vii + )\nt1000#1#1 ultrasparc t1#1.0 ghz#32 gb#one 3.5 sata or two 2.5 sas#march 2006\nt2000#2#1 ultrasparc t1#1.0 , 1.2 , 1.4 ghz#64 gb#up to four 2.5 sas#december 2005\nt5120#1#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#november 2007\nt5140#1#2 ultrasparc t2 +#1.2 , 1.4 ghz#128 gb#up to eight 2.5 sas#april 2008\nt5220#2#1 ultrasparc t2#1.2 , 1.4 ghz#128 gb#up to sixteen 2.5 sas#november 2007\nt5240#2#2 ultrasparc t2 +#1.2 , 1.4 ghz#256 gb#up to sixteen 2.5 sas#april 2008\n",
        "pandas_code": "all(df[df['model'].isin(['m3000', 't2000', 't5220', 't5240'])]['ru'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 175,
        "statement": "the earliest solar car team be start in 1990 and be midnight sun ( university of waterloo )",
        "label": 1,
        "table_caption": "list of solar car teams",
        "table_text": "team name (affiliation)#year started#number of cars#current car#car#website\nblue sky solar racing ( university of toronto )#1998#7#b - 7#77#english\n\u00e9clipse ( \u00e9cole de technologie sup\u00e9rieure )#1992#7#\u00e9clipse 7#92#french english\nesteban ( \u00e9cole polytechnique de montr\u00e9al )#1998#6#esteban vi#55#french english\nmcmaster solar car project ( mcmaster university )#1997#4#phoenix ii#116#english\nmidnight sun ( university of waterloo )#1990#10#midnight sun x#24#english\nomega solar ( durham college and uoit )#2008#1#arctic sun#none#english\n",
        "pandas_code": "df[df['year started'] == df['year started'].min()]['team name (affiliation)'].values[0] == 'midnight sun ( university of waterloo )'",
        "pandas_eval": "True"
    },
    {
        "id": 176,
        "statement": "the average number of car on a team be 5.83",
        "label": 1,
        "table_caption": "list of solar car teams",
        "table_text": "team name (affiliation)#year started#number of cars#current car#car#website\nblue sky solar racing ( university of toronto )#1998#7#b - 7#77#english\n\u00e9clipse ( \u00e9cole de technologie sup\u00e9rieure )#1992#7#\u00e9clipse 7#92#french english\nesteban ( \u00e9cole polytechnique de montr\u00e9al )#1998#6#esteban vi#55#french english\nmcmaster solar car project ( mcmaster university )#1997#4#phoenix ii#116#english\nmidnight sun ( university of waterloo )#1990#10#midnight sun x#24#english\nomega solar ( durham college and uoit )#2008#1#arctic sun#none#english\n",
        "pandas_code": "abs(df['number of cars'].mean() - 5.83) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 177,
        "statement": "midnight sun ( university of waterloo ) have more car than blue sky solar racing ( university of toronto )",
        "label": 1,
        "table_caption": "list of solar car teams",
        "table_text": "team name (affiliation)#year started#number of cars#current car#car#website\nblue sky solar racing ( university of toronto )#1998#7#b - 7#77#english\n\u00e9clipse ( \u00e9cole de technologie sup\u00e9rieure )#1992#7#\u00e9clipse 7#92#french english\nesteban ( \u00e9cole polytechnique de montr\u00e9al )#1998#6#esteban vi#55#french english\nmcmaster solar car project ( mcmaster university )#1997#4#phoenix ii#116#english\nmidnight sun ( university of waterloo )#1990#10#midnight sun x#24#english\nomega solar ( durham college and uoit )#2008#1#arctic sun#none#english\n",
        "pandas_code": "df[df['team name (affiliation)'] == 'midnight sun ( university of waterloo )']['number of cars'].values[0] > df[df['team name (affiliation)'] == 'blue sky solar racing ( university of toronto )']['number of cars'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 178,
        "statement": "the average year a team be start be 1997",
        "label": 1,
        "table_caption": "list of solar car teams",
        "table_text": "team name (affiliation)#year started#number of cars#current car#car#website\nblue sky solar racing ( university of toronto )#1998#7#b - 7#77#english\n\u00e9clipse ( \u00e9cole de technologie sup\u00e9rieure )#1992#7#\u00e9clipse 7#92#french english\nesteban ( \u00e9cole polytechnique de montr\u00e9al )#1998#6#esteban vi#55#french english\nmcmaster solar car project ( mcmaster university )#1997#4#phoenix ii#116#english\nmidnight sun ( university of waterloo )#1990#10#midnight sun x#24#english\nomega solar ( durham college and uoit )#2008#1#arctic sun#none#english\n",
        "pandas_code": "abs(df['year started'].mean() - 1997) < 1",
        "pandas_eval": "True"
    },
    {
        "id": 180,
        "statement": "keith wisniewski win 18 fight in the first round or only round of the fight",
        "label": 1,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "df[(df['res'] == 'win') & ((df['round'] == 1) | (df['round'].isna()))].shape[0] == 18",
        "pandas_eval": "True"
    },
    {
        "id": 182,
        "statement": "keith wisniewski be involve in 5 fight which be decide by split decision",
        "label": 1,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "(df['method'].eq('decision (split)').sum() == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 183,
        "statement": "the shortest fight which keith wisniewski won be 21 second",
        "label": 1,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "df[(df['res'] == 'win') & (df['time'] == '0:21')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 184,
        "statement": "keith wisniewski be involve in 6 fight which be end by a tko",
        "label": 1,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "len(df[df['method'].str.contains('tko', case=False)]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 185,
        "statement": "keith wisniewski win 17 fight in the first round or only round of the fight",
        "label": 0,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "(df['res'] == 'win').sum() == 17 and (df[df['res'] == 'win']['round'] == 1).all()",
        "pandas_eval": "False"
    },
    {
        "id": 186,
        "statement": "the longest round in a fight which keith wisniewski won be 31 minute 20 second",
        "label": 0,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "df[(df['res'] == 'win') & (df['time'] == '31:30')].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 187,
        "statement": "keith wisniewski be involve in 4 fight which be decide by split decision",
        "label": 0,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "(df['method'].str.contains('decision (split)').sum() == 4)",
        "pandas_eval": "False"
    },
    {
        "id": 188,
        "statement": "the shortest fight which keith wisniewski won be 31 second",
        "label": 0,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "df[(df['res'] == 'win') & (df['time'] == '0:31')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 189,
        "statement": "keith wisniewski be involve in 5 fight which be end by a tko",
        "label": 0,
        "table_caption": "keith wisniewski",
        "table_text": "res#record#opponent#method#event#round#time#location\nloss#28 - 15 - 1#ivan jorge#decision (unanimous)#ufc fight night : teixeira vs bader#3#5:00#belo horizonte , brazil\nloss#28 - 14 - 1#chris clements#decision (split)#ufc 145#3#5:00#atlanta , georgia , united states\nloss#28 - 13 - 1#josh neer#tko (doctor stoppage)#ufc live : cruz vs johnson#2#5:00#washington , dc , united states\nwin#28 - 12 - 1#chris wilson#decision (unanimous)#hoosier fc 7: validation#3#5:00#valparaiso , indiana , united states\nwin#27 - 12 - 1#randy crawford#ko (knee)#cutthroat mma : supremacy 2#1#0:21#hammond , indiana , united states\nwin#26 - 12 - 1#ted worthington#submission (heel hook)#xtreme fighting organization 35#2#3:56#chicago , illinois , united states\nwin#25 - 12 - 1#pete spratt#submission (rear - naked choke)#hoosier fc 2: it 's on#1#4:07#hammond , indiana , united states\nwin#24 - 12 - 1#tristan yunker#tko (punches)#war in the yard#2#1:12#anderson , indiana , united states\nwin#23 - 12 - 1#johnny davis#submission (rear - naked choke)#c3: corral combat classic 2#1#2:00#hammond , indiana , united states\nloss#22 - 12 - 1#carlo prater#decision (split)#art of war 3#3#5:00#dallas , texas , united states\nloss#22 - 11 - 1#erik oganov#decision (split)#bodog fight : usa vs russia#3#5:00#vancouver , british columbia , canada\nloss#22 - 10 - 1#rory markham#tko (corner stoppage)#ifl : world championship semifinals#3#4:00#portland , oregon , united states\nloss#22 - 9 - 1#jorge masvidal#decision (majority)#bodogfight : to the brink of war#3#5:00#costa rica\nloss#22 - 8 - 1#nick thompson#decision (unanimous)#ufc 56#3#5:00#las vegas , nevada , united states\nwin#22 - 7 - 1#carlo prater#decision (unanimous)#ffc 15: fiesta las vegas#3#5:00#las vegas , nevada , united states\nwin#21 - 7 - 1#musail allaudinov#tko (punches)#euphoria : usa vs russia#3#1:04#atlantic city , new jersey , united states\nloss#20 - 7 - 1#shinya aoki#submission (standing armlock)#shooto : 1 / 29 in korakuen hall#1#2:22#tokyo , japan\nwin#20 - 6 - 1#carlo prater#decision (split)#freestyle fighting championships 13#3#5:00#biloxi , mississippi , united states\nwin#19 - 6 - 1#derrick noble#decision (unanimous)#ihc 8: ethereal#3#5:00#hammond , indiana , united states\nwin#18 - 6 - 1#steve berger#decision (unanimous)#combat : do fighting challenge 1#3#5:00#cicero , illinois , united states\nwin#17 - 6 - 1#kyle jensen#submission (rear - naked choke)#extreme challenge 59#1#2:19#medina , minnesota , united states\nloss#16 - 6 - 1#jason black#decision#xtreme kage kombat#3#5:00#des moines , iowa , united states\nwin#16 - 5 - 1#chris moore#submission (rear - naked choke)#ihc 7: the crucible#1#1:59#hammond , indiana , united states\nwin#15 - 5 - 1#jorge santiago#ko (punches)#absolute fighting championships 6#3#2:14#fort lauderdale , florida , united states\nwin#14 - 5 - 1#danila veselov#submission (guillotine choke)#m - 1 mfc : russia vs the world 6#1#1:54#moscow , russia\nwin#13 - 5 - 1#eddie sanchez#submission (choke)#tfc 8: hell raiser#1#n / a#toledo , ohio , united states\nwin#12 - 5 - 1#nuri shakir#submission (guillotine choke)#usmma 3: ring of fury#1#0:46#boston , massachusetts , united states\nwin#11 - 5 - 1#marco macera#tko (punches)#battle of new orleans 4#1#1:32#metairie , louisiana , united states\nwin#10 - 5 - 1#matt lee#tko (punches)#usmma 2: ring of fury#2#4:19#lowell , massachusetts , united states\nloss#9 - 5 - 1#john renken#submission (kneebar)#hooknshoot : relentless#2#1:43#evansville , indiana , united states\nwin#9 - 4 - 1#eric pettit#submission (rear - naked choke)#sc 1: the awakening#1#n / a#canton , illinois , united states\nwin#8 - 4 - 1#jason glabus#submission (choke)#chicago challenge 7#1#n / a#chicago , illinois , united states\nwin#7 - 4 - 1#dale carson#submission (rear - naked choke)#hooknshoot : trial 3#1#n / a#tell city , indiana , united states\nloss#6 - 4 - 1#cj fernandes#submission (punches)#tfc 1: fightzone 1#1#31:30#fort wayne , indiana , united states\nwin#6 - 3 - 1#angelo popofski#decision (unanimous)#hooknshoot : millennium#1#15:00#n / a\nloss#5 - 3 - 1#adrian serrano#submission (americana)#hooknshoot : texas heat#1#6:30#n / a\nwin#5 - 2 - 1#pat heidenreich#submission (armbar)#night of the diamonds#1#n / a#n / a\ndraw#4 - 2 - 1#jason glabus#draw#chicago challenge 6#1#10:00#chicago , illinois , united states\nloss#4 - 2#shonie carter#ko (spinning back fist)#indiana martial arts challenge 3#1#0:40#indiana , united states\nwin#4 - 1#jim rhodes#submission (armbar)#chicago challenge 5#1#n / a#chicago , illinois , united states\nwin#3 - 1#josh cate#submission (triangle choke)#fearless freestyle fighting 3#1#5:08#n / a\nwin#2 - 1#brandon bledsoe#submission (rear - naked choke)#fearless freestyle fighting 3#1#2:07#n / a\nwin#1 - 1#robert humphreys#decision (unanimous)#fearless freestyle fighting 2#1#15:00#n / a\nloss#0 - 1#joey gilbert#decision (split)#jkd : challenge 2#3#5:00#chicago , illinois , united states\n",
        "pandas_code": "(df['method'].str.contains('tko', case=False, na=False).sum()) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 190,
        "statement": "only 1 orlando player come from the jacksonville school / club team",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "len(df[df['school / club team'] == 'jacksonville']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 192,
        "statement": "mile simon play for orlando before jeryl sasser join the team",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "df[df['player'] == 'miles simon']['years in orlando'].str.split(' - ').iloc[0][1] < df[df['player'] == 'jeryl sasser']['years in orlando'].str.split(' - ').iloc[0][0]",
        "pandas_eval": "True"
    },
    {
        "id": 194,
        "statement": "there be 3 player with the surname smith",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "len(df[df['player'].str.contains(' smith$', case=False, regex=True)]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 195,
        "statement": "there be several player from the jacksonville school / club team",
        "label": 0,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "len(df[df['school / club team'] == 'jacksonville']) > 1",
        "pandas_eval": "False"
    },
    {
        "id": 196,
        "statement": "mile simon join the team after jeryl sassner",
        "label": 0,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "df.loc[df['player'] == 'miles simon', 'years in orlando'].iloc[0].split(' - ')[0] > df.loc[df['player'] == 'jeryl sasser', 'years in orlando'].iloc[0].split(' - ')[1]",
        "pandas_eval": "False"
    },
    {
        "id": 197,
        "statement": "dennis scott and danny schayes be never on the team together",
        "label": 0,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "any((df['player'] == 'dennis scott') & (df['years in orlando'].apply(lambda x: any(y in df[df['player'] == 'danny schayes']['years in orlando'].iloc[0].split(' - ') for y in x.split(' - ')))))",
        "pandas_eval": "False"
    },
    {
        "id": 198,
        "statement": "each player from orlando have a unique surname",
        "label": 0,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#nationality#position#years in orlando#school / club team\njeryl sasser#united states#guard#2001 - 2003#southern methodist\njeryl sasser#united states#guard#2001 - 2003#southern methodist\ndanny schayes#united states#forward - center#1996 - 1999#syracuse\ndennis scott#united states#guard - forward#1990 - 1997#georgia tech\nrony seikaly#lebanon#center#1996 - 1998#syracuse\nrony seikaly#united states#center#1996 - 1998#syracuse\nbrian shaw#united states#guard#1994 - 1997#uc santa barbara\nmiles simon#united states#guard#1998 - 1999#arizona\nscott skiles#united states#guard#1989 - 1994#michigan state\nishmael smith#united states#guard#2012 - 2013#wake forest\nkenny smith#united states#guard#1996#north carolina\notis smith#united states#guard - forward#1989 - 1992#jacksonville\nfelton spencer#united states#center#1996#louisville\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\ndeshawn stevenson#united states#guard#2004 - 2006#washington union hs\nrod strickland#united states#guard#2003 - 2004#depaul\nderek strong#united states#forward#1996 - 2000#xavier (ohio)\n",
        "pandas_code": "df['player'].apply(lambda x: x.split()[-1]).nunique() == df.shape[0]",
        "pandas_eval": "False"
    },
    {
        "id": 199,
        "statement": "the detroit tiger play the yankee 4 time in may 2008",
        "label": 1,
        "table_caption": "2008 detroit tigers season",
        "table_text": "date#opponent#score#loss#attendance#record\nmay 1#yankees#8 - 4#albaladejo (0 - 1)#50993#14 - 15\nmay 2#twins#11 - 1#galarraga (2 - 1)#33628#14 - 16\nmay 3#twins#4 - 1#verlander (1 - 5)#28985#14 - 17\nmay 4#twins#7 - 6#miner (1 - 2)#29821#14 - 18\nmay 5#red sox#6 - 3#bonderman (2 - 3)#39478#14 - 19\nmay 6#red sox#5 - 0#robertson (1 - 4)#38564#14 - 20\nmay 7#red sox#10 - 9#papelbon (2 - 1)#38062#15 - 20\nmay 8#red sox#5 - 1#verlander (1 - 6)#38952#15 - 21\nmay 9#yankees#6 - 5#igawa (0 - 1)#44062#16 - 21\nmay 10#yankees#5 - 2#bonderman (2 - 4)#44580#16 - 22\nmay 11#yankees#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1\nmay 13#royals#3 - 2#cruceta (0 - 1)#11703#16 - 23\nmay 14#royals#2 - 0#verlander (1 - 7)#14053#16 - 24\nmay 15#royals#8 - 4#rogers (3 - 4)#34734#16 - 25\nmay 16#d - backs#4 - 3#miner (1 - 3)#33531#16 - 26\nmay 17#d - backs#3 - 2#scherzer (0 - 2)#48804#17 - 26\nmay 18#d - backs#4 - 0#robertson (1 - 4)#38793#17 - 27\nmay 20#mariners#12 - 8#silva (3 - 3)#39463#18 - 27\nmay 21#mariners#9 - 4#washburn (2 - 6)#36495#19 - 27\nmay 22#mariners#9 - 2#batista (3 - 6)#40166#20 - 27\nmay 23#twins#9 - 4#galarraga (3 - 2)#40732#20 - 28\nmay 24#twins#19 - 3#bonser (2 - 6)#41137#21 - 28\nmay 25#twins#6 - 1#cruceta (0 - 2)#42413#21 - 29\nmay 26#angels#1 - 0#dolsi (0 - 1)#41031#21 - 30\nmay 27#angels#3 - 2#l\u00f3pez (2 - 1)#36569#21 - 31\nmay 28#angels#6 - 2#saunders (8 - 2)#42191#22 - 31\nmay 30#mariners#7 - 4#silva (3 - 5)#34019#23 - 31\nmay 31#mariners#5 - 0#verlander (2 - 8)#33441#23 - 32\n",
        "pandas_code": "len(df[df['opponent'].str.contains('yankee', case=False)]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 200,
        "statement": "the detroit tiger lose the first 6 game they play in may 2008",
        "label": 1,
        "table_caption": "2008 detroit tigers season",
        "table_text": "date#opponent#score#loss#attendance#record\nmay 1#yankees#8 - 4#albaladejo (0 - 1)#50993#14 - 15\nmay 2#twins#11 - 1#galarraga (2 - 1)#33628#14 - 16\nmay 3#twins#4 - 1#verlander (1 - 5)#28985#14 - 17\nmay 4#twins#7 - 6#miner (1 - 2)#29821#14 - 18\nmay 5#red sox#6 - 3#bonderman (2 - 3)#39478#14 - 19\nmay 6#red sox#5 - 0#robertson (1 - 4)#38564#14 - 20\nmay 7#red sox#10 - 9#papelbon (2 - 1)#38062#15 - 20\nmay 8#red sox#5 - 1#verlander (1 - 6)#38952#15 - 21\nmay 9#yankees#6 - 5#igawa (0 - 1)#44062#16 - 21\nmay 10#yankees#5 - 2#bonderman (2 - 4)#44580#16 - 22\nmay 11#yankees#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1#postponed (rain) - rescheduled for september 1\nmay 13#royals#3 - 2#cruceta (0 - 1)#11703#16 - 23\nmay 14#royals#2 - 0#verlander (1 - 7)#14053#16 - 24\nmay 15#royals#8 - 4#rogers (3 - 4)#34734#16 - 25\nmay 16#d - backs#4 - 3#miner (1 - 3)#33531#16 - 26\nmay 17#d - backs#3 - 2#scherzer (0 - 2)#48804#17 - 26\nmay 18#d - backs#4 - 0#robertson (1 - 4)#38793#17 - 27\nmay 20#mariners#12 - 8#silva (3 - 3)#39463#18 - 27\nmay 21#mariners#9 - 4#washburn (2 - 6)#36495#19 - 27\nmay 22#mariners#9 - 2#batista (3 - 6)#40166#20 - 27\nmay 23#twins#9 - 4#galarraga (3 - 2)#40732#20 - 28\nmay 24#twins#19 - 3#bonser (2 - 6)#41137#21 - 28\nmay 25#twins#6 - 1#cruceta (0 - 2)#42413#21 - 29\nmay 26#angels#1 - 0#dolsi (0 - 1)#41031#21 - 30\nmay 27#angels#3 - 2#l\u00f3pez (2 - 1)#36569#21 - 31\nmay 28#angels#6 - 2#saunders (8 - 2)#42191#22 - 31\nmay 30#mariners#7 - 4#silva (3 - 5)#34019#23 - 31\nmay 31#mariners#5 - 0#verlander (2 - 8)#33441#23 - 32\n",
        "pandas_code": "all(df['record'].iloc[:6].apply(lambda x: int(x.split(' - ')[0]) < int(x.split(' - ')[1])))",
        "pandas_eval": "True"
    },
    {
        "id": 204,
        "statement": "in game 1 on mary 23 , dwyane wade have the high in point (25) and udonis haslem have the high in rebound (9) while in game 2 shaquille o'neal have the high in rebound (17)",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 23#detroit#w 91 - 86 (ot)#dwyane wade (25)#udonis haslem (9)#dwyane wade (5)#the palace of auburn hills 22076#1 - 0\n2#may 25#detroit#l 88 - 92 (ot)#dwyane wade (32)#shaquille o'neal (17)#dwyane wade (5)#the palace of auburn hills 22076#1 - 1\n3#may 27#detroit#w 98 - 83 (ot)#dwyane wade (35)#shaquille o'neal (12)#antoine walker , dwyane wade (4)#american airlines arena 20245#2 - 1\n4#may 29#detroit#w 89 - 78 (ot)#dwyane wade (31)#shaquille o'neal (9)#dwyane wade (5)#american airlines arena 20248#3 - 1\n5#may 31#detroit#l 78 - 91 (ot)#dwyane wade (23)#udonis haslem (10)#jason williams (6)#the palace of auburn hills 22076#3 - 2\n",
        "pandas_code": "(df.loc[df['game'] == 1, 'date'].iloc[0] == 'may 23') & (df.loc[df['game'] == 1, 'high points'].iloc[0] == 'dwyane wade (25)') & (df.loc[df['game'] == 1, 'high rebounds'].iloc[0] == 'udonis haslem (9)') & (df.loc[df['game'] == 2, 'high rebounds'].iloc[0] == \"shaquille o'neal (17)\")",
        "pandas_eval": "True"
    },
    {
        "id": 205,
        "statement": "dwyane wade have the high in assist in game 1 , 2 , and 3 with 5 while jason williams have the high in assist in game 5 with 6",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 23#detroit#w 91 - 86 (ot)#dwyane wade (25)#udonis haslem (9)#dwyane wade (5)#the palace of auburn hills 22076#1 - 0\n2#may 25#detroit#l 88 - 92 (ot)#dwyane wade (32)#shaquille o'neal (17)#dwyane wade (5)#the palace of auburn hills 22076#1 - 1\n3#may 27#detroit#w 98 - 83 (ot)#dwyane wade (35)#shaquille o'neal (12)#antoine walker , dwyane wade (4)#american airlines arena 20245#2 - 1\n4#may 29#detroit#w 89 - 78 (ot)#dwyane wade (31)#shaquille o'neal (9)#dwyane wade (5)#american airlines arena 20248#3 - 1\n5#may 31#detroit#l 78 - 91 (ot)#dwyane wade (23)#udonis haslem (10)#jason williams (6)#the palace of auburn hills 22076#3 - 2\n",
        "pandas_code": "all(df.loc[df['game'].isin([1, 2, 3]), 'high assists'].str.contains('dwyane wade')) and df.loc[df['game'] == 5, 'high assists'].str.contains('jason williams').all()",
        "pandas_eval": "True"
    },
    {
        "id": 206,
        "statement": "in game 3 , dwyane wade have a high of 35 point and shaquille o'neal have a high of 12 rebound at the american airline arena with 20245",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 23#detroit#w 91 - 86 (ot)#dwyane wade (25)#udonis haslem (9)#dwyane wade (5)#the palace of auburn hills 22076#1 - 0\n2#may 25#detroit#l 88 - 92 (ot)#dwyane wade (32)#shaquille o'neal (17)#dwyane wade (5)#the palace of auburn hills 22076#1 - 1\n3#may 27#detroit#w 98 - 83 (ot)#dwyane wade (35)#shaquille o'neal (12)#antoine walker , dwyane wade (4)#american airlines arena 20245#2 - 1\n4#may 29#detroit#w 89 - 78 (ot)#dwyane wade (31)#shaquille o'neal (9)#dwyane wade (5)#american airlines arena 20248#3 - 1\n5#may 31#detroit#l 78 - 91 (ot)#dwyane wade (23)#udonis haslem (10)#jason williams (6)#the palace of auburn hills 22076#3 - 2\n",
        "pandas_code": "(df.loc[df['game'] == 3, ['high points', 'high rebounds', 'location attendance']].values == ['dwyane wade (35)', \"shaquille o'neal (12)\", 'american airlines arena 20245']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 207,
        "statement": "game 5 take place on may 31 against detroit and the miami heat lose 78 - 91 at the palace of auburns hill with attendance 22076",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 23#detroit#w 91 - 86 (ot)#dwyane wade (25)#udonis haslem (9)#dwyane wade (5)#the palace of auburn hills 22076#1 - 0\n2#may 25#detroit#l 88 - 92 (ot)#dwyane wade (32)#shaquille o'neal (17)#dwyane wade (5)#the palace of auburn hills 22076#1 - 1\n3#may 27#detroit#w 98 - 83 (ot)#dwyane wade (35)#shaquille o'neal (12)#antoine walker , dwyane wade (4)#american airlines arena 20245#2 - 1\n4#may 29#detroit#w 89 - 78 (ot)#dwyane wade (31)#shaquille o'neal (9)#dwyane wade (5)#american airlines arena 20248#3 - 1\n5#may 31#detroit#l 78 - 91 (ot)#dwyane wade (23)#udonis haslem (10)#jason williams (6)#the palace of auburn hills 22076#3 - 2\n",
        "pandas_code": "((df['game'] == 5) & (df['date'] == 'may 31') & (df['team'] == 'detroit') & (df['score'] == 'l 78 - 91 (ot)') & (df['location attendance'] == 'the palace of auburn hills 22076')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 208,
        "statement": "shaquille o'neal have the high in rebound in game 2 , 3 , and 4 which take place at the palace of auburns with attendance 22076 , american airline arena with attendance 20245 , and american airline arena with attendance 20248",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 23#detroit#w 91 - 86 (ot)#dwyane wade (25)#udonis haslem (9)#dwyane wade (5)#the palace of auburn hills 22076#1 - 0\n2#may 25#detroit#l 88 - 92 (ot)#dwyane wade (32)#shaquille o'neal (17)#dwyane wade (5)#the palace of auburn hills 22076#1 - 1\n3#may 27#detroit#w 98 - 83 (ot)#dwyane wade (35)#shaquille o'neal (12)#antoine walker , dwyane wade (4)#american airlines arena 20245#2 - 1\n4#may 29#detroit#w 89 - 78 (ot)#dwyane wade (31)#shaquille o'neal (9)#dwyane wade (5)#american airlines arena 20248#3 - 1\n5#may 31#detroit#l 78 - 91 (ot)#dwyane wade (23)#udonis haslem (10)#jason williams (6)#the palace of auburn hills 22076#3 - 2\n",
        "pandas_code": "all(df.loc[df['game'].isin([2, 3, 4]), 'high rebounds'].str.contains(\"shaquille o'neal\")) and all(df.loc[df['game'].isin([2, 3, 4]), 'location attendance'].isin(['the palace of auburn hills 22076', 'american airlines arena 20245', 'american airlines arena 20248']))",
        "pandas_eval": "True"
    },
    {
        "id": 209,
        "statement": "altref church have additional stop to account for the year (1997)",
        "label": 1,
        "table_caption": "j\u00fcrgen ahrend",
        "table_text": "year#opus#town#church#kind#manuals#stops\n1954 / 88#1#larrelt (d)#larrelt church#r#i / p#11\n1955#4#westerhusen (d)#westerhusen church#r#i / p#7\n1957#9#uttum (d)#uttum church#r#i#9\n1957 (1997)#10#veldhausen (d)#altref church#nb#i (ii / p)#6 (13)\n1959 / 2002#18#scheveningen (nl)#zorgvlietkerk#nb#iii / p#26\n1961#25#rysum (d)#rysum church#r#i#7\n1961#27#aurich (d)#lambertikirche#nb#ii / p#25\n1962#29#espel (nl)#ref church#nb#i#7\n1962#30#bremen (d)#st martini#nb#iii / p#33\n1963#34#wassenaar (nl)#kivietkerk#nb#ii / p#21\n1965#41#amsterdam (nl)#oude kerk (transept organ)#nb#ii / p#17\n1965#43#amsterdam (nl)#oude waalse kerk#r#ii / p#26\n1965#40#groningen (nl)#magnaliakerk#nb#ii#13\n1965#42#den haag (nl)#johanneskapel#nb#ii / p#14\n1966#45#bremen (d)#ev church oberneuland#nb#ii / p#22\n1967#49#castrop - rauxel (d)#church schwerin - frolinde#nb#iii / p#27\n1968#51#aalten (nl)#geref zuiderkerk#nb#ii / p#16\n1968#58#haarlem (nl)#mennonite church#nb#iii / p#24\n1969#62#hamburg (d)#ref church altona#nb#ii / p#13\n1966 / 69 / 87#65#marienhafe (d)#marienkirche#r#ii / p#20\n1970#68#frankfurt am main (d)#cantate domino#nb#iii / p#33\n1970#69#uelsen (d)#ref church#nb#ii / p#20\n1970 / 76#70#innsbruck (a)#hofkirche#r#ii / p#15\n1963 - 71#74#leer (d)#gro\u00e3\u00ffe kirche#r#iii / p#37\n",
        "pandas_code": "df.loc[df['church'] == 'altref church', 'stops'].values[0] == '6 (13)'",
        "pandas_eval": "True"
    },
    {
        "id": 210,
        "statement": "marienkirche be the only church with 3 separate year list",
        "label": 1,
        "table_caption": "j\u00fcrgen ahrend",
        "table_text": "year#opus#town#church#kind#manuals#stops\n1954 / 88#1#larrelt (d)#larrelt church#r#i / p#11\n1955#4#westerhusen (d)#westerhusen church#r#i / p#7\n1957#9#uttum (d)#uttum church#r#i#9\n1957 (1997)#10#veldhausen (d)#altref church#nb#i (ii / p)#6 (13)\n1959 / 2002#18#scheveningen (nl)#zorgvlietkerk#nb#iii / p#26\n1961#25#rysum (d)#rysum church#r#i#7\n1961#27#aurich (d)#lambertikirche#nb#ii / p#25\n1962#29#espel (nl)#ref church#nb#i#7\n1962#30#bremen (d)#st martini#nb#iii / p#33\n1963#34#wassenaar (nl)#kivietkerk#nb#ii / p#21\n1965#41#amsterdam (nl)#oude kerk (transept organ)#nb#ii / p#17\n1965#43#amsterdam (nl)#oude waalse kerk#r#ii / p#26\n1965#40#groningen (nl)#magnaliakerk#nb#ii#13\n1965#42#den haag (nl)#johanneskapel#nb#ii / p#14\n1966#45#bremen (d)#ev church oberneuland#nb#ii / p#22\n1967#49#castrop - rauxel (d)#church schwerin - frolinde#nb#iii / p#27\n1968#51#aalten (nl)#geref zuiderkerk#nb#ii / p#16\n1968#58#haarlem (nl)#mennonite church#nb#iii / p#24\n1969#62#hamburg (d)#ref church altona#nb#ii / p#13\n1966 / 69 / 87#65#marienhafe (d)#marienkirche#r#ii / p#20\n1970#68#frankfurt am main (d)#cantate domino#nb#iii / p#33\n1970#69#uelsen (d)#ref church#nb#ii / p#20\n1970 / 76#70#innsbruck (a)#hofkirche#r#ii / p#15\n1963 - 71#74#leer (d)#gro\u00e3\u00ffe kirche#r#iii / p#37\n",
        "pandas_code": "df[df['church'] == 'marienkirche']['year'].str.split(' / ').explode().nunique() == 3 and (df[df['church'] != 'marienkirche']['year'].str.split(' / ').explode().groupby(df['church']).nunique() != 3).all()",
        "pandas_eval": "True"
    },
    {
        "id": 211,
        "statement": "gro\u00e3\u00ffe kirche be the only church with a span of consecutive year 1963 - 74",
        "label": 1,
        "table_caption": "j\u00fcrgen ahrend",
        "table_text": "year#opus#town#church#kind#manuals#stops\n1954 / 88#1#larrelt (d)#larrelt church#r#i / p#11\n1955#4#westerhusen (d)#westerhusen church#r#i / p#7\n1957#9#uttum (d)#uttum church#r#i#9\n1957 (1997)#10#veldhausen (d)#altref church#nb#i (ii / p)#6 (13)\n1959 / 2002#18#scheveningen (nl)#zorgvlietkerk#nb#iii / p#26\n1961#25#rysum (d)#rysum church#r#i#7\n1961#27#aurich (d)#lambertikirche#nb#ii / p#25\n1962#29#espel (nl)#ref church#nb#i#7\n1962#30#bremen (d)#st martini#nb#iii / p#33\n1963#34#wassenaar (nl)#kivietkerk#nb#ii / p#21\n1965#41#amsterdam (nl)#oude kerk (transept organ)#nb#ii / p#17\n1965#43#amsterdam (nl)#oude waalse kerk#r#ii / p#26\n1965#40#groningen (nl)#magnaliakerk#nb#ii#13\n1965#42#den haag (nl)#johanneskapel#nb#ii / p#14\n1966#45#bremen (d)#ev church oberneuland#nb#ii / p#22\n1967#49#castrop - rauxel (d)#church schwerin - frolinde#nb#iii / p#27\n1968#51#aalten (nl)#geref zuiderkerk#nb#ii / p#16\n1968#58#haarlem (nl)#mennonite church#nb#iii / p#24\n1969#62#hamburg (d)#ref church altona#nb#ii / p#13\n1966 / 69 / 87#65#marienhafe (d)#marienkirche#r#ii / p#20\n1970#68#frankfurt am main (d)#cantate domino#nb#iii / p#33\n1970#69#uelsen (d)#ref church#nb#ii / p#20\n1970 / 76#70#innsbruck (a)#hofkirche#r#ii / p#15\n1963 - 71#74#leer (d)#gro\u00e3\u00ffe kirche#r#iii / p#37\n",
        "pandas_code": "df[(df['church'] == 'gro\u00e3\u00ffe kirche') & (df['year'] == '1963 - 71')].shape[0] == 1 and df[df['year'].str.contains('-')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 212,
        "statement": "3 church have more than 30 stop with the designation (d) in their town",
        "label": 1,
        "table_caption": "j\u00fcrgen ahrend",
        "table_text": "year#opus#town#church#kind#manuals#stops\n1954 / 88#1#larrelt (d)#larrelt church#r#i / p#11\n1955#4#westerhusen (d)#westerhusen church#r#i / p#7\n1957#9#uttum (d)#uttum church#r#i#9\n1957 (1997)#10#veldhausen (d)#altref church#nb#i (ii / p)#6 (13)\n1959 / 2002#18#scheveningen (nl)#zorgvlietkerk#nb#iii / p#26\n1961#25#rysum (d)#rysum church#r#i#7\n1961#27#aurich (d)#lambertikirche#nb#ii / p#25\n1962#29#espel (nl)#ref church#nb#i#7\n1962#30#bremen (d)#st martini#nb#iii / p#33\n1963#34#wassenaar (nl)#kivietkerk#nb#ii / p#21\n1965#41#amsterdam (nl)#oude kerk (transept organ)#nb#ii / p#17\n1965#43#amsterdam (nl)#oude waalse kerk#r#ii / p#26\n1965#40#groningen (nl)#magnaliakerk#nb#ii#13\n1965#42#den haag (nl)#johanneskapel#nb#ii / p#14\n1966#45#bremen (d)#ev church oberneuland#nb#ii / p#22\n1967#49#castrop - rauxel (d)#church schwerin - frolinde#nb#iii / p#27\n1968#51#aalten (nl)#geref zuiderkerk#nb#ii / p#16\n1968#58#haarlem (nl)#mennonite church#nb#iii / p#24\n1969#62#hamburg (d)#ref church altona#nb#ii / p#13\n1966 / 69 / 87#65#marienhafe (d)#marienkirche#r#ii / p#20\n1970#68#frankfurt am main (d)#cantate domino#nb#iii / p#33\n1970#69#uelsen (d)#ref church#nb#ii / p#20\n1970 / 76#70#innsbruck (a)#hofkirche#r#ii / p#15\n1963 - 71#74#leer (d)#gro\u00e3\u00ffe kirche#r#iii / p#37\n",
        "pandas_code": "len(df[(df['stops'].str.extract('(\\d+)')[0].astype(int) > 30) & (df['town'].str.contains('\\(d\\)'))]['church'].unique()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 213,
        "statement": "magnaliakerk be the only church with a single 'ii' manual",
        "label": 1,
        "table_caption": "j\u00fcrgen ahrend",
        "table_text": "year#opus#town#church#kind#manuals#stops\n1954 / 88#1#larrelt (d)#larrelt church#r#i / p#11\n1955#4#westerhusen (d)#westerhusen church#r#i / p#7\n1957#9#uttum (d)#uttum church#r#i#9\n1957 (1997)#10#veldhausen (d)#altref church#nb#i (ii / p)#6 (13)\n1959 / 2002#18#scheveningen (nl)#zorgvlietkerk#nb#iii / p#26\n1961#25#rysum (d)#rysum church#r#i#7\n1961#27#aurich (d)#lambertikirche#nb#ii / p#25\n1962#29#espel (nl)#ref church#nb#i#7\n1962#30#bremen (d)#st martini#nb#iii / p#33\n1963#34#wassenaar (nl)#kivietkerk#nb#ii / p#21\n1965#41#amsterdam (nl)#oude kerk (transept organ)#nb#ii / p#17\n1965#43#amsterdam (nl)#oude waalse kerk#r#ii / p#26\n1965#40#groningen (nl)#magnaliakerk#nb#ii#13\n1965#42#den haag (nl)#johanneskapel#nb#ii / p#14\n1966#45#bremen (d)#ev church oberneuland#nb#ii / p#22\n1967#49#castrop - rauxel (d)#church schwerin - frolinde#nb#iii / p#27\n1968#51#aalten (nl)#geref zuiderkerk#nb#ii / p#16\n1968#58#haarlem (nl)#mennonite church#nb#iii / p#24\n1969#62#hamburg (d)#ref church altona#nb#ii / p#13\n1966 / 69 / 87#65#marienhafe (d)#marienkirche#r#ii / p#20\n1970#68#frankfurt am main (d)#cantate domino#nb#iii / p#33\n1970#69#uelsen (d)#ref church#nb#ii / p#20\n1970 / 76#70#innsbruck (a)#hofkirche#r#ii / p#15\n1963 - 71#74#leer (d)#gro\u00e3\u00ffe kirche#r#iii / p#37\n",
        "pandas_code": "(df[df['church'] == 'magnaliakerk']['manuals'].eq('ii').all()) & (df[df['manuals'] == 'ii'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 214,
        "statement": "vince carter have the highest point or tie for highest point in 9 game",
        "label": 1,
        "table_caption": "1999 - 2000 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n30#january 4#portland#l 90 - 114 (ot)#tracy mcgrady (18)#kevin willis (8)#doug christie , tracy mcgrady (4)#air canada centre 19800#17 - 13\n31#january 6#sacramento#w 101 - 89 (ot)#charles oakley (20)#charles oakley (16)#alvin williams (10)#air canada centre 19800#18 - 13\n32#january 7#atlanta#l 97 - 105 (ot)#vince carter (34)#vince carter , charles oakley , kevin willis (7)#doug christie (5)#philips arena 14452#18 - 14\n33#january 9#vancouver#l 97 - 107 (ot)#vince carter , antonio davis (20)#vince carter (10)#vince carter , charles oakley (6)#air canada centre 19188#18 - 15\n34#january 11#washington#l 89 - 117 (ot)#vince carter (19)#antonio davis (15)#muggsy bogues (6)#mci center 13610#18 - 16\n35#january 12#orlando#w 108 - 102 (ot)#vince carter (30)#antonio davis (9)#vince carter (9)#air canada centre 17241#19 - 16\n36#january 14#milwaukee#w 115 - 110 (ot)#vince carter (47)#charles oakley (12)#doug christie (8)#air canada centre 19246#20 - 16\n37#january 15#milwaukee#l 97 - 118 (ot)#doug christie (31)#kevin willis (12)#muggsy bogues , vince carter (5)#bradley center 18717#20 - 17\n38#january 17#charlotte#l 94 - 115 (ot)#vince carter (24)#michael stewart (8)#vince carter (6)#charlotte coliseum 20278#20 - 18\n39#january 19#boston#l 90 - 94 (ot)#vince carter (20)#charles oakley (10)#muggsy bogues , alvin williams (5)#fleetcenter 16124#20 - 19\n40#january 23#seattle#w 94 - 77 (ot)#antonio davis , tracy mcgrady (17)#kevin willis (12)#doug christie (6)#air canada centre 19800#21 - 19\n41#january 26#washington#w 120 - 105 (ot)#vince carter (26)#kevin willis (9)#charles oakley (8)#air canada centre 17582#22 - 19\n42#january 28#miami#w 108 - 93 (ot)#vince carter (23)#antonio davis (12)#charles oakley (7)#air canada centre 19800#23 - 19\n",
        "pandas_code": "(df['high points'].str.contains('vince carter').sum() == 9)",
        "pandas_eval": "True"
    },
    {
        "id": 215,
        "statement": "4 game have the same location and same attendance",
        "label": 1,
        "table_caption": "1999 - 2000 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n30#january 4#portland#l 90 - 114 (ot)#tracy mcgrady (18)#kevin willis (8)#doug christie , tracy mcgrady (4)#air canada centre 19800#17 - 13\n31#january 6#sacramento#w 101 - 89 (ot)#charles oakley (20)#charles oakley (16)#alvin williams (10)#air canada centre 19800#18 - 13\n32#january 7#atlanta#l 97 - 105 (ot)#vince carter (34)#vince carter , charles oakley , kevin willis (7)#doug christie (5)#philips arena 14452#18 - 14\n33#january 9#vancouver#l 97 - 107 (ot)#vince carter , antonio davis (20)#vince carter (10)#vince carter , charles oakley (6)#air canada centre 19188#18 - 15\n34#january 11#washington#l 89 - 117 (ot)#vince carter (19)#antonio davis (15)#muggsy bogues (6)#mci center 13610#18 - 16\n35#january 12#orlando#w 108 - 102 (ot)#vince carter (30)#antonio davis (9)#vince carter (9)#air canada centre 17241#19 - 16\n36#january 14#milwaukee#w 115 - 110 (ot)#vince carter (47)#charles oakley (12)#doug christie (8)#air canada centre 19246#20 - 16\n37#january 15#milwaukee#l 97 - 118 (ot)#doug christie (31)#kevin willis (12)#muggsy bogues , vince carter (5)#bradley center 18717#20 - 17\n38#january 17#charlotte#l 94 - 115 (ot)#vince carter (24)#michael stewart (8)#vince carter (6)#charlotte coliseum 20278#20 - 18\n39#january 19#boston#l 90 - 94 (ot)#vince carter (20)#charles oakley (10)#muggsy bogues , alvin williams (5)#fleetcenter 16124#20 - 19\n40#january 23#seattle#w 94 - 77 (ot)#antonio davis , tracy mcgrady (17)#kevin willis (12)#doug christie (6)#air canada centre 19800#21 - 19\n41#january 26#washington#w 120 - 105 (ot)#vince carter (26)#kevin willis (9)#charles oakley (8)#air canada centre 17582#22 - 19\n42#january 28#miami#w 108 - 93 (ot)#vince carter (23)#antonio davis (12)#charles oakley (7)#air canada centre 19800#23 - 19\n",
        "pandas_code": "df['location attendance'].value_counts().max() >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 216,
        "statement": "there be 6 win out of 13 game",
        "label": 1,
        "table_caption": "1999 - 2000 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n30#january 4#portland#l 90 - 114 (ot)#tracy mcgrady (18)#kevin willis (8)#doug christie , tracy mcgrady (4)#air canada centre 19800#17 - 13\n31#january 6#sacramento#w 101 - 89 (ot)#charles oakley (20)#charles oakley (16)#alvin williams (10)#air canada centre 19800#18 - 13\n32#january 7#atlanta#l 97 - 105 (ot)#vince carter (34)#vince carter , charles oakley , kevin willis (7)#doug christie (5)#philips arena 14452#18 - 14\n33#january 9#vancouver#l 97 - 107 (ot)#vince carter , antonio davis (20)#vince carter (10)#vince carter , charles oakley (6)#air canada centre 19188#18 - 15\n34#january 11#washington#l 89 - 117 (ot)#vince carter (19)#antonio davis (15)#muggsy bogues (6)#mci center 13610#18 - 16\n35#january 12#orlando#w 108 - 102 (ot)#vince carter (30)#antonio davis (9)#vince carter (9)#air canada centre 17241#19 - 16\n36#january 14#milwaukee#w 115 - 110 (ot)#vince carter (47)#charles oakley (12)#doug christie (8)#air canada centre 19246#20 - 16\n37#january 15#milwaukee#l 97 - 118 (ot)#doug christie (31)#kevin willis (12)#muggsy bogues , vince carter (5)#bradley center 18717#20 - 17\n38#january 17#charlotte#l 94 - 115 (ot)#vince carter (24)#michael stewart (8)#vince carter (6)#charlotte coliseum 20278#20 - 18\n39#january 19#boston#l 90 - 94 (ot)#vince carter (20)#charles oakley (10)#muggsy bogues , alvin williams (5)#fleetcenter 16124#20 - 19\n40#january 23#seattle#w 94 - 77 (ot)#antonio davis , tracy mcgrady (17)#kevin willis (12)#doug christie (6)#air canada centre 19800#21 - 19\n41#january 26#washington#w 120 - 105 (ot)#vince carter (26)#kevin willis (9)#charles oakley (8)#air canada centre 17582#22 - 19\n42#january 28#miami#w 108 - 93 (ot)#vince carter (23)#antonio davis (12)#charles oakley (7)#air canada centre 19800#23 - 19\n",
        "pandas_code": "(df['score'].str.startswith('w').sum() == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 217,
        "statement": "charlotte coliseum have the highest attendance with 20278 , follow by air canada centre 19800",
        "label": 1,
        "table_caption": "1999 - 2000 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n30#january 4#portland#l 90 - 114 (ot)#tracy mcgrady (18)#kevin willis (8)#doug christie , tracy mcgrady (4)#air canada centre 19800#17 - 13\n31#january 6#sacramento#w 101 - 89 (ot)#charles oakley (20)#charles oakley (16)#alvin williams (10)#air canada centre 19800#18 - 13\n32#january 7#atlanta#l 97 - 105 (ot)#vince carter (34)#vince carter , charles oakley , kevin willis (7)#doug christie (5)#philips arena 14452#18 - 14\n33#january 9#vancouver#l 97 - 107 (ot)#vince carter , antonio davis (20)#vince carter (10)#vince carter , charles oakley (6)#air canada centre 19188#18 - 15\n34#january 11#washington#l 89 - 117 (ot)#vince carter (19)#antonio davis (15)#muggsy bogues (6)#mci center 13610#18 - 16\n35#january 12#orlando#w 108 - 102 (ot)#vince carter (30)#antonio davis (9)#vince carter (9)#air canada centre 17241#19 - 16\n36#january 14#milwaukee#w 115 - 110 (ot)#vince carter (47)#charles oakley (12)#doug christie (8)#air canada centre 19246#20 - 16\n37#january 15#milwaukee#l 97 - 118 (ot)#doug christie (31)#kevin willis (12)#muggsy bogues , vince carter (5)#bradley center 18717#20 - 17\n38#january 17#charlotte#l 94 - 115 (ot)#vince carter (24)#michael stewart (8)#vince carter (6)#charlotte coliseum 20278#20 - 18\n39#january 19#boston#l 90 - 94 (ot)#vince carter (20)#charles oakley (10)#muggsy bogues , alvin williams (5)#fleetcenter 16124#20 - 19\n40#january 23#seattle#w 94 - 77 (ot)#antonio davis , tracy mcgrady (17)#kevin willis (12)#doug christie (6)#air canada centre 19800#21 - 19\n41#january 26#washington#w 120 - 105 (ot)#vince carter (26)#kevin willis (9)#charles oakley (8)#air canada centre 17582#22 - 19\n42#january 28#miami#w 108 - 93 (ot)#vince carter (23)#antonio davis (12)#charles oakley (7)#air canada centre 19800#23 - 19\n",
        "pandas_code": "(df['location attendance'].str.extract('(\\\\d+)').astype(int).max() == 20278).item()",
        "pandas_eval": "True"
    },
    {
        "id": 218,
        "statement": "vince carter have more high point on january 14 than he do on january 17",
        "label": 1,
        "table_caption": "1999 - 2000 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n30#january 4#portland#l 90 - 114 (ot)#tracy mcgrady (18)#kevin willis (8)#doug christie , tracy mcgrady (4)#air canada centre 19800#17 - 13\n31#january 6#sacramento#w 101 - 89 (ot)#charles oakley (20)#charles oakley (16)#alvin williams (10)#air canada centre 19800#18 - 13\n32#january 7#atlanta#l 97 - 105 (ot)#vince carter (34)#vince carter , charles oakley , kevin willis (7)#doug christie (5)#philips arena 14452#18 - 14\n33#january 9#vancouver#l 97 - 107 (ot)#vince carter , antonio davis (20)#vince carter (10)#vince carter , charles oakley (6)#air canada centre 19188#18 - 15\n34#january 11#washington#l 89 - 117 (ot)#vince carter (19)#antonio davis (15)#muggsy bogues (6)#mci center 13610#18 - 16\n35#january 12#orlando#w 108 - 102 (ot)#vince carter (30)#antonio davis (9)#vince carter (9)#air canada centre 17241#19 - 16\n36#january 14#milwaukee#w 115 - 110 (ot)#vince carter (47)#charles oakley (12)#doug christie (8)#air canada centre 19246#20 - 16\n37#january 15#milwaukee#l 97 - 118 (ot)#doug christie (31)#kevin willis (12)#muggsy bogues , vince carter (5)#bradley center 18717#20 - 17\n38#january 17#charlotte#l 94 - 115 (ot)#vince carter (24)#michael stewart (8)#vince carter (6)#charlotte coliseum 20278#20 - 18\n39#january 19#boston#l 90 - 94 (ot)#vince carter (20)#charles oakley (10)#muggsy bogues , alvin williams (5)#fleetcenter 16124#20 - 19\n40#january 23#seattle#w 94 - 77 (ot)#antonio davis , tracy mcgrady (17)#kevin willis (12)#doug christie (6)#air canada centre 19800#21 - 19\n41#january 26#washington#w 120 - 105 (ot)#vince carter (26)#kevin willis (9)#charles oakley (8)#air canada centre 17582#22 - 19\n42#january 28#miami#w 108 - 93 (ot)#vince carter (23)#antonio davis (12)#charles oakley (7)#air canada centre 19800#23 - 19\n",
        "pandas_code": "int(df[df['date'] == 'january 14']['high points'].str.extract(r'(\\d+)')[0].iloc[0]) > int(df[df['date'] == 'january 17']['high points'].str.extract(r'(\\d+)')[0].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 219,
        "statement": "the united state play a test match on 16 / 06 / 2001",
        "label": 1,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'united states') & (df['date'] == '16 / 06 / 2001') & (df['status'] == 'test match')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 220,
        "statement": "canada play a game report by bbc report at swangard stadium",
        "label": 1,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'canada') & (df['report'] == 'bbc report') & (df['venue'] == 'swangard stadium , burnaby')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 221,
        "statement": "british columbia play a tour match at thunderbird stadium in vancouver",
        "label": 1,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'british columbia') & (df['status'] == 'tour match') & (df['venue'] == 'thunderbird stadium , vancouver')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 222,
        "statement": "canada play a first test at fletcher field in markham",
        "label": 1,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'canada') & (df['status'] == 'first test') & (df['venue'] == \"fletcher 's fields , markham\")).any()",
        "pandas_eval": "True"
    },
    {
        "id": 223,
        "statement": "united state a play a tour match in las angeles",
        "label": 1,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "df[(df['opposing team'] == \"united states 'a'\") & (df['status'] == 'tour match') & (df['venue'] == 'los angeles')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 224,
        "statement": "the united state play tour match on june 16th , 2001",
        "label": 0,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'united states') & (df['date'] == '16 / 06 / 2001') & (df['status'] == 'tour match')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 225,
        "statement": "canada play a game report by bbc report outside of swangard stadium",
        "label": 0,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'canada') & (df['report'] == 'bbc report') & (df['venue'] != 'swangard stadium , burnaby')).any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 226,
        "statement": "british columbia play a tour match outside of thunderbird stadium in vancouver",
        "label": 0,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'british columbia') & (df['status'] == 'tour match') & (df['venue'] != 'thunderbird stadium , vancouver')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 227,
        "statement": "canada play a tour game outside of fletcher field in markham",
        "label": 0,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == 'canada') & (df['venue'] != 'fletcher \\'s fields , markham') & (df['status'] == 'tour match')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 228,
        "statement": "united state a play a tour match outside of los angeles",
        "label": 0,
        "table_caption": "2001 england rugby union tour of north america",
        "table_text": "opposing team#against#date#venue#status#report\ncanada#10#02 / 06 / 2001#fletcher 's fields , markham#first test#bbc report\nbritish columbia#19#05 / 06 / 2001#thunderbird stadium , vancouver#tour match#bbc report\ncanada#20#09 / 06 / 2001#swangard stadium , burnaby#second test#bbc report\nunited states 'a'#21#12 / 06 / 2001#los angeles#tour match#bbc report\nunited states#19#16 / 06 / 2001#balboa stadium , san diego#test match#bbc report\n",
        "pandas_code": "((df['opposing team'] == \"united states 'a'\") & (df['status'] == 'tour match') & (df['venue'] != 'los angeles')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 229,
        "statement": "truck robinson have the highest rebs (rebounds) total 2505 in 1978 followed by clifford robinson with 1330 in 1997",
        "label": 1,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "(df[df['player'] == 'truck robinson']['rebs'].values[0] == 2505) & (df[df['player'] == 'clifford robinson']['rebs'].values[0] == 1330) & (df[df['player'] == 'truck robinson']['from'].values[0] == 1978) & (df[df['player'] == 'clifford robinson']['from'].values[0] == 1997) & (df['rebs'].max() == 2505)",
        "pandas_eval": "True"
    },
    {
        "id": 230,
        "statement": "terrence rencher have the least rebs (rebounds) total 2 in 1995 and the least (assists) total 0 the same year",
        "label": 1,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "(df[df['player'] == 'terrence rencher']['rebs'].values[0] == 2) & (df[df['player'] == 'terrence rencher']['asts'].values[0] == 0) & (df[df['player'] == 'terrence rencher']['from'].values[0] == 1995)",
        "pandas_eval": "True"
    },
    {
        "id": 231,
        "statement": "the phoenix sun have 2 player with a position of sg (shooting guard) that be both from michigan state",
        "label": 1,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "(df[(df['pos'] == 'sg') & (df['school / country'] == 'michigan state')].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 232,
        "statement": "the phoenix sun have 5 player with 2 different (pos) position",
        "label": 1,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "len(df[df['pos'].str.contains(' / ')]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 233,
        "statement": "clifford robinson have 1 more reb (rebound) than stefano rusconi",
        "label": 1,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "df[df['player'] == 'clifford robinson']['rebs'].values[0] >= df[df['player'] == 'stefano rusconi']['rebs'].values[0] + 1",
        "pandas_eval": "True"
    },
    {
        "id": 234,
        "statement": "clifford robinson have the most rebound total 1330 follow by rodney rodgers with 1047 in 1999",
        "label": 0,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 235,
        "statement": "terrence rancher have the most amount of rebound in 1995",
        "label": 0,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "df[(df['from'] == 1995) & (df['rebs'] == df[df['from'] == 1995]['rebs'].max())]['player'].eq('terrence rencher').any()",
        "pandas_eval": "False"
    },
    {
        "id": 236,
        "statement": "none of the player from the phoenix sun with a position of sg be from michigan state",
        "label": 0,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "not any((df['pos'] == 'sg') & (df['school / country'] == 'michigan state'))",
        "pandas_eval": "False"
    },
    {
        "id": 237,
        "statement": "the phoenix sun have only 5 player with 3 different position",
        "label": 0,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "len(df['pos'].unique()) == 3 and len(df) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 238,
        "statement": "clifford robinson have as many rebound as stefano rusconi",
        "label": 0,
        "table_caption": "phoenix suns all - time roster",
        "table_text": "player#pos#from#school / country#rebs#asts\nkurt rambis#f#1989#santa clara#783#209\njoe reaves#sf#1973#bethel#8#1\nmichael redd#sg#2011#ohio state#77#33\nterrence rencher#pg#1995#texas#2#0\nshawn respert#sg#1998#michigan state#13#8\njason richardson#sg#2008#michigan state#776#289\nquentin richardson#g / f#2004#depaul#479#158\npat riley#sg#1975#kentucky#47#57\nrick robey#c / f#1983#kentucky#345#128\nclifford robinson#f / c#1997#connecticut#1330#759\nrumeal robinson#pg#1996#michigan#7#8\ntruck robinson#pf#1978#tennessee state#2505#566\nrodney rogers#f#1999#wake forest#1047#422\njalen rose#g / f#2006#michigan#23#16\ntrevor ruffin#pg#1994#hawaii#23#48\nstefano rusconi#c / f#1995#italy#6#3\n",
        "pandas_code": "df[df['player'] == 'clifford robinson']['rebs'].values[0] == df[df['player'] == 'stefano rusconi']['rebs'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 239,
        "statement": "ji - young oh have 2 1 best finish",
        "label": 1,
        "table_caption": "ji - young oh",
        "table_text": "year#tournaments played#cuts made#wins#2nd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2007#22#10#0#0#1#6#148876#72#72.46#81\n2008#29#26#1#0#4#1#680225#26#71.65#22\n2009#25#20#1#0#3#1#558316#24#71.90#34\n2010#18#10#0#0#0#t12#98384#71#73.42#92\n2011#11#1#0#0#0#t54#5456#148#74.42#125\n",
        "pandas_code": "df[df['best finish'].isin(['1', 't1'])].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 240,
        "statement": "ji - young oh play more tournament in 2008 than any other year",
        "label": 1,
        "table_caption": "ji - young oh",
        "table_text": "year#tournaments played#cuts made#wins#2nd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2007#22#10#0#0#1#6#148876#72#72.46#81\n2008#29#26#1#0#4#1#680225#26#71.65#22\n2009#25#20#1#0#3#1#558316#24#71.90#34\n2010#18#10#0#0#0#t12#98384#71#73.42#92\n2011#11#1#0#0#0#t54#5456#148#74.42#125\n",
        "pandas_code": "df.loc[df['year'] == 2008, 'tournaments played'].values[0] == df['tournaments played'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 241,
        "statement": "ji - young oh pleayed the least tournament in 2011",
        "label": 1,
        "table_caption": "ji - young oh",
        "table_text": "year#tournaments played#cuts made#wins#2nd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2007#22#10#0#0#1#6#148876#72#72.46#81\n2008#29#26#1#0#4#1#680225#26#71.65#22\n2009#25#20#1#0#3#1#558316#24#71.90#34\n2010#18#10#0#0#0#t12#98384#71#73.42#92\n2011#11#1#0#0#0#t54#5456#148#74.42#125\n",
        "pandas_code": "df.loc[df['year'] == 2011, 'tournaments played'].values[0] == df['tournaments played'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 242,
        "statement": "ji - young oh win in 2008 and 2009 tournament",
        "label": 1,
        "table_caption": "ji - young oh",
        "table_text": "year#tournaments played#cuts made#wins#2nd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2007#22#10#0#0#1#6#148876#72#72.46#81\n2008#29#26#1#0#4#1#680225#26#71.65#22\n2009#25#20#1#0#3#1#558316#24#71.90#34\n2010#18#10#0#0#0#t12#98384#71#73.42#92\n2011#11#1#0#0#0#t54#5456#148#74.42#125\n",
        "pandas_code": "all(df[(df['year'].isin([2008, 2009])) & (df['wins'] > 0)]['wins'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 243,
        "statement": "ji - young oh 's total tournament win earnings be the highest in 2008",
        "label": 1,
        "table_caption": "ji - young oh",
        "table_text": "year#tournaments played#cuts made#wins#2nd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2007#22#10#0#0#1#6#148876#72#72.46#81\n2008#29#26#1#0#4#1#680225#26#71.65#22\n2009#25#20#1#0#3#1#558316#24#71.90#34\n2010#18#10#0#0#0#t12#98384#71#73.42#92\n2011#11#1#0#0#0#t54#5456#148#74.42#125\n",
        "pandas_code": "df[df['year'] == 2008]['earnings'].max() == df['earnings'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 244,
        "statement": "university of florida ha the largest siz by acerage with 2000 acre",
        "label": 1,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df[df['university'] == 'university of florida']['campus area (acres)'].values[0] == 2000",
        "pandas_eval": "True"
    },
    {
        "id": 245,
        "statement": "university of florida rank highest of all university in florida in kiplinger 's top 100 at 2nd overall in the united state",
        "label": 1,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df[df['university'] == 'university of florida'][\"kiplinger 's top 100 values\"].values[0] == '2nd overall in the united states'",
        "pandas_eval": "True"
    },
    {
        "id": 246,
        "statement": "the endowment of florida a&m university be 5 million higher than the endowment of university of central florida",
        "label": 1,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "(int(df[df['university'] == 'florida a&m university']['endowment as of 2008'].str.replace(' million', '').values[0]) - int(df[df['university'] == 'university of central florida']['endowment as of 2008'].str.replace(' million', '').values[0])) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 247,
        "statement": "new college of florida , have the smallest endowment of any university with only 33 million",
        "label": 1,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df[df['university'] == 'new college of florida']['endowment as of 2008'].values[0] == '33 million'",
        "pandas_eval": "True"
    },
    {
        "id": 248,
        "statement": "new college of florida also have the smallest enrollment by far with only 769 student",
        "label": 1,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df[df['university'] == 'new college of florida']['enrollment as of 2008'].iloc[0] == 769",
        "pandas_eval": "True"
    },
    {
        "id": 249,
        "statement": "university of florida be the youngest university to be establish",
        "label": 0,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df[df['university'] == 'university of florida']['established'].values[0] == df['established'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 250,
        "statement": "all of the university be rank in the kiplinger 's top 100 value",
        "label": 0,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "all(df[\"kiplinger 's top 100 values\"].str.contains('overall'))",
        "pandas_eval": "False"
    },
    {
        "id": 251,
        "statement": "the endowment of florida a&m university be 5 and a half million higher than the endowment of university of central florida",
        "label": 0,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "(int(df[df['university'] == 'florida a&m university']['endowment as of 2008'].str.replace(' million', '').iloc[0]) == int(df[df['university'] == 'university of central florida']['endowment as of 2008'].str.replace(' million', '').iloc[0]) + 5.5)",
        "pandas_eval": "False"
    },
    {
        "id": 252,
        "statement": "new college of florida , have the third largest endowment of any university with only 33 million",
        "label": 0,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "df['endowment as of 2008'].apply(lambda x: float(x.split()[0]) * (1000 if 'billion' in x else 1)).rank(method='min', ascending=False).loc[df['university'] == 'new college of florida'].iloc[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 253,
        "statement": "new college of florida have the lowest enrollment 2008 with 1000",
        "label": 0,
        "table_caption": "florida board of governors",
        "table_text": "university#location#established#endowment as of 2008#campus area (acres)#kiplinger 's top 100 values#enrollment as of 2008\nflorida a&m university#tallahassee , florida#1887#119 million#419#n / a#11567\nflorida atlantic university#boca raton , florida#1961#182 million#850#n / a#26525\nflorida gulf coast university#fort myers , florida#1991#39 million#760#n / a#9387\nflorida international university#miami , florida#1965#97 million#573#n / a#38614\nflorida state university#tallahassee , florida#1851#570 million#1200#17th overall in the united states#41002\nnew college of florida#sarasota , florida#1960#33 million#144#8th overall in the united states#769\nuniversity of central florida#orlando , florida#1963#114 million#1415#42nd overall in the united states#48699\nuniversity of florida#gainesville , florida#1853#1.3 billion#2000#2nd overall in the united states#52084\nuniversity of north florida#jacksonville , florida#1969#95 million#1300#n / a#16570\nuniversity of south florida#tampa , florida#1956#360 million#1913#75th overall in the united states#45524\nuniversity of west florida#pensacola , florida#1963#61 million#1600#n / a#10394\n",
        "pandas_code": "(df[df['university'] == 'new college of florida']['enrollment as of 2008'].iloc[0] == 1000) and (df['enrollment as of 2008'].min() == 1000)",
        "pandas_eval": "False"
    },
    {
        "id": 254,
        "statement": "the average speed of a rider during the 2007 manx grand prix be 88.48 mph",
        "label": 1,
        "table_caption": "2007 manx grand prix",
        "table_text": "rank#rider#team#speed#time\n1#ewan hamilton#242cc suzuki#93.937 mph#1:36.23.79\n2#peter richardson#248cc suzuki#92.909 mph#1:37.27.77\n3#david smith#246cc suzuki#92.613 mph#1:37.46.48\n4#peter symes#250cc suzuki#92.425 mph#1:37.58.38\n5#tom jackson#248cc suzuki#89.932 mph#1:40.41.34\n6#mervyn stratford#247cc greeves silverstone#85.971 mph#1:45.19.70\n7#roger jones#248cc suzuki#85.523 mph#1:45.52.84\n8#tony mason#249cc honda#83.662 mph#1:45.52.84\n9#kevin murphy#250cc ducati#79.320 mph#1:54.09.63\n",
        "pandas_code": "abs(df['speed'].str.replace(' mph', '').astype(float).mean() - 88.48) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 255,
        "statement": "peter symes have a faster time than tom jackson",
        "label": 1,
        "table_caption": "2007 manx grand prix",
        "table_text": "rank#rider#team#speed#time\n1#ewan hamilton#242cc suzuki#93.937 mph#1:36.23.79\n2#peter richardson#248cc suzuki#92.909 mph#1:37.27.77\n3#david smith#246cc suzuki#92.613 mph#1:37.46.48\n4#peter symes#250cc suzuki#92.425 mph#1:37.58.38\n5#tom jackson#248cc suzuki#89.932 mph#1:40.41.34\n6#mervyn stratford#247cc greeves silverstone#85.971 mph#1:45.19.70\n7#roger jones#248cc suzuki#85.523 mph#1:45.52.84\n8#tony mason#249cc honda#83.662 mph#1:45.52.84\n9#kevin murphy#250cc ducati#79.320 mph#1:54.09.63\n",
        "pandas_code": "df[df['rider'] == 'peter symes']['time'].values[0] < df[df['rider'] == 'tom jackson']['time'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 256,
        "statement": "248cc suzuki have more rider than any other team at the 2007 manx grand prix",
        "label": 1,
        "table_caption": "2007 manx grand prix",
        "table_text": "rank#rider#team#speed#time\n1#ewan hamilton#242cc suzuki#93.937 mph#1:36.23.79\n2#peter richardson#248cc suzuki#92.909 mph#1:37.27.77\n3#david smith#246cc suzuki#92.613 mph#1:37.46.48\n4#peter symes#250cc suzuki#92.425 mph#1:37.58.38\n5#tom jackson#248cc suzuki#89.932 mph#1:40.41.34\n6#mervyn stratford#247cc greeves silverstone#85.971 mph#1:45.19.70\n7#roger jones#248cc suzuki#85.523 mph#1:45.52.84\n8#tony mason#249cc honda#83.662 mph#1:45.52.84\n9#kevin murphy#250cc ducati#79.320 mph#1:54.09.63\n",
        "pandas_code": "df[df['team'] == '248cc suzuki'].shape[0] > df[df['team'] != '248cc suzuki'].groupby('team').size().max()",
        "pandas_eval": "True"
    },
    {
        "id": 257,
        "statement": "tony mason 's speed be 4.342 mph faster than kevin murphy 's",
        "label": 1,
        "table_caption": "2007 manx grand prix",
        "table_text": "rank#rider#team#speed#time\n1#ewan hamilton#242cc suzuki#93.937 mph#1:36.23.79\n2#peter richardson#248cc suzuki#92.909 mph#1:37.27.77\n3#david smith#246cc suzuki#92.613 mph#1:37.46.48\n4#peter symes#250cc suzuki#92.425 mph#1:37.58.38\n5#tom jackson#248cc suzuki#89.932 mph#1:40.41.34\n6#mervyn stratford#247cc greeves silverstone#85.971 mph#1:45.19.70\n7#roger jones#248cc suzuki#85.523 mph#1:45.52.84\n8#tony mason#249cc honda#83.662 mph#1:45.52.84\n9#kevin murphy#250cc ducati#79.320 mph#1:54.09.63\n",
        "pandas_code": "abs(float(df[df['rider'] == 'tony mason']['speed'].str.replace(' mph', '').iloc[0]) - float(df[df['rider'] == 'kevin murphy']['speed'].str.replace(' mph', '').iloc[0]) - 4.342) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 258,
        "statement": "roger jones and tony mason have the same time at the 2007 manx grand prix",
        "label": 1,
        "table_caption": "2007 manx grand prix",
        "table_text": "rank#rider#team#speed#time\n1#ewan hamilton#242cc suzuki#93.937 mph#1:36.23.79\n2#peter richardson#248cc suzuki#92.909 mph#1:37.27.77\n3#david smith#246cc suzuki#92.613 mph#1:37.46.48\n4#peter symes#250cc suzuki#92.425 mph#1:37.58.38\n5#tom jackson#248cc suzuki#89.932 mph#1:40.41.34\n6#mervyn stratford#247cc greeves silverstone#85.971 mph#1:45.19.70\n7#roger jones#248cc suzuki#85.523 mph#1:45.52.84\n8#tony mason#249cc honda#83.662 mph#1:45.52.84\n9#kevin murphy#250cc ducati#79.320 mph#1:54.09.63\n",
        "pandas_code": "df[df['rider'].isin(['roger jones', 'tony mason'])]['time'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 259,
        "statement": "marie krko\u0161kov\u00e1 win the women single 4 year in a row",
        "label": 1,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "((df['women \\'s singles'] == 'marie krko\u0161kov\u00e1').rolling(4).sum().eq(4).any())",
        "pandas_eval": "True"
    },
    {
        "id": 260,
        "statement": "petr pavel win more men single than karel lakom\u00fd",
        "label": 1,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "(df[\"men 's singles\"].value_counts()['petr pavel'] > df[\"men 's singles\"].value_counts()['karel lakom\u00fd'])",
        "pandas_eval": "True"
    },
    {
        "id": 261,
        "statement": "eva lacinov\u00e1 win the women double 3 year in a row with 2 different people",
        "label": 1,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "any(df['women \\'s doubles'].iloc[i:i+3].apply(lambda x: 'eva lacinov\u00e1' in x).all() and len(set([x.split(' / ')[0] if 'eva lacinov\u00e1' == x.split(' / ')[1] else x.split(' / ')[1] for x in df['women \\'s doubles'].iloc[i:i+3]])) == 2 for i in range(len(df) - 2))",
        "pandas_eval": "True"
    },
    {
        "id": 262,
        "statement": "zuzana kalivovova win the mixed double 2 year in a row with 2 different people",
        "label": 1,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "(df['mixed doubles'].iloc[-2:].apply(lambda x: 'zuzana kalivodov\u00e1' in x).all()) and (df['mixed doubles'].iloc[-2].split(' / ')[0] != df['mixed doubles'].iloc[-1].split(' / ')[0])",
        "pandas_eval": "True"
    },
    {
        "id": 263,
        "statement": "petr pavel win a medal before petr janda",
        "label": 1,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "df[df[\"men 's singles\"] == 'petr pavel']['year'].min() < df[df[\"men 's singles\"] == 'petr janda']['year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 264,
        "statement": "maria holobrad\u00e1 win the women 's single 4 year in a row",
        "label": 0,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "(df[df[\"women 's singles\"] == 'maria holobrad\u00e1'].groupby('year').size() >= 4).any()",
        "pandas_eval": "False"
    },
    {
        "id": 265,
        "statement": "petr pavel win fewer men single than karel lakom\u00fd",
        "label": 0,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "(df[\"men 's singles\"].value_counts()['petr pavel'] < df[\"men 's singles\"].value_counts()['karel lakom\u00fd'])",
        "pandas_eval": "False"
    },
    {
        "id": 266,
        "statement": "eva lacinov\u00e1 win the women double 3 year in a row with 3 different people",
        "label": 0,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "(df['women \\'s doubles'].iloc[21:24].apply(lambda x: 'eva lacinov\u00e1' in x).all()) & (len(set([pair.split(' / ')[1] if 'eva lacinov\u00e1' == pair.split(' / ')[0] else pair.split(' / ')[0] for pair in df['women \\'s doubles'].iloc[21:24]])) == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 267,
        "statement": "petr pavel win the mixed double 2 year in a row with 2 different people",
        "label": 0,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "((df['mixed doubles'].iloc[4] == 'petr pavel / jaroslava krahulcov\u00e1') & (df['mixed doubles'].iloc[5] == 'petr pavel / jaroslava krahulcov\u00e1')) == False",
        "pandas_eval": "False"
    },
    {
        "id": 268,
        "statement": "petr pavel win a medal before miroslav pezlar",
        "label": 0,
        "table_caption": "czechoslovak national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1965#miroslav pezlar#vera perinov\u00e1#miroslav pezlar / ladislav rehor#vera perinov\u00e1 / miluse bilcikov\u00e1#petr nadvorn\u00edk / vera perinov\u00e1\n1966#jan novotn\u00fd#hana rerichov\u00e1#miroslav pezlar / ladislav rehor#dana povoln\u00e1 / milena kuzelov\u00e1#jan novotn\u00fd / hana rerichov\u00e1\n1967#ladislav rehor#milena kuzelov\u00e1#jan chmela / ji\u0159\u00ed chmela#milena kuzelov\u00e1 / alexandra fadrna#zbynek schwarz / jaroslava krahulcov\u00e1\n1968#petr pavel#jaroslava krahulcov\u00e1#josef brejcha / petr freimann#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr freimann / benediktov\u00e1\n1969#petr pavel#jaroslava krahulcov\u00e1#tomas dohnal / petr pavel#marie krko\u0161kov\u00e1 / vera mydlarov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1970#petr pavel#marie krko\u0161kov\u00e1#zdenek kozesnik / josef kraus#jaroslava krahulcov\u00e1 / anna skodov\u00e1#petr pavel / jaroslava krahulcov\u00e1\n1971#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#zdenek kozesnik / mojmir skalick\u00fd#marie krko\u0161kov\u00e1 / olga zrnov\u00e1#mojmir skalicky / marie krko\u0161kov\u00e1\n1972#miroslav \u0161r\u00e1mek#marie krko\u0161kov\u00e1#miroslav \u0161r\u00e1mek / ales guttenberger#tatjana pravdov\u00e1 / olga zrnov\u00e1#miroslav \u0161r\u00e1mek / olga zrnov\u00e1\n1973#karel myskovsky#marie krko\u0161kov\u00e1#j reis / j vachna#marie krko\u0161kov\u00e1 / jitka trminkov\u00e1#karel myskovsky / marie krko\u0161kov\u00e1\n1974#karel lakom\u00fd#maria holobrad\u00e1#karel lakom\u00fd / vladimir janda#maria holobrad\u00e1 / helena turcinkov\u00e1#vladimir janda / helena turcinkov\u00e1\n1975#karel lakom\u00fd#helena turcinkov\u00e1#karel lakom\u00fd / vladimir janda#helena turcinkov\u00e1 / buresov\u00e1#karel lakom\u00fd / zuzana vale\u010dkov\u00e1\n1976#michal mal\u00fd#jirina hubertov\u00e1#vladimir janda / lubos sedlak#helena turcinkov\u00e1 / jirina hubertov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1977#michal mal\u00fd#zuzana vale\u010dkov\u00e1#michal mal\u00fd / ladislav tandler#milena hamralov\u00e1 / zuzana vale\u010dkov\u00e1#michal mal\u00fd / zuzana vale\u010dkov\u00e1\n1978#ladislav tandler#alena nejedlov\u00e1#juraj lenart / ladislav tandler#milena hamralov\u00e1 / alena nejedlov\u00e1#ladislav tandler / alena nejedlov\u00e1\n1979#lubomir cechovsk\u00fd#alena nejedlov\u00e1#petr cech / vladimir hrub\u00fd#eli\u0161ka klu\u010dkov\u00e1 / alena nejedlov\u00e1#stanislav kraus / alena nejedlov\u00e1\n1980#richard hobzik#eli\u0161ka klu\u010dkov\u00e1#radek goldmann / richard hobzik#sona karwaczykov\u00e1 / eli\u0161ka klu\u010dkov\u00e1#richard hobzik / eli\u0161ka klu\u010dkov\u00e1\n1981#richard hobzik#jitka cuprov\u00e1#ji\u0159\u00ed dufek / richard hobzik#irena ferencov\u00e1 / yweta trminkov\u00e1#richard hobzik / jitka cuprov\u00e1\n1982#ji\u0159\u00ed dufek#irena ferencov\u00e1#ji\u0159\u00ed dufek / tomas zapletal#jaroslava balcarov\u00e1 / irena ferencov\u00e1#ji\u0159\u00ed dufek / irena ferencov\u00e1\n1983#roman janostik#jaroslava balcarov\u00e1#ji\u0159\u00ed hlavaty / pavel kubis#jaroslava balcarov\u00e1 / jana drapakov\u00e1#ji\u0159\u00ed hlavaty / radka viktorinov\u00e1\n1984#radek heger#jaroslava balcarov\u00e1#radek heger / roman janostik#jaroslava balcarov\u00e1 / jana mrstinov\u00e1#jaroslav hawlik / jaroslava balcarov\u00e1\n1985#tomasz mendrek#jitka lacinov\u00e1#pavel brazda / tomasz mendrek#radka viktorinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / radka viktorinov\u00e1\n1986#tomasz mendrek#jitka lacinov\u00e1#radek svoboda / tomasz mendrek#eva lacinov\u00e1 / jitka lacinov\u00e1#tomasz mendrek / jitka lacinov\u00e1\n1987#radek svoboda#jitka lacinov\u00e1#radek svoboda / martin vrkoslav#eva lacinov\u00e1 / jitka lacinov\u00e1#martin vrkoslav / jitka lacinov\u00e1\n1988#martin kulhavy#eva lacinov\u00e1#radek gregor / jan jurka#eva lacinov\u00e1 / dana matou\u0161kov\u00e1#jan doucha / eva lacinov\u00e1\n1989#petr janda#veronika vopalenska#petr janda / petr horsky#petra hlubu\u010dkov\u00e1 / dana matou\u0161kov\u00e1#petr janda / dana matou\u0161kov\u00e1\n1990#petr janda#veronika vopalenska#petr janda / daniel gaspar#veronika vopalenska / diana stadnikov\u00e1#ladislav oros / eva melounov\u00e1\n1991#martin skoupil#marketa koudelkov\u00e1#jan lanik / pavel weigner#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#igor paar / zuzana kalivodov\u00e1\n1992#jan kosteleck\u00fd#marketa koudelkov\u00e1#jan kosteleck\u00fd / jan panos#marketa koudelkov\u00e1 / zuzana kalivodov\u00e1#martin panos / zuzana kalivodov\u00e1\n",
        "pandas_code": "df[df[\"men 's singles\"] == 'petr pavel']['year'].min() < df[df[\"men 's singles\"] == 'miroslav pezlar']['year'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 270,
        "statement": "mark skaife wont the atcc round 1 , 2 , and 3 during the 1994 australian tour car season",
        "label": 1,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "all(df[df['series'].isin(['atcc round 1', 'atcc round 2', 'atcc round 3'])]['winner'] == 'mark skaife')",
        "pandas_eval": "True"
    },
    {
        "id": 271,
        "statement": "dick johnson john bowe win back to back game for sep 4 and oct 2 during the 1994 australian tour car season",
        "label": 1,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "((df['date'] == '4 sep') & (df['winner'] == 'dick johnson john bowe')).any() and ((df['date'] == '2 oct') & (df['winner'] == 'dick johnson john bowe')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 273,
        "statement": "john bowe win both of the 2 day conference during the 1994 australian tour car season",
        "label": 1,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "df[(df['winner'].str.contains('john bowe')) & (df['date'].str.contains('-'))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 274,
        "statement": "mark skaife wont the atcc round 1 through 4 during the 1994 australian tour car season",
        "label": 0,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "all(df[(df['series'].str.contains('atcc round')) & (df['series'].str.contains('1|2|3|4'))]['winner'] == 'mark skaife')",
        "pandas_eval": "False"
    },
    {
        "id": 275,
        "statement": "dick johnson and john bowe win back to back game for aug 7 and aug 28 during the 1994 australian tour car season",
        "label": 0,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "(df.loc[df['date'].isin(['7 aug', '28 aug']), 'winner'].str.contains('dick johnson|john bowe').all())",
        "pandas_eval": "False"
    },
    {
        "id": 276,
        "statement": "john bowe only win 1 day conferences during the 1994 australian tour car season",
        "label": 0,
        "table_caption": "1994 australian touring car season",
        "table_text": "date#series#circuit#city / state#winner#team\n30 jan#winfield triple challenge#eastern creek raceway#sydney , new south wales#glenn seton#glenn seton racing\n27 feb#atcc round 1#amaroo park#sydney , new south wales#mark skaife#gibson motor sport\n6 mar#atcc round 2#sandown international raceway#melbourne , victoria#mark skaife#gibson motor sport\n13 mar#atcc round 3#symmons plains raceway#launceston , tasmania#mark skaife#gibson motor sport\n19 - 20 mar#courier mail gold coast 100#surfers paradise street circuit#surfers paradise , queensland#john bowe#dick johnson racing\n10 apr#atcc round 4#phillip island grand prix circuit#phillip island , victoria#glenn seton#glenn seton racing\n17 apr#amc round 1#eastern creek raceway#sydney , new south wales#tony longhurst#logamo racing\n24 apr#atcc round 5#lakeside international raceway#brisbane , queensland#larry perkins#castrol perkins racing\n15 may#atcc round 6#winton motor raceway#benalla , victoria#glenn seton#glenn seton racing\n22 may#amc round 2#phillip island grand prix circuit#phillip island , victoria#paul morris#logamo racing\n5 jun#atcc round 7#eastern creek raceway#sydney , new south wales#peter brock#holden racing team\n19 jun#amc round 3#winton motor raceway#benalla , victoria#paul morris#logamo racing\n26 jun#atcc round 8#mallala motor sport park#adelaide , south australia#mark skaife#gibson motor sport\n3 jul#atcc round 9#barbagallo raceway#perth , western australia#alan jones#glenn seton racing\n17 jul#amc round 4#lakeside international raceway#brisbane , queensland#tony longhurst#logamo racing\n24 jul#atcc round 10#oran park raceway#sydney , new south wales#glenn seton#glenn seton racing\n7 aug#amc round 5#mallala motor sport park#adelaide , south australia#tony longhurst#logamo racing\n28 aug#amc round 6#oran park raceway#sydney , new south wales#paul morris#logamo racing\n4 sep#sandown 500#sandown international raceway#melbourne , victoria#dick johnson john bowe#dick johnson racing\n2 oct#tooheys 1000#mount panorama circuit#bathurst , new south wales#dick johnson john bowe#dick johnson racing\n12 - 13 nov#sensational adelaide touring cars#adelaide street circuit#adelaide , south australia#john bowe#dick johnson racing\n",
        "pandas_code": "len(df[(df['winner'] == 'john bowe') & (df['series'].str.contains('atcc'))]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 277,
        "statement": "3 member of the 77th united state congress resign",
        "label": 1,
        "table_caption": "77th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\noklahoma 7th#sam c massingale (d)#died january 17 , 1941#victor wickersham (d)#april 1 , 1941\nnew york 17th#kenneth f simpson (r)#died january 25 , 1941#joseph c baldwin (r)#march 11 , 1941\nalabama 7th#walter w bankhead (d)#resigned february 1 , 1941#carter manasco (d)#june 24 , 1941\nmaryland 6th#william d byron (d)#died february 27 , 1941#katharine byron (d)#may 27 , 1941\nnew york 42nd#pius l schwert (d)#died march 11 , 1941#john c butler (r)#april 22 , 1941\nnorth carolina 5th#alonzo d folger (d)#died april 30 , 1941#john h folger (d)#june 14 , 1941\nnew york 14th#morris m edelstein (d)#died june 4 , 1941#arthur g klein (d)#july 29 , 1941\nwisconsin 1st#stephen bolles (r)#died july 8 , 1941#lawrence h smith (r)#august 29 , 1941\npennsylvania 15th#albert g rutherford (r)#died august 10 , 1941#wilson d gillette (r)#november 4 , 1941\ncolorado 4th#edward t taylor (d)#died september 3 , 1941#robert f rockwell (r)#december 9 , 1941\ncalifornia 17th#lee e geyer (d)#died october 11 , 1941#cecil r king (d)#august 25 , 1942\nmassachusetts 7th#lawrence j connery (d)#died october 19 , 1941#thomas j lane (d)#december 30 , 1941\npennsylvania 11th#patrick j boland (d)#died may 18 , 1942#veronica g boland (d)#november 3 , 1942\ncalifornia 3rd#frank h buck (d)#died september 17 , 1942#vacant until the next congress#vacant until the next congress\npennsylvania 25th#charles i faddis (d)#resigned december 4 , 1942 to enter the us army#vacant until the next congress#vacant until the next congress\nillinois 6th#a f maciejewski (d)#resigned december 6 , 1942#vacant until the next congress#vacant until the next congress\n",
        "pandas_code": "df['reason for change'].str.contains('resigned').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 279,
        "statement": "frank h buck be the latest of the 77th united state congress to die",
        "label": 1,
        "table_caption": "77th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\noklahoma 7th#sam c massingale (d)#died january 17 , 1941#victor wickersham (d)#april 1 , 1941\nnew york 17th#kenneth f simpson (r)#died january 25 , 1941#joseph c baldwin (r)#march 11 , 1941\nalabama 7th#walter w bankhead (d)#resigned february 1 , 1941#carter manasco (d)#june 24 , 1941\nmaryland 6th#william d byron (d)#died february 27 , 1941#katharine byron (d)#may 27 , 1941\nnew york 42nd#pius l schwert (d)#died march 11 , 1941#john c butler (r)#april 22 , 1941\nnorth carolina 5th#alonzo d folger (d)#died april 30 , 1941#john h folger (d)#june 14 , 1941\nnew york 14th#morris m edelstein (d)#died june 4 , 1941#arthur g klein (d)#july 29 , 1941\nwisconsin 1st#stephen bolles (r)#died july 8 , 1941#lawrence h smith (r)#august 29 , 1941\npennsylvania 15th#albert g rutherford (r)#died august 10 , 1941#wilson d gillette (r)#november 4 , 1941\ncolorado 4th#edward t taylor (d)#died september 3 , 1941#robert f rockwell (r)#december 9 , 1941\ncalifornia 17th#lee e geyer (d)#died october 11 , 1941#cecil r king (d)#august 25 , 1942\nmassachusetts 7th#lawrence j connery (d)#died october 19 , 1941#thomas j lane (d)#december 30 , 1941\npennsylvania 11th#patrick j boland (d)#died may 18 , 1942#veronica g boland (d)#november 3 , 1942\ncalifornia 3rd#frank h buck (d)#died september 17 , 1942#vacant until the next congress#vacant until the next congress\npennsylvania 25th#charles i faddis (d)#resigned december 4 , 1942 to enter the us army#vacant until the next congress#vacant until the next congress\nillinois 6th#a f maciejewski (d)#resigned december 6 , 1942#vacant until the next congress#vacant until the next congress\n",
        "pandas_code": "df[df['vacator'].str.contains('frank h buck')]['reason for change'].str.extract(r'(\\d{4})')[0].astype(int).max() == df['reason for change'].str.extract(r'(\\d{4})')[0].astype(int).max()",
        "pandas_eval": "True"
    },
    {
        "id": 280,
        "statement": "3 vacate seat remain vacant until the next congress",
        "label": 1,
        "table_caption": "77th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\noklahoma 7th#sam c massingale (d)#died january 17 , 1941#victor wickersham (d)#april 1 , 1941\nnew york 17th#kenneth f simpson (r)#died january 25 , 1941#joseph c baldwin (r)#march 11 , 1941\nalabama 7th#walter w bankhead (d)#resigned february 1 , 1941#carter manasco (d)#june 24 , 1941\nmaryland 6th#william d byron (d)#died february 27 , 1941#katharine byron (d)#may 27 , 1941\nnew york 42nd#pius l schwert (d)#died march 11 , 1941#john c butler (r)#april 22 , 1941\nnorth carolina 5th#alonzo d folger (d)#died april 30 , 1941#john h folger (d)#june 14 , 1941\nnew york 14th#morris m edelstein (d)#died june 4 , 1941#arthur g klein (d)#july 29 , 1941\nwisconsin 1st#stephen bolles (r)#died july 8 , 1941#lawrence h smith (r)#august 29 , 1941\npennsylvania 15th#albert g rutherford (r)#died august 10 , 1941#wilson d gillette (r)#november 4 , 1941\ncolorado 4th#edward t taylor (d)#died september 3 , 1941#robert f rockwell (r)#december 9 , 1941\ncalifornia 17th#lee e geyer (d)#died october 11 , 1941#cecil r king (d)#august 25 , 1942\nmassachusetts 7th#lawrence j connery (d)#died october 19 , 1941#thomas j lane (d)#december 30 , 1941\npennsylvania 11th#patrick j boland (d)#died may 18 , 1942#veronica g boland (d)#november 3 , 1942\ncalifornia 3rd#frank h buck (d)#died september 17 , 1942#vacant until the next congress#vacant until the next congress\npennsylvania 25th#charles i faddis (d)#resigned december 4 , 1942 to enter the us army#vacant until the next congress#vacant until the next congress\nillinois 6th#a f maciejewski (d)#resigned december 6 , 1942#vacant until the next congress#vacant until the next congress\n",
        "pandas_code": "df[df['successor'].str.contains('vacant until the next congress', na=False)].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 281,
        "statement": "3 deceased member of the 77th congress be from new york",
        "label": 1,
        "table_caption": "77th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\noklahoma 7th#sam c massingale (d)#died january 17 , 1941#victor wickersham (d)#april 1 , 1941\nnew york 17th#kenneth f simpson (r)#died january 25 , 1941#joseph c baldwin (r)#march 11 , 1941\nalabama 7th#walter w bankhead (d)#resigned february 1 , 1941#carter manasco (d)#june 24 , 1941\nmaryland 6th#william d byron (d)#died february 27 , 1941#katharine byron (d)#may 27 , 1941\nnew york 42nd#pius l schwert (d)#died march 11 , 1941#john c butler (r)#april 22 , 1941\nnorth carolina 5th#alonzo d folger (d)#died april 30 , 1941#john h folger (d)#june 14 , 1941\nnew york 14th#morris m edelstein (d)#died june 4 , 1941#arthur g klein (d)#july 29 , 1941\nwisconsin 1st#stephen bolles (r)#died july 8 , 1941#lawrence h smith (r)#august 29 , 1941\npennsylvania 15th#albert g rutherford (r)#died august 10 , 1941#wilson d gillette (r)#november 4 , 1941\ncolorado 4th#edward t taylor (d)#died september 3 , 1941#robert f rockwell (r)#december 9 , 1941\ncalifornia 17th#lee e geyer (d)#died october 11 , 1941#cecil r king (d)#august 25 , 1942\nmassachusetts 7th#lawrence j connery (d)#died october 19 , 1941#thomas j lane (d)#december 30 , 1941\npennsylvania 11th#patrick j boland (d)#died may 18 , 1942#veronica g boland (d)#november 3 , 1942\ncalifornia 3rd#frank h buck (d)#died september 17 , 1942#vacant until the next congress#vacant until the next congress\npennsylvania 25th#charles i faddis (d)#resigned december 4 , 1942 to enter the us army#vacant until the next congress#vacant until the next congress\nillinois 6th#a f maciejewski (d)#resigned december 6 , 1942#vacant until the next congress#vacant until the next congress\n",
        "pandas_code": "df[df['reason for change'].str.contains('died') & df['district'].str.contains('new york')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 282,
        "statement": "from the binibining pilipinas the 2000 miss universe philippine be nina ricci alagao",
        "label": 1,
        "table_caption": "binibining pilipinas",
        "table_text": "year#miss universe philippines#binibining pilipinas - world#binibining pilipinas - international#binibining pilipinas - tourism#first runner - up#second runner - up\n2000#nina ricci alagao#katherine annwen de guzman#joanna maria pe\u5358aloza#not awarded#maria cristina tan#nicole hofer\n2001#zorayda ruth andam#gilrhea quinzon#maricarl tolosa#not awarded#michelle reyes#maricar balagtas\n2002#karen loren agustin#katherine anne manalo#kristine alzar#not awarded#margaret - ann bayot#maria lourdes magno\n2003#carla gay balingit#maria rafaela yunon#jhezarie javier#not awarded#kate sephora baesa#noela mae evangelista\n2004#maricar balagtas#maria karla bautista#margaret ann bayot#not awarded#tracy ann javelona#princess jasmine tiongson\n2005#gionna cabrera#carlene aguilar#precious lara quigaman#wendy valdez#wendy valdez#melanie lariosa ediza\n2006#lia andrea ramos#anna maris igpit#denille lou valmonte#not awarded#rosalyn sirikit santiago#jeanne bernadette bello\n2007#anna theresa licaros#margaret nales wilson#nadia lee cien shami#not awarded#leizel verses#abigail lesley cruz\n2008#jennifer barrientos#janina san miguel#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2008#jennifer barrientos#danielle casta\u5358o#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2009#bianca manalo#marie - ann umali#melody gersbach#not awarded#richell angalot#regina hahn\n",
        "pandas_code": "df.loc[df['year'] == 2000, 'miss universe philippines'].iloc[0] == 'nina ricci alagao'",
        "pandas_eval": "True"
    },
    {
        "id": 283,
        "statement": "from the binibining pilipinas the 2001 miss universe philippine be zorayda ruth andam",
        "label": 1,
        "table_caption": "binibining pilipinas",
        "table_text": "year#miss universe philippines#binibining pilipinas - world#binibining pilipinas - international#binibining pilipinas - tourism#first runner - up#second runner - up\n2000#nina ricci alagao#katherine annwen de guzman#joanna maria pe\u5358aloza#not awarded#maria cristina tan#nicole hofer\n2001#zorayda ruth andam#gilrhea quinzon#maricarl tolosa#not awarded#michelle reyes#maricar balagtas\n2002#karen loren agustin#katherine anne manalo#kristine alzar#not awarded#margaret - ann bayot#maria lourdes magno\n2003#carla gay balingit#maria rafaela yunon#jhezarie javier#not awarded#kate sephora baesa#noela mae evangelista\n2004#maricar balagtas#maria karla bautista#margaret ann bayot#not awarded#tracy ann javelona#princess jasmine tiongson\n2005#gionna cabrera#carlene aguilar#precious lara quigaman#wendy valdez#wendy valdez#melanie lariosa ediza\n2006#lia andrea ramos#anna maris igpit#denille lou valmonte#not awarded#rosalyn sirikit santiago#jeanne bernadette bello\n2007#anna theresa licaros#margaret nales wilson#nadia lee cien shami#not awarded#leizel verses#abigail lesley cruz\n2008#jennifer barrientos#janina san miguel#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2008#jennifer barrientos#danielle casta\u5358o#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2009#bianca manalo#marie - ann umali#melody gersbach#not awarded#richell angalot#regina hahn\n",
        "pandas_code": "df.loc[df['year'] == 2001, 'miss universe philippines'].iloc[0] == 'zorayda ruth andam'",
        "pandas_eval": "True"
    },
    {
        "id": 284,
        "statement": "from the binibining pilipinas the 2002 miss universe philippine be karen loren agustin",
        "label": 1,
        "table_caption": "binibining pilipinas",
        "table_text": "year#miss universe philippines#binibining pilipinas - world#binibining pilipinas - international#binibining pilipinas - tourism#first runner - up#second runner - up\n2000#nina ricci alagao#katherine annwen de guzman#joanna maria pe\u5358aloza#not awarded#maria cristina tan#nicole hofer\n2001#zorayda ruth andam#gilrhea quinzon#maricarl tolosa#not awarded#michelle reyes#maricar balagtas\n2002#karen loren agustin#katherine anne manalo#kristine alzar#not awarded#margaret - ann bayot#maria lourdes magno\n2003#carla gay balingit#maria rafaela yunon#jhezarie javier#not awarded#kate sephora baesa#noela mae evangelista\n2004#maricar balagtas#maria karla bautista#margaret ann bayot#not awarded#tracy ann javelona#princess jasmine tiongson\n2005#gionna cabrera#carlene aguilar#precious lara quigaman#wendy valdez#wendy valdez#melanie lariosa ediza\n2006#lia andrea ramos#anna maris igpit#denille lou valmonte#not awarded#rosalyn sirikit santiago#jeanne bernadette bello\n2007#anna theresa licaros#margaret nales wilson#nadia lee cien shami#not awarded#leizel verses#abigail lesley cruz\n2008#jennifer barrientos#janina san miguel#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2008#jennifer barrientos#danielle casta\u5358o#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2009#bianca manalo#marie - ann umali#melody gersbach#not awarded#richell angalot#regina hahn\n",
        "pandas_code": "df.loc[df['year'] == 2002, 'miss universe philippines'].iloc[0] == 'karen loren agustin'",
        "pandas_eval": "True"
    },
    {
        "id": 285,
        "statement": "from the binibining pilipinas the 2003 miss universe philippine be carla gay balingit",
        "label": 1,
        "table_caption": "binibining pilipinas",
        "table_text": "year#miss universe philippines#binibining pilipinas - world#binibining pilipinas - international#binibining pilipinas - tourism#first runner - up#second runner - up\n2000#nina ricci alagao#katherine annwen de guzman#joanna maria pe\u5358aloza#not awarded#maria cristina tan#nicole hofer\n2001#zorayda ruth andam#gilrhea quinzon#maricarl tolosa#not awarded#michelle reyes#maricar balagtas\n2002#karen loren agustin#katherine anne manalo#kristine alzar#not awarded#margaret - ann bayot#maria lourdes magno\n2003#carla gay balingit#maria rafaela yunon#jhezarie javier#not awarded#kate sephora baesa#noela mae evangelista\n2004#maricar balagtas#maria karla bautista#margaret ann bayot#not awarded#tracy ann javelona#princess jasmine tiongson\n2005#gionna cabrera#carlene aguilar#precious lara quigaman#wendy valdez#wendy valdez#melanie lariosa ediza\n2006#lia andrea ramos#anna maris igpit#denille lou valmonte#not awarded#rosalyn sirikit santiago#jeanne bernadette bello\n2007#anna theresa licaros#margaret nales wilson#nadia lee cien shami#not awarded#leizel verses#abigail lesley cruz\n2008#jennifer barrientos#janina san miguel#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2008#jennifer barrientos#danielle casta\u5358o#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2009#bianca manalo#marie - ann umali#melody gersbach#not awarded#richell angalot#regina hahn\n",
        "pandas_code": "df.loc[df['year'] == 2003, 'miss universe philippines'].iloc[0] == 'carla gay balingit'",
        "pandas_eval": "True"
    },
    {
        "id": 286,
        "statement": "from the binibining pilipinas the 2004 miss universe philippine be maricar balagtas",
        "label": 1,
        "table_caption": "binibining pilipinas",
        "table_text": "year#miss universe philippines#binibining pilipinas - world#binibining pilipinas - international#binibining pilipinas - tourism#first runner - up#second runner - up\n2000#nina ricci alagao#katherine annwen de guzman#joanna maria pe\u5358aloza#not awarded#maria cristina tan#nicole hofer\n2001#zorayda ruth andam#gilrhea quinzon#maricarl tolosa#not awarded#michelle reyes#maricar balagtas\n2002#karen loren agustin#katherine anne manalo#kristine alzar#not awarded#margaret - ann bayot#maria lourdes magno\n2003#carla gay balingit#maria rafaela yunon#jhezarie javier#not awarded#kate sephora baesa#noela mae evangelista\n2004#maricar balagtas#maria karla bautista#margaret ann bayot#not awarded#tracy ann javelona#princess jasmine tiongson\n2005#gionna cabrera#carlene aguilar#precious lara quigaman#wendy valdez#wendy valdez#melanie lariosa ediza\n2006#lia andrea ramos#anna maris igpit#denille lou valmonte#not awarded#rosalyn sirikit santiago#jeanne bernadette bello\n2007#anna theresa licaros#margaret nales wilson#nadia lee cien shami#not awarded#leizel verses#abigail lesley cruz\n2008#jennifer barrientos#janina san miguel#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2008#jennifer barrientos#danielle casta\u5358o#patricia fernandez#not awarded#danielle casta\u5358o#elizabeth jacqueline nacuspag\n2009#bianca manalo#marie - ann umali#melody gersbach#not awarded#richell angalot#regina hahn\n",
        "pandas_code": "df.loc[df['year'] == 2004, 'miss universe philippines'].iloc[0] == 'maricar balagtas'",
        "pandas_eval": "True"
    },
    {
        "id": 287,
        "statement": "measha brueggergosman be 1 of 3 celebrity who have an original air date in january 2008",
        "label": 1,
        "table_caption": "who do you think you are? (canadian tv series)",
        "table_text": "total no#celebrity#director#original air date#viewers\n1#shaun majumder#scott harper#11 october 2007#n / a\n2#margot kidder#margaret slaght#18 october 2007#n / a\n3#steven page#david langer#25 october 2007#n / a\n4#sonja smits#karen pinker#1 november 2007#n / a\n5#chantal kreviazuk#nadine schwartz#8 november 2007#n / a\n6#major - general lewis mackenzie#richard martyn#15 november 2007#n / a\n7#mary walsh#matt gallagher#22 november 2007#n / a\n8#randy bachman#margaret slaght#29 november 2007#n / a\n9#scott thompson#scott harper#6 december 2007#n / a\n10#don cherry#richard martyn#10 january 2008#n / a\n11#measha brueggergosman#karen pinker#17 january 2008#n / a\n12#margaret trudeau#peter findlay#24 january 2008#n / a\n",
        "pandas_code": "len(df[(df['celebrity'] == 'measha brueggergosman') & (df['original air date'].str.contains('january 2008'))]) == 1 and len(df[df['original air date'].str.contains('january 2008')]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 288,
        "statement": "sonja smits and measha brueggergosman both have the same director",
        "label": 1,
        "table_caption": "who do you think you are? (canadian tv series)",
        "table_text": "total no#celebrity#director#original air date#viewers\n1#shaun majumder#scott harper#11 october 2007#n / a\n2#margot kidder#margaret slaght#18 october 2007#n / a\n3#steven page#david langer#25 october 2007#n / a\n4#sonja smits#karen pinker#1 november 2007#n / a\n5#chantal kreviazuk#nadine schwartz#8 november 2007#n / a\n6#major - general lewis mackenzie#richard martyn#15 november 2007#n / a\n7#mary walsh#matt gallagher#22 november 2007#n / a\n8#randy bachman#margaret slaght#29 november 2007#n / a\n9#scott thompson#scott harper#6 december 2007#n / a\n10#don cherry#richard martyn#10 january 2008#n / a\n11#measha brueggergosman#karen pinker#17 january 2008#n / a\n12#margaret trudeau#peter findlay#24 january 2008#n / a\n",
        "pandas_code": "df[df['celebrity'].isin(['sonja smits', 'measha brueggergosman'])].duplicated(subset='director', keep=False).all()",
        "pandas_eval": "True"
    },
    {
        "id": 289,
        "statement": "steven page have an original air date 3 month before don cherry",
        "label": 1,
        "table_caption": "who do you think you are? (canadian tv series)",
        "table_text": "total no#celebrity#director#original air date#viewers\n1#shaun majumder#scott harper#11 october 2007#n / a\n2#margot kidder#margaret slaght#18 october 2007#n / a\n3#steven page#david langer#25 october 2007#n / a\n4#sonja smits#karen pinker#1 november 2007#n / a\n5#chantal kreviazuk#nadine schwartz#8 november 2007#n / a\n6#major - general lewis mackenzie#richard martyn#15 november 2007#n / a\n7#mary walsh#matt gallagher#22 november 2007#n / a\n8#randy bachman#margaret slaght#29 november 2007#n / a\n9#scott thompson#scott harper#6 december 2007#n / a\n10#don cherry#richard martyn#10 january 2008#n / a\n11#measha brueggergosman#karen pinker#17 january 2008#n / a\n12#margaret trudeau#peter findlay#24 january 2008#n / a\n",
        "pandas_code": "(pd.to_datetime(df[df['celebrity'] == 'steven page']['original air date'].iloc[0]) + pd.DateOffset(months=3) >= pd.to_datetime(df[df['celebrity'] == 'don cherry']['original air date'].iloc[0]))",
        "pandas_eval": "True"
    },
    {
        "id": 290,
        "statement": "sonja smits have an original air date 1 week before chantal kreviazuk",
        "label": 1,
        "table_caption": "who do you think you are? (canadian tv series)",
        "table_text": "total no#celebrity#director#original air date#viewers\n1#shaun majumder#scott harper#11 october 2007#n / a\n2#margot kidder#margaret slaght#18 october 2007#n / a\n3#steven page#david langer#25 october 2007#n / a\n4#sonja smits#karen pinker#1 november 2007#n / a\n5#chantal kreviazuk#nadine schwartz#8 november 2007#n / a\n6#major - general lewis mackenzie#richard martyn#15 november 2007#n / a\n7#mary walsh#matt gallagher#22 november 2007#n / a\n8#randy bachman#margaret slaght#29 november 2007#n / a\n9#scott thompson#scott harper#6 december 2007#n / a\n10#don cherry#richard martyn#10 january 2008#n / a\n11#measha brueggergosman#karen pinker#17 january 2008#n / a\n12#margaret trudeau#peter findlay#24 january 2008#n / a\n",
        "pandas_code": "(df.loc[df['celebrity'] == 'sonja smits', 'original air date'].values[0] == '1 november 2007') & (df.loc[df['celebrity'] == 'chantal kreviazuk', 'original air date'].values[0] == '8 november 2007')",
        "pandas_eval": "True"
    },
    {
        "id": 291,
        "statement": "scott thompson be the only celebrity to have an original air date in december",
        "label": 1,
        "table_caption": "who do you think you are? (canadian tv series)",
        "table_text": "total no#celebrity#director#original air date#viewers\n1#shaun majumder#scott harper#11 october 2007#n / a\n2#margot kidder#margaret slaght#18 october 2007#n / a\n3#steven page#david langer#25 october 2007#n / a\n4#sonja smits#karen pinker#1 november 2007#n / a\n5#chantal kreviazuk#nadine schwartz#8 november 2007#n / a\n6#major - general lewis mackenzie#richard martyn#15 november 2007#n / a\n7#mary walsh#matt gallagher#22 november 2007#n / a\n8#randy bachman#margaret slaght#29 november 2007#n / a\n9#scott thompson#scott harper#6 december 2007#n / a\n10#don cherry#richard martyn#10 january 2008#n / a\n11#measha brueggergosman#karen pinker#17 january 2008#n / a\n12#margaret trudeau#peter findlay#24 january 2008#n / a\n",
        "pandas_code": "(df[df['celebrity'] == 'scott thompson']['original air date'].str.contains('december').any()) & (~df[df['celebrity'] != 'scott thompson']['original air date'].str.contains('december').any())",
        "pandas_eval": "True"
    },
    {
        "id": 292,
        "statement": "the most expensive headphone be headphone model gs1000i",
        "label": 1,
        "table_caption": "grado labs",
        "table_text": "headphone model#headphone class#driver - matched db#construction#earpads#termination#us msrp\nigrado#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug#49\nsr60i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#79\nsr80i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#99\nsr125i#prestige#0.1#plastic#comfort pads#1 / 4 (6.5 mm) plug#150\nsr225i#prestige#0.05#plastic#bowls#1 / 4 (6.5 mm) plug#200\nsr325is#prestige#0.05#aluminum alloy / plastic inner sleeve#bowls#1 / 4 (6.5 mm) plug#295\nrs2i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#495\nrs1i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#695\ngs1000i#statement#0.05#hand - crafted mahogany#circumaural bowls#1 / 4 (6.5 mm) plug#995\nps500#professional#0.05#hand - crafted mahogany / aluminum#bowls#1 / 4 (6.5 mm) plug#595\n",
        "pandas_code": "df.loc[df['us msrp'].idxmax(), 'headphone model'] == 'gs1000i'",
        "pandas_eval": "True"
    },
    {
        "id": 293,
        "statement": "igrado cost leaa than sr60i",
        "label": 1,
        "table_caption": "grado labs",
        "table_text": "headphone model#headphone class#driver - matched db#construction#earpads#termination#us msrp\nigrado#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug#49\nsr60i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#79\nsr80i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#99\nsr125i#prestige#0.1#plastic#comfort pads#1 / 4 (6.5 mm) plug#150\nsr225i#prestige#0.05#plastic#bowls#1 / 4 (6.5 mm) plug#200\nsr325is#prestige#0.05#aluminum alloy / plastic inner sleeve#bowls#1 / 4 (6.5 mm) plug#295\nrs2i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#495\nrs1i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#695\ngs1000i#statement#0.05#hand - crafted mahogany#circumaural bowls#1 / 4 (6.5 mm) plug#995\nps500#professional#0.05#hand - crafted mahogany / aluminum#bowls#1 / 4 (6.5 mm) plug#595\n",
        "pandas_code": "df[df['headphone model'] == 'igrado']['us msrp'].values[0] < df[df['headphone model'] == 'sr60i']['us msrp'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 295,
        "statement": "sr325is be the only 1 that be make of aluminum alloy / plastic inner sleeve",
        "label": 1,
        "table_caption": "grado labs",
        "table_text": "headphone model#headphone class#driver - matched db#construction#earpads#termination#us msrp\nigrado#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug#49\nsr60i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#79\nsr80i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#99\nsr125i#prestige#0.1#plastic#comfort pads#1 / 4 (6.5 mm) plug#150\nsr225i#prestige#0.05#plastic#bowls#1 / 4 (6.5 mm) plug#200\nsr325is#prestige#0.05#aluminum alloy / plastic inner sleeve#bowls#1 / 4 (6.5 mm) plug#295\nrs2i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#495\nrs1i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#695\ngs1000i#statement#0.05#hand - crafted mahogany#circumaural bowls#1 / 4 (6.5 mm) plug#995\nps500#professional#0.05#hand - crafted mahogany / aluminum#bowls#1 / 4 (6.5 mm) plug#595\n",
        "pandas_code": "df[df['construction'] == 'aluminum alloy / plastic inner sleeve']['headphone model'].eq('sr325is').all()",
        "pandas_eval": "True"
    },
    {
        "id": 296,
        "statement": "1 / 4 (6.5 mm) plug be the termination of all but 3 headphone",
        "label": 1,
        "table_caption": "grado labs",
        "table_text": "headphone model#headphone class#driver - matched db#construction#earpads#termination#us msrp\nigrado#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug#49\nsr60i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#79\nsr80i#prestige#0.1#plastic#comfort pads#1 / 8 (3.5 mm) plug with 1 / 4 adaptor#99\nsr125i#prestige#0.1#plastic#comfort pads#1 / 4 (6.5 mm) plug#150\nsr225i#prestige#0.05#plastic#bowls#1 / 4 (6.5 mm) plug#200\nsr325is#prestige#0.05#aluminum alloy / plastic inner sleeve#bowls#1 / 4 (6.5 mm) plug#295\nrs2i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#495\nrs1i#reference#0.05#hand - crafted mahogany#bowls#1 / 4 (6.5 mm) plug#695\ngs1000i#statement#0.05#hand - crafted mahogany#circumaural bowls#1 / 4 (6.5 mm) plug#995\nps500#professional#0.05#hand - crafted mahogany / aluminum#bowls#1 / 4 (6.5 mm) plug#595\n",
        "pandas_code": "(df['termination'] == '1 / 4 (6.5 mm) plug').sum() == len(df) - 3",
        "pandas_eval": "True"
    },
    {
        "id": 298,
        "statement": "vince carter have the highest number of point in 1 game with 43 point",
        "label": 1,
        "table_caption": "2001 - 02 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n60#march 1#portland#l 81 - 91 (ot)#vince carter (25)#antonio davis , hakeem olajuwon (8)#chris childs (7)#air canada centre 19800#29 - 31\n61#march 3#philadelphia#l 84 - 96 (ot)#antonio davis (26)#antonio davis (9)#alvin williams (6)#air canada centre 19800#29 - 32\n62#march 5#houston#l 109 - 112 (ot)#vince carter (43)#vince carter , hakeem olajuwon (7)#alvin williams (9)#compaq center 14221#29 - 33\n63#march 7#dallas#l 103 - 122 (ot)#vince carter (19)#keon clark , antonio davis (15)#alvin williams (7)#american airlines center 19945#29 - 34\n64#march 8#miami#w 83 - 74 (ot)#antonio davis (23)#antonio davis (10)#chris childs (6)#american airlines arena 16500#30 - 34\n65#march 10#orlando#l 79 - 92 (ot)#vince carter (16)#antonio davis (12)#chris childs (7)#td waterhouse centre 16171#30 - 35\n66#march 12#new jersey#l 84 - 86 (ot)#antonio davis (27)#antonio davis , jerome williams (13)#vince carter (4)#continental airlines arena 16105#30 - 36\n67#march 17#sacramento#l 113 - 116 (ot)#vince carter (22)#hakeem olajuwon (13)#chris childs (7)#air canada centre 19800#30 - 37\n68#march 19#minnesota#l 80 - 112 (ot)#morris peterson (19)#antonio davis (13)#alvin williams (7)#target center 17010#30 - 38\n69#march 22#cleveland#w 94 - 80 (ot)#morris peterson (18)#keon clark (10)#alvin williams (4)#gund arena 17847#31 - 38\n70#march 24#washington#w 92 - 91 (ot)#morris peterson (26)#antonio davis (9)#alvin williams (9)#air canada centre 19800#32 - 38\n71#march 27#miami#w 81 - 80 (ot)#morris peterson (21)#antonio davis , jerome williams (10)#chris childs (6)#air canada centre 19800#33 - 38\n72#march 28#atlanta#w 85 - 83 (ot)#antonio davis , morris peterson (15)#antonio davis (9)#chris childs (7)#philips arena 12036#34 - 38\n",
        "pandas_code": "df['high points'].str.contains('vince carter \\(43\\)').any()",
        "pandas_eval": "True"
    },
    {
        "id": 299,
        "statement": "antonio davis have the most rebound throughout the raptor season",
        "label": 1,
        "table_caption": "2001 - 02 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n60#march 1#portland#l 81 - 91 (ot)#vince carter (25)#antonio davis , hakeem olajuwon (8)#chris childs (7)#air canada centre 19800#29 - 31\n61#march 3#philadelphia#l 84 - 96 (ot)#antonio davis (26)#antonio davis (9)#alvin williams (6)#air canada centre 19800#29 - 32\n62#march 5#houston#l 109 - 112 (ot)#vince carter (43)#vince carter , hakeem olajuwon (7)#alvin williams (9)#compaq center 14221#29 - 33\n63#march 7#dallas#l 103 - 122 (ot)#vince carter (19)#keon clark , antonio davis (15)#alvin williams (7)#american airlines center 19945#29 - 34\n64#march 8#miami#w 83 - 74 (ot)#antonio davis (23)#antonio davis (10)#chris childs (6)#american airlines arena 16500#30 - 34\n65#march 10#orlando#l 79 - 92 (ot)#vince carter (16)#antonio davis (12)#chris childs (7)#td waterhouse centre 16171#30 - 35\n66#march 12#new jersey#l 84 - 86 (ot)#antonio davis (27)#antonio davis , jerome williams (13)#vince carter (4)#continental airlines arena 16105#30 - 36\n67#march 17#sacramento#l 113 - 116 (ot)#vince carter (22)#hakeem olajuwon (13)#chris childs (7)#air canada centre 19800#30 - 37\n68#march 19#minnesota#l 80 - 112 (ot)#morris peterson (19)#antonio davis (13)#alvin williams (7)#target center 17010#30 - 38\n69#march 22#cleveland#w 94 - 80 (ot)#morris peterson (18)#keon clark (10)#alvin williams (4)#gund arena 17847#31 - 38\n70#march 24#washington#w 92 - 91 (ot)#morris peterson (26)#antonio davis (9)#alvin williams (9)#air canada centre 19800#32 - 38\n71#march 27#miami#w 81 - 80 (ot)#morris peterson (21)#antonio davis , jerome williams (10)#chris childs (6)#air canada centre 19800#33 - 38\n72#march 28#atlanta#w 85 - 83 (ot)#antonio davis , morris peterson (15)#antonio davis (9)#chris childs (7)#philips arena 12036#34 - 38\n",
        "pandas_code": "(df['high rebounds'].str.contains('antonio davis').sum() > df['high rebounds'].str.contains('vince carter').sum()) and (df['high rebounds'].str.contains('antonio davis').sum() > df['high rebounds'].str.contains('morris peterson').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 300,
        "statement": "the highest attended game be against dallas on march 7",
        "label": 1,
        "table_caption": "2001 - 02 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n60#march 1#portland#l 81 - 91 (ot)#vince carter (25)#antonio davis , hakeem olajuwon (8)#chris childs (7)#air canada centre 19800#29 - 31\n61#march 3#philadelphia#l 84 - 96 (ot)#antonio davis (26)#antonio davis (9)#alvin williams (6)#air canada centre 19800#29 - 32\n62#march 5#houston#l 109 - 112 (ot)#vince carter (43)#vince carter , hakeem olajuwon (7)#alvin williams (9)#compaq center 14221#29 - 33\n63#march 7#dallas#l 103 - 122 (ot)#vince carter (19)#keon clark , antonio davis (15)#alvin williams (7)#american airlines center 19945#29 - 34\n64#march 8#miami#w 83 - 74 (ot)#antonio davis (23)#antonio davis (10)#chris childs (6)#american airlines arena 16500#30 - 34\n65#march 10#orlando#l 79 - 92 (ot)#vince carter (16)#antonio davis (12)#chris childs (7)#td waterhouse centre 16171#30 - 35\n66#march 12#new jersey#l 84 - 86 (ot)#antonio davis (27)#antonio davis , jerome williams (13)#vince carter (4)#continental airlines arena 16105#30 - 36\n67#march 17#sacramento#l 113 - 116 (ot)#vince carter (22)#hakeem olajuwon (13)#chris childs (7)#air canada centre 19800#30 - 37\n68#march 19#minnesota#l 80 - 112 (ot)#morris peterson (19)#antonio davis (13)#alvin williams (7)#target center 17010#30 - 38\n69#march 22#cleveland#w 94 - 80 (ot)#morris peterson (18)#keon clark (10)#alvin williams (4)#gund arena 17847#31 - 38\n70#march 24#washington#w 92 - 91 (ot)#morris peterson (26)#antonio davis (9)#alvin williams (9)#air canada centre 19800#32 - 38\n71#march 27#miami#w 81 - 80 (ot)#morris peterson (21)#antonio davis , jerome williams (10)#chris childs (6)#air canada centre 19800#33 - 38\n72#march 28#atlanta#w 85 - 83 (ot)#antonio davis , morris peterson (15)#antonio davis (9)#chris childs (7)#philips arena 12036#34 - 38\n",
        "pandas_code": "df.loc[df['team'] == 'dallas', 'location attendance'].max() == 'american airlines center 19945'",
        "pandas_eval": "True"
    },
    {
        "id": 301,
        "statement": "the longest break between game be 5 day between the game against new jersey and the game against sacramento",
        "label": 1,
        "table_caption": "2001 - 02 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n60#march 1#portland#l 81 - 91 (ot)#vince carter (25)#antonio davis , hakeem olajuwon (8)#chris childs (7)#air canada centre 19800#29 - 31\n61#march 3#philadelphia#l 84 - 96 (ot)#antonio davis (26)#antonio davis (9)#alvin williams (6)#air canada centre 19800#29 - 32\n62#march 5#houston#l 109 - 112 (ot)#vince carter (43)#vince carter , hakeem olajuwon (7)#alvin williams (9)#compaq center 14221#29 - 33\n63#march 7#dallas#l 103 - 122 (ot)#vince carter (19)#keon clark , antonio davis (15)#alvin williams (7)#american airlines center 19945#29 - 34\n64#march 8#miami#w 83 - 74 (ot)#antonio davis (23)#antonio davis (10)#chris childs (6)#american airlines arena 16500#30 - 34\n65#march 10#orlando#l 79 - 92 (ot)#vince carter (16)#antonio davis (12)#chris childs (7)#td waterhouse centre 16171#30 - 35\n66#march 12#new jersey#l 84 - 86 (ot)#antonio davis (27)#antonio davis , jerome williams (13)#vince carter (4)#continental airlines arena 16105#30 - 36\n67#march 17#sacramento#l 113 - 116 (ot)#vince carter (22)#hakeem olajuwon (13)#chris childs (7)#air canada centre 19800#30 - 37\n68#march 19#minnesota#l 80 - 112 (ot)#morris peterson (19)#antonio davis (13)#alvin williams (7)#target center 17010#30 - 38\n69#march 22#cleveland#w 94 - 80 (ot)#morris peterson (18)#keon clark (10)#alvin williams (4)#gund arena 17847#31 - 38\n70#march 24#washington#w 92 - 91 (ot)#morris peterson (26)#antonio davis (9)#alvin williams (9)#air canada centre 19800#32 - 38\n71#march 27#miami#w 81 - 80 (ot)#morris peterson (21)#antonio davis , jerome williams (10)#chris childs (6)#air canada centre 19800#33 - 38\n72#march 28#atlanta#w 85 - 83 (ot)#antonio davis , morris peterson (15)#antonio davis (9)#chris childs (7)#philips arena 12036#34 - 38\n",
        "pandas_code": "(pd.to_datetime(df[df['team'] == 'sacramento']['date'].values[0], format='%B %d') - pd.to_datetime(df[df['team'] == 'new jersey']['date'].values[0], format='%B %d')).days == 5",
        "pandas_eval": "True"
    },
    {
        "id": 303,
        "statement": "steve smith who play the position mf , have 5 league goal , the same as bob newton who play the position fw",
        "label": 1,
        "table_caption": "1975 - 76 huddersfield town f.c. season",
        "table_text": "name#position#league apps#league goals#fa cup apps#fa cup goals#league cup apps#league cup goals#total apps#total goals\nsteve baines#df#41#1#4#1#3#0#48#2\nrod belfitt#fw#28#6#4#2#3#0#35#8\nmick butler#mf#9 (1)#2#0#0#0#0#9 (1)#2\nbobby campbell#fw#11#1#0#0#3#1#14#2\nwillie coulson#mf#2#0#0#0#0#0#2#0\nterry dolan#mf#29 (1)#1#3#0#3#1#35 (1)#2\nbarry endean#mf#2 (2)#0#0#0#0#0#2 (2)#0\nfranny firth#fw#16#2#0#0#0#0#16#2\nmartin fowler#mf#6 (2)#0#0#0#0#0#6 (2)#0\npaul garner#df#16#0#0#0#3#0#19#0\ncolin garwood#fw#12#6#2#0#0#0#14#6\nwayne goldthorpe#fw#6 (2)#2#0#0#0#0#6 (2)#2\nterry gray#fw#29#15#4#2#3#2#36#19\npeter hart#mf#17 (2)#1#0#0#0#0#17 (2)#1\ngeoff hutt#df#20#0#4#0#3#0#27#0\njimmy lawson#mf#22 (3)#4#3#1#0#0#25 (3)#5\nlloyd maitland#mf#9 (6)#1#1#0#0#0#10 (6)#1\ngrahame mcgifford#df#3#0#0#0#0#0#3#0\nbob newton#fw#16 (2)#5#0#0#0#0#16 (2)#5\nbrian o'neil#mf#38#1#3#0#3#0#44#1\nterry poole#gk#4#0#0#0#0#0#4#0\njohn saunders#df#17#0#0#0#3#0#20#0\narnie sidebottom#df#9#2#0#0#0#0#9#2\nchris simpkin#df#25#0#4#0#0#0#29#0\nsteve smith#mf#41#5#4#0#3#0#48#5\nalan sweeney#df#36#0#4#0#0#0#40#0\ndick taylor#gk#42#0#4#0#3#0#49#0\n",
        "pandas_code": "(df.loc[df['name'] == 'steve smith', 'league goals'].values[0] == 5) & (df.loc[df['name'] == 'bob newton', 'league goals'].values[0] == 5) & (df.loc[df['name'] == 'steve smith', 'position'].values[0] == 'mf') & (df.loc[df['name'] == 'bob newton', 'position'].values[0] == 'fw')",
        "pandas_eval": "True"
    },
    {
        "id": 304,
        "statement": "jimmy lawson , peter hart , brian o'neil , and martin fowler all play the mf position",
        "label": 1,
        "table_caption": "1975 - 76 huddersfield town f.c. season",
        "table_text": "name#position#league apps#league goals#fa cup apps#fa cup goals#league cup apps#league cup goals#total apps#total goals\nsteve baines#df#41#1#4#1#3#0#48#2\nrod belfitt#fw#28#6#4#2#3#0#35#8\nmick butler#mf#9 (1)#2#0#0#0#0#9 (1)#2\nbobby campbell#fw#11#1#0#0#3#1#14#2\nwillie coulson#mf#2#0#0#0#0#0#2#0\nterry dolan#mf#29 (1)#1#3#0#3#1#35 (1)#2\nbarry endean#mf#2 (2)#0#0#0#0#0#2 (2)#0\nfranny firth#fw#16#2#0#0#0#0#16#2\nmartin fowler#mf#6 (2)#0#0#0#0#0#6 (2)#0\npaul garner#df#16#0#0#0#3#0#19#0\ncolin garwood#fw#12#6#2#0#0#0#14#6\nwayne goldthorpe#fw#6 (2)#2#0#0#0#0#6 (2)#2\nterry gray#fw#29#15#4#2#3#2#36#19\npeter hart#mf#17 (2)#1#0#0#0#0#17 (2)#1\ngeoff hutt#df#20#0#4#0#3#0#27#0\njimmy lawson#mf#22 (3)#4#3#1#0#0#25 (3)#5\nlloyd maitland#mf#9 (6)#1#1#0#0#0#10 (6)#1\ngrahame mcgifford#df#3#0#0#0#0#0#3#0\nbob newton#fw#16 (2)#5#0#0#0#0#16 (2)#5\nbrian o'neil#mf#38#1#3#0#3#0#44#1\nterry poole#gk#4#0#0#0#0#0#4#0\njohn saunders#df#17#0#0#0#3#0#20#0\narnie sidebottom#df#9#2#0#0#0#0#9#2\nchris simpkin#df#25#0#4#0#0#0#29#0\nsteve smith#mf#41#5#4#0#3#0#48#5\nalan sweeney#df#36#0#4#0#0#0#40#0\ndick taylor#gk#42#0#4#0#3#0#49#0\n",
        "pandas_code": "all(df[df['name'].isin(['jimmy lawson', 'peter hart', \"brian o'neil\", 'martin fowler'])]['position'] == 'mf')",
        "pandas_eval": "True"
    },
    {
        "id": 305,
        "statement": "terry gray have 15 league goal , 10 more league goal than bob newton",
        "label": 1,
        "table_caption": "1975 - 76 huddersfield town f.c. season",
        "table_text": "name#position#league apps#league goals#fa cup apps#fa cup goals#league cup apps#league cup goals#total apps#total goals\nsteve baines#df#41#1#4#1#3#0#48#2\nrod belfitt#fw#28#6#4#2#3#0#35#8\nmick butler#mf#9 (1)#2#0#0#0#0#9 (1)#2\nbobby campbell#fw#11#1#0#0#3#1#14#2\nwillie coulson#mf#2#0#0#0#0#0#2#0\nterry dolan#mf#29 (1)#1#3#0#3#1#35 (1)#2\nbarry endean#mf#2 (2)#0#0#0#0#0#2 (2)#0\nfranny firth#fw#16#2#0#0#0#0#16#2\nmartin fowler#mf#6 (2)#0#0#0#0#0#6 (2)#0\npaul garner#df#16#0#0#0#3#0#19#0\ncolin garwood#fw#12#6#2#0#0#0#14#6\nwayne goldthorpe#fw#6 (2)#2#0#0#0#0#6 (2)#2\nterry gray#fw#29#15#4#2#3#2#36#19\npeter hart#mf#17 (2)#1#0#0#0#0#17 (2)#1\ngeoff hutt#df#20#0#4#0#3#0#27#0\njimmy lawson#mf#22 (3)#4#3#1#0#0#25 (3)#5\nlloyd maitland#mf#9 (6)#1#1#0#0#0#10 (6)#1\ngrahame mcgifford#df#3#0#0#0#0#0#3#0\nbob newton#fw#16 (2)#5#0#0#0#0#16 (2)#5\nbrian o'neil#mf#38#1#3#0#3#0#44#1\nterry poole#gk#4#0#0#0#0#0#4#0\njohn saunders#df#17#0#0#0#3#0#20#0\narnie sidebottom#df#9#2#0#0#0#0#9#2\nchris simpkin#df#25#0#4#0#0#0#29#0\nsteve smith#mf#41#5#4#0#3#0#48#5\nalan sweeney#df#36#0#4#0#0#0#40#0\ndick taylor#gk#42#0#4#0#3#0#49#0\n",
        "pandas_code": "(df[df['name'] == 'terry gray']['league goals'].values[0] == 15) & (df[df['name'] == 'terry gray']['league goals'].values[0] - df[df['name'] == 'bob newton']['league goals'].values[0] == 10)",
        "pandas_eval": "True"
    },
    {
        "id": 306,
        "statement": "steve baines , rod belfitt , bobby campbell , and terry dolan all have 3 league cup apps",
        "label": 1,
        "table_caption": "1975 - 76 huddersfield town f.c. season",
        "table_text": "name#position#league apps#league goals#fa cup apps#fa cup goals#league cup apps#league cup goals#total apps#total goals\nsteve baines#df#41#1#4#1#3#0#48#2\nrod belfitt#fw#28#6#4#2#3#0#35#8\nmick butler#mf#9 (1)#2#0#0#0#0#9 (1)#2\nbobby campbell#fw#11#1#0#0#3#1#14#2\nwillie coulson#mf#2#0#0#0#0#0#2#0\nterry dolan#mf#29 (1)#1#3#0#3#1#35 (1)#2\nbarry endean#mf#2 (2)#0#0#0#0#0#2 (2)#0\nfranny firth#fw#16#2#0#0#0#0#16#2\nmartin fowler#mf#6 (2)#0#0#0#0#0#6 (2)#0\npaul garner#df#16#0#0#0#3#0#19#0\ncolin garwood#fw#12#6#2#0#0#0#14#6\nwayne goldthorpe#fw#6 (2)#2#0#0#0#0#6 (2)#2\nterry gray#fw#29#15#4#2#3#2#36#19\npeter hart#mf#17 (2)#1#0#0#0#0#17 (2)#1\ngeoff hutt#df#20#0#4#0#3#0#27#0\njimmy lawson#mf#22 (3)#4#3#1#0#0#25 (3)#5\nlloyd maitland#mf#9 (6)#1#1#0#0#0#10 (6)#1\ngrahame mcgifford#df#3#0#0#0#0#0#3#0\nbob newton#fw#16 (2)#5#0#0#0#0#16 (2)#5\nbrian o'neil#mf#38#1#3#0#3#0#44#1\nterry poole#gk#4#0#0#0#0#0#4#0\njohn saunders#df#17#0#0#0#3#0#20#0\narnie sidebottom#df#9#2#0#0#0#0#9#2\nchris simpkin#df#25#0#4#0#0#0#29#0\nsteve smith#mf#41#5#4#0#3#0#48#5\nalan sweeney#df#36#0#4#0#0#0#40#0\ndick taylor#gk#42#0#4#0#3#0#49#0\n",
        "pandas_code": "all(df[df['name'].isin(['steve baines', 'rod belfitt', 'bobby campbell', 'terry dolan'])]['league cup apps'].astype(str) == '3')",
        "pandas_eval": "True"
    },
    {
        "id": 307,
        "statement": "an israeli film be submit each year during the 1980s",
        "label": 1,
        "table_caption": "list of israeli submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1964 (37th)#sallah#sallah shabati (\u05e1\u05d0\u05dc\u05d7 \u05e9\u05d1\u05ea\u05d9)#ephraim kishon#nominee\n1966 (39th)#the flying matchmaker#shnei kuni leml (\u05e9\u05e0\u05d9 \u05e7\u05d5\u05e0\u05d9 \u05dc\u05de\u05dc)#israel becker#not nominated\n1968 (41st)#every bastard a king#kol mamzer melech (\u05db\u05dc \u05de\u05de\u05d6\u05e8 \u05de\u05dc\u05da)#uri zohar#not nominated\n1969 (42nd)#siege#matzor (\u05de\u05e6\u05d5\u05e8)#gilberto tofano#not nominated\n1971 (44th)#the policeman#hashoter azoulay (\u05d4\u05e9\u05d5\u05d8\u05e8 \u05d0\u05d6\u05d5\u05dc\u05d0\u05d9)#ephraim kishon#nominee\n1972 (45th)#i love you rosa#ani ohev otach roza (\u05d0\u05e0\u05d9 \u05d0\u05d5\u05d4\u05d1 \u05d0\u05d5\u05ea\u05da \u05e8\u05d5\u05d6\u05d4)#mosh\u00e9 mizrahi#nominee\n1973 (46th)#the house on chelouche street#habayit birhov chelouche (\u05d4\u05d1\u05d9\u05ea \u05d1\u05e8\u05d7\u05d5\u05d1 \u05e9\u05dc\u05d5\u05e9)#mosh\u00e9 mizrahi#nominee\n1975 (48th)#my michael#michael sheli (\u05de\u05d9\u05db\u05d0\u05dc \u05e9\u05dc\u05d9)#dan wolman#not nominated\n1977 (50th)#operation thunderbolt#mivtza yonatan (\u05de\u05d1\u05e6\u05e2 \u05d9\u05d5\u05e0\u05ea\u05df)#menahem golan#nominee\n1978 (51st)#lemon popsicle#eskimo limon (\u05d0\u05e1\u05e7\u05d9\u05de\u05d5 \u05dc\u05d9\u05de\u05d5\u05df)#boaz davidson#not nominated\n1979 (52nd)#moments#rega'im (\u05e8\u05d2\u05e2\u05d9\u05dd)#michal bat - adam#not nominated\n1980 (53rd)#the thin line#al hevel dak (\u05e2\u05dc \u05d7\u05d1\u05dc \u05d3\u05e7)#michal bat - adam#not nominated\n1981 (54th)#a thousand little kisses#elef neshikot ktanot (\u05d0\u05dc\u05e3 \u05e0\u05e9\u05d9\u05e7\u05d5\u05ea \u05e7\u05d8\u05e0\u05d5\u05ea)#mira recanati#not nominated\n1982 (55th)#hamsin#hamsin (\u05d7\u05de\u05e1\u05d9\u05df)#daniel wachsmann#not nominated\n1983 (56th)#a married couple#zug nasuy (\u05d6\u05d5\u05d2 \u05e0\u05e9\u05d5\u05d9)#yitzhak yeshurun#not nominated\n1984 (57th)#beyond the walls#me'ahorey hasoragim (\u05de\u05d0\u05d7\u05d5\u05e8\u05d9 \u05d4\u05e1\u05d5\u05e8\u05d2\u05d9\u05dd)#uri barbash#nominee\n1985 (58th)#when night falls#ad sof halayla (\u05e2\u05d3 \u05e1\u05d5\u05e3 \u05d4\u05dc\u05d9\u05dc\u05d4)#eitan green#not nominated\n1986 (59th)#avanti popolo#avanti popolo (\u05d0\u05d5\u05d5\u05e0\u05d8\u05d9 \u05e4\u05d5\u05e4\u05d5\u05dc\u05d5)#rafi bukai#not nominated\n1987 (60th)#i don't give a damn#lo sam zayin (\u05dc\u05d0 \u05e9\u05dd \u05d6\u05d9\u05df)#shmuel imberman#not nominated\n1988 (61st)#the summer of aviya#hakayitz shel aviya (\u05d4\u05e7\u05d9\u05e5 \u05e9\u05dc \u05d0\u05d1\u05d9\u05d4)#eli cohen#not nominated\n1989 (62nd)#one of us#ehad mishelanu (\u05d0\u05d7\u05d3 \u05de\u05e9\u05dc\u05e0\u05d5)#uri barbash#not nominated\n1990 (63rd)#the lookout#shuroo (\u05e9\u05d5\u05e8\u05d5)#savi gavison#not nominated\n1991 (64th)#beyond the sea#me'ever layam (\u05de\u05e2\u05d1\u05e8 \u05dc\u05d9\u05dd)#jacob goldwasser#not nominated\n1992 (65th)#life according to agfa#ha'hayim al pi agfa (\u05d4\u05d7\u05d9\u05d9\u05dd \u05e2\u05dc \u05e4\u05d9 \u05d0\u05d2\u05e4\u05d0)#assi dayan#not nominated\n1994 (67th)#sh'chur#sh'chur (\u05e9\u05d7\u05d5\u05e8)#shmuel hasfari#not nominated\n1995 (68th)#lovesick on nana street#hole ahava beshikun gimel (\u05d7\u05d5\u05dc\u05d4 \u05d0\u05d4\u05d1\u05d4 \u05d1\u05e9\u05d9\u05db\u05d5\u05df \u05d2\u05d9\u05de\u05dc)#savi gavison#not nominated\n1997 (70th)#pick a card#afula express (\u05e2\u05e4\u05d5\u05dc\u05d4 \u05d0\u05e7\u05e1\u05e4\u05e8\u05e1)#julie shles#not nominated\n1998 (71st)#circus palestine#kirkas palestina (\u05e7\u05e8\u05e7\u05e1 \u05e4\u05dc\u05e9\u05ea\u05d9\u05e0\u05d4)#eyal halfon#not nominated\n1999 (72nd)#yana 's friends#hahaverim shel yana (\u05d4\u05d7\u05d1\u05e8\u05d9\u05dd \u05e9\u05dc \u05d9\u05d0\u05e0\u05d4)#arik kaplun#not nominated\n2000 (73rd)#time of favor#hahesder (\u05d4\u05d4\u05e1\u05d3\u05e8)#joseph cedar#not nominated\n2001 (74th)#late marriage#hatuna me'uheret (\u05d7\u05ea\u05d5\u05e0\u05d4 \u05de\u05d0\u05d5\u05d7\u05e8\u05ea)#dover kosahvili#not nominated\n2002 (75th)#broken wings#knafayim shvurot (\u05db\u05e0\u05e4\u05d9\u05d9\u05dd \u05e9\u05d1\u05d5\u05e8\u05d5\u05ea)#nir bergman#not nominated\n2003 (76th)#nina 's tragedies#ha'asonot shel nina (\u05d4\u05d0\u05e1\u05d5\u05e0\u05d5\u05ea \u05e9\u05dc \u05e0\u05d9\u05e0\u05d4)#savi gavison#not nominated\n2004 (77th)#campfire#medurat hashevet (\u05de\u05d3\u05d5\u05e8\u05ea \u05d4\u05e9\u05d1\u05d8)#joseph cedar#not nominated\n2005: (78th)#what a wonderful place#eize makom nifla (\u05d0\u05d9\u05d6\u05d4 \u05de\u05e7\u05d5\u05dd \u05e0\u05e4\u05dc\u05d0)#eyal halfon#not nominated\n2006 (79th)#sweet mud#adama meshuga'at (\u05d0\u05d3\u05de\u05d4 \u05de\u05e9\u05d5\u05d2\u05e2\u05ea)#dror shaul#not nominated\n2007 (80th)#beaufort#beaufort (\u05d1\u05d5\u05e4\u05d5\u05e8)#joseph cedar#nominee\n2008 (81st)#waltz with bashir#vals im bashir (\u05d5\u05d0\u05dc\u05e1 \u05e2\u05dd \u05d1\u05d0\u05e9\u05d9\u05e8)#ari folman#nominee\n2011 (84th)#footnote#he'arat shulayim (\u05d4\u05e2\u05e8\u05ea \u05e9\u05d5\u05dc\u05d9\u05d9\u05dd)#joseph cedar#nominee\n2012 (85th)#fill the void#l'male et hehalal (\u05dc\u05de\u05dc\u05d0 \u05d0\u05ea \u05d4\u05d7\u05dc\u05dc)#rama burshtein#not nominated\n",
        "pandas_code": "all(year in df['year (ceremony)'].str[:4].astype(int).unique() for year in range(1980, 1990))",
        "pandas_eval": "True"
    },
    {
        "id": 308,
        "statement": "savi gavison direct 3 of the film that be submit",
        "label": 1,
        "table_caption": "list of israeli submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1964 (37th)#sallah#sallah shabati (\u05e1\u05d0\u05dc\u05d7 \u05e9\u05d1\u05ea\u05d9)#ephraim kishon#nominee\n1966 (39th)#the flying matchmaker#shnei kuni leml (\u05e9\u05e0\u05d9 \u05e7\u05d5\u05e0\u05d9 \u05dc\u05de\u05dc)#israel becker#not nominated\n1968 (41st)#every bastard a king#kol mamzer melech (\u05db\u05dc \u05de\u05de\u05d6\u05e8 \u05de\u05dc\u05da)#uri zohar#not nominated\n1969 (42nd)#siege#matzor (\u05de\u05e6\u05d5\u05e8)#gilberto tofano#not nominated\n1971 (44th)#the policeman#hashoter azoulay (\u05d4\u05e9\u05d5\u05d8\u05e8 \u05d0\u05d6\u05d5\u05dc\u05d0\u05d9)#ephraim kishon#nominee\n1972 (45th)#i love you rosa#ani ohev otach roza (\u05d0\u05e0\u05d9 \u05d0\u05d5\u05d4\u05d1 \u05d0\u05d5\u05ea\u05da \u05e8\u05d5\u05d6\u05d4)#mosh\u00e9 mizrahi#nominee\n1973 (46th)#the house on chelouche street#habayit birhov chelouche (\u05d4\u05d1\u05d9\u05ea \u05d1\u05e8\u05d7\u05d5\u05d1 \u05e9\u05dc\u05d5\u05e9)#mosh\u00e9 mizrahi#nominee\n1975 (48th)#my michael#michael sheli (\u05de\u05d9\u05db\u05d0\u05dc \u05e9\u05dc\u05d9)#dan wolman#not nominated\n1977 (50th)#operation thunderbolt#mivtza yonatan (\u05de\u05d1\u05e6\u05e2 \u05d9\u05d5\u05e0\u05ea\u05df)#menahem golan#nominee\n1978 (51st)#lemon popsicle#eskimo limon (\u05d0\u05e1\u05e7\u05d9\u05de\u05d5 \u05dc\u05d9\u05de\u05d5\u05df)#boaz davidson#not nominated\n1979 (52nd)#moments#rega'im (\u05e8\u05d2\u05e2\u05d9\u05dd)#michal bat - adam#not nominated\n1980 (53rd)#the thin line#al hevel dak (\u05e2\u05dc \u05d7\u05d1\u05dc \u05d3\u05e7)#michal bat - adam#not nominated\n1981 (54th)#a thousand little kisses#elef neshikot ktanot (\u05d0\u05dc\u05e3 \u05e0\u05e9\u05d9\u05e7\u05d5\u05ea \u05e7\u05d8\u05e0\u05d5\u05ea)#mira recanati#not nominated\n1982 (55th)#hamsin#hamsin (\u05d7\u05de\u05e1\u05d9\u05df)#daniel wachsmann#not nominated\n1983 (56th)#a married couple#zug nasuy (\u05d6\u05d5\u05d2 \u05e0\u05e9\u05d5\u05d9)#yitzhak yeshurun#not nominated\n1984 (57th)#beyond the walls#me'ahorey hasoragim (\u05de\u05d0\u05d7\u05d5\u05e8\u05d9 \u05d4\u05e1\u05d5\u05e8\u05d2\u05d9\u05dd)#uri barbash#nominee\n1985 (58th)#when night falls#ad sof halayla (\u05e2\u05d3 \u05e1\u05d5\u05e3 \u05d4\u05dc\u05d9\u05dc\u05d4)#eitan green#not nominated\n1986 (59th)#avanti popolo#avanti popolo (\u05d0\u05d5\u05d5\u05e0\u05d8\u05d9 \u05e4\u05d5\u05e4\u05d5\u05dc\u05d5)#rafi bukai#not nominated\n1987 (60th)#i don't give a damn#lo sam zayin (\u05dc\u05d0 \u05e9\u05dd \u05d6\u05d9\u05df)#shmuel imberman#not nominated\n1988 (61st)#the summer of aviya#hakayitz shel aviya (\u05d4\u05e7\u05d9\u05e5 \u05e9\u05dc \u05d0\u05d1\u05d9\u05d4)#eli cohen#not nominated\n1989 (62nd)#one of us#ehad mishelanu (\u05d0\u05d7\u05d3 \u05de\u05e9\u05dc\u05e0\u05d5)#uri barbash#not nominated\n1990 (63rd)#the lookout#shuroo (\u05e9\u05d5\u05e8\u05d5)#savi gavison#not nominated\n1991 (64th)#beyond the sea#me'ever layam (\u05de\u05e2\u05d1\u05e8 \u05dc\u05d9\u05dd)#jacob goldwasser#not nominated\n1992 (65th)#life according to agfa#ha'hayim al pi agfa (\u05d4\u05d7\u05d9\u05d9\u05dd \u05e2\u05dc \u05e4\u05d9 \u05d0\u05d2\u05e4\u05d0)#assi dayan#not nominated\n1994 (67th)#sh'chur#sh'chur (\u05e9\u05d7\u05d5\u05e8)#shmuel hasfari#not nominated\n1995 (68th)#lovesick on nana street#hole ahava beshikun gimel (\u05d7\u05d5\u05dc\u05d4 \u05d0\u05d4\u05d1\u05d4 \u05d1\u05e9\u05d9\u05db\u05d5\u05df \u05d2\u05d9\u05de\u05dc)#savi gavison#not nominated\n1997 (70th)#pick a card#afula express (\u05e2\u05e4\u05d5\u05dc\u05d4 \u05d0\u05e7\u05e1\u05e4\u05e8\u05e1)#julie shles#not nominated\n1998 (71st)#circus palestine#kirkas palestina (\u05e7\u05e8\u05e7\u05e1 \u05e4\u05dc\u05e9\u05ea\u05d9\u05e0\u05d4)#eyal halfon#not nominated\n1999 (72nd)#yana 's friends#hahaverim shel yana (\u05d4\u05d7\u05d1\u05e8\u05d9\u05dd \u05e9\u05dc \u05d9\u05d0\u05e0\u05d4)#arik kaplun#not nominated\n2000 (73rd)#time of favor#hahesder (\u05d4\u05d4\u05e1\u05d3\u05e8)#joseph cedar#not nominated\n2001 (74th)#late marriage#hatuna me'uheret (\u05d7\u05ea\u05d5\u05e0\u05d4 \u05de\u05d0\u05d5\u05d7\u05e8\u05ea)#dover kosahvili#not nominated\n2002 (75th)#broken wings#knafayim shvurot (\u05db\u05e0\u05e4\u05d9\u05d9\u05dd \u05e9\u05d1\u05d5\u05e8\u05d5\u05ea)#nir bergman#not nominated\n2003 (76th)#nina 's tragedies#ha'asonot shel nina (\u05d4\u05d0\u05e1\u05d5\u05e0\u05d5\u05ea \u05e9\u05dc \u05e0\u05d9\u05e0\u05d4)#savi gavison#not nominated\n2004 (77th)#campfire#medurat hashevet (\u05de\u05d3\u05d5\u05e8\u05ea \u05d4\u05e9\u05d1\u05d8)#joseph cedar#not nominated\n2005: (78th)#what a wonderful place#eize makom nifla (\u05d0\u05d9\u05d6\u05d4 \u05de\u05e7\u05d5\u05dd \u05e0\u05e4\u05dc\u05d0)#eyal halfon#not nominated\n2006 (79th)#sweet mud#adama meshuga'at (\u05d0\u05d3\u05de\u05d4 \u05de\u05e9\u05d5\u05d2\u05e2\u05ea)#dror shaul#not nominated\n2007 (80th)#beaufort#beaufort (\u05d1\u05d5\u05e4\u05d5\u05e8)#joseph cedar#nominee\n2008 (81st)#waltz with bashir#vals im bashir (\u05d5\u05d0\u05dc\u05e1 \u05e2\u05dd \u05d1\u05d0\u05e9\u05d9\u05e8)#ari folman#nominee\n2011 (84th)#footnote#he'arat shulayim (\u05d4\u05e2\u05e8\u05ea \u05e9\u05d5\u05dc\u05d9\u05d9\u05dd)#joseph cedar#nominee\n2012 (85th)#fill the void#l'male et hehalal (\u05dc\u05de\u05dc\u05d0 \u05d0\u05ea \u05d4\u05d7\u05dc\u05dc)#rama burshtein#not nominated\n",
        "pandas_code": "df[df['director'] == 'savi gavison'].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 309,
        "statement": "joseph cedar direct the most movie of those that be submit",
        "label": 1,
        "table_caption": "list of israeli submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1964 (37th)#sallah#sallah shabati (\u05e1\u05d0\u05dc\u05d7 \u05e9\u05d1\u05ea\u05d9)#ephraim kishon#nominee\n1966 (39th)#the flying matchmaker#shnei kuni leml (\u05e9\u05e0\u05d9 \u05e7\u05d5\u05e0\u05d9 \u05dc\u05de\u05dc)#israel becker#not nominated\n1968 (41st)#every bastard a king#kol mamzer melech (\u05db\u05dc \u05de\u05de\u05d6\u05e8 \u05de\u05dc\u05da)#uri zohar#not nominated\n1969 (42nd)#siege#matzor (\u05de\u05e6\u05d5\u05e8)#gilberto tofano#not nominated\n1971 (44th)#the policeman#hashoter azoulay (\u05d4\u05e9\u05d5\u05d8\u05e8 \u05d0\u05d6\u05d5\u05dc\u05d0\u05d9)#ephraim kishon#nominee\n1972 (45th)#i love you rosa#ani ohev otach roza (\u05d0\u05e0\u05d9 \u05d0\u05d5\u05d4\u05d1 \u05d0\u05d5\u05ea\u05da \u05e8\u05d5\u05d6\u05d4)#mosh\u00e9 mizrahi#nominee\n1973 (46th)#the house on chelouche street#habayit birhov chelouche (\u05d4\u05d1\u05d9\u05ea \u05d1\u05e8\u05d7\u05d5\u05d1 \u05e9\u05dc\u05d5\u05e9)#mosh\u00e9 mizrahi#nominee\n1975 (48th)#my michael#michael sheli (\u05de\u05d9\u05db\u05d0\u05dc \u05e9\u05dc\u05d9)#dan wolman#not nominated\n1977 (50th)#operation thunderbolt#mivtza yonatan (\u05de\u05d1\u05e6\u05e2 \u05d9\u05d5\u05e0\u05ea\u05df)#menahem golan#nominee\n1978 (51st)#lemon popsicle#eskimo limon (\u05d0\u05e1\u05e7\u05d9\u05de\u05d5 \u05dc\u05d9\u05de\u05d5\u05df)#boaz davidson#not nominated\n1979 (52nd)#moments#rega'im (\u05e8\u05d2\u05e2\u05d9\u05dd)#michal bat - adam#not nominated\n1980 (53rd)#the thin line#al hevel dak (\u05e2\u05dc \u05d7\u05d1\u05dc \u05d3\u05e7)#michal bat - adam#not nominated\n1981 (54th)#a thousand little kisses#elef neshikot ktanot (\u05d0\u05dc\u05e3 \u05e0\u05e9\u05d9\u05e7\u05d5\u05ea \u05e7\u05d8\u05e0\u05d5\u05ea)#mira recanati#not nominated\n1982 (55th)#hamsin#hamsin (\u05d7\u05de\u05e1\u05d9\u05df)#daniel wachsmann#not nominated\n1983 (56th)#a married couple#zug nasuy (\u05d6\u05d5\u05d2 \u05e0\u05e9\u05d5\u05d9)#yitzhak yeshurun#not nominated\n1984 (57th)#beyond the walls#me'ahorey hasoragim (\u05de\u05d0\u05d7\u05d5\u05e8\u05d9 \u05d4\u05e1\u05d5\u05e8\u05d2\u05d9\u05dd)#uri barbash#nominee\n1985 (58th)#when night falls#ad sof halayla (\u05e2\u05d3 \u05e1\u05d5\u05e3 \u05d4\u05dc\u05d9\u05dc\u05d4)#eitan green#not nominated\n1986 (59th)#avanti popolo#avanti popolo (\u05d0\u05d5\u05d5\u05e0\u05d8\u05d9 \u05e4\u05d5\u05e4\u05d5\u05dc\u05d5)#rafi bukai#not nominated\n1987 (60th)#i don't give a damn#lo sam zayin (\u05dc\u05d0 \u05e9\u05dd \u05d6\u05d9\u05df)#shmuel imberman#not nominated\n1988 (61st)#the summer of aviya#hakayitz shel aviya (\u05d4\u05e7\u05d9\u05e5 \u05e9\u05dc \u05d0\u05d1\u05d9\u05d4)#eli cohen#not nominated\n1989 (62nd)#one of us#ehad mishelanu (\u05d0\u05d7\u05d3 \u05de\u05e9\u05dc\u05e0\u05d5)#uri barbash#not nominated\n1990 (63rd)#the lookout#shuroo (\u05e9\u05d5\u05e8\u05d5)#savi gavison#not nominated\n1991 (64th)#beyond the sea#me'ever layam (\u05de\u05e2\u05d1\u05e8 \u05dc\u05d9\u05dd)#jacob goldwasser#not nominated\n1992 (65th)#life according to agfa#ha'hayim al pi agfa (\u05d4\u05d7\u05d9\u05d9\u05dd \u05e2\u05dc \u05e4\u05d9 \u05d0\u05d2\u05e4\u05d0)#assi dayan#not nominated\n1994 (67th)#sh'chur#sh'chur (\u05e9\u05d7\u05d5\u05e8)#shmuel hasfari#not nominated\n1995 (68th)#lovesick on nana street#hole ahava beshikun gimel (\u05d7\u05d5\u05dc\u05d4 \u05d0\u05d4\u05d1\u05d4 \u05d1\u05e9\u05d9\u05db\u05d5\u05df \u05d2\u05d9\u05de\u05dc)#savi gavison#not nominated\n1997 (70th)#pick a card#afula express (\u05e2\u05e4\u05d5\u05dc\u05d4 \u05d0\u05e7\u05e1\u05e4\u05e8\u05e1)#julie shles#not nominated\n1998 (71st)#circus palestine#kirkas palestina (\u05e7\u05e8\u05e7\u05e1 \u05e4\u05dc\u05e9\u05ea\u05d9\u05e0\u05d4)#eyal halfon#not nominated\n1999 (72nd)#yana 's friends#hahaverim shel yana (\u05d4\u05d7\u05d1\u05e8\u05d9\u05dd \u05e9\u05dc \u05d9\u05d0\u05e0\u05d4)#arik kaplun#not nominated\n2000 (73rd)#time of favor#hahesder (\u05d4\u05d4\u05e1\u05d3\u05e8)#joseph cedar#not nominated\n2001 (74th)#late marriage#hatuna me'uheret (\u05d7\u05ea\u05d5\u05e0\u05d4 \u05de\u05d0\u05d5\u05d7\u05e8\u05ea)#dover kosahvili#not nominated\n2002 (75th)#broken wings#knafayim shvurot (\u05db\u05e0\u05e4\u05d9\u05d9\u05dd \u05e9\u05d1\u05d5\u05e8\u05d5\u05ea)#nir bergman#not nominated\n2003 (76th)#nina 's tragedies#ha'asonot shel nina (\u05d4\u05d0\u05e1\u05d5\u05e0\u05d5\u05ea \u05e9\u05dc \u05e0\u05d9\u05e0\u05d4)#savi gavison#not nominated\n2004 (77th)#campfire#medurat hashevet (\u05de\u05d3\u05d5\u05e8\u05ea \u05d4\u05e9\u05d1\u05d8)#joseph cedar#not nominated\n2005: (78th)#what a wonderful place#eize makom nifla (\u05d0\u05d9\u05d6\u05d4 \u05de\u05e7\u05d5\u05dd \u05e0\u05e4\u05dc\u05d0)#eyal halfon#not nominated\n2006 (79th)#sweet mud#adama meshuga'at (\u05d0\u05d3\u05de\u05d4 \u05de\u05e9\u05d5\u05d2\u05e2\u05ea)#dror shaul#not nominated\n2007 (80th)#beaufort#beaufort (\u05d1\u05d5\u05e4\u05d5\u05e8)#joseph cedar#nominee\n2008 (81st)#waltz with bashir#vals im bashir (\u05d5\u05d0\u05dc\u05e1 \u05e2\u05dd \u05d1\u05d0\u05e9\u05d9\u05e8)#ari folman#nominee\n2011 (84th)#footnote#he'arat shulayim (\u05d4\u05e2\u05e8\u05ea \u05e9\u05d5\u05dc\u05d9\u05d9\u05dd)#joseph cedar#nominee\n2012 (85th)#fill the void#l'male et hehalal (\u05dc\u05de\u05dc\u05d0 \u05d0\u05ea \u05d4\u05d7\u05dc\u05dc)#rama burshtein#not nominated\n",
        "pandas_code": "df[df['director'] == 'joseph cedar'].shape[0] == df['director'].value_counts().max()",
        "pandas_eval": "True"
    },
    {
        "id": 310,
        "statement": "mosh\u00e9 mizrahi direct more film than rama burshtein",
        "label": 1,
        "table_caption": "list of israeli submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1964 (37th)#sallah#sallah shabati (\u05e1\u05d0\u05dc\u05d7 \u05e9\u05d1\u05ea\u05d9)#ephraim kishon#nominee\n1966 (39th)#the flying matchmaker#shnei kuni leml (\u05e9\u05e0\u05d9 \u05e7\u05d5\u05e0\u05d9 \u05dc\u05de\u05dc)#israel becker#not nominated\n1968 (41st)#every bastard a king#kol mamzer melech (\u05db\u05dc \u05de\u05de\u05d6\u05e8 \u05de\u05dc\u05da)#uri zohar#not nominated\n1969 (42nd)#siege#matzor (\u05de\u05e6\u05d5\u05e8)#gilberto tofano#not nominated\n1971 (44th)#the policeman#hashoter azoulay (\u05d4\u05e9\u05d5\u05d8\u05e8 \u05d0\u05d6\u05d5\u05dc\u05d0\u05d9)#ephraim kishon#nominee\n1972 (45th)#i love you rosa#ani ohev otach roza (\u05d0\u05e0\u05d9 \u05d0\u05d5\u05d4\u05d1 \u05d0\u05d5\u05ea\u05da \u05e8\u05d5\u05d6\u05d4)#mosh\u00e9 mizrahi#nominee\n1973 (46th)#the house on chelouche street#habayit birhov chelouche (\u05d4\u05d1\u05d9\u05ea \u05d1\u05e8\u05d7\u05d5\u05d1 \u05e9\u05dc\u05d5\u05e9)#mosh\u00e9 mizrahi#nominee\n1975 (48th)#my michael#michael sheli (\u05de\u05d9\u05db\u05d0\u05dc \u05e9\u05dc\u05d9)#dan wolman#not nominated\n1977 (50th)#operation thunderbolt#mivtza yonatan (\u05de\u05d1\u05e6\u05e2 \u05d9\u05d5\u05e0\u05ea\u05df)#menahem golan#nominee\n1978 (51st)#lemon popsicle#eskimo limon (\u05d0\u05e1\u05e7\u05d9\u05de\u05d5 \u05dc\u05d9\u05de\u05d5\u05df)#boaz davidson#not nominated\n1979 (52nd)#moments#rega'im (\u05e8\u05d2\u05e2\u05d9\u05dd)#michal bat - adam#not nominated\n1980 (53rd)#the thin line#al hevel dak (\u05e2\u05dc \u05d7\u05d1\u05dc \u05d3\u05e7)#michal bat - adam#not nominated\n1981 (54th)#a thousand little kisses#elef neshikot ktanot (\u05d0\u05dc\u05e3 \u05e0\u05e9\u05d9\u05e7\u05d5\u05ea \u05e7\u05d8\u05e0\u05d5\u05ea)#mira recanati#not nominated\n1982 (55th)#hamsin#hamsin (\u05d7\u05de\u05e1\u05d9\u05df)#daniel wachsmann#not nominated\n1983 (56th)#a married couple#zug nasuy (\u05d6\u05d5\u05d2 \u05e0\u05e9\u05d5\u05d9)#yitzhak yeshurun#not nominated\n1984 (57th)#beyond the walls#me'ahorey hasoragim (\u05de\u05d0\u05d7\u05d5\u05e8\u05d9 \u05d4\u05e1\u05d5\u05e8\u05d2\u05d9\u05dd)#uri barbash#nominee\n1985 (58th)#when night falls#ad sof halayla (\u05e2\u05d3 \u05e1\u05d5\u05e3 \u05d4\u05dc\u05d9\u05dc\u05d4)#eitan green#not nominated\n1986 (59th)#avanti popolo#avanti popolo (\u05d0\u05d5\u05d5\u05e0\u05d8\u05d9 \u05e4\u05d5\u05e4\u05d5\u05dc\u05d5)#rafi bukai#not nominated\n1987 (60th)#i don't give a damn#lo sam zayin (\u05dc\u05d0 \u05e9\u05dd \u05d6\u05d9\u05df)#shmuel imberman#not nominated\n1988 (61st)#the summer of aviya#hakayitz shel aviya (\u05d4\u05e7\u05d9\u05e5 \u05e9\u05dc \u05d0\u05d1\u05d9\u05d4)#eli cohen#not nominated\n1989 (62nd)#one of us#ehad mishelanu (\u05d0\u05d7\u05d3 \u05de\u05e9\u05dc\u05e0\u05d5)#uri barbash#not nominated\n1990 (63rd)#the lookout#shuroo (\u05e9\u05d5\u05e8\u05d5)#savi gavison#not nominated\n1991 (64th)#beyond the sea#me'ever layam (\u05de\u05e2\u05d1\u05e8 \u05dc\u05d9\u05dd)#jacob goldwasser#not nominated\n1992 (65th)#life according to agfa#ha'hayim al pi agfa (\u05d4\u05d7\u05d9\u05d9\u05dd \u05e2\u05dc \u05e4\u05d9 \u05d0\u05d2\u05e4\u05d0)#assi dayan#not nominated\n1994 (67th)#sh'chur#sh'chur (\u05e9\u05d7\u05d5\u05e8)#shmuel hasfari#not nominated\n1995 (68th)#lovesick on nana street#hole ahava beshikun gimel (\u05d7\u05d5\u05dc\u05d4 \u05d0\u05d4\u05d1\u05d4 \u05d1\u05e9\u05d9\u05db\u05d5\u05df \u05d2\u05d9\u05de\u05dc)#savi gavison#not nominated\n1997 (70th)#pick a card#afula express (\u05e2\u05e4\u05d5\u05dc\u05d4 \u05d0\u05e7\u05e1\u05e4\u05e8\u05e1)#julie shles#not nominated\n1998 (71st)#circus palestine#kirkas palestina (\u05e7\u05e8\u05e7\u05e1 \u05e4\u05dc\u05e9\u05ea\u05d9\u05e0\u05d4)#eyal halfon#not nominated\n1999 (72nd)#yana 's friends#hahaverim shel yana (\u05d4\u05d7\u05d1\u05e8\u05d9\u05dd \u05e9\u05dc \u05d9\u05d0\u05e0\u05d4)#arik kaplun#not nominated\n2000 (73rd)#time of favor#hahesder (\u05d4\u05d4\u05e1\u05d3\u05e8)#joseph cedar#not nominated\n2001 (74th)#late marriage#hatuna me'uheret (\u05d7\u05ea\u05d5\u05e0\u05d4 \u05de\u05d0\u05d5\u05d7\u05e8\u05ea)#dover kosahvili#not nominated\n2002 (75th)#broken wings#knafayim shvurot (\u05db\u05e0\u05e4\u05d9\u05d9\u05dd \u05e9\u05d1\u05d5\u05e8\u05d5\u05ea)#nir bergman#not nominated\n2003 (76th)#nina 's tragedies#ha'asonot shel nina (\u05d4\u05d0\u05e1\u05d5\u05e0\u05d5\u05ea \u05e9\u05dc \u05e0\u05d9\u05e0\u05d4)#savi gavison#not nominated\n2004 (77th)#campfire#medurat hashevet (\u05de\u05d3\u05d5\u05e8\u05ea \u05d4\u05e9\u05d1\u05d8)#joseph cedar#not nominated\n2005: (78th)#what a wonderful place#eize makom nifla (\u05d0\u05d9\u05d6\u05d4 \u05de\u05e7\u05d5\u05dd \u05e0\u05e4\u05dc\u05d0)#eyal halfon#not nominated\n2006 (79th)#sweet mud#adama meshuga'at (\u05d0\u05d3\u05de\u05d4 \u05de\u05e9\u05d5\u05d2\u05e2\u05ea)#dror shaul#not nominated\n2007 (80th)#beaufort#beaufort (\u05d1\u05d5\u05e4\u05d5\u05e8)#joseph cedar#nominee\n2008 (81st)#waltz with bashir#vals im bashir (\u05d5\u05d0\u05dc\u05e1 \u05e2\u05dd \u05d1\u05d0\u05e9\u05d9\u05e8)#ari folman#nominee\n2011 (84th)#footnote#he'arat shulayim (\u05d4\u05e2\u05e8\u05ea \u05e9\u05d5\u05dc\u05d9\u05d9\u05dd)#joseph cedar#nominee\n2012 (85th)#fill the void#l'male et hehalal (\u05dc\u05de\u05dc\u05d0 \u05d0\u05ea \u05d4\u05d7\u05dc\u05dc)#rama burshtein#not nominated\n",
        "pandas_code": "(df['director'].value_counts()['mosh\u00e9 mizrahi'] > df['director'].value_counts()['rama burshtein'])",
        "pandas_eval": "True"
    },
    {
        "id": 311,
        "statement": "no film make in the 1990s be nominate",
        "label": 1,
        "table_caption": "list of israeli submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1964 (37th)#sallah#sallah shabati (\u05e1\u05d0\u05dc\u05d7 \u05e9\u05d1\u05ea\u05d9)#ephraim kishon#nominee\n1966 (39th)#the flying matchmaker#shnei kuni leml (\u05e9\u05e0\u05d9 \u05e7\u05d5\u05e0\u05d9 \u05dc\u05de\u05dc)#israel becker#not nominated\n1968 (41st)#every bastard a king#kol mamzer melech (\u05db\u05dc \u05de\u05de\u05d6\u05e8 \u05de\u05dc\u05da)#uri zohar#not nominated\n1969 (42nd)#siege#matzor (\u05de\u05e6\u05d5\u05e8)#gilberto tofano#not nominated\n1971 (44th)#the policeman#hashoter azoulay (\u05d4\u05e9\u05d5\u05d8\u05e8 \u05d0\u05d6\u05d5\u05dc\u05d0\u05d9)#ephraim kishon#nominee\n1972 (45th)#i love you rosa#ani ohev otach roza (\u05d0\u05e0\u05d9 \u05d0\u05d5\u05d4\u05d1 \u05d0\u05d5\u05ea\u05da \u05e8\u05d5\u05d6\u05d4)#mosh\u00e9 mizrahi#nominee\n1973 (46th)#the house on chelouche street#habayit birhov chelouche (\u05d4\u05d1\u05d9\u05ea \u05d1\u05e8\u05d7\u05d5\u05d1 \u05e9\u05dc\u05d5\u05e9)#mosh\u00e9 mizrahi#nominee\n1975 (48th)#my michael#michael sheli (\u05de\u05d9\u05db\u05d0\u05dc \u05e9\u05dc\u05d9)#dan wolman#not nominated\n1977 (50th)#operation thunderbolt#mivtza yonatan (\u05de\u05d1\u05e6\u05e2 \u05d9\u05d5\u05e0\u05ea\u05df)#menahem golan#nominee\n1978 (51st)#lemon popsicle#eskimo limon (\u05d0\u05e1\u05e7\u05d9\u05de\u05d5 \u05dc\u05d9\u05de\u05d5\u05df)#boaz davidson#not nominated\n1979 (52nd)#moments#rega'im (\u05e8\u05d2\u05e2\u05d9\u05dd)#michal bat - adam#not nominated\n1980 (53rd)#the thin line#al hevel dak (\u05e2\u05dc \u05d7\u05d1\u05dc \u05d3\u05e7)#michal bat - adam#not nominated\n1981 (54th)#a thousand little kisses#elef neshikot ktanot (\u05d0\u05dc\u05e3 \u05e0\u05e9\u05d9\u05e7\u05d5\u05ea \u05e7\u05d8\u05e0\u05d5\u05ea)#mira recanati#not nominated\n1982 (55th)#hamsin#hamsin (\u05d7\u05de\u05e1\u05d9\u05df)#daniel wachsmann#not nominated\n1983 (56th)#a married couple#zug nasuy (\u05d6\u05d5\u05d2 \u05e0\u05e9\u05d5\u05d9)#yitzhak yeshurun#not nominated\n1984 (57th)#beyond the walls#me'ahorey hasoragim (\u05de\u05d0\u05d7\u05d5\u05e8\u05d9 \u05d4\u05e1\u05d5\u05e8\u05d2\u05d9\u05dd)#uri barbash#nominee\n1985 (58th)#when night falls#ad sof halayla (\u05e2\u05d3 \u05e1\u05d5\u05e3 \u05d4\u05dc\u05d9\u05dc\u05d4)#eitan green#not nominated\n1986 (59th)#avanti popolo#avanti popolo (\u05d0\u05d5\u05d5\u05e0\u05d8\u05d9 \u05e4\u05d5\u05e4\u05d5\u05dc\u05d5)#rafi bukai#not nominated\n1987 (60th)#i don't give a damn#lo sam zayin (\u05dc\u05d0 \u05e9\u05dd \u05d6\u05d9\u05df)#shmuel imberman#not nominated\n1988 (61st)#the summer of aviya#hakayitz shel aviya (\u05d4\u05e7\u05d9\u05e5 \u05e9\u05dc \u05d0\u05d1\u05d9\u05d4)#eli cohen#not nominated\n1989 (62nd)#one of us#ehad mishelanu (\u05d0\u05d7\u05d3 \u05de\u05e9\u05dc\u05e0\u05d5)#uri barbash#not nominated\n1990 (63rd)#the lookout#shuroo (\u05e9\u05d5\u05e8\u05d5)#savi gavison#not nominated\n1991 (64th)#beyond the sea#me'ever layam (\u05de\u05e2\u05d1\u05e8 \u05dc\u05d9\u05dd)#jacob goldwasser#not nominated\n1992 (65th)#life according to agfa#ha'hayim al pi agfa (\u05d4\u05d7\u05d9\u05d9\u05dd \u05e2\u05dc \u05e4\u05d9 \u05d0\u05d2\u05e4\u05d0)#assi dayan#not nominated\n1994 (67th)#sh'chur#sh'chur (\u05e9\u05d7\u05d5\u05e8)#shmuel hasfari#not nominated\n1995 (68th)#lovesick on nana street#hole ahava beshikun gimel (\u05d7\u05d5\u05dc\u05d4 \u05d0\u05d4\u05d1\u05d4 \u05d1\u05e9\u05d9\u05db\u05d5\u05df \u05d2\u05d9\u05de\u05dc)#savi gavison#not nominated\n1997 (70th)#pick a card#afula express (\u05e2\u05e4\u05d5\u05dc\u05d4 \u05d0\u05e7\u05e1\u05e4\u05e8\u05e1)#julie shles#not nominated\n1998 (71st)#circus palestine#kirkas palestina (\u05e7\u05e8\u05e7\u05e1 \u05e4\u05dc\u05e9\u05ea\u05d9\u05e0\u05d4)#eyal halfon#not nominated\n1999 (72nd)#yana 's friends#hahaverim shel yana (\u05d4\u05d7\u05d1\u05e8\u05d9\u05dd \u05e9\u05dc \u05d9\u05d0\u05e0\u05d4)#arik kaplun#not nominated\n2000 (73rd)#time of favor#hahesder (\u05d4\u05d4\u05e1\u05d3\u05e8)#joseph cedar#not nominated\n2001 (74th)#late marriage#hatuna me'uheret (\u05d7\u05ea\u05d5\u05e0\u05d4 \u05de\u05d0\u05d5\u05d7\u05e8\u05ea)#dover kosahvili#not nominated\n2002 (75th)#broken wings#knafayim shvurot (\u05db\u05e0\u05e4\u05d9\u05d9\u05dd \u05e9\u05d1\u05d5\u05e8\u05d5\u05ea)#nir bergman#not nominated\n2003 (76th)#nina 's tragedies#ha'asonot shel nina (\u05d4\u05d0\u05e1\u05d5\u05e0\u05d5\u05ea \u05e9\u05dc \u05e0\u05d9\u05e0\u05d4)#savi gavison#not nominated\n2004 (77th)#campfire#medurat hashevet (\u05de\u05d3\u05d5\u05e8\u05ea \u05d4\u05e9\u05d1\u05d8)#joseph cedar#not nominated\n2005: (78th)#what a wonderful place#eize makom nifla (\u05d0\u05d9\u05d6\u05d4 \u05de\u05e7\u05d5\u05dd \u05e0\u05e4\u05dc\u05d0)#eyal halfon#not nominated\n2006 (79th)#sweet mud#adama meshuga'at (\u05d0\u05d3\u05de\u05d4 \u05de\u05e9\u05d5\u05d2\u05e2\u05ea)#dror shaul#not nominated\n2007 (80th)#beaufort#beaufort (\u05d1\u05d5\u05e4\u05d5\u05e8)#joseph cedar#nominee\n2008 (81st)#waltz with bashir#vals im bashir (\u05d5\u05d0\u05dc\u05e1 \u05e2\u05dd \u05d1\u05d0\u05e9\u05d9\u05e8)#ari folman#nominee\n2011 (84th)#footnote#he'arat shulayim (\u05d4\u05e2\u05e8\u05ea \u05e9\u05d5\u05dc\u05d9\u05d9\u05dd)#joseph cedar#nominee\n2012 (85th)#fill the void#l'male et hehalal (\u05dc\u05de\u05dc\u05d0 \u05d0\u05ea \u05d4\u05d7\u05dc\u05dc)#rama burshtein#not nominated\n",
        "pandas_code": "df[df['year (ceremony)'].str.contains('199')]['result'].eq('nominee').any() == False",
        "pandas_eval": "True"
    },
    {
        "id": 312,
        "statement": "dublin county and waterford county be tie for 8th place",
        "label": 1,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "len(df[(df['county'].isin(['dublin', 'waterford'])) & (df['rank'] == 8)]['total'].unique()) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 313,
        "statement": "eddie keher be rank both 1st and second place",
        "label": 1,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "df[(df['player'] == 'eddie keher') & (df['rank'].isin([1, 2]))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 314,
        "statement": "philly grime and s\u00e9amus power both play for waterford",
        "label": 1,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[df['player'].isin(['philly grimes', 's\u00e9amus power'])]['county'] == 'waterford')",
        "pandas_eval": "True"
    },
    {
        "id": 315,
        "statement": "eddie keher and tom walsh both play for kikenny county",
        "label": 1,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[(df['player'] == 'eddie keher') | (df['player'] == 'tom walsh')]['county'] == 'kilkenny')",
        "pandas_eval": "True"
    },
    {
        "id": 316,
        "statement": "mick kennedy and fran whelan both play for dublin county",
        "label": 1,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[(df['player'] == 'mick kennedy') | (df['player'] == 'fran whelan')]['county'] == 'dublin')",
        "pandas_eval": "True"
    },
    {
        "id": 317,
        "statement": "dublin county and waterford county be tie for 4th place",
        "label": 0,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "(df[(df['county'] == 'dublin') | (df['county'] == 'waterford')]['rank'].value_counts().loc[4] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 318,
        "statement": "eddie keher be rank both second and third place",
        "label": 0,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "(df[(df['player'] == 'eddie keher') & (df['rank'].isin([2, 3]))].shape[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 319,
        "statement": "philly grime and s\u00e9amus power both play for dublin",
        "label": 0,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[(df['player'] == 'philly grimes') | (df['player'] == 's\u00e9amus power')]['county'] == 'dublin')",
        "pandas_eval": "False"
    },
    {
        "id": 320,
        "statement": "eddie keher and tom walsh both play for cork county",
        "label": 0,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[(df['player'] == 'eddie keher') | (df['player'] == 'tom walsh')]['county'] == 'cork')",
        "pandas_eval": "False"
    },
    {
        "id": 321,
        "statement": "mick kennedy and fran whelan both play for carlow county",
        "label": 0,
        "table_caption": "1963 all - ireland senior hurling championship",
        "table_text": "rank#player#county#tally#total#opposition\n1#eddie keher#kilkenny#0 - 14#14#waterford\n2#eddie keher#kilkenny#2 - 5#11#wexford\n3#jimmy smyth#clare#1 - 7#10#cork\n4#richard browne#cork#3 - 0#9#clare\n4#se\u00e1n mcloughlin#tipperary#3 - 0#9#waterford\n4#s\u00e9amus power#waterford#3 - 0#9#kilkenny\n4#willie walsh#carlow#2 - 3#9#dublin\n8#fran whelan#dublin#1 - 5#8#carlow\n8#philly grimes#waterford#0 - 8#8#tipperary\n10#tom walsh#kilkenny#2 - 1#7#waterford\n10#mick kennedy#dublin#1 - 4#7#carlow\n10#philly grimes#waterford#1 - 4#7#kilkenny\n",
        "pandas_code": "all(df[(df['player'] == 'mick kennedy') | (df['player'] == 'fran whelan')]['county'] == 'carlow')",
        "pandas_eval": "False"
    },
    {
        "id": 322,
        "statement": "between 2006 and 2009 , the only club represent be if elfsborg",
        "label": 1,
        "table_caption": "denni avdi\u0107",
        "table_text": "season#club#country#competition#apps#goals\n2006 - 07#if elfsborg#sweden#allsvenskan#19#0\n2007 - 08#if elfsborg#sweden#allsvenskan#26#4\n2008 - 09#if elfsborg#sweden#allsvenskan#30#3\n2009 - 10#if elfsborg#sweden#allsvenskan#29#19\n2010 - 11#werder bremen#germany#bundesliga#7#0\n2011 - 12#werder bremen ii#germany#regionalliga nord#12#0\n2012 - 13#pec zwolle#netherlands#eredivisie#24#8\n",
        "pandas_code": "all(df[(df['season'].str[:4].astype(int) >= 2006) & (df['season'].str[:4].astype(int) <= 2009)]['club'] == 'if elfsborg')",
        "pandas_eval": "True"
    },
    {
        "id": 323,
        "statement": "the highest number of apps in a single season happen in the 2008 - 09 season",
        "label": 1,
        "table_caption": "denni avdi\u0107",
        "table_text": "season#club#country#competition#apps#goals\n2006 - 07#if elfsborg#sweden#allsvenskan#19#0\n2007 - 08#if elfsborg#sweden#allsvenskan#26#4\n2008 - 09#if elfsborg#sweden#allsvenskan#30#3\n2009 - 10#if elfsborg#sweden#allsvenskan#29#19\n2010 - 11#werder bremen#germany#bundesliga#7#0\n2011 - 12#werder bremen ii#germany#regionalliga nord#12#0\n2012 - 13#pec zwolle#netherlands#eredivisie#24#8\n",
        "pandas_code": "df.loc[df['apps'].idxmax(), 'season'] == '2008 - 09'",
        "pandas_eval": "True"
    },
    {
        "id": 324,
        "statement": "the average number of goal in the 2010 - 11 and 2011 - 12 season be 0",
        "label": 1,
        "table_caption": "denni avdi\u0107",
        "table_text": "season#club#country#competition#apps#goals\n2006 - 07#if elfsborg#sweden#allsvenskan#19#0\n2007 - 08#if elfsborg#sweden#allsvenskan#26#4\n2008 - 09#if elfsborg#sweden#allsvenskan#30#3\n2009 - 10#if elfsborg#sweden#allsvenskan#29#19\n2010 - 11#werder bremen#germany#bundesliga#7#0\n2011 - 12#werder bremen ii#germany#regionalliga nord#12#0\n2012 - 13#pec zwolle#netherlands#eredivisie#24#8\n",
        "pandas_code": "df[(df['season'] == '2010 - 11') | (df['season'] == '2011 - 12')]['goals'].mean() == 0",
        "pandas_eval": "True"
    },
    {
        "id": 325,
        "statement": "the lowest number of apps happen in a season before denni avdi\u0107 play for the netherlands",
        "label": 1,
        "table_caption": "denni avdi\u0107",
        "table_text": "season#club#country#competition#apps#goals\n2006 - 07#if elfsborg#sweden#allsvenskan#19#0\n2007 - 08#if elfsborg#sweden#allsvenskan#26#4\n2008 - 09#if elfsborg#sweden#allsvenskan#30#3\n2009 - 10#if elfsborg#sweden#allsvenskan#29#19\n2010 - 11#werder bremen#germany#bundesliga#7#0\n2011 - 12#werder bremen ii#germany#regionalliga nord#12#0\n2012 - 13#pec zwolle#netherlands#eredivisie#24#8\n",
        "pandas_code": "(df[df['country'] == 'netherlands']['apps'].min() > df[df['country'] != 'netherlands']['apps'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 326,
        "statement": "the season with the highest number of goal happen after the season with the highest number of apps",
        "label": 1,
        "table_caption": "denni avdi\u0107",
        "table_text": "season#club#country#competition#apps#goals\n2006 - 07#if elfsborg#sweden#allsvenskan#19#0\n2007 - 08#if elfsborg#sweden#allsvenskan#26#4\n2008 - 09#if elfsborg#sweden#allsvenskan#30#3\n2009 - 10#if elfsborg#sweden#allsvenskan#29#19\n2010 - 11#werder bremen#germany#bundesliga#7#0\n2011 - 12#werder bremen ii#germany#regionalliga nord#12#0\n2012 - 13#pec zwolle#netherlands#eredivisie#24#8\n",
        "pandas_code": "(df.loc[df['goals'].idxmax(), 'season'] > df.loc[df['apps'].idxmax(), 'season'])",
        "pandas_eval": "True"
    },
    {
        "id": 327,
        "statement": "in 1950 united state house of representative election , 2 democrat be re - elect in pennsylvania",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\npennsylvania 2#william t granahan#democratic#1948#re - elected#william t granahan (d) 57.0% max slepin (r) 43.0%\npennsylvania 3#hardie scott#republican#1946#re - elected#hardie scott (r) 50.3% maurice s osser (d) 49.7%\npennsylvania 9#paul b dague#republican#1946#re - elected#paul b dague (r) 67.2% philip ragan (d) 32.8%\npennsylvania 12#ivor d fenton#republican#1938#re - elected#ivor d fenton (r) 56.8% james h gildea (d) 43.2%\npennsylvania 19#leon h gavin#republican#1942#re - elected#leon h gavin (r) 62.8% fred c barr (d) 37.2%\npennsylvania 21#james f lind#democratic#1948#re - elected#james f lind (d) 52.2% francis worley (r) 47.8%\n",
        "pandas_code": "len(df[(df['party'] == 'democratic') & (df['result'] == 're - elected')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 328,
        "statement": "in 1950 united state house of representative election , 4 republican be re - elect in pennsylvania",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\npennsylvania 2#william t granahan#democratic#1948#re - elected#william t granahan (d) 57.0% max slepin (r) 43.0%\npennsylvania 3#hardie scott#republican#1946#re - elected#hardie scott (r) 50.3% maurice s osser (d) 49.7%\npennsylvania 9#paul b dague#republican#1946#re - elected#paul b dague (r) 67.2% philip ragan (d) 32.8%\npennsylvania 12#ivor d fenton#republican#1938#re - elected#ivor d fenton (r) 56.8% james h gildea (d) 43.2%\npennsylvania 19#leon h gavin#republican#1942#re - elected#leon h gavin (r) 62.8% fred c barr (d) 37.2%\npennsylvania 21#james f lind#democratic#1948#re - elected#james f lind (d) 52.2% francis worley (r) 47.8%\n",
        "pandas_code": "len(df[(df['party'] == 'republican') & (df['result'] == 're - elected')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 329,
        "statement": "out of the 6 pennsyvanian representative , ivor d fenton have hold office the longest",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\npennsylvania 2#william t granahan#democratic#1948#re - elected#william t granahan (d) 57.0% max slepin (r) 43.0%\npennsylvania 3#hardie scott#republican#1946#re - elected#hardie scott (r) 50.3% maurice s osser (d) 49.7%\npennsylvania 9#paul b dague#republican#1946#re - elected#paul b dague (r) 67.2% philip ragan (d) 32.8%\npennsylvania 12#ivor d fenton#republican#1938#re - elected#ivor d fenton (r) 56.8% james h gildea (d) 43.2%\npennsylvania 19#leon h gavin#republican#1942#re - elected#leon h gavin (r) 62.8% fred c barr (d) 37.2%\npennsylvania 21#james f lind#democratic#1948#re - elected#james f lind (d) 52.2% francis worley (r) 47.8%\n",
        "pandas_code": "df[df['incumbent'] == 'ivor d fenton']['first elected'].min() == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 330,
        "statement": "out of the 6 pennsyvanian representative , william t granahann and james f lind be elect to office most recently",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\npennsylvania 2#william t granahan#democratic#1948#re - elected#william t granahan (d) 57.0% max slepin (r) 43.0%\npennsylvania 3#hardie scott#republican#1946#re - elected#hardie scott (r) 50.3% maurice s osser (d) 49.7%\npennsylvania 9#paul b dague#republican#1946#re - elected#paul b dague (r) 67.2% philip ragan (d) 32.8%\npennsylvania 12#ivor d fenton#republican#1938#re - elected#ivor d fenton (r) 56.8% james h gildea (d) 43.2%\npennsylvania 19#leon h gavin#republican#1942#re - elected#leon h gavin (r) 62.8% fred c barr (d) 37.2%\npennsylvania 21#james f lind#democratic#1948#re - elected#james f lind (d) 52.2% francis worley (r) 47.8%\n",
        "pandas_code": "all(df[df['incumbent'].isin(['william t granahan', 'james f lind'])]['first elected'] == df['first elected'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 331,
        "statement": "all 6 of the candidate from pennsylvania to win the 1950 united state house of representative election be candidate who have be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\npennsylvania 2#william t granahan#democratic#1948#re - elected#william t granahan (d) 57.0% max slepin (r) 43.0%\npennsylvania 3#hardie scott#republican#1946#re - elected#hardie scott (r) 50.3% maurice s osser (d) 49.7%\npennsylvania 9#paul b dague#republican#1946#re - elected#paul b dague (r) 67.2% philip ragan (d) 32.8%\npennsylvania 12#ivor d fenton#republican#1938#re - elected#ivor d fenton (r) 56.8% james h gildea (d) 43.2%\npennsylvania 19#leon h gavin#republican#1942#re - elected#leon h gavin (r) 62.8% fred c barr (d) 37.2%\npennsylvania 21#james f lind#democratic#1948#re - elected#james f lind (d) 52.2% francis worley (r) 47.8%\n",
        "pandas_code": "all(df[df['district'].str.contains('pennsylvania')]['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 332,
        "statement": "the highest attendance during july be 29126",
        "label": 1,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df['attendance'].max() == 29126",
        "pandas_eval": "True"
    },
    {
        "id": 333,
        "statement": "the lowest attendance during july be 6532",
        "label": 1,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df[df['date'].str.contains('july')]['attendance'].min() == 6532",
        "pandas_eval": "True"
    },
    {
        "id": 334,
        "statement": "3 game be play against the white sox",
        "label": 1,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "len(df[df['opponent'] == 'white sox']) >= 3",
        "pandas_eval": "True"
    },
    {
        "id": 335,
        "statement": "the toronto blue jays win 16 of the 27 game play",
        "label": 1,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[-1] - df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[0] == 15 and len(df) == 27",
        "pandas_eval": "True"
    },
    {
        "id": 337,
        "statement": "the lowest attendance during july be 29126",
        "label": 0,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df[df['date'].str.contains('july')]['attendance'].min() == 29126",
        "pandas_eval": "False"
    },
    {
        "id": 338,
        "statement": "the highest attendance during july be 6532",
        "label": 0,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df[df['date'].str.contains('july')]['attendance'].max() == 6532",
        "pandas_eval": "False"
    },
    {
        "id": 339,
        "statement": "3 game be play against the ranger",
        "label": 0,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "len(df[df['opponent'] == 'rangers']) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 340,
        "statement": "the toronto blue jays win 18 of the 27 game play",
        "label": 0,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[-1] - df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[0] == 18",
        "pandas_eval": "False"
    },
    {
        "id": 341,
        "statement": "the toronto blue jays win 11 of the 27 game play",
        "label": 0,
        "table_caption": "1982 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\njuly 1#mariners#4 - 3#leal (5 - 6)#21004#33 - 41\njuly 2#twins#9 - 4#havens (3 - 6)#7503#34 - 41\njuly 3#twins#2 - 1#clancy (7 - 5)#9591#34 - 42\njuly 4#twins#4 - 3#stieb (6 - 10)#6532#34 - 43\njuly 5#rangers#3 - 2#leal (5 - 7)#29126#34 - 44\njuly 6#rangers#4 - 3#tanana (4 - 10)#9657#35 - 44\njuly 7#royals#3 - 1#clancy (7 - 6)#22217#35 - 45\njuly 8#royals#5 - 4#armstrong (2 - 3)#24409#36 - 45\njuly 9#white sox#7 - 6#dotson (3 - 10)#15131#37 - 45\njuly 10#white sox#6 - 5#gott (1 - 5)#17035#37 - 46\njuly 11#white sox#16 - 7#clancy (7 - 7)#16169#37 - 47\njuly 15#rangers#5 - 1#honeycutt (4 - 10)#14123#38 - 47\njuly 16#rangers#6 - 0#hough (7 - 8)#13359#39 - 47\njuly 17#rangers#11 - 3#butcher (0 - 1)#17080#40 - 47\njuly 18#rangers#5 - 4 (10)#darwin (6 - 4)#15512#41 - 47\njuly 19#royals#4 - 2#black (3 - 3)#16466#42 - 47\njuly 20#royals#9 - 2#gura (10 - 7)#18552#43 - 47\njuly 21#royals#9 - 7#clancy (8 - 8)#19152#43 - 48\njuly 22#white sox#3 - 2#mclaughlin (8 - 4)#21875#43 - 49\njuly 23#white sox#7 - 1#barnes (0 - 2)#27770#44 - 49\njuly 24#white sox#8 - 1#lamp (7 - 5)#21821#45 - 49\njuly 25#white sox#5 - 3#clancy (8 - 9)#17452#45 - 50\njuly 26#red sox#3 - 2#gott (1 - 6)#22261#45 - 51\njuly 27#red sox#3 - 1#tudor (6 - 8)#27077#46 - 51\njuly 28#red sox#9 - 7#jackson (3 - 7)#18627#46 - 52\njuly 30#tigers#6 - 5 (12)#james (0 - 2)#18262#47 - 52\njuly 31#tigers#1 - 0 (10)#rucker (1 - 1)#21007#48 - 52\n",
        "pandas_code": "df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[-1] - df['record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[0] == 11",
        "pandas_eval": "False"
    },
    {
        "id": 342,
        "statement": "fulham be the only team to achieve a high score of 4",
        "label": 1,
        "table_caption": "2004 - 05 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#derby county#1 - 1#fulham#22040\nreplay#fulham#4 - 2#derby county#15528\n2#manchester united#3 - 0#middlesbrough#67251\n3#blackburn rovers#3 - 0#colchester united#10634\n4#chelsea#2 - 0#birmingham city#40379\n5#west ham united#1 - 1#sheffield united#25449\nreplay#sheffield united#1 - 1#west ham united#15067\nsheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties\n6#oldham athletic#0 - 1#bolton wanderers#12029\n7#arsenal#2 - 0#wolverhampton wanderers#37135\n8#everton#3 - 0#sunderland#33186\n9#nottingham forest#1 - 0#peterborough#16774\n10#brentford#0 - 0#hartlepool#8967\nreplay#hartlepool#0 - 1#brentford#7580\n11#reading#1 - 2#leicester city#14825\n12#burnley#2 - 0#bournemouth#9944\n13#southampton#2 - 1#portsmouth#29453\n14#west bromwich albion#1 - 1#tottenham hotspur#22441\nreplay#tottenham hotspur#3 - 1#west bromwich albion#27860\n15#newcastle united#3 - 1#coventry city#44044\n16#charlton athletic#3 - 2#yeovil town#22873\n",
        "pandas_code": "(df['score'] == '4 - 2').sum() == 1 and df[df['score'] == '4 - 2']['home team'].iloc[0] == 'fulham'",
        "pandas_eval": "True"
    },
    {
        "id": 345,
        "statement": "middlesbrough be 1 of 9 team to lose with a score of 0",
        "label": 1,
        "table_caption": "2004 - 05 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#derby county#1 - 1#fulham#22040\nreplay#fulham#4 - 2#derby county#15528\n2#manchester united#3 - 0#middlesbrough#67251\n3#blackburn rovers#3 - 0#colchester united#10634\n4#chelsea#2 - 0#birmingham city#40379\n5#west ham united#1 - 1#sheffield united#25449\nreplay#sheffield united#1 - 1#west ham united#15067\nsheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties#sheffield united won on penalties\n6#oldham athletic#0 - 1#bolton wanderers#12029\n7#arsenal#2 - 0#wolverhampton wanderers#37135\n8#everton#3 - 0#sunderland#33186\n9#nottingham forest#1 - 0#peterborough#16774\n10#brentford#0 - 0#hartlepool#8967\nreplay#hartlepool#0 - 1#brentford#7580\n11#reading#1 - 2#leicester city#14825\n12#burnley#2 - 0#bournemouth#9944\n13#southampton#2 - 1#portsmouth#29453\n14#west bromwich albion#1 - 1#tottenham hotspur#22441\nreplay#tottenham hotspur#3 - 1#west bromwich albion#27860\n15#newcastle united#3 - 1#coventry city#44044\n16#charlton athletic#3 - 2#yeovil town#22873\n",
        "pandas_code": "(df[df['away team'] == 'middlesbrough']['score'] == '3 - 0').any()",
        "pandas_eval": "True"
    },
    {
        "id": 347,
        "statement": "cfl draft player from 7 college",
        "label": 1,
        "table_caption": "2009 cfl draft",
        "table_text": "pick#cfl team#player#position#college\n33#hamilton tiger - cats#guillarme allard - cameus#rb#laval\n34#toronto argonauts#gordon sawler#dl#st francis xavier\n35#winnipeg blue bombers#peter quinney#fb#wilfrid laurier\n36#edmonton eskimos#eric lee#rb#weber state\n37#bc lions#jonathan pierre - etienne#de#montreal\n38#hamilton tiger - cats (via saskatchewan)#raymond wladichuk#db#simon fraser\n39#montreal alouettes#beno\u00e3\u00aet boulanger#rb#sherbrooke\n",
        "pandas_code": "len(df['college'].unique()) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 348,
        "statement": "peter quinney be draftedfor the fb position while gordon sawler get the dl position",
        "label": 1,
        "table_caption": "2009 cfl draft",
        "table_text": "pick#cfl team#player#position#college\n33#hamilton tiger - cats#guillarme allard - cameus#rb#laval\n34#toronto argonauts#gordon sawler#dl#st francis xavier\n35#winnipeg blue bombers#peter quinney#fb#wilfrid laurier\n36#edmonton eskimos#eric lee#rb#weber state\n37#bc lions#jonathan pierre - etienne#de#montreal\n38#hamilton tiger - cats (via saskatchewan)#raymond wladichuk#db#simon fraser\n39#montreal alouettes#beno\u00e3\u00aet boulanger#rb#sherbrooke\n",
        "pandas_code": "(df[df['player'] == 'peter quinney']['position'].values[0] == 'fb') & (df[df['player'] == 'gordon sawler']['position'].values[0] == 'dl')",
        "pandas_eval": "True"
    },
    {
        "id": 349,
        "statement": "there be 7 pick on the table",
        "label": 1,
        "table_caption": "2009 cfl draft",
        "table_text": "pick#cfl team#player#position#college\n33#hamilton tiger - cats#guillarme allard - cameus#rb#laval\n34#toronto argonauts#gordon sawler#dl#st francis xavier\n35#winnipeg blue bombers#peter quinney#fb#wilfrid laurier\n36#edmonton eskimos#eric lee#rb#weber state\n37#bc lions#jonathan pierre - etienne#de#montreal\n38#hamilton tiger - cats (via saskatchewan)#raymond wladichuk#db#simon fraser\n39#montreal alouettes#beno\u00e3\u00aet boulanger#rb#sherbrooke\n",
        "pandas_code": "len(df['pick']) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 350,
        "statement": "pick 33 and 38 be from the hamilton tiger - cat cfl team",
        "label": 1,
        "table_caption": "2009 cfl draft",
        "table_text": "pick#cfl team#player#position#college\n33#hamilton tiger - cats#guillarme allard - cameus#rb#laval\n34#toronto argonauts#gordon sawler#dl#st francis xavier\n35#winnipeg blue bombers#peter quinney#fb#wilfrid laurier\n36#edmonton eskimos#eric lee#rb#weber state\n37#bc lions#jonathan pierre - etienne#de#montreal\n38#hamilton tiger - cats (via saskatchewan)#raymond wladichuk#db#simon fraser\n39#montreal alouettes#beno\u00e3\u00aet boulanger#rb#sherbrooke\n",
        "pandas_code": "(df[df['pick'].isin([33, 38])]['cfl team'].str.contains('hamilton tiger - cats')).all()",
        "pandas_eval": "True"
    },
    {
        "id": 351,
        "statement": "3 be draft for the rb position",
        "label": 1,
        "table_caption": "2009 cfl draft",
        "table_text": "pick#cfl team#player#position#college\n33#hamilton tiger - cats#guillarme allard - cameus#rb#laval\n34#toronto argonauts#gordon sawler#dl#st francis xavier\n35#winnipeg blue bombers#peter quinney#fb#wilfrid laurier\n36#edmonton eskimos#eric lee#rb#weber state\n37#bc lions#jonathan pierre - etienne#de#montreal\n38#hamilton tiger - cats (via saskatchewan)#raymond wladichuk#db#simon fraser\n39#montreal alouettes#beno\u00e3\u00aet boulanger#rb#sherbrooke\n",
        "pandas_code": "len(df[df['position'] == 'rb']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 352,
        "statement": "the venue be flemington more than anything else",
        "label": 1,
        "table_caption": "weekend hussler",
        "table_text": "result#date#race#venue#group#distance#weight (kg)#time#jockey#winner / 2nd\n4th#19 aug 2007#3yo maiden#sale#na#1208 m#57.0#1:12.89#l nolen#1st - aceland street\nwon#2 sep 2007#3yo maiden#cranbourne#na#1200 m#57.0#1:11.65#c symons#2nd - embracing\nwon#19 sep 2007#3yo rst72#sandown#na#1300 m#57.0#1:15.94#b rawiller#2nd - simplest\nwon#30 sep 2007#3yo open#sandown#na#1400 m#57.0#1:24.23#b rawiller#2nd - viatorian\nwon#13 oct 2007#caulfield guineas#caulfield#g1#1600 m#55.5#1:36.42#b rawiller#2nd - scenic blast\nwon#3 nov 2007#ascot vale stakes#flemington#g1#1200 m#55.5#1:08.95#b rawiller#2nd - bel mer\n10th#10 nov 2007#emirates stakes#flemington#g1#1600 m#51.5#1:35.98#d nikolic#1st - tears i cry\nwon#23 feb 2008#oakleigh plate#caulfield#g1#1100 m#53.0#1:03.22#b rawiller#2nd - magnus\nwon#8 mar 2008#newmarket handicap#flemington#g1#1200 m#56.0#1:08.97#b rawiller#2nd - magnus\nwon#29 mar 2008#randwick guineas#randwick#g1#1600 m#56.5#1:36.30#b rawiller#2nd - triple honour\n",
        "pandas_code": "df['venue'].value_counts().idxmax() == 'flemington'",
        "pandas_eval": "True"
    },
    {
        "id": 353,
        "statement": "the venue be caulfield more than it be randwick",
        "label": 1,
        "table_caption": "weekend hussler",
        "table_text": "result#date#race#venue#group#distance#weight (kg)#time#jockey#winner / 2nd\n4th#19 aug 2007#3yo maiden#sale#na#1208 m#57.0#1:12.89#l nolen#1st - aceland street\nwon#2 sep 2007#3yo maiden#cranbourne#na#1200 m#57.0#1:11.65#c symons#2nd - embracing\nwon#19 sep 2007#3yo rst72#sandown#na#1300 m#57.0#1:15.94#b rawiller#2nd - simplest\nwon#30 sep 2007#3yo open#sandown#na#1400 m#57.0#1:24.23#b rawiller#2nd - viatorian\nwon#13 oct 2007#caulfield guineas#caulfield#g1#1600 m#55.5#1:36.42#b rawiller#2nd - scenic blast\nwon#3 nov 2007#ascot vale stakes#flemington#g1#1200 m#55.5#1:08.95#b rawiller#2nd - bel mer\n10th#10 nov 2007#emirates stakes#flemington#g1#1600 m#51.5#1:35.98#d nikolic#1st - tears i cry\nwon#23 feb 2008#oakleigh plate#caulfield#g1#1100 m#53.0#1:03.22#b rawiller#2nd - magnus\nwon#8 mar 2008#newmarket handicap#flemington#g1#1200 m#56.0#1:08.97#b rawiller#2nd - magnus\nwon#29 mar 2008#randwick guineas#randwick#g1#1600 m#56.5#1:36.30#b rawiller#2nd - triple honour\n",
        "pandas_code": "(df['venue'].value_counts()['caulfield'] > df['venue'].value_counts()['randwick'])",
        "pandas_eval": "True"
    },
    {
        "id": 354,
        "statement": "the highest weight be 57.0",
        "label": 1,
        "table_caption": "weekend hussler",
        "table_text": "result#date#race#venue#group#distance#weight (kg)#time#jockey#winner / 2nd\n4th#19 aug 2007#3yo maiden#sale#na#1208 m#57.0#1:12.89#l nolen#1st - aceland street\nwon#2 sep 2007#3yo maiden#cranbourne#na#1200 m#57.0#1:11.65#c symons#2nd - embracing\nwon#19 sep 2007#3yo rst72#sandown#na#1300 m#57.0#1:15.94#b rawiller#2nd - simplest\nwon#30 sep 2007#3yo open#sandown#na#1400 m#57.0#1:24.23#b rawiller#2nd - viatorian\nwon#13 oct 2007#caulfield guineas#caulfield#g1#1600 m#55.5#1:36.42#b rawiller#2nd - scenic blast\nwon#3 nov 2007#ascot vale stakes#flemington#g1#1200 m#55.5#1:08.95#b rawiller#2nd - bel mer\n10th#10 nov 2007#emirates stakes#flemington#g1#1600 m#51.5#1:35.98#d nikolic#1st - tears i cry\nwon#23 feb 2008#oakleigh plate#caulfield#g1#1100 m#53.0#1:03.22#b rawiller#2nd - magnus\nwon#8 mar 2008#newmarket handicap#flemington#g1#1200 m#56.0#1:08.97#b rawiller#2nd - magnus\nwon#29 mar 2008#randwick guineas#randwick#g1#1600 m#56.5#1:36.30#b rawiller#2nd - triple honour\n",
        "pandas_code": "df['weight (kg)'].max() == 57.0",
        "pandas_eval": "True"
    },
    {
        "id": 355,
        "statement": "the shortest time be 1:03.22",
        "label": 1,
        "table_caption": "weekend hussler",
        "table_text": "result#date#race#venue#group#distance#weight (kg)#time#jockey#winner / 2nd\n4th#19 aug 2007#3yo maiden#sale#na#1208 m#57.0#1:12.89#l nolen#1st - aceland street\nwon#2 sep 2007#3yo maiden#cranbourne#na#1200 m#57.0#1:11.65#c symons#2nd - embracing\nwon#19 sep 2007#3yo rst72#sandown#na#1300 m#57.0#1:15.94#b rawiller#2nd - simplest\nwon#30 sep 2007#3yo open#sandown#na#1400 m#57.0#1:24.23#b rawiller#2nd - viatorian\nwon#13 oct 2007#caulfield guineas#caulfield#g1#1600 m#55.5#1:36.42#b rawiller#2nd - scenic blast\nwon#3 nov 2007#ascot vale stakes#flemington#g1#1200 m#55.5#1:08.95#b rawiller#2nd - bel mer\n10th#10 nov 2007#emirates stakes#flemington#g1#1600 m#51.5#1:35.98#d nikolic#1st - tears i cry\nwon#23 feb 2008#oakleigh plate#caulfield#g1#1100 m#53.0#1:03.22#b rawiller#2nd - magnus\nwon#8 mar 2008#newmarket handicap#flemington#g1#1200 m#56.0#1:08.97#b rawiller#2nd - magnus\nwon#29 mar 2008#randwick guineas#randwick#g1#1600 m#56.5#1:36.30#b rawiller#2nd - triple honour\n",
        "pandas_code": "df['time'].min() == '1:03.22'",
        "pandas_eval": "True"
    },
    {
        "id": 356,
        "statement": "b rawiller be the jockey 6 time more than d nikolic",
        "label": 1,
        "table_caption": "weekend hussler",
        "table_text": "result#date#race#venue#group#distance#weight (kg)#time#jockey#winner / 2nd\n4th#19 aug 2007#3yo maiden#sale#na#1208 m#57.0#1:12.89#l nolen#1st - aceland street\nwon#2 sep 2007#3yo maiden#cranbourne#na#1200 m#57.0#1:11.65#c symons#2nd - embracing\nwon#19 sep 2007#3yo rst72#sandown#na#1300 m#57.0#1:15.94#b rawiller#2nd - simplest\nwon#30 sep 2007#3yo open#sandown#na#1400 m#57.0#1:24.23#b rawiller#2nd - viatorian\nwon#13 oct 2007#caulfield guineas#caulfield#g1#1600 m#55.5#1:36.42#b rawiller#2nd - scenic blast\nwon#3 nov 2007#ascot vale stakes#flemington#g1#1200 m#55.5#1:08.95#b rawiller#2nd - bel mer\n10th#10 nov 2007#emirates stakes#flemington#g1#1600 m#51.5#1:35.98#d nikolic#1st - tears i cry\nwon#23 feb 2008#oakleigh plate#caulfield#g1#1100 m#53.0#1:03.22#b rawiller#2nd - magnus\nwon#8 mar 2008#newmarket handicap#flemington#g1#1200 m#56.0#1:08.97#b rawiller#2nd - magnus\nwon#29 mar 2008#randwick guineas#randwick#g1#1600 m#56.5#1:36.30#b rawiller#2nd - triple honour\n",
        "pandas_code": "(df['jockey'].value_counts()['b rawiller'] > df['jockey'].value_counts()['d nikolic'] * 6)",
        "pandas_eval": "True"
    },
    {
        "id": 358,
        "statement": "5 garfield episode 1 's begin with the word , the",
        "label": 1,
        "table_caption": "list of garfield and friends episodes",
        "table_text": "episode#garfield episode 1#us acres episode#garfield episode 2#original airdate\nshow 106#the legend of johnny ragweedseed#grape expectations (part 1)#catch as cats can't#september 17 , 1994\nshow 107#a matter of conscience#grape expectations (part 2)#top ten#september 17 , 1994\nshow 108#change of mind#temp trouble#the perfect match#september 24 , 1994\nshow 109#my fair feline#double trouble talk#half - baked alaska#september 24 , 1994\nshow 110#puss in high - tops#egg over easy (part 1)#the beast from beyond#october 1 , 1994\nshow 111#model behavior#egg over easy (part 2)#another ant episode#october 1 , 1994\nshow 112#the guy of her dreams#the discount of monte cristo#the fairy dogmother#october 8 , 1994\nshow 113#the stand - up mouse#daydream doctor#happy garfield day#october 8 , 1994\nshow 114#sit on it#kiddy korner#brainware broadcast#october 15 , 1994\nshow 115#suburban jungle#the thing in the box#the feline philosopher#october 22 , 1994\nshow 116#thoroughly mixed - up mouse#the old man of the mountain#food fighter#october 29 , 1994\nshow 117#the jelly roger#the farmyard feline philosopher#dogmother 2#november 5 , 1994\nshow 118#alley katta and the 40 thieves#if it 's tuesday this must be alpha centauri#clash of the titans#november 19 , 1994\nshow 119#canned laughter#deja vu#the man who hated cats#november 26 , 1994\nshow 120#the horror hostess (part 1)#newsworthy wade#the horror hostess (part 2)#december 3 , 1994\n",
        "pandas_code": "sum(df['garfield episode 1'].str.startswith('the ')) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 359,
        "statement": "6 garfield episode 2 's begin with the word , the",
        "label": 1,
        "table_caption": "list of garfield and friends episodes",
        "table_text": "episode#garfield episode 1#us acres episode#garfield episode 2#original airdate\nshow 106#the legend of johnny ragweedseed#grape expectations (part 1)#catch as cats can't#september 17 , 1994\nshow 107#a matter of conscience#grape expectations (part 2)#top ten#september 17 , 1994\nshow 108#change of mind#temp trouble#the perfect match#september 24 , 1994\nshow 109#my fair feline#double trouble talk#half - baked alaska#september 24 , 1994\nshow 110#puss in high - tops#egg over easy (part 1)#the beast from beyond#october 1 , 1994\nshow 111#model behavior#egg over easy (part 2)#another ant episode#october 1 , 1994\nshow 112#the guy of her dreams#the discount of monte cristo#the fairy dogmother#october 8 , 1994\nshow 113#the stand - up mouse#daydream doctor#happy garfield day#october 8 , 1994\nshow 114#sit on it#kiddy korner#brainware broadcast#october 15 , 1994\nshow 115#suburban jungle#the thing in the box#the feline philosopher#october 22 , 1994\nshow 116#thoroughly mixed - up mouse#the old man of the mountain#food fighter#october 29 , 1994\nshow 117#the jelly roger#the farmyard feline philosopher#dogmother 2#november 5 , 1994\nshow 118#alley katta and the 40 thieves#if it 's tuesday this must be alpha centauri#clash of the titans#november 19 , 1994\nshow 119#canned laughter#deja vu#the man who hated cats#november 26 , 1994\nshow 120#the horror hostess (part 1)#newsworthy wade#the horror hostess (part 2)#december 3 , 1994\n",
        "pandas_code": "(df['garfield episode 2'].str.startswith('the').sum() == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 360,
        "statement": "3 show have 2 part : the horror hostess , egg over easy , and grape expectation",
        "label": 1,
        "table_caption": "list of garfield and friends episodes",
        "table_text": "episode#garfield episode 1#us acres episode#garfield episode 2#original airdate\nshow 106#the legend of johnny ragweedseed#grape expectations (part 1)#catch as cats can't#september 17 , 1994\nshow 107#a matter of conscience#grape expectations (part 2)#top ten#september 17 , 1994\nshow 108#change of mind#temp trouble#the perfect match#september 24 , 1994\nshow 109#my fair feline#double trouble talk#half - baked alaska#september 24 , 1994\nshow 110#puss in high - tops#egg over easy (part 1)#the beast from beyond#october 1 , 1994\nshow 111#model behavior#egg over easy (part 2)#another ant episode#october 1 , 1994\nshow 112#the guy of her dreams#the discount of monte cristo#the fairy dogmother#october 8 , 1994\nshow 113#the stand - up mouse#daydream doctor#happy garfield day#october 8 , 1994\nshow 114#sit on it#kiddy korner#brainware broadcast#october 15 , 1994\nshow 115#suburban jungle#the thing in the box#the feline philosopher#october 22 , 1994\nshow 116#thoroughly mixed - up mouse#the old man of the mountain#food fighter#october 29 , 1994\nshow 117#the jelly roger#the farmyard feline philosopher#dogmother 2#november 5 , 1994\nshow 118#alley katta and the 40 thieves#if it 's tuesday this must be alpha centauri#clash of the titans#november 19 , 1994\nshow 119#canned laughter#deja vu#the man who hated cats#november 26 , 1994\nshow 120#the horror hostess (part 1)#newsworthy wade#the horror hostess (part 2)#december 3 , 1994\n",
        "pandas_code": "(df['garfield episode 1'].str.contains('the horror hostess', case=False).sum() + df['garfield episode 2'].str.contains('the horror hostess', case=False).sum() == 2) and (df['us acres episode'].str.contains('egg over easy', case=False).sum() == 2) and (df['us acres episode'].str.contains('grape expectations', case=False).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 362,
        "statement": "disseminate intravascular coagulation be the worst condition in acute liver failure",
        "label": 1,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "df.loc[df['condition'] == 'disseminated intravascular coagulation', ['prothrombin time', 'partial thromboplastin time', 'bleeding time', 'platelet count']].isin(['prolonged', 'decreased']).all().all()",
        "pandas_eval": "True"
    },
    {
        "id": 363,
        "statement": "both hemophilia and factor xii deficiency have prolong partial thromboplastin time",
        "label": 1,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "all(df[df['condition'].isin(['hemophilia', 'factor xii deficiency'])]['partial thromboplastin time'] == 'prolonged')",
        "pandas_eval": "True"
    },
    {
        "id": 364,
        "statement": "there be 4 condition in which platelet count be decrease",
        "label": 1,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "len(df[df['platelet count'].str.contains('decreased')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 366,
        "statement": "c1inh deficiency be the only condition with shorten partial thromboplastin time",
        "label": 1,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "len(df[(df['partial thromboplastin time'] == 'shortened') & (df['condition'] == 'c1inh deficiency')]) == 1 and len(df[df['partial thromboplastin time'] == 'shortened']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 367,
        "statement": "both hemopholia and factor xii deficiency have a shorten partial thromboplastin time",
        "label": 0,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "all(df[df['condition'].isin(['hemophilia', 'factor xii deficiency'])]['partial thromboplastin time'] == 'shortened')",
        "pandas_eval": "False"
    },
    {
        "id": 368,
        "statement": "there be no condition in which platelet count be decrease",
        "label": 0,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "not any(df['platelet count'] == 'decreased')",
        "pandas_eval": "False"
    },
    {
        "id": 369,
        "statement": "c1inh deficiency and 2 other condition have a shorten partial thromboplastin time",
        "label": 0,
        "table_caption": "acute liver failure",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\nc1inh deficiency#unaffected#shortened#unaffected#unaffected\n",
        "pandas_code": "len(df[df['partial thromboplastin time'] == 'shortened']) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 371,
        "statement": "don corvan direct more episode than anyone else in season 4",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n52#1#initiation#michael zinberg#frank dungan & jeff stein & tony sheehan#october 30 , 1987#5 m03\n53#2#tv george#alan bergmann#frank dungan & jeff stein & tony sheehan#november 06 , 1987#5 m02\n54#3#triangle#alan bergmann#frank dungan & jeff stein & tony sheehan#november 13 , 1987#5 m01\n55#4#marsha 's job#don corvan#liz sage#november 20 , 1987#5 m07\n56#5#moonlighting#gerren keith#frank dungan & jeff stein & tony sheehan#november 27 , 1987#5 m04\n57#6#the wedding#tony singletary#jeff stein#december 04 , 1987#5 m08\n58#7#fall guy#don corvan#doug steckler#december 11 , 1987#5 m10\n59#8#christmas story#don corvan#doug steckler#december 18 , 1987#5 m11\n60#9#gi george#don corvan#jeff greenstein & jeff strauss#january 08 , 1988#5 m06\n61#10#kevin 's model#michael zinberg#jim kearns#january 15 , 1988#5 m12\n62#11#commentary#michael zinberg#jack carrerrow#january 22 , 1988#5 m13\n63#12#the diary#howard storm#mitzi mccall brill & adrienne armstrong#january 29 , 1988#5 m05\n64#13#the trip : part 1#don corvan#frank dungan & jeff stein & tony sheehan#february 05 , 1988#5 m16\n65#14#the trip : part 2#don corvan#frank dungan & jeff stein & tony sheehan#february 12 , 1988#5 m17\n66#15#foxtrot#don corvan#doug steckler#march 04 , 1988#5 m18\n67#16#heather 's monk#don corvan#jim kearns#march 11 , 1988#5 m20\n68#17#kevin nightingale#don corvan#liz sage#march 18 , 1988#5 m15\n69#18#the apartment#tony sheehan#frank dungan & jeff stein & tony sheehan#march 25 , 1988#5 m19\n70#19#graduation#don corvan#liz sage#april 29 , 1988#5 m22\n",
        "pandas_code": "df[df['season'] == 4]['directed by'].value_counts().idxmax() == 'don corvan'",
        "pandas_eval": "True"
    },
    {
        "id": 372,
        "statement": "graduation be the title of the episode with the most recent air date",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n52#1#initiation#michael zinberg#frank dungan & jeff stein & tony sheehan#october 30 , 1987#5 m03\n53#2#tv george#alan bergmann#frank dungan & jeff stein & tony sheehan#november 06 , 1987#5 m02\n54#3#triangle#alan bergmann#frank dungan & jeff stein & tony sheehan#november 13 , 1987#5 m01\n55#4#marsha 's job#don corvan#liz sage#november 20 , 1987#5 m07\n56#5#moonlighting#gerren keith#frank dungan & jeff stein & tony sheehan#november 27 , 1987#5 m04\n57#6#the wedding#tony singletary#jeff stein#december 04 , 1987#5 m08\n58#7#fall guy#don corvan#doug steckler#december 11 , 1987#5 m10\n59#8#christmas story#don corvan#doug steckler#december 18 , 1987#5 m11\n60#9#gi george#don corvan#jeff greenstein & jeff strauss#january 08 , 1988#5 m06\n61#10#kevin 's model#michael zinberg#jim kearns#january 15 , 1988#5 m12\n62#11#commentary#michael zinberg#jack carrerrow#january 22 , 1988#5 m13\n63#12#the diary#howard storm#mitzi mccall brill & adrienne armstrong#january 29 , 1988#5 m05\n64#13#the trip : part 1#don corvan#frank dungan & jeff stein & tony sheehan#february 05 , 1988#5 m16\n65#14#the trip : part 2#don corvan#frank dungan & jeff stein & tony sheehan#february 12 , 1988#5 m17\n66#15#foxtrot#don corvan#doug steckler#march 04 , 1988#5 m18\n67#16#heather 's monk#don corvan#jim kearns#march 11 , 1988#5 m20\n68#17#kevin nightingale#don corvan#liz sage#march 18 , 1988#5 m15\n69#18#the apartment#tony sheehan#frank dungan & jeff stein & tony sheehan#march 25 , 1988#5 m19\n70#19#graduation#don corvan#liz sage#april 29 , 1988#5 m22\n",
        "pandas_code": "df.loc[pd.to_datetime(df['original air date']).idxmax(), 'title'] == 'graduation'",
        "pandas_eval": "True"
    },
    {
        "id": 373,
        "statement": "7 different episode have 3 writer work together",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n52#1#initiation#michael zinberg#frank dungan & jeff stein & tony sheehan#october 30 , 1987#5 m03\n53#2#tv george#alan bergmann#frank dungan & jeff stein & tony sheehan#november 06 , 1987#5 m02\n54#3#triangle#alan bergmann#frank dungan & jeff stein & tony sheehan#november 13 , 1987#5 m01\n55#4#marsha 's job#don corvan#liz sage#november 20 , 1987#5 m07\n56#5#moonlighting#gerren keith#frank dungan & jeff stein & tony sheehan#november 27 , 1987#5 m04\n57#6#the wedding#tony singletary#jeff stein#december 04 , 1987#5 m08\n58#7#fall guy#don corvan#doug steckler#december 11 , 1987#5 m10\n59#8#christmas story#don corvan#doug steckler#december 18 , 1987#5 m11\n60#9#gi george#don corvan#jeff greenstein & jeff strauss#january 08 , 1988#5 m06\n61#10#kevin 's model#michael zinberg#jim kearns#january 15 , 1988#5 m12\n62#11#commentary#michael zinberg#jack carrerrow#january 22 , 1988#5 m13\n63#12#the diary#howard storm#mitzi mccall brill & adrienne armstrong#january 29 , 1988#5 m05\n64#13#the trip : part 1#don corvan#frank dungan & jeff stein & tony sheehan#february 05 , 1988#5 m16\n65#14#the trip : part 2#don corvan#frank dungan & jeff stein & tony sheehan#february 12 , 1988#5 m17\n66#15#foxtrot#don corvan#doug steckler#march 04 , 1988#5 m18\n67#16#heather 's monk#don corvan#jim kearns#march 11 , 1988#5 m20\n68#17#kevin nightingale#don corvan#liz sage#march 18 , 1988#5 m15\n69#18#the apartment#tony sheehan#frank dungan & jeff stein & tony sheehan#march 25 , 1988#5 m19\n70#19#graduation#don corvan#liz sage#april 29 , 1988#5 m22\n",
        "pandas_code": "len(df[df['written by'].apply(lambda x: len(set(x.split(' & '))) == 3)]) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 374,
        "statement": "liz sage never work with another person when write episode",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n52#1#initiation#michael zinberg#frank dungan & jeff stein & tony sheehan#october 30 , 1987#5 m03\n53#2#tv george#alan bergmann#frank dungan & jeff stein & tony sheehan#november 06 , 1987#5 m02\n54#3#triangle#alan bergmann#frank dungan & jeff stein & tony sheehan#november 13 , 1987#5 m01\n55#4#marsha 's job#don corvan#liz sage#november 20 , 1987#5 m07\n56#5#moonlighting#gerren keith#frank dungan & jeff stein & tony sheehan#november 27 , 1987#5 m04\n57#6#the wedding#tony singletary#jeff stein#december 04 , 1987#5 m08\n58#7#fall guy#don corvan#doug steckler#december 11 , 1987#5 m10\n59#8#christmas story#don corvan#doug steckler#december 18 , 1987#5 m11\n60#9#gi george#don corvan#jeff greenstein & jeff strauss#january 08 , 1988#5 m06\n61#10#kevin 's model#michael zinberg#jim kearns#january 15 , 1988#5 m12\n62#11#commentary#michael zinberg#jack carrerrow#january 22 , 1988#5 m13\n63#12#the diary#howard storm#mitzi mccall brill & adrienne armstrong#january 29 , 1988#5 m05\n64#13#the trip : part 1#don corvan#frank dungan & jeff stein & tony sheehan#february 05 , 1988#5 m16\n65#14#the trip : part 2#don corvan#frank dungan & jeff stein & tony sheehan#february 12 , 1988#5 m17\n66#15#foxtrot#don corvan#doug steckler#march 04 , 1988#5 m18\n67#16#heather 's monk#don corvan#jim kearns#march 11 , 1988#5 m20\n68#17#kevin nightingale#don corvan#liz sage#march 18 , 1988#5 m15\n69#18#the apartment#tony sheehan#frank dungan & jeff stein & tony sheehan#march 25 , 1988#5 m19\n70#19#graduation#don corvan#liz sage#april 29 , 1988#5 m22\n",
        "pandas_code": "all(df[df['written by'].str.contains('liz sage')]['written by'].apply(lambda x: len(x.split(' & ')) == 1))",
        "pandas_eval": "True"
    },
    {
        "id": 375,
        "statement": "the lowest attendance figure for a game be 10111",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n2#november 1#dallas#l 85 - 95 (ot)#rashad mccants (18)#al jefferson (12)#randy foye (6)#target center 16893#1 - 1\n3#november 2#oklahoma city#l 85 - 88 (ot)#al jefferson (24)#al jefferson (13)#randy foye (6)#ford center 18163#1 - 2\n4#november 5#san antonio#l 125 - 129 (2ot)#al jefferson (30)#al jefferson (14)#sebastian telfair (10)#target center 11112#1 - 3\n5#november 7#sacramento#l 109 - 121 (ot)#kevin love (20)#al jefferson (9)#sebastian telfair (7)#arco arena 10592#1 - 4\n6#november 8#portland#l 93 - 97 (ot)#al jefferson (27)#kevin love (7)#al jefferson , randy foye (5)#rose garden 20599#1 - 5\n7#november 11#golden state#l 110 - 113 (ot)#al jefferson (25)#al jefferson (12)#randy foye (8)#oracle arena 17422#1 - 6\n8#november 15#portland#l 83 - 88 (ot)#al jefferson (26)#al jefferson (6)#mike miller (5)#target center 12213#1 - 7\n9#november 16#denver#l 84 - 90 (ot)#al jefferson (20)#al jefferson (14)#randy foye (6)#pepsi center 16721#1 - 8\n10#november 19#philadelphia#w 102 - 96 (ot)#al jefferson (25)#mike miller (10)#sebastian telfair (8)#target center 10111#2 - 8\n11#november 21#boston#l 78 - 95 (ot)#al jefferson (21)#craig smith (7)#craig smith (4)#target center 19107#2 - 9\n12#november 23#detroit#w 106 - 80 (ot)#randy foye (23)#craig smith (9)#randy foye (14)#the palace of auburn hills 22076#3 - 9\n13#november 26#phoenix#l 102 - 110 (ot)#al jefferson (28)#al jefferson (17)#mike miller (6)#target center 11708#3 - 10\n14#november 28#oklahoma city#w 105 - 103 (ot)#craig smith (23)#al jefferson (9)#randy foye (7)#ford center 18229#4 - 10\n",
        "pandas_code": "df['location attendance'].str.extract('(\\d+)')[0].astype(int).min() == 10111",
        "pandas_eval": "True"
    },
    {
        "id": 378,
        "statement": "al jefferson lead the team in rebound in 9 different game",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n2#november 1#dallas#l 85 - 95 (ot)#rashad mccants (18)#al jefferson (12)#randy foye (6)#target center 16893#1 - 1\n3#november 2#oklahoma city#l 85 - 88 (ot)#al jefferson (24)#al jefferson (13)#randy foye (6)#ford center 18163#1 - 2\n4#november 5#san antonio#l 125 - 129 (2ot)#al jefferson (30)#al jefferson (14)#sebastian telfair (10)#target center 11112#1 - 3\n5#november 7#sacramento#l 109 - 121 (ot)#kevin love (20)#al jefferson (9)#sebastian telfair (7)#arco arena 10592#1 - 4\n6#november 8#portland#l 93 - 97 (ot)#al jefferson (27)#kevin love (7)#al jefferson , randy foye (5)#rose garden 20599#1 - 5\n7#november 11#golden state#l 110 - 113 (ot)#al jefferson (25)#al jefferson (12)#randy foye (8)#oracle arena 17422#1 - 6\n8#november 15#portland#l 83 - 88 (ot)#al jefferson (26)#al jefferson (6)#mike miller (5)#target center 12213#1 - 7\n9#november 16#denver#l 84 - 90 (ot)#al jefferson (20)#al jefferson (14)#randy foye (6)#pepsi center 16721#1 - 8\n10#november 19#philadelphia#w 102 - 96 (ot)#al jefferson (25)#mike miller (10)#sebastian telfair (8)#target center 10111#2 - 8\n11#november 21#boston#l 78 - 95 (ot)#al jefferson (21)#craig smith (7)#craig smith (4)#target center 19107#2 - 9\n12#november 23#detroit#w 106 - 80 (ot)#randy foye (23)#craig smith (9)#randy foye (14)#the palace of auburn hills 22076#3 - 9\n13#november 26#phoenix#l 102 - 110 (ot)#al jefferson (28)#al jefferson (17)#mike miller (6)#target center 11708#3 - 10\n14#november 28#oklahoma city#w 105 - 103 (ot)#craig smith (23)#al jefferson (9)#randy foye (7)#ford center 18229#4 - 10\n",
        "pandas_code": "df['high rebounds'].str.contains('al jefferson').sum() == 9",
        "pandas_eval": "True"
    },
    {
        "id": 381,
        "statement": "there be 10 different constructor for the 18 driver in this race",
        "label": 1,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "len(df['constructor'].unique()) == 9",
        "pandas_eval": "True"
    },
    {
        "id": 383,
        "statement": "the 2 driver whose constructor be minardi - cosworth have to retire from the race",
        "label": 1,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "all(df[df['constructor'] == 'minardi - cosworth']['time / retired'].str.contains('spun off|gearbox'))",
        "pandas_eval": "True"
    },
    {
        "id": 384,
        "statement": "jarno trulli and ralf schumacher be only 0.772 second apart from each other",
        "label": 1,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "abs(df[df['driver'].isin(['jarno trulli', 'ralf schumacher'])]['time / retired'].str.extract(r'\\+\\s*(\\d+\\.\\d+)')[0].astype(float).diff().abs().iloc[-1] - 0.772) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 385,
        "statement": "more than 6 of the 18 driver finish the race",
        "label": 0,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "(df['time / retired'].str.contains('\\\\+').sum() <= 6)",
        "pandas_eval": "False"
    },
    {
        "id": 386,
        "statement": "there be less than 10 different constructor for the 18 driver in this race",
        "label": 0,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "len(df['constructor'].unique()) >= 10",
        "pandas_eval": "False"
    },
    {
        "id": 387,
        "statement": "the bottom 5 position driver do not retire from the race",
        "label": 0,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "all(df.sort_values('grid', ascending=False).head(5)['time / retired'].str.contains('\\+'))",
        "pandas_eval": "False"
    },
    {
        "id": 388,
        "statement": "the 2 driver whose constructor be minardi - cosworth finish the race",
        "label": 0,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "all(df[df['constructor'] == 'minardi - cosworth']['time / retired'].str.contains('\\+'))",
        "pandas_eval": "False"
    },
    {
        "id": 389,
        "statement": "jarno trulli and ralf schumacher be more than 0.772 second apart from each other",
        "label": 0,
        "table_caption": "2005 spanish grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nkimi r\u00e4ikk\u00f6nen#mclaren - mercedes#66#1:27:16.830#1\nfernando alonso#renault#66#+ 27.652#3\njarno trulli#toyota#66#+ 45.947#5\nralf schumacher#toyota#66#+ 46.719#4\ngiancarlo fisichella#renault#66#+ 57.936#6\nmark webber#williams - bmw#66#+ 1:08.542#2\njuan pablo montoya#mclaren - mercedes#65#+ 1 lap#7\ndavid coulthard#red bull - cosworth#65#+ 1 lap#9\nrubens barrichello#ferrari#65#+ 1 lap#16\nnick heidfeld#williams - bmw#65#+ 1 lap#17\nfelipe massa#sauber - petronas#63#wheel rim#10\ntiago monteiro#jordan - toyota#63#+ 3 laps#18\nnarain karthikeyan#jordan - toyota#63#+ 3 laps#13\njacques villeneuve#sauber - petronas#51#engine#12\nmichael schumacher#ferrari#46#puncture#8\nchristijan albers#minardi - cosworth#19#gearbox#14\npatrick friesacher#minardi - cosworth#11#spun off#15\nvitantonio liuzzi#red bull - cosworth#9#spun off#11\n",
        "pandas_code": "abs(df[df['driver'].isin(['jarno trulli', 'ralf schumacher'])]['time / retired'].str.extract(r'\\+\\s*(\\d+):(\\d+)\\.(\\d+)').astype(float).dot([60, 1, 0.001]).diff().abs().iloc[-1] > 0.772)",
        "pandas_eval": "False"
    },
    {
        "id": 390,
        "statement": "among all ceremony , no title have be nominate",
        "label": 1,
        "table_caption": "list of portuguese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#portuguese title#director#result\n1980: (53rd)#morning undersea#manh\u00e3 submersa#lauro ant\u00f3nio#not nominated\n1982: (55th)#francisca#francisca#manoel de oliveira#not nominated\n1983: (56th)#no trace of sin#sem sombra de pecado#jos\u00e9 fonseca e costa#not nominated\n1988: (61st)#hard times#tempos dif\u00edceis#jo\u00e3o botelho#not nominated\n1989: (62nd)#the cannibals#os canibais#manoel de oliveira#not nominated\n1990: (63rd)#the king 's trial#o processo do rei#jo\u00e3o m\u00e1rio grilo#not nominated\n1991: (64th)#o sangue#o sangue#pedro costa#not nominated\n1992: (65th)#day of despair#o dia do desespero#manoel de oliveira#not nominated\n1993: (66th)#abraham 's valley#vale abra\u00e3o#manoel de oliveira#not nominated\n1994: (67th)#three palm trees#tr\u00eas palmeiras#jo\u00e3o botelho#not nominated\n1995: (68th)#god 's comedy#a divina com\u00e9dia#manoel de oliveira#not nominated\n1997: (70th)#journey to the beginning of the world#viagem ao princ\u00edpio do mundo#manoel de oliveira#not nominated\n1998: (71st)#anxiety#inquietude#manoel de oliveira#not nominated\n1999: (72nd)#the mutants#os mutantes#teresa villaverde#not nominated\n2000: (73rd)#too late#tarde demais#jos\u00e9 nascimento#not nominated\n2001: (74th)#camarate#camarate#lu\u00eds filipe rocha#not nominated\n2003: (76th)#a talking picture#um filme falado#manoel de oliveira#not nominated\n2004: (77th)#the miracle according to salom\u00e9#o milagre segundo salom\u00e9#m\u00e1rio barroso#not nominated\n2005: (78th)#noite escura#noite escura#jo\u00e3o canijo#not nominated\n2006: (79th)#alice#alice#marco martins#not nominated\n2007: (80th)#belle toujours#belle toujours#manoel de oliveira#not nominated\n2008: (81st)#our beloved month of august#aquele querido m\u00eas de agosto#miguel gomes#not nominated\n2009: (82nd)#doomed love#um amor de perdi\u00e7\u00e3o#m\u00e1rio barroso#not nominated\n2010: (83rd)#to die like a man#morrer como um homem#jo\u00e3o pedro rodrigues#not nominated\n2012: (85th)#blood of my blood#sangue do meu sangue#jo\u00e3o canijo#not nominated\n",
        "pandas_code": "df['result'].eq('not nominated').all()",
        "pandas_eval": "True"
    },
    {
        "id": 391,
        "statement": "manoel de oliveira be include in the ceremony but be never nominate",
        "label": 1,
        "table_caption": "list of portuguese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#portuguese title#director#result\n1980: (53rd)#morning undersea#manh\u00e3 submersa#lauro ant\u00f3nio#not nominated\n1982: (55th)#francisca#francisca#manoel de oliveira#not nominated\n1983: (56th)#no trace of sin#sem sombra de pecado#jos\u00e9 fonseca e costa#not nominated\n1988: (61st)#hard times#tempos dif\u00edceis#jo\u00e3o botelho#not nominated\n1989: (62nd)#the cannibals#os canibais#manoel de oliveira#not nominated\n1990: (63rd)#the king 's trial#o processo do rei#jo\u00e3o m\u00e1rio grilo#not nominated\n1991: (64th)#o sangue#o sangue#pedro costa#not nominated\n1992: (65th)#day of despair#o dia do desespero#manoel de oliveira#not nominated\n1993: (66th)#abraham 's valley#vale abra\u00e3o#manoel de oliveira#not nominated\n1994: (67th)#three palm trees#tr\u00eas palmeiras#jo\u00e3o botelho#not nominated\n1995: (68th)#god 's comedy#a divina com\u00e9dia#manoel de oliveira#not nominated\n1997: (70th)#journey to the beginning of the world#viagem ao princ\u00edpio do mundo#manoel de oliveira#not nominated\n1998: (71st)#anxiety#inquietude#manoel de oliveira#not nominated\n1999: (72nd)#the mutants#os mutantes#teresa villaverde#not nominated\n2000: (73rd)#too late#tarde demais#jos\u00e9 nascimento#not nominated\n2001: (74th)#camarate#camarate#lu\u00eds filipe rocha#not nominated\n2003: (76th)#a talking picture#um filme falado#manoel de oliveira#not nominated\n2004: (77th)#the miracle according to salom\u00e9#o milagre segundo salom\u00e9#m\u00e1rio barroso#not nominated\n2005: (78th)#noite escura#noite escura#jo\u00e3o canijo#not nominated\n2006: (79th)#alice#alice#marco martins#not nominated\n2007: (80th)#belle toujours#belle toujours#manoel de oliveira#not nominated\n2008: (81st)#our beloved month of august#aquele querido m\u00eas de agosto#miguel gomes#not nominated\n2009: (82nd)#doomed love#um amor de perdi\u00e7\u00e3o#m\u00e1rio barroso#not nominated\n2010: (83rd)#to die like a man#morrer como um homem#jo\u00e3o pedro rodrigues#not nominated\n2012: (85th)#blood of my blood#sangue do meu sangue#jo\u00e3o canijo#not nominated\n",
        "pandas_code": "all(df[df['director'] == 'manoel de oliveira']['result'] == 'not nominated')",
        "pandas_eval": "True"
    },
    {
        "id": 392,
        "statement": "there be no director nominate for the ceremony",
        "label": 1,
        "table_caption": "list of portuguese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#portuguese title#director#result\n1980: (53rd)#morning undersea#manh\u00e3 submersa#lauro ant\u00f3nio#not nominated\n1982: (55th)#francisca#francisca#manoel de oliveira#not nominated\n1983: (56th)#no trace of sin#sem sombra de pecado#jos\u00e9 fonseca e costa#not nominated\n1988: (61st)#hard times#tempos dif\u00edceis#jo\u00e3o botelho#not nominated\n1989: (62nd)#the cannibals#os canibais#manoel de oliveira#not nominated\n1990: (63rd)#the king 's trial#o processo do rei#jo\u00e3o m\u00e1rio grilo#not nominated\n1991: (64th)#o sangue#o sangue#pedro costa#not nominated\n1992: (65th)#day of despair#o dia do desespero#manoel de oliveira#not nominated\n1993: (66th)#abraham 's valley#vale abra\u00e3o#manoel de oliveira#not nominated\n1994: (67th)#three palm trees#tr\u00eas palmeiras#jo\u00e3o botelho#not nominated\n1995: (68th)#god 's comedy#a divina com\u00e9dia#manoel de oliveira#not nominated\n1997: (70th)#journey to the beginning of the world#viagem ao princ\u00edpio do mundo#manoel de oliveira#not nominated\n1998: (71st)#anxiety#inquietude#manoel de oliveira#not nominated\n1999: (72nd)#the mutants#os mutantes#teresa villaverde#not nominated\n2000: (73rd)#too late#tarde demais#jos\u00e9 nascimento#not nominated\n2001: (74th)#camarate#camarate#lu\u00eds filipe rocha#not nominated\n2003: (76th)#a talking picture#um filme falado#manoel de oliveira#not nominated\n2004: (77th)#the miracle according to salom\u00e9#o milagre segundo salom\u00e9#m\u00e1rio barroso#not nominated\n2005: (78th)#noite escura#noite escura#jo\u00e3o canijo#not nominated\n2006: (79th)#alice#alice#marco martins#not nominated\n2007: (80th)#belle toujours#belle toujours#manoel de oliveira#not nominated\n2008: (81st)#our beloved month of august#aquele querido m\u00eas de agosto#miguel gomes#not nominated\n2009: (82nd)#doomed love#um amor de perdi\u00e7\u00e3o#m\u00e1rio barroso#not nominated\n2010: (83rd)#to die like a man#morrer como um homem#jo\u00e3o pedro rodrigues#not nominated\n2012: (85th)#blood of my blood#sangue do meu sangue#jo\u00e3o canijo#not nominated\n",
        "pandas_code": "df[df['result'] == 'not nominated']['director'].nunique() == df['director'].nunique()",
        "pandas_eval": "True"
    },
    {
        "id": 393,
        "statement": "out of all the 25 ceremony manoel de oliveira have participate in 9 ceremony",
        "label": 1,
        "table_caption": "list of portuguese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#portuguese title#director#result\n1980: (53rd)#morning undersea#manh\u00e3 submersa#lauro ant\u00f3nio#not nominated\n1982: (55th)#francisca#francisca#manoel de oliveira#not nominated\n1983: (56th)#no trace of sin#sem sombra de pecado#jos\u00e9 fonseca e costa#not nominated\n1988: (61st)#hard times#tempos dif\u00edceis#jo\u00e3o botelho#not nominated\n1989: (62nd)#the cannibals#os canibais#manoel de oliveira#not nominated\n1990: (63rd)#the king 's trial#o processo do rei#jo\u00e3o m\u00e1rio grilo#not nominated\n1991: (64th)#o sangue#o sangue#pedro costa#not nominated\n1992: (65th)#day of despair#o dia do desespero#manoel de oliveira#not nominated\n1993: (66th)#abraham 's valley#vale abra\u00e3o#manoel de oliveira#not nominated\n1994: (67th)#three palm trees#tr\u00eas palmeiras#jo\u00e3o botelho#not nominated\n1995: (68th)#god 's comedy#a divina com\u00e9dia#manoel de oliveira#not nominated\n1997: (70th)#journey to the beginning of the world#viagem ao princ\u00edpio do mundo#manoel de oliveira#not nominated\n1998: (71st)#anxiety#inquietude#manoel de oliveira#not nominated\n1999: (72nd)#the mutants#os mutantes#teresa villaverde#not nominated\n2000: (73rd)#too late#tarde demais#jos\u00e9 nascimento#not nominated\n2001: (74th)#camarate#camarate#lu\u00eds filipe rocha#not nominated\n2003: (76th)#a talking picture#um filme falado#manoel de oliveira#not nominated\n2004: (77th)#the miracle according to salom\u00e9#o milagre segundo salom\u00e9#m\u00e1rio barroso#not nominated\n2005: (78th)#noite escura#noite escura#jo\u00e3o canijo#not nominated\n2006: (79th)#alice#alice#marco martins#not nominated\n2007: (80th)#belle toujours#belle toujours#manoel de oliveira#not nominated\n2008: (81st)#our beloved month of august#aquele querido m\u00eas de agosto#miguel gomes#not nominated\n2009: (82nd)#doomed love#um amor de perdi\u00e7\u00e3o#m\u00e1rio barroso#not nominated\n2010: (83rd)#to die like a man#morrer como um homem#jo\u00e3o pedro rodrigues#not nominated\n2012: (85th)#blood of my blood#sangue do meu sangue#jo\u00e3o canijo#not nominated\n",
        "pandas_code": "df[df['director'] == 'manoel de oliveira'].shape[0] == 9",
        "pandas_eval": "True"
    },
    {
        "id": 394,
        "statement": "in 2006 the film alice direct by marco martin be not nominate for the ceremony",
        "label": 1,
        "table_caption": "list of portuguese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#portuguese title#director#result\n1980: (53rd)#morning undersea#manh\u00e3 submersa#lauro ant\u00f3nio#not nominated\n1982: (55th)#francisca#francisca#manoel de oliveira#not nominated\n1983: (56th)#no trace of sin#sem sombra de pecado#jos\u00e9 fonseca e costa#not nominated\n1988: (61st)#hard times#tempos dif\u00edceis#jo\u00e3o botelho#not nominated\n1989: (62nd)#the cannibals#os canibais#manoel de oliveira#not nominated\n1990: (63rd)#the king 's trial#o processo do rei#jo\u00e3o m\u00e1rio grilo#not nominated\n1991: (64th)#o sangue#o sangue#pedro costa#not nominated\n1992: (65th)#day of despair#o dia do desespero#manoel de oliveira#not nominated\n1993: (66th)#abraham 's valley#vale abra\u00e3o#manoel de oliveira#not nominated\n1994: (67th)#three palm trees#tr\u00eas palmeiras#jo\u00e3o botelho#not nominated\n1995: (68th)#god 's comedy#a divina com\u00e9dia#manoel de oliveira#not nominated\n1997: (70th)#journey to the beginning of the world#viagem ao princ\u00edpio do mundo#manoel de oliveira#not nominated\n1998: (71st)#anxiety#inquietude#manoel de oliveira#not nominated\n1999: (72nd)#the mutants#os mutantes#teresa villaverde#not nominated\n2000: (73rd)#too late#tarde demais#jos\u00e9 nascimento#not nominated\n2001: (74th)#camarate#camarate#lu\u00eds filipe rocha#not nominated\n2003: (76th)#a talking picture#um filme falado#manoel de oliveira#not nominated\n2004: (77th)#the miracle according to salom\u00e9#o milagre segundo salom\u00e9#m\u00e1rio barroso#not nominated\n2005: (78th)#noite escura#noite escura#jo\u00e3o canijo#not nominated\n2006: (79th)#alice#alice#marco martins#not nominated\n2007: (80th)#belle toujours#belle toujours#manoel de oliveira#not nominated\n2008: (81st)#our beloved month of august#aquele querido m\u00eas de agosto#miguel gomes#not nominated\n2009: (82nd)#doomed love#um amor de perdi\u00e7\u00e3o#m\u00e1rio barroso#not nominated\n2010: (83rd)#to die like a man#morrer como um homem#jo\u00e3o pedro rodrigues#not nominated\n2012: (85th)#blood of my blood#sangue do meu sangue#jo\u00e3o canijo#not nominated\n",
        "pandas_code": "(df['year (ceremony)'].str.contains('2006') & (df['portuguese title'] == 'alice') & (df['director'] == 'marco martins') & (df['result'] == 'not nominated')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 395,
        "statement": "allan arkush be the only person to direct 4 episode",
        "label": 1,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "(df['directed by'].value_counts() == 4).sum() == 1 and df['directed by'].value_counts().idxmax() == 'allan arkush'",
        "pandas_eval": "True"
    },
    {
        "id": 396,
        "statement": "all but 1 episode have atleast 11.27 million viewer",
        "label": 1,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df['us viewers (millions)'].ge(11.27).sum() == len(df) - 1",
        "pandas_eval": "True"
    },
    {
        "id": 397,
        "statement": "joyce chopra be the only person who didn't direct more than 1 episode",
        "label": 1,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df['directed by'].value_counts().eq(1).sum() == 1 and df['directed by'].value_counts().eq(1).idxmax() == 'joyce chopra'",
        "pandas_eval": "True"
    },
    {
        "id": 398,
        "statement": "kathy mccormick write both the most and the least viewed episode",
        "label": 1,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "(df[df['written by'].str.contains('kathy mccormick')]['us viewers (millions)'].max() == df['us viewers (millions)'].max()) and (df[df['written by'].str.contains('kathy mccormick')]['us viewers (millions)'].min() == df['us viewers (millions)'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 399,
        "statement": "all of the episode stephen williams direct have atleast 11.94 million viewer",
        "label": 1,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "all(df[df['directed by'] == 'stephen williams']['us viewers (millions)'] >= 11.94)",
        "pandas_eval": "True"
    },
    {
        "id": 400,
        "statement": "allan arkush be the only person to direct 6 episode",
        "label": 0,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df['directed by'].value_counts().eq(6).any()",
        "pandas_eval": "False"
    },
    {
        "id": 401,
        "statement": "all but 1 episode have less than 11.27 million viewer",
        "label": 0,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df['us viewers (millions)'].lt(11.27).sum() == len(df) - 1",
        "pandas_eval": "False"
    },
    {
        "id": 402,
        "statement": "joyce chopra be the only person who direct more than 1 episode",
        "label": 0,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df['directed by'].value_counts().eq(1).all()",
        "pandas_eval": "False"
    },
    {
        "id": 403,
        "statement": "kathy mccormick write the least viewed episode",
        "label": 0,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "df.loc[df['written by'].str.contains('kathy mccormick'), 'us viewers (millions)'].min() != df['us viewers (millions)'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 404,
        "statement": "all of the episode stephen williams direct have atleast 12.79 million viewer",
        "label": 0,
        "table_caption": "list of crossing jordan episodes",
        "table_text": "no in series#no in season#title#directed by#written by#us viewers (millions)#original air date\n58#1#devil may care#allan arkush#jim praytor , andi bushell#12.79#march 7 , 2004\n47#2#slam dunk#michael gershman#kathy mccormick , sharon lee watson#9.69#march 12 , 2004\n48#3#'til death do us part#michael gershman#damon lindelof , tim kring#12.79#march 14 , 2004\n50#5#dead or alive#stephen williams#aron eli coleite#11.94#march 21 , 2004\n51#6#second chances#allan arkush#kathy mccormick#14.67#march 28 , 2004\n52#7#missing pieces#stephen williams#david amann#12.94#april 4 , 2004\n53#8#most likely#joyce chopra#gary glasberg#13.31#april 18 , 2004\n54#9#all the news fit to print#allan arkush#kira arne#11.27#april 25 , 2004\n55#10#revealed#allan arkush#damon lindelof#11.86#may 9 , 2004\n56#11#he said , she said#michael gershman#jim praytor , andi bushell#12.64#may 16 , 2004\n57#12#dead in the water#stephen williams#gary glasberg#12.94#may 23 , 2004\n",
        "pandas_code": "all(df[df['directed by'] == 'stephen williams']['us viewers (millions)'] >= 12.79)",
        "pandas_eval": "False"
    },
    {
        "id": 405,
        "statement": "bangladesh have the highest population density at 1034 pop per km square while japan as the lowest population density at 339 pop per km square",
        "label": 1,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "(df.loc[df['country / region'] == 'bangladesh', 'density (pop per km 2 )'].values[0] == 1034) & (df.loc[df['country / region'] == 'japan', 'density (pop per km 2 )'].values[0] == 339)",
        "pandas_eval": "True"
    },
    {
        "id": 406,
        "statement": "no country have a population density greater than 1100 pop per km square while no country have a population density lower than 300 pop per km square",
        "label": 1,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "(df['density (pop per km 2 )'] > 1100).any() == False and (df['density (pop per km 2 )'] < 300).any() == False",
        "pandas_eval": "True"
    },
    {
        "id": 407,
        "statement": "south korea be rank third in population density with 48456369 people while rwanda be fourth with 10718379 people",
        "label": 1,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "(df[df['country / region'] == 'south korea']['rank'].values[0] == 3) & (df[df['country / region'] == 'south korea']['population'].values[0] == 48456369) & (df[df['country / region'] == 'rwanda']['rank'].values[0] == 4) & (df[df['country / region'] == 'rwanda']['population'].values[0] == 10718379)",
        "pandas_eval": "True"
    },
    {
        "id": 408,
        "statement": "no country be rank lower than 9th on this list",
        "label": 1,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "all(df['rank'] <= 9)",
        "pandas_eval": "True"
    },
    {
        "id": 409,
        "statement": "the country with the largest population be india with 1210193422 people and japan with 127960000 people",
        "label": 1,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "df[df['population'] == df['population'].max()]['country / region'].values[0] == 'india' and df[df['country / region'] == 'japan']['population'].values[0] == 127960000",
        "pandas_eval": "True"
    },
    {
        "id": 410,
        "statement": "bangladesh have the lowest population density while japan have the highest population density",
        "label": 0,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "df.loc[df['country / region'] == 'bangladesh', 'density (pop per km 2 )'].values[0] == df['density (pop per km 2 )'].min() and df.loc[df['country / region'] == 'japan', 'density (pop per km 2 )'].values[0] == df['density (pop per km 2 )'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 411,
        "statement": "the top 9 country with the highest population density all have density greater than 400 pop per km square",
        "label": 0,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "all(df['density (pop per km 2 )'] > 400)",
        "pandas_eval": "False"
    },
    {
        "id": 412,
        "statement": "south korea be rank third in population density and have 10 time the population of the fourth ranking , rwanda",
        "label": 0,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "(df.loc[df['country / region'] == 'south korea', 'rank'].values[0] == 3) and (df.loc[df['country / region'] == 'south korea', 'population'].values[0] == 10 * df.loc[df['country / region'] == 'rwanda', 'population'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 413,
        "statement": "the table show the population density of the top 100 most population - dense country",
        "label": 0,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "len(df) == 100",
        "pandas_eval": "False"
    },
    {
        "id": 414,
        "statement": "the country with the largest total population on this list be bangladesh and taiwan",
        "label": 0,
        "table_caption": "population density",
        "table_text": "rank#country / region#population#area (km 2 )#density (pop per km 2 )\n1#bangladesh#152518015#147570#1034\n2#taiwan (roc)#22955395#36190#634\n3#south korea#48456369#99538#487\n4#rwanda#10718379#26338#407\n5#netherlands#16760000#41526#404\n6#india#1210193422#3185263#380\n7#belgium#11007020#30528#361\n8#sri lanka#20653000#65610#345\n9#japan#127960000#377944#339\n",
        "pandas_code": "(df.loc[df['population'].idxmax(), 'country / region'] == 'bangladesh') and (df.loc[df['population'].idxmax(), 'country / region'] == 'taiwan (roc)')",
        "pandas_eval": "False"
    },
    {
        "id": 415,
        "statement": "ontario have over 2 time as many south asian in 2011 as any other province",
        "label": 1,
        "table_caption": "south asian canadian",
        "table_text": "province#south asians 2001#% 2001#south asians 2011#% 2011\nontario#554870#4.9%#1003180#7.9%\nbritish columbia#210295#5.4%#311265#7.2%\nalberta#69580#2.4%#159055#4.4%\nquebec#59510#0.8%#91400#1.2%\nmanitoba#12875#1.2%#26220#2.2%\nsaskatchewan#4090#0.4%#12620#1.3%\nnova scotia#2895#0.3%#5935#0.7%\nnew brunswick#1415#0.2%#3090#0.4%\nnewfoundland and labrador#1010#0.2%#2005#0.4%\nprince edward island#115#0.1%#500#0.4%\nyukon#205#0.7%#340#1.0%\nnorthwest territories#190#0.5%#200#0.5%\nnunavut#30#0.1%#115#0.4%\n",
        "pandas_code": "df[df['province'] == 'ontario']['south asians 2011'].values[0] > 2 * df[df['province'] != 'ontario']['south asians 2011'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 416,
        "statement": "the south asian in alberta more than double between 2001 and 2011",
        "label": 1,
        "table_caption": "south asian canadian",
        "table_text": "province#south asians 2001#% 2001#south asians 2011#% 2011\nontario#554870#4.9%#1003180#7.9%\nbritish columbia#210295#5.4%#311265#7.2%\nalberta#69580#2.4%#159055#4.4%\nquebec#59510#0.8%#91400#1.2%\nmanitoba#12875#1.2%#26220#2.2%\nsaskatchewan#4090#0.4%#12620#1.3%\nnova scotia#2895#0.3%#5935#0.7%\nnew brunswick#1415#0.2%#3090#0.4%\nnewfoundland and labrador#1010#0.2%#2005#0.4%\nprince edward island#115#0.1%#500#0.4%\nyukon#205#0.7%#340#1.0%\nnorthwest territories#190#0.5%#200#0.5%\nnunavut#30#0.1%#115#0.4%\n",
        "pandas_code": "df[df['province'] == 'alberta']['south asians 2011'].values[0] > 2 * df[df['province'] == 'alberta']['south asians 2001'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 417,
        "statement": "4 province be over 1% 2001",
        "label": 1,
        "table_caption": "south asian canadian",
        "table_text": "province#south asians 2001#% 2001#south asians 2011#% 2011\nontario#554870#4.9%#1003180#7.9%\nbritish columbia#210295#5.4%#311265#7.2%\nalberta#69580#2.4%#159055#4.4%\nquebec#59510#0.8%#91400#1.2%\nmanitoba#12875#1.2%#26220#2.2%\nsaskatchewan#4090#0.4%#12620#1.3%\nnova scotia#2895#0.3%#5935#0.7%\nnew brunswick#1415#0.2%#3090#0.4%\nnewfoundland and labrador#1010#0.2%#2005#0.4%\nprince edward island#115#0.1%#500#0.4%\nyukon#205#0.7%#340#1.0%\nnorthwest territories#190#0.5%#200#0.5%\nnunavut#30#0.1%#115#0.4%\n",
        "pandas_code": "len(df[df['% 2001'].str.replace('%', '').astype(float) > 1]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 418,
        "statement": "the percentage of south asian in all territory increase between 2001 and 2011 with the exception of the northwest territories",
        "label": 1,
        "table_caption": "south asian canadian",
        "table_text": "province#south asians 2001#% 2001#south asians 2011#% 2011\nontario#554870#4.9%#1003180#7.9%\nbritish columbia#210295#5.4%#311265#7.2%\nalberta#69580#2.4%#159055#4.4%\nquebec#59510#0.8%#91400#1.2%\nmanitoba#12875#1.2%#26220#2.2%\nsaskatchewan#4090#0.4%#12620#1.3%\nnova scotia#2895#0.3%#5935#0.7%\nnew brunswick#1415#0.2%#3090#0.4%\nnewfoundland and labrador#1010#0.2%#2005#0.4%\nprince edward island#115#0.1%#500#0.4%\nyukon#205#0.7%#340#1.0%\nnorthwest territories#190#0.5%#200#0.5%\nnunavut#30#0.1%#115#0.4%\n",
        "pandas_code": "all(df[df['province'] != 'northwest territories']['% 2011'].str.strip('%').astype(float) > df[df['province'] != 'northwest territories']['% 2001'].str.strip('%').astype(float))",
        "pandas_eval": "True"
    },
    {
        "id": 419,
        "statement": "more south asian come to nunavut between 2001 and 2011 than the northwest territories",
        "label": 1,
        "table_caption": "south asian canadian",
        "table_text": "province#south asians 2001#% 2001#south asians 2011#% 2011\nontario#554870#4.9%#1003180#7.9%\nbritish columbia#210295#5.4%#311265#7.2%\nalberta#69580#2.4%#159055#4.4%\nquebec#59510#0.8%#91400#1.2%\nmanitoba#12875#1.2%#26220#2.2%\nsaskatchewan#4090#0.4%#12620#1.3%\nnova scotia#2895#0.3%#5935#0.7%\nnew brunswick#1415#0.2%#3090#0.4%\nnewfoundland and labrador#1010#0.2%#2005#0.4%\nprince edward island#115#0.1%#500#0.4%\nyukon#205#0.7%#340#1.0%\nnorthwest territories#190#0.5%#200#0.5%\nnunavut#30#0.1%#115#0.4%\n",
        "pandas_code": "(df.loc[df['province'] == 'nunavut', 'south asians 2011'].values[0] - df.loc[df['province'] == 'nunavut', 'south asians 2001'].values[0]) > (df.loc[df['province'] == 'northwest territories', 'south asians 2011'].values[0] - df.loc[df['province'] == 'northwest territories', 'south asians 2001'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 421,
        "statement": "michael schumacher be 1st in the grid and finish in second",
        "label": 1,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "(df[df['driver'] == 'michael schumacher']['grid'].iloc[0] == 1) & (df[df['driver'] == 'michael schumacher']['time / retired'].iloc[0] == '+ 20.235')",
        "pandas_eval": "True"
    },
    {
        "id": 422,
        "statement": "at the end of the race tarso marque be 3 lap behind",
        "label": 1,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "df[df['driver'] == 'tarso marques']['time / retired'].iloc[0] == '+ 3 laps'",
        "pandas_eval": "True"
    },
    {
        "id": 423,
        "statement": "ferrari have 2 driver in the race , michael schumacher and rubens barrichello",
        "label": 1,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "len(df[(df['constructor'] == 'ferrari') & (df['driver'].isin(['michael schumacher', 'rubens barrichello']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 424,
        "statement": "during the race 4 driver be involve in a collision or accident",
        "label": 1,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "len(df[df['time / retired'].str.contains('collision|accident', na=False)]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 425,
        "statement": "only 2 driver finish the 2001 canadian grand prix",
        "label": 0,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "len(df[df['time / retired'].str.contains(r'^\\d+:\\d+:\\d+\\.\\d+$', na=False)]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 426,
        "statement": "kimi r\u00e4ikk\u00f6nen be 1st in the grid and finish in second",
        "label": 0,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "(df[df['driver'] == 'kimi r\u00e4ikk\u00f6nen']['grid'].values[0] == 1) & (df[df['driver'] == 'kimi r\u00e4ikk\u00f6nen']['time / retired'].values[0] == '+ 20.235')",
        "pandas_eval": "False"
    },
    {
        "id": 427,
        "statement": "at the end of the race jenson button be 3 lap behind",
        "label": 0,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "df[df['driver'] == 'jenson button']['laps'].iloc[0] == df['laps'].max() - 3",
        "pandas_eval": "False"
    },
    {
        "id": 428,
        "statement": "ferrari have 2 driver in the race , jos verstappen and mika h\u00e4kkinen",
        "label": 0,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "(df['constructor'] == 'ferrari').sum() == 1 and not set(['jos verstappen', 'mika h\u00e4kkinen']).issubset(df[df['constructor'] == 'ferrari']['driver'])",
        "pandas_eval": "False"
    },
    {
        "id": 429,
        "statement": "during the race 10 driver be involve in a collision or accident",
        "label": 0,
        "table_caption": "2001 canadian grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nralf schumacher#williams - bmw#69#1:34:31.522#2\nmichael schumacher#ferrari#69#+ 20.235#1\nmika h\u00e4kkinen#mclaren - mercedes#69#+ 40.672#8\nkimi r\u00e4ikk\u00f6nen#sauber - petronas#69#+ 1:08.116#7\njean alesi#prost - acer#69#+ 1:10.435#16\npedro de la rosa#jaguar - cosworth#68#+ 1 lap#14\nricardo zonta#jordan - honda#68#+ 1 lap#12\nluciano burti#prost - acer#68#+ 1 lap#19\ntarso marques#minardi - european#66#+ 3 laps#21\njos verstappen#arrows - asiatech#65#brakes#13\njarno trulli#jordan - honda#63#brakes#4\ndavid coulthard#mclaren - mercedes#54#engine#3\nolivier panis#bar - honda#38#brakes#6\njacques villeneuve#bar - honda#34#driveshaft#9\nenrique bernoldi#arrows - asiatech#24#engine#17\njuan pablo montoya#williams - bmw#19#accident#10\nrubens barrichello#ferrari#19#spun off#5\njenson button#benetton - renault#17#oil leak#20\nfernando alonso#minardi - european#7#transmission#22\nnick heidfeld#sauber - petronas#1#collision#11\neddie irvine#jaguar - cosworth#1#collision#15\ngiancarlo fisichella#benetton - renault#0#collision#18\n",
        "pandas_code": "df[df['time / retired'].str.contains('collision|accident', na=False)].shape[0] == 10",
        "pandas_eval": "False"
    },
    {
        "id": 430,
        "statement": "central florida be a champion 3 time more",
        "label": 1,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "df[df['champion'] == 'central florida'].shape[0] >= 3",
        "pandas_eval": "True"
    },
    {
        "id": 431,
        "statement": "north carolina state win 3 in 2005 , 2006 , 2007",
        "label": 1,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "df[(df['year'].isin([2005, 2006, 2007])) & (df['champion'] == 'north carolina state')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 434,
        "statement": "morrisville , north carolina have host the final 4 time",
        "label": 1,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "df[df['location'].str.contains('morrisville , nc')].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 435,
        "statement": "central florida be runner up in 2004 , 2005 , and 2006",
        "label": 0,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "not df[(df['year'].isin([2004, 2005, 2006])) & (df['runner - up'] == 'central florida')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 436,
        "statement": "north carolina state be runner up in 2006",
        "label": 0,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "df[(df['year'] == 2006) & (df['runner - up'] == 'north carolina state')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 437,
        "statement": "elon university score 8 - 5 in 2000",
        "label": 0,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "(df[(df['year'] == 2000) & (df['champion'] == 'elon university') & (df['score'] == '8 - 5')].shape[0] > 0)",
        "pandas_eval": "False"
    },
    {
        "id": 438,
        "statement": "north carolina state beat east carolina in 2006",
        "label": 0,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "((df['year'] == 2006) & (df['champion'] == 'north carolina state') & (df['runner - up'] == 'east carolina')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 439,
        "statement": "morrisville , north carolina host unc charlotte in 2006",
        "label": 0,
        "table_caption": "southeastern collegiate roller hockey league",
        "table_text": "year#champion#score#runner - up#location#semi - finalist 1#semi - finalist 2\n2000#elon university#5 - 4ot#north carolina state#durham , nc#na#na\n2001#east carolina#na#unc charlotte#morrisville , nc#north carolina state#na\n2002#unc charlotte#na#elon#charlotte , nc#east carolina#north carolina state\n2003#north carolina state#7 - 6#elon university#morrisville , nc#east carolina#south carolina\n2004#central florida#7 - 4#south florida#ellenton , fl#florida#florida atlantic\n2004#north carolina state#9 - 1#east carolina#morrisville , nc#unc - charlotte#south carolina\n2005#central florida#5 - 4#florida#ellenton , fl#south florida#florida atlantic\n2005#north carolina state#8 - 5#unc - charlotte#morrisville , nc#east carolina#south carolina\n2006#north carolina state#6 - 3#south florida#snellville , ga#central florida#florida\n2007#north carolina state#5 - 4 ot#florida international#snellville , ga#central florida#florida state\n2008#central florida#6 - 2#florida international#snellville , ga#florida#florida state\n",
        "pandas_code": "((df['location'] == 'morrisville , nc') & (df['year'] == 2006) & ((df['champion'] == 'unc charlotte') | (df['runner - up'] == 'unc charlotte') | (df['semi - finalist 1'] == 'unc charlotte') | (df['semi - finalist 2'] == 'unc charlotte'))).any()",
        "pandas_eval": "False"
    },
    {
        "id": 440,
        "statement": "5 out of 6 candidate run unopposed during georgia 's 1936 house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#hugh peterson#democratic#1934#re - elected#hugh peterson (d) unopposed\ngeorgia 2#edward e cox#democratic#1924#re - elected#edward e cox (d) unopposed\ngeorgia 3#bryant t castellow#democratic#1932#retired democratic hold#stephen pace (d) unopposed\ngeorgia 4#emmett marshall owen#democratic#1932#re - elected#emmett marshall owen (d) unopposed\ngeorgia 6#carl vinson#democratic#1914#re - elected#carl vinson (d) unopposed\ngeorgia 8#braswell deen#democratic#1932#re - elected#braswell deen (d) 94.5% ben j ford (r) 5.5%\n",
        "pandas_code": "sum(df['candidates'].str.contains('unopposed')) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 441,
        "statement": "the average year an incumbent be first elect be 1928",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#hugh peterson#democratic#1934#re - elected#hugh peterson (d) unopposed\ngeorgia 2#edward e cox#democratic#1924#re - elected#edward e cox (d) unopposed\ngeorgia 3#bryant t castellow#democratic#1932#retired democratic hold#stephen pace (d) unopposed\ngeorgia 4#emmett marshall owen#democratic#1932#re - elected#emmett marshall owen (d) unopposed\ngeorgia 6#carl vinson#democratic#1914#re - elected#carl vinson (d) unopposed\ngeorgia 8#braswell deen#democratic#1932#re - elected#braswell deen (d) 94.5% ben j ford (r) 5.5%\n",
        "pandas_code": "df['first elected'].mean() == 1928",
        "pandas_eval": "True"
    },
    {
        "id": 442,
        "statement": "all candidate come from the democratic party during georgia 's 1936 house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#hugh peterson#democratic#1934#re - elected#hugh peterson (d) unopposed\ngeorgia 2#edward e cox#democratic#1924#re - elected#edward e cox (d) unopposed\ngeorgia 3#bryant t castellow#democratic#1932#retired democratic hold#stephen pace (d) unopposed\ngeorgia 4#emmett marshall owen#democratic#1932#re - elected#emmett marshall owen (d) unopposed\ngeorgia 6#carl vinson#democratic#1914#re - elected#carl vinson (d) unopposed\ngeorgia 8#braswell deen#democratic#1932#re - elected#braswell deen (d) 94.5% ben j ford (r) 5.5%\n",
        "pandas_code": "all(df['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 443,
        "statement": "carl vinson have be in office the longest of the incumbent in georgia 's 1936 house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#hugh peterson#democratic#1934#re - elected#hugh peterson (d) unopposed\ngeorgia 2#edward e cox#democratic#1924#re - elected#edward e cox (d) unopposed\ngeorgia 3#bryant t castellow#democratic#1932#retired democratic hold#stephen pace (d) unopposed\ngeorgia 4#emmett marshall owen#democratic#1932#re - elected#emmett marshall owen (d) unopposed\ngeorgia 6#carl vinson#democratic#1914#re - elected#carl vinson (d) unopposed\ngeorgia 8#braswell deen#democratic#1932#re - elected#braswell deen (d) 94.5% ben j ford (r) 5.5%\n",
        "pandas_code": "df.loc[df['incumbent'] == 'carl vinson', 'first elected'].iloc[0] == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 444,
        "statement": "the only incumbent not re - elect during georgia 's 1936 house of representative election retire the democratic party 's hold in their district",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#hugh peterson#democratic#1934#re - elected#hugh peterson (d) unopposed\ngeorgia 2#edward e cox#democratic#1924#re - elected#edward e cox (d) unopposed\ngeorgia 3#bryant t castellow#democratic#1932#retired democratic hold#stephen pace (d) unopposed\ngeorgia 4#emmett marshall owen#democratic#1932#re - elected#emmett marshall owen (d) unopposed\ngeorgia 6#carl vinson#democratic#1914#re - elected#carl vinson (d) unopposed\ngeorgia 8#braswell deen#democratic#1932#re - elected#braswell deen (d) 94.5% ben j ford (r) 5.5%\n",
        "pandas_code": "df[df['result'] == 'retired democratic hold']['incumbent'].iloc[0] == 'bryant t castellow'",
        "pandas_eval": "True"
    },
    {
        "id": 445,
        "statement": "3 of these tournament occur in consecutive year from 2002 to 2004",
        "label": 1,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df[df['date'].str.extract('(\\\\d{4})')[0].astype(int).between(2002, 2004)]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 446,
        "statement": "3 out of these 5 tournament play by patty schnyder occur on a carpet surface",
        "label": 1,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "df[df['surface'] == 'carpet'].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 447,
        "statement": "patty schnyder partner with barbara schett more than any other partner",
        "label": 1,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "df['partner'].value_counts().idxmax() == 'barbara schett'",
        "pandas_eval": "True"
    },
    {
        "id": 448,
        "statement": "in all 5 tournament , patty schnyder never face the same opponent 2 time in the final",
        "label": 1,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df['opponent in the final'].unique()) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 449,
        "statement": "the 2 tournament occur in germany be in different city and with different surface",
        "label": 1,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "(df[df['tournament'].str.contains('germany', case=False)].groupby('tournament')['surface'].nunique().size == 2) & (df[df['tournament'].str.contains('germany', case=False)]['tournament'].nunique() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 450,
        "statement": "4 of these tournament occur in consecutive year from 2002 to 2004",
        "label": 0,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "(df['date'].str.extract(r'(\\d{4})')[0].astype(int).between(2002, 2004).sum() == 4)",
        "pandas_eval": "False"
    },
    {
        "id": 451,
        "statement": "3 out of these 5 tournament play by patty schnyder occur on a clay surface",
        "label": 0,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "df[df['surface'] == 'clay'].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 452,
        "statement": "patty schnyder partner with barbara schett fewer than any other partner",
        "label": 0,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "df['partner'].value_counts().idxmax() != 'barbara schett'",
        "pandas_eval": "False"
    },
    {
        "id": 453,
        "statement": "in all 5 tournament , patty schnyder always face the same opponent 2 time in the final",
        "label": 0,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df['opponent in the final'].unique()) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 454,
        "statement": "the 2 tournament occur in germany be in the same city and with the same surface",
        "label": 0,
        "table_caption": "patty schnyder",
        "table_text": "date#tournament#surface#partner#opponent in the final#score\n3 may 1998#hamburg , germany#clay#barbara schett#martina hingis jana novotn\u00e1#7 - 6 , 3 - 6 , 6 - 3\n17 february 2002#antwerp , belgium#carpet#magdalena maleeva#nathalie dechy meilen tu#6 - 3 , 6 - 7 , 6 - 3\n9 february 2003#paris , france#carpet#barbara schett#marion bartoli st\u00e9phanie cohen - aloro#2 - 6 , 6 - 2 , 7 - 6\n15 february 2004#paris , france#carpet#barbara schett#silvia farina elia francesca schiavone#6 - 3 , 6 - 2\n5 october 2008#stuttgart , germany#hard#anna - lena gr\u00f6nefeld#kv\u011bta peschke rennae stubbs#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df[(df['tournament'].str.contains('germany')) & (df['surface'] == df[df['tournament'].str.contains('germany')]['surface'].iloc[0]) & (df['tournament'].str.split(',').str[0] == df[df['tournament'].str.contains('germany')]['tournament'].str.split(',').str[0].iloc[0])]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 456,
        "statement": "the attendance be exactly the same for the game on sept 16 and sept 22",
        "label": 1,
        "table_caption": "1974 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record#attendance\n1#sept 16#oakland raiders#win#21#20#22#1 - 0#80020\n2#sept 22#miami dolphins#loss#16#24#16#1 - 1#80020\n3#sept 29#new york jets#win#16#12#17#2 - 1#76978\n4#oct 6#green bay packers#win#27#7#22#3 - 1#56267\n5#oct 13#baltimore colts#win#27#14#15#4 - 1#40626\n6#oct 20#new england patriots#win#30#28#19#5 - 1#78935\n7#oct 27#chicago bears#win#16#6#16#6 - 1#78084\n8#nov 3#new england patriots#win#29#28#22#7 - 1#61279\n9#nov 10#houston oilers#loss#9#21#16#7 - 2#79144\n10#nov 17#miami dolphins#loss#28#35#16#7 - 3#69313\n11#nov 24#cleveland browns#win#15#10#10#8 - 3#66504\n12#dec 1#baltimore colts#win#6#0#9#9 - 3#75325\n13#dec 8#new york jets#loss#10#20#12#9 - 4#61091\n",
        "pandas_code": "df[df['date'].isin(['sept 16', 'sept 22'])]['attendance'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 457,
        "statement": "the bill score their fewest first down when they play the baltimore colts the second time",
        "label": 1,
        "table_caption": "1974 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record#attendance\n1#sept 16#oakland raiders#win#21#20#22#1 - 0#80020\n2#sept 22#miami dolphins#loss#16#24#16#1 - 1#80020\n3#sept 29#new york jets#win#16#12#17#2 - 1#76978\n4#oct 6#green bay packers#win#27#7#22#3 - 1#56267\n5#oct 13#baltimore colts#win#27#14#15#4 - 1#40626\n6#oct 20#new england patriots#win#30#28#19#5 - 1#78935\n7#oct 27#chicago bears#win#16#6#16#6 - 1#78084\n8#nov 3#new england patriots#win#29#28#22#7 - 1#61279\n9#nov 10#houston oilers#loss#9#21#16#7 - 2#79144\n10#nov 17#miami dolphins#loss#28#35#16#7 - 3#69313\n11#nov 24#cleveland browns#win#15#10#10#8 - 3#66504\n12#dec 1#baltimore colts#win#6#0#9#9 - 3#75325\n13#dec 8#new york jets#loss#10#20#12#9 - 4#61091\n",
        "pandas_code": "df.loc[df['opponent'] == 'baltimore colts', 'bills first downs'].min() == df.loc[df['game'] == 12, 'bills first downs'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 458,
        "statement": "attendance be at it 's lowest when the bill play at the baltimore colt",
        "label": 1,
        "table_caption": "1974 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record#attendance\n1#sept 16#oakland raiders#win#21#20#22#1 - 0#80020\n2#sept 22#miami dolphins#loss#16#24#16#1 - 1#80020\n3#sept 29#new york jets#win#16#12#17#2 - 1#76978\n4#oct 6#green bay packers#win#27#7#22#3 - 1#56267\n5#oct 13#baltimore colts#win#27#14#15#4 - 1#40626\n6#oct 20#new england patriots#win#30#28#19#5 - 1#78935\n7#oct 27#chicago bears#win#16#6#16#6 - 1#78084\n8#nov 3#new england patriots#win#29#28#22#7 - 1#61279\n9#nov 10#houston oilers#loss#9#21#16#7 - 2#79144\n10#nov 17#miami dolphins#loss#28#35#16#7 - 3#69313\n11#nov 24#cleveland browns#win#15#10#10#8 - 3#66504\n12#dec 1#baltimore colts#win#6#0#9#9 - 3#75325\n13#dec 8#new york jets#loss#10#20#12#9 - 4#61091\n",
        "pandas_code": "df.loc[df['opponent'] == 'baltimore colts', 'attendance'].min() == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 459,
        "statement": "the bill score their lowest point in all 13 game but still result in a win",
        "label": 1,
        "table_caption": "1974 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record#attendance\n1#sept 16#oakland raiders#win#21#20#22#1 - 0#80020\n2#sept 22#miami dolphins#loss#16#24#16#1 - 1#80020\n3#sept 29#new york jets#win#16#12#17#2 - 1#76978\n4#oct 6#green bay packers#win#27#7#22#3 - 1#56267\n5#oct 13#baltimore colts#win#27#14#15#4 - 1#40626\n6#oct 20#new england patriots#win#30#28#19#5 - 1#78935\n7#oct 27#chicago bears#win#16#6#16#6 - 1#78084\n8#nov 3#new england patriots#win#29#28#22#7 - 1#61279\n9#nov 10#houston oilers#loss#9#21#16#7 - 2#79144\n10#nov 17#miami dolphins#loss#28#35#16#7 - 3#69313\n11#nov 24#cleveland browns#win#15#10#10#8 - 3#66504\n12#dec 1#baltimore colts#win#6#0#9#9 - 3#75325\n13#dec 8#new york jets#loss#10#20#12#9 - 4#61091\n",
        "pandas_code": "df.loc[df['bills points'].idxmin(), 'result'] == 'win'",
        "pandas_eval": "True"
    },
    {
        "id": 460,
        "statement": "portugal , kazakhstan , norway , slovenia , and luxembourg all have 1 holder",
        "label": 1,
        "table_caption": "vuelta a espa\u00f1a records and statistics",
        "table_text": "rank#country#jerseys#vuelta wins#points#kom#combo#most recent cyclist#most recent date#different holders\n1#spain#631#31#15#45#12#daniel moreno#2013 , stage 9#85\n2#france#155#9#5#7#2#sylvain chavanel#2011 , stage 7#24\n3#belgium#140#7#13#1#2#philippe gilbert#2010 , stage 7#26\n4#italy#100#5#4#3#1#vincenzo nibali#2013 , stage 18#18\n5#switzerland#89#5#2#2#1#fabian cancellara#2009 , stage 7#5\n6#germany#50#4#7#0#0#andr\u00e9 greipel#2009 , stage 6#7\n7#netherlands#45#2#5#1#0#bauke mollema#2011 , stage 9#10\n8#russia#30#2#0#1#2#denis menchov#2007 , stage 21#3\n9#united kingdom#26#0#1#0#0#bradley wiggins#2011 , stage 14#6\n10#colombia#23#1#0#6#1#santiago botero#2001 , stage 7#4\n11#ireland#17#1#4#0#2#nicolas roche#2013 , stage 8#3\n12#united states#12#1#0#0#1#chris horner#2013 , stage 21#3\n13 =#portugal#5#0#0#0#0#joaquim agostinho#1976 , stage 16#1\n13 =#kazakhstan#5#1#0#0#1#alexander vinokourov#2006 , stage 21#1\n13 =#australia#5#0#0#1#0#cadel evans#2009 , stage 8#2\n13 =#denmark#5#0#0#0#0#jakob fuglsang#2011 , stage 1#2\n17 =#norway#3#0#1#0#0#thor hushovd#2006 , stage 4#1\n17 =#slovenia#3#0#0#0#0#janez brajkovi\u010d#2013 , stage 1#1\n18#luxembourg#2#0#0#0#0#beno\u00eet joachim#2004 , stage 4#1\n",
        "pandas_code": "all(df[df['country'].isin(['portugal', 'kazakhstan', 'norway', 'slovenia', 'luxembourg'])]['different holders'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 461,
        "statement": "spain have more holder than france",
        "label": 1,
        "table_caption": "vuelta a espa\u00f1a records and statistics",
        "table_text": "rank#country#jerseys#vuelta wins#points#kom#combo#most recent cyclist#most recent date#different holders\n1#spain#631#31#15#45#12#daniel moreno#2013 , stage 9#85\n2#france#155#9#5#7#2#sylvain chavanel#2011 , stage 7#24\n3#belgium#140#7#13#1#2#philippe gilbert#2010 , stage 7#26\n4#italy#100#5#4#3#1#vincenzo nibali#2013 , stage 18#18\n5#switzerland#89#5#2#2#1#fabian cancellara#2009 , stage 7#5\n6#germany#50#4#7#0#0#andr\u00e9 greipel#2009 , stage 6#7\n7#netherlands#45#2#5#1#0#bauke mollema#2011 , stage 9#10\n8#russia#30#2#0#1#2#denis menchov#2007 , stage 21#3\n9#united kingdom#26#0#1#0#0#bradley wiggins#2011 , stage 14#6\n10#colombia#23#1#0#6#1#santiago botero#2001 , stage 7#4\n11#ireland#17#1#4#0#2#nicolas roche#2013 , stage 8#3\n12#united states#12#1#0#0#1#chris horner#2013 , stage 21#3\n13 =#portugal#5#0#0#0#0#joaquim agostinho#1976 , stage 16#1\n13 =#kazakhstan#5#1#0#0#1#alexander vinokourov#2006 , stage 21#1\n13 =#australia#5#0#0#1#0#cadel evans#2009 , stage 8#2\n13 =#denmark#5#0#0#0#0#jakob fuglsang#2011 , stage 1#2\n17 =#norway#3#0#1#0#0#thor hushovd#2006 , stage 4#1\n17 =#slovenia#3#0#0#0#0#janez brajkovi\u010d#2013 , stage 1#1\n18#luxembourg#2#0#0#0#0#beno\u00eet joachim#2004 , stage 4#1\n",
        "pandas_code": "df[df['country'] == 'spain']['different holders'].values[0] > df[df['country'] == 'france']['different holders'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 462,
        "statement": "france and belgium both have more than 20 holder",
        "label": 1,
        "table_caption": "vuelta a espa\u00f1a records and statistics",
        "table_text": "rank#country#jerseys#vuelta wins#points#kom#combo#most recent cyclist#most recent date#different holders\n1#spain#631#31#15#45#12#daniel moreno#2013 , stage 9#85\n2#france#155#9#5#7#2#sylvain chavanel#2011 , stage 7#24\n3#belgium#140#7#13#1#2#philippe gilbert#2010 , stage 7#26\n4#italy#100#5#4#3#1#vincenzo nibali#2013 , stage 18#18\n5#switzerland#89#5#2#2#1#fabian cancellara#2009 , stage 7#5\n6#germany#50#4#7#0#0#andr\u00e9 greipel#2009 , stage 6#7\n7#netherlands#45#2#5#1#0#bauke mollema#2011 , stage 9#10\n8#russia#30#2#0#1#2#denis menchov#2007 , stage 21#3\n9#united kingdom#26#0#1#0#0#bradley wiggins#2011 , stage 14#6\n10#colombia#23#1#0#6#1#santiago botero#2001 , stage 7#4\n11#ireland#17#1#4#0#2#nicolas roche#2013 , stage 8#3\n12#united states#12#1#0#0#1#chris horner#2013 , stage 21#3\n13 =#portugal#5#0#0#0#0#joaquim agostinho#1976 , stage 16#1\n13 =#kazakhstan#5#1#0#0#1#alexander vinokourov#2006 , stage 21#1\n13 =#australia#5#0#0#1#0#cadel evans#2009 , stage 8#2\n13 =#denmark#5#0#0#0#0#jakob fuglsang#2011 , stage 1#2\n17 =#norway#3#0#1#0#0#thor hushovd#2006 , stage 4#1\n17 =#slovenia#3#0#0#0#0#janez brajkovi\u010d#2013 , stage 1#1\n18#luxembourg#2#0#0#0#0#beno\u00eet joachim#2004 , stage 4#1\n",
        "pandas_code": "(df[df['country'].isin(['france', 'belgium'])]['different holders'] > 20).all()",
        "pandas_eval": "True"
    },
    {
        "id": 463,
        "statement": "spain , france , and belgium all have more than 100 jersey",
        "label": 1,
        "table_caption": "vuelta a espa\u00f1a records and statistics",
        "table_text": "rank#country#jerseys#vuelta wins#points#kom#combo#most recent cyclist#most recent date#different holders\n1#spain#631#31#15#45#12#daniel moreno#2013 , stage 9#85\n2#france#155#9#5#7#2#sylvain chavanel#2011 , stage 7#24\n3#belgium#140#7#13#1#2#philippe gilbert#2010 , stage 7#26\n4#italy#100#5#4#3#1#vincenzo nibali#2013 , stage 18#18\n5#switzerland#89#5#2#2#1#fabian cancellara#2009 , stage 7#5\n6#germany#50#4#7#0#0#andr\u00e9 greipel#2009 , stage 6#7\n7#netherlands#45#2#5#1#0#bauke mollema#2011 , stage 9#10\n8#russia#30#2#0#1#2#denis menchov#2007 , stage 21#3\n9#united kingdom#26#0#1#0#0#bradley wiggins#2011 , stage 14#6\n10#colombia#23#1#0#6#1#santiago botero#2001 , stage 7#4\n11#ireland#17#1#4#0#2#nicolas roche#2013 , stage 8#3\n12#united states#12#1#0#0#1#chris horner#2013 , stage 21#3\n13 =#portugal#5#0#0#0#0#joaquim agostinho#1976 , stage 16#1\n13 =#kazakhstan#5#1#0#0#1#alexander vinokourov#2006 , stage 21#1\n13 =#australia#5#0#0#1#0#cadel evans#2009 , stage 8#2\n13 =#denmark#5#0#0#0#0#jakob fuglsang#2011 , stage 1#2\n17 =#norway#3#0#1#0#0#thor hushovd#2006 , stage 4#1\n17 =#slovenia#3#0#0#0#0#janez brajkovi\u010d#2013 , stage 1#1\n18#luxembourg#2#0#0#0#0#beno\u00eet joachim#2004 , stage 4#1\n",
        "pandas_code": "all(df[df['country'].isin(['spain', 'france', 'belgium'])]['jerseys'] > 100)",
        "pandas_eval": "True"
    },
    {
        "id": 464,
        "statement": "switzerland have less holder than the netherlands",
        "label": 1,
        "table_caption": "vuelta a espa\u00f1a records and statistics",
        "table_text": "rank#country#jerseys#vuelta wins#points#kom#combo#most recent cyclist#most recent date#different holders\n1#spain#631#31#15#45#12#daniel moreno#2013 , stage 9#85\n2#france#155#9#5#7#2#sylvain chavanel#2011 , stage 7#24\n3#belgium#140#7#13#1#2#philippe gilbert#2010 , stage 7#26\n4#italy#100#5#4#3#1#vincenzo nibali#2013 , stage 18#18\n5#switzerland#89#5#2#2#1#fabian cancellara#2009 , stage 7#5\n6#germany#50#4#7#0#0#andr\u00e9 greipel#2009 , stage 6#7\n7#netherlands#45#2#5#1#0#bauke mollema#2011 , stage 9#10\n8#russia#30#2#0#1#2#denis menchov#2007 , stage 21#3\n9#united kingdom#26#0#1#0#0#bradley wiggins#2011 , stage 14#6\n10#colombia#23#1#0#6#1#santiago botero#2001 , stage 7#4\n11#ireland#17#1#4#0#2#nicolas roche#2013 , stage 8#3\n12#united states#12#1#0#0#1#chris horner#2013 , stage 21#3\n13 =#portugal#5#0#0#0#0#joaquim agostinho#1976 , stage 16#1\n13 =#kazakhstan#5#1#0#0#1#alexander vinokourov#2006 , stage 21#1\n13 =#australia#5#0#0#1#0#cadel evans#2009 , stage 8#2\n13 =#denmark#5#0#0#0#0#jakob fuglsang#2011 , stage 1#2\n17 =#norway#3#0#1#0#0#thor hushovd#2006 , stage 4#1\n17 =#slovenia#3#0#0#0#0#janez brajkovi\u010d#2013 , stage 1#1\n18#luxembourg#2#0#0#0#0#beno\u00eet joachim#2004 , stage 4#1\n",
        "pandas_code": "(df[df['country'] == 'switzerland']['different holders'].values[0] < df[df['country'] == 'netherlands']['different holders'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 465,
        "statement": "sydney fc be 1 of 3 club with an australian marquee",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "len(df[df['australian marquee'] != 'none']) == 3 and 'sydney fc' in df[df['australian marquee'] != 'none']['club'].values",
        "pandas_eval": "True"
    },
    {
        "id": 466,
        "statement": "melbourne heart and newcastle jet be the only 2 club who have not announce a vice - captain",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "df[df['vice - captain'] == 'tba']['club'].isin(['melbourne heart', 'newcastle jets']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 467,
        "statement": "adelaide united and wellington phoenix do not have an australian marquee or an international marquee",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df.loc[df['club'].isin(['adelaide united', 'wellington phoenix']), ['australian marquee', 'international marquee']] == 'none').all().all()",
        "pandas_eval": "True"
    },
    {
        "id": 468,
        "statement": "melbourne heart and western sydney wanderer do not have an australian marquee , but do have an international marquee",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df.loc[df['club'].isin(['melbourne heart', 'western sydney wanderers']), 'australian marquee'].eq('none').all()) & (df.loc[df['club'].isin(['melbourne heart', 'western sydney wanderers']), 'international marquee'].ne('none').all())",
        "pandas_eval": "True"
    },
    {
        "id": 469,
        "statement": "brisbane roar have 2 more marquee than adelaide united",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df.loc[df['club'] == 'brisbane roar', ['australian marquee', 'international marquee']].apply(lambda x: x != 'none').sum().sum() - df.loc[df['club'] == 'adelaide united', ['australian marquee', 'international marquee']].apply(lambda x: x != 'none').sum().sum()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 470,
        "statement": "sydney fc be 1 of 4 club with an australian marquee",
        "label": 0,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "df[df['club'] == 'sydney fc']['australian marquee'].iloc[0] == 'none'",
        "pandas_eval": "False"
    },
    {
        "id": 471,
        "statement": "melbourne heart and newcastle jet be the only 2 club who have not announce a captain",
        "label": 0,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df[df['club'].isin(['melbourne heart', 'newcastle jets'])]['captain'] == 'none').all() and (df[~df['club'].isin(['melbourne heart', 'newcastle jets'])]['captain'] != 'none').all()",
        "pandas_eval": "False"
    },
    {
        "id": 472,
        "statement": "adelaide united and wellington phoenix do not have an australian marquee but have an international marquee",
        "label": 0,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "((df['club'] == 'adelaide united') | (df['club'] == 'wellington phoenix')).all() & (df[df['club'].isin(['adelaide united', 'wellington phoenix'])]['australian marquee'] == 'none').all() & (df[df['club'].isin(['adelaide united', 'wellington phoenix'])]['international marquee'] != 'none').all()",
        "pandas_eval": "False"
    },
    {
        "id": 473,
        "statement": "melbourne heart and western sydney wanderer have an australian marquee , but do not have an international marquee",
        "label": 0,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df.loc[df['club'].isin(['melbourne heart', 'western sydney wanderers']), 'australian marquee'].ne('none').all()) & (df.loc[df['club'].isin(['melbourne heart', 'western sydney wanderers']), 'international marquee'].eq('none').all())",
        "pandas_eval": "False"
    },
    {
        "id": 474,
        "statement": "brisbane roar have 3 more marquee than adelaide united",
        "label": 0,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\nwestern sydney wanderers#none#shinji ono#none#michael beauchamp#nikolai topor - stanley\n",
        "pandas_code": "(df.loc[df['club'] == 'brisbane roar', ['australian marquee', 'international marquee', 'junior marquee player']].count().sum() - df.loc[df['club'] == 'adelaide united', ['australian marquee', 'international marquee', 'junior marquee player']].count().sum()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 475,
        "statement": "tony elmer be the only rider that be rid a yamaha",
        "label": 1,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "len(df[df['rider'].str.contains('yamaha', case=False)]) == 1 and df[df['rider'].str.contains('yamaha', case=False)]['rider'].iloc[0] == 'tony elmer / darren marshall 600cc ireson yamaha'",
        "pandas_eval": "True"
    },
    {
        "id": 476,
        "statement": "john holden and klaus klaffenb\u00f6ck both achieve a time under 20' on june 4",
        "label": 1,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "all(df[df['rider'].isin(['john holden / andrew winkle 600cc lcr suzuki', 'klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda'])]['fri 4 june'].str.extract(r\"(\\d+'\\d+\\.\\d+)\")[0].apply(lambda x: float(x.split(\"'\")[0]) + float(x.split(\"'\")[1]) / 60) < 20)",
        "pandas_eval": "True"
    },
    {
        "id": 477,
        "statement": "conrad harrison achieve a rank above the rank of tim reef",
        "label": 1,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "df[df['rider'].str.contains('conrad harrison')]['rank'].values[0] < df[df['rider'].str.contains('tim reeves')]['rank'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 480,
        "statement": "only 2 rider be rid an yamaha",
        "label": 0,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "df[df['rider'].str.contains('yamaha', case=False)].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 481,
        "statement": "none of the rider achieve a time under 20'",
        "label": 0,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "all(df[['mon 31 may', 'tues 1 june', 'wed 2 june', 'thurs 3 june', 'fri 4 june']].apply(lambda x: x.str.contains(\"'\\d{2}'\\d{2}\\.\\d{2}\\s\\d{2,3}\\.\\d{3}\\smph\") & ~x.str.contains(\"19'\\d{2}\\.\\d{2}\\s\\d{2,3}\\.\\d{3}\\smph\")).all())",
        "pandas_eval": "False"
    },
    {
        "id": 482,
        "statement": "conrad harrison achieve a rank above the rank of john holden",
        "label": 0,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "df[df['rider'].str.contains('conrad harrison')]['rank'].values[0] < df[df['rider'].str.contains('john holden')]['rank'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 483,
        "statement": "none of the rider achieve a time under 20' more than 1 time",
        "label": 0,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "all(df[['mon 31 may', 'tues 1 june', 'wed 2 june', 'thurs 3 june', 'fri 4 june']].apply(lambda x: x.str.contains(r\"\\d{1,2}'\\d{2}\\.\\d{2}\") & x.str.extract(r\"(\\d{1,2})'\\d{2}\\.\\d{2}\")[0].astype(float) < 20).sum(axis=1) <= 1)",
        "pandas_eval": "False"
    },
    {
        "id": 484,
        "statement": "tony elmer and paul knapton be the only rider to officially not record a time for 1 of the event",
        "label": 0,
        "table_caption": "2010 isle of man tt",
        "table_text": "rank#rider#sat 29 may#mon 31 may#tues 1 june#wed 2 june#thurs 3 june#fri 4 june\n2#klaus klaffenb\u00f6ck / dan sayle 600cc lcr honda#cancelled no time#20'15.35 111.761 mph#20'05.79 112.647 mph#19'55.92 113.576 mph#19'50.47 114.096 mph#19'56.64 113.508 mph\n3#john holden / andrew winkle 600cc lcr suzuki#cancelled no time#20'17.36 111.576 mph#20'09.86 112.267 mph#20'04.82 112.737 mph#20'15.90 111.710 mph#19'59.43 113.224 mph\n4#simon neary / paul knapton 600cc honda#cancelled no time#20'24.08 110.964 mph#20'05.64 112.661 mph#20'11.98 112.071 mph#20'00.19 113.172 mph#20'01.41 113.058 mph\n5#conrad harrison / kerry williams 600cc honda#cancelled no time#20'50.30 108.636 mph#20'27.78 110.629 mph#20'25.77 110.810 mph#20'13.17 111.962 mph#20'29.39 110.484 mph\n6#tim reeves / dipash chauhan 600cc honda#cancelled no time#-- no time#20'59.60 107.834 mph#20'45.81 109.028 mph#20'26.35 110.758 mph#37'03.92 61.076 mph\n7#gary bryan / gary partridge 600cc honda#cancelled no time#21'21.24 106.013 mph#21'09.41 107.001 mph#20'47.90 108.845 mph#20'27.35 110.668 mph#20'40.91 109.459 mph\n8#roy hanks / dave wells 600cc suzuki#cancelled no time#21'36.43 104.771 mph#21'05.27 107.351 mph#20'50.62 108.608 mph#20'27.93 110.615 mph#-- no time\n9#tony elmer / darren marshall 600cc ireson yamaha#cancelled no time#21'35.11 108.877 mph#21'02.66 107.573 mph#20'43.24 109.253 mph#20'28.72 110.554 mph#20'39.74 109.562 mph\n",
        "pandas_code": "df[df['rider'].str.contains('tony elmer|paul knapton')].apply(lambda x: x[['sat 29 may', 'mon 31 may', 'tues 1 june', 'wed 2 june', 'thurs 3 june', 'fri 4 june']].str.contains('no time').sum(), axis=1).eq(1).all() and df[~df['rider'].str.contains('tony elmer|paul knapton')].apply(lambda x: x[['sat 29 may', 'mon 31 may', 'tues 1 june', 'wed 2 june', 'thurs 3 june', 'fri 4 june']].str.contains('no time').sum(), axis=1).eq(0).all()",
        "pandas_eval": "False"
    },
    {
        "id": 485,
        "statement": "matt barnes and jud buechler both play guard - forward",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nmatt barnes#22#united states#guard - forward#2009 - 2010#ucla\nandre barrett#11#united states#guard#2005#seton hall\nbrandon bass#30#united states#forward#2009 - 2011#louisiana state\ntony battie#4#united states#forward - center#2004 - 2009#texas tech\ndavid benoit#2#united states#forward#1998#alabama\nkeith bogans#3#united states#guard#2003 - 2004#kentucky\nkeith bogans#10#united states#guard#2006 - 2009#kentucky\nanthony bonner#24#united states#forward#1995 - 1996#st louis\nanthony bowie#14#united states#guard#1991 - 1996#oklahoma\nearl boykins#11#united states#guard#1999#eastern michigan\nmichael bradley#7#united states#forward#2004 - 2005#villanova\ndee brown#7#united states#guard#2000 - 2002#jacksonville\njud buechler#30#united states#guard - forward#2001 - 2002#arizona\n",
        "pandas_code": "all(df[df['player'].isin(['matt barnes', 'jud buechler'])]['position'] == 'guard - forward')",
        "pandas_eval": "True"
    },
    {
        "id": 486,
        "statement": "tonny battie be the only 1 who play forward - center",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nmatt barnes#22#united states#guard - forward#2009 - 2010#ucla\nandre barrett#11#united states#guard#2005#seton hall\nbrandon bass#30#united states#forward#2009 - 2011#louisiana state\ntony battie#4#united states#forward - center#2004 - 2009#texas tech\ndavid benoit#2#united states#forward#1998#alabama\nkeith bogans#3#united states#guard#2003 - 2004#kentucky\nkeith bogans#10#united states#guard#2006 - 2009#kentucky\nanthony bonner#24#united states#forward#1995 - 1996#st louis\nanthony bowie#14#united states#guard#1991 - 1996#oklahoma\nearl boykins#11#united states#guard#1999#eastern michigan\nmichael bradley#7#united states#forward#2004 - 2005#villanova\ndee brown#7#united states#guard#2000 - 2002#jacksonville\njud buechler#30#united states#guard - forward#2001 - 2002#arizona\n",
        "pandas_code": "(df[df['position'] == 'forward - center']['player'].nunique() == 1) and (df[df['position'] == 'forward - center']['player'].iloc[0] == 'tony battie')",
        "pandas_eval": "True"
    },
    {
        "id": 487,
        "statement": "keith bogans play as both number 3 and 10 for kentucky , from 2003 - 04 and 2006 - 09 respectively",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nmatt barnes#22#united states#guard - forward#2009 - 2010#ucla\nandre barrett#11#united states#guard#2005#seton hall\nbrandon bass#30#united states#forward#2009 - 2011#louisiana state\ntony battie#4#united states#forward - center#2004 - 2009#texas tech\ndavid benoit#2#united states#forward#1998#alabama\nkeith bogans#3#united states#guard#2003 - 2004#kentucky\nkeith bogans#10#united states#guard#2006 - 2009#kentucky\nanthony bonner#24#united states#forward#1995 - 1996#st louis\nanthony bowie#14#united states#guard#1991 - 1996#oklahoma\nearl boykins#11#united states#guard#1999#eastern michigan\nmichael bradley#7#united states#forward#2004 - 2005#villanova\ndee brown#7#united states#guard#2000 - 2002#jacksonville\njud buechler#30#united states#guard - forward#2001 - 2002#arizona\n",
        "pandas_code": "((df['player'] == 'keith bogans') & (df['no'].isin([3, 10])) & (df['school / club team'] == 'kentucky') & (df['years in orlando'].isin(['2003 - 2004', '2006 - 2009']))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 488,
        "statement": "brandon bass , david benoit , anthony bonner , and michael bradley all play forward",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nmatt barnes#22#united states#guard - forward#2009 - 2010#ucla\nandre barrett#11#united states#guard#2005#seton hall\nbrandon bass#30#united states#forward#2009 - 2011#louisiana state\ntony battie#4#united states#forward - center#2004 - 2009#texas tech\ndavid benoit#2#united states#forward#1998#alabama\nkeith bogans#3#united states#guard#2003 - 2004#kentucky\nkeith bogans#10#united states#guard#2006 - 2009#kentucky\nanthony bonner#24#united states#forward#1995 - 1996#st louis\nanthony bowie#14#united states#guard#1991 - 1996#oklahoma\nearl boykins#11#united states#guard#1999#eastern michigan\nmichael bradley#7#united states#forward#2004 - 2005#villanova\ndee brown#7#united states#guard#2000 - 2002#jacksonville\njud buechler#30#united states#guard - forward#2001 - 2002#arizona\n",
        "pandas_code": "all(df[df['player'].isin(['brandon bass', 'david benoit', 'anthony bonner', 'michael bradley'])]['position'].str.contains('forward'))",
        "pandas_eval": "True"
    },
    {
        "id": 489,
        "statement": "michael bradley and dee brown be both number 7 , for villanova and jacksonville respectively",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nmatt barnes#22#united states#guard - forward#2009 - 2010#ucla\nandre barrett#11#united states#guard#2005#seton hall\nbrandon bass#30#united states#forward#2009 - 2011#louisiana state\ntony battie#4#united states#forward - center#2004 - 2009#texas tech\ndavid benoit#2#united states#forward#1998#alabama\nkeith bogans#3#united states#guard#2003 - 2004#kentucky\nkeith bogans#10#united states#guard#2006 - 2009#kentucky\nanthony bonner#24#united states#forward#1995 - 1996#st louis\nanthony bowie#14#united states#guard#1991 - 1996#oklahoma\nearl boykins#11#united states#guard#1999#eastern michigan\nmichael bradley#7#united states#forward#2004 - 2005#villanova\ndee brown#7#united states#guard#2000 - 2002#jacksonville\njud buechler#30#united states#guard - forward#2001 - 2002#arizona\n",
        "pandas_code": "(df.loc[df['player'] == 'michael bradley', ['no', 'school / club team']].values.tolist() == [[7, 'villanova']]) and (df.loc[df['player'] == 'dee brown', ['no', 'school / club team']].values.tolist() == [[7, 'jacksonville']])",
        "pandas_eval": "True"
    },
    {
        "id": 490,
        "statement": "in the 2010 melbourne football club season 3 team have no loss",
        "label": 1,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "(df['lost'] == 0).sum() >= 3",
        "pandas_eval": "True"
    },
    {
        "id": 491,
        "statement": "melbourne play 1 game against st kilda that result in a loss",
        "label": 1,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "(df[(df['opponent'] == 'st kilda') & (df['played'] == 1) & (df['lost'] == 1)].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 492,
        "statement": "collingwood be the opponent that result in a 162 score of point against",
        "label": 1,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "df[df['opponent'] == 'collingwood']['points against'].iloc[0] == 162",
        "pandas_eval": "True"
    },
    {
        "id": 493,
        "statement": "the richmond and brisbane lion game both result in 2 win",
        "label": 1,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "all(df[(df['opponent'] == 'richmond') | (df['opponent'] == 'brisbane lions')]['won'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 494,
        "statement": "the fremantle game result in a higher percentage than carlton",
        "label": 1,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "df[df['opponent'] == 'fremantle']['percentage (%)'].values[0] > df[df['opponent'] == 'carlton']['percentage (%)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 495,
        "statement": "in the 2010 melbourne football club season 3 team have 3 loss",
        "label": 0,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "len(df[df['lost'] == 3]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 496,
        "statement": "melbourne play 1 game against hawthorn that result in a draw",
        "label": 0,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "((df['opponent'] == 'hawthorn') & (df['played'] == 1) & (df['drew'] == 1)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 497,
        "statement": "collingwood be the opponent that result in a 150 score of point against",
        "label": 0,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "df[df['opponent'] == 'collingwood']['points against'].values[0] == 150",
        "pandas_eval": "False"
    },
    {
        "id": 498,
        "statement": "the richmond andcarlton game both result in 3 win",
        "label": 0,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "((df['opponent'] == 'richmond') & (df['won'] == 3)).any() & ((df['opponent'] == 'carlton') & (df['won'] == 3)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 499,
        "statement": "the fremantle game result in a lower percentage than geelong",
        "label": 0,
        "table_caption": "2010 melbourne football club season",
        "table_text": "opponent#played#won#lost#drew#premiership points#points for#points against#percentage (%)\nrichmond#2#2#0#0#8#242#158#153.16\nbrisbane lions#2#2#0#0#8#177#117#151.28\nsydney#1#1#0#0#4#142#69#205.80\nessendon#1#1#0#0#4#122#103#118.45\nport adelaide#2#1#1#0#4#194#222#87.39\nadelaide#2#1#1#0#4#130#158#82.28\ncollingwood#2#0#1#1#2#161#162#99.38\nwestern bulldogs#1#0#1#0#0#66#70#94.29\nfremantle#1#0#1#0#0#71#82#86.59\nnorth melbourne#2#0#2#0#0#181#217#83.41\nst kilda#1#0#1#0#0#65#100#65.00\nhawthorn#2#0#2#0#0#139#216#64.35\nwest coast#1#0#1#0#0#46#75#61.33\ncarlton#1#0#1#0#0#58#166#59.41\ngeelong#1#0#1#0#0#67#121#55.37\n",
        "pandas_code": "df[df['opponent'] == 'fremantle']['percentage (%)'].values[0] < df[df['opponent'] == 'geelong']['percentage (%)'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 500,
        "statement": "both the longest and shortest film length take place in the country of soviet union",
        "label": 1,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "(df.loc[df['length'].str.extract('(\\d+)')[0].astype(int).idxmax(), 'country'] == 'soviet union') and (df.loc[df['length'].str.extract('(\\d+)')[0].astype(int).idxmin(), 'country'] == 'soviet union')",
        "pandas_eval": "True"
    },
    {
        "id": 501,
        "statement": "1983 be the only year in which more than 1 title be release",
        "label": 1,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "df['year'].value_counts().eq(2).sum() == 1 and df['year'].value_counts().idxmax() == 1983",
        "pandas_eval": "True"
    },
    {
        "id": 505,
        "statement": "both the longest and shortest film length take place in the country of italy",
        "label": 0,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "(df.loc[df['length'].str.extract('(\\\\d+)').astype(int).idxmax(), 'country'] == 'italy').item() & (df.loc[df['length'].str.extract('(\\\\d+)').astype(int).idxmin(), 'country'] == 'italy').item()",
        "pandas_eval": "False"
    },
    {
        "id": 506,
        "statement": "1983 be the only year in which 3 title be release",
        "label": 0,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "df['year'].value_counts().eq(3).sum() == 1 and df['year'].value_counts().idxmax() == 1983",
        "pandas_eval": "False"
    },
    {
        "id": 507,
        "statement": "sweden be the only country to appear 3 time on the chart",
        "label": 0,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "df[df['country'].str.contains('sweden')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 508,
        "statement": "the longest time between year be 3 , which happen 2 time between 1962 and 1966 , and again from 1980 - 1984",
        "label": 0,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "(df['year'].diff().abs().eq(3).sum() == 2) and (df['year'].diff().abs().eq(4).sum() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 509,
        "statement": "voyage in time be the only english title to have be film in 2 different country",
        "label": 0,
        "table_caption": "andrei tarkovsky filmography",
        "table_text": "year#english title#original title#country#length\n1956#the killers#\u0443\u0431\u0438\u0439\u0446\u044b#soviet union#19 min\n1959#there will be no leave today#\u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0443\u0432\u043e\u043b\u044c\u043d\u0435\u043d\u0438\u044f \u043d\u0435 \u0431\u0443\u0434\u0435\u0442#soviet union#46 min\n1961#the steamroller and the violin#\u043a\u0430\u0442\u043e\u043a \u0438 \u0441\u043a\u0440\u0438\u043f\u043a\u0430#soviet union#46 min\n1962#ivan 's childhood#\u0438\u0432\u0430\u043d\u043e\u0432\u043e \u0434\u0435\u0442\u0441\u0442\u0432\u043e#soviet union#95 min\n1966#andrei rublev#\u0430\u043d\u0434\u0440\u0435\u0439 \u0440\u0443\u0431\u043b\u0451\u0432#soviet union#205 min\n1972#solaris#\u0441\u043e\u043b\u044f\u0440\u0438\u0441#soviet union#165 min\n1975#the mirror#\u0437\u0435\u0440\u043a\u0430\u043b\u043e#soviet union#107 min\n1979#stalker#\u0441\u0442\u0430\u043b\u043a\u0435\u0440#soviet union#164 min\n1983#nostalghia#nostalghia#italy / soviet union#125 min\n1983#voyage in time#tempo di viaggio#italy#63 min\n1986#the sacrifice#offret#sweden#149 min\n",
        "pandas_code": "len(df[(df['english title'] == 'voyage in time') & (df['country'].str.contains('/'))]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 510,
        "statement": "austria and sweden be tie for the highest number of victories during a season with 13 victories",
        "label": 1,
        "table_caption": "fis alpine ski world cup",
        "table_text": "country#season#victories#downhill#super g#giant slalom#slalom#combined\nsweden#1978 - 1979#13#-#na#10#3#-\naustria#2000 - 2001#13#5#3#5#-#-\nfrance#1967#12#5#na#4#3#na\nsweden#1979 - 1980#11#-#na#6#5#-\nluxembourg#1984 - 1985#11#-#2#2#7#-\nswitzerland#1986 - 1987#11#5#1#3#-#2\nitaly#1994 - 1995#11#-#-#4#7#-\nsweden#1976 - 1977#10#-#na#3#7#-\nsweden#1980 - 1981#10#-#na#6#4#-\naustria#1997 - 1998#10#2#4#3#-#1\naustria#1999 - 2000#10#3#4#3#-#-\naustria#2001 - 2002#10#6#3#1#-#-\n",
        "pandas_code": "(df[df['victories'] == 13]['country'].value_counts().loc[['austria', 'sweden']] == 1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 511,
        "statement": "out of the 12 position on this list , sweden appear 4 time",
        "label": 1,
        "table_caption": "fis alpine ski world cup",
        "table_text": "country#season#victories#downhill#super g#giant slalom#slalom#combined\nsweden#1978 - 1979#13#-#na#10#3#-\naustria#2000 - 2001#13#5#3#5#-#-\nfrance#1967#12#5#na#4#3#na\nsweden#1979 - 1980#11#-#na#6#5#-\nluxembourg#1984 - 1985#11#-#2#2#7#-\nswitzerland#1986 - 1987#11#5#1#3#-#2\nitaly#1994 - 1995#11#-#-#4#7#-\nsweden#1976 - 1977#10#-#na#3#7#-\nsweden#1980 - 1981#10#-#na#6#4#-\naustria#1997 - 1998#10#2#4#3#-#1\naustria#1999 - 2000#10#3#4#3#-#-\naustria#2001 - 2002#10#6#3#1#-#-\n",
        "pandas_code": "df['country'].value_counts()['sweden'] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 512,
        "statement": "on this list , austria have both the lowest number of victories during a season and the highest number of victories during a season",
        "label": 1,
        "table_caption": "fis alpine ski world cup",
        "table_text": "country#season#victories#downhill#super g#giant slalom#slalom#combined\nsweden#1978 - 1979#13#-#na#10#3#-\naustria#2000 - 2001#13#5#3#5#-#-\nfrance#1967#12#5#na#4#3#na\nsweden#1979 - 1980#11#-#na#6#5#-\nluxembourg#1984 - 1985#11#-#2#2#7#-\nswitzerland#1986 - 1987#11#5#1#3#-#2\nitaly#1994 - 1995#11#-#-#4#7#-\nsweden#1976 - 1977#10#-#na#3#7#-\nsweden#1980 - 1981#10#-#na#6#4#-\naustria#1997 - 1998#10#2#4#3#-#1\naustria#1999 - 2000#10#3#4#3#-#-\naustria#2001 - 2002#10#6#3#1#-#-\n",
        "pandas_code": "(df[df['country'] == 'austria']['victories'].min() == df['victories'].min()) & (df[df['country'] == 'austria']['victories'].max() == df['victories'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 513,
        "statement": "with 10 giant slalom victories , sweden have earn the highest number of slalom victories during a single season",
        "label": 1,
        "table_caption": "fis alpine ski world cup",
        "table_text": "country#season#victories#downhill#super g#giant slalom#slalom#combined\nsweden#1978 - 1979#13#-#na#10#3#-\naustria#2000 - 2001#13#5#3#5#-#-\nfrance#1967#12#5#na#4#3#na\nsweden#1979 - 1980#11#-#na#6#5#-\nluxembourg#1984 - 1985#11#-#2#2#7#-\nswitzerland#1986 - 1987#11#5#1#3#-#2\nitaly#1994 - 1995#11#-#-#4#7#-\nsweden#1976 - 1977#10#-#na#3#7#-\nsweden#1980 - 1981#10#-#na#6#4#-\naustria#1997 - 1998#10#2#4#3#-#1\naustria#1999 - 2000#10#3#4#3#-#-\naustria#2001 - 2002#10#6#3#1#-#-\n",
        "pandas_code": "(df[(df['giant slalom'] == 10) & (df['country'] == 'sweden')].shape[0] > 0) and (df['slalom'].max() == '7')",
        "pandas_eval": "True"
    },
    {
        "id": 514,
        "statement": "out of the team list , austria earn the highest number of downhill victories in a single season during austria 's 2001 - 2002 season",
        "label": 1,
        "table_caption": "fis alpine ski world cup",
        "table_text": "country#season#victories#downhill#super g#giant slalom#slalom#combined\nsweden#1978 - 1979#13#-#na#10#3#-\naustria#2000 - 2001#13#5#3#5#-#-\nfrance#1967#12#5#na#4#3#na\nsweden#1979 - 1980#11#-#na#6#5#-\nluxembourg#1984 - 1985#11#-#2#2#7#-\nswitzerland#1986 - 1987#11#5#1#3#-#2\nitaly#1994 - 1995#11#-#-#4#7#-\nsweden#1976 - 1977#10#-#na#3#7#-\nsweden#1980 - 1981#10#-#na#6#4#-\naustria#1997 - 1998#10#2#4#3#-#1\naustria#1999 - 2000#10#3#4#3#-#-\naustria#2001 - 2002#10#6#3#1#-#-\n",
        "pandas_code": "df[(df['country'] == 'austria') & (df['season'] == '2001 - 2002')]['downhill'].max() == df['downhill'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 515,
        "statement": "spain have 1 more tennis stadium than united state",
        "label": 1,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df[df['country'] == 'spain'].shape[0] - df[df['country'] == 'united states'].shape[0]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 516,
        "statement": "the combination capacity of all united state stadium be less than capacity of 1 stadium in australia",
        "label": 1,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "df[df['country'] == 'united states']['capacity'].sum() < df[df['country'] == 'australia']['capacity'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 517,
        "statement": "united state only have 1 stadium in the top 10 stadium rank",
        "label": 1,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df[df['country'] == 'united states']['rank'] <= 10).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 518,
        "statement": "czech republic have a higher ranking stadium than israel",
        "label": 1,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "df[df['country'] == 'czech republic']['rank'].min() < df[df['country'] == 'israel']['rank'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 519,
        "statement": "only 4 stadium have less than 10 thousand capacity seating",
        "label": 1,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df['capacity'] < 10000).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 520,
        "statement": "spain have 1 less tennis stadium than united state",
        "label": 0,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df[df['country'] == 'spain'].shape[0] == df[df['country'] == 'united states'].shape[0] - 1)",
        "pandas_eval": "False"
    },
    {
        "id": 521,
        "statement": "the combination capacity of all united state stadium be more than capacity of 1 stadium in australia",
        "label": 0,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "df[df['country'] == 'united states']['capacity'].sum() > df[df['country'] == 'australia']['capacity'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 522,
        "statement": "united state only have 2 stadium in the top 10 stadium ranking",
        "label": 0,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df[df['country'] == 'united states']['rank'] <= 10).sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 523,
        "statement": "czech republic have a higher ranking stadium than ireland",
        "label": 0,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "(df[df['country'] == 'czech republic']['rank'].min() < df[df['country'] == 'ireland']['rank'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 524,
        "statement": "only 5 stadium have less than 10 thousand capacity seating",
        "label": 0,
        "table_caption": "list of tennis stadiums by capacity",
        "table_text": "rank#stadium#capacity#city#country\n1#queensland sport and athletics centre#49000#brisbane#australia\n2#estadio ol\u00edmpico de sevilla#27200#seville#spain\n3#belgrade arena#23000#belgrade#serbia\n4#las ventas#21000#madrid#spain\n5#royal dublin society#6000#dublin#ireland\n6#o2 arena (prague)#17000#prague#czech republic\n7#palau sant jordi#16500#barcelona#spain\n8#estadio mary ter\u00e1n de weiss#14510#buenos aires#argentina\n9#palacio de deportes de santander#14000#santander#spain\n10#memorial coliseum#12000#portland , oregon#united states\n11#nokia arena#11700#tel aviv#israel\n12#public auditorium#11500#cleveland#united states\n13#mediolanum forum#11200#milan#italy\n14#bill graham civic auditorium#7000#san francisco#united states\n15#sibamac arena#4500#bratislava#slovakia\n16#idrottens hus#2400#helsingborg#sweden\n",
        "pandas_code": "len(df[df['capacity'] < 10000]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 525,
        "statement": "only 1 of the successor for the 89th united state congress be a replublican",
        "label": 1,
        "table_caption": "89th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\nsouth carolina (3)#olin d johnston (d)#died april 18 , 1965#donald s russell (d)#april 22 , 1965\nsouth carolina (3)#donald s russell (d)#successor elected november 8 , 1965#ernest hollings (d)#november 9 , 1965\nvirginia (1)#harry f byrd (d)#resigned november 10 , 1965#harry f byrd , jr (d)#november 12 , 1965\nmichigan (2)#patrick v mcnamara (d)#died april 30 , 1966#robert p griffin (r)#may 11 , 1966\nvirginia (2)#a willis robertson (d)#resigned december 30 , 1966#william b spong , jr (d)#december 31 , 1966\n",
        "pandas_code": "(df['successor'].str.contains('\\\\(r\\\\)', case=False, regex=True).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 526,
        "statement": "2 of the vacators reason for change for the 89th united state congress be because of death",
        "label": 1,
        "table_caption": "89th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\nsouth carolina (3)#olin d johnston (d)#died april 18 , 1965#donald s russell (d)#april 22 , 1965\nsouth carolina (3)#donald s russell (d)#successor elected november 8 , 1965#ernest hollings (d)#november 9 , 1965\nvirginia (1)#harry f byrd (d)#resigned november 10 , 1965#harry f byrd , jr (d)#november 12 , 1965\nmichigan (2)#patrick v mcnamara (d)#died april 30 , 1966#robert p griffin (r)#may 11 , 1966\nvirginia (2)#a willis robertson (d)#resigned december 30 , 1966#william b spong , jr (d)#december 31 , 1966\n",
        "pandas_code": "df['reason for change'].str.contains('died').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 527,
        "statement": "donald s russell become a successor before harry f byrd",
        "label": 1,
        "table_caption": "89th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\nsouth carolina (3)#olin d johnston (d)#died april 18 , 1965#donald s russell (d)#april 22 , 1965\nsouth carolina (3)#donald s russell (d)#successor elected november 8 , 1965#ernest hollings (d)#november 9 , 1965\nvirginia (1)#harry f byrd (d)#resigned november 10 , 1965#harry f byrd , jr (d)#november 12 , 1965\nmichigan (2)#patrick v mcnamara (d)#died april 30 , 1966#robert p griffin (r)#may 11 , 1966\nvirginia (2)#a willis robertson (d)#resigned december 30 , 1966#william b spong , jr (d)#december 31 , 1966\n",
        "pandas_code": "pd.to_datetime(df.loc[df['vacator'] == 'donald s russell (d)', 'date of successors formal installation'].iloc[0], format='%B %d , %Y') < pd.to_datetime(df.loc[df['vacator'] == 'harry f byrd (d)', 'date of successors formal installation'].iloc[0], format='%B %d , %Y')",
        "pandas_eval": "True"
    },
    {
        "id": 528,
        "statement": "all of the vactors for the 89th united state congress be democrat",
        "label": 1,
        "table_caption": "89th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\nsouth carolina (3)#olin d johnston (d)#died april 18 , 1965#donald s russell (d)#april 22 , 1965\nsouth carolina (3)#donald s russell (d)#successor elected november 8 , 1965#ernest hollings (d)#november 9 , 1965\nvirginia (1)#harry f byrd (d)#resigned november 10 , 1965#harry f byrd , jr (d)#november 12 , 1965\nmichigan (2)#patrick v mcnamara (d)#died april 30 , 1966#robert p griffin (r)#may 11 , 1966\nvirginia (2)#a willis robertson (d)#resigned december 30 , 1966#william b spong , jr (d)#december 31 , 1966\n",
        "pandas_code": "all(df['vacator'].str.contains('(d)'))",
        "pandas_eval": "True"
    },
    {
        "id": 529,
        "statement": "2 of the vacators come from south carolina (3)",
        "label": 1,
        "table_caption": "89th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\nsouth carolina (3)#olin d johnston (d)#died april 18 , 1965#donald s russell (d)#april 22 , 1965\nsouth carolina (3)#donald s russell (d)#successor elected november 8 , 1965#ernest hollings (d)#november 9 , 1965\nvirginia (1)#harry f byrd (d)#resigned november 10 , 1965#harry f byrd , jr (d)#november 12 , 1965\nmichigan (2)#patrick v mcnamara (d)#died april 30 , 1966#robert p griffin (r)#may 11 , 1966\nvirginia (2)#a willis robertson (d)#resigned december 30 , 1966#william b spong , jr (d)#december 31 , 1966\n",
        "pandas_code": "df[df['state (class)'] == 'south carolina (3)']['vacator'].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 530,
        "statement": "jos\u00e9 calder\u00f3n play for toronto from 2005 - 2013 as a guard while marcus camby play for toronto from 1996 - 98 as a center",
        "label": 1,
        "table_caption": "toronto raptors all - time roster",
        "table_text": "player#no#nationality#position#years in toronto#school / club team\njos\u00e9 calder\u00f3n#8#spain#guard#2005 - 2013#tau cer\u00e1mica (spain)\nmarcus camby#21#united states#center#1996 - 98#massachusetts\nanthony carter#25#united states#guard#2011 - 12#hawaii\nvince carter#15#united states#guard - forward#1998 - 2004#north carolina\nchris childs#1#united states#guard#2001 - 02#boise state\ndoug christie#13#united states#forward#1996 - 2000#pepperdine\nkeon clark#7#united states#forward - center#2001 - 02#unlv\nomar cook#1#united states#guard#2005 - 06#st john 's\ntyrone corbin#23#united states#guard - forward#2000 - 01#depaul\nwilliam cunningham#54#united states#center#1999#temple\nearl cureton#35#united states#forward#1996 - 97#detroit\ndell curry#30#united states#guard#1999 - 2002#virginia tech\n",
        "pandas_code": "(df[(df['player'] == 'jos\u00e9 calder\u00f3n') & (df['years in toronto'] == '2005 - 2013') & (df['position'] == 'guard')].shape[0] > 0) & (df[(df['player'] == 'marcus camby') & (df['years in toronto'] == '1996 - 98') & (df['position'] == 'center')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 531,
        "statement": "dell curry play guard for the toronto raptor from 1999 - 2002 and be from virginia tech",
        "label": 1,
        "table_caption": "toronto raptors all - time roster",
        "table_text": "player#no#nationality#position#years in toronto#school / club team\njos\u00e9 calder\u00f3n#8#spain#guard#2005 - 2013#tau cer\u00e1mica (spain)\nmarcus camby#21#united states#center#1996 - 98#massachusetts\nanthony carter#25#united states#guard#2011 - 12#hawaii\nvince carter#15#united states#guard - forward#1998 - 2004#north carolina\nchris childs#1#united states#guard#2001 - 02#boise state\ndoug christie#13#united states#forward#1996 - 2000#pepperdine\nkeon clark#7#united states#forward - center#2001 - 02#unlv\nomar cook#1#united states#guard#2005 - 06#st john 's\ntyrone corbin#23#united states#guard - forward#2000 - 01#depaul\nwilliam cunningham#54#united states#center#1999#temple\nearl cureton#35#united states#forward#1996 - 97#detroit\ndell curry#30#united states#guard#1999 - 2002#virginia tech\n",
        "pandas_code": "df[(df['player'] == 'dell curry') & (df['position'].str.contains('guard')) & (df['years in toronto'] == '1999 - 2002') & (df['school / club team'] == 'virginia tech')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 532,
        "statement": "vince carter be no 15 for the toronto raptor and play as a guard - forward from 1998 - 2004 while chris child be no 1 and play as a guard from 2001 - 02",
        "label": 1,
        "table_caption": "toronto raptors all - time roster",
        "table_text": "player#no#nationality#position#years in toronto#school / club team\njos\u00e9 calder\u00f3n#8#spain#guard#2005 - 2013#tau cer\u00e1mica (spain)\nmarcus camby#21#united states#center#1996 - 98#massachusetts\nanthony carter#25#united states#guard#2011 - 12#hawaii\nvince carter#15#united states#guard - forward#1998 - 2004#north carolina\nchris childs#1#united states#guard#2001 - 02#boise state\ndoug christie#13#united states#forward#1996 - 2000#pepperdine\nkeon clark#7#united states#forward - center#2001 - 02#unlv\nomar cook#1#united states#guard#2005 - 06#st john 's\ntyrone corbin#23#united states#guard - forward#2000 - 01#depaul\nwilliam cunningham#54#united states#center#1999#temple\nearl cureton#35#united states#forward#1996 - 97#detroit\ndell curry#30#united states#guard#1999 - 2002#virginia tech\n",
        "pandas_code": "((df['player'] == 'vince carter') & (df['no'] == 15) & (df['position'] == 'guard - forward') & (df['years in toronto'] == '1998 - 2004')).any() & ((df['player'] == 'chris childs') & (df['no'] == 1) & (df['position'] == 'guard') & (df['years in toronto'] == '2001 - 02')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 533,
        "statement": "earl cureton and doug christie both played forward for the toronto raptor in 1996 - 97 and 1996 - 2000 , respectively",
        "label": 1,
        "table_caption": "toronto raptors all - time roster",
        "table_text": "player#no#nationality#position#years in toronto#school / club team\njos\u00e9 calder\u00f3n#8#spain#guard#2005 - 2013#tau cer\u00e1mica (spain)\nmarcus camby#21#united states#center#1996 - 98#massachusetts\nanthony carter#25#united states#guard#2011 - 12#hawaii\nvince carter#15#united states#guard - forward#1998 - 2004#north carolina\nchris childs#1#united states#guard#2001 - 02#boise state\ndoug christie#13#united states#forward#1996 - 2000#pepperdine\nkeon clark#7#united states#forward - center#2001 - 02#unlv\nomar cook#1#united states#guard#2005 - 06#st john 's\ntyrone corbin#23#united states#guard - forward#2000 - 01#depaul\nwilliam cunningham#54#united states#center#1999#temple\nearl cureton#35#united states#forward#1996 - 97#detroit\ndell curry#30#united states#guard#1999 - 2002#virginia tech\n",
        "pandas_code": "all(df[(df['player'].isin(['earl cureton', 'doug christie'])) & (df['position'].str.contains('forward')) & (df['years in toronto'].isin(['1996 - 97', '1996 - 2000']))].groupby('player').size() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 534,
        "statement": "omar cook play for the toronto raptor from 2005 - 06 and be from st john 's while tyrone corbin play for the raptor from 2000 - 01 and be from depaul",
        "label": 1,
        "table_caption": "toronto raptors all - time roster",
        "table_text": "player#no#nationality#position#years in toronto#school / club team\njos\u00e9 calder\u00f3n#8#spain#guard#2005 - 2013#tau cer\u00e1mica (spain)\nmarcus camby#21#united states#center#1996 - 98#massachusetts\nanthony carter#25#united states#guard#2011 - 12#hawaii\nvince carter#15#united states#guard - forward#1998 - 2004#north carolina\nchris childs#1#united states#guard#2001 - 02#boise state\ndoug christie#13#united states#forward#1996 - 2000#pepperdine\nkeon clark#7#united states#forward - center#2001 - 02#unlv\nomar cook#1#united states#guard#2005 - 06#st john 's\ntyrone corbin#23#united states#guard - forward#2000 - 01#depaul\nwilliam cunningham#54#united states#center#1999#temple\nearl cureton#35#united states#forward#1996 - 97#detroit\ndell curry#30#united states#guard#1999 - 2002#virginia tech\n",
        "pandas_code": "(df[(df['player'] == 'omar cook') & (df['years in toronto'] == '2005 - 06') & (df['school / club team'] == \"st john 's\")].shape[0] > 0) & (df[(df['player'] == 'tyrone corbin') & (df['years in toronto'] == '2000 - 01') & (df['school / club team'] == 'depaul')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 535,
        "statement": "lupus , bo\u00f6tes , virgo , and libra be all tie for the constellation with the most object in this section , with 3 each",
        "label": 1,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "(df['constellation'].value_counts().loc[['lupus', 'bo\u00f6tes', 'virgo', 'libra']] == 3).all()",
        "pandas_eval": "True"
    },
    {
        "id": 536,
        "statement": "there be 10 galaxy in this section , of 6 different type , and only 5 non - galaxy",
        "label": 1,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "(df['object type'].str.contains('galaxy').sum() == 10) & (df[df['object type'].str.contains('galaxy')]['object type'].nunique() == 5) & ((~df['object type'].str.contains('galaxy')).sum() == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 537,
        "statement": "circinus be the constellation with the fewest object in this section , as it only have 1",
        "label": 1,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "df['constellation'].value_counts().idxmin() == 'circinus'",
        "pandas_eval": "True"
    },
    {
        "id": 538,
        "statement": "5890 have the highest right ascension of the object in the list",
        "label": 1,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "df.loc[df['ngc number'] == 5890, 'right ascension ( j2000 )'].values[0] == df['right ascension ( j2000 )'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 540,
        "statement": "there be 10 galaxy in this section of 7 different type and only 6 non - galaxy",
        "label": 0,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "(df['object type'].str.contains('galaxy').sum() == 10) & (df[df['object type'].str.contains('galaxy')]['object type'].nunique() == 7) & ((~df['object type'].str.contains('galaxy')).sum() == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 541,
        "statement": "circinus be the constellation with the fewest object in this section , as it only have 1",
        "label": 0,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "df['constellation'].value_counts().min() == df['constellation'].value_counts()['circinus'] and df['constellation'].value_counts().value_counts().iloc[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 542,
        "statement": "5890 , number 5866 , have the highest right ascension of the object in the list with 15h17 m50.1s",
        "label": 0,
        "table_caption": "list of ngc objects (5001 - 6000)",
        "table_text": "ngc number#object type#constellation#right ascension ( j2000 )#declination ( j2000 )\n5822#open cluster#lupus#15h04 m# degree24\u2032\n5823#open cluster#circinus#15h05 m44.8s# degree37\u203230\u2033\n5824#globular cluster#lupus#15h03 m58.5s# degree04\u203204\u2033\n5825#elliptical galaxy#bo\u00f6tes#14h54 m31.5s# degree38\u203231\u2033\n5838#lenticular galaxy#virgo#15h05 m26.3s# degree05\u203257\u2033\n5846#elliptical galaxy#virgo#15h06 m29.4s# degree36\u203219\u2033\n5850#spiral galaxy#virgo#15h07 m07.8s# degree32\u203239\u2033\n5866#lenticular galaxy#draco#15h06 m29.5s# degree45\u203247\u2033\n5877#triple star#lupus#15h12 m53.1s# degree55\u203238\u2033\n5879#galaxy#draco#15h09 m46.8s# degree00\u203201\u2033\n5882#planetary nebula#libra#15h16 m49.9s# degree38\u203258\u2033\n5885#barred spiral galaxy#libra#15h15 m04.1s# degree05\u203210.0\u2033\n5886#elliptical galaxy#bo\u00f6tes#15h12 m45.4s# degree12\u203202\u2033\n5888#barred spiral galaxy#bo\u00f6tes#15h13 m07.4s# degree15\u203252\u2033\n5890#lenticular galaxy#libra#15h17 m51.1s# degree35\u203219\u2033\n",
        "pandas_code": "(df['right ascension ( j2000 )'].max() == '15h17 m50.1s')",
        "pandas_eval": "False"
    },
    {
        "id": 543,
        "statement": "all but 1 of the 1986 us open golfer be from the united state",
        "label": 1,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "df[df['country'] != 'united states'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 544,
        "statement": "jack nicklaus win 4 us open 's , which be more than any other golfer",
        "label": 1,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "df[df['player'] == 'jack nicklaus']['year (s) won'].str.split(' , ').explode().nunique() > df[df['player'] != 'jack nicklaus']['year (s) won'].str.split(' , ').explode().groupby(df['player']).nunique().max()",
        "pandas_eval": "True"
    },
    {
        "id": 545,
        "statement": "andy north and lee trevino both win the us open 2 time before",
        "label": 1,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "all(df[df['player'].isin(['andy north', 'lee trevino'])]['year (s) won'].str.count(',').add(1) == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 546,
        "statement": "andy north have the highest total of all golfer in the 1986 us open",
        "label": 1,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "df[df['player'] == 'andy north']['total'].max() == df['total'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 547,
        "statement": "david graham be the only golfer , in the 1986 us open from australia",
        "label": 1,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "len(df[(df['player'] == 'david graham') & (df['country'] == 'australia')]) == 1 and len(df[df['country'] == 'australia']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 548,
        "statement": "all of the 1986 us open golfer be from the united state",
        "label": 0,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "all(df['country'] == 'united states')",
        "pandas_eval": "False"
    },
    {
        "id": 549,
        "statement": "jack nicklaus never win a us open",
        "label": 0,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "not any('1980' in year for year in df[df['player'] == 'jack nicklaus']['year (s) won'])",
        "pandas_eval": "False"
    },
    {
        "id": 550,
        "statement": "andy north and lee trevino have only win 1 us open each",
        "label": 0,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "all(df[df['player'].isin(['andy north', 'lee trevino'])]['year (s) won'].apply(lambda x: len(x.split(','))) == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 551,
        "statement": "andy north win the 1986 us open",
        "label": 0,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "df[df['player'] == 'andy north']['year (s) won'].str.contains('1986').any()",
        "pandas_eval": "False"
    },
    {
        "id": 552,
        "statement": "david graham be from the united state",
        "label": 0,
        "table_caption": "1986 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\nlee trevino#united states#1968 , 1971#282#+ 2#t4\njack nicklaus#united states#1962 , 1967 , 1972 , 1980#284#+ 4#t8\nfuzzy zoeller#united states#1984#288#+ 8#t15\ndavid graham#australia#1981#288#+ 8#t15\ntom watson#united states#1982#289#+ 9#t24\nlarry nelson#united states#1983#291#+ 11#t35\njohnny miller#united states#1973#293#+ 13#t45\nhubert green#united states#1977#296#+ 16#t55\nandy north#united states#1978 , 1985#302#+ 22#68\n",
        "pandas_code": "df[df['player'] == 'david graham']['country'].iloc[0] == 'united states'",
        "pandas_eval": "False"
    },
    {
        "id": 553,
        "statement": "3 programme be on the same channel as the original",
        "label": 1,
        "table_caption": "2008 in british television",
        "table_text": "programme#date (s) of original removal#original channel#date (s) of return#new channel (s)\nmr and mrs as all star mr & mrs#1999#itv#12 april 2008#n / a (same channel as original)\nitv news at ten#5 march 1999 30 january 2004#itv#22 january 2001 14 january 2008#n / a (same channel as original)\ngladiators#1 january 2000#itv#11 may 2008#sky1\nsuperstars#2005#bbc one#july 2008#five\nit'll be alright on the night#18 march 2006#itv#20 september 2008#n / a (same channel as original)\n",
        "pandas_code": "df[df['new channel (s)'].str.contains('same channel as original')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 554,
        "statement": "gladiator return before superstar",
        "label": 1,
        "table_caption": "2008 in british television",
        "table_text": "programme#date (s) of original removal#original channel#date (s) of return#new channel (s)\nmr and mrs as all star mr & mrs#1999#itv#12 april 2008#n / a (same channel as original)\nitv news at ten#5 march 1999 30 january 2004#itv#22 january 2001 14 january 2008#n / a (same channel as original)\ngladiators#1 january 2000#itv#11 may 2008#sky1\nsuperstars#2005#bbc one#july 2008#five\nit'll be alright on the night#18 march 2006#itv#20 september 2008#n / a (same channel as original)\n",
        "pandas_code": "df.loc[df['programme'] == 'gladiators', 'date (s) of return'].iloc[0] < df.loc[df['programme'] == 'superstars', 'date (s) of return'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 555,
        "statement": "gladiator be originally remove after mr and mr as all star mr & mr",
        "label": 1,
        "table_caption": "2008 in british television",
        "table_text": "programme#date (s) of original removal#original channel#date (s) of return#new channel (s)\nmr and mrs as all star mr & mrs#1999#itv#12 april 2008#n / a (same channel as original)\nitv news at ten#5 march 1999 30 january 2004#itv#22 january 2001 14 january 2008#n / a (same channel as original)\ngladiators#1 january 2000#itv#11 may 2008#sky1\nsuperstars#2005#bbc one#july 2008#five\nit'll be alright on the night#18 march 2006#itv#20 september 2008#n / a (same channel as original)\n",
        "pandas_code": "pd.to_datetime(df[df['programme'] == 'gladiators']['date (s) of original removal'].iloc[0]) > pd.to_datetime(df[df['programme'] == 'mr and mrs as all star mr & mrs']['date (s) of original removal'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 556,
        "statement": "4 programme be originally on the same original channel",
        "label": 1,
        "table_caption": "2008 in british television",
        "table_text": "programme#date (s) of original removal#original channel#date (s) of return#new channel (s)\nmr and mrs as all star mr & mrs#1999#itv#12 april 2008#n / a (same channel as original)\nitv news at ten#5 march 1999 30 january 2004#itv#22 january 2001 14 january 2008#n / a (same channel as original)\ngladiators#1 january 2000#itv#11 may 2008#sky1\nsuperstars#2005#bbc one#july 2008#five\nit'll be alright on the night#18 march 2006#itv#20 september 2008#n / a (same channel as original)\n",
        "pandas_code": "df['original channel'].value_counts().max() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 558,
        "statement": "2 countries' weekly schedule of most daring be unkown",
        "label": 1,
        "table_caption": "most daring",
        "table_text": "country#tv network (s)#series premiere#weekly schedule#status\naustralia#fox8#unknown#weekdays 2:30 pm#currently airing\nbelgium#2be#unknown#mondays 8:00 pm#currently airing\nbrazil#trutv#unknown#saturdays 11:00 pm#currently airing\nestonia#kanal 12#unknown#weekends#currently airing\ngreece#skai tv#unknown#weekends 3:00 pm#currently airing\nindia#axn india#season 5 & 6#monday to thursday 11:00 pm#currently airing\nitaly#sky italia#unknown#unknown#currently airing\nnorway#viasat 4#unknown#fridays 8:35 pm#currently airing\npakistan#axn#unknown#unknown#currently airing\npoland#polsat play#season 3 & 4#every day 7:00 pm#currently airing\nunited arab emirates#mbc action#unknown#thursday 4:00 pm#currently airing\n",
        "pandas_code": "(df['weekly schedule'] == 'unknown').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 560,
        "statement": "pole air series premiere before india series premiere of most dare",
        "label": 1,
        "table_caption": "most daring",
        "table_text": "country#tv network (s)#series premiere#weekly schedule#status\naustralia#fox8#unknown#weekdays 2:30 pm#currently airing\nbelgium#2be#unknown#mondays 8:00 pm#currently airing\nbrazil#trutv#unknown#saturdays 11:00 pm#currently airing\nestonia#kanal 12#unknown#weekends#currently airing\ngreece#skai tv#unknown#weekends 3:00 pm#currently airing\nindia#axn india#season 5 & 6#monday to thursday 11:00 pm#currently airing\nitaly#sky italia#unknown#unknown#currently airing\nnorway#viasat 4#unknown#fridays 8:35 pm#currently airing\npakistan#axn#unknown#unknown#currently airing\npoland#polsat play#season 3 & 4#every day 7:00 pm#currently airing\nunited arab emirates#mbc action#unknown#thursday 4:00 pm#currently airing\n",
        "pandas_code": "df.loc[df['country'] == 'poland', 'series premiere'].iloc[0] < df.loc[df['country'] == 'india', 'series premiere'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 561,
        "statement": "all but 2 county be unknown to what series it premier",
        "label": 1,
        "table_caption": "most daring",
        "table_text": "country#tv network (s)#series premiere#weekly schedule#status\naustralia#fox8#unknown#weekdays 2:30 pm#currently airing\nbelgium#2be#unknown#mondays 8:00 pm#currently airing\nbrazil#trutv#unknown#saturdays 11:00 pm#currently airing\nestonia#kanal 12#unknown#weekends#currently airing\ngreece#skai tv#unknown#weekends 3:00 pm#currently airing\nindia#axn india#season 5 & 6#monday to thursday 11:00 pm#currently airing\nitaly#sky italia#unknown#unknown#currently airing\nnorway#viasat 4#unknown#fridays 8:35 pm#currently airing\npakistan#axn#unknown#unknown#currently airing\npoland#polsat play#season 3 & 4#every day 7:00 pm#currently airing\nunited arab emirates#mbc action#unknown#thursday 4:00 pm#currently airing\n",
        "pandas_code": "df['series premiere'].value_counts().get('unknown', 0) == len(df) - 2",
        "pandas_eval": "True"
    },
    {
        "id": 562,
        "statement": "4 country schedule most daring after 7 pm",
        "label": 1,
        "table_caption": "most daring",
        "table_text": "country#tv network (s)#series premiere#weekly schedule#status\naustralia#fox8#unknown#weekdays 2:30 pm#currently airing\nbelgium#2be#unknown#mondays 8:00 pm#currently airing\nbrazil#trutv#unknown#saturdays 11:00 pm#currently airing\nestonia#kanal 12#unknown#weekends#currently airing\ngreece#skai tv#unknown#weekends 3:00 pm#currently airing\nindia#axn india#season 5 & 6#monday to thursday 11:00 pm#currently airing\nitaly#sky italia#unknown#unknown#currently airing\nnorway#viasat 4#unknown#fridays 8:35 pm#currently airing\npakistan#axn#unknown#unknown#currently airing\npoland#polsat play#season 3 & 4#every day 7:00 pm#currently airing\nunited arab emirates#mbc action#unknown#thursday 4:00 pm#currently airing\n",
        "pandas_code": "len(df[df['weekly schedule'].str.contains(r'\\b(?:8|9|10|11|12):\\d{2} pm\\b', na=False)]) >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 563,
        "statement": "team dundee have a higher average than team queen of the south",
        "label": 1,
        "table_caption": "2007 - 08 scottish first division",
        "table_text": "team#stadium#capacity#highest#lowest#average\ndundee#dens park#11856#6192#3146#4264\ndunfermline athletic#east end park#11998#4946#2444#3754\nst johnstone#mcdiarmid park#10673#6279#1686#2969\ngreenock morton#cappielow#5741#3818#2228#2727\npartick thistle#firhill stadium#10887#4914#1149#2591\nhamilton academical#new douglas park#5396#5078#1410#2468\nqueen of the south#palmerston park#6412#3005#1484#1965\nlivingston#almondvale stadium#10016#2865#1125#1726\nclyde#broadwood stadium#8006#1979#910#1298\n",
        "pandas_code": "df[df['team'] == 'dundee']['average'].values[0] > df[df['team'] == 'queen of the south']['average'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 564,
        "statement": "the capacity for den park be 2 time the capacity of cappielow",
        "label": 1,
        "table_caption": "2007 - 08 scottish first division",
        "table_text": "team#stadium#capacity#highest#lowest#average\ndundee#dens park#11856#6192#3146#4264\ndunfermline athletic#east end park#11998#4946#2444#3754\nst johnstone#mcdiarmid park#10673#6279#1686#2969\ngreenock morton#cappielow#5741#3818#2228#2727\npartick thistle#firhill stadium#10887#4914#1149#2591\nhamilton academical#new douglas park#5396#5078#1410#2468\nqueen of the south#palmerston park#6412#3005#1484#1965\nlivingston#almondvale stadium#10016#2865#1125#1726\nclyde#broadwood stadium#8006#1979#910#1298\n",
        "pandas_code": "abs(df.loc[df['stadium'] == 'dens park', 'capacity'].values[0] - 2 * df.loc[df['stadium'] == 'cappielow', 'capacity'].values[0]) < 1000",
        "pandas_eval": "True"
    },
    {
        "id": 565,
        "statement": "the average for team livingston and team clyde be 1512",
        "label": 1,
        "table_caption": "2007 - 08 scottish first division",
        "table_text": "team#stadium#capacity#highest#lowest#average\ndundee#dens park#11856#6192#3146#4264\ndunfermline athletic#east end park#11998#4946#2444#3754\nst johnstone#mcdiarmid park#10673#6279#1686#2969\ngreenock morton#cappielow#5741#3818#2228#2727\npartick thistle#firhill stadium#10887#4914#1149#2591\nhamilton academical#new douglas park#5396#5078#1410#2468\nqueen of the south#palmerston park#6412#3005#1484#1965\nlivingston#almondvale stadium#10016#2865#1125#1726\nclyde#broadwood stadium#8006#1979#910#1298\n",
        "pandas_code": "(df[df['team'].isin(['livingston', 'clyde'])]['average'].mean() == 1512)",
        "pandas_eval": "True"
    },
    {
        "id": 566,
        "statement": "the average for all 9 team be 2640.2222",
        "label": 1,
        "table_caption": "2007 - 08 scottish first division",
        "table_text": "team#stadium#capacity#highest#lowest#average\ndundee#dens park#11856#6192#3146#4264\ndunfermline athletic#east end park#11998#4946#2444#3754\nst johnstone#mcdiarmid park#10673#6279#1686#2969\ngreenock morton#cappielow#5741#3818#2228#2727\npartick thistle#firhill stadium#10887#4914#1149#2591\nhamilton academical#new douglas park#5396#5078#1410#2468\nqueen of the south#palmerston park#6412#3005#1484#1965\nlivingston#almondvale stadium#10016#2865#1125#1726\nclyde#broadwood stadium#8006#1979#910#1298\n",
        "pandas_code": "abs(df['average'].mean() - 2640.2222) < 0.0001",
        "pandas_eval": "True"
    },
    {
        "id": 567,
        "statement": "there be 5 stadium with a capacity of 11000 or more",
        "label": 1,
        "table_caption": "2007 - 08 scottish first division",
        "table_text": "team#stadium#capacity#highest#lowest#average\ndundee#dens park#11856#6192#3146#4264\ndunfermline athletic#east end park#11998#4946#2444#3754\nst johnstone#mcdiarmid park#10673#6279#1686#2969\ngreenock morton#cappielow#5741#3818#2228#2727\npartick thistle#firhill stadium#10887#4914#1149#2591\nhamilton academical#new douglas park#5396#5078#1410#2468\nqueen of the south#palmerston park#6412#3005#1484#1965\nlivingston#almondvale stadium#10016#2865#1125#1726\nclyde#broadwood stadium#8006#1979#910#1298\n",
        "pandas_code": "(df['capacity'] >= 11000).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 568,
        "statement": "3 television franchise be from the region / country of uk",
        "label": 1,
        "table_caption": "list of television show franchises",
        "table_text": "region / country#local name#network#dates aired#starring\nunited kingdom#till death us do part#bbc one#1965 - 1968 , 1970 , 1972 - 1975#warren mitchell as alf garnett\nunited kingdom#till death#itv#1981#warren mitchell as alf garnett\nunited kingdom#in sickness and in health#bbc one#1985 - 1992#warren mitchell as alf garnett\nunited states#all in the family#cbs#1971 - 1979#carroll o'connor as archie bunker\nunited states#archie bunker 's place#cbs#1979 - 1983#carroll o'connor as archie bunker\ngermany#ein herz und eine seele#wdr , ard#1973 - 1976#heinz schubert as alfred tetzlaff\n",
        "pandas_code": "len(df[df['region / country'] == 'united kingdom']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 569,
        "statement": "in sickness and in health and till death us do part have bbc one as their network",
        "label": 1,
        "table_caption": "list of television show franchises",
        "table_text": "region / country#local name#network#dates aired#starring\nunited kingdom#till death us do part#bbc one#1965 - 1968 , 1970 , 1972 - 1975#warren mitchell as alf garnett\nunited kingdom#till death#itv#1981#warren mitchell as alf garnett\nunited kingdom#in sickness and in health#bbc one#1985 - 1992#warren mitchell as alf garnett\nunited states#all in the family#cbs#1971 - 1979#carroll o'connor as archie bunker\nunited states#archie bunker 's place#cbs#1979 - 1983#carroll o'connor as archie bunker\ngermany#ein herz und eine seele#wdr , ard#1973 - 1976#heinz schubert as alfred tetzlaff\n",
        "pandas_code": "df[df['local name'].isin(['in sickness and in health', 'till death us do part'])]['network'].eq('bbc one').all()",
        "pandas_eval": "True"
    },
    {
        "id": 570,
        "statement": "till death be air the shortest",
        "label": 1,
        "table_caption": "list of television show franchises",
        "table_text": "region / country#local name#network#dates aired#starring\nunited kingdom#till death us do part#bbc one#1965 - 1968 , 1970 , 1972 - 1975#warren mitchell as alf garnett\nunited kingdom#till death#itv#1981#warren mitchell as alf garnett\nunited kingdom#in sickness and in health#bbc one#1985 - 1992#warren mitchell as alf garnett\nunited states#all in the family#cbs#1971 - 1979#carroll o'connor as archie bunker\nunited states#archie bunker 's place#cbs#1979 - 1983#carroll o'connor as archie bunker\ngermany#ein herz und eine seele#wdr , ard#1973 - 1976#heinz schubert as alfred tetzlaff\n",
        "pandas_code": "df.loc[df['local name'] == 'till death', 'dates aired'].iloc[0] == '1981'",
        "pandas_eval": "True"
    },
    {
        "id": 571,
        "statement": "cbs be the network for the show in the us",
        "label": 1,
        "table_caption": "list of television show franchises",
        "table_text": "region / country#local name#network#dates aired#starring\nunited kingdom#till death us do part#bbc one#1965 - 1968 , 1970 , 1972 - 1975#warren mitchell as alf garnett\nunited kingdom#till death#itv#1981#warren mitchell as alf garnett\nunited kingdom#in sickness and in health#bbc one#1985 - 1992#warren mitchell as alf garnett\nunited states#all in the family#cbs#1971 - 1979#carroll o'connor as archie bunker\nunited states#archie bunker 's place#cbs#1979 - 1983#carroll o'connor as archie bunker\ngermany#ein herz und eine seele#wdr , ard#1973 - 1976#heinz schubert as alfred tetzlaff\n",
        "pandas_code": "df[(df['region / country'] == 'united states') & (df['network'] == 'cbs')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 572,
        "statement": "till death us do part be air the longest",
        "label": 1,
        "table_caption": "list of television show franchises",
        "table_text": "region / country#local name#network#dates aired#starring\nunited kingdom#till death us do part#bbc one#1965 - 1968 , 1970 , 1972 - 1975#warren mitchell as alf garnett\nunited kingdom#till death#itv#1981#warren mitchell as alf garnett\nunited kingdom#in sickness and in health#bbc one#1985 - 1992#warren mitchell as alf garnett\nunited states#all in the family#cbs#1971 - 1979#carroll o'connor as archie bunker\nunited states#archie bunker 's place#cbs#1979 - 1983#carroll o'connor as archie bunker\ngermany#ein herz und eine seele#wdr , ard#1973 - 1976#heinz schubert as alfred tetzlaff\n",
        "pandas_code": "df[df['local name'] == 'till death us do part']['dates aired'].str.split(' - ').apply(lambda x: int(x[-1].split(',')[0]) - int(x[0].split(',')[0])).max() == max(df['dates aired'].str.split(' - ').apply(lambda x: int(x[-1].split(',')[0]) - int(x[0].split(',')[0])))",
        "pandas_eval": "True"
    },
    {
        "id": 573,
        "statement": "israel get lesser rank than japan",
        "label": 1,
        "table_caption": "list of asian countries by gdp per capita",
        "table_text": "asian rank#world rank#country#gdp per capita#gdp world rank\n1#1#qatar#85638#69\n2#4#brunei#50790#113\n3#5#singapore#49754#44\n4#9#kuwait#39344#56\n5#14#united arab emirates#37941#55\n6#22#japan#33596#3\n7#24#bahrain#31899#105\n8#26#republic of china (taiwan)#30322#19\n9#31#israel#27147#52\n10#34#south korea#24803#14\n11#36#oman#23987#77\n12#38#saudi arabia#22852#22\n13#59#malaysia#13385#30\n14#66#lebanon#11279#84\n15#71#iran#10570#18\n16#83#thailand#7907#24\n17#100#people 's republic of china#7325#2\n18#105#jordan#6976#99\n19#106#bhutan#6962#n / a\n20#109#maldives#4603#n / a\n21#111#syria#6892#63\n22#113#sri lanka#6765#65\n23#120#indonesia#6728#16\n24#122#philippines#3383#37\n25#124#mongolia#3222#141\n26#127#pakistan#2594#26\n27#128#vietnam#2589#46\n28#129#india#2563#4\n29#107#east timor#4770#156\n30#132#yemen#2343#81\n31#136#laos#2054#128\n32#140#papua new guinea#1974#131\n33#143#cambodia#1818#103\n34#153#bangladesh#1311#48\n35#159#nepal#3397#96\n36#161#burma#1040#78\n",
        "pandas_code": "df[df['country'] == 'israel']['world rank'].values[0] > df[df['country'] == 'japan']['world rank'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 574,
        "statement": "india get higher rank than bangladesh and nepal",
        "label": 1,
        "table_caption": "list of asian countries by gdp per capita",
        "table_text": "asian rank#world rank#country#gdp per capita#gdp world rank\n1#1#qatar#85638#69\n2#4#brunei#50790#113\n3#5#singapore#49754#44\n4#9#kuwait#39344#56\n5#14#united arab emirates#37941#55\n6#22#japan#33596#3\n7#24#bahrain#31899#105\n8#26#republic of china (taiwan)#30322#19\n9#31#israel#27147#52\n10#34#south korea#24803#14\n11#36#oman#23987#77\n12#38#saudi arabia#22852#22\n13#59#malaysia#13385#30\n14#66#lebanon#11279#84\n15#71#iran#10570#18\n16#83#thailand#7907#24\n17#100#people 's republic of china#7325#2\n18#105#jordan#6976#99\n19#106#bhutan#6962#n / a\n20#109#maldives#4603#n / a\n21#111#syria#6892#63\n22#113#sri lanka#6765#65\n23#120#indonesia#6728#16\n24#122#philippines#3383#37\n25#124#mongolia#3222#141\n26#127#pakistan#2594#26\n27#128#vietnam#2589#46\n28#129#india#2563#4\n29#107#east timor#4770#156\n30#132#yemen#2343#81\n31#136#laos#2054#128\n32#140#papua new guinea#1974#131\n33#143#cambodia#1818#103\n34#153#bangladesh#1311#48\n35#159#nepal#3397#96\n36#161#burma#1040#78\n",
        "pandas_code": "df[df['country'] == 'india']['world rank'].values[0] < df[df['country'] == 'bangladesh']['world rank'].values[0] and df[df['country'] == 'india']['world rank'].values[0] < df[df['country'] == 'nepal']['world rank'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 575,
        "statement": "gdp per caput of lao be higher than bangladesh",
        "label": 1,
        "table_caption": "list of asian countries by gdp per capita",
        "table_text": "asian rank#world rank#country#gdp per capita#gdp world rank\n1#1#qatar#85638#69\n2#4#brunei#50790#113\n3#5#singapore#49754#44\n4#9#kuwait#39344#56\n5#14#united arab emirates#37941#55\n6#22#japan#33596#3\n7#24#bahrain#31899#105\n8#26#republic of china (taiwan)#30322#19\n9#31#israel#27147#52\n10#34#south korea#24803#14\n11#36#oman#23987#77\n12#38#saudi arabia#22852#22\n13#59#malaysia#13385#30\n14#66#lebanon#11279#84\n15#71#iran#10570#18\n16#83#thailand#7907#24\n17#100#people 's republic of china#7325#2\n18#105#jordan#6976#99\n19#106#bhutan#6962#n / a\n20#109#maldives#4603#n / a\n21#111#syria#6892#63\n22#113#sri lanka#6765#65\n23#120#indonesia#6728#16\n24#122#philippines#3383#37\n25#124#mongolia#3222#141\n26#127#pakistan#2594#26\n27#128#vietnam#2589#46\n28#129#india#2563#4\n29#107#east timor#4770#156\n30#132#yemen#2343#81\n31#136#laos#2054#128\n32#140#papua new guinea#1974#131\n33#143#cambodia#1818#103\n34#153#bangladesh#1311#48\n35#159#nepal#3397#96\n36#161#burma#1040#78\n",
        "pandas_code": "df[df['country'] == 'laos']['gdp per capita'].values[0] > df[df['country'] == 'bangladesh']['gdp per capita'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 576,
        "statement": "gdp world rank of singapore be higher than bahrain",
        "label": 1,
        "table_caption": "list of asian countries by gdp per capita",
        "table_text": "asian rank#world rank#country#gdp per capita#gdp world rank\n1#1#qatar#85638#69\n2#4#brunei#50790#113\n3#5#singapore#49754#44\n4#9#kuwait#39344#56\n5#14#united arab emirates#37941#55\n6#22#japan#33596#3\n7#24#bahrain#31899#105\n8#26#republic of china (taiwan)#30322#19\n9#31#israel#27147#52\n10#34#south korea#24803#14\n11#36#oman#23987#77\n12#38#saudi arabia#22852#22\n13#59#malaysia#13385#30\n14#66#lebanon#11279#84\n15#71#iran#10570#18\n16#83#thailand#7907#24\n17#100#people 's republic of china#7325#2\n18#105#jordan#6976#99\n19#106#bhutan#6962#n / a\n20#109#maldives#4603#n / a\n21#111#syria#6892#63\n22#113#sri lanka#6765#65\n23#120#indonesia#6728#16\n24#122#philippines#3383#37\n25#124#mongolia#3222#141\n26#127#pakistan#2594#26\n27#128#vietnam#2589#46\n28#129#india#2563#4\n29#107#east timor#4770#156\n30#132#yemen#2343#81\n31#136#laos#2054#128\n32#140#papua new guinea#1974#131\n33#143#cambodia#1818#103\n34#153#bangladesh#1311#48\n35#159#nepal#3397#96\n36#161#burma#1040#78\n",
        "pandas_code": "int(df[df['country'] == 'singapore']['gdp world rank'].values[0]) < int(df[df['country'] == 'bahrain']['gdp world rank'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 577,
        "statement": "world rank of kuwait be far higher than saudi arabia",
        "label": 1,
        "table_caption": "list of asian countries by gdp per capita",
        "table_text": "asian rank#world rank#country#gdp per capita#gdp world rank\n1#1#qatar#85638#69\n2#4#brunei#50790#113\n3#5#singapore#49754#44\n4#9#kuwait#39344#56\n5#14#united arab emirates#37941#55\n6#22#japan#33596#3\n7#24#bahrain#31899#105\n8#26#republic of china (taiwan)#30322#19\n9#31#israel#27147#52\n10#34#south korea#24803#14\n11#36#oman#23987#77\n12#38#saudi arabia#22852#22\n13#59#malaysia#13385#30\n14#66#lebanon#11279#84\n15#71#iran#10570#18\n16#83#thailand#7907#24\n17#100#people 's republic of china#7325#2\n18#105#jordan#6976#99\n19#106#bhutan#6962#n / a\n20#109#maldives#4603#n / a\n21#111#syria#6892#63\n22#113#sri lanka#6765#65\n23#120#indonesia#6728#16\n24#122#philippines#3383#37\n25#124#mongolia#3222#141\n26#127#pakistan#2594#26\n27#128#vietnam#2589#46\n28#129#india#2563#4\n29#107#east timor#4770#156\n30#132#yemen#2343#81\n31#136#laos#2054#128\n32#140#papua new guinea#1974#131\n33#143#cambodia#1818#103\n34#153#bangladesh#1311#48\n35#159#nepal#3397#96\n36#161#burma#1040#78\n",
        "pandas_code": "(df[df['country'] == 'kuwait']['world rank'].values[0] < df[df['country'] == 'saudi arabia']['world rank'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 578,
        "statement": "there be a total of 8 different country who participate in the issf event",
        "label": 1,
        "table_caption": "2008 issf world cup final (shotgun)",
        "table_text": "shooter#event#rank points#score points#total\nerminio frasca ( ita )#wcf 2007#defending champion#defending champion#defending champion\ndavid kosteleck\u00fd ( cze )#og beijing#olympic gold medalist#olympic gold medalist#olympic gold medalist\ngiovanni pellielo ( ita )#og beijing#olympic silver medalist#olympic silver medalist#olympic silver medalist\naleksei alipov ( rus )#og beijing#olympic bronze medalist#olympic bronze medalist#olympic bronze medalist\nmichael diamond ( aus )#wc kerrville#15#13#28\nst\u00e9phane clamens ( fra )#wc belgrade#15#12#27\nm\u00e1rio filipovi\u010d ( svk )#wc beijing#15#8#23\nbret erickson ( usa )#wc suhl#10#12#22\ndominic grazioli ( usa )#wc kerrville#8#13#21\nmassimo fabbrizi ( ita )#wc belgrade#10#11#21\nmassimiliano mola ( ita )#wc kerrville#10#10#20\nfrancesco amici ( smr )#wc belgrade#8#10#18\n",
        "pandas_code": "len(df['shooter'].str.extract(r'\\((.*?)\\)')[0].unique()) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 579,
        "statement": "michael diamond au score a total of 28 point for first place and francesco amici smr score a total of 18 point for last place",
        "label": 1,
        "table_caption": "2008 issf world cup final (shotgun)",
        "table_text": "shooter#event#rank points#score points#total\nerminio frasca ( ita )#wcf 2007#defending champion#defending champion#defending champion\ndavid kosteleck\u00fd ( cze )#og beijing#olympic gold medalist#olympic gold medalist#olympic gold medalist\ngiovanni pellielo ( ita )#og beijing#olympic silver medalist#olympic silver medalist#olympic silver medalist\naleksei alipov ( rus )#og beijing#olympic bronze medalist#olympic bronze medalist#olympic bronze medalist\nmichael diamond ( aus )#wc kerrville#15#13#28\nst\u00e9phane clamens ( fra )#wc belgrade#15#12#27\nm\u00e1rio filipovi\u010d ( svk )#wc beijing#15#8#23\nbret erickson ( usa )#wc suhl#10#12#22\ndominic grazioli ( usa )#wc kerrville#8#13#21\nmassimo fabbrizi ( ita )#wc belgrade#10#11#21\nmassimiliano mola ( ita )#wc kerrville#10#10#20\nfrancesco amici ( smr )#wc belgrade#8#10#18\n",
        "pandas_code": "(df[df['shooter'] == 'michael diamond ( aus )']['total'].iloc[0] == '28') & (df[df['shooter'] == 'francesco amici ( smr )']['total'].iloc[0] == '18')",
        "pandas_eval": "True"
    },
    {
        "id": 580,
        "statement": "david kosteleck\u00fd cze win gold , giovanni pellielo of ita won silver , and aleksei alipov ru win bronze",
        "label": 1,
        "table_caption": "2008 issf world cup final (shotgun)",
        "table_text": "shooter#event#rank points#score points#total\nerminio frasca ( ita )#wcf 2007#defending champion#defending champion#defending champion\ndavid kosteleck\u00fd ( cze )#og beijing#olympic gold medalist#olympic gold medalist#olympic gold medalist\ngiovanni pellielo ( ita )#og beijing#olympic silver medalist#olympic silver medalist#olympic silver medalist\naleksei alipov ( rus )#og beijing#olympic bronze medalist#olympic bronze medalist#olympic bronze medalist\nmichael diamond ( aus )#wc kerrville#15#13#28\nst\u00e9phane clamens ( fra )#wc belgrade#15#12#27\nm\u00e1rio filipovi\u010d ( svk )#wc beijing#15#8#23\nbret erickson ( usa )#wc suhl#10#12#22\ndominic grazioli ( usa )#wc kerrville#8#13#21\nmassimo fabbrizi ( ita )#wc belgrade#10#11#21\nmassimiliano mola ( ita )#wc kerrville#10#10#20\nfrancesco amici ( smr )#wc belgrade#8#10#18\n",
        "pandas_code": "(df[(df['shooter'].str.contains('david kosteleck\u00fd')) & (df['rank points'] == 'olympic gold medalist')].shape[0] == 1) & (df[(df['shooter'].str.contains('giovanni pellielo')) & (df['rank points'] == 'olympic silver medalist')].shape[0] == 1) & (df[(df['shooter'].str.contains('aleksei alipov')) & (df['rank points'] == 'olympic bronze medalist')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 581,
        "statement": "the issf olympic shoot event take place at the og beijing",
        "label": 1,
        "table_caption": "2008 issf world cup final (shotgun)",
        "table_text": "shooter#event#rank points#score points#total\nerminio frasca ( ita )#wcf 2007#defending champion#defending champion#defending champion\ndavid kosteleck\u00fd ( cze )#og beijing#olympic gold medalist#olympic gold medalist#olympic gold medalist\ngiovanni pellielo ( ita )#og beijing#olympic silver medalist#olympic silver medalist#olympic silver medalist\naleksei alipov ( rus )#og beijing#olympic bronze medalist#olympic bronze medalist#olympic bronze medalist\nmichael diamond ( aus )#wc kerrville#15#13#28\nst\u00e9phane clamens ( fra )#wc belgrade#15#12#27\nm\u00e1rio filipovi\u010d ( svk )#wc beijing#15#8#23\nbret erickson ( usa )#wc suhl#10#12#22\ndominic grazioli ( usa )#wc kerrville#8#13#21\nmassimo fabbrizi ( ita )#wc belgrade#10#11#21\nmassimiliano mola ( ita )#wc kerrville#10#10#20\nfrancesco amici ( smr )#wc belgrade#8#10#18\n",
        "pandas_code": "any(df['event'] == 'og beijing')",
        "pandas_eval": "True"
    },
    {
        "id": 582,
        "statement": "erminio frasca of ita be the defend champion of the issf event",
        "label": 1,
        "table_caption": "2008 issf world cup final (shotgun)",
        "table_text": "shooter#event#rank points#score points#total\nerminio frasca ( ita )#wcf 2007#defending champion#defending champion#defending champion\ndavid kosteleck\u00fd ( cze )#og beijing#olympic gold medalist#olympic gold medalist#olympic gold medalist\ngiovanni pellielo ( ita )#og beijing#olympic silver medalist#olympic silver medalist#olympic silver medalist\naleksei alipov ( rus )#og beijing#olympic bronze medalist#olympic bronze medalist#olympic bronze medalist\nmichael diamond ( aus )#wc kerrville#15#13#28\nst\u00e9phane clamens ( fra )#wc belgrade#15#12#27\nm\u00e1rio filipovi\u010d ( svk )#wc beijing#15#8#23\nbret erickson ( usa )#wc suhl#10#12#22\ndominic grazioli ( usa )#wc kerrville#8#13#21\nmassimo fabbrizi ( ita )#wc belgrade#10#11#21\nmassimiliano mola ( ita )#wc kerrville#10#10#20\nfrancesco amici ( smr )#wc belgrade#8#10#18\n",
        "pandas_code": "df[df['shooter'] == 'erminio frasca ( ita )']['rank points'].iloc[0] == 'defending champion'",
        "pandas_eval": "True"
    },
    {
        "id": 583,
        "statement": "northern hawk have more horsepower than any other member of the american seafood fleet",
        "label": 1,
        "table_caption": "american seafoods",
        "table_text": "name#length#tonnage#built by#year#engines#horsepowers#former names\namerican dynasty#272.0 feet#3471#mangone shipyard , houston , tx#1974#2 , bergen diesel , brm - 8#8000#artabaze , bure , sea bure\namerican triumph#285.0 feet#4294#ls baier &co , portland , or#1961#2 , w\ucc44rtsil\ucc44 , 8r32d#7939#acona\nnorthern jaeger#337 feet#3732#levingston shipbuilding , orange , tx#1969#2 , mak m453c#6322#jaeger , inagua ranger ii , wisco ranger\nnorthern eagle#344.1 feet#4437#ulstein hatlo norway#1966#2 , bergen diesel , brm - 8#6590#mauna kea , hawaiian princess\nnorthern hawk#310.1 feet#3732#brount marine corp , warren , ri#1981#2 , bergen diesel , brm - 8#8790#state trust\nocean rover#223.0 feet#4345#mcdermott shipyards , amelia , la#1973#3 , w\ucc44rtsil\ucc44#7080#enterprise\n",
        "pandas_code": "df.loc[df['name'] == 'northern hawk', 'horsepowers'].values[0] > df.loc[df['name'] != 'northern hawk', 'horsepowers'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 584,
        "statement": "the average tonnage of a member of the american seafood fleet be 4001.83",
        "label": 1,
        "table_caption": "american seafoods",
        "table_text": "name#length#tonnage#built by#year#engines#horsepowers#former names\namerican dynasty#272.0 feet#3471#mangone shipyard , houston , tx#1974#2 , bergen diesel , brm - 8#8000#artabaze , bure , sea bure\namerican triumph#285.0 feet#4294#ls baier &co , portland , or#1961#2 , w\ucc44rtsil\ucc44 , 8r32d#7939#acona\nnorthern jaeger#337 feet#3732#levingston shipbuilding , orange , tx#1969#2 , mak m453c#6322#jaeger , inagua ranger ii , wisco ranger\nnorthern eagle#344.1 feet#4437#ulstein hatlo norway#1966#2 , bergen diesel , brm - 8#6590#mauna kea , hawaiian princess\nnorthern hawk#310.1 feet#3732#brount marine corp , warren , ri#1981#2 , bergen diesel , brm - 8#8790#state trust\nocean rover#223.0 feet#4345#mcdermott shipyards , amelia , la#1973#3 , w\ucc44rtsil\ucc44#7080#enterprise\n",
        "pandas_code": "abs(df['tonnage'].mean() - 4001.83) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 585,
        "statement": "ocean rover be shorter than american dynasty",
        "label": 1,
        "table_caption": "american seafoods",
        "table_text": "name#length#tonnage#built by#year#engines#horsepowers#former names\namerican dynasty#272.0 feet#3471#mangone shipyard , houston , tx#1974#2 , bergen diesel , brm - 8#8000#artabaze , bure , sea bure\namerican triumph#285.0 feet#4294#ls baier &co , portland , or#1961#2 , w\ucc44rtsil\ucc44 , 8r32d#7939#acona\nnorthern jaeger#337 feet#3732#levingston shipbuilding , orange , tx#1969#2 , mak m453c#6322#jaeger , inagua ranger ii , wisco ranger\nnorthern eagle#344.1 feet#4437#ulstein hatlo norway#1966#2 , bergen diesel , brm - 8#6590#mauna kea , hawaiian princess\nnorthern hawk#310.1 feet#3732#brount marine corp , warren , ri#1981#2 , bergen diesel , brm - 8#8790#state trust\nocean rover#223.0 feet#4345#mcdermott shipyards , amelia , la#1973#3 , w\ucc44rtsil\ucc44#7080#enterprise\n",
        "pandas_code": "df.loc[df['name'] == 'ocean rover', 'length'].values[0] < df.loc[df['name'] == 'american dynasty', 'length'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 586,
        "statement": "3 member of the american seafood fleet be longer than 300 foot",
        "label": 1,
        "table_caption": "american seafoods",
        "table_text": "name#length#tonnage#built by#year#engines#horsepowers#former names\namerican dynasty#272.0 feet#3471#mangone shipyard , houston , tx#1974#2 , bergen diesel , brm - 8#8000#artabaze , bure , sea bure\namerican triumph#285.0 feet#4294#ls baier &co , portland , or#1961#2 , w\ucc44rtsil\ucc44 , 8r32d#7939#acona\nnorthern jaeger#337 feet#3732#levingston shipbuilding , orange , tx#1969#2 , mak m453c#6322#jaeger , inagua ranger ii , wisco ranger\nnorthern eagle#344.1 feet#4437#ulstein hatlo norway#1966#2 , bergen diesel , brm - 8#6590#mauna kea , hawaiian princess\nnorthern hawk#310.1 feet#3732#brount marine corp , warren , ri#1981#2 , bergen diesel , brm - 8#8790#state trust\nocean rover#223.0 feet#4345#mcdermott shipyards , amelia , la#1973#3 , w\ucc44rtsil\ucc44#7080#enterprise\n",
        "pandas_code": "sum(df['length'].str.replace(' feet', '').astype(float) > 300) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 587,
        "statement": "american triumph be build 20 year earlier than northern hawk",
        "label": 1,
        "table_caption": "american seafoods",
        "table_text": "name#length#tonnage#built by#year#engines#horsepowers#former names\namerican dynasty#272.0 feet#3471#mangone shipyard , houston , tx#1974#2 , bergen diesel , brm - 8#8000#artabaze , bure , sea bure\namerican triumph#285.0 feet#4294#ls baier &co , portland , or#1961#2 , w\ucc44rtsil\ucc44 , 8r32d#7939#acona\nnorthern jaeger#337 feet#3732#levingston shipbuilding , orange , tx#1969#2 , mak m453c#6322#jaeger , inagua ranger ii , wisco ranger\nnorthern eagle#344.1 feet#4437#ulstein hatlo norway#1966#2 , bergen diesel , brm - 8#6590#mauna kea , hawaiian princess\nnorthern hawk#310.1 feet#3732#brount marine corp , warren , ri#1981#2 , bergen diesel , brm - 8#8790#state trust\nocean rover#223.0 feet#4345#mcdermott shipyards , amelia , la#1973#3 , w\ucc44rtsil\ucc44#7080#enterprise\n",
        "pandas_code": "df[df['name'] == 'american triumph']['year'].values[0] == df[df['name'] == 'northern hawk']['year'].values[0] - 20",
        "pandas_eval": "True"
    },
    {
        "id": 588,
        "statement": "only 1 representative retire to pursue another political office",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1998",
        "table_text": "district#incumbent#party#first elected#results#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 64% william holst (d) 36%\nnew york 2#rick lazio#republican#1992#re - elected#rick lazio (r) 67% john bace (d) 30%\nnew york 3#peter king#republican#1992#re - elected#peter king (r) 65% kevin langberg (d) 35%\nnew york 4#carolyn mccarthy#democratic#1996#re - elected#carolyn mccarthy (d) 53% gregory becker (r) 47%\nnew york 5#gary ackerman#democratic#1983#re - elected#gary ackerman (d) 65% david pinzon (r) 34%\nnew york 6#gregory w meeks#democratic#february 3 , 1998 (special)#re - elected#gregory w meeks (d) unopposed\nnew york 7#thomas manton#democratic#1984#retired democratic hold#joseph crowley (d) 68% james dillon (r) 28%\nnew york 8#jerrold nadler#democratic#1992#re - elected#jerrold nadler (d) 86% ted howard (r) 14%\nnew york 9#charles schumer#democratic#1980#retired to run for us senate democratic hold#anthony weiner (d) 66% leslie telano (r) 24%\nnew york 10#ed towns#democratic#1982#re - elected#ed towns (d) 92% ernestine brown (r) 7%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 89% david greene (r) 10%\nnew york 12#nydia velazquez#democratic#1992#re - elected#nydia velazquez (d) 83% rosemarie markgraf (r) 13%\nnew york 13#vito fossella#republican#1997#re - elected#vito fossella (r) 66% eugene prisco (d) 34%\nnew york 15#charles b rangel#democratic#1970#re - elected#charles b rangel (d) 94% david cunningham (r) 6%\nnew york 16#jose serrano#democratic#1990#re - elected#jose serrano (d) 96% thomas bayley (r) 4%\nnew york 17#eliot engel#democratic#1988#re - elected#eliot engel (d) 88% peter fiumefreddo (r) 12%\nnew york 18#nita lowey#democratic#1988#re - elected#nita lowey (d) unopposed\nnew york 19#sue kelly#republican#1994#re - elected#sue kelly (r) 63% dick collins (d) 34%\nnew york 20#benjamin gilman#republican#1972#re - elected#benjamin gilman (r) 58% paul feiner (d) 40%\nnew york 21#mike mcnulty#democratic#1988#re - elected#mike mcnulty (d) 74% lauren ayers (r) 26%\nnew york 22#gerald solomon#republican#1978#retired republican hold#john sweeney (r) 56% jean bordewich (d) 43%\nnew york 23#sherwood boehlert#republican#1982#re - elected#sherwood boehlert (r) unopposed\nnew york 24#john mchugh#republican#1992#re - elected#john mchugh (r) 79% neil tallon (d) 21%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 69% yvonne rothenberg (d) 31%\nnew york 27#bill paxon#republican#1988#retired republican hold#tom reynolds (r) 58% bill cook (d) 42%\nnew york 28#louise slaughter#democratic#1986#re - elected#louise slaughter (d) 65% richard kaplan (r) 31%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 58% chris collins (r) 41%\nnew york 30#jack quinn#republican#1992#re - elected#jack quinn (r) 68% crystal peoples (d) 32%\n",
        "pandas_code": "df[df['results'].str.contains('retired to run for', na=False)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 589,
        "statement": "2 representative retire their seat for party hold",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1998",
        "table_text": "district#incumbent#party#first elected#results#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 64% william holst (d) 36%\nnew york 2#rick lazio#republican#1992#re - elected#rick lazio (r) 67% john bace (d) 30%\nnew york 3#peter king#republican#1992#re - elected#peter king (r) 65% kevin langberg (d) 35%\nnew york 4#carolyn mccarthy#democratic#1996#re - elected#carolyn mccarthy (d) 53% gregory becker (r) 47%\nnew york 5#gary ackerman#democratic#1983#re - elected#gary ackerman (d) 65% david pinzon (r) 34%\nnew york 6#gregory w meeks#democratic#february 3 , 1998 (special)#re - elected#gregory w meeks (d) unopposed\nnew york 7#thomas manton#democratic#1984#retired democratic hold#joseph crowley (d) 68% james dillon (r) 28%\nnew york 8#jerrold nadler#democratic#1992#re - elected#jerrold nadler (d) 86% ted howard (r) 14%\nnew york 9#charles schumer#democratic#1980#retired to run for us senate democratic hold#anthony weiner (d) 66% leslie telano (r) 24%\nnew york 10#ed towns#democratic#1982#re - elected#ed towns (d) 92% ernestine brown (r) 7%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 89% david greene (r) 10%\nnew york 12#nydia velazquez#democratic#1992#re - elected#nydia velazquez (d) 83% rosemarie markgraf (r) 13%\nnew york 13#vito fossella#republican#1997#re - elected#vito fossella (r) 66% eugene prisco (d) 34%\nnew york 15#charles b rangel#democratic#1970#re - elected#charles b rangel (d) 94% david cunningham (r) 6%\nnew york 16#jose serrano#democratic#1990#re - elected#jose serrano (d) 96% thomas bayley (r) 4%\nnew york 17#eliot engel#democratic#1988#re - elected#eliot engel (d) 88% peter fiumefreddo (r) 12%\nnew york 18#nita lowey#democratic#1988#re - elected#nita lowey (d) unopposed\nnew york 19#sue kelly#republican#1994#re - elected#sue kelly (r) 63% dick collins (d) 34%\nnew york 20#benjamin gilman#republican#1972#re - elected#benjamin gilman (r) 58% paul feiner (d) 40%\nnew york 21#mike mcnulty#democratic#1988#re - elected#mike mcnulty (d) 74% lauren ayers (r) 26%\nnew york 22#gerald solomon#republican#1978#retired republican hold#john sweeney (r) 56% jean bordewich (d) 43%\nnew york 23#sherwood boehlert#republican#1982#re - elected#sherwood boehlert (r) unopposed\nnew york 24#john mchugh#republican#1992#re - elected#john mchugh (r) 79% neil tallon (d) 21%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 69% yvonne rothenberg (d) 31%\nnew york 27#bill paxon#republican#1988#retired republican hold#tom reynolds (r) 58% bill cook (d) 42%\nnew york 28#louise slaughter#democratic#1986#re - elected#louise slaughter (d) 65% richard kaplan (r) 31%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 58% chris collins (r) 41%\nnew york 30#jack quinn#republican#1992#re - elected#jack quinn (r) 68% crystal peoples (d) 32%\n",
        "pandas_code": "df['results'].str.contains('retired.*hold').sum() >= 2",
        "pandas_eval": "True"
    },
    {
        "id": 591,
        "statement": "john lafalce be the earliest elect representative",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1998",
        "table_text": "district#incumbent#party#first elected#results#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 64% william holst (d) 36%\nnew york 2#rick lazio#republican#1992#re - elected#rick lazio (r) 67% john bace (d) 30%\nnew york 3#peter king#republican#1992#re - elected#peter king (r) 65% kevin langberg (d) 35%\nnew york 4#carolyn mccarthy#democratic#1996#re - elected#carolyn mccarthy (d) 53% gregory becker (r) 47%\nnew york 5#gary ackerman#democratic#1983#re - elected#gary ackerman (d) 65% david pinzon (r) 34%\nnew york 6#gregory w meeks#democratic#february 3 , 1998 (special)#re - elected#gregory w meeks (d) unopposed\nnew york 7#thomas manton#democratic#1984#retired democratic hold#joseph crowley (d) 68% james dillon (r) 28%\nnew york 8#jerrold nadler#democratic#1992#re - elected#jerrold nadler (d) 86% ted howard (r) 14%\nnew york 9#charles schumer#democratic#1980#retired to run for us senate democratic hold#anthony weiner (d) 66% leslie telano (r) 24%\nnew york 10#ed towns#democratic#1982#re - elected#ed towns (d) 92% ernestine brown (r) 7%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 89% david greene (r) 10%\nnew york 12#nydia velazquez#democratic#1992#re - elected#nydia velazquez (d) 83% rosemarie markgraf (r) 13%\nnew york 13#vito fossella#republican#1997#re - elected#vito fossella (r) 66% eugene prisco (d) 34%\nnew york 15#charles b rangel#democratic#1970#re - elected#charles b rangel (d) 94% david cunningham (r) 6%\nnew york 16#jose serrano#democratic#1990#re - elected#jose serrano (d) 96% thomas bayley (r) 4%\nnew york 17#eliot engel#democratic#1988#re - elected#eliot engel (d) 88% peter fiumefreddo (r) 12%\nnew york 18#nita lowey#democratic#1988#re - elected#nita lowey (d) unopposed\nnew york 19#sue kelly#republican#1994#re - elected#sue kelly (r) 63% dick collins (d) 34%\nnew york 20#benjamin gilman#republican#1972#re - elected#benjamin gilman (r) 58% paul feiner (d) 40%\nnew york 21#mike mcnulty#democratic#1988#re - elected#mike mcnulty (d) 74% lauren ayers (r) 26%\nnew york 22#gerald solomon#republican#1978#retired republican hold#john sweeney (r) 56% jean bordewich (d) 43%\nnew york 23#sherwood boehlert#republican#1982#re - elected#sherwood boehlert (r) unopposed\nnew york 24#john mchugh#republican#1992#re - elected#john mchugh (r) 79% neil tallon (d) 21%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 69% yvonne rothenberg (d) 31%\nnew york 27#bill paxon#republican#1988#retired republican hold#tom reynolds (r) 58% bill cook (d) 42%\nnew york 28#louise slaughter#democratic#1986#re - elected#louise slaughter (d) 65% richard kaplan (r) 31%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 58% chris collins (r) 41%\nnew york 30#jack quinn#republican#1992#re - elected#jack quinn (r) 68% crystal peoples (d) 32%\n",
        "pandas_code": "df.loc[df['incumbent'] == 'john lafalce', 'first elected'].iloc[0] == '1974'",
        "pandas_eval": "True"
    },
    {
        "id": 592,
        "statement": "gregory w meeks be the only representative elect through a special election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1998",
        "table_text": "district#incumbent#party#first elected#results#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 64% william holst (d) 36%\nnew york 2#rick lazio#republican#1992#re - elected#rick lazio (r) 67% john bace (d) 30%\nnew york 3#peter king#republican#1992#re - elected#peter king (r) 65% kevin langberg (d) 35%\nnew york 4#carolyn mccarthy#democratic#1996#re - elected#carolyn mccarthy (d) 53% gregory becker (r) 47%\nnew york 5#gary ackerman#democratic#1983#re - elected#gary ackerman (d) 65% david pinzon (r) 34%\nnew york 6#gregory w meeks#democratic#february 3 , 1998 (special)#re - elected#gregory w meeks (d) unopposed\nnew york 7#thomas manton#democratic#1984#retired democratic hold#joseph crowley (d) 68% james dillon (r) 28%\nnew york 8#jerrold nadler#democratic#1992#re - elected#jerrold nadler (d) 86% ted howard (r) 14%\nnew york 9#charles schumer#democratic#1980#retired to run for us senate democratic hold#anthony weiner (d) 66% leslie telano (r) 24%\nnew york 10#ed towns#democratic#1982#re - elected#ed towns (d) 92% ernestine brown (r) 7%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 89% david greene (r) 10%\nnew york 12#nydia velazquez#democratic#1992#re - elected#nydia velazquez (d) 83% rosemarie markgraf (r) 13%\nnew york 13#vito fossella#republican#1997#re - elected#vito fossella (r) 66% eugene prisco (d) 34%\nnew york 15#charles b rangel#democratic#1970#re - elected#charles b rangel (d) 94% david cunningham (r) 6%\nnew york 16#jose serrano#democratic#1990#re - elected#jose serrano (d) 96% thomas bayley (r) 4%\nnew york 17#eliot engel#democratic#1988#re - elected#eliot engel (d) 88% peter fiumefreddo (r) 12%\nnew york 18#nita lowey#democratic#1988#re - elected#nita lowey (d) unopposed\nnew york 19#sue kelly#republican#1994#re - elected#sue kelly (r) 63% dick collins (d) 34%\nnew york 20#benjamin gilman#republican#1972#re - elected#benjamin gilman (r) 58% paul feiner (d) 40%\nnew york 21#mike mcnulty#democratic#1988#re - elected#mike mcnulty (d) 74% lauren ayers (r) 26%\nnew york 22#gerald solomon#republican#1978#retired republican hold#john sweeney (r) 56% jean bordewich (d) 43%\nnew york 23#sherwood boehlert#republican#1982#re - elected#sherwood boehlert (r) unopposed\nnew york 24#john mchugh#republican#1992#re - elected#john mchugh (r) 79% neil tallon (d) 21%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 69% yvonne rothenberg (d) 31%\nnew york 27#bill paxon#republican#1988#retired republican hold#tom reynolds (r) 58% bill cook (d) 42%\nnew york 28#louise slaughter#democratic#1986#re - elected#louise slaughter (d) 65% richard kaplan (r) 31%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 58% chris collins (r) 41%\nnew york 30#jack quinn#republican#1992#re - elected#jack quinn (r) 68% crystal peoples (d) 32%\n",
        "pandas_code": "df[df['first elected'].str.contains('special', case=False, na=False)]['incumbent'].eq('gregory w meeks').all()",
        "pandas_eval": "True"
    },
    {
        "id": 593,
        "statement": "there be 2 republican from il in the house in 1988",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 1#charles hayes#democratic#1983#re - elected#charles hayes (d) 96.0% stephen j evans (r) 4.0%\nillinois 2#gus savage#democratic#1980#re - elected#gus savage (d) 82.7% william t hespel (r) 17.3%\nillinois 3#marty russo#democratic#1974#re - elected#marty russo (d) 62.2% joseph j mccarthy (r) 37.8%\nillinois 6#henry hyde#republican#1974#re - elected#henry hyde (r) 73.7% william j andrle (d) 26.3%\nillinois 7#cardiss collins#democratic#1973#re - elected#cardiss collins (d) unopposed\nillinois 12#phil crane#republican#1969#re - elected#phil crane (r) 75.2% john a leonardi (d) 24.8%\nillinois 17#lane evans#democratic#1982#re - elected#lane evans (d) 64.9% william e stewart (r) 35.1%\n",
        "pandas_code": "(df['party'] == 'republican').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 594,
        "statement": "there be more democrat than republican from the illinois district in the us house of representative",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 1#charles hayes#democratic#1983#re - elected#charles hayes (d) 96.0% stephen j evans (r) 4.0%\nillinois 2#gus savage#democratic#1980#re - elected#gus savage (d) 82.7% william t hespel (r) 17.3%\nillinois 3#marty russo#democratic#1974#re - elected#marty russo (d) 62.2% joseph j mccarthy (r) 37.8%\nillinois 6#henry hyde#republican#1974#re - elected#henry hyde (r) 73.7% william j andrle (d) 26.3%\nillinois 7#cardiss collins#democratic#1973#re - elected#cardiss collins (d) unopposed\nillinois 12#phil crane#republican#1969#re - elected#phil crane (r) 75.2% john a leonardi (d) 24.8%\nillinois 17#lane evans#democratic#1982#re - elected#lane evans (d) 64.9% william e stewart (r) 35.1%\n",
        "pandas_code": "df[df['district'].str.contains('illinois')]['party'].value_counts().idxmax() == 'democratic'",
        "pandas_eval": "True"
    },
    {
        "id": 595,
        "statement": "marty russo and henry hyde be both first elect in 1974",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 1#charles hayes#democratic#1983#re - elected#charles hayes (d) 96.0% stephen j evans (r) 4.0%\nillinois 2#gus savage#democratic#1980#re - elected#gus savage (d) 82.7% william t hespel (r) 17.3%\nillinois 3#marty russo#democratic#1974#re - elected#marty russo (d) 62.2% joseph j mccarthy (r) 37.8%\nillinois 6#henry hyde#republican#1974#re - elected#henry hyde (r) 73.7% william j andrle (d) 26.3%\nillinois 7#cardiss collins#democratic#1973#re - elected#cardiss collins (d) unopposed\nillinois 12#phil crane#republican#1969#re - elected#phil crane (r) 75.2% john a leonardi (d) 24.8%\nillinois 17#lane evans#democratic#1982#re - elected#lane evans (d) 64.9% william e stewart (r) 35.1%\n",
        "pandas_code": "(df[df['incumbent'].isin(['marty russo', 'henry hyde'])]['first elected'] == 1974).all()",
        "pandas_eval": "True"
    },
    {
        "id": 596,
        "statement": "phil crane be the earliest candidate first elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 1#charles hayes#democratic#1983#re - elected#charles hayes (d) 96.0% stephen j evans (r) 4.0%\nillinois 2#gus savage#democratic#1980#re - elected#gus savage (d) 82.7% william t hespel (r) 17.3%\nillinois 3#marty russo#democratic#1974#re - elected#marty russo (d) 62.2% joseph j mccarthy (r) 37.8%\nillinois 6#henry hyde#republican#1974#re - elected#henry hyde (r) 73.7% william j andrle (d) 26.3%\nillinois 7#cardiss collins#democratic#1973#re - elected#cardiss collins (d) unopposed\nillinois 12#phil crane#republican#1969#re - elected#phil crane (r) 75.2% john a leonardi (d) 24.8%\nillinois 17#lane evans#democratic#1982#re - elected#lane evans (d) 64.9% william e stewart (r) 35.1%\n",
        "pandas_code": "df[df['incumbent'] == 'phil crane']['first elected'].min() == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 597,
        "statement": "elect official be from 7 district of illinois",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 1#charles hayes#democratic#1983#re - elected#charles hayes (d) 96.0% stephen j evans (r) 4.0%\nillinois 2#gus savage#democratic#1980#re - elected#gus savage (d) 82.7% william t hespel (r) 17.3%\nillinois 3#marty russo#democratic#1974#re - elected#marty russo (d) 62.2% joseph j mccarthy (r) 37.8%\nillinois 6#henry hyde#republican#1974#re - elected#henry hyde (r) 73.7% william j andrle (d) 26.3%\nillinois 7#cardiss collins#democratic#1973#re - elected#cardiss collins (d) unopposed\nillinois 12#phil crane#republican#1969#re - elected#phil crane (r) 75.2% john a leonardi (d) 24.8%\nillinois 17#lane evans#democratic#1982#re - elected#lane evans (d) 64.9% william e stewart (r) 35.1%\n",
        "pandas_code": "df[df['district'] == 'illinois 7']['incumbent'].values[0] == 'cardiss collins'",
        "pandas_eval": "True"
    },
    {
        "id": 598,
        "statement": "the builder , g&swr kilmarnock , build after the north british locomotive of 1913",
        "label": 1,
        "table_caption": "locomotives of the glasgow and south western railway",
        "table_text": "class#wheels#date#builder#no built#1919 nos#lms class#lms nos\n4 - 4 - 0#131#1913#north british#6#331 - 336#3p#14510 - 5\n4 - 4 - 0#137#1915#g&swr kilmarnock#6#325 - 330#3p#14516 - 21\n0 - 6 - 0t#5#1917#north british#3#322 - 324#2f#16377 - 9\n0 - 6 - 2t#45#1915 - 17#north british#18#11 - 28#3f#16410 - 27 (later 16910 - 27)\n0 - 6 - 0#279#1913#north british#15#71 - 85#4f#17750 - 64\n",
        "pandas_code": "(df[df['builder'] == 'g&swr kilmarnock']['date'].iloc[0] > df[df['builder'] == 'north british']['date'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 599,
        "statement": "the first north british locomotive and the g&swr kilmarnock both have class 4 - 4 - 0",
        "label": 1,
        "table_caption": "locomotives of the glasgow and south western railway",
        "table_text": "class#wheels#date#builder#no built#1919 nos#lms class#lms nos\n4 - 4 - 0#131#1913#north british#6#331 - 336#3p#14510 - 5\n4 - 4 - 0#137#1915#g&swr kilmarnock#6#325 - 330#3p#14516 - 21\n0 - 6 - 0t#5#1917#north british#3#322 - 324#2f#16377 - 9\n0 - 6 - 2t#45#1915 - 17#north british#18#11 - 28#3f#16410 - 27 (later 16910 - 27)\n0 - 6 - 0#279#1913#north british#15#71 - 85#4f#17750 - 64\n",
        "pandas_code": "(df[df['builder'] == 'north british'].iloc[0]['class'] == '4 - 4 - 0') & (df[df['builder'] == 'g&swr kilmarnock'].iloc[0]['class'] == '4 - 4 - 0')",
        "pandas_eval": "True"
    },
    {
        "id": 600,
        "statement": "g&swr kilmarnock do not build on the glasgow and south western railway in 1917",
        "label": 1,
        "table_caption": "locomotives of the glasgow and south western railway",
        "table_text": "class#wheels#date#builder#no built#1919 nos#lms class#lms nos\n4 - 4 - 0#131#1913#north british#6#331 - 336#3p#14510 - 5\n4 - 4 - 0#137#1915#g&swr kilmarnock#6#325 - 330#3p#14516 - 21\n0 - 6 - 0t#5#1917#north british#3#322 - 324#2f#16377 - 9\n0 - 6 - 2t#45#1915 - 17#north british#18#11 - 28#3f#16410 - 27 (later 16910 - 27)\n0 - 6 - 0#279#1913#north british#15#71 - 85#4f#17750 - 64\n",
        "pandas_code": "not any((df['builder'] == 'g&swr kilmarnock') & (df['date'] == '1917'))",
        "pandas_eval": "True"
    },
    {
        "id": 601,
        "statement": "1919 nos 322 - 324 be build after 1919 nos 325 - 330",
        "label": 1,
        "table_caption": "locomotives of the glasgow and south western railway",
        "table_text": "class#wheels#date#builder#no built#1919 nos#lms class#lms nos\n4 - 4 - 0#131#1913#north british#6#331 - 336#3p#14510 - 5\n4 - 4 - 0#137#1915#g&swr kilmarnock#6#325 - 330#3p#14516 - 21\n0 - 6 - 0t#5#1917#north british#3#322 - 324#2f#16377 - 9\n0 - 6 - 2t#45#1915 - 17#north british#18#11 - 28#3f#16410 - 27 (later 16910 - 27)\n0 - 6 - 0#279#1913#north british#15#71 - 85#4f#17750 - 64\n",
        "pandas_code": "df.loc[df['1919 nos'] == '322 - 324', 'date'].values[0] > df.loc[df['1919 nos'] == '325 - 330', 'date'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 602,
        "statement": "the north british builder build 4 of the 5 locomotive of the glasgow and south western railway",
        "label": 1,
        "table_caption": "locomotives of the glasgow and south western railway",
        "table_text": "class#wheels#date#builder#no built#1919 nos#lms class#lms nos\n4 - 4 - 0#131#1913#north british#6#331 - 336#3p#14510 - 5\n4 - 4 - 0#137#1915#g&swr kilmarnock#6#325 - 330#3p#14516 - 21\n0 - 6 - 0t#5#1917#north british#3#322 - 324#2f#16377 - 9\n0 - 6 - 2t#45#1915 - 17#north british#18#11 - 28#3f#16410 - 27 (later 16910 - 27)\n0 - 6 - 0#279#1913#north british#15#71 - 85#4f#17750 - 64\n",
        "pandas_code": "(df['builder'] == 'north british').sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 603,
        "statement": "goran relji\u0107 win with a record of 5 - 0 against petr kelner as compare to a record of 1 - 0 with bojan spalevic",
        "label": 1,
        "table_caption": "goran relji\u0107",
        "table_text": "res#record#opponent#method#event#round#location\nwin#12 - 4#nikolay osokin#ko (liver kick)#draka 2013#1#vladivostok , russia\nloss#11 - 4#jan b\u0142achowicz#decision (unanimous)#ksw 22#3#warsaw , poland\nwin#11 - 3#gadji magomedov#decision (unanimous)#draka 8#4#nakhodka , russia\nwin#10 - 3#rogent lloret#tko (punches)#s&hc 5#1#geneva , switzerland\nwin#9 - 3#goran scepanovic#tko (punches)#bilic - eric security fight night 6#1#zagreb , croatia\nloss#8 - 3#krzysztof soszynski#decision (unanimous)#ufc 122#3#oberhausen , germany\nloss#8 - 2#kendall grove#decision (split)#ufc 116#3#nevada , united states\nloss#8 - 1#cb dollaway#decision (unanimous)#ufc 110#3#sydney , australia\nwin#8 - 0#wilson gouveia#tko (punches)#ufc 84#2#nevada , united states\nwin#7 - 0#waldemar golinski#decision (unanimous)#be 2 - boxing explosion 2#2#celje , slovenia\nwin#6 - 0#tomek smykowski#submission (armbar)#cf - the real deal#1#maribor , slovenia\nwin#5 - 0#petr kelner#submission (armbar)#ultimate fight - challenge 1#2#samobor , croatia\nwin#4 - 0#bojan mihajlovic#submission ( kimura )#ns 3 - noc skorpiona 3#1#\u0161ibenik , croatia\nwin#3 - 0#nenad djuric#tko (punches)#ns 1 - noc skorpiona 1#2#zadar , croatia\nwin#2 - 0#andrej bregar#submission ( arm triangle choke )#trboulje 1 - croatia vs slovenia#1#ljubljana , slovenia\nwin#1 - 0#bojan spalevic#submission ( armbar )#ufd - ultimate fight dubravc#1#zagreb , croatia\n",
        "pandas_code": "(df[(df['res'] == 'win') & (df['record'] == '5 - 0') & (df['opponent'] == 'petr kelner')].shape[0] > 0) & (df[(df['res'] == 'win') & (df['record'] == '1 - 0') & (df['opponent'] == 'bojan spalevic')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 604,
        "statement": "there be only 1 match goran relji\u0107 win with ko kick) method",
        "label": 1,
        "table_caption": "goran relji\u0107",
        "table_text": "res#record#opponent#method#event#round#location\nwin#12 - 4#nikolay osokin#ko (liver kick)#draka 2013#1#vladivostok , russia\nloss#11 - 4#jan b\u0142achowicz#decision (unanimous)#ksw 22#3#warsaw , poland\nwin#11 - 3#gadji magomedov#decision (unanimous)#draka 8#4#nakhodka , russia\nwin#10 - 3#rogent lloret#tko (punches)#s&hc 5#1#geneva , switzerland\nwin#9 - 3#goran scepanovic#tko (punches)#bilic - eric security fight night 6#1#zagreb , croatia\nloss#8 - 3#krzysztof soszynski#decision (unanimous)#ufc 122#3#oberhausen , germany\nloss#8 - 2#kendall grove#decision (split)#ufc 116#3#nevada , united states\nloss#8 - 1#cb dollaway#decision (unanimous)#ufc 110#3#sydney , australia\nwin#8 - 0#wilson gouveia#tko (punches)#ufc 84#2#nevada , united states\nwin#7 - 0#waldemar golinski#decision (unanimous)#be 2 - boxing explosion 2#2#celje , slovenia\nwin#6 - 0#tomek smykowski#submission (armbar)#cf - the real deal#1#maribor , slovenia\nwin#5 - 0#petr kelner#submission (armbar)#ultimate fight - challenge 1#2#samobor , croatia\nwin#4 - 0#bojan mihajlovic#submission ( kimura )#ns 3 - noc skorpiona 3#1#\u0161ibenik , croatia\nwin#3 - 0#nenad djuric#tko (punches)#ns 1 - noc skorpiona 1#2#zadar , croatia\nwin#2 - 0#andrej bregar#submission ( arm triangle choke )#trboulje 1 - croatia vs slovenia#1#ljubljana , slovenia\nwin#1 - 0#bojan spalevic#submission ( armbar )#ufd - ultimate fight dubravc#1#zagreb , croatia\n",
        "pandas_code": "len(df[(df['res'] == 'win') & (df['method'] == 'ko (liver kick)')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 605,
        "statement": "the highest win record of goran relji\u0107 be 12 - 4",
        "label": 1,
        "table_caption": "goran relji\u0107",
        "table_text": "res#record#opponent#method#event#round#location\nwin#12 - 4#nikolay osokin#ko (liver kick)#draka 2013#1#vladivostok , russia\nloss#11 - 4#jan b\u0142achowicz#decision (unanimous)#ksw 22#3#warsaw , poland\nwin#11 - 3#gadji magomedov#decision (unanimous)#draka 8#4#nakhodka , russia\nwin#10 - 3#rogent lloret#tko (punches)#s&hc 5#1#geneva , switzerland\nwin#9 - 3#goran scepanovic#tko (punches)#bilic - eric security fight night 6#1#zagreb , croatia\nloss#8 - 3#krzysztof soszynski#decision (unanimous)#ufc 122#3#oberhausen , germany\nloss#8 - 2#kendall grove#decision (split)#ufc 116#3#nevada , united states\nloss#8 - 1#cb dollaway#decision (unanimous)#ufc 110#3#sydney , australia\nwin#8 - 0#wilson gouveia#tko (punches)#ufc 84#2#nevada , united states\nwin#7 - 0#waldemar golinski#decision (unanimous)#be 2 - boxing explosion 2#2#celje , slovenia\nwin#6 - 0#tomek smykowski#submission (armbar)#cf - the real deal#1#maribor , slovenia\nwin#5 - 0#petr kelner#submission (armbar)#ultimate fight - challenge 1#2#samobor , croatia\nwin#4 - 0#bojan mihajlovic#submission ( kimura )#ns 3 - noc skorpiona 3#1#\u0161ibenik , croatia\nwin#3 - 0#nenad djuric#tko (punches)#ns 1 - noc skorpiona 1#2#zadar , croatia\nwin#2 - 0#andrej bregar#submission ( arm triangle choke )#trboulje 1 - croatia vs slovenia#1#ljubljana , slovenia\nwin#1 - 0#bojan spalevic#submission ( armbar )#ufd - ultimate fight dubravc#1#zagreb , croatia\n",
        "pandas_code": "df[df['res'] == 'win']['record'].apply(lambda x: int(x.split(' - ')[0])).max() == 12",
        "pandas_eval": "True"
    },
    {
        "id": 607,
        "statement": "goran relji\u0107 lose a total of 4 match and win a total of 12 match",
        "label": 1,
        "table_caption": "goran relji\u0107",
        "table_text": "res#record#opponent#method#event#round#location\nwin#12 - 4#nikolay osokin#ko (liver kick)#draka 2013#1#vladivostok , russia\nloss#11 - 4#jan b\u0142achowicz#decision (unanimous)#ksw 22#3#warsaw , poland\nwin#11 - 3#gadji magomedov#decision (unanimous)#draka 8#4#nakhodka , russia\nwin#10 - 3#rogent lloret#tko (punches)#s&hc 5#1#geneva , switzerland\nwin#9 - 3#goran scepanovic#tko (punches)#bilic - eric security fight night 6#1#zagreb , croatia\nloss#8 - 3#krzysztof soszynski#decision (unanimous)#ufc 122#3#oberhausen , germany\nloss#8 - 2#kendall grove#decision (split)#ufc 116#3#nevada , united states\nloss#8 - 1#cb dollaway#decision (unanimous)#ufc 110#3#sydney , australia\nwin#8 - 0#wilson gouveia#tko (punches)#ufc 84#2#nevada , united states\nwin#7 - 0#waldemar golinski#decision (unanimous)#be 2 - boxing explosion 2#2#celje , slovenia\nwin#6 - 0#tomek smykowski#submission (armbar)#cf - the real deal#1#maribor , slovenia\nwin#5 - 0#petr kelner#submission (armbar)#ultimate fight - challenge 1#2#samobor , croatia\nwin#4 - 0#bojan mihajlovic#submission ( kimura )#ns 3 - noc skorpiona 3#1#\u0161ibenik , croatia\nwin#3 - 0#nenad djuric#tko (punches)#ns 1 - noc skorpiona 1#2#zadar , croatia\nwin#2 - 0#andrej bregar#submission ( arm triangle choke )#trboulje 1 - croatia vs slovenia#1#ljubljana , slovenia\nwin#1 - 0#bojan spalevic#submission ( armbar )#ufd - ultimate fight dubravc#1#zagreb , croatia\n",
        "pandas_code": "(df['res'].value_counts().get('loss', 0) == 4) & (df['res'].value_counts().get('win', 0) == 12)",
        "pandas_eval": "True"
    },
    {
        "id": 608,
        "statement": "only 2 of the representative on the list be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nohio 5#del latta#republican#1958#re - elected#del latta (r) 71.1% carl g sherer (d) 28.9%\nohio 6#bill harsha#republican#1960#re - elected#bill harsha (r) 67.8% raymond h stevens (d) 32.2%\nohio 8#jackson edward betts#republican#1950#re - elected#jackson edward betts (r) unopposed\nohio 10#clarence e miller#republican#1966#re - elected#clarence e miller (r) 66.5% doug arnett (d) 33.5%\nohio 11#j william stanton#republican#1964#re - elected#j william stanton (r) 68.2% ralph rudd (d) 31.8%\nohio 16#frank t bow#republican#1950#re - elected#frank t bow (r) 56.2% virgil l musser (d) 43.8%\nohio 18#wayne l hays#democratic#1948#re - elected#wayne l hays (d) 68.3% robert stewart (r) 31.7%\nohio 21#louis stokes#democratic#1968#re - elected#louis stokes (d) 77.6% bill mack (r) 22.4%\n",
        "pandas_code": "(df['party'] == 'democratic').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 609,
        "statement": "jackson edward betts be the only candidate that be uncontested",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nohio 5#del latta#republican#1958#re - elected#del latta (r) 71.1% carl g sherer (d) 28.9%\nohio 6#bill harsha#republican#1960#re - elected#bill harsha (r) 67.8% raymond h stevens (d) 32.2%\nohio 8#jackson edward betts#republican#1950#re - elected#jackson edward betts (r) unopposed\nohio 10#clarence e miller#republican#1966#re - elected#clarence e miller (r) 66.5% doug arnett (d) 33.5%\nohio 11#j william stanton#republican#1964#re - elected#j william stanton (r) 68.2% ralph rudd (d) 31.8%\nohio 16#frank t bow#republican#1950#re - elected#frank t bow (r) 56.2% virgil l musser (d) 43.8%\nohio 18#wayne l hays#democratic#1948#re - elected#wayne l hays (d) 68.3% robert stewart (r) 31.7%\nohio 21#louis stokes#democratic#1968#re - elected#louis stokes (d) 77.6% bill mack (r) 22.4%\n",
        "pandas_code": "df[df['incumbent'] == 'jackson edward betts']['candidates'].eq('jackson edward betts (r) unopposed').all()",
        "pandas_eval": "True"
    },
    {
        "id": 610,
        "statement": "jackson edward betts be first elect 10 year before bill harsha",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nohio 5#del latta#republican#1958#re - elected#del latta (r) 71.1% carl g sherer (d) 28.9%\nohio 6#bill harsha#republican#1960#re - elected#bill harsha (r) 67.8% raymond h stevens (d) 32.2%\nohio 8#jackson edward betts#republican#1950#re - elected#jackson edward betts (r) unopposed\nohio 10#clarence e miller#republican#1966#re - elected#clarence e miller (r) 66.5% doug arnett (d) 33.5%\nohio 11#j william stanton#republican#1964#re - elected#j william stanton (r) 68.2% ralph rudd (d) 31.8%\nohio 16#frank t bow#republican#1950#re - elected#frank t bow (r) 56.2% virgil l musser (d) 43.8%\nohio 18#wayne l hays#democratic#1948#re - elected#wayne l hays (d) 68.3% robert stewart (r) 31.7%\nohio 21#louis stokes#democratic#1968#re - elected#louis stokes (d) 77.6% bill mack (r) 22.4%\n",
        "pandas_code": "(df[df['incumbent'] == 'jackson edward betts']['first elected'].values[0] == df[df['incumbent'] == 'bill harsha']['first elected'].values[0] - 10)",
        "pandas_eval": "True"
    },
    {
        "id": 612,
        "statement": "louis stokes be the candidate that most recently first elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nohio 5#del latta#republican#1958#re - elected#del latta (r) 71.1% carl g sherer (d) 28.9%\nohio 6#bill harsha#republican#1960#re - elected#bill harsha (r) 67.8% raymond h stevens (d) 32.2%\nohio 8#jackson edward betts#republican#1950#re - elected#jackson edward betts (r) unopposed\nohio 10#clarence e miller#republican#1966#re - elected#clarence e miller (r) 66.5% doug arnett (d) 33.5%\nohio 11#j william stanton#republican#1964#re - elected#j william stanton (r) 68.2% ralph rudd (d) 31.8%\nohio 16#frank t bow#republican#1950#re - elected#frank t bow (r) 56.2% virgil l musser (d) 43.8%\nohio 18#wayne l hays#democratic#1948#re - elected#wayne l hays (d) 68.3% robert stewart (r) 31.7%\nohio 21#louis stokes#democratic#1968#re - elected#louis stokes (d) 77.6% bill mack (r) 22.4%\n",
        "pandas_code": "df[df['incumbent'] == 'louis stokes']['first elected'].max() == df['first elected'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 613,
        "statement": "the locomotive with 4 - 4 - 0 wheel arrangement ( whyte notation ) be own by western and atlantic railroad 49 texasand dardanelle and russelville 8",
        "label": 1,
        "table_caption": "cooke locomotive and machine works",
        "table_text": "serial number#wheel arrangement ( whyte notation )#build date#operational owner (s)#disposition\nunknown#4 - 4 - 0#october 1856#western and atlantic railroad 49 texas#static display in grant park , atlanta , georgia\n1555#2 - 6 - 0#february 1884#colorado and southern railway 9#georgetown loop railroad , silver plume , colorado\n1861#4 - 4 - 0#february 1888#dardanelle and russelville 8#nevada state railroad museum , carson city , nevada\n2053#4 - 6 - 0#october , 1890#union pacific railroad 1242#lion 's park , cheyenne , wyoming\n2054#4 - 6 - 0#october , 1890#union pacific railroad 1243#durham western heritage museum , omaha , nebraska\n2202#4 - 6 - 0#april 1892#texas and new orleans railroad 319#riverdale , georgia\n2341#4 - 6 - 0#july 1896#southern pacific railroad 2248#grapevine vintage railroad , grapevine , texas\n",
        "pandas_code": "((df['wheel arrangement ( whyte notation )'] == '4 - 4 - 0') & (df['operational owner (s)'].isin(['western and atlantic railroad 49 texas', 'dardanelle and russelville 8']))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 614,
        "statement": "the locomotive with serial number 2053 and 2054 have union pacific railroad operational owner (s)",
        "label": 1,
        "table_caption": "cooke locomotive and machine works",
        "table_text": "serial number#wheel arrangement ( whyte notation )#build date#operational owner (s)#disposition\nunknown#4 - 4 - 0#october 1856#western and atlantic railroad 49 texas#static display in grant park , atlanta , georgia\n1555#2 - 6 - 0#february 1884#colorado and southern railway 9#georgetown loop railroad , silver plume , colorado\n1861#4 - 4 - 0#february 1888#dardanelle and russelville 8#nevada state railroad museum , carson city , nevada\n2053#4 - 6 - 0#october , 1890#union pacific railroad 1242#lion 's park , cheyenne , wyoming\n2054#4 - 6 - 0#october , 1890#union pacific railroad 1243#durham western heritage museum , omaha , nebraska\n2202#4 - 6 - 0#april 1892#texas and new orleans railroad 319#riverdale , georgia\n2341#4 - 6 - 0#july 1896#southern pacific railroad 2248#grapevine vintage railroad , grapevine , texas\n",
        "pandas_code": "all(df[df['serial number'].isin(['2053', '2054'])]['operational owner (s)'].str.contains('union pacific railroad'))",
        "pandas_eval": "True"
    },
    {
        "id": 615,
        "statement": "the locomotive build in april 1892 and july 1896 be dispose in riverdale , georgia and grapevine vintage railroad , grapevine , texas respectively",
        "label": 1,
        "table_caption": "cooke locomotive and machine works",
        "table_text": "serial number#wheel arrangement ( whyte notation )#build date#operational owner (s)#disposition\nunknown#4 - 4 - 0#october 1856#western and atlantic railroad 49 texas#static display in grant park , atlanta , georgia\n1555#2 - 6 - 0#february 1884#colorado and southern railway 9#georgetown loop railroad , silver plume , colorado\n1861#4 - 4 - 0#february 1888#dardanelle and russelville 8#nevada state railroad museum , carson city , nevada\n2053#4 - 6 - 0#october , 1890#union pacific railroad 1242#lion 's park , cheyenne , wyoming\n2054#4 - 6 - 0#october , 1890#union pacific railroad 1243#durham western heritage museum , omaha , nebraska\n2202#4 - 6 - 0#april 1892#texas and new orleans railroad 319#riverdale , georgia\n2341#4 - 6 - 0#july 1896#southern pacific railroad 2248#grapevine vintage railroad , grapevine , texas\n",
        "pandas_code": "df[(df['build date'].isin(['april 1892', 'july 1896'])) & (df['disposition'].isin(['riverdale , georgia', 'grapevine vintage railroad , grapevine , texas']))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 616,
        "statement": "the wheel arrangement ( whyte notation ) for serial number 2053 , 2054 , 2202 and 2341 be 4 - 6 - 0",
        "label": 1,
        "table_caption": "cooke locomotive and machine works",
        "table_text": "serial number#wheel arrangement ( whyte notation )#build date#operational owner (s)#disposition\nunknown#4 - 4 - 0#october 1856#western and atlantic railroad 49 texas#static display in grant park , atlanta , georgia\n1555#2 - 6 - 0#february 1884#colorado and southern railway 9#georgetown loop railroad , silver plume , colorado\n1861#4 - 4 - 0#february 1888#dardanelle and russelville 8#nevada state railroad museum , carson city , nevada\n2053#4 - 6 - 0#october , 1890#union pacific railroad 1242#lion 's park , cheyenne , wyoming\n2054#4 - 6 - 0#october , 1890#union pacific railroad 1243#durham western heritage museum , omaha , nebraska\n2202#4 - 6 - 0#april 1892#texas and new orleans railroad 319#riverdale , georgia\n2341#4 - 6 - 0#july 1896#southern pacific railroad 2248#grapevine vintage railroad , grapevine , texas\n",
        "pandas_code": "all(df[df['serial number'].isin(['2053', '2054', '2202', '2341'])]['wheel arrangement ( whyte notation )'] == '4 - 6 - 0')",
        "pandas_eval": "True"
    },
    {
        "id": 617,
        "statement": "the locomotive with serial number 2202 be build 4 year earlier than that with serial 2341",
        "label": 1,
        "table_caption": "cooke locomotive and machine works",
        "table_text": "serial number#wheel arrangement ( whyte notation )#build date#operational owner (s)#disposition\nunknown#4 - 4 - 0#october 1856#western and atlantic railroad 49 texas#static display in grant park , atlanta , georgia\n1555#2 - 6 - 0#february 1884#colorado and southern railway 9#georgetown loop railroad , silver plume , colorado\n1861#4 - 4 - 0#february 1888#dardanelle and russelville 8#nevada state railroad museum , carson city , nevada\n2053#4 - 6 - 0#october , 1890#union pacific railroad 1242#lion 's park , cheyenne , wyoming\n2054#4 - 6 - 0#october , 1890#union pacific railroad 1243#durham western heritage museum , omaha , nebraska\n2202#4 - 6 - 0#april 1892#texas and new orleans railroad 319#riverdale , georgia\n2341#4 - 6 - 0#july 1896#southern pacific railroad 2248#grapevine vintage railroad , grapevine , texas\n",
        "pandas_code": "(df[df['serial number'] == '2202']['build date'].values[0].split()[-1] < df[df['serial number'] == '2341']['build date'].values[0].split()[-1]) and (int(df[df['serial number'] == '2341']['build date'].values[0].split()[-1]) - int(df[df['serial number'] == '2202']['build date'].values[0].split()[-1]) == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 618,
        "statement": "ue lleida have more than 2 time as many win as cfj mollerussa",
        "label": 1,
        "table_caption": "1988 - 89 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#cd castell\u00f3n#38#51 + 13#21#9#8#49#29#+ 20\n2#rayo vallecano#38#49 + 11#19#11#8#61#36#+ 25\n3#cd tenerife#38#48 + 10#20#8#10#54#36#+ 18\n4#rcd mallorca#38#48 + 10#21#6#11#51#26#+ 25\n5#recreativo de huelva#38#42 + 4#16#10#12#46#36#+ 10\n6#racing de santander#38#42 + 4#17#8#13#56#43#+ 13\n7#ud salamanca#38#42 + 4#14#14#10#35#33#+ 2\n8#sestao#38#41 + 3#14#13#11#39#32#+ 7\n9#ue figueres#38#41 + 3#16#9#13#52#50#+ 2\n10#deportivo de la coru\u00f1a#38#40 + 2#16#8#14#43#35#+ 8\n11#ud las palmas#38#40 + 2#15#10#13#52#53#- 1\n12#xerez cd#38#40 + 2#13#14#11#40#38#+ 2\n13#ce sabadell fc#38#39 + 1#15#9#14#49#43#+ 6\n14#real burgos#38#36 - 2#9#18#11#27#34#- 7\n15#castilla cf#38#36 - 2#13#10#15#50#59#- 9\n16#sd eibar#38#34 - 4#8#18#12#36#42#- 6\n17#barcelona atl\u00e8tic#38#28 - 10#8#12#18#42#58#- 16\n18#ud alzira#38#26 - 12#9#8#21#29#58#- 29\n19#ue lleida#38#26 - 12#8#10#20#29#43#- 16\n20#cfj mollerussa#38#11 - 27#3#5#30#19#75#- 56\n",
        "pandas_code": "(df[df['club'] == 'ue lleida']['wins'].values[0] > 2 * df[df['club'] == 'cfj mollerussa']['wins'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 619,
        "statement": "sd eibar and barcelona atl\u00e8tic have the same number of win (8) but sd eibar have 12 loss and barcelona atl\u00e8tic have 18",
        "label": 1,
        "table_caption": "1988 - 89 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#cd castell\u00f3n#38#51 + 13#21#9#8#49#29#+ 20\n2#rayo vallecano#38#49 + 11#19#11#8#61#36#+ 25\n3#cd tenerife#38#48 + 10#20#8#10#54#36#+ 18\n4#rcd mallorca#38#48 + 10#21#6#11#51#26#+ 25\n5#recreativo de huelva#38#42 + 4#16#10#12#46#36#+ 10\n6#racing de santander#38#42 + 4#17#8#13#56#43#+ 13\n7#ud salamanca#38#42 + 4#14#14#10#35#33#+ 2\n8#sestao#38#41 + 3#14#13#11#39#32#+ 7\n9#ue figueres#38#41 + 3#16#9#13#52#50#+ 2\n10#deportivo de la coru\u00f1a#38#40 + 2#16#8#14#43#35#+ 8\n11#ud las palmas#38#40 + 2#15#10#13#52#53#- 1\n12#xerez cd#38#40 + 2#13#14#11#40#38#+ 2\n13#ce sabadell fc#38#39 + 1#15#9#14#49#43#+ 6\n14#real burgos#38#36 - 2#9#18#11#27#34#- 7\n15#castilla cf#38#36 - 2#13#10#15#50#59#- 9\n16#sd eibar#38#34 - 4#8#18#12#36#42#- 6\n17#barcelona atl\u00e8tic#38#28 - 10#8#12#18#42#58#- 16\n18#ud alzira#38#26 - 12#9#8#21#29#58#- 29\n19#ue lleida#38#26 - 12#8#10#20#29#43#- 16\n20#cfj mollerussa#38#11 - 27#3#5#30#19#75#- 56\n",
        "pandas_code": "(df[df['club'] == 'sd eibar']['wins'].values[0] == df[df['club'] == 'barcelona atl\u00e8tic']['wins'].values[0]) & (df[df['club'] == 'sd eibar']['losses'].values[0] == 12) & (df[df['club'] == 'barcelona atl\u00e8tic']['losses'].values[0] == 18)",
        "pandas_eval": "True"
    },
    {
        "id": 620,
        "statement": "ce sabadell fc have 6 more win than real burgos , but 1 less win than deportivo de la coru\u00f1a",
        "label": 1,
        "table_caption": "1988 - 89 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#cd castell\u00f3n#38#51 + 13#21#9#8#49#29#+ 20\n2#rayo vallecano#38#49 + 11#19#11#8#61#36#+ 25\n3#cd tenerife#38#48 + 10#20#8#10#54#36#+ 18\n4#rcd mallorca#38#48 + 10#21#6#11#51#26#+ 25\n5#recreativo de huelva#38#42 + 4#16#10#12#46#36#+ 10\n6#racing de santander#38#42 + 4#17#8#13#56#43#+ 13\n7#ud salamanca#38#42 + 4#14#14#10#35#33#+ 2\n8#sestao#38#41 + 3#14#13#11#39#32#+ 7\n9#ue figueres#38#41 + 3#16#9#13#52#50#+ 2\n10#deportivo de la coru\u00f1a#38#40 + 2#16#8#14#43#35#+ 8\n11#ud las palmas#38#40 + 2#15#10#13#52#53#- 1\n12#xerez cd#38#40 + 2#13#14#11#40#38#+ 2\n13#ce sabadell fc#38#39 + 1#15#9#14#49#43#+ 6\n14#real burgos#38#36 - 2#9#18#11#27#34#- 7\n15#castilla cf#38#36 - 2#13#10#15#50#59#- 9\n16#sd eibar#38#34 - 4#8#18#12#36#42#- 6\n17#barcelona atl\u00e8tic#38#28 - 10#8#12#18#42#58#- 16\n18#ud alzira#38#26 - 12#9#8#21#29#58#- 29\n19#ue lleida#38#26 - 12#8#10#20#29#43#- 16\n20#cfj mollerussa#38#11 - 27#3#5#30#19#75#- 56\n",
        "pandas_code": "(df.loc[df['club'] == 'ce sabadell fc', 'wins'].values[0] == df.loc[df['club'] == 'real burgos', 'wins'].values[0] + 6) & (df.loc[df['club'] == 'ce sabadell fc', 'wins'].values[0] == df.loc[df['club'] == 'deportivo de la coru\u00f1a', 'wins'].values[0] - 1)",
        "pandas_eval": "True"
    },
    {
        "id": 621,
        "statement": "ce sabadell fc have 15 loss compare to real burgos with only 9 , but ce sabadell fc be in a higher position than real burgos",
        "label": 1,
        "table_caption": "1988 - 89 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#cd castell\u00f3n#38#51 + 13#21#9#8#49#29#+ 20\n2#rayo vallecano#38#49 + 11#19#11#8#61#36#+ 25\n3#cd tenerife#38#48 + 10#20#8#10#54#36#+ 18\n4#rcd mallorca#38#48 + 10#21#6#11#51#26#+ 25\n5#recreativo de huelva#38#42 + 4#16#10#12#46#36#+ 10\n6#racing de santander#38#42 + 4#17#8#13#56#43#+ 13\n7#ud salamanca#38#42 + 4#14#14#10#35#33#+ 2\n8#sestao#38#41 + 3#14#13#11#39#32#+ 7\n9#ue figueres#38#41 + 3#16#9#13#52#50#+ 2\n10#deportivo de la coru\u00f1a#38#40 + 2#16#8#14#43#35#+ 8\n11#ud las palmas#38#40 + 2#15#10#13#52#53#- 1\n12#xerez cd#38#40 + 2#13#14#11#40#38#+ 2\n13#ce sabadell fc#38#39 + 1#15#9#14#49#43#+ 6\n14#real burgos#38#36 - 2#9#18#11#27#34#- 7\n15#castilla cf#38#36 - 2#13#10#15#50#59#- 9\n16#sd eibar#38#34 - 4#8#18#12#36#42#- 6\n17#barcelona atl\u00e8tic#38#28 - 10#8#12#18#42#58#- 16\n18#ud alzira#38#26 - 12#9#8#21#29#58#- 29\n19#ue lleida#38#26 - 12#8#10#20#29#43#- 16\n20#cfj mollerussa#38#11 - 27#3#5#30#19#75#- 56\n",
        "pandas_code": "(df[df['club'] == 'ce sabadell fc']['losses'].values[0] == 14) & (df[df['club'] == 'real burgos']['losses'].values[0] == 11) & (df[df['club'] == 'ce sabadell fc']['position'].values[0] < df[df['club'] == 'real burgos']['position'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 622,
        "statement": "club cd castell\u00f3n in position 1 with 21 win , have 18 more win than the club cfj mollerussa who have only 3 win",
        "label": 1,
        "table_caption": "1988 - 89 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#cd castell\u00f3n#38#51 + 13#21#9#8#49#29#+ 20\n2#rayo vallecano#38#49 + 11#19#11#8#61#36#+ 25\n3#cd tenerife#38#48 + 10#20#8#10#54#36#+ 18\n4#rcd mallorca#38#48 + 10#21#6#11#51#26#+ 25\n5#recreativo de huelva#38#42 + 4#16#10#12#46#36#+ 10\n6#racing de santander#38#42 + 4#17#8#13#56#43#+ 13\n7#ud salamanca#38#42 + 4#14#14#10#35#33#+ 2\n8#sestao#38#41 + 3#14#13#11#39#32#+ 7\n9#ue figueres#38#41 + 3#16#9#13#52#50#+ 2\n10#deportivo de la coru\u00f1a#38#40 + 2#16#8#14#43#35#+ 8\n11#ud las palmas#38#40 + 2#15#10#13#52#53#- 1\n12#xerez cd#38#40 + 2#13#14#11#40#38#+ 2\n13#ce sabadell fc#38#39 + 1#15#9#14#49#43#+ 6\n14#real burgos#38#36 - 2#9#18#11#27#34#- 7\n15#castilla cf#38#36 - 2#13#10#15#50#59#- 9\n16#sd eibar#38#34 - 4#8#18#12#36#42#- 6\n17#barcelona atl\u00e8tic#38#28 - 10#8#12#18#42#58#- 16\n18#ud alzira#38#26 - 12#9#8#21#29#58#- 29\n19#ue lleida#38#26 - 12#8#10#20#29#43#- 16\n20#cfj mollerussa#38#11 - 27#3#5#30#19#75#- 56\n",
        "pandas_code": "(df.loc[df['club'] == 'cd castell\u00f3n', 'wins'].values[0] - df.loc[df['club'] == 'cfj mollerussa', 'wins'].values[0]) == 18",
        "pandas_eval": "True"
    },
    {
        "id": 623,
        "statement": "both match on carpet surface result in loss",
        "label": 1,
        "table_caption": "maria jo\u00e3o koehler",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#bulgaria#carpet#dia evtimova tsvetana pironkova#loss#1 - 6 , 2 - 6\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#the netherlands#carpet#nicole thijssen pauline wong#loss#2 - 6 , 4 - 6\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#frederica piedade#switzerland#hard#sarah moundir amra sadikovic#loss#5 - 7 , 7 - 5 , 6 - 4\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#neuza silva#romania#hard#irina - camelia begu ioana raluca olaru#win#7 - 5 , 7 - 5\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#michelle larcher de brito#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n",
        "pandas_code": "all(df[df['surface'] == 'carpet']['w - l'] == 'loss')",
        "pandas_eval": "True"
    },
    {
        "id": 624,
        "statement": "both match on clay surface result in win",
        "label": 1,
        "table_caption": "maria jo\u00e3o koehler",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#bulgaria#carpet#dia evtimova tsvetana pironkova#loss#1 - 6 , 2 - 6\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#the netherlands#carpet#nicole thijssen pauline wong#loss#2 - 6 , 4 - 6\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#frederica piedade#switzerland#hard#sarah moundir amra sadikovic#loss#5 - 7 , 7 - 5 , 6 - 4\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#neuza silva#romania#hard#irina - camelia begu ioana raluca olaru#win#7 - 5 , 7 - 5\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#michelle larcher de brito#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n",
        "pandas_code": "all(df[df['surface'] == 'clay']['w - l'] == 'win')",
        "pandas_eval": "True"
    },
    {
        "id": 625,
        "statement": "both match when partner with magali lattre be loss",
        "label": 1,
        "table_caption": "maria jo\u00e3o koehler",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#bulgaria#carpet#dia evtimova tsvetana pironkova#loss#1 - 6 , 2 - 6\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#the netherlands#carpet#nicole thijssen pauline wong#loss#2 - 6 , 4 - 6\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#frederica piedade#switzerland#hard#sarah moundir amra sadikovic#loss#5 - 7 , 7 - 5 , 6 - 4\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#neuza silva#romania#hard#irina - camelia begu ioana raluca olaru#win#7 - 5 , 7 - 5\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#michelle larcher de brito#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n",
        "pandas_code": "all(df[df['partnering'] == 'magali de lattre']['w - l'] == 'loss')",
        "pandas_eval": "True"
    },
    {
        "id": 627,
        "statement": "the record on hard surface be 1 win and 2 loss",
        "label": 1,
        "table_caption": "maria jo\u00e3o koehler",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#bulgaria#carpet#dia evtimova tsvetana pironkova#loss#1 - 6 , 2 - 6\n2008 fed cup europe / africa group i#rr#30 january - 3 february 2008#magali de lattre#the netherlands#carpet#nicole thijssen pauline wong#loss#2 - 6 , 4 - 6\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#frederica piedade#switzerland#hard#sarah moundir amra sadikovic#loss#5 - 7 , 7 - 5 , 6 - 4\n2010 fed cup europe / africa group i#rr#4 - 5 february 2010#neuza silva#romania#hard#irina - camelia begu ioana raluca olaru#win#7 - 5 , 7 - 5\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#michelle larcher de brito#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#michelle larcher de brito#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n",
        "pandas_code": "(df[df['surface'] == 'hard']['w - l'].value_counts() == pd.Series({'loss': 2, 'win': 1})).all()",
        "pandas_eval": "True"
    },
    {
        "id": 628,
        "statement": "constituency number 56 and 57 be both from the district of damoh",
        "label": 1,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "all(df[df['constituency number'].isin(['56', '57'])]['district'] == 'damoh')",
        "pandas_eval": "True"
    },
    {
        "id": 629,
        "statement": "all but hatta have none state on the reserve for ( sc / st / none) column",
        "label": 1,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "df[(df['name'] != 'hatta') & (df['name'] != 'total :')]['reserved for ( sc / st / none)'].eq('none').all()",
        "pandas_eval": "True"
    },
    {
        "id": 630,
        "statement": "banda 's number of electorate (2009) be 885 more than that of rehli",
        "label": 1,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "(df[df['name'] == 'banda']['number of electorates (2009)'].values[0] - df[df['name'] == 'rehli']['number of electorates (2009)'].values[0]) == 885",
        "pandas_eval": "True"
    },
    {
        "id": 631,
        "statement": "the highest number of electorate (2009) be 185489 follow by 176993 and 176108",
        "label": 1,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "(df[df['name'] != 'total :']['number of electorates (2009)'].nlargest(3).tolist() == [185489, 176993, 176108])",
        "pandas_eval": "True"
    },
    {
        "id": 632,
        "statement": "malhara be the only 1 from district chhatarpur , 3 from sagar and 4 from damoh",
        "label": 1,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "len(df[df['district'] == 'chhatarpur']) == 1 and len(df[df['district'] == 'sagar']) == 3 and len(df[df['district'] == 'damoh']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 633,
        "statement": "constituency number 56 and 57 be the only 1 from the district of damoh",
        "label": 0,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "((df['district'] == 'damoh') & (df['constituency number'].isin(['56', '57']))).sum() == 2 and (df['district'] == 'damoh').sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 634,
        "statement": "all but hatta and banda have none state on the reserve for ( sc / st / none) column",
        "label": 0,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "all(df[~df['name'].isin(['hatta', 'banda'])]['reserved for ( sc / st / none)'] == 'none')",
        "pandas_eval": "False"
    },
    {
        "id": 635,
        "statement": "banda 's number of electorate (2009) be 885 more than that of rehli and lower than deori 's",
        "label": 0,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "(df[df['name'] == 'banda']['number of electorates (2009)'].values[0] == df[df['name'] == 'rehli']['number of electorates (2009)'].values[0] + 885) & (df[df['name'] == 'banda']['number of electorates (2009)'].values[0] < df[df['name'] == 'deori']['number of electorates (2009)'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 636,
        "statement": "the highest number of electorate (2009) be from constituency number 53 , 185489 follow by 176993 and 176108",
        "label": 0,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "df.loc[df['number of electorates (2009)'].idxmax(), 'constituency number'] == '53' and df['number of electorates (2009)'].sort_values(ascending=False).iloc[:3].tolist() == [185489, 176993, 176108]",
        "pandas_eval": "False"
    },
    {
        "id": 637,
        "statement": "malhara , the 1 with the lowest number of electorate , be the only 1 from district chhatarpur , 3 from sagar and 4 from damoh",
        "label": 0,
        "table_caption": "damoh (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n38#deori#none#sagar#160350\n39#rehli#none#sagar#176108\n42#banda#none#sagar#176993\n53#malhara#none#chhatarpur#150503\n54#pathariya#none#damoh#165758\n55#damoh#none#damoh#185489\n56#jabera#none#damoh#169816\n57#hatta#sc#damoh#173217\ntotal :#total :#total :#total :#1358234\n",
        "pandas_code": "df[df['name'] == 'malhara']['number of electorates (2009)'].min() != df['number of electorates (2009)'].min() or len(df[df['district'] == 'chhatarpur']) != 1 or len(df[df['district'] == 'sagar']) != 3 or len(df[df['district'] == 'damoh']) != 4",
        "pandas_eval": "False"
    },
    {
        "id": 638,
        "statement": "dwight howard have the high point the most time",
        "label": 1,
        "table_caption": "2008 - 09 orlando magic season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#76ers#l 98 - 100 (ot)#dwight howard (31)#dwight howard (16)#rafer alston (5)#amway arena 17461#0 - 1\n2#april 22#76ers#w 96 - 87 (ot)#courtney lee (24)#dwight howard (10)#rashard lewis (6)#amway arena 17461#1 - 1\n3#april 24#76ers#l 94 - 96 (ot)#dwight howard (36)#dwight howard (11)#courtney lee (5)#wachovia center 16492#1 - 2\n4#april 26#76ers#w 84 - 81 (ot)#dwight howard (18)#dwight howard (18)#rafer alston (5)#wachovia center 16464#2 - 2\n5#april 28#76ers#w 91 - 78 (ot)#dwight howard , rashard lewis (24)#dwight howard (24)#rafer alston , hedo t\u00fcrko\u011flu (4)#amway arena 17461#3 - 2\n",
        "pandas_code": "df['high points'].str.contains('dwight howard').sum() > (max([df['high points'].str.contains(player).sum() for player in df['high points'].str.extract(r'\\\\((.*?)\\\\)')[0].dropna().unique() if player != 'dwight howard']) if any(player != 'dwight howard' for player in df['high points'].str.extract(r'\\\\((.*?)\\\\)')[0].dropna().unique()) else 0)",
        "pandas_eval": "True"
    },
    {
        "id": 639,
        "statement": "dwight howard high rebound be higher on april 28 than on april 26",
        "label": 1,
        "table_caption": "2008 - 09 orlando magic season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#76ers#l 98 - 100 (ot)#dwight howard (31)#dwight howard (16)#rafer alston (5)#amway arena 17461#0 - 1\n2#april 22#76ers#w 96 - 87 (ot)#courtney lee (24)#dwight howard (10)#rashard lewis (6)#amway arena 17461#1 - 1\n3#april 24#76ers#l 94 - 96 (ot)#dwight howard (36)#dwight howard (11)#courtney lee (5)#wachovia center 16492#1 - 2\n4#april 26#76ers#w 84 - 81 (ot)#dwight howard (18)#dwight howard (18)#rafer alston (5)#wachovia center 16464#2 - 2\n5#april 28#76ers#w 91 - 78 (ot)#dwight howard , rashard lewis (24)#dwight howard (24)#rafer alston , hedo t\u00fcrko\u011flu (4)#amway arena 17461#3 - 2\n",
        "pandas_code": "int(df[df['date'] == 'april 28']['high rebounds'].str.extract(r'(\\d+)')[0].iloc[0]) > int(df[df['date'] == 'april 26']['high rebounds'].str.extract(r'(\\d+)')[0].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 640,
        "statement": "the lowest attendance be on april 26",
        "label": 1,
        "table_caption": "2008 - 09 orlando magic season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#76ers#l 98 - 100 (ot)#dwight howard (31)#dwight howard (16)#rafer alston (5)#amway arena 17461#0 - 1\n2#april 22#76ers#w 96 - 87 (ot)#courtney lee (24)#dwight howard (10)#rashard lewis (6)#amway arena 17461#1 - 1\n3#april 24#76ers#l 94 - 96 (ot)#dwight howard (36)#dwight howard (11)#courtney lee (5)#wachovia center 16492#1 - 2\n4#april 26#76ers#w 84 - 81 (ot)#dwight howard (18)#dwight howard (18)#rafer alston (5)#wachovia center 16464#2 - 2\n5#april 28#76ers#w 91 - 78 (ot)#dwight howard , rashard lewis (24)#dwight howard (24)#rafer alston , hedo t\u00fcrko\u011flu (4)#amway arena 17461#3 - 2\n",
        "pandas_code": "(df[df['date'] == 'april 26']['location attendance'].str.extract('(\\\\d+)').astype(int).iloc[0] == df['location attendance'].str.extract('(\\\\d+)').astype(int).min()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 641,
        "statement": "the highest attendance happen 3 time",
        "label": 1,
        "table_caption": "2008 - 09 orlando magic season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#76ers#l 98 - 100 (ot)#dwight howard (31)#dwight howard (16)#rafer alston (5)#amway arena 17461#0 - 1\n2#april 22#76ers#w 96 - 87 (ot)#courtney lee (24)#dwight howard (10)#rashard lewis (6)#amway arena 17461#1 - 1\n3#april 24#76ers#l 94 - 96 (ot)#dwight howard (36)#dwight howard (11)#courtney lee (5)#wachovia center 16492#1 - 2\n4#april 26#76ers#w 84 - 81 (ot)#dwight howard (18)#dwight howard (18)#rafer alston (5)#wachovia center 16464#2 - 2\n5#april 28#76ers#w 91 - 78 (ot)#dwight howard , rashard lewis (24)#dwight howard (24)#rafer alston , hedo t\u00fcrko\u011flu (4)#amway arena 17461#3 - 2\n",
        "pandas_code": "df['location attendance'].str.extract(r'(\\d+)').astype(int).value_counts().max() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 642,
        "statement": "the game on april 28 have a higher attendance than the game on april 24",
        "label": 1,
        "table_caption": "2008 - 09 orlando magic season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#76ers#l 98 - 100 (ot)#dwight howard (31)#dwight howard (16)#rafer alston (5)#amway arena 17461#0 - 1\n2#april 22#76ers#w 96 - 87 (ot)#courtney lee (24)#dwight howard (10)#rashard lewis (6)#amway arena 17461#1 - 1\n3#april 24#76ers#l 94 - 96 (ot)#dwight howard (36)#dwight howard (11)#courtney lee (5)#wachovia center 16492#1 - 2\n4#april 26#76ers#w 84 - 81 (ot)#dwight howard (18)#dwight howard (18)#rafer alston (5)#wachovia center 16464#2 - 2\n5#april 28#76ers#w 91 - 78 (ot)#dwight howard , rashard lewis (24)#dwight howard (24)#rafer alston , hedo t\u00fcrko\u011flu (4)#amway arena 17461#3 - 2\n",
        "pandas_code": "int(df[df['date'] == 'april 28']['location attendance'].str.extract('(\\\\d+)').iloc[0]) > int(df[df['date'] == 'april 24']['location attendance'].str.extract('(\\\\d+)').iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 643,
        "statement": "in the 2008 - 09 russian volleyball super league 2 head coach share the same first name",
        "label": 1,
        "table_caption": "2008 - 09 russian volleyball super league",
        "table_text": "previous season 2007 / 2008#team#town#arena (capacity)#website#head coach#foreign players (max 2)\n1#dynamo#moscow#dynamo sports palace (5 000)#wwwvcdynamoru#daniele bagnoli#dante amaral alexis gonzalez\n2#iskra#odintsovo#volleyball sportiv complex (3 500)#iskravolleycom#zoran gaji\u0107#giba jochen sch\u00f6ps\n3#zenit#kazan#basket - hall arena (7 000)#wwwzenit - kazancom#vladimir alekno#lloy ball clay stanley\n4#lokomotiv#novosibirsk#skk sever (2 500)#wwwlokovolleyru#vladimir babakin#william priddy h\u00e9ctor soto\n6#ural#ufa#foc neftyanik bashkortostana#wwwvolleyufaru#yuriy marichev#gabe gardner p\u00e9ter veres\n7#fakel#novy urengoy#csc gazodobytchik (800)#wwwfakelvolleyru#boris kolchin#sean rooney kevin hansen\n9#yaroslavich#yaroslavl#sk atlant#wwwyarvolleyru#roberto serniotti#dallas soonias frederick winters\n10#yugra - samotlor#niznevartovsk#sc neftaynik (2000)#wwwugra - samotlorru#yuriy korotkevich#sergey shulga\npromoted#nova#novokuybyshevsk#sc octan#wwwnovavolleynarodru#aleksandr cherny#cristian casoli kostadin stoykov\n",
        "pandas_code": "df['head coach'].apply(lambda x: x.split()[0]).duplicated(keep=False).any()",
        "pandas_eval": "True"
    },
    {
        "id": 644,
        "statement": "volleyball sportiv complex have a larger arena capacity skk sever",
        "label": 1,
        "table_caption": "2008 - 09 russian volleyball super league",
        "table_text": "previous season 2007 / 2008#team#town#arena (capacity)#website#head coach#foreign players (max 2)\n1#dynamo#moscow#dynamo sports palace (5 000)#wwwvcdynamoru#daniele bagnoli#dante amaral alexis gonzalez\n2#iskra#odintsovo#volleyball sportiv complex (3 500)#iskravolleycom#zoran gaji\u0107#giba jochen sch\u00f6ps\n3#zenit#kazan#basket - hall arena (7 000)#wwwzenit - kazancom#vladimir alekno#lloy ball clay stanley\n4#lokomotiv#novosibirsk#skk sever (2 500)#wwwlokovolleyru#vladimir babakin#william priddy h\u00e9ctor soto\n6#ural#ufa#foc neftyanik bashkortostana#wwwvolleyufaru#yuriy marichev#gabe gardner p\u00e9ter veres\n7#fakel#novy urengoy#csc gazodobytchik (800)#wwwfakelvolleyru#boris kolchin#sean rooney kevin hansen\n9#yaroslavich#yaroslavl#sk atlant#wwwyarvolleyru#roberto serniotti#dallas soonias frederick winters\n10#yugra - samotlor#niznevartovsk#sc neftaynik (2000)#wwwugra - samotlorru#yuriy korotkevich#sergey shulga\npromoted#nova#novokuybyshevsk#sc octan#wwwnovavolleynarodru#aleksandr cherny#cristian casoli kostadin stoykov\n",
        "pandas_code": "df[df['arena (capacity)'].str.contains('volleyball sportiv complex')]['arena (capacity)'].str.extract('(\\d+)')[0].astype(int).values[0] > df[df['arena (capacity)'].str.contains('skk sever')]['arena (capacity)'].str.extract('(\\d+)')[0].astype(int).values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 645,
        "statement": "sc neftaynik have a larger arena capacity csc gazodobytchik",
        "label": 1,
        "table_caption": "2008 - 09 russian volleyball super league",
        "table_text": "previous season 2007 / 2008#team#town#arena (capacity)#website#head coach#foreign players (max 2)\n1#dynamo#moscow#dynamo sports palace (5 000)#wwwvcdynamoru#daniele bagnoli#dante amaral alexis gonzalez\n2#iskra#odintsovo#volleyball sportiv complex (3 500)#iskravolleycom#zoran gaji\u0107#giba jochen sch\u00f6ps\n3#zenit#kazan#basket - hall arena (7 000)#wwwzenit - kazancom#vladimir alekno#lloy ball clay stanley\n4#lokomotiv#novosibirsk#skk sever (2 500)#wwwlokovolleyru#vladimir babakin#william priddy h\u00e9ctor soto\n6#ural#ufa#foc neftyanik bashkortostana#wwwvolleyufaru#yuriy marichev#gabe gardner p\u00e9ter veres\n7#fakel#novy urengoy#csc gazodobytchik (800)#wwwfakelvolleyru#boris kolchin#sean rooney kevin hansen\n9#yaroslavich#yaroslavl#sk atlant#wwwyarvolleyru#roberto serniotti#dallas soonias frederick winters\n10#yugra - samotlor#niznevartovsk#sc neftaynik (2000)#wwwugra - samotlorru#yuriy korotkevich#sergey shulga\npromoted#nova#novokuybyshevsk#sc octan#wwwnovavolleynarodru#aleksandr cherny#cristian casoli kostadin stoykov\n",
        "pandas_code": "int(df[df['team'] == 'yugra - samotlor']['arena (capacity)'].str.extract(r'(\\d+)')[0].values[0]) > int(df[df['team'] == 'fakel']['arena (capacity)'].str.extract(r'(\\d+)')[0].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 646,
        "statement": "basket - hall arena have a larger arena capacity dynamo sport palace",
        "label": 1,
        "table_caption": "2008 - 09 russian volleyball super league",
        "table_text": "previous season 2007 / 2008#team#town#arena (capacity)#website#head coach#foreign players (max 2)\n1#dynamo#moscow#dynamo sports palace (5 000)#wwwvcdynamoru#daniele bagnoli#dante amaral alexis gonzalez\n2#iskra#odintsovo#volleyball sportiv complex (3 500)#iskravolleycom#zoran gaji\u0107#giba jochen sch\u00f6ps\n3#zenit#kazan#basket - hall arena (7 000)#wwwzenit - kazancom#vladimir alekno#lloy ball clay stanley\n4#lokomotiv#novosibirsk#skk sever (2 500)#wwwlokovolleyru#vladimir babakin#william priddy h\u00e9ctor soto\n6#ural#ufa#foc neftyanik bashkortostana#wwwvolleyufaru#yuriy marichev#gabe gardner p\u00e9ter veres\n7#fakel#novy urengoy#csc gazodobytchik (800)#wwwfakelvolleyru#boris kolchin#sean rooney kevin hansen\n9#yaroslavich#yaroslavl#sk atlant#wwwyarvolleyru#roberto serniotti#dallas soonias frederick winters\n10#yugra - samotlor#niznevartovsk#sc neftaynik (2000)#wwwugra - samotlorru#yuriy korotkevich#sergey shulga\npromoted#nova#novokuybyshevsk#sc octan#wwwnovavolleynarodru#aleksandr cherny#cristian casoli kostadin stoykov\n",
        "pandas_code": "int(df[df['arena (capacity)'].str.contains('basket - hall arena')]['arena (capacity)'].str.extract(r'\\((.*?)\\)')[0].str.replace(' ', '').iloc[0]) > int(df[df['arena (capacity)'].str.contains('dynamo sports palace')]['arena (capacity)'].str.extract(r'\\((.*?)\\)')[0].str.replace(' ', '').iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 648,
        "statement": "among the 1st member , edmund mordaunt be elect at the earliest date",
        "label": 1,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['1st member'] == 'edmund mordaunt', 'elected'].min() == '1553'",
        "pandas_eval": "True"
    },
    {
        "id": 649,
        "statement": "among the 1st member , george gascoign be elect at the latest date",
        "label": 1,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['1st member'] == 'george gascoigne', 'elected'].iloc[0] == df['elected'].iloc[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 650,
        "statement": "the only 2 1st member elect in 1554 be edmund mordaun and william hall",
        "label": 1,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "all(df[df['elected'].str.contains('1554')]['1st member'].isin(['edmund mordaunt', 'william hall']))",
        "pandas_eval": "True"
    },
    {
        "id": 651,
        "statement": "the only unknown 1st member be elect in 1555",
        "label": 1,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df[(df['1st member'] == 'unknown') & (df['elected'] == '1555')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 652,
        "statement": "the 2nd member that be assemble at the latest date be thomas leigh",
        "label": 1,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['assembled'] == df['assembled'].max(), '2nd member'].iloc[0] == 'thomas leigh'",
        "pandas_eval": "True"
    },
    {
        "id": 653,
        "statement": "among the 2nd member , edmund mordaunt be elect at the earliest date",
        "label": 0,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['2nd member'] == 'edmund mordaunt', 'elected'].min() == df['elected'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 654,
        "statement": "among the 1st member , george gascoign be elect at the earliest date",
        "label": 0,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['1st member'] == 'george gascoigne', 'elected'].iloc[0] == df['elected'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 655,
        "statement": "the only 2 1st member elect in 1558 be edmund mordaun and william hall",
        "label": 0,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "any(df[df['elected'].str.contains('1558')]['1st member'].isin(['edmund mordaunt', 'william hall']))",
        "pandas_eval": "False"
    },
    {
        "id": 656,
        "statement": "the only unknown 1st member be elect in 1553",
        "label": 0,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "not df[(df['1st member'] == 'unknown') & (df['elected'] == '1553')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 657,
        "statement": "the 1st member that be assemble at the latest date be thomas leigh",
        "label": 0,
        "table_caption": "bedford (uk parliament constituency)",
        "table_text": "summoned#elected#assembled#dissolved#1st member#2nd member\n14 august 1553#1553#5 october 1553#5 december 1553#edmund mordaunt#thomas leigh\n17 february 1554#1554#2 april 1554#3 may 1554#edmund mordaunt#thomas leigh\n3 october 1554#1554#12 november 1554#16 january 1555#william hall#john williams\n3 september 1555#1555#21 october 1555#9 december 1555#unknown#unknown\n6 december 1557#1557 / 58#20 january 1558#17 november 1558#george gascoigne#thomas leigh\n",
        "pandas_code": "df.loc[df['assembled'].idxmax(), '1st member'] == 'thomas leigh'",
        "pandas_eval": "False"
    },
    {
        "id": 658,
        "statement": "the only game list on cbs happen in november",
        "label": 1,
        "table_caption": "1977 pittsburgh steelers season",
        "table_text": "week#date#opponent#time ( et )#tv#result#record#game site\n1#monday september 19#san francisco 49ers#9:00 pm#abc#w 27 - 0#1 - 0#three rivers stadium\n2#sunday september 25#oakland raiders#4:00 pm#nbc#l 16 - 7#1 - 1#three rivers stadium\n3#sunday october 2#cleveland browns#1:00 pm#nbc#w 28 - 14#2 - 1#cleveland municipal stadium\n4#sunday october 9#houston oilers#2:00 pm#nbc#l 27 - 10#2 - 2#astrodome\n5#monday october 17#cincinnati bengals#1:00 pm#abc#w 20 - 14#3 - 2#three rivers stadium\n6#sunday october 23#houston oilers#1:00 pm#nbc#w 27 - 10#4 - 2#three rivers stadium\n7#sunday october 30#baltimore colts#4:00 pm#nbc#l 31 - 14#4 - 3#memorial stadium\n8#sunday november 6#denver broncos#4:00 pm#nbc#l 21 - 7#4 - 4#mile high stadium\n9#sunday november 13#cleveland browns#1:00 pm#nbc#w 35 - 31#5 - 4#three rivers stadium\n10#sunday november 20#dallas cowboys#4:00 pm#cbs#w 28 - 13#6 - 4#three rivers stadium\n11#sunday november 27#new york jets#1:00 pm#nbc#w 23 - 20#7 - 4#shea stadium\n12#sunday december 4#seattle seahawks#1:00 pm#nbc#w 30 - 20#8 - 4#three rivers stadium\n13#saturday december 10#cincinnati bengals#1:00 pm#nbc#l 17 - 10#8 - 5#riverfront stadium\n",
        "pandas_code": "(df['tv'] == 'cbs').sum() == 1 and df[df['tv'] == 'cbs']['date'].str.contains('november').all()",
        "pandas_eval": "True"
    },
    {
        "id": 659,
        "statement": "over 5 of the game list be at the three river stadium",
        "label": 1,
        "table_caption": "1977 pittsburgh steelers season",
        "table_text": "week#date#opponent#time ( et )#tv#result#record#game site\n1#monday september 19#san francisco 49ers#9:00 pm#abc#w 27 - 0#1 - 0#three rivers stadium\n2#sunday september 25#oakland raiders#4:00 pm#nbc#l 16 - 7#1 - 1#three rivers stadium\n3#sunday october 2#cleveland browns#1:00 pm#nbc#w 28 - 14#2 - 1#cleveland municipal stadium\n4#sunday october 9#houston oilers#2:00 pm#nbc#l 27 - 10#2 - 2#astrodome\n5#monday october 17#cincinnati bengals#1:00 pm#abc#w 20 - 14#3 - 2#three rivers stadium\n6#sunday october 23#houston oilers#1:00 pm#nbc#w 27 - 10#4 - 2#three rivers stadium\n7#sunday october 30#baltimore colts#4:00 pm#nbc#l 31 - 14#4 - 3#memorial stadium\n8#sunday november 6#denver broncos#4:00 pm#nbc#l 21 - 7#4 - 4#mile high stadium\n9#sunday november 13#cleveland browns#1:00 pm#nbc#w 35 - 31#5 - 4#three rivers stadium\n10#sunday november 20#dallas cowboys#4:00 pm#cbs#w 28 - 13#6 - 4#three rivers stadium\n11#sunday november 27#new york jets#1:00 pm#nbc#w 23 - 20#7 - 4#shea stadium\n12#sunday december 4#seattle seahawks#1:00 pm#nbc#w 30 - 20#8 - 4#three rivers stadium\n13#saturday december 10#cincinnati bengals#1:00 pm#nbc#l 17 - 10#8 - 5#riverfront stadium\n",
        "pandas_code": "(df['game site'].value_counts()['three rivers stadium'] > 5)",
        "pandas_eval": "True"
    },
    {
        "id": 660,
        "statement": "the only game at the astrodome happen on october 9 , a sunday",
        "label": 1,
        "table_caption": "1977 pittsburgh steelers season",
        "table_text": "week#date#opponent#time ( et )#tv#result#record#game site\n1#monday september 19#san francisco 49ers#9:00 pm#abc#w 27 - 0#1 - 0#three rivers stadium\n2#sunday september 25#oakland raiders#4:00 pm#nbc#l 16 - 7#1 - 1#three rivers stadium\n3#sunday october 2#cleveland browns#1:00 pm#nbc#w 28 - 14#2 - 1#cleveland municipal stadium\n4#sunday october 9#houston oilers#2:00 pm#nbc#l 27 - 10#2 - 2#astrodome\n5#monday october 17#cincinnati bengals#1:00 pm#abc#w 20 - 14#3 - 2#three rivers stadium\n6#sunday october 23#houston oilers#1:00 pm#nbc#w 27 - 10#4 - 2#three rivers stadium\n7#sunday october 30#baltimore colts#4:00 pm#nbc#l 31 - 14#4 - 3#memorial stadium\n8#sunday november 6#denver broncos#4:00 pm#nbc#l 21 - 7#4 - 4#mile high stadium\n9#sunday november 13#cleveland browns#1:00 pm#nbc#w 35 - 31#5 - 4#three rivers stadium\n10#sunday november 20#dallas cowboys#4:00 pm#cbs#w 28 - 13#6 - 4#three rivers stadium\n11#sunday november 27#new york jets#1:00 pm#nbc#w 23 - 20#7 - 4#shea stadium\n12#sunday december 4#seattle seahawks#1:00 pm#nbc#w 30 - 20#8 - 4#three rivers stadium\n13#saturday december 10#cincinnati bengals#1:00 pm#nbc#l 17 - 10#8 - 5#riverfront stadium\n",
        "pandas_code": "df[(df['game site'] == 'astrodome') & (df['date'] == 'sunday october 9')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 661,
        "statement": "both of the game on abc be a victory , with result of 27 - 0 and 20 - 14",
        "label": 1,
        "table_caption": "1977 pittsburgh steelers season",
        "table_text": "week#date#opponent#time ( et )#tv#result#record#game site\n1#monday september 19#san francisco 49ers#9:00 pm#abc#w 27 - 0#1 - 0#three rivers stadium\n2#sunday september 25#oakland raiders#4:00 pm#nbc#l 16 - 7#1 - 1#three rivers stadium\n3#sunday october 2#cleveland browns#1:00 pm#nbc#w 28 - 14#2 - 1#cleveland municipal stadium\n4#sunday october 9#houston oilers#2:00 pm#nbc#l 27 - 10#2 - 2#astrodome\n5#monday october 17#cincinnati bengals#1:00 pm#abc#w 20 - 14#3 - 2#three rivers stadium\n6#sunday october 23#houston oilers#1:00 pm#nbc#w 27 - 10#4 - 2#three rivers stadium\n7#sunday october 30#baltimore colts#4:00 pm#nbc#l 31 - 14#4 - 3#memorial stadium\n8#sunday november 6#denver broncos#4:00 pm#nbc#l 21 - 7#4 - 4#mile high stadium\n9#sunday november 13#cleveland browns#1:00 pm#nbc#w 35 - 31#5 - 4#three rivers stadium\n10#sunday november 20#dallas cowboys#4:00 pm#cbs#w 28 - 13#6 - 4#three rivers stadium\n11#sunday november 27#new york jets#1:00 pm#nbc#w 23 - 20#7 - 4#shea stadium\n12#sunday december 4#seattle seahawks#1:00 pm#nbc#w 30 - 20#8 - 4#three rivers stadium\n13#saturday december 10#cincinnati bengals#1:00 pm#nbc#l 17 - 10#8 - 5#riverfront stadium\n",
        "pandas_code": "all(df[df['tv'] == 'abc']['result'].isin(['w 27 - 0', 'w 20 - 14']))",
        "pandas_eval": "True"
    },
    {
        "id": 662,
        "statement": "only 2 of the game list occur in december",
        "label": 1,
        "table_caption": "1977 pittsburgh steelers season",
        "table_text": "week#date#opponent#time ( et )#tv#result#record#game site\n1#monday september 19#san francisco 49ers#9:00 pm#abc#w 27 - 0#1 - 0#three rivers stadium\n2#sunday september 25#oakland raiders#4:00 pm#nbc#l 16 - 7#1 - 1#three rivers stadium\n3#sunday october 2#cleveland browns#1:00 pm#nbc#w 28 - 14#2 - 1#cleveland municipal stadium\n4#sunday october 9#houston oilers#2:00 pm#nbc#l 27 - 10#2 - 2#astrodome\n5#monday october 17#cincinnati bengals#1:00 pm#abc#w 20 - 14#3 - 2#three rivers stadium\n6#sunday october 23#houston oilers#1:00 pm#nbc#w 27 - 10#4 - 2#three rivers stadium\n7#sunday october 30#baltimore colts#4:00 pm#nbc#l 31 - 14#4 - 3#memorial stadium\n8#sunday november 6#denver broncos#4:00 pm#nbc#l 21 - 7#4 - 4#mile high stadium\n9#sunday november 13#cleveland browns#1:00 pm#nbc#w 35 - 31#5 - 4#three rivers stadium\n10#sunday november 20#dallas cowboys#4:00 pm#cbs#w 28 - 13#6 - 4#three rivers stadium\n11#sunday november 27#new york jets#1:00 pm#nbc#w 23 - 20#7 - 4#shea stadium\n12#sunday december 4#seattle seahawks#1:00 pm#nbc#w 30 - 20#8 - 4#three rivers stadium\n13#saturday december 10#cincinnati bengals#1:00 pm#nbc#l 17 - 10#8 - 5#riverfront stadium\n",
        "pandas_code": "len(df[df['date'].str.contains('december')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 663,
        "statement": "picasso have the most number of track recording compare to other artist",
        "label": 1,
        "table_caption": "maison ikkoku cd single memorial file",
        "table_text": "disc#track#english title#japanese title#r\u014dmaji title#artist#track time\n1#1#hello sadness!#\u60b2\u3057\u307f\u3088\u3053\u3093\u306b\u3061\u306f#kanashimi yo konnichi wa#yuki saito#4:00\n1#2#forgotten things when moving#\u304a\u5f15\u8d8a\u3057\u30fb\u5fd8\u308c\u3082\u306e#ohikkoshi wasuremono#yuki saito#4:44\n2#3#will tomorrow be sunny#\u3042\u3057\u305f\u6674\u308c\u308b\u304b#ashita hareru ka#takao kisugi#4:35\n2#4#just a little farther#\u3082\u3046\u5c11\u3057\u9060\u304f#m\u014d sukoshi t\u014dku#takao kisugi#4:10\n3#5#cinema#\u30b7\u30fb\u30cd\u30fb\u30de#shinema#picasso#3:56\n3#6#bgm : on the way home 2#bgm - \u5e30\u308a\u9053\uff12#bijiemu kaerimichi ts\u016b#instrumental#3:26\n4#7#alone again#\u30a2\u30ed\u30fc\u30f3\u30fb\u30a2\u30b2\u30a4\u30f3#ar\u014dn agein#gilbert o 'sullivan#3:40\n4#8#get down#\u30b2\u30c3\u30c8\u30fb\u30c0\u30a6\u30f3#getto daun#gilbert o 'sullivan#2:38\n5#9#premonition#\u4e88\u611f#yokan#kyoko otonashi#3:53\n5#10#toward the entrance to a dream#\u5922\u306e\u5165\u53e3\u3078\u2026#yume no iriguchi e#kyoko otonashi#4:03\n6#11#fantasy#\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc#fantaj\u012b#picasso#4:03\n6#12#picture of teary eyes#\u6fe1\u308c\u305f\u77b3\u306e\u30d4\u30af\u30c1\u30e3\u30fc#nureta hitomi no pikuch\u0101#picasso#3:36\n7#13#i love you#\u597d\u304d\u3055#suki sa#anzen chitai#2:49\n7#14#enveloped in memories#\u601d\u3044\u51fa\u306b\u3064\u3064\u307e\u308c\u3066#omoide ni tsutsumarete#anzen chitai#2:40\n8#15#sunny shiny morning#\u30b5\u30cb\u30fc\u3000\u30b7\u30e3\u30a4\u30cb\u30fc\u3000\u30e2\u30fc\u30cb\u30f3\u30b0#san\u012b shain\u012b m\u014dningu#kiyonori matsuo#3:48\n8#16#younger girl#\u30e4\u30f3\u30ac\u30fc\u30ac\u30fc\u30eb#yang\u0101 g\u0101ru#kiyonori matsuo#3:15\n9#17#goodbye sketch#\u30b5\u30e8\u30ca\u30e9\u306e\u7d20\u63cf#sayonara no dessan#picasso#4:34\n9#18#sand atelier#\u7802\u306eatelier#suna no atorie#picasso#3:57\n10#19#melody#\u30e1\u30ed\u30c7\u30a3\u30fc#merod\u012b#kyoko otonashi#3:51\n10#20#excitement#\u3068\u304d\u3081\u304d#tokimeki#kyoko otonashi#3:59\n11#21#sunny spot#\u967d\u3060\u307e\u308a#hidamari#k\u014dz\u014d murashita#4:05\n11#22#symphony of wind#bgm : \u98a8\u306e\u30b7\u30f3\u30d5\u30a9\u30cb\u30fc#bijiemu : kaze no shinfon\u012b#instrumental#4:08\n12#23#begin the night#\u30d3\u30ae\u30f3\u30fb\u30b6\u30fb\u30ca\u30a4\u30c8#bigin za naito#picasso#4:32\n12#24#the tenderness burns#\u611b\u3057\u3055\u304c\u3001\u71c3\u3048\u308b\u3002#itoshisa ga , moeru#picasso#4:33\n13#25#glass kiss#\u785d\u5b50\u306e\u30ad\u30c3\u30b9#garasu no kissu#rika himenogi#4:28\n",
        "pandas_code": "df['artist'].value_counts().idxmax() == 'picasso'",
        "pandas_eval": "True"
    },
    {
        "id": 664,
        "statement": "it 's just rika himenogi who have a single track record at maison ikkoku cd single memorial file",
        "label": 1,
        "table_caption": "maison ikkoku cd single memorial file",
        "table_text": "disc#track#english title#japanese title#r\u014dmaji title#artist#track time\n1#1#hello sadness!#\u60b2\u3057\u307f\u3088\u3053\u3093\u306b\u3061\u306f#kanashimi yo konnichi wa#yuki saito#4:00\n1#2#forgotten things when moving#\u304a\u5f15\u8d8a\u3057\u30fb\u5fd8\u308c\u3082\u306e#ohikkoshi wasuremono#yuki saito#4:44\n2#3#will tomorrow be sunny#\u3042\u3057\u305f\u6674\u308c\u308b\u304b#ashita hareru ka#takao kisugi#4:35\n2#4#just a little farther#\u3082\u3046\u5c11\u3057\u9060\u304f#m\u014d sukoshi t\u014dku#takao kisugi#4:10\n3#5#cinema#\u30b7\u30fb\u30cd\u30fb\u30de#shinema#picasso#3:56\n3#6#bgm : on the way home 2#bgm - \u5e30\u308a\u9053\uff12#bijiemu kaerimichi ts\u016b#instrumental#3:26\n4#7#alone again#\u30a2\u30ed\u30fc\u30f3\u30fb\u30a2\u30b2\u30a4\u30f3#ar\u014dn agein#gilbert o 'sullivan#3:40\n4#8#get down#\u30b2\u30c3\u30c8\u30fb\u30c0\u30a6\u30f3#getto daun#gilbert o 'sullivan#2:38\n5#9#premonition#\u4e88\u611f#yokan#kyoko otonashi#3:53\n5#10#toward the entrance to a dream#\u5922\u306e\u5165\u53e3\u3078\u2026#yume no iriguchi e#kyoko otonashi#4:03\n6#11#fantasy#\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc#fantaj\u012b#picasso#4:03\n6#12#picture of teary eyes#\u6fe1\u308c\u305f\u77b3\u306e\u30d4\u30af\u30c1\u30e3\u30fc#nureta hitomi no pikuch\u0101#picasso#3:36\n7#13#i love you#\u597d\u304d\u3055#suki sa#anzen chitai#2:49\n7#14#enveloped in memories#\u601d\u3044\u51fa\u306b\u3064\u3064\u307e\u308c\u3066#omoide ni tsutsumarete#anzen chitai#2:40\n8#15#sunny shiny morning#\u30b5\u30cb\u30fc\u3000\u30b7\u30e3\u30a4\u30cb\u30fc\u3000\u30e2\u30fc\u30cb\u30f3\u30b0#san\u012b shain\u012b m\u014dningu#kiyonori matsuo#3:48\n8#16#younger girl#\u30e4\u30f3\u30ac\u30fc\u30ac\u30fc\u30eb#yang\u0101 g\u0101ru#kiyonori matsuo#3:15\n9#17#goodbye sketch#\u30b5\u30e8\u30ca\u30e9\u306e\u7d20\u63cf#sayonara no dessan#picasso#4:34\n9#18#sand atelier#\u7802\u306eatelier#suna no atorie#picasso#3:57\n10#19#melody#\u30e1\u30ed\u30c7\u30a3\u30fc#merod\u012b#kyoko otonashi#3:51\n10#20#excitement#\u3068\u304d\u3081\u304d#tokimeki#kyoko otonashi#3:59\n11#21#sunny spot#\u967d\u3060\u307e\u308a#hidamari#k\u014dz\u014d murashita#4:05\n11#22#symphony of wind#bgm : \u98a8\u306e\u30b7\u30f3\u30d5\u30a9\u30cb\u30fc#bijiemu : kaze no shinfon\u012b#instrumental#4:08\n12#23#begin the night#\u30d3\u30ae\u30f3\u30fb\u30b6\u30fb\u30ca\u30a4\u30c8#bigin za naito#picasso#4:32\n12#24#the tenderness burns#\u611b\u3057\u3055\u304c\u3001\u71c3\u3048\u308b\u3002#itoshisa ga , moeru#picasso#4:33\n13#25#glass kiss#\u785d\u5b50\u306e\u30ad\u30c3\u30b9#garasu no kissu#rika himenogi#4:28\n",
        "pandas_code": "df[df['artist'] == 'rika himenogi'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 665,
        "statement": "forgotten thing when moving have the longest track time at maison ikkoku cd single memorial file",
        "label": 1,
        "table_caption": "maison ikkoku cd single memorial file",
        "table_text": "disc#track#english title#japanese title#r\u014dmaji title#artist#track time\n1#1#hello sadness!#\u60b2\u3057\u307f\u3088\u3053\u3093\u306b\u3061\u306f#kanashimi yo konnichi wa#yuki saito#4:00\n1#2#forgotten things when moving#\u304a\u5f15\u8d8a\u3057\u30fb\u5fd8\u308c\u3082\u306e#ohikkoshi wasuremono#yuki saito#4:44\n2#3#will tomorrow be sunny#\u3042\u3057\u305f\u6674\u308c\u308b\u304b#ashita hareru ka#takao kisugi#4:35\n2#4#just a little farther#\u3082\u3046\u5c11\u3057\u9060\u304f#m\u014d sukoshi t\u014dku#takao kisugi#4:10\n3#5#cinema#\u30b7\u30fb\u30cd\u30fb\u30de#shinema#picasso#3:56\n3#6#bgm : on the way home 2#bgm - \u5e30\u308a\u9053\uff12#bijiemu kaerimichi ts\u016b#instrumental#3:26\n4#7#alone again#\u30a2\u30ed\u30fc\u30f3\u30fb\u30a2\u30b2\u30a4\u30f3#ar\u014dn agein#gilbert o 'sullivan#3:40\n4#8#get down#\u30b2\u30c3\u30c8\u30fb\u30c0\u30a6\u30f3#getto daun#gilbert o 'sullivan#2:38\n5#9#premonition#\u4e88\u611f#yokan#kyoko otonashi#3:53\n5#10#toward the entrance to a dream#\u5922\u306e\u5165\u53e3\u3078\u2026#yume no iriguchi e#kyoko otonashi#4:03\n6#11#fantasy#\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc#fantaj\u012b#picasso#4:03\n6#12#picture of teary eyes#\u6fe1\u308c\u305f\u77b3\u306e\u30d4\u30af\u30c1\u30e3\u30fc#nureta hitomi no pikuch\u0101#picasso#3:36\n7#13#i love you#\u597d\u304d\u3055#suki sa#anzen chitai#2:49\n7#14#enveloped in memories#\u601d\u3044\u51fa\u306b\u3064\u3064\u307e\u308c\u3066#omoide ni tsutsumarete#anzen chitai#2:40\n8#15#sunny shiny morning#\u30b5\u30cb\u30fc\u3000\u30b7\u30e3\u30a4\u30cb\u30fc\u3000\u30e2\u30fc\u30cb\u30f3\u30b0#san\u012b shain\u012b m\u014dningu#kiyonori matsuo#3:48\n8#16#younger girl#\u30e4\u30f3\u30ac\u30fc\u30ac\u30fc\u30eb#yang\u0101 g\u0101ru#kiyonori matsuo#3:15\n9#17#goodbye sketch#\u30b5\u30e8\u30ca\u30e9\u306e\u7d20\u63cf#sayonara no dessan#picasso#4:34\n9#18#sand atelier#\u7802\u306eatelier#suna no atorie#picasso#3:57\n10#19#melody#\u30e1\u30ed\u30c7\u30a3\u30fc#merod\u012b#kyoko otonashi#3:51\n10#20#excitement#\u3068\u304d\u3081\u304d#tokimeki#kyoko otonashi#3:59\n11#21#sunny spot#\u967d\u3060\u307e\u308a#hidamari#k\u014dz\u014d murashita#4:05\n11#22#symphony of wind#bgm : \u98a8\u306e\u30b7\u30f3\u30d5\u30a9\u30cb\u30fc#bijiemu : kaze no shinfon\u012b#instrumental#4:08\n12#23#begin the night#\u30d3\u30ae\u30f3\u30fb\u30b6\u30fb\u30ca\u30a4\u30c8#bigin za naito#picasso#4:32\n12#24#the tenderness burns#\u611b\u3057\u3055\u304c\u3001\u71c3\u3048\u308b\u3002#itoshisa ga , moeru#picasso#4:33\n13#25#glass kiss#\u785d\u5b50\u306e\u30ad\u30c3\u30b9#garasu no kissu#rika himenogi#4:28\n",
        "pandas_code": "df.loc[df['english title'] == 'forgotten things when moving', 'track time'].iloc[0] == max(df['track time'])",
        "pandas_eval": "True"
    },
    {
        "id": 666,
        "statement": "gilbert o 'sullivan 's get down be the shortest track at maison ikkoku cd single memorial file",
        "label": 1,
        "table_caption": "maison ikkoku cd single memorial file",
        "table_text": "disc#track#english title#japanese title#r\u014dmaji title#artist#track time\n1#1#hello sadness!#\u60b2\u3057\u307f\u3088\u3053\u3093\u306b\u3061\u306f#kanashimi yo konnichi wa#yuki saito#4:00\n1#2#forgotten things when moving#\u304a\u5f15\u8d8a\u3057\u30fb\u5fd8\u308c\u3082\u306e#ohikkoshi wasuremono#yuki saito#4:44\n2#3#will tomorrow be sunny#\u3042\u3057\u305f\u6674\u308c\u308b\u304b#ashita hareru ka#takao kisugi#4:35\n2#4#just a little farther#\u3082\u3046\u5c11\u3057\u9060\u304f#m\u014d sukoshi t\u014dku#takao kisugi#4:10\n3#5#cinema#\u30b7\u30fb\u30cd\u30fb\u30de#shinema#picasso#3:56\n3#6#bgm : on the way home 2#bgm - \u5e30\u308a\u9053\uff12#bijiemu kaerimichi ts\u016b#instrumental#3:26\n4#7#alone again#\u30a2\u30ed\u30fc\u30f3\u30fb\u30a2\u30b2\u30a4\u30f3#ar\u014dn agein#gilbert o 'sullivan#3:40\n4#8#get down#\u30b2\u30c3\u30c8\u30fb\u30c0\u30a6\u30f3#getto daun#gilbert o 'sullivan#2:38\n5#9#premonition#\u4e88\u611f#yokan#kyoko otonashi#3:53\n5#10#toward the entrance to a dream#\u5922\u306e\u5165\u53e3\u3078\u2026#yume no iriguchi e#kyoko otonashi#4:03\n6#11#fantasy#\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc#fantaj\u012b#picasso#4:03\n6#12#picture of teary eyes#\u6fe1\u308c\u305f\u77b3\u306e\u30d4\u30af\u30c1\u30e3\u30fc#nureta hitomi no pikuch\u0101#picasso#3:36\n7#13#i love you#\u597d\u304d\u3055#suki sa#anzen chitai#2:49\n7#14#enveloped in memories#\u601d\u3044\u51fa\u306b\u3064\u3064\u307e\u308c\u3066#omoide ni tsutsumarete#anzen chitai#2:40\n8#15#sunny shiny morning#\u30b5\u30cb\u30fc\u3000\u30b7\u30e3\u30a4\u30cb\u30fc\u3000\u30e2\u30fc\u30cb\u30f3\u30b0#san\u012b shain\u012b m\u014dningu#kiyonori matsuo#3:48\n8#16#younger girl#\u30e4\u30f3\u30ac\u30fc\u30ac\u30fc\u30eb#yang\u0101 g\u0101ru#kiyonori matsuo#3:15\n9#17#goodbye sketch#\u30b5\u30e8\u30ca\u30e9\u306e\u7d20\u63cf#sayonara no dessan#picasso#4:34\n9#18#sand atelier#\u7802\u306eatelier#suna no atorie#picasso#3:57\n10#19#melody#\u30e1\u30ed\u30c7\u30a3\u30fc#merod\u012b#kyoko otonashi#3:51\n10#20#excitement#\u3068\u304d\u3081\u304d#tokimeki#kyoko otonashi#3:59\n11#21#sunny spot#\u967d\u3060\u307e\u308a#hidamari#k\u014dz\u014d murashita#4:05\n11#22#symphony of wind#bgm : \u98a8\u306e\u30b7\u30f3\u30d5\u30a9\u30cb\u30fc#bijiemu : kaze no shinfon\u012b#instrumental#4:08\n12#23#begin the night#\u30d3\u30ae\u30f3\u30fb\u30b6\u30fb\u30ca\u30a4\u30c8#bigin za naito#picasso#4:32\n12#24#the tenderness burns#\u611b\u3057\u3055\u304c\u3001\u71c3\u3048\u308b\u3002#itoshisa ga , moeru#picasso#4:33\n13#25#glass kiss#\u785d\u5b50\u306e\u30ad\u30c3\u30b9#garasu no kissu#rika himenogi#4:28\n",
        "pandas_code": "df[df['artist'] == \"gilbert o 'sullivan\"]['track time'].min() == '2:38'",
        "pandas_eval": "True"
    },
    {
        "id": 667,
        "statement": "envelop in memories by anzen chitai be the second shortest track maison ikkoku cd single memorial file",
        "label": 1,
        "table_caption": "maison ikkoku cd single memorial file",
        "table_text": "disc#track#english title#japanese title#r\u014dmaji title#artist#track time\n1#1#hello sadness!#\u60b2\u3057\u307f\u3088\u3053\u3093\u306b\u3061\u306f#kanashimi yo konnichi wa#yuki saito#4:00\n1#2#forgotten things when moving#\u304a\u5f15\u8d8a\u3057\u30fb\u5fd8\u308c\u3082\u306e#ohikkoshi wasuremono#yuki saito#4:44\n2#3#will tomorrow be sunny#\u3042\u3057\u305f\u6674\u308c\u308b\u304b#ashita hareru ka#takao kisugi#4:35\n2#4#just a little farther#\u3082\u3046\u5c11\u3057\u9060\u304f#m\u014d sukoshi t\u014dku#takao kisugi#4:10\n3#5#cinema#\u30b7\u30fb\u30cd\u30fb\u30de#shinema#picasso#3:56\n3#6#bgm : on the way home 2#bgm - \u5e30\u308a\u9053\uff12#bijiemu kaerimichi ts\u016b#instrumental#3:26\n4#7#alone again#\u30a2\u30ed\u30fc\u30f3\u30fb\u30a2\u30b2\u30a4\u30f3#ar\u014dn agein#gilbert o 'sullivan#3:40\n4#8#get down#\u30b2\u30c3\u30c8\u30fb\u30c0\u30a6\u30f3#getto daun#gilbert o 'sullivan#2:38\n5#9#premonition#\u4e88\u611f#yokan#kyoko otonashi#3:53\n5#10#toward the entrance to a dream#\u5922\u306e\u5165\u53e3\u3078\u2026#yume no iriguchi e#kyoko otonashi#4:03\n6#11#fantasy#\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc#fantaj\u012b#picasso#4:03\n6#12#picture of teary eyes#\u6fe1\u308c\u305f\u77b3\u306e\u30d4\u30af\u30c1\u30e3\u30fc#nureta hitomi no pikuch\u0101#picasso#3:36\n7#13#i love you#\u597d\u304d\u3055#suki sa#anzen chitai#2:49\n7#14#enveloped in memories#\u601d\u3044\u51fa\u306b\u3064\u3064\u307e\u308c\u3066#omoide ni tsutsumarete#anzen chitai#2:40\n8#15#sunny shiny morning#\u30b5\u30cb\u30fc\u3000\u30b7\u30e3\u30a4\u30cb\u30fc\u3000\u30e2\u30fc\u30cb\u30f3\u30b0#san\u012b shain\u012b m\u014dningu#kiyonori matsuo#3:48\n8#16#younger girl#\u30e4\u30f3\u30ac\u30fc\u30ac\u30fc\u30eb#yang\u0101 g\u0101ru#kiyonori matsuo#3:15\n9#17#goodbye sketch#\u30b5\u30e8\u30ca\u30e9\u306e\u7d20\u63cf#sayonara no dessan#picasso#4:34\n9#18#sand atelier#\u7802\u306eatelier#suna no atorie#picasso#3:57\n10#19#melody#\u30e1\u30ed\u30c7\u30a3\u30fc#merod\u012b#kyoko otonashi#3:51\n10#20#excitement#\u3068\u304d\u3081\u304d#tokimeki#kyoko otonashi#3:59\n11#21#sunny spot#\u967d\u3060\u307e\u308a#hidamari#k\u014dz\u014d murashita#4:05\n11#22#symphony of wind#bgm : \u98a8\u306e\u30b7\u30f3\u30d5\u30a9\u30cb\u30fc#bijiemu : kaze no shinfon\u012b#instrumental#4:08\n12#23#begin the night#\u30d3\u30ae\u30f3\u30fb\u30b6\u30fb\u30ca\u30a4\u30c8#bigin za naito#picasso#4:32\n12#24#the tenderness burns#\u611b\u3057\u3055\u304c\u3001\u71c3\u3048\u308b\u3002#itoshisa ga , moeru#picasso#4:33\n13#25#glass kiss#\u785d\u5b50\u306e\u30ad\u30c3\u30b9#garasu no kissu#rika himenogi#4:28\n",
        "pandas_code": "df[(df['english title'] == 'enveloped in memories') & (df['artist'] == 'anzen chitai')]['track time'].iloc[0] == sorted(df['track time'])[1]",
        "pandas_eval": "True"
    },
    {
        "id": 668,
        "statement": "joey rassett place lower than bobby wadkins",
        "label": 1,
        "table_caption": "1983 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\nt1#seve ballesteros#spain#69#- 2\nt1#john mahaffey#united states#69#- 2\nt1#bob murphy#united states#69#- 2\n4#bruce devlin#australia#70#- 1\nt5#lou graham#united states#71#e\nt5#bobby wadkins#united states#71#e\nt5#d a weibring#united states#71#e\nt8#jim booros#united states#72#+ 1\nt8#frank conner#united states#72#+ 1\nt8#raymond floyd#united states#72#+ 1\nt8#hale irwin#united states#72#+ 1\nt8#jim nelford#canada#72#+ 1\nt8#nick price#zimbabwe#72#+ 1\nt8#joey rassett#united states#72#+ 1\nt8#lanny wadkins#united states#72#+ 1\nt8#tom watson#united states#72#+ 1\n",
        "pandas_code": "int(df[df['player'] == 'joey rassett']['place'].values[0].lstrip('t')) > int(df[df['player'] == 'bobby wadkins']['place'].values[0].lstrip('t'))",
        "pandas_eval": "True"
    },
    {
        "id": 669,
        "statement": "seve ballesteros be the only player from spain",
        "label": 1,
        "table_caption": "1983 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\nt1#seve ballesteros#spain#69#- 2\nt1#john mahaffey#united states#69#- 2\nt1#bob murphy#united states#69#- 2\n4#bruce devlin#australia#70#- 1\nt5#lou graham#united states#71#e\nt5#bobby wadkins#united states#71#e\nt5#d a weibring#united states#71#e\nt8#jim booros#united states#72#+ 1\nt8#frank conner#united states#72#+ 1\nt8#raymond floyd#united states#72#+ 1\nt8#hale irwin#united states#72#+ 1\nt8#jim nelford#canada#72#+ 1\nt8#nick price#zimbabwe#72#+ 1\nt8#joey rassett#united states#72#+ 1\nt8#lanny wadkins#united states#72#+ 1\nt8#tom watson#united states#72#+ 1\n",
        "pandas_code": "len(df[(df['player'] == 'seve ballesteros') & (df['country'] == 'spain')]) == 1 and len(df[df['country'] == 'spain']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 670,
        "statement": "there be 4 player who be not from the united state",
        "label": 1,
        "table_caption": "1983 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\nt1#seve ballesteros#spain#69#- 2\nt1#john mahaffey#united states#69#- 2\nt1#bob murphy#united states#69#- 2\n4#bruce devlin#australia#70#- 1\nt5#lou graham#united states#71#e\nt5#bobby wadkins#united states#71#e\nt5#d a weibring#united states#71#e\nt8#jim booros#united states#72#+ 1\nt8#frank conner#united states#72#+ 1\nt8#raymond floyd#united states#72#+ 1\nt8#hale irwin#united states#72#+ 1\nt8#jim nelford#canada#72#+ 1\nt8#nick price#zimbabwe#72#+ 1\nt8#joey rassett#united states#72#+ 1\nt8#lanny wadkins#united states#72#+ 1\nt8#tom watson#united states#72#+ 1\n",
        "pandas_code": "len(df[df['country'] != 'united states']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 671,
        "statement": "raymond floyd and jim nelford have the same score",
        "label": 1,
        "table_caption": "1983 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\nt1#seve ballesteros#spain#69#- 2\nt1#john mahaffey#united states#69#- 2\nt1#bob murphy#united states#69#- 2\n4#bruce devlin#australia#70#- 1\nt5#lou graham#united states#71#e\nt5#bobby wadkins#united states#71#e\nt5#d a weibring#united states#71#e\nt8#jim booros#united states#72#+ 1\nt8#frank conner#united states#72#+ 1\nt8#raymond floyd#united states#72#+ 1\nt8#hale irwin#united states#72#+ 1\nt8#jim nelford#canada#72#+ 1\nt8#nick price#zimbabwe#72#+ 1\nt8#joey rassett#united states#72#+ 1\nt8#lanny wadkins#united states#72#+ 1\nt8#tom watson#united states#72#+ 1\n",
        "pandas_code": "df[df['player'].isin(['raymond floyd', 'jim nelford'])]['score'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 672,
        "statement": "bruce devlin place higher than jim booros",
        "label": 1,
        "table_caption": "1983 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\nt1#seve ballesteros#spain#69#- 2\nt1#john mahaffey#united states#69#- 2\nt1#bob murphy#united states#69#- 2\n4#bruce devlin#australia#70#- 1\nt5#lou graham#united states#71#e\nt5#bobby wadkins#united states#71#e\nt5#d a weibring#united states#71#e\nt8#jim booros#united states#72#+ 1\nt8#frank conner#united states#72#+ 1\nt8#raymond floyd#united states#72#+ 1\nt8#hale irwin#united states#72#+ 1\nt8#jim nelford#canada#72#+ 1\nt8#nick price#zimbabwe#72#+ 1\nt8#joey rassett#united states#72#+ 1\nt8#lanny wadkins#united states#72#+ 1\nt8#tom watson#united states#72#+ 1\n",
        "pandas_code": "df[df['player'] == 'bruce devlin']['place'].values[0] < df[df['player'] == 'jim booros']['place'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 673,
        "statement": "ioannis bourousis be the tallest center player of fiba eurobasket 2007",
        "label": 1,
        "table_caption": "fiba eurobasket 2007 squads",
        "table_text": "no#player#height#position#year born#current club\n4#theodoros papaloukas#2.00#guard#1977#cska moscow\n5#ioannis bourousis#2.13#center#1983#olympiacos\n6#nikolaos zisis#1.95#guard#1983#cska moscow\n7#vasileios spanoulis#1.92#guard#1982#panathinaikos\n8#panagiotis vasilopoulos#2.01#forward#1984#olympiacos\n9#michalis pelekanos#1.98#forward#1981#real madrid\n10#nikolaos chatzivrettas#1.95#guard#1977#panathinaikos\n11#dimosthenis dikoudis#2.06#forward#1977#panathinaikos\n12#konstantinos tsartsaris#2.09#center#1979#panathinaikos\n13#dimitris diamantidis#1.96#guard#1980#panathinaikos\n14#lazaros papadopoulos#2.10#center#1980#real madrid\n",
        "pandas_code": "df[df['position'] == 'center']['height'].max() == df[df['player'] == 'ioannis bourousis']['height'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 674,
        "statement": "there be 5 player from panathinaikos club on the list",
        "label": 1,
        "table_caption": "fiba eurobasket 2007 squads",
        "table_text": "no#player#height#position#year born#current club\n4#theodoros papaloukas#2.00#guard#1977#cska moscow\n5#ioannis bourousis#2.13#center#1983#olympiacos\n6#nikolaos zisis#1.95#guard#1983#cska moscow\n7#vasileios spanoulis#1.92#guard#1982#panathinaikos\n8#panagiotis vasilopoulos#2.01#forward#1984#olympiacos\n9#michalis pelekanos#1.98#forward#1981#real madrid\n10#nikolaos chatzivrettas#1.95#guard#1977#panathinaikos\n11#dimosthenis dikoudis#2.06#forward#1977#panathinaikos\n12#konstantinos tsartsaris#2.09#center#1979#panathinaikos\n13#dimitris diamantidis#1.96#guard#1980#panathinaikos\n14#lazaros papadopoulos#2.10#center#1980#real madrid\n",
        "pandas_code": "(df['current club'] == 'panathinaikos').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 675,
        "statement": "they list 5 guard from different club",
        "label": 1,
        "table_caption": "fiba eurobasket 2007 squads",
        "table_text": "no#player#height#position#year born#current club\n4#theodoros papaloukas#2.00#guard#1977#cska moscow\n5#ioannis bourousis#2.13#center#1983#olympiacos\n6#nikolaos zisis#1.95#guard#1983#cska moscow\n7#vasileios spanoulis#1.92#guard#1982#panathinaikos\n8#panagiotis vasilopoulos#2.01#forward#1984#olympiacos\n9#michalis pelekanos#1.98#forward#1981#real madrid\n10#nikolaos chatzivrettas#1.95#guard#1977#panathinaikos\n11#dimosthenis dikoudis#2.06#forward#1977#panathinaikos\n12#konstantinos tsartsaris#2.09#center#1979#panathinaikos\n13#dimitris diamantidis#1.96#guard#1980#panathinaikos\n14#lazaros papadopoulos#2.10#center#1980#real madrid\n",
        "pandas_code": "len(df[df['position'] == 'guard']) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 676,
        "statement": "there be 3 player who be bear year 1977",
        "label": 1,
        "table_caption": "fiba eurobasket 2007 squads",
        "table_text": "no#player#height#position#year born#current club\n4#theodoros papaloukas#2.00#guard#1977#cska moscow\n5#ioannis bourousis#2.13#center#1983#olympiacos\n6#nikolaos zisis#1.95#guard#1983#cska moscow\n7#vasileios spanoulis#1.92#guard#1982#panathinaikos\n8#panagiotis vasilopoulos#2.01#forward#1984#olympiacos\n9#michalis pelekanos#1.98#forward#1981#real madrid\n10#nikolaos chatzivrettas#1.95#guard#1977#panathinaikos\n11#dimosthenis dikoudis#2.06#forward#1977#panathinaikos\n12#konstantinos tsartsaris#2.09#center#1979#panathinaikos\n13#dimitris diamantidis#1.96#guard#1980#panathinaikos\n14#lazaros papadopoulos#2.10#center#1980#real madrid\n",
        "pandas_code": "len(df[df['year born'] == 1977]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 677,
        "statement": "the youngest player be panagiotis vasilopoulos of the club olympiacos",
        "label": 1,
        "table_caption": "fiba eurobasket 2007 squads",
        "table_text": "no#player#height#position#year born#current club\n4#theodoros papaloukas#2.00#guard#1977#cska moscow\n5#ioannis bourousis#2.13#center#1983#olympiacos\n6#nikolaos zisis#1.95#guard#1983#cska moscow\n7#vasileios spanoulis#1.92#guard#1982#panathinaikos\n8#panagiotis vasilopoulos#2.01#forward#1984#olympiacos\n9#michalis pelekanos#1.98#forward#1981#real madrid\n10#nikolaos chatzivrettas#1.95#guard#1977#panathinaikos\n11#dimosthenis dikoudis#2.06#forward#1977#panathinaikos\n12#konstantinos tsartsaris#2.09#center#1979#panathinaikos\n13#dimitris diamantidis#1.96#guard#1980#panathinaikos\n14#lazaros papadopoulos#2.10#center#1980#real madrid\n",
        "pandas_code": "df[df['year born'] == df['year born'].max()].iloc[0]['player'] == 'panagiotis vasilopoulos' and df[df['year born'] == df['year born'].max()].iloc[0]['current club'] == 'olympiacos'",
        "pandas_eval": "True"
    },
    {
        "id": 678,
        "statement": "mark calcavecchia , tom watson , and jack nicklaus all play for the united state",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['mark calcavecchia', 'tom watson', 'jack nicklaus'])]['country'] == 'united states')",
        "pandas_eval": "True"
    },
    {
        "id": 679,
        "statement": "tom watson have the most win out of anyone list , in 1975 , 1977 , 1980 , 1982 , 1983",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "df[df['player'] == 'tom watson']['year (s) won'].str.split(' , ').explode().nunique() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 680,
        "statement": "mark calcavecchia , nick price , and sandy lyle all only have 1 win each",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['mark calcavecchia', 'nick price', 'sandy lyle'])]['year (s) won'].apply(lambda x: len(x.split(','))) == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 681,
        "statement": "seve ballesteros , nick faldo , and nick price all have a total score of 291 , each",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['seve ballesteros', 'nick faldo', 'nick price'])]['total'] == 291)",
        "pandas_eval": "True"
    },
    {
        "id": 682,
        "statement": "greg norman be the only player list for australia , while nick price be the only player list for zimbabwe",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "(df[df['country'] == 'australia']['player'].tolist() == ['greg norman']) & (df[df['country'] == 'zimbabwe']['player'].tolist() == ['nick price'])",
        "pandas_eval": "True"
    },
    {
        "id": 683,
        "statement": "mark calcavecchia , tom watson , and jack nicklaus all play for australia",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['mark calcavecchia', 'tom watson', 'jack nicklaus'])]['country'] == 'australia')",
        "pandas_eval": "False"
    },
    {
        "id": 684,
        "statement": "tom watson have the least win out of anyone list , in 1975 , 1977 , 1980 , 1982 , 1983",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "df['year (s) won'].apply(lambda x: len(x.split(' , '))).min() == len(df[df['player'] == 'tom watson']['year (s) won'].iloc[0].split(' , '))",
        "pandas_eval": "False"
    },
    {
        "id": 685,
        "statement": "mark calcavecchia , nick price , and sandy lyle all only have 2 win each",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['mark calcavecchia', 'nick price', 'sandy lyle'])]['year (s) won'].apply(lambda x: len(x.split(' , '))) == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 686,
        "statement": "seve ballesteros , nick faldo , and nick price all have a total score of 290 , each",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "all(df[df['player'].isin(['seve ballesteros', 'nick faldo', 'nick price'])]['total'] == 290)",
        "pandas_eval": "False"
    },
    {
        "id": 687,
        "statement": "greg norman be the only player list for zimbabwe , while nick price be the only player list for australia",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par#finish\ngreg norman#australia#1986 , 1993#287#1#t15\nmark calcavecchia#united states#1989#289#+ 1#t24\ntom watson#united states#1975 , 1977 , 1980 , 1982 , 1983#290#+ 2#t31\nseve ballesteros#spain#1979 , 1984 , 1988#291#+ 3#t40\nnick faldo#england#1987 , 1990 , 1992#291#+ 3#t40\nnick price#zimbabwe#1994#291#+ 3#t40\ngary player#south africa#1959 , 1968 , 1974#295#+ 7#t68\njack nicklaus#united states#1966 , 1970 , 1978#296#+ 8#t79\nsandy lyle#scotland#1985#296#+ 8#t79\n",
        "pandas_code": "(df[df['country'] == 'zimbabwe']['player'].eq('greg norman').all() and df[df['country'] == 'australia']['player'].eq('nick price').all())",
        "pandas_eval": "False"
    },
    {
        "id": 688,
        "statement": "out of 8 season , all of them be host by rannvijay singh",
        "label": 1,
        "table_caption": "mtv roadies",
        "table_text": "season#network#host#contestants#winner#destination\n2#mtv india#rannvijay singh#10#ayushmann khurrana#india\n3#mtv india#rannvijay singh#12#parul shahi#india\n4#mtv india#rannvijay singh#13#anthony yeh#india\n5#mtv india#rannvijay singh#13#ashutosh kaushik#thailand , malaysia\n6#mtv india#rannvijay singh#20#nauman sait#australia\n7#mtv india#rannvijay singh#23#anwar syed#africa\n8#mtv india#rannvijay singh#13#aanchal khurrana#brazil\n9#mtv india#rannvijay singh#13#vikas khoker#united states\n",
        "pandas_code": "all(df['host'] == 'rannvijay singh')",
        "pandas_eval": "True"
    },
    {
        "id": 689,
        "statement": "out of 8 season , all of them come from the same network name mtv india",
        "label": 1,
        "table_caption": "mtv roadies",
        "table_text": "season#network#host#contestants#winner#destination\n2#mtv india#rannvijay singh#10#ayushmann khurrana#india\n3#mtv india#rannvijay singh#12#parul shahi#india\n4#mtv india#rannvijay singh#13#anthony yeh#india\n5#mtv india#rannvijay singh#13#ashutosh kaushik#thailand , malaysia\n6#mtv india#rannvijay singh#20#nauman sait#australia\n7#mtv india#rannvijay singh#23#anwar syed#africa\n8#mtv india#rannvijay singh#13#aanchal khurrana#brazil\n9#mtv india#rannvijay singh#13#vikas khoker#united states\n",
        "pandas_code": "df['network'].eq('mtv india').all()",
        "pandas_eval": "True"
    },
    {
        "id": 690,
        "statement": "out of 8 season , 3 of which share the same destination to india",
        "label": 1,
        "table_caption": "mtv roadies",
        "table_text": "season#network#host#contestants#winner#destination\n2#mtv india#rannvijay singh#10#ayushmann khurrana#india\n3#mtv india#rannvijay singh#12#parul shahi#india\n4#mtv india#rannvijay singh#13#anthony yeh#india\n5#mtv india#rannvijay singh#13#ashutosh kaushik#thailand , malaysia\n6#mtv india#rannvijay singh#20#nauman sait#australia\n7#mtv india#rannvijay singh#23#anwar syed#africa\n8#mtv india#rannvijay singh#13#aanchal khurrana#brazil\n9#mtv india#rannvijay singh#13#vikas khoker#united states\n",
        "pandas_code": "(df['destination'].value_counts()['india'] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 691,
        "statement": "the number of contestant from season 2 - 9 have an average of 10 - 25 contestant",
        "label": 1,
        "table_caption": "mtv roadies",
        "table_text": "season#network#host#contestants#winner#destination\n2#mtv india#rannvijay singh#10#ayushmann khurrana#india\n3#mtv india#rannvijay singh#12#parul shahi#india\n4#mtv india#rannvijay singh#13#anthony yeh#india\n5#mtv india#rannvijay singh#13#ashutosh kaushik#thailand , malaysia\n6#mtv india#rannvijay singh#20#nauman sait#australia\n7#mtv india#rannvijay singh#23#anwar syed#africa\n8#mtv india#rannvijay singh#13#aanchal khurrana#brazil\n9#mtv india#rannvijay singh#13#vikas khoker#united states\n",
        "pandas_code": "(df['contestants'].between(10, 25).all())",
        "pandas_eval": "True"
    },
    {
        "id": 693,
        "statement": "born to be bunnies and firemen or firewomen both air in september 2004",
        "label": 1,
        "table_caption": "list of the bellflower bunnies episodes",
        "table_text": "official#tf1#french title#english title#air date (france)#original beechwood bunny tale / source material\n5#5#l'exploit de tante zinia#born to be bunnies#22 september 2004#l'exploit de tante zinia\n6#8#les passiflore m\u00e8nent l'enqu\u00eate#bunnies on a case#13 october 2004#les passiflore m\u00e8nent l'enqu\u00eate\n7#6#les beignets flamb\u00e9s#firemen or firewomen#29 september 2004#les beignets flamb\u00e9s\n10#9#l'invention d'on\u00e9sime#papa bramble 's invention#20 october 2004#l'invention d'on\u00e9sime passiflore\n12#12#en avant la musique#face the music#17 november 2004#en avant , la musique!\n14#26#dentdelion et le b\u00e9b\u00e9 marmotte#dandelion and the baby groundhog#23 february 2005#teleplay by val\u00e9rie baranski\n15#13#la cl\u00e9 du bonheur#the key to happiness#24 november 2004#teleplay by val\u00e9rie baranski\n16#14#pirouette sous les feux de la rampe#in the limelight#1 december 2004#teleplay by val\u00e9rie baranski\n17#15#coup de foudre aux airelles#love at first sight#8 december 2004#teleplay by val\u00e9rie baranski\n18#22#au pays des fant\u00f4mes#weird country#26 january 2005#teleplay by val\u00e9rie baranski\n19#21#dentdelion fait son cin\u00e9ma#the silver screen#19 january 2005#teleplay by val\u00e9rie baranski\n20#23#agaric et le sorcier#periwinkle and the witch doctor#2 february 2005#teleplay by val\u00e9rie baranski\n21#18#le pacte du lac#the monster of blueberry lake#29 december 2004#teleplay by val\u00e9rie baranski\n22#16#le secret d'on\u00e9sime#papa bramble 's secret#15 december 2004#teleplay by val\u00e9rie baranski\n23#19#vive les vacances#holiday with love#5 january 2005#teleplay by val\u00e9rie baranski\n24#20#chez les petits savants#the science academy#12 january 2005#teleplay by val\u00e9rie baranski\n25#24#au cur de la source#the heart of the spring#9 february 2005#teleplay by val\u00e9rie baranski\n",
        "pandas_code": "all(df[df['english title'].isin(['born to be bunnies', 'firemen or firewomen'])]['air date (france)'].str.contains('september 2004'))",
        "pandas_eval": "True"
    },
    {
        "id": 694,
        "statement": "bunnies on a case and papa bramble 's invention both air in october 2004",
        "label": 1,
        "table_caption": "list of the bellflower bunnies episodes",
        "table_text": "official#tf1#french title#english title#air date (france)#original beechwood bunny tale / source material\n5#5#l'exploit de tante zinia#born to be bunnies#22 september 2004#l'exploit de tante zinia\n6#8#les passiflore m\u00e8nent l'enqu\u00eate#bunnies on a case#13 october 2004#les passiflore m\u00e8nent l'enqu\u00eate\n7#6#les beignets flamb\u00e9s#firemen or firewomen#29 september 2004#les beignets flamb\u00e9s\n10#9#l'invention d'on\u00e9sime#papa bramble 's invention#20 october 2004#l'invention d'on\u00e9sime passiflore\n12#12#en avant la musique#face the music#17 november 2004#en avant , la musique!\n14#26#dentdelion et le b\u00e9b\u00e9 marmotte#dandelion and the baby groundhog#23 february 2005#teleplay by val\u00e9rie baranski\n15#13#la cl\u00e9 du bonheur#the key to happiness#24 november 2004#teleplay by val\u00e9rie baranski\n16#14#pirouette sous les feux de la rampe#in the limelight#1 december 2004#teleplay by val\u00e9rie baranski\n17#15#coup de foudre aux airelles#love at first sight#8 december 2004#teleplay by val\u00e9rie baranski\n18#22#au pays des fant\u00f4mes#weird country#26 january 2005#teleplay by val\u00e9rie baranski\n19#21#dentdelion fait son cin\u00e9ma#the silver screen#19 january 2005#teleplay by val\u00e9rie baranski\n20#23#agaric et le sorcier#periwinkle and the witch doctor#2 february 2005#teleplay by val\u00e9rie baranski\n21#18#le pacte du lac#the monster of blueberry lake#29 december 2004#teleplay by val\u00e9rie baranski\n22#16#le secret d'on\u00e9sime#papa bramble 's secret#15 december 2004#teleplay by val\u00e9rie baranski\n23#19#vive les vacances#holiday with love#5 january 2005#teleplay by val\u00e9rie baranski\n24#20#chez les petits savants#the science academy#12 january 2005#teleplay by val\u00e9rie baranski\n25#24#au cur de la source#the heart of the spring#9 february 2005#teleplay by val\u00e9rie baranski\n",
        "pandas_code": "all(df.loc[df['english title'].isin(['bunnies on a case', \"papa bramble 's invention\"]), 'air date (france)'].str.contains('october 2004'))",
        "pandas_eval": "True"
    },
    {
        "id": 696,
        "statement": "in the limelight , love at first site , the monster of blueberry lake , and papa bramble 's secret all air in december 2004",
        "label": 1,
        "table_caption": "list of the bellflower bunnies episodes",
        "table_text": "official#tf1#french title#english title#air date (france)#original beechwood bunny tale / source material\n5#5#l'exploit de tante zinia#born to be bunnies#22 september 2004#l'exploit de tante zinia\n6#8#les passiflore m\u00e8nent l'enqu\u00eate#bunnies on a case#13 october 2004#les passiflore m\u00e8nent l'enqu\u00eate\n7#6#les beignets flamb\u00e9s#firemen or firewomen#29 september 2004#les beignets flamb\u00e9s\n10#9#l'invention d'on\u00e9sime#papa bramble 's invention#20 october 2004#l'invention d'on\u00e9sime passiflore\n12#12#en avant la musique#face the music#17 november 2004#en avant , la musique!\n14#26#dentdelion et le b\u00e9b\u00e9 marmotte#dandelion and the baby groundhog#23 february 2005#teleplay by val\u00e9rie baranski\n15#13#la cl\u00e9 du bonheur#the key to happiness#24 november 2004#teleplay by val\u00e9rie baranski\n16#14#pirouette sous les feux de la rampe#in the limelight#1 december 2004#teleplay by val\u00e9rie baranski\n17#15#coup de foudre aux airelles#love at first sight#8 december 2004#teleplay by val\u00e9rie baranski\n18#22#au pays des fant\u00f4mes#weird country#26 january 2005#teleplay by val\u00e9rie baranski\n19#21#dentdelion fait son cin\u00e9ma#the silver screen#19 january 2005#teleplay by val\u00e9rie baranski\n20#23#agaric et le sorcier#periwinkle and the witch doctor#2 february 2005#teleplay by val\u00e9rie baranski\n21#18#le pacte du lac#the monster of blueberry lake#29 december 2004#teleplay by val\u00e9rie baranski\n22#16#le secret d'on\u00e9sime#papa bramble 's secret#15 december 2004#teleplay by val\u00e9rie baranski\n23#19#vive les vacances#holiday with love#5 january 2005#teleplay by val\u00e9rie baranski\n24#20#chez les petits savants#the science academy#12 january 2005#teleplay by val\u00e9rie baranski\n25#24#au cur de la source#the heart of the spring#9 february 2005#teleplay by val\u00e9rie baranski\n",
        "pandas_code": "all(df[df['english title'].isin(['in the limelight', 'love at first sight', 'the monster of blueberry lake', \"papa bramble 's secret\"])]['air date (france)'].str.contains('december 2004'))",
        "pandas_eval": "True"
    },
    {
        "id": 697,
        "statement": "dandelion and the baby groundhog , periwinkle and the witch doctor , and the heart of the spring all air in february 2005",
        "label": 1,
        "table_caption": "list of the bellflower bunnies episodes",
        "table_text": "official#tf1#french title#english title#air date (france)#original beechwood bunny tale / source material\n5#5#l'exploit de tante zinia#born to be bunnies#22 september 2004#l'exploit de tante zinia\n6#8#les passiflore m\u00e8nent l'enqu\u00eate#bunnies on a case#13 october 2004#les passiflore m\u00e8nent l'enqu\u00eate\n7#6#les beignets flamb\u00e9s#firemen or firewomen#29 september 2004#les beignets flamb\u00e9s\n10#9#l'invention d'on\u00e9sime#papa bramble 's invention#20 october 2004#l'invention d'on\u00e9sime passiflore\n12#12#en avant la musique#face the music#17 november 2004#en avant , la musique!\n14#26#dentdelion et le b\u00e9b\u00e9 marmotte#dandelion and the baby groundhog#23 february 2005#teleplay by val\u00e9rie baranski\n15#13#la cl\u00e9 du bonheur#the key to happiness#24 november 2004#teleplay by val\u00e9rie baranski\n16#14#pirouette sous les feux de la rampe#in the limelight#1 december 2004#teleplay by val\u00e9rie baranski\n17#15#coup de foudre aux airelles#love at first sight#8 december 2004#teleplay by val\u00e9rie baranski\n18#22#au pays des fant\u00f4mes#weird country#26 january 2005#teleplay by val\u00e9rie baranski\n19#21#dentdelion fait son cin\u00e9ma#the silver screen#19 january 2005#teleplay by val\u00e9rie baranski\n20#23#agaric et le sorcier#periwinkle and the witch doctor#2 february 2005#teleplay by val\u00e9rie baranski\n21#18#le pacte du lac#the monster of blueberry lake#29 december 2004#teleplay by val\u00e9rie baranski\n22#16#le secret d'on\u00e9sime#papa bramble 's secret#15 december 2004#teleplay by val\u00e9rie baranski\n23#19#vive les vacances#holiday with love#5 january 2005#teleplay by val\u00e9rie baranski\n24#20#chez les petits savants#the science academy#12 january 2005#teleplay by val\u00e9rie baranski\n25#24#au cur de la source#the heart of the spring#9 february 2005#teleplay by val\u00e9rie baranski\n",
        "pandas_code": "all(df[df['english title'].isin(['dandelion and the baby groundhog', 'periwinkle and the witch doctor', 'the heart of the spring'])]['air date (france)'].str.contains('february 2005'))",
        "pandas_eval": "True"
    },
    {
        "id": 698,
        "statement": "sarajevo b , c have have the most season in the top division",
        "label": 1,
        "table_caption": "premier league of bosnia and herzegovina",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in premier league a#first season of current spell in top division#top division titles#last top division title\nborac b#003 3rd#1961 - 62#23#9#2008 - 09#1#2010 - 11\n\u010delik b , c#004 4th#1966 - 67#30#13#2000 - 01#3 d#1996 - 97\ngo\u0161k (r)#015 15th#2011 - 12#2#2#2011 - 12#0#n / a\ngradina (r)#016 16th#2012 - 13#1#1#2012 - 13#0#n / a\nleotar b , c#008 8th#2002 - 03#11#11#2002 - 03#1#2002 - 03\nolimpic#005 5th#2000 - 01#6#6#2009 - 10#0#n / a\nradnik#012 12th#2006 - 07#3#3#2012 - 13#1 e#1998 - 99\nrudar#011 11th#2009 - 10#4#4#2009 - 10#0#n / a\nsarajevo b , c#002 2nd#1947 - 48#55#13#1958 - 59#4 f#2006 - 07\nslavija#007 7th#1930#17#9#2004 - 05#0#n / a\n\u0161iroki brijeg b , c#006 6th#2000 - 01#13#13#2000 - 01#6 g#2005 - 06\ntravnik#014 14th#2000 - 01#10#10#2007 - 08#0#n / a\nvele\u017e b#013 13th#1952 - 53#48#10#2006 - 07#0#n / a\nzrinjski b , c#009 9th#2000 - 01#13#13#2000 - 01#2#2008 - 09\nzvijezda#010 10th#2008 - 09#5#5#2008 - 09#0#n / a\n",
        "pandas_code": "df.loc[df['club'] == 'sarajevo b , c', 'number of seasons in top division'].values[0] == df['number of seasons in top division'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 699,
        "statement": "each club have have an average of 8 season in premier league a",
        "label": 1,
        "table_caption": "premier league of bosnia and herzegovina",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in premier league a#first season of current spell in top division#top division titles#last top division title\nborac b#003 3rd#1961 - 62#23#9#2008 - 09#1#2010 - 11\n\u010delik b , c#004 4th#1966 - 67#30#13#2000 - 01#3 d#1996 - 97\ngo\u0161k (r)#015 15th#2011 - 12#2#2#2011 - 12#0#n / a\ngradina (r)#016 16th#2012 - 13#1#1#2012 - 13#0#n / a\nleotar b , c#008 8th#2002 - 03#11#11#2002 - 03#1#2002 - 03\nolimpic#005 5th#2000 - 01#6#6#2009 - 10#0#n / a\nradnik#012 12th#2006 - 07#3#3#2012 - 13#1 e#1998 - 99\nrudar#011 11th#2009 - 10#4#4#2009 - 10#0#n / a\nsarajevo b , c#002 2nd#1947 - 48#55#13#1958 - 59#4 f#2006 - 07\nslavija#007 7th#1930#17#9#2004 - 05#0#n / a\n\u0161iroki brijeg b , c#006 6th#2000 - 01#13#13#2000 - 01#6 g#2005 - 06\ntravnik#014 14th#2000 - 01#10#10#2007 - 08#0#n / a\nvele\u017e b#013 13th#1952 - 53#48#10#2006 - 07#0#n / a\nzrinjski b , c#009 9th#2000 - 01#13#13#2000 - 01#2#2008 - 09\nzvijezda#010 10th#2008 - 09#5#5#2008 - 09#0#n / a\n",
        "pandas_code": "df['number of seasons in premier league a'].mean() >= 8",
        "pandas_eval": "True"
    },
    {
        "id": 700,
        "statement": "borac b have more season in the top division than gradina (r)",
        "label": 1,
        "table_caption": "premier league of bosnia and herzegovina",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in premier league a#first season of current spell in top division#top division titles#last top division title\nborac b#003 3rd#1961 - 62#23#9#2008 - 09#1#2010 - 11\n\u010delik b , c#004 4th#1966 - 67#30#13#2000 - 01#3 d#1996 - 97\ngo\u0161k (r)#015 15th#2011 - 12#2#2#2011 - 12#0#n / a\ngradina (r)#016 16th#2012 - 13#1#1#2012 - 13#0#n / a\nleotar b , c#008 8th#2002 - 03#11#11#2002 - 03#1#2002 - 03\nolimpic#005 5th#2000 - 01#6#6#2009 - 10#0#n / a\nradnik#012 12th#2006 - 07#3#3#2012 - 13#1 e#1998 - 99\nrudar#011 11th#2009 - 10#4#4#2009 - 10#0#n / a\nsarajevo b , c#002 2nd#1947 - 48#55#13#1958 - 59#4 f#2006 - 07\nslavija#007 7th#1930#17#9#2004 - 05#0#n / a\n\u0161iroki brijeg b , c#006 6th#2000 - 01#13#13#2000 - 01#6 g#2005 - 06\ntravnik#014 14th#2000 - 01#10#10#2007 - 08#0#n / a\nvele\u017e b#013 13th#1952 - 53#48#10#2006 - 07#0#n / a\nzrinjski b , c#009 9th#2000 - 01#13#13#2000 - 01#2#2008 - 09\nzvijezda#010 10th#2008 - 09#5#5#2008 - 09#0#n / a\n",
        "pandas_code": "df[df['club'] == 'borac b']['number of seasons in top division'].values[0] > df[df['club'] == 'gradina (r)']['number of seasons in top division'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 701,
        "statement": "4 team have have 13 season in premier league a",
        "label": 1,
        "table_caption": "premier league of bosnia and herzegovina",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in premier league a#first season of current spell in top division#top division titles#last top division title\nborac b#003 3rd#1961 - 62#23#9#2008 - 09#1#2010 - 11\n\u010delik b , c#004 4th#1966 - 67#30#13#2000 - 01#3 d#1996 - 97\ngo\u0161k (r)#015 15th#2011 - 12#2#2#2011 - 12#0#n / a\ngradina (r)#016 16th#2012 - 13#1#1#2012 - 13#0#n / a\nleotar b , c#008 8th#2002 - 03#11#11#2002 - 03#1#2002 - 03\nolimpic#005 5th#2000 - 01#6#6#2009 - 10#0#n / a\nradnik#012 12th#2006 - 07#3#3#2012 - 13#1 e#1998 - 99\nrudar#011 11th#2009 - 10#4#4#2009 - 10#0#n / a\nsarajevo b , c#002 2nd#1947 - 48#55#13#1958 - 59#4 f#2006 - 07\nslavija#007 7th#1930#17#9#2004 - 05#0#n / a\n\u0161iroki brijeg b , c#006 6th#2000 - 01#13#13#2000 - 01#6 g#2005 - 06\ntravnik#014 14th#2000 - 01#10#10#2007 - 08#0#n / a\nvele\u017e b#013 13th#1952 - 53#48#10#2006 - 07#0#n / a\nzrinjski b , c#009 9th#2000 - 01#13#13#2000 - 01#2#2008 - 09\nzvijezda#010 10th#2008 - 09#5#5#2008 - 09#0#n / a\n",
        "pandas_code": "(df['number of seasons in premier league a'] == 13).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 702,
        "statement": "slavija have their first season of current spell in the top division 5 year earlier than rudar",
        "label": 1,
        "table_caption": "premier league of bosnia and herzegovina",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in premier league a#first season of current spell in top division#top division titles#last top division title\nborac b#003 3rd#1961 - 62#23#9#2008 - 09#1#2010 - 11\n\u010delik b , c#004 4th#1966 - 67#30#13#2000 - 01#3 d#1996 - 97\ngo\u0161k (r)#015 15th#2011 - 12#2#2#2011 - 12#0#n / a\ngradina (r)#016 16th#2012 - 13#1#1#2012 - 13#0#n / a\nleotar b , c#008 8th#2002 - 03#11#11#2002 - 03#1#2002 - 03\nolimpic#005 5th#2000 - 01#6#6#2009 - 10#0#n / a\nradnik#012 12th#2006 - 07#3#3#2012 - 13#1 e#1998 - 99\nrudar#011 11th#2009 - 10#4#4#2009 - 10#0#n / a\nsarajevo b , c#002 2nd#1947 - 48#55#13#1958 - 59#4 f#2006 - 07\nslavija#007 7th#1930#17#9#2004 - 05#0#n / a\n\u0161iroki brijeg b , c#006 6th#2000 - 01#13#13#2000 - 01#6 g#2005 - 06\ntravnik#014 14th#2000 - 01#10#10#2007 - 08#0#n / a\nvele\u017e b#013 13th#1952 - 53#48#10#2006 - 07#0#n / a\nzrinjski b , c#009 9th#2000 - 01#13#13#2000 - 01#2#2008 - 09\nzvijezda#010 10th#2008 - 09#5#5#2008 - 09#0#n / a\n",
        "pandas_code": "(df[df['club'] == 'slavija']['first season of current spell in top division'].values[0].split(' - ')[0] == str(int(df[df['club'] == 'rudar']['first season of current spell in top division'].values[0].split(' - ')[0]) - 5))",
        "pandas_eval": "True"
    },
    {
        "id": 703,
        "statement": "the game at lake oval have a crowd 2 time the size of the game at western oval have",
        "label": 1,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "df[df['venue'] == 'lake oval']['crowd'].values[0] == 2 * df[df['venue'] == 'western oval']['crowd'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 704,
        "statement": "footscray be the team with the highest home team score",
        "label": 1,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "df.loc[df['home team score'].apply(lambda x: int(x.split('.')[0])).idxmax(), 'home team'] == 'footscray'",
        "pandas_eval": "True"
    },
    {
        "id": 705,
        "statement": "st kilda be not 1 of the home team on 17 april 1948",
        "label": 1,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "('st kilda' not in df['home team'].values) and ('17 april 1948' in df['date'].values)",
        "pandas_eval": "True"
    },
    {
        "id": 706,
        "statement": "richmond be 1 of 4 away team that have an away team score higher than 10.00",
        "label": 1,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "len(df[df['away team'] == 'richmond'][df['away team score'].str.extract(r'(\\d+)\\.')[0].astype(int) > 10]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 708,
        "statement": "the game at lake oval have a crowd 4 time the size the game at western oval have",
        "label": 0,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "(df[df['venue'] == 'lake oval']['crowd'].values[0] == 4 * df[df['venue'] == 'western oval']['crowd'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 710,
        "statement": "st kilda be not 1 of the away team on 17 april 1948",
        "label": 0,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "not any(df[(df['date'] == '17 april 1948') & (df['away team'] == 'st kilda')])",
        "pandas_eval": "False"
    },
    {
        "id": 711,
        "statement": "richmond be the only away team that have an away team score higher than 10.00",
        "label": 0,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "((df['away team'] == 'richmond') & (df['away team score'].str.extract(r'(\\\\d+)\\\\.')[0].astype(float) > 10)).any() & (df[df['away team score'].str.extract(r'(\\\\d+)\\\\.')[0].astype(float) > 10]['away team'].nunique() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 712,
        "statement": "melbourne have a home team score that be 3.38 point lower than what hawthorn have",
        "label": 0,
        "table_caption": "1948 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#21.19 (145)#st kilda#9.11 (65)#western oval#14000#17 april 1948\nfitzroy#13.14 (92)#geelong#9.16 (70)#brunswick street oval#17000#17 april 1948\nsouth melbourne#18.15 (123)#richmond#17.6 (108)#lake oval#28000#17 april 1948\nmelbourne#12.5 (77)#essendon#13.18 (96)#mcg#29000#17 april 1948\nnorth melbourne#9.12 (66)#collingwood#11.20 (86)#arden street oval#20000#17 april 1948\nhawthorn#11.10 (76)#carlton#17.15 (117)#glenferrie oval#16000#17 april 1948\n",
        "pandas_code": "(df[df['home team'] == 'melbourne']['home team score'].str.extract(r'(\\d+)\\.(\\d+)').astype(int).apply(lambda x: x[0] * 6 + x[1], axis=1).iloc[0] - df[df['home team'] == 'hawthorn']['home team score'].str.extract(r'(\\d+)\\.(\\d+)').astype(int).apply(lambda x: x[0] * 6 + x[1], axis=1).iloc[0]) == -3.38",
        "pandas_eval": "False"
    },
    {
        "id": 713,
        "statement": "on may 15 san antonio play the new orleans hornet with a score of 80 - 99 and the high poitns and assist go to paul (21 and 8)",
        "label": 1,
        "table_caption": "2007 - 08 new orleans hornets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 3#san antonio#101 - 82#west (30)#chandler (15)#paul (13)#new orleans arena 18040#1 - 0\n2#may 5#san antonio#102 - 84#paul (30)#chandler (11)#paul (12)#new orleans arena 17927#2 - 0\n3#may 8#san antonio#99 - 110#paul (35)#west (12)#paul (9)#at&t center 18797#2 - 1\n4#may 11#san antonio#80 - 100#paul (23)#armstrong , paul (6)#paul (5)#at&t center 18797#2 - 2\n5#may 13#san antonio#101 - 79#west (38)#west (14)#paul (14)#new orleans arena 18246#3 - 2\n6#may 15#san antonio#80 - 99#paul (21)#five - way tie (6)#paul (8)#at&t center 18797#3 - 3\n",
        "pandas_code": "(df[(df['date'] == 'may 15') & (df['team'] == 'san antonio') & (df['score'] == '80 - 99') & (df['high points'] == 'paul (21)') & (df['high assists'] == 'paul (8)')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 714,
        "statement": "when the highest assist be paul (14) the location attendance be new orleans arena 18246 and the date be may 13",
        "label": 1,
        "table_caption": "2007 - 08 new orleans hornets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 3#san antonio#101 - 82#west (30)#chandler (15)#paul (13)#new orleans arena 18040#1 - 0\n2#may 5#san antonio#102 - 84#paul (30)#chandler (11)#paul (12)#new orleans arena 17927#2 - 0\n3#may 8#san antonio#99 - 110#paul (35)#west (12)#paul (9)#at&t center 18797#2 - 1\n4#may 11#san antonio#80 - 100#paul (23)#armstrong , paul (6)#paul (5)#at&t center 18797#2 - 2\n5#may 13#san antonio#101 - 79#west (38)#west (14)#paul (14)#new orleans arena 18246#3 - 2\n6#may 15#san antonio#80 - 99#paul (21)#five - way tie (6)#paul (8)#at&t center 18797#3 - 3\n",
        "pandas_code": "((df['high assists'] == 'paul (14)') & (df['location attendance'] == 'new orleans arena 18246') & (df['date'] == 'may 13')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 715,
        "statement": "when the date be may 8 the score be 99 - 110 and location attendance at&t center 18797 and when the date be may 5 the score be 102 - 84 with location attendance new orleans arena 17927",
        "label": 1,
        "table_caption": "2007 - 08 new orleans hornets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 3#san antonio#101 - 82#west (30)#chandler (15)#paul (13)#new orleans arena 18040#1 - 0\n2#may 5#san antonio#102 - 84#paul (30)#chandler (11)#paul (12)#new orleans arena 17927#2 - 0\n3#may 8#san antonio#99 - 110#paul (35)#west (12)#paul (9)#at&t center 18797#2 - 1\n4#may 11#san antonio#80 - 100#paul (23)#armstrong , paul (6)#paul (5)#at&t center 18797#2 - 2\n5#may 13#san antonio#101 - 79#west (38)#west (14)#paul (14)#new orleans arena 18246#3 - 2\n6#may 15#san antonio#80 - 99#paul (21)#five - way tie (6)#paul (8)#at&t center 18797#3 - 3\n",
        "pandas_code": "(df[(df['date'] == 'may 8') & (df['score'] == '99 - 110') & (df['location attendance'] == 'at&t center 18797')].shape[0] > 0) & (df[(df['date'] == 'may 5') & (df['score'] == '102 - 84') & (df['location attendance'] == 'new orleans arena 17927')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 716,
        "statement": "west (12) have the high in rebound on may 8 and armstrong , paul (6) have the high in rebound on may 11 when the score be 99 - 110 and 80 - 100",
        "label": 1,
        "table_caption": "2007 - 08 new orleans hornets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 3#san antonio#101 - 82#west (30)#chandler (15)#paul (13)#new orleans arena 18040#1 - 0\n2#may 5#san antonio#102 - 84#paul (30)#chandler (11)#paul (12)#new orleans arena 17927#2 - 0\n3#may 8#san antonio#99 - 110#paul (35)#west (12)#paul (9)#at&t center 18797#2 - 1\n4#may 11#san antonio#80 - 100#paul (23)#armstrong , paul (6)#paul (5)#at&t center 18797#2 - 2\n5#may 13#san antonio#101 - 79#west (38)#west (14)#paul (14)#new orleans arena 18246#3 - 2\n6#may 15#san antonio#80 - 99#paul (21)#five - way tie (6)#paul (8)#at&t center 18797#3 - 3\n",
        "pandas_code": "((df['date'] == 'may 8') & (df['high rebounds'] == 'west (12)') & (df['score'] == '99 - 110')).any() and ((df['date'] == 'may 11') & (df['high rebounds'] == 'armstrong , paul (6)') & (df['score'] == '80 - 100')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 717,
        "statement": "when the series be 3 - 3 the location attendance be at&t center 18797 and the date may 15 with a score of 80 - 99",
        "label": 1,
        "table_caption": "2007 - 08 new orleans hornets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#may 3#san antonio#101 - 82#west (30)#chandler (15)#paul (13)#new orleans arena 18040#1 - 0\n2#may 5#san antonio#102 - 84#paul (30)#chandler (11)#paul (12)#new orleans arena 17927#2 - 0\n3#may 8#san antonio#99 - 110#paul (35)#west (12)#paul (9)#at&t center 18797#2 - 1\n4#may 11#san antonio#80 - 100#paul (23)#armstrong , paul (6)#paul (5)#at&t center 18797#2 - 2\n5#may 13#san antonio#101 - 79#west (38)#west (14)#paul (14)#new orleans arena 18246#3 - 2\n6#may 15#san antonio#80 - 99#paul (21)#five - way tie (6)#paul (8)#at&t center 18797#3 - 3\n",
        "pandas_code": "df[(df['series'] == '3 - 3') & (df['location attendance'] == 'at&t center 18797') & (df['date'] == 'may 15') & (df['score'] == '80 - 99')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 718,
        "statement": "the philadelphia flyer play the ny islander 3 time in the 1979 - 80 season",
        "label": 1,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "len(df[((df['visitor'] == 'philadelphia') & (df['home'] == 'ny islanders')) | ((df['visitor'] == 'ny islanders') & (df['home'] == 'philadelphia'))]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 719,
        "statement": "the philadelphia flyer play the philadelphia 3 time in the 1979 - 80 season",
        "label": 1,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "df[df['visitor'] == 'philadelphia'].shape[0] + df[df['home'] == 'philadelphia'].shape[0] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 720,
        "statement": "the same number of fan attend the may 13th , may 15 , and may 22 game",
        "label": 1,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "len(set(df[df['date'].isin(['may 13', 'may 15', 'may 22'])]['attendance'])) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 721,
        "statement": "the series be on tied 1 to 1 on may 15 during then 1979 - 80 philadelphia flyer season",
        "label": 1,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "df[(df['date'] == 'may 15') & (df['series'] == 'series tied 1 - 1')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 722,
        "statement": "the game on may 15 have the biggest point difference with the philadelphia flyer lose by 5 point",
        "label": 1,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "(df[df['date'] == 'may 15']['score'].str.split(' - ').apply(lambda x: abs(int(x[0]) - int(x[1]))) == 5).all()",
        "pandas_eval": "True"
    },
    {
        "id": 723,
        "statement": "the philadelphia flyer play the ny islander 4 time in the 1979 - 80 season",
        "label": 0,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "(df['visitor'].value_counts()['ny islanders'] + df['home'].value_counts()['ny islanders']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 724,
        "statement": "the philadelphia flyer play the philadelphia 2 time in the 1979 - 80 season",
        "label": 0,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "len(df[df['visitor'] == 'philadelphia']) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 725,
        "statement": "the same number of fan attend the may 13th , may 15 , and may 17 game",
        "label": 0,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "len(set(df[df['date'].isin(['may 13', 'may 15', 'may 17'])]['attendance'])) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 726,
        "statement": "the series be on tied 2 to 2 on may 15 during then 1979 - 80 philadelphia flyer season",
        "label": 0,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "not df[(df['date'] == 'may 15') & (df['series'] == 'series tied 1 - 1')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 727,
        "statement": "the game on may 15 have the biggest point difference with the philadelphia flyer lose by 3 point",
        "label": 0,
        "table_caption": "1979 - 80 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#series\nmay 13#ny islanders#4 - 3#philadelphia#peeters#17077#islanders lead 1 - 0\nmay 15#ny islanders#3 - 8#philadelphia#peeters#17077#series tied 1 - 1\nmay 17#philadelphia#2 - 6#ny islanders#myre#14995#islanders lead 2 - 1\nmay 19#philadelphia#2 - 5#ny islanders#peeters#14995#islanders lead 3 - 1\nmay 22#ny islanders#3 - 6#philadelphia#peeters#17077#islanders lead 3 - 2\nmay 24#philadelphia#4 - 5#ny islanders#peeters#14995#islanders win 4 - 2\n",
        "pandas_code": "(df[df['date'] == 'may 15']['score'].str.split(' - ').apply(lambda x: abs(int(x[0]) - int(x[1]))).max() == 5) and ('philadelphia' in df[df['date'] == 'may 15']['visitor'].values)",
        "pandas_eval": "False"
    },
    {
        "id": 728,
        "statement": "major general sir dennis pack , command the 9th brigade , lose 18 officer and 107 men , which be more than any other unit",
        "label": 1,
        "table_caption": "quatre bras order of battle",
        "table_text": "unit#commander#complement#killed#wounded#missing\n5th division#lieutenant general sir thomas picton#312 off 4792 men#6 off 84 men#49 off 531 men#3 off 27 men\n8th brigade#major general sir james kempt#167 off 2348 men#4 off 68 men#44 off 522 men#1 off 0 men\n1st battalion , 32nd (cornwall) regiment of foot#major (brevet lieutenant colonel) john hicks#44 off 605 men#2 off 21 men#20 off 153 men#0 off 0 men\n79th regiment of foot (cameron highlanders)#lieutenant - colonel neil douglas#46 off 656 men#2 off 28 men#16 off 258 men#1 off 0 men\n9th brigade#major general sir dennis pack#177 off 2133 men#18 off 107 men#57 off 728 men#0 off 17 men\n2nd battalion , 44th (east essex) regiment of foot#lieutenant - colonel john millert hamerton#36 off 427 men#2 off 10 men#15 off 94 men#0 off 17 men\n92nd regiment of foot (gordon highlanders)#lieutenant - colonel john cameron of fassifern#47 off 568 men#6 off 35 men#16 off 226 men#0 off 0 men\n4th hanoverian brigade#colonel charles best#140 off 2444 men#2 off 16 men#4 off 39 men#2 off 27 men\nlandwehr battalion l\u00e3\u00bcneberg#lieutenant colonel ludwig von romdohr#24 off 582 men#0 off 4 men#2 off 8 men#0 off 4 men\nlandwehr battalion munden#major ferdinand von schmid#37 off 590 men#0 off 6 men#0 off 14 men#0 off 13 men\nlandwehr battalion osterode#major claus von reden#35 off 621 men#1 off 1 man#0 off 3 men#0 off 0 men\n",
        "pandas_code": "df[df['commander'] == 'major general sir dennis pack']['killed'].str.extract(r'(\\d+) off (\\d+) men').astype(int).apply(lambda x: x[0] + x[1], axis=1).max() == 125",
        "pandas_eval": "True"
    },
    {
        "id": 729,
        "statement": "lieutenant general sir thomas picton , command the fifth division , have 4792 men , which be more than any other unit",
        "label": 1,
        "table_caption": "quatre bras order of battle",
        "table_text": "unit#commander#complement#killed#wounded#missing\n5th division#lieutenant general sir thomas picton#312 off 4792 men#6 off 84 men#49 off 531 men#3 off 27 men\n8th brigade#major general sir james kempt#167 off 2348 men#4 off 68 men#44 off 522 men#1 off 0 men\n1st battalion , 32nd (cornwall) regiment of foot#major (brevet lieutenant colonel) john hicks#44 off 605 men#2 off 21 men#20 off 153 men#0 off 0 men\n79th regiment of foot (cameron highlanders)#lieutenant - colonel neil douglas#46 off 656 men#2 off 28 men#16 off 258 men#1 off 0 men\n9th brigade#major general sir dennis pack#177 off 2133 men#18 off 107 men#57 off 728 men#0 off 17 men\n2nd battalion , 44th (east essex) regiment of foot#lieutenant - colonel john millert hamerton#36 off 427 men#2 off 10 men#15 off 94 men#0 off 17 men\n92nd regiment of foot (gordon highlanders)#lieutenant - colonel john cameron of fassifern#47 off 568 men#6 off 35 men#16 off 226 men#0 off 0 men\n4th hanoverian brigade#colonel charles best#140 off 2444 men#2 off 16 men#4 off 39 men#2 off 27 men\nlandwehr battalion l\u00e3\u00bcneberg#lieutenant colonel ludwig von romdohr#24 off 582 men#0 off 4 men#2 off 8 men#0 off 4 men\nlandwehr battalion munden#major ferdinand von schmid#37 off 590 men#0 off 6 men#0 off 14 men#0 off 13 men\nlandwehr battalion osterode#major claus von reden#35 off 621 men#1 off 1 man#0 off 3 men#0 off 0 men\n",
        "pandas_code": "(df[df['commander'] == 'lieutenant general sir thomas picton']['complement'].str.extract('(\\\\d+)').astype(int).iloc[0] > df[df['commander'] != 'lieutenant general sir thomas picton']['complement'].str.extract('(\\\\d+)').astype(int).max()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 730,
        "statement": "landwehr battalionll\u00e3\u00bcneberg and landwehr battalion munden each have only 1 officer kill at the battle of quatras bra",
        "label": 1,
        "table_caption": "quatre bras order of battle",
        "table_text": "unit#commander#complement#killed#wounded#missing\n5th division#lieutenant general sir thomas picton#312 off 4792 men#6 off 84 men#49 off 531 men#3 off 27 men\n8th brigade#major general sir james kempt#167 off 2348 men#4 off 68 men#44 off 522 men#1 off 0 men\n1st battalion , 32nd (cornwall) regiment of foot#major (brevet lieutenant colonel) john hicks#44 off 605 men#2 off 21 men#20 off 153 men#0 off 0 men\n79th regiment of foot (cameron highlanders)#lieutenant - colonel neil douglas#46 off 656 men#2 off 28 men#16 off 258 men#1 off 0 men\n9th brigade#major general sir dennis pack#177 off 2133 men#18 off 107 men#57 off 728 men#0 off 17 men\n2nd battalion , 44th (east essex) regiment of foot#lieutenant - colonel john millert hamerton#36 off 427 men#2 off 10 men#15 off 94 men#0 off 17 men\n92nd regiment of foot (gordon highlanders)#lieutenant - colonel john cameron of fassifern#47 off 568 men#6 off 35 men#16 off 226 men#0 off 0 men\n4th hanoverian brigade#colonel charles best#140 off 2444 men#2 off 16 men#4 off 39 men#2 off 27 men\nlandwehr battalion l\u00e3\u00bcneberg#lieutenant colonel ludwig von romdohr#24 off 582 men#0 off 4 men#2 off 8 men#0 off 4 men\nlandwehr battalion munden#major ferdinand von schmid#37 off 590 men#0 off 6 men#0 off 14 men#0 off 13 men\nlandwehr battalion osterode#major claus von reden#35 off 621 men#1 off 1 man#0 off 3 men#0 off 0 men\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 731,
        "statement": "less than 8 officer be report miss after the battle of quatras bra",
        "label": 1,
        "table_caption": "quatre bras order of battle",
        "table_text": "unit#commander#complement#killed#wounded#missing\n5th division#lieutenant general sir thomas picton#312 off 4792 men#6 off 84 men#49 off 531 men#3 off 27 men\n8th brigade#major general sir james kempt#167 off 2348 men#4 off 68 men#44 off 522 men#1 off 0 men\n1st battalion , 32nd (cornwall) regiment of foot#major (brevet lieutenant colonel) john hicks#44 off 605 men#2 off 21 men#20 off 153 men#0 off 0 men\n79th regiment of foot (cameron highlanders)#lieutenant - colonel neil douglas#46 off 656 men#2 off 28 men#16 off 258 men#1 off 0 men\n9th brigade#major general sir dennis pack#177 off 2133 men#18 off 107 men#57 off 728 men#0 off 17 men\n2nd battalion , 44th (east essex) regiment of foot#lieutenant - colonel john millert hamerton#36 off 427 men#2 off 10 men#15 off 94 men#0 off 17 men\n92nd regiment of foot (gordon highlanders)#lieutenant - colonel john cameron of fassifern#47 off 568 men#6 off 35 men#16 off 226 men#0 off 0 men\n4th hanoverian brigade#colonel charles best#140 off 2444 men#2 off 16 men#4 off 39 men#2 off 27 men\nlandwehr battalion l\u00e3\u00bcneberg#lieutenant colonel ludwig von romdohr#24 off 582 men#0 off 4 men#2 off 8 men#0 off 4 men\nlandwehr battalion munden#major ferdinand von schmid#37 off 590 men#0 off 6 men#0 off 14 men#0 off 13 men\nlandwehr battalion osterode#major claus von reden#35 off 621 men#1 off 1 man#0 off 3 men#0 off 0 men\n",
        "pandas_code": "df['missing'].apply(lambda x: int(x.split(' off ')[0])).sum() < 8",
        "pandas_eval": "True"
    },
    {
        "id": 732,
        "statement": "forty 3 officer be kill , from all the unit combine",
        "label": 1,
        "table_caption": "quatre bras order of battle",
        "table_text": "unit#commander#complement#killed#wounded#missing\n5th division#lieutenant general sir thomas picton#312 off 4792 men#6 off 84 men#49 off 531 men#3 off 27 men\n8th brigade#major general sir james kempt#167 off 2348 men#4 off 68 men#44 off 522 men#1 off 0 men\n1st battalion , 32nd (cornwall) regiment of foot#major (brevet lieutenant colonel) john hicks#44 off 605 men#2 off 21 men#20 off 153 men#0 off 0 men\n79th regiment of foot (cameron highlanders)#lieutenant - colonel neil douglas#46 off 656 men#2 off 28 men#16 off 258 men#1 off 0 men\n9th brigade#major general sir dennis pack#177 off 2133 men#18 off 107 men#57 off 728 men#0 off 17 men\n2nd battalion , 44th (east essex) regiment of foot#lieutenant - colonel john millert hamerton#36 off 427 men#2 off 10 men#15 off 94 men#0 off 17 men\n92nd regiment of foot (gordon highlanders)#lieutenant - colonel john cameron of fassifern#47 off 568 men#6 off 35 men#16 off 226 men#0 off 0 men\n4th hanoverian brigade#colonel charles best#140 off 2444 men#2 off 16 men#4 off 39 men#2 off 27 men\nlandwehr battalion l\u00e3\u00bcneberg#lieutenant colonel ludwig von romdohr#24 off 582 men#0 off 4 men#2 off 8 men#0 off 4 men\nlandwehr battalion munden#major ferdinand von schmid#37 off 590 men#0 off 6 men#0 off 14 men#0 off 13 men\nlandwehr battalion osterode#major claus von reden#35 off 621 men#1 off 1 man#0 off 3 men#0 off 0 men\n",
        "pandas_code": "df['killed'].apply(lambda x: int(x.split(' off ')[0])).sum() == 43",
        "pandas_eval": "True"
    },
    {
        "id": 733,
        "statement": "william st onge (d) die before michael j kirwan (d)",
        "label": 1,
        "table_caption": "91st united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\ntennessee 8th#robert a everett (d)#died january 26 , 1969#ed jones (d)#march 25 , 1969\nmassachusetts 6th#william h bates (r)#died june 22 , 1969#michael j harrington (d)#september 30 , 1969\nillinois 6th#daniel j ronan (d)#died august 13 , 1969#george w collins (d)#november 3 , 1970\ncalifornia 24th#glenard p lipscomb (r)#died february 1 , 1970#john h rousselot (r)#june 30 , 1970\ncalifornia 35th#james b utt (r)#died march 1 , 1970#john g schmitz (r)#june 30 , 1970\nconnecticut 2nd#william st onge (d)#died may 1 , 1970#robert h steele (r)#november 3 , 1970\nohio 19th#michael j kirwan (d)#died july 27 , 1970#charles j carney (d)#november 3 , 1970\npennsylvania 9th#george watkins (r)#died august 7 , 1970#john h ware iii (r)#november 3 , 1970\nillinois 1st#william l dawson (d)#died november 9 , 1970#vacant#not filled this term\n",
        "pandas_code": "pd.to_datetime(df[df['vacator'] == 'william st onge (d)']['reason for change'].values[0].split('died ')[1], format='%B %d , %Y') < pd.to_datetime(df[df['vacator'] == 'michael j kirwan (d)']['reason for change'].values[0].split('died ')[1], format='%B %d , %Y')",
        "pandas_eval": "True"
    },
    {
        "id": 734,
        "statement": "john g schmitz (r) be seat the same day as john h rousselot (r)",
        "label": 1,
        "table_caption": "91st united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\ntennessee 8th#robert a everett (d)#died january 26 , 1969#ed jones (d)#march 25 , 1969\nmassachusetts 6th#william h bates (r)#died june 22 , 1969#michael j harrington (d)#september 30 , 1969\nillinois 6th#daniel j ronan (d)#died august 13 , 1969#george w collins (d)#november 3 , 1970\ncalifornia 24th#glenard p lipscomb (r)#died february 1 , 1970#john h rousselot (r)#june 30 , 1970\ncalifornia 35th#james b utt (r)#died march 1 , 1970#john g schmitz (r)#june 30 , 1970\nconnecticut 2nd#william st onge (d)#died may 1 , 1970#robert h steele (r)#november 3 , 1970\nohio 19th#michael j kirwan (d)#died july 27 , 1970#charles j carney (d)#november 3 , 1970\npennsylvania 9th#george watkins (r)#died august 7 , 1970#john h ware iii (r)#november 3 , 1970\nillinois 1st#william l dawson (d)#died november 9 , 1970#vacant#not filled this term\n",
        "pandas_code": "df[(df['successor'] == 'john g schmitz (r)') & (df['date successor seated'] == 'june 30 , 1970')].shape[0] > 0 and df[(df['successor'] == 'john h rousselot (r)') & (df['date successor seated'] == 'june 30 , 1970')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 735,
        "statement": "william l dawson (d) be the only 1 on the list without an immidiate successor",
        "label": 1,
        "table_caption": "91st united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\ntennessee 8th#robert a everett (d)#died january 26 , 1969#ed jones (d)#march 25 , 1969\nmassachusetts 6th#william h bates (r)#died june 22 , 1969#michael j harrington (d)#september 30 , 1969\nillinois 6th#daniel j ronan (d)#died august 13 , 1969#george w collins (d)#november 3 , 1970\ncalifornia 24th#glenard p lipscomb (r)#died february 1 , 1970#john h rousselot (r)#june 30 , 1970\ncalifornia 35th#james b utt (r)#died march 1 , 1970#john g schmitz (r)#june 30 , 1970\nconnecticut 2nd#william st onge (d)#died may 1 , 1970#robert h steele (r)#november 3 , 1970\nohio 19th#michael j kirwan (d)#died july 27 , 1970#charles j carney (d)#november 3 , 1970\npennsylvania 9th#george watkins (r)#died august 7 , 1970#john h ware iii (r)#november 3 , 1970\nillinois 1st#william l dawson (d)#died november 9 , 1970#vacant#not filled this term\n",
        "pandas_code": "df[df['vacator'] == 'william l dawson (d)']['successor'].eq('vacant').all()",
        "pandas_eval": "True"
    },
    {
        "id": 736,
        "statement": "ed jones (d) be seat before michael j harrington (d)",
        "label": 1,
        "table_caption": "91st united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\ntennessee 8th#robert a everett (d)#died january 26 , 1969#ed jones (d)#march 25 , 1969\nmassachusetts 6th#william h bates (r)#died june 22 , 1969#michael j harrington (d)#september 30 , 1969\nillinois 6th#daniel j ronan (d)#died august 13 , 1969#george w collins (d)#november 3 , 1970\ncalifornia 24th#glenard p lipscomb (r)#died february 1 , 1970#john h rousselot (r)#june 30 , 1970\ncalifornia 35th#james b utt (r)#died march 1 , 1970#john g schmitz (r)#june 30 , 1970\nconnecticut 2nd#william st onge (d)#died may 1 , 1970#robert h steele (r)#november 3 , 1970\nohio 19th#michael j kirwan (d)#died july 27 , 1970#charles j carney (d)#november 3 , 1970\npennsylvania 9th#george watkins (r)#died august 7 , 1970#john h ware iii (r)#november 3 , 1970\nillinois 1st#william l dawson (d)#died november 9 , 1970#vacant#not filled this term\n",
        "pandas_code": "df[df['successor'] == 'ed jones (d)']['date successor seated'].iloc[0] < df[df['successor'] == 'michael j harrington (d)']['date successor seated'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 737,
        "statement": "connecticut 2nd , ohio 19th and pennsylvania 9th have successor seat on the same day",
        "label": 1,
        "table_caption": "91st united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\ntennessee 8th#robert a everett (d)#died january 26 , 1969#ed jones (d)#march 25 , 1969\nmassachusetts 6th#william h bates (r)#died june 22 , 1969#michael j harrington (d)#september 30 , 1969\nillinois 6th#daniel j ronan (d)#died august 13 , 1969#george w collins (d)#november 3 , 1970\ncalifornia 24th#glenard p lipscomb (r)#died february 1 , 1970#john h rousselot (r)#june 30 , 1970\ncalifornia 35th#james b utt (r)#died march 1 , 1970#john g schmitz (r)#june 30 , 1970\nconnecticut 2nd#william st onge (d)#died may 1 , 1970#robert h steele (r)#november 3 , 1970\nohio 19th#michael j kirwan (d)#died july 27 , 1970#charles j carney (d)#november 3 , 1970\npennsylvania 9th#george watkins (r)#died august 7 , 1970#john h ware iii (r)#november 3 , 1970\nillinois 1st#william l dawson (d)#died november 9 , 1970#vacant#not filled this term\n",
        "pandas_code": "len(df[(df['district'].isin(['connecticut 2nd', 'ohio 19th', 'pennsylvania 9th'])) & (df['date successor seated'] == 'november 3 , 1970')]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 738,
        "statement": "charles barkley have 11 high rebound on april 2 and april 6",
        "label": 1,
        "table_caption": "1992 - 93 phoenix suns season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n69#april 2#boston#w 118 - 114#charles barkley (37)#charles barkley (11)#kevin johnson (9)#boston garden 14890#54 - 15\n70#april 4#indiana#w 110 - 100#charles barkley (32)#charles barkley (13)#kevin johnson (8)#market square arena 16530#55 - 15\n71#april 6#la lakers#w 115 - 114#kevin johnson (32)#charles barkley (11)#dan majerle (8)#america west arena 19023#56 - 15\n72#april 8#sacramento#w 123 - 114#cedric ceballos (28)#mark west (13)#kevin johnson (7)#arco arena#57 - 15\n73#april 9#denver#w 98 - 97#charles barkley (26)#charles barkley (19)#charles barkley (12)#america west arena 19023#58 - 15\n74#april 11#utah#w 112 - 99#kevin johnson (29)#charles barkley (7)#kevin johnson (9)#america west arena 19023#59 - 15\n75#april 12#la clippers#l 104 - 111#richard dumas (28)#richard dumas (12)#danny ainge , dan majerle (6)#los angeles memorial sports arena 15989#59 - 16\n76#april 14#minnesota#w 98 - 84#dan majerle (25)#cedric ceballos (13)#kevin johnson (10)#america west arena 19023#60 - 16\n77#april 16#seattle#l 102 - 108#cedric ceballos (21)#tom chambers , jerrod mustaf (7)#kevin johnson , dan majerle (7)#america west arena 19023#60 - 17\n78#april 17#utah#l 101 - 110#cedric ceballos (25)#cedric ceballos (10)#negele knight (6)#delta center 19911#60 - 18\n79#april 19#houston#l 97 - 111#kevin johnson (18)#jerrod mustaf (8)#kevin johnson (7)#america west arena 19023#60 - 19\n80#april 22#portland#w 115 - 114#charles barkley (25)#cedric ceballos (9)#kevin johnson (14)#memorial coliseum 12888#61 - 19\n81#april 24#san antonio#w 99 - 97#richard dumas (23)#cedric ceballos (11)#danny ainge , frank johnson , negele knight (6)#america west arena 19023#62 - 19\n",
        "pandas_code": "all(df[(df['date'].isin(['april 2', 'april 6'])) & (df['high rebounds'].str.contains('charles barkley'))]['high rebounds'].str.extract(r'\\((\\d+)\\)')[0].astype(int) == 11)",
        "pandas_eval": "True"
    },
    {
        "id": 739,
        "statement": "the highest number of high point for charles barkley be 5 more than the highest number of high point for kevin johnson",
        "label": 1,
        "table_caption": "1992 - 93 phoenix suns season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n69#april 2#boston#w 118 - 114#charles barkley (37)#charles barkley (11)#kevin johnson (9)#boston garden 14890#54 - 15\n70#april 4#indiana#w 110 - 100#charles barkley (32)#charles barkley (13)#kevin johnson (8)#market square arena 16530#55 - 15\n71#april 6#la lakers#w 115 - 114#kevin johnson (32)#charles barkley (11)#dan majerle (8)#america west arena 19023#56 - 15\n72#april 8#sacramento#w 123 - 114#cedric ceballos (28)#mark west (13)#kevin johnson (7)#arco arena#57 - 15\n73#april 9#denver#w 98 - 97#charles barkley (26)#charles barkley (19)#charles barkley (12)#america west arena 19023#58 - 15\n74#april 11#utah#w 112 - 99#kevin johnson (29)#charles barkley (7)#kevin johnson (9)#america west arena 19023#59 - 15\n75#april 12#la clippers#l 104 - 111#richard dumas (28)#richard dumas (12)#danny ainge , dan majerle (6)#los angeles memorial sports arena 15989#59 - 16\n76#april 14#minnesota#w 98 - 84#dan majerle (25)#cedric ceballos (13)#kevin johnson (10)#america west arena 19023#60 - 16\n77#april 16#seattle#l 102 - 108#cedric ceballos (21)#tom chambers , jerrod mustaf (7)#kevin johnson , dan majerle (7)#america west arena 19023#60 - 17\n78#april 17#utah#l 101 - 110#cedric ceballos (25)#cedric ceballos (10)#negele knight (6)#delta center 19911#60 - 18\n79#april 19#houston#l 97 - 111#kevin johnson (18)#jerrod mustaf (8)#kevin johnson (7)#america west arena 19023#60 - 19\n80#april 22#portland#w 115 - 114#charles barkley (25)#cedric ceballos (9)#kevin johnson (14)#memorial coliseum 12888#61 - 19\n81#april 24#san antonio#w 99 - 97#richard dumas (23)#cedric ceballos (11)#danny ainge , frank johnson , negele knight (6)#america west arena 19023#62 - 19\n",
        "pandas_code": "df[df['high points'].str.contains('charles barkley')].apply(lambda x: int(x['high points'].split('(')[1].split(')')[0]), axis=1).max() - df[df['high points'].str.contains('kevin johnson')].apply(lambda x: int(x['high points'].split('(')[1].split(')')[0]), axis=1).max() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 740,
        "statement": "america west arena be the location use most often",
        "label": 1,
        "table_caption": "1992 - 93 phoenix suns season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n69#april 2#boston#w 118 - 114#charles barkley (37)#charles barkley (11)#kevin johnson (9)#boston garden 14890#54 - 15\n70#april 4#indiana#w 110 - 100#charles barkley (32)#charles barkley (13)#kevin johnson (8)#market square arena 16530#55 - 15\n71#april 6#la lakers#w 115 - 114#kevin johnson (32)#charles barkley (11)#dan majerle (8)#america west arena 19023#56 - 15\n72#april 8#sacramento#w 123 - 114#cedric ceballos (28)#mark west (13)#kevin johnson (7)#arco arena#57 - 15\n73#april 9#denver#w 98 - 97#charles barkley (26)#charles barkley (19)#charles barkley (12)#america west arena 19023#58 - 15\n74#april 11#utah#w 112 - 99#kevin johnson (29)#charles barkley (7)#kevin johnson (9)#america west arena 19023#59 - 15\n75#april 12#la clippers#l 104 - 111#richard dumas (28)#richard dumas (12)#danny ainge , dan majerle (6)#los angeles memorial sports arena 15989#59 - 16\n76#april 14#minnesota#w 98 - 84#dan majerle (25)#cedric ceballos (13)#kevin johnson (10)#america west arena 19023#60 - 16\n77#april 16#seattle#l 102 - 108#cedric ceballos (21)#tom chambers , jerrod mustaf (7)#kevin johnson , dan majerle (7)#america west arena 19023#60 - 17\n78#april 17#utah#l 101 - 110#cedric ceballos (25)#cedric ceballos (10)#negele knight (6)#delta center 19911#60 - 18\n79#april 19#houston#l 97 - 111#kevin johnson (18)#jerrod mustaf (8)#kevin johnson (7)#america west arena 19023#60 - 19\n80#april 22#portland#w 115 - 114#charles barkley (25)#cedric ceballos (9)#kevin johnson (14)#memorial coliseum 12888#61 - 19\n81#april 24#san antonio#w 99 - 97#richard dumas (23)#cedric ceballos (11)#danny ainge , frank johnson , negele knight (6)#america west arena 19023#62 - 19\n",
        "pandas_code": "df['location attendance'].str.contains('america west arena').sum() > (len(df) / 2)",
        "pandas_eval": "True"
    },
    {
        "id": 741,
        "statement": "charles barkley have the most game with the most high point with 4",
        "label": 1,
        "table_caption": "1992 - 93 phoenix suns season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n69#april 2#boston#w 118 - 114#charles barkley (37)#charles barkley (11)#kevin johnson (9)#boston garden 14890#54 - 15\n70#april 4#indiana#w 110 - 100#charles barkley (32)#charles barkley (13)#kevin johnson (8)#market square arena 16530#55 - 15\n71#april 6#la lakers#w 115 - 114#kevin johnson (32)#charles barkley (11)#dan majerle (8)#america west arena 19023#56 - 15\n72#april 8#sacramento#w 123 - 114#cedric ceballos (28)#mark west (13)#kevin johnson (7)#arco arena#57 - 15\n73#april 9#denver#w 98 - 97#charles barkley (26)#charles barkley (19)#charles barkley (12)#america west arena 19023#58 - 15\n74#april 11#utah#w 112 - 99#kevin johnson (29)#charles barkley (7)#kevin johnson (9)#america west arena 19023#59 - 15\n75#april 12#la clippers#l 104 - 111#richard dumas (28)#richard dumas (12)#danny ainge , dan majerle (6)#los angeles memorial sports arena 15989#59 - 16\n76#april 14#minnesota#w 98 - 84#dan majerle (25)#cedric ceballos (13)#kevin johnson (10)#america west arena 19023#60 - 16\n77#april 16#seattle#l 102 - 108#cedric ceballos (21)#tom chambers , jerrod mustaf (7)#kevin johnson , dan majerle (7)#america west arena 19023#60 - 17\n78#april 17#utah#l 101 - 110#cedric ceballos (25)#cedric ceballos (10)#negele knight (6)#delta center 19911#60 - 18\n79#april 19#houston#l 97 - 111#kevin johnson (18)#jerrod mustaf (8)#kevin johnson (7)#america west arena 19023#60 - 19\n80#april 22#portland#w 115 - 114#charles barkley (25)#cedric ceballos (9)#kevin johnson (14)#memorial coliseum 12888#61 - 19\n81#april 24#san antonio#w 99 - 97#richard dumas (23)#cedric ceballos (11)#danny ainge , frank johnson , negele knight (6)#america west arena 19023#62 - 19\n",
        "pandas_code": "(df['high points'].str.contains('charles barkley').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 743,
        "statement": "attendance be less than 50000 5 time during the 1971 san diego charger season",
        "label": 1,
        "table_caption": "1971 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#kansas city chiefs#w 21 - 14#54061\n2#september 26 , 1971#oakland raiders#l 34 - 0#54084\n3#october 3 , 1971#pittsburgh steelers#l 21 - 17#44339\n4#october 10 , 1971#kansas city chiefs#l 31 - 10#50514\n5#october 17 , 1971#denver broncos#l 20 - 16#51200\n6#october 23 , 1971#buffalo bills#w 20 - 3#49261\n7#october 31 , 1971#new york jets#w 49 - 21#44786\n8#november 7 , 1971#new york giants#l 35 - 17#62905\n9#november 15 , 1971#st louis cardinals#w 20 - 17#46486\n10#november 21 , 1971#oakland raiders#l 34 - 33#54681\n11#november 28 , 1971#cincinnati bengals#l 31 - 0#59580\n12#december 5 , 1971#minnesota vikings#w 30 - 14#54505\n13#december 12 , 1971#denver broncos#w 45 - 17#44347\n14#december 19 , 1971#houston oilers#l 49 - 33#35959\n",
        "pandas_code": "bool((df['attendance'] < 50000).sum() == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 744,
        "statement": "the first game of the season be play on september 19 and the last on december 19",
        "label": 1,
        "table_caption": "1971 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#kansas city chiefs#w 21 - 14#54061\n2#september 26 , 1971#oakland raiders#l 34 - 0#54084\n3#october 3 , 1971#pittsburgh steelers#l 21 - 17#44339\n4#october 10 , 1971#kansas city chiefs#l 31 - 10#50514\n5#october 17 , 1971#denver broncos#l 20 - 16#51200\n6#october 23 , 1971#buffalo bills#w 20 - 3#49261\n7#october 31 , 1971#new york jets#w 49 - 21#44786\n8#november 7 , 1971#new york giants#l 35 - 17#62905\n9#november 15 , 1971#st louis cardinals#w 20 - 17#46486\n10#november 21 , 1971#oakland raiders#l 34 - 33#54681\n11#november 28 , 1971#cincinnati bengals#l 31 - 0#59580\n12#december 5 , 1971#minnesota vikings#w 30 - 14#54505\n13#december 12 , 1971#denver broncos#w 45 - 17#44347\n14#december 19 , 1971#houston oilers#l 49 - 33#35959\n",
        "pandas_code": "(df['date'].iloc[0] == 'september 19 , 1971') & (df['date'].iloc[-1] == 'december 19 , 1971')",
        "pandas_eval": "True"
    },
    {
        "id": 745,
        "statement": "the san diego charger play against the oakland raider 2 time",
        "label": 1,
        "table_caption": "1971 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#kansas city chiefs#w 21 - 14#54061\n2#september 26 , 1971#oakland raiders#l 34 - 0#54084\n3#october 3 , 1971#pittsburgh steelers#l 21 - 17#44339\n4#october 10 , 1971#kansas city chiefs#l 31 - 10#50514\n5#october 17 , 1971#denver broncos#l 20 - 16#51200\n6#october 23 , 1971#buffalo bills#w 20 - 3#49261\n7#october 31 , 1971#new york jets#w 49 - 21#44786\n8#november 7 , 1971#new york giants#l 35 - 17#62905\n9#november 15 , 1971#st louis cardinals#w 20 - 17#46486\n10#november 21 , 1971#oakland raiders#l 34 - 33#54681\n11#november 28 , 1971#cincinnati bengals#l 31 - 0#59580\n12#december 5 , 1971#minnesota vikings#w 30 - 14#54505\n13#december 12 , 1971#denver broncos#w 45 - 17#44347\n14#december 19 , 1971#houston oilers#l 49 - 33#35959\n",
        "pandas_code": "(df['opponent'] == 'oakland raiders').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 746,
        "statement": "the san diego charger win 6 game and lose 8 game",
        "label": 1,
        "table_caption": "1971 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#kansas city chiefs#w 21 - 14#54061\n2#september 26 , 1971#oakland raiders#l 34 - 0#54084\n3#october 3 , 1971#pittsburgh steelers#l 21 - 17#44339\n4#october 10 , 1971#kansas city chiefs#l 31 - 10#50514\n5#october 17 , 1971#denver broncos#l 20 - 16#51200\n6#october 23 , 1971#buffalo bills#w 20 - 3#49261\n7#october 31 , 1971#new york jets#w 49 - 21#44786\n8#november 7 , 1971#new york giants#l 35 - 17#62905\n9#november 15 , 1971#st louis cardinals#w 20 - 17#46486\n10#november 21 , 1971#oakland raiders#l 34 - 33#54681\n11#november 28 , 1971#cincinnati bengals#l 31 - 0#59580\n12#december 5 , 1971#minnesota vikings#w 30 - 14#54505\n13#december 12 , 1971#denver broncos#w 45 - 17#44347\n14#december 19 , 1971#houston oilers#l 49 - 33#35959\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 6) & (df['result'].str.startswith('l').sum() == 8)",
        "pandas_eval": "True"
    },
    {
        "id": 747,
        "statement": "they score zero point in 2 different loss , september 26 and november 28",
        "label": 1,
        "table_caption": "1971 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#kansas city chiefs#w 21 - 14#54061\n2#september 26 , 1971#oakland raiders#l 34 - 0#54084\n3#october 3 , 1971#pittsburgh steelers#l 21 - 17#44339\n4#october 10 , 1971#kansas city chiefs#l 31 - 10#50514\n5#october 17 , 1971#denver broncos#l 20 - 16#51200\n6#october 23 , 1971#buffalo bills#w 20 - 3#49261\n7#october 31 , 1971#new york jets#w 49 - 21#44786\n8#november 7 , 1971#new york giants#l 35 - 17#62905\n9#november 15 , 1971#st louis cardinals#w 20 - 17#46486\n10#november 21 , 1971#oakland raiders#l 34 - 33#54681\n11#november 28 , 1971#cincinnati bengals#l 31 - 0#59580\n12#december 5 , 1971#minnesota vikings#w 30 - 14#54505\n13#december 12 , 1971#denver broncos#w 45 - 17#44347\n14#december 19 , 1971#houston oilers#l 49 - 33#35959\n",
        "pandas_code": "len(df[(df['result'].str.startswith('l')) & (df['result'].str.contains('0')) & ((df['date'] == 'september 26 , 1971') | (df['date'] == 'november 28 , 1971'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 748,
        "statement": "the 2010 suzuki cup competition on december 5 and 6",
        "label": 1,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "((df['competition'] == '2010 aff suzuki cup') & (df['date'].str.contains('5 december 2010|6 december 2010'))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 749,
        "statement": "after the qualifier in september 4 friendly event be hold",
        "label": 1,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "df[(df['date'] > '4 september 2011') & (df['competition'] == 'friendly')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 750,
        "statement": "their be a friendly play in muscat after the 2 world cup event on november 9",
        "label": 1,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "df[(df['venue'] == 'muscat , oman') & (df['competition'] == 'friendly') & (pd.to_datetime(df['date'], format='%d %B %Y') > pd.to_datetime('9 november 2007', format='%d %B %Y'))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 751,
        "statement": "their be 10 event hold in the month of november",
        "label": 1,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "len(df[df['date'].str.contains('november')]) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 753,
        "statement": "before november 9th there be a friendly play in muscat before 2 world cup event",
        "label": 0,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "((pd.to_datetime(df['date'], format='%d %B %Y') < pd.to_datetime('9 November 2007', format='%d %B %Y')) & (df['venue'] == 'muscat , oman') & (df['competition'] == 'friendly')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 754,
        "statement": "the lowest number of event be play in the month of november",
        "label": 0,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "df[df['date'].str.contains('november')].shape[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 755,
        "statement": "more than half of the event on the chart be hold in kallang singapore",
        "label": 0,
        "table_caption": "aleksandar \u0111uri\u0107",
        "table_text": "date#venue#score#result#competition\n9 november 2007#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n9 november 2007#kallang , singapore#2 - 0#2 - 0#2010 fifa world cup qualification\n24 january 2008#muscat , oman#2 - 0#2 - 0#friendly\n26 march 2008#kallang , singapore#1 - 0#2 - 0#2010 fifa world cup qualification\n2 june 2008#kallang , singapore#1 - 1#3 - 7#2010 fifa world cup qualification\n29 november 2008#petaling jaya , malaysia#1 - 0#2 - 2#friendly\n29 november 2008#petaling jaya , malaysia#2 - 1#2 - 2#friendly\n22 october 2009#ho chi minh city , vietnam#1 - 0#4 - 2#2009 ho chi minh city int'l football cup\n22 october 2009#ho chi minh city , vietnam#2 - 1#4 - 2#2009 ho chi minh city int'l football cup\n24 october 2009#ho chi minh city , vietnam#1 - 1#2 - 2#2009 ho chi minh city int'l football cup\n4 november 2009#kallang , singapore#1 - 0#3 - 1#friendly\n4 november 2009#kallang , singapore#3 - 1#3 - 1#friendly\n18 november 2009#bangkok , thailand#1 - 0#1 - 0#2011 afc asian cup qualification\n2 november 2010#hanoi , vietnam#1 - 0#1 - 2#vff cup\n2 december 2010#hanoi , vietnam#1 - 0#1 - 1#2010 aff suzuki cup\n5 december 2010#hanoi , vietnam#1 - 1#2 - 1#2010 aff suzuki cup\n7 june 2011#kallang , singapore#4 - 0#4 - 0#friendly\n18 july 2011#kallang , singapore#1 - 0#3 - 2#friendly\n18 july 2011#kallang , singapore#2 - 1#3 - 2#friendly\n23 july 2011#kallang , singapore#1 - 1#5 - 3#2014 fifa world cup qualification\n23 july 2011#kallang , singaopre#5 - 3#5 - 3#2014 fifa world cup qualification\n2 september 2011#kunming , china#1 - 0#1 - 2#2014 fifa world cup qualification\n7 october 2011#kallang , singapore#2 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#1 - 0#2 - 0#friendly\n15 august 2012#jalan besar , singapore#2 - 0#2 - 0#friendly\n19 november 2012#jurong west , singapore#4 - 0#4 - 0#friendly\n25 november 2012#kuala lumpur , malaysia#3 - 0#3 - 0#2012 aff suzuki cup\n",
        "pandas_code": "(df['venue'].str.contains('kallang , singapore').sum() / len(df)) > 0.5",
        "pandas_eval": "False"
    },
    {
        "id": 757,
        "statement": "the united state be 1 of 3 country to win 4 , draw 2 and lost 1 game",
        "label": 1,
        "table_caption": "1985 world ice hockey championships",
        "table_text": "games#drawn#lost#points difference#points\n7#1#0#54 - 13#13\n7#0#1#36 - 13#12\n7#1#1#45 - 22#11\n7#0#3#51 - 29#8\n7#0#4#16 - 23#6\n7#0#5#27 - 45#4\n7#0#6#18 - 56#2\n7#0#7#09 - 55#0\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 758,
        "statement": "in the 1985 world ice hockey championship , east germany didn't have a single win",
        "label": 1,
        "table_caption": "1985 world ice hockey championships",
        "table_text": "games#drawn#lost#points difference#points\n7#1#0#54 - 13#13\n7#0#1#36 - 13#12\n7#1#1#45 - 22#11\n7#0#3#51 - 29#8\n7#0#4#16 - 23#6\n7#0#5#27 - 45#4\n7#0#6#18 - 56#2\n7#0#7#09 - 55#0\n",
        "pandas_code": "all(df[df['points'] == 0]['lost'] == df[df['points'] == 0]['games'])",
        "pandas_eval": "True"
    },
    {
        "id": 760,
        "statement": "sweden , west germany and east germany each lost 5 game",
        "label": 1,
        "table_caption": "1985 world ice hockey championships",
        "table_text": "games#drawn#lost#points difference#points\n7#1#0#54 - 13#13\n7#0#1#36 - 13#12\n7#1#1#45 - 22#11\n7#0#3#51 - 29#8\n7#0#4#16 - 23#6\n7#0#5#27 - 45#4\n7#0#6#18 - 56#2\n7#0#7#09 - 55#0\n",
        "pandas_code": "all(df[df['lost'] == 5].index.isin([5]))",
        "pandas_eval": "True"
    },
    {
        "id": 762,
        "statement": "patriot have a record of 6 - 1 when score more than 30 point",
        "label": 1,
        "table_caption": "1980 new england patriots season",
        "table_text": "week#opponent#result#stadium#record#attendance\n1#cleveland browns#w 34 - 17#schaefer stadium#1 - 0#49222\n2#atlanta falcons#l 21 - 37#schaefer stadium#1 - 1#48321\n3#seattle seahawks#w 37 - 31#kingdome#2 - 1#61035\n4#denver broncos#w 23 - 14#schaefer stadium#3 - 1#59602\n5#new york jets#w 21 - 11#shea stadium#4 - 1#53603\n6#miami dolphins#w 34 - 0#schaefer stadium#5 - 1#60377\n7#baltimore colts#w 37 - 21#memorial stadium#6 - 1#53924\n8#buffalo bills#l 13 - 31#rich stadium#6 - 2#75092\n9#new york jets#w 34 - 21#schaefer stadium#7 - 2#60834\n10#houston oilers#l 34 - 38#astrodome#7 - 3#51524\n11#los angeles rams#l 14 - 17#schaefer stadium#7 - 4#60609\n12#baltimore colts#w 47 - 21#schaefer stadium#8 - 4#60994\n13#san francisco 49ers#l 17 - 21#candlestick park#8 - 5#45254\n14#miami dolphins#l 13 - 16#miami orange bowl#8 - 6#63292\n15#buffalo bills#w 24 - 2#schaefer stadium#9 - 6#58324\n",
        "pandas_code": "df[(df['result'].str.startswith('w')) & (df['result'].str.extract(r'(\\d+) - \\d+')[0].astype(int) > 30) & (df['record'] == '6 - 1')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 763,
        "statement": "at schaefer stadium , the patriot only have 2 game under 50000 attendance",
        "label": 1,
        "table_caption": "1980 new england patriots season",
        "table_text": "week#opponent#result#stadium#record#attendance\n1#cleveland browns#w 34 - 17#schaefer stadium#1 - 0#49222\n2#atlanta falcons#l 21 - 37#schaefer stadium#1 - 1#48321\n3#seattle seahawks#w 37 - 31#kingdome#2 - 1#61035\n4#denver broncos#w 23 - 14#schaefer stadium#3 - 1#59602\n5#new york jets#w 21 - 11#shea stadium#4 - 1#53603\n6#miami dolphins#w 34 - 0#schaefer stadium#5 - 1#60377\n7#baltimore colts#w 37 - 21#memorial stadium#6 - 1#53924\n8#buffalo bills#l 13 - 31#rich stadium#6 - 2#75092\n9#new york jets#w 34 - 21#schaefer stadium#7 - 2#60834\n10#houston oilers#l 34 - 38#astrodome#7 - 3#51524\n11#los angeles rams#l 14 - 17#schaefer stadium#7 - 4#60609\n12#baltimore colts#w 47 - 21#schaefer stadium#8 - 4#60994\n13#san francisco 49ers#l 17 - 21#candlestick park#8 - 5#45254\n14#miami dolphins#l 13 - 16#miami orange bowl#8 - 6#63292\n15#buffalo bills#w 24 - 2#schaefer stadium#9 - 6#58324\n",
        "pandas_code": "len(df[(df['stadium'] == 'schaefer stadium') & (df['attendance'] < 50000)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 764,
        "statement": "patriot have a 5 - game win streak",
        "label": 1,
        "table_caption": "1980 new england patriots season",
        "table_text": "week#opponent#result#stadium#record#attendance\n1#cleveland browns#w 34 - 17#schaefer stadium#1 - 0#49222\n2#atlanta falcons#l 21 - 37#schaefer stadium#1 - 1#48321\n3#seattle seahawks#w 37 - 31#kingdome#2 - 1#61035\n4#denver broncos#w 23 - 14#schaefer stadium#3 - 1#59602\n5#new york jets#w 21 - 11#shea stadium#4 - 1#53603\n6#miami dolphins#w 34 - 0#schaefer stadium#5 - 1#60377\n7#baltimore colts#w 37 - 21#memorial stadium#6 - 1#53924\n8#buffalo bills#l 13 - 31#rich stadium#6 - 2#75092\n9#new york jets#w 34 - 21#schaefer stadium#7 - 2#60834\n10#houston oilers#l 34 - 38#astrodome#7 - 3#51524\n11#los angeles rams#l 14 - 17#schaefer stadium#7 - 4#60609\n12#baltimore colts#w 47 - 21#schaefer stadium#8 - 4#60994\n13#san francisco 49ers#l 17 - 21#candlestick park#8 - 5#45254\n14#miami dolphins#l 13 - 16#miami orange bowl#8 - 6#63292\n15#buffalo bills#w 24 - 2#schaefer stadium#9 - 6#58324\n",
        "pandas_code": "any(df['result'].str.startswith('w').rolling(window=5).apply(lambda x: x.all(), raw=True).dropna() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 766,
        "statement": "both lithuanian litas and latvian lat have official target date in the month of january",
        "label": 1,
        "table_caption": "euro convergence criteria",
        "table_text": "currency#code#entry erm ii#central rate#official target date\nbulgarian lev#bgn#-#1.95583#-\ncroatian kuna#hrk#-#-#-\nczech koruna#czk#-#-#-\ndanish krone#dkk#1 january 1999#7.46038#formal opt - out\nhungarian forint#huf#-#-#-\nlatvian lats#lvl#2 may 2005#0.702804#1 january 2014\nlithuanian litas#ltl#28 june 2004#3.45280#1 january 2015\npolish z\u0142oty#pln#-#-#-\nromanian leu#ron#-#-#-\nswedish krona#sek#not considered#-#de facto opt - out\nbritish pound sterling gibraltar pound#gbp gip#not considered#-#formal opt - out\n",
        "pandas_code": "all(df[df['currency'].isin(['lithuanian litas', 'latvian lats'])]['official target date'].str.contains('january', case=False, na=False))",
        "pandas_eval": "True"
    },
    {
        "id": 767,
        "statement": "the bulgarian lev do not have a larger central rate than the danish krone",
        "label": 1,
        "table_caption": "euro convergence criteria",
        "table_text": "currency#code#entry erm ii#central rate#official target date\nbulgarian lev#bgn#-#1.95583#-\ncroatian kuna#hrk#-#-#-\nczech koruna#czk#-#-#-\ndanish krone#dkk#1 january 1999#7.46038#formal opt - out\nhungarian forint#huf#-#-#-\nlatvian lats#lvl#2 may 2005#0.702804#1 january 2014\nlithuanian litas#ltl#28 june 2004#3.45280#1 january 2015\npolish z\u0142oty#pln#-#-#-\nromanian leu#ron#-#-#-\nswedish krona#sek#not considered#-#de facto opt - out\nbritish pound sterling gibraltar pound#gbp gip#not considered#-#formal opt - out\n",
        "pandas_code": "(df[df['currency'] == 'bulgarian lev']['central rate'].values[0] > df[df['currency'] == 'danish krone']['central rate'].values[0]) == False",
        "pandas_eval": "True"
    },
    {
        "id": 768,
        "statement": "the british pound sterling gibraltar pound and the danish krone be formal opt - out",
        "label": 1,
        "table_caption": "euro convergence criteria",
        "table_text": "currency#code#entry erm ii#central rate#official target date\nbulgarian lev#bgn#-#1.95583#-\ncroatian kuna#hrk#-#-#-\nczech koruna#czk#-#-#-\ndanish krone#dkk#1 january 1999#7.46038#formal opt - out\nhungarian forint#huf#-#-#-\nlatvian lats#lvl#2 may 2005#0.702804#1 january 2014\nlithuanian litas#ltl#28 june 2004#3.45280#1 january 2015\npolish z\u0142oty#pln#-#-#-\nromanian leu#ron#-#-#-\nswedish krona#sek#not considered#-#de facto opt - out\nbritish pound sterling gibraltar pound#gbp gip#not considered#-#formal opt - out\n",
        "pandas_code": "df[df['currency'].isin(['british pound sterling gibraltar pound', 'danish krone'])]['official target date'].eq('formal opt - out').all()",
        "pandas_eval": "True"
    },
    {
        "id": 769,
        "statement": "the swedish krona and the british pound sterling gibraltar pound be not consider for the european exchange rate mechanism",
        "label": 1,
        "table_caption": "euro convergence criteria",
        "table_text": "currency#code#entry erm ii#central rate#official target date\nbulgarian lev#bgn#-#1.95583#-\ncroatian kuna#hrk#-#-#-\nczech koruna#czk#-#-#-\ndanish krone#dkk#1 january 1999#7.46038#formal opt - out\nhungarian forint#huf#-#-#-\nlatvian lats#lvl#2 may 2005#0.702804#1 january 2014\nlithuanian litas#ltl#28 june 2004#3.45280#1 january 2015\npolish z\u0142oty#pln#-#-#-\nromanian leu#ron#-#-#-\nswedish krona#sek#not considered#-#de facto opt - out\nbritish pound sterling gibraltar pound#gbp gip#not considered#-#formal opt - out\n",
        "pandas_code": "all(df[df['currency'].isin(['swedish krona', 'british pound sterling gibraltar pound'])]['entry erm ii'] == 'not considered')",
        "pandas_eval": "True"
    },
    {
        "id": 770,
        "statement": "the danish krone enter the european exchange rate mechanism earlier than the latvian lat",
        "label": 1,
        "table_caption": "euro convergence criteria",
        "table_text": "currency#code#entry erm ii#central rate#official target date\nbulgarian lev#bgn#-#1.95583#-\ncroatian kuna#hrk#-#-#-\nczech koruna#czk#-#-#-\ndanish krone#dkk#1 january 1999#7.46038#formal opt - out\nhungarian forint#huf#-#-#-\nlatvian lats#lvl#2 may 2005#0.702804#1 january 2014\nlithuanian litas#ltl#28 june 2004#3.45280#1 january 2015\npolish z\u0142oty#pln#-#-#-\nromanian leu#ron#-#-#-\nswedish krona#sek#not considered#-#de facto opt - out\nbritish pound sterling gibraltar pound#gbp gip#not considered#-#formal opt - out\n",
        "pandas_code": "df[df['currency'] == 'danish krone']['entry erm ii'].values[0] < df[df['currency'] == 'latvian lats']['entry erm ii'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 771,
        "statement": "the television episode the night of the eccentric air before the episode the night of the golden cobra",
        "label": 1,
        "table_caption": "list of the wild wild west episodes",
        "table_text": "series#season#title#director (s)#writer (s)#airdate#production code#arties disguises\n29#1#the night of the eccentrics#robert sparr#charles bennett#september 16 , 1966#34#aide to president juarez\n30#2#the night of the golden cobra#irving j moore#henry sharp#september 23 , 1966#29#east indian magician\n31#3#the night of the raven#irving j moore#edward di lorenzo#september 30 , 1966#32#(none)\n33#5#the night of the returning dead#richard donner#john kneubuhl#october 14 , 1966#35#(none)\n34#6#the night of the flying pie plate#robert sparr#dan ullman#october 21 , 1966#38#dirk dejohn , gem expert mr spock type\n35#7#the night of the poisonous posey#alan crosland jr#teleplay : leigh chapman story : donn mullally#october 28 , 1966#39#villain ascot sam\n36#8#the night of the bottomless pit#robert sparr#ken kolb#november 4 , 1966#36#pierre gaspard , former legionnaire\n37#9#the night of the watery death#irving j moore#michael edwards (see note below)#november 11 , 1966#40#swedish sailor\n38#10#the night of the green terror#robert sparr#john kneubuhl#november 18 , 1966#41#indian woman\n39#11#the night of the ready - made corpse#irving j moore#ken kolb (and bob wood , uncredited)#november 25 , 1966#37#bespectacled old man thomas link (criminal)\n40#12#the night of the man - eating house#alan crosland jr#john kneubuhl#december 2 , 1966#33#(none)\n42#14#the night of the infernal machine#sherman marks#shimon wincelberg#december 23 , 1966#31#herr ostropolyer , pastry chef\n44#16#the night of the tottering tontine#irving j moore#elon packard and norman hudis#january 6 , 1967#44#blond secretary to dr raven angus macgordon\n45#17#the night of the feathered fury#robert sparr#henry sharp#january 13 , 1967#46#organ grinder bald uncle hansi\n46#18#the night of the gypsy peril#alan crosland jr#ken kolb#january 20 , 1967#45#uncle moe , the peddler\n47#19#the night of the tartar#charles r rondeau#robert c dennis and earl barret#february 3 , 1967#49#russian immigrant theodore rimsky\n48#20#the night of the vicious valentine#irving j moore#leigh chapman#february 10 , 1967#48#a jewish tailor\n50#22#the night of the deadly bubble#irving j moore#michael edwards#february 24 , 1967#50#an old blind beggar a drunken sailor\n51#23#the night of the surreal mccoy#alan crosland jr#john kneubuhl#march 3 , 1967#51#gunfighter lightning mccoy\n53#25#the night of the deadly blossom#alan crosland jr#daniel mainwaring#march 17 , 1967#53#a stevedore a red - suited villain\n54#26#the night of the cadre#leon benson#digby wolfe#march 24 , 1967#54#kelton , a traveling salesman a prison guard\n",
        "pandas_code": "df[df['title'] == 'the night of the eccentrics']['airdate'].values[0] < df[df['title'] == 'the night of the golden cobra']['airdate'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 772,
        "statement": "all of the episode title begin with the word the night of the",
        "label": 1,
        "table_caption": "list of the wild wild west episodes",
        "table_text": "series#season#title#director (s)#writer (s)#airdate#production code#arties disguises\n29#1#the night of the eccentrics#robert sparr#charles bennett#september 16 , 1966#34#aide to president juarez\n30#2#the night of the golden cobra#irving j moore#henry sharp#september 23 , 1966#29#east indian magician\n31#3#the night of the raven#irving j moore#edward di lorenzo#september 30 , 1966#32#(none)\n33#5#the night of the returning dead#richard donner#john kneubuhl#october 14 , 1966#35#(none)\n34#6#the night of the flying pie plate#robert sparr#dan ullman#october 21 , 1966#38#dirk dejohn , gem expert mr spock type\n35#7#the night of the poisonous posey#alan crosland jr#teleplay : leigh chapman story : donn mullally#october 28 , 1966#39#villain ascot sam\n36#8#the night of the bottomless pit#robert sparr#ken kolb#november 4 , 1966#36#pierre gaspard , former legionnaire\n37#9#the night of the watery death#irving j moore#michael edwards (see note below)#november 11 , 1966#40#swedish sailor\n38#10#the night of the green terror#robert sparr#john kneubuhl#november 18 , 1966#41#indian woman\n39#11#the night of the ready - made corpse#irving j moore#ken kolb (and bob wood , uncredited)#november 25 , 1966#37#bespectacled old man thomas link (criminal)\n40#12#the night of the man - eating house#alan crosland jr#john kneubuhl#december 2 , 1966#33#(none)\n42#14#the night of the infernal machine#sherman marks#shimon wincelberg#december 23 , 1966#31#herr ostropolyer , pastry chef\n44#16#the night of the tottering tontine#irving j moore#elon packard and norman hudis#january 6 , 1967#44#blond secretary to dr raven angus macgordon\n45#17#the night of the feathered fury#robert sparr#henry sharp#january 13 , 1967#46#organ grinder bald uncle hansi\n46#18#the night of the gypsy peril#alan crosland jr#ken kolb#january 20 , 1967#45#uncle moe , the peddler\n47#19#the night of the tartar#charles r rondeau#robert c dennis and earl barret#february 3 , 1967#49#russian immigrant theodore rimsky\n48#20#the night of the vicious valentine#irving j moore#leigh chapman#february 10 , 1967#48#a jewish tailor\n50#22#the night of the deadly bubble#irving j moore#michael edwards#february 24 , 1967#50#an old blind beggar a drunken sailor\n51#23#the night of the surreal mccoy#alan crosland jr#john kneubuhl#march 3 , 1967#51#gunfighter lightning mccoy\n53#25#the night of the deadly blossom#alan crosland jr#daniel mainwaring#march 17 , 1967#53#a stevedore a red - suited villain\n54#26#the night of the cadre#leon benson#digby wolfe#march 24 , 1967#54#kelton , a traveling salesman a prison guard\n",
        "pandas_code": "all(df['title'].str.startswith('the night of the'))",
        "pandas_eval": "True"
    },
    {
        "id": 773,
        "statement": "the director robert sparr direct more than 1 episode of the television show the wild wild west",
        "label": 1,
        "table_caption": "list of the wild wild west episodes",
        "table_text": "series#season#title#director (s)#writer (s)#airdate#production code#arties disguises\n29#1#the night of the eccentrics#robert sparr#charles bennett#september 16 , 1966#34#aide to president juarez\n30#2#the night of the golden cobra#irving j moore#henry sharp#september 23 , 1966#29#east indian magician\n31#3#the night of the raven#irving j moore#edward di lorenzo#september 30 , 1966#32#(none)\n33#5#the night of the returning dead#richard donner#john kneubuhl#october 14 , 1966#35#(none)\n34#6#the night of the flying pie plate#robert sparr#dan ullman#october 21 , 1966#38#dirk dejohn , gem expert mr spock type\n35#7#the night of the poisonous posey#alan crosland jr#teleplay : leigh chapman story : donn mullally#october 28 , 1966#39#villain ascot sam\n36#8#the night of the bottomless pit#robert sparr#ken kolb#november 4 , 1966#36#pierre gaspard , former legionnaire\n37#9#the night of the watery death#irving j moore#michael edwards (see note below)#november 11 , 1966#40#swedish sailor\n38#10#the night of the green terror#robert sparr#john kneubuhl#november 18 , 1966#41#indian woman\n39#11#the night of the ready - made corpse#irving j moore#ken kolb (and bob wood , uncredited)#november 25 , 1966#37#bespectacled old man thomas link (criminal)\n40#12#the night of the man - eating house#alan crosland jr#john kneubuhl#december 2 , 1966#33#(none)\n42#14#the night of the infernal machine#sherman marks#shimon wincelberg#december 23 , 1966#31#herr ostropolyer , pastry chef\n44#16#the night of the tottering tontine#irving j moore#elon packard and norman hudis#january 6 , 1967#44#blond secretary to dr raven angus macgordon\n45#17#the night of the feathered fury#robert sparr#henry sharp#january 13 , 1967#46#organ grinder bald uncle hansi\n46#18#the night of the gypsy peril#alan crosland jr#ken kolb#january 20 , 1967#45#uncle moe , the peddler\n47#19#the night of the tartar#charles r rondeau#robert c dennis and earl barret#february 3 , 1967#49#russian immigrant theodore rimsky\n48#20#the night of the vicious valentine#irving j moore#leigh chapman#february 10 , 1967#48#a jewish tailor\n50#22#the night of the deadly bubble#irving j moore#michael edwards#february 24 , 1967#50#an old blind beggar a drunken sailor\n51#23#the night of the surreal mccoy#alan crosland jr#john kneubuhl#march 3 , 1967#51#gunfighter lightning mccoy\n53#25#the night of the deadly blossom#alan crosland jr#daniel mainwaring#march 17 , 1967#53#a stevedore a red - suited villain\n54#26#the night of the cadre#leon benson#digby wolfe#march 24 , 1967#54#kelton , a traveling salesman a prison guard\n",
        "pandas_code": "df['director (s)'].value_counts().get('robert sparr', 0) > 1",
        "pandas_eval": "True"
    },
    {
        "id": 774,
        "statement": "the writer ken kolb write 3 episode of the television show the wild wild west during the year of 1966 and 1967",
        "label": 1,
        "table_caption": "list of the wild wild west episodes",
        "table_text": "series#season#title#director (s)#writer (s)#airdate#production code#arties disguises\n29#1#the night of the eccentrics#robert sparr#charles bennett#september 16 , 1966#34#aide to president juarez\n30#2#the night of the golden cobra#irving j moore#henry sharp#september 23 , 1966#29#east indian magician\n31#3#the night of the raven#irving j moore#edward di lorenzo#september 30 , 1966#32#(none)\n33#5#the night of the returning dead#richard donner#john kneubuhl#october 14 , 1966#35#(none)\n34#6#the night of the flying pie plate#robert sparr#dan ullman#october 21 , 1966#38#dirk dejohn , gem expert mr spock type\n35#7#the night of the poisonous posey#alan crosland jr#teleplay : leigh chapman story : donn mullally#october 28 , 1966#39#villain ascot sam\n36#8#the night of the bottomless pit#robert sparr#ken kolb#november 4 , 1966#36#pierre gaspard , former legionnaire\n37#9#the night of the watery death#irving j moore#michael edwards (see note below)#november 11 , 1966#40#swedish sailor\n38#10#the night of the green terror#robert sparr#john kneubuhl#november 18 , 1966#41#indian woman\n39#11#the night of the ready - made corpse#irving j moore#ken kolb (and bob wood , uncredited)#november 25 , 1966#37#bespectacled old man thomas link (criminal)\n40#12#the night of the man - eating house#alan crosland jr#john kneubuhl#december 2 , 1966#33#(none)\n42#14#the night of the infernal machine#sherman marks#shimon wincelberg#december 23 , 1966#31#herr ostropolyer , pastry chef\n44#16#the night of the tottering tontine#irving j moore#elon packard and norman hudis#january 6 , 1967#44#blond secretary to dr raven angus macgordon\n45#17#the night of the feathered fury#robert sparr#henry sharp#january 13 , 1967#46#organ grinder bald uncle hansi\n46#18#the night of the gypsy peril#alan crosland jr#ken kolb#january 20 , 1967#45#uncle moe , the peddler\n47#19#the night of the tartar#charles r rondeau#robert c dennis and earl barret#february 3 , 1967#49#russian immigrant theodore rimsky\n48#20#the night of the vicious valentine#irving j moore#leigh chapman#february 10 , 1967#48#a jewish tailor\n50#22#the night of the deadly bubble#irving j moore#michael edwards#february 24 , 1967#50#an old blind beggar a drunken sailor\n51#23#the night of the surreal mccoy#alan crosland jr#john kneubuhl#march 3 , 1967#51#gunfighter lightning mccoy\n53#25#the night of the deadly blossom#alan crosland jr#daniel mainwaring#march 17 , 1967#53#a stevedore a red - suited villain\n54#26#the night of the cadre#leon benson#digby wolfe#march 24 , 1967#54#kelton , a traveling salesman a prison guard\n",
        "pandas_code": "(df['writer (s)'].str.contains('ken kolb').sum() == 3) and (df[df['writer (s)'].str.contains('ken kolb')]['airdate'].str.contains('1966|1967').all())",
        "pandas_eval": "True"
    },
    {
        "id": 775,
        "statement": "irving j moore direct 2 more episode than robert sparr",
        "label": 1,
        "table_caption": "list of the wild wild west episodes",
        "table_text": "series#season#title#director (s)#writer (s)#airdate#production code#arties disguises\n29#1#the night of the eccentrics#robert sparr#charles bennett#september 16 , 1966#34#aide to president juarez\n30#2#the night of the golden cobra#irving j moore#henry sharp#september 23 , 1966#29#east indian magician\n31#3#the night of the raven#irving j moore#edward di lorenzo#september 30 , 1966#32#(none)\n33#5#the night of the returning dead#richard donner#john kneubuhl#october 14 , 1966#35#(none)\n34#6#the night of the flying pie plate#robert sparr#dan ullman#october 21 , 1966#38#dirk dejohn , gem expert mr spock type\n35#7#the night of the poisonous posey#alan crosland jr#teleplay : leigh chapman story : donn mullally#october 28 , 1966#39#villain ascot sam\n36#8#the night of the bottomless pit#robert sparr#ken kolb#november 4 , 1966#36#pierre gaspard , former legionnaire\n37#9#the night of the watery death#irving j moore#michael edwards (see note below)#november 11 , 1966#40#swedish sailor\n38#10#the night of the green terror#robert sparr#john kneubuhl#november 18 , 1966#41#indian woman\n39#11#the night of the ready - made corpse#irving j moore#ken kolb (and bob wood , uncredited)#november 25 , 1966#37#bespectacled old man thomas link (criminal)\n40#12#the night of the man - eating house#alan crosland jr#john kneubuhl#december 2 , 1966#33#(none)\n42#14#the night of the infernal machine#sherman marks#shimon wincelberg#december 23 , 1966#31#herr ostropolyer , pastry chef\n44#16#the night of the tottering tontine#irving j moore#elon packard and norman hudis#january 6 , 1967#44#blond secretary to dr raven angus macgordon\n45#17#the night of the feathered fury#robert sparr#henry sharp#january 13 , 1967#46#organ grinder bald uncle hansi\n46#18#the night of the gypsy peril#alan crosland jr#ken kolb#january 20 , 1967#45#uncle moe , the peddler\n47#19#the night of the tartar#charles r rondeau#robert c dennis and earl barret#february 3 , 1967#49#russian immigrant theodore rimsky\n48#20#the night of the vicious valentine#irving j moore#leigh chapman#february 10 , 1967#48#a jewish tailor\n50#22#the night of the deadly bubble#irving j moore#michael edwards#february 24 , 1967#50#an old blind beggar a drunken sailor\n51#23#the night of the surreal mccoy#alan crosland jr#john kneubuhl#march 3 , 1967#51#gunfighter lightning mccoy\n53#25#the night of the deadly blossom#alan crosland jr#daniel mainwaring#march 17 , 1967#53#a stevedore a red - suited villain\n54#26#the night of the cadre#leon benson#digby wolfe#march 24 , 1967#54#kelton , a traveling salesman a prison guard\n",
        "pandas_code": "(df['director (s)'].value_counts()['irving j moore'] - df['director (s)'].value_counts()['robert sparr']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 776,
        "statement": "there be more game in the year 1992 than any other year",
        "label": 1,
        "table_caption": "mark woodforde",
        "table_text": "outcome#year#championship#surface#partner#opponents in the final#score in the final\nwinner#1992#australian open#hard#nicole provis#arantxa s\u00e1nchez vicario todd woodbridge#6 - 3 , 4 - 6 , 11 - 9\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#lori mcneil bryan shelton#6 - 2 , 6 - 3\nwinner#1992#us open#hard#nicole provis#helena sukov\u00e1 tom nijssen#4 - 6 , 6 - 3 , 6 - 3\nwinner#1993#wimbledon#grass#martina navr\u00e1tilov\u00e1#manon bollegraf tom nijssen#6 - 3 , 6 - 4\nrunner - up#1993#us open#hard#martina navr\u00e1tilov\u00e1#helena sukov\u00e1 todd woodbridge#3 - 6 , 6 - 7\nwinner#1996#australian open#hard#larisa savchenko neiland#nicole arendt luke jensen#4 - 6 , 7 - 5 , 6 - 0\n",
        "pandas_code": "df[df['year'] == 1992].shape[0] > df[df['year'] != 1992].groupby('year').size().max()",
        "pandas_eval": "True"
    },
    {
        "id": 777,
        "statement": "hard be most often choose surface",
        "label": 1,
        "table_caption": "mark woodforde",
        "table_text": "outcome#year#championship#surface#partner#opponents in the final#score in the final\nwinner#1992#australian open#hard#nicole provis#arantxa s\u00e1nchez vicario todd woodbridge#6 - 3 , 4 - 6 , 11 - 9\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#lori mcneil bryan shelton#6 - 2 , 6 - 3\nwinner#1992#us open#hard#nicole provis#helena sukov\u00e1 tom nijssen#4 - 6 , 6 - 3 , 6 - 3\nwinner#1993#wimbledon#grass#martina navr\u00e1tilov\u00e1#manon bollegraf tom nijssen#6 - 3 , 6 - 4\nrunner - up#1993#us open#hard#martina navr\u00e1tilov\u00e1#helena sukov\u00e1 todd woodbridge#3 - 6 , 6 - 7\nwinner#1996#australian open#hard#larisa savchenko neiland#nicole arendt luke jensen#4 - 6 , 7 - 5 , 6 - 0\n",
        "pandas_code": "df['surface'].value_counts().idxmax() == 'hard'",
        "pandas_eval": "True"
    },
    {
        "id": 778,
        "statement": "mark woodforde be only a runner - up in 1993",
        "label": 1,
        "table_caption": "mark woodforde",
        "table_text": "outcome#year#championship#surface#partner#opponents in the final#score in the final\nwinner#1992#australian open#hard#nicole provis#arantxa s\u00e1nchez vicario todd woodbridge#6 - 3 , 4 - 6 , 11 - 9\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#lori mcneil bryan shelton#6 - 2 , 6 - 3\nwinner#1992#us open#hard#nicole provis#helena sukov\u00e1 tom nijssen#4 - 6 , 6 - 3 , 6 - 3\nwinner#1993#wimbledon#grass#martina navr\u00e1tilov\u00e1#manon bollegraf tom nijssen#6 - 3 , 6 - 4\nrunner - up#1993#us open#hard#martina navr\u00e1tilov\u00e1#helena sukov\u00e1 todd woodbridge#3 - 6 , 6 - 7\nwinner#1996#australian open#hard#larisa savchenko neiland#nicole arendt luke jensen#4 - 6 , 7 - 5 , 6 - 0\n",
        "pandas_code": "all(df[(df['year'] == 1993) & (df['outcome'] == 'runner - up')]['outcome'] == 'runner - up') and all(df[(df['year'] == 1993) & (df['outcome'] != 'runner - up')]['outcome'] != 'runner - up')",
        "pandas_eval": "True"
    },
    {
        "id": 779,
        "statement": "there be a year where mark woodforde be both a runner - up and a winner",
        "label": 1,
        "table_caption": "mark woodforde",
        "table_text": "outcome#year#championship#surface#partner#opponents in the final#score in the final\nwinner#1992#australian open#hard#nicole provis#arantxa s\u00e1nchez vicario todd woodbridge#6 - 3 , 4 - 6 , 11 - 9\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#lori mcneil bryan shelton#6 - 2 , 6 - 3\nwinner#1992#us open#hard#nicole provis#helena sukov\u00e1 tom nijssen#4 - 6 , 6 - 3 , 6 - 3\nwinner#1993#wimbledon#grass#martina navr\u00e1tilov\u00e1#manon bollegraf tom nijssen#6 - 3 , 6 - 4\nrunner - up#1993#us open#hard#martina navr\u00e1tilov\u00e1#helena sukov\u00e1 todd woodbridge#3 - 6 , 6 - 7\nwinner#1996#australian open#hard#larisa savchenko neiland#nicole arendt luke jensen#4 - 6 , 7 - 5 , 6 - 0\n",
        "pandas_code": "any(df[df['outcome'] == 'runner - up']['year'].isin(df[df['outcome'] == 'winner']['year']))",
        "pandas_eval": "True"
    },
    {
        "id": 780,
        "statement": "the australian open appear 2 time on the list",
        "label": 1,
        "table_caption": "mark woodforde",
        "table_text": "outcome#year#championship#surface#partner#opponents in the final#score in the final\nwinner#1992#australian open#hard#nicole provis#arantxa s\u00e1nchez vicario todd woodbridge#6 - 3 , 4 - 6 , 11 - 9\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#lori mcneil bryan shelton#6 - 2 , 6 - 3\nwinner#1992#us open#hard#nicole provis#helena sukov\u00e1 tom nijssen#4 - 6 , 6 - 3 , 6 - 3\nwinner#1993#wimbledon#grass#martina navr\u00e1tilov\u00e1#manon bollegraf tom nijssen#6 - 3 , 6 - 4\nrunner - up#1993#us open#hard#martina navr\u00e1tilov\u00e1#helena sukov\u00e1 todd woodbridge#3 - 6 , 6 - 7\nwinner#1996#australian open#hard#larisa savchenko neiland#nicole arendt luke jensen#4 - 6 , 7 - 5 , 6 - 0\n",
        "pandas_code": "len(df[df['championship'] == 'australian open']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 781,
        "statement": "the atlanta hawk and the philadelphia 76ers be the opponent the same number of time",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df['opponent'].value_counts().loc[['atlanta hawks', 'philadelphia 76ers']].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 782,
        "statement": "there be 7 game that take place at boston garden",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "len(df[df['location'] == 'boston garden']) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 783,
        "statement": "the game on dec 1 and dec 19 be against the same team",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df[df['date'].isin(['sat dec 1', 'wed dec 19'])]['opponent'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 784,
        "statement": "the game against the charlotte hornet take place before the game against the indiana pacer",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df[df['opponent'] == 'charlotte hornets']['date'].iloc[0] < df[df['opponent'] == 'indiana pacers']['date'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 785,
        "statement": "both game against the atlanta hawk take place after dec 20",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "all(pd.to_datetime(df[df['opponent'] == 'atlanta hawks']['date'], format='%a %b %d').dt.day > 20)",
        "pandas_eval": "True"
    },
    {
        "id": 786,
        "statement": "the denver nuggents be the opponets more that any other team",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "(df['opponent'].value_counts().idxmax() == 'denver nuggets')",
        "pandas_eval": "False"
    },
    {
        "id": 787,
        "statement": "the boston garden have the least amount of game play at this location",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df['location'].value_counts().idxmin() == 'boston garden'",
        "pandas_eval": "False"
    },
    {
        "id": 788,
        "statement": "the game on dec 20 and dec 28 be against the same opponent",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df[df['date'].isin(['thu dec 20', 'fri dec 28'])]['opponent'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 789,
        "statement": "the game against the atlanta at the omni be the 1st game play",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "df[(df['opponent'] == 'atlanta hawks') & (df['location'] == 'the omni')].index[0] == df.index[0]",
        "pandas_eval": "False"
    },
    {
        "id": 790,
        "statement": "both game against the philadelphia 76ers take place after dec 26",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n15#sat dec 1#philadelphia 76ers#110 - 116#the spectrum#12 - 3\n16#mon dec 3#seattle supersonics#135 - 102#boston garden#13 - 3\n17#wed dec 5#denver nuggets#148 - 140#boston garden#14 - 3\n18#fri dec 7#dallas mavericks#112 - 104#reunion arena#15 - 3\n19#sat dec 8#san antonio spurs#96 - 102#hemisfair arena#15 - 4\n20#mon dec 10#houston rockets#107 - 95#the summit#16 - 4\n21#wed dec 12#milwaukee bucks#129 - 111#boston garden#17 - 4\n22#fri dec 14#detroit pistons#108 - 100#boston garden#18 - 4\n23#sat dec 15#miami heat#114 - 100#miami arena#19 - 4\n24#wed dec 19#philadelphia 76ers#115 - 105#boston garden#20 - 4\n25#thu dec 20#charlotte hornets#115 - 96#charlotte coliseum#21 - 4\n26#sun dec 23#atlanta hawks#132 - 104#boston garden#22 - 4\n27#wed dec 26#indiana pacers#152 - 132#boston garden#23 - 4\n28#fri dec 28#atlanta hawks#114 - 131#the omni#23 - 5\n",
        "pandas_code": "((df['opponent'] == 'philadelphia 76ers') & (df['date'].apply(lambda x: int(x.split(' ')[-1]) > 26))).any()",
        "pandas_eval": "False"
    },
    {
        "id": 791,
        "statement": "in the year 1996 the atlanta silverback did not qualify for the playoff",
        "label": 1,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1996]['playoffs'].values[0] == 'did not qualify'",
        "pandas_eval": "True"
    },
    {
        "id": 792,
        "statement": "in the year 1997 the atlanta silverback did not qualify for the playoff",
        "label": 1,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1997]['playoffs'].values[0] == 'did not qualify'",
        "pandas_eval": "True"
    },
    {
        "id": 793,
        "statement": "in the year 1998 the atlanta silverback did not qualify for the playoff",
        "label": 1,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1998]['playoffs'].values[0] == 'did not qualify'",
        "pandas_eval": "True"
    },
    {
        "id": 794,
        "statement": "in the year 1999 the atlanta silverback did not qualify for the playoff",
        "label": 1,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1999]['playoffs'].values[0] == 'did not qualify'",
        "pandas_eval": "True"
    },
    {
        "id": 795,
        "statement": "in the year 2000 the atlanta silverback did not qualify for the playoff",
        "label": 1,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 2000]['playoffs'].values[0] == 'did not qualify'",
        "pandas_eval": "True"
    },
    {
        "id": 796,
        "statement": "in the year 2007 the atlanta silverback did not qualify for the playoff",
        "label": 0,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 2007]['playoffs'].iloc[0] == 'did not qualify'",
        "pandas_eval": "False"
    },
    {
        "id": 797,
        "statement": "in the year 1997 the atlanta silverback qualify for the playoff",
        "label": 0,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df.loc[df['year'] == 1997, 'playoffs'].values[0] != 'did not qualify'",
        "pandas_eval": "False"
    },
    {
        "id": 798,
        "statement": "in the year 1998 the atlanta silverback qualify for the playoff",
        "label": 0,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1998]['playoffs'].values[0] != 'did not qualify'",
        "pandas_eval": "False"
    },
    {
        "id": 799,
        "statement": "in the year 1999 the atlanta silverback did qualify for the playoff",
        "label": 0,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 1999]['playoffs'].iloc[0] == 'did qualify'",
        "pandas_eval": "False"
    },
    {
        "id": 800,
        "statement": "in the year 2001 the atlanta silverback did not qualify for the playoff",
        "label": 0,
        "table_caption": "atlanta silverbacks",
        "table_text": "year#division#league#reg season#playoffs#open cup#avg attendance\n1995#2#a - league#4th#finalist#2nd round#2632\n1996#2#a - league#7th#did not qualify#did not qualify#4315\n1997#2#usisl a - league#7th , central#did not qualify#did not qualify#2406\n1998#2#usisl a - league#6th , atlantic#did not qualify#did not qualify#923\n1999#2#usl a - league#5th , atlantic#did not qualify#did not qualify#2703\n2000#2#usl a - league#6th , atlantic#did not qualify#did not qualify#3327\n2001#2#usl a - league#5th , central#did not qualify#did not qualify#1094\n2002#2#usl a - league#3rd , southeast#1st round#3rd round#1006\n2003#2#usl a - league#5th , southeast#did not qualify#3rd round#1200\n2004#2#usl a - league#5th , eastern#did not qualify#4th round#1662\n2005#2#usl first division#8th#did not qualify#3rd round#1724\n2006#2#usl first division#8th#did not qualify#3rd round#2298\n2007#2#usl first division#4th#finalist#3rd round#2372\n2008#2#usl first division#9th#did not qualify#2nd round#2281\n2009#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2010#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus#on hiatus\n2011#2#nasl#8th#did not qualify#denied entry#2866\n2012#2#nasl#7th#did not qualify#3rd round#4505\n2013#2#nasl#1st - spring 7th - fall#hosting nasl soccer bowl#3rd round#5039\n",
        "pandas_code": "df[df['year'] == 2001]['playoffs'].values[0] != 'did not qualify'",
        "pandas_eval": "False"
    },
    {
        "id": 801,
        "statement": "pistol be the mostly use weapon on the table",
        "label": 1,
        "table_caption": "eagle riders",
        "table_text": "gatchaman#eagle riders#rank#bird uniform#weapon#mecha#japanese voice actor#voice actor (eagle riders)\nken washio#hunter harris#g1#eagle#razor boomerang#airplane#katsuji mori#richard cansino\njoe asakura#joe thax#g2#condor#pistol#race car#isao sasaki#bryan cranston\njun#kelly jennar#g3#swan#yo - yo#motorcycle#kazuko sugiyama#heidi noelle lenhart\njinpei#mickey dugan#g4#swallow#bolo#dune buggy#yoku shioya#mona marshall\nryu nakanishi#ollie keeawani#g5#owl#pistol#god phoenix#shingo kanemoto#paul schrier\n",
        "pandas_code": "df['weapon'].value_counts().idxmax() == 'pistol'",
        "pandas_eval": "True"
    },
    {
        "id": 802,
        "statement": "hunter harris come first in the table while ollie keeawani come fifth",
        "label": 1,
        "table_caption": "eagle riders",
        "table_text": "gatchaman#eagle riders#rank#bird uniform#weapon#mecha#japanese voice actor#voice actor (eagle riders)\nken washio#hunter harris#g1#eagle#razor boomerang#airplane#katsuji mori#richard cansino\njoe asakura#joe thax#g2#condor#pistol#race car#isao sasaki#bryan cranston\njun#kelly jennar#g3#swan#yo - yo#motorcycle#kazuko sugiyama#heidi noelle lenhart\njinpei#mickey dugan#g4#swallow#bolo#dune buggy#yoku shioya#mona marshall\nryu nakanishi#ollie keeawani#g5#owl#pistol#god phoenix#shingo kanemoto#paul schrier\n",
        "pandas_code": "(df['eagle riders'].iloc[0] == 'hunter harris') & (df['eagle riders'].iloc[4] == 'ollie keeawani')",
        "pandas_eval": "True"
    },
    {
        "id": 803,
        "statement": "motorcycle be the mecha of the third rank player",
        "label": 1,
        "table_caption": "eagle riders",
        "table_text": "gatchaman#eagle riders#rank#bird uniform#weapon#mecha#japanese voice actor#voice actor (eagle riders)\nken washio#hunter harris#g1#eagle#razor boomerang#airplane#katsuji mori#richard cansino\njoe asakura#joe thax#g2#condor#pistol#race car#isao sasaki#bryan cranston\njun#kelly jennar#g3#swan#yo - yo#motorcycle#kazuko sugiyama#heidi noelle lenhart\njinpei#mickey dugan#g4#swallow#bolo#dune buggy#yoku shioya#mona marshall\nryu nakanishi#ollie keeawani#g5#owl#pistol#god phoenix#shingo kanemoto#paul schrier\n",
        "pandas_code": "df.loc[df['rank'] == 'g3', 'mecha'].iloc[0] == 'motorcycle'",
        "pandas_eval": "True"
    },
    {
        "id": 804,
        "statement": "richard cansino be the voice actor for hunter harris while katsuji mori be the japanese voice actor",
        "label": 1,
        "table_caption": "eagle riders",
        "table_text": "gatchaman#eagle riders#rank#bird uniform#weapon#mecha#japanese voice actor#voice actor (eagle riders)\nken washio#hunter harris#g1#eagle#razor boomerang#airplane#katsuji mori#richard cansino\njoe asakura#joe thax#g2#condor#pistol#race car#isao sasaki#bryan cranston\njun#kelly jennar#g3#swan#yo - yo#motorcycle#kazuko sugiyama#heidi noelle lenhart\njinpei#mickey dugan#g4#swallow#bolo#dune buggy#yoku shioya#mona marshall\nryu nakanishi#ollie keeawani#g5#owl#pistol#god phoenix#shingo kanemoto#paul schrier\n",
        "pandas_code": "df[(df['voice actor (eagle riders)'] == 'richard cansino') & (df['eagle riders'] == 'hunter harris') & (df['japanese voice actor'] == 'katsuji mori')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 805,
        "statement": "joe thax with the pistol weapon beat kelly jennar with the yo - yo to the second position",
        "label": 1,
        "table_caption": "eagle riders",
        "table_text": "gatchaman#eagle riders#rank#bird uniform#weapon#mecha#japanese voice actor#voice actor (eagle riders)\nken washio#hunter harris#g1#eagle#razor boomerang#airplane#katsuji mori#richard cansino\njoe asakura#joe thax#g2#condor#pistol#race car#isao sasaki#bryan cranston\njun#kelly jennar#g3#swan#yo - yo#motorcycle#kazuko sugiyama#heidi noelle lenhart\njinpei#mickey dugan#g4#swallow#bolo#dune buggy#yoku shioya#mona marshall\nryu nakanishi#ollie keeawani#g5#owl#pistol#god phoenix#shingo kanemoto#paul schrier\n",
        "pandas_code": "df[(df['eagle riders'] == 'joe thax') & (df['weapon'] == 'pistol') & (df['rank'] == 'g2')].empty == False and df[(df['eagle riders'] == 'kelly jennar') & (df['weapon'] == 'yo - yo') & (df['rank'] == 'g3')].empty == False",
        "pandas_eval": "True"
    },
    {
        "id": 806,
        "statement": "the english name for sunday have a thai name of \u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c and the color red be associate with it",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#transcription#color#sanskrit word#planet\nsunday#\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c#wan athit#red#aditya#sun\nmonday#\u0e27\u0e31\u0e19\u0e08\u0e31\u0e19\u0e17\u0e23\u0e4c#wan chan#yellow#chandra#moon\ntuesday#\u0e27\u0e31\u0e19\u0e2d\u0e31\u0e07\u0e04\u0e32\u0e23#wan angkhan#pink#angaraka#mars\nwednesday#\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18#wan phut#green#budha#mercury\nthursday#\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35#wan phruehatsabodi#orange#brihaspati#jupiter\nfriday#\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c#wan suk#blue#shukra#venus\nsaturday#\u0e27\u0e31\u0e19\u0e40\u0e2a\u0e32\u0e23\u0e4c#wan sao#purple#shani#saturn\n",
        "pandas_code": "df[(df['english name'] == 'sunday') & (df['thai name'] == '\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c') & (df['color'] == 'red')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 807,
        "statement": "the color green and the planet mercury be associate with the english name wednesday",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#transcription#color#sanskrit word#planet\nsunday#\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c#wan athit#red#aditya#sun\nmonday#\u0e27\u0e31\u0e19\u0e08\u0e31\u0e19\u0e17\u0e23\u0e4c#wan chan#yellow#chandra#moon\ntuesday#\u0e27\u0e31\u0e19\u0e2d\u0e31\u0e07\u0e04\u0e32\u0e23#wan angkhan#pink#angaraka#mars\nwednesday#\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18#wan phut#green#budha#mercury\nthursday#\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35#wan phruehatsabodi#orange#brihaspati#jupiter\nfriday#\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c#wan suk#blue#shukra#venus\nsaturday#\u0e27\u0e31\u0e19\u0e40\u0e2a\u0e32\u0e23\u0e4c#wan sao#purple#shani#saturn\n",
        "pandas_code": "df[(df['english name'] == 'wednesday') & (df['color'] == 'green') & (df['planet'] == 'mercury')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 808,
        "statement": "the thai name for the english name friday be \u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c and the transcription for it be wan suk",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#transcription#color#sanskrit word#planet\nsunday#\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c#wan athit#red#aditya#sun\nmonday#\u0e27\u0e31\u0e19\u0e08\u0e31\u0e19\u0e17\u0e23\u0e4c#wan chan#yellow#chandra#moon\ntuesday#\u0e27\u0e31\u0e19\u0e2d\u0e31\u0e07\u0e04\u0e32\u0e23#wan angkhan#pink#angaraka#mars\nwednesday#\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18#wan phut#green#budha#mercury\nthursday#\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35#wan phruehatsabodi#orange#brihaspati#jupiter\nfriday#\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c#wan suk#blue#shukra#venus\nsaturday#\u0e27\u0e31\u0e19\u0e40\u0e2a\u0e32\u0e23\u0e4c#wan sao#purple#shani#saturn\n",
        "pandas_code": "((df['english name'] == 'friday') & (df['thai name'] == '\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c') & (df['transcription'] == 'wan suk')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 809,
        "statement": "saturn and venus be the planet associate with the english name saturday and friday , respectively",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#transcription#color#sanskrit word#planet\nsunday#\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c#wan athit#red#aditya#sun\nmonday#\u0e27\u0e31\u0e19\u0e08\u0e31\u0e19\u0e17\u0e23\u0e4c#wan chan#yellow#chandra#moon\ntuesday#\u0e27\u0e31\u0e19\u0e2d\u0e31\u0e07\u0e04\u0e32\u0e23#wan angkhan#pink#angaraka#mars\nwednesday#\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18#wan phut#green#budha#mercury\nthursday#\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35#wan phruehatsabodi#orange#brihaspati#jupiter\nfriday#\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c#wan suk#blue#shukra#venus\nsaturday#\u0e27\u0e31\u0e19\u0e40\u0e2a\u0e32\u0e23\u0e4c#wan sao#purple#shani#saturn\n",
        "pandas_code": "(df[df['english name'] == 'saturday']['planet'].values[0] == 'saturn') & (df[df['english name'] == 'friday']['planet'].values[0] == 'venus')",
        "pandas_eval": "True"
    },
    {
        "id": 810,
        "statement": "\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35 be the thai name for the english name thursday , and be represent by the plant jupiter",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#transcription#color#sanskrit word#planet\nsunday#\u0e27\u0e31\u0e19\u0e2d\u0e32\u0e17\u0e34\u0e15\u0e22\u0e4c#wan athit#red#aditya#sun\nmonday#\u0e27\u0e31\u0e19\u0e08\u0e31\u0e19\u0e17\u0e23\u0e4c#wan chan#yellow#chandra#moon\ntuesday#\u0e27\u0e31\u0e19\u0e2d\u0e31\u0e07\u0e04\u0e32\u0e23#wan angkhan#pink#angaraka#mars\nwednesday#\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18#wan phut#green#budha#mercury\nthursday#\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35#wan phruehatsabodi#orange#brihaspati#jupiter\nfriday#\u0e27\u0e31\u0e19\u0e28\u0e38\u0e01\u0e23\u0e4c#wan suk#blue#shukra#venus\nsaturday#\u0e27\u0e31\u0e19\u0e40\u0e2a\u0e32\u0e23\u0e4c#wan sao#purple#shani#saturn\n",
        "pandas_code": "df[(df['thai name'] == '\u0e27\u0e31\u0e19\u0e1e\u0e24\u0e2b\u0e31\u0e2a\u0e1a\u0e14\u0e35') & (df['english name'] == 'thursday') & (df['planet'] == 'jupiter')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 811,
        "statement": "all 5 election result in the candidate be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1954",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#john w heselton#republican#1944#re - elected#john w heselton (r) 55.6% john j dwyer (d) 44.4%\nmassachusetts 3#philip philbin#democratic#1942#re - elected#philip philbin (d) unopposed\nmassachusetts 5#edith nourse rogers#republican#1925#re - elected#edith nourse rogers (r) unopposed\nmassachusetts 7#thomas j lane#democratic#1941#re - elected#thomas j lane (d) unopposed\nmassachusetts 11#tip o'neill#democratic#1952#re - elected#tip o'neill (d) 78.2% charles s bolster (r) 21.8%\n",
        "pandas_code": "all(df['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 812,
        "statement": "there be 3 democrat and 2 republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1954",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#john w heselton#republican#1944#re - elected#john w heselton (r) 55.6% john j dwyer (d) 44.4%\nmassachusetts 3#philip philbin#democratic#1942#re - elected#philip philbin (d) unopposed\nmassachusetts 5#edith nourse rogers#republican#1925#re - elected#edith nourse rogers (r) unopposed\nmassachusetts 7#thomas j lane#democratic#1941#re - elected#thomas j lane (d) unopposed\nmassachusetts 11#tip o'neill#democratic#1952#re - elected#tip o'neill (d) 78.2% charles s bolster (r) 21.8%\n",
        "pandas_code": "df['party'].value_counts().to_dict() == {'democratic': 3, 'republican': 2}",
        "pandas_eval": "True"
    },
    {
        "id": 813,
        "statement": "3 election be run without opposition",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1954",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#john w heselton#republican#1944#re - elected#john w heselton (r) 55.6% john j dwyer (d) 44.4%\nmassachusetts 3#philip philbin#democratic#1942#re - elected#philip philbin (d) unopposed\nmassachusetts 5#edith nourse rogers#republican#1925#re - elected#edith nourse rogers (r) unopposed\nmassachusetts 7#thomas j lane#democratic#1941#re - elected#thomas j lane (d) unopposed\nmassachusetts 11#tip o'neill#democratic#1952#re - elected#tip o'neill (d) 78.2% charles s bolster (r) 21.8%\n",
        "pandas_code": "df['candidates'].str.contains('unopposed').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 814,
        "statement": "edith nourse rogers be the earliest - elect representative",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1954",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#john w heselton#republican#1944#re - elected#john w heselton (r) 55.6% john j dwyer (d) 44.4%\nmassachusetts 3#philip philbin#democratic#1942#re - elected#philip philbin (d) unopposed\nmassachusetts 5#edith nourse rogers#republican#1925#re - elected#edith nourse rogers (r) unopposed\nmassachusetts 7#thomas j lane#democratic#1941#re - elected#thomas j lane (d) unopposed\nmassachusetts 11#tip o'neill#democratic#1952#re - elected#tip o'neill (d) 78.2% charles s bolster (r) 21.8%\n",
        "pandas_code": "df[df['incumbent'] == 'edith nourse rogers']['first elected'].min() == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 816,
        "statement": "there be 4 mutation which have no attribute",
        "label": 1,
        "table_caption": "dom events",
        "table_text": "category#type#attribute#description#bubbles#cancelable\nmouse#dragstart#ondragstart#fired on an element when a drag is started#yes#yes\nkeyboard#keyup#onkeyup#fires when a key on the keyboard is released#yes#yes\nhtml frame / object#resize#onresize#fires when a document view is resized#yes#no\nhtml frame / object#scroll#onscroll#fires when a document view is scrolled#yes#no\nhtml form#submit#onsubmit#fires when a form is submitted#yes#yes\nhtml form#reset#onreset#fires when a form is reset#yes#no\nmutation#domsubtreemodified#(none)#fires when the subtree is modified#yes#no\nmutation#domnoderemoved#(none)#fires when a node has been removed from a dom - tree#yes#no\nmutation#domnoderemovedfromdocument#(none)#fires when a node is being removed from a document#no#no\nmutation#domattrmodified#(none)#fires when an attribute has been modified#yes#no\n",
        "pandas_code": "len(df[(df['category'] == 'mutation') & (df['attribute'] == '(none)')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 817,
        "statement": "a dragstart mouse is fire on an element when a drag is start",
        "label": 1,
        "table_caption": "dom events",
        "table_text": "category#type#attribute#description#bubbles#cancelable\nmouse#dragstart#ondragstart#fired on an element when a drag is started#yes#yes\nkeyboard#keyup#onkeyup#fires when a key on the keyboard is released#yes#yes\nhtml frame / object#resize#onresize#fires when a document view is resized#yes#no\nhtml frame / object#scroll#onscroll#fires when a document view is scrolled#yes#no\nhtml form#submit#onsubmit#fires when a form is submitted#yes#yes\nhtml form#reset#onreset#fires when a form is reset#yes#no\nmutation#domsubtreemodified#(none)#fires when the subtree is modified#yes#no\nmutation#domnoderemoved#(none)#fires when a node has been removed from a dom - tree#yes#no\nmutation#domnoderemovedfromdocument#(none)#fires when a node is being removed from a document#no#no\nmutation#domattrmodified#(none)#fires when an attribute has been modified#yes#no\n",
        "pandas_code": "df[(df['category'] == 'mouse') & (df['type'] == 'dragstart') & (df['description'] == 'fired on an element when a drag is started')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 818,
        "statement": "a html frame have an on scroll attribute that fire when a document view is scroll",
        "label": 1,
        "table_caption": "dom events",
        "table_text": "category#type#attribute#description#bubbles#cancelable\nmouse#dragstart#ondragstart#fired on an element when a drag is started#yes#yes\nkeyboard#keyup#onkeyup#fires when a key on the keyboard is released#yes#yes\nhtml frame / object#resize#onresize#fires when a document view is resized#yes#no\nhtml frame / object#scroll#onscroll#fires when a document view is scrolled#yes#no\nhtml form#submit#onsubmit#fires when a form is submitted#yes#yes\nhtml form#reset#onreset#fires when a form is reset#yes#no\nmutation#domsubtreemodified#(none)#fires when the subtree is modified#yes#no\nmutation#domnoderemoved#(none)#fires when a node has been removed from a dom - tree#yes#no\nmutation#domnoderemovedfromdocument#(none)#fires when a node is being removed from a document#no#no\nmutation#domattrmodified#(none)#fires when an attribute has been modified#yes#no\n",
        "pandas_code": "any((df['category'] == 'html frame / object') & (df['type'] == 'scroll') & (df['attribute'] == 'onscroll') & (df['description'] == 'fires when a document view is scrolled'))",
        "pandas_eval": "True"
    },
    {
        "id": 819,
        "statement": "there be 3 out of 10 type that be cancel - able in the dom event",
        "label": 1,
        "table_caption": "dom events",
        "table_text": "category#type#attribute#description#bubbles#cancelable\nmouse#dragstart#ondragstart#fired on an element when a drag is started#yes#yes\nkeyboard#keyup#onkeyup#fires when a key on the keyboard is released#yes#yes\nhtml frame / object#resize#onresize#fires when a document view is resized#yes#no\nhtml frame / object#scroll#onscroll#fires when a document view is scrolled#yes#no\nhtml form#submit#onsubmit#fires when a form is submitted#yes#yes\nhtml form#reset#onreset#fires when a form is reset#yes#no\nmutation#domsubtreemodified#(none)#fires when the subtree is modified#yes#no\nmutation#domnoderemoved#(none)#fires when a node has been removed from a dom - tree#yes#no\nmutation#domnoderemovedfromdocument#(none)#fires when a node is being removed from a document#no#no\nmutation#domattrmodified#(none)#fires when an attribute has been modified#yes#no\n",
        "pandas_code": "df[df['cancelable'] == 'yes'].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 820,
        "statement": "there be only 1 category that have an onreset attribute that fire when a form is reset",
        "label": 1,
        "table_caption": "dom events",
        "table_text": "category#type#attribute#description#bubbles#cancelable\nmouse#dragstart#ondragstart#fired on an element when a drag is started#yes#yes\nkeyboard#keyup#onkeyup#fires when a key on the keyboard is released#yes#yes\nhtml frame / object#resize#onresize#fires when a document view is resized#yes#no\nhtml frame / object#scroll#onscroll#fires when a document view is scrolled#yes#no\nhtml form#submit#onsubmit#fires when a form is submitted#yes#yes\nhtml form#reset#onreset#fires when a form is reset#yes#no\nmutation#domsubtreemodified#(none)#fires when the subtree is modified#yes#no\nmutation#domnoderemoved#(none)#fires when a node has been removed from a dom - tree#yes#no\nmutation#domnoderemovedfromdocument#(none)#fires when a node is being removed from a document#no#no\nmutation#domattrmodified#(none)#fires when an attribute has been modified#yes#no\n",
        "pandas_code": "len(df[(df['attribute'] == 'onreset') & (df['description'] == 'fires when a form is reset')]['category'].unique()) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 821,
        "statement": "only 3 of the list team have win a premiership",
        "label": 1,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "df['no of premierships'].gt(0).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 822,
        "statement": "the bulldog be the only team to have only compete for 1 year",
        "label": 1,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "len(df[(df['nickname'] == 'bulldogs') & (df['years in competition'].str.contains(r'^\\d{4}$'))]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 823,
        "statement": "the demon , with 8 premiership , win more premiership than the rest of the team combine",
        "label": 1,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "df[df['nickname'] == 'demons']['no of premierships'].values[0] > df[df['nickname'] != 'demons']['no of premierships'].sum()",
        "pandas_eval": "True"
    },
    {
        "id": 824,
        "statement": "more than half the team start in 1982 , and none start before then",
        "label": 1,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "((df['years in competition'].str.startswith('1982').sum() / len(df)) > 0.5) and (df['years in competition'].str.extract(r'^(\\d{4})')[0].astype(int).min() >= 1982)",
        "pandas_eval": "True"
    },
    {
        "id": 825,
        "statement": "none of the team have hold the premiership for more than 3 year in a row",
        "label": 1,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "all(df['premiership years'].apply(lambda x: max([len(list(g)) for k, g in __import__('itertools').groupby(x.split(' , ')) if k != 'none']) <= 3 if x != 'none' else True))",
        "pandas_eval": "True"
    },
    {
        "id": 826,
        "statement": "only two of the list team have win a premiership",
        "label": 0,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "df['no of premierships'].eq(0).sum() == len(df) - 2",
        "pandas_eval": "False"
    },
    {
        "id": 827,
        "statement": "the rosellas be the only team to have only compete for 1 year",
        "label": 0,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "len(df[(df['nickname'] == 'rosellas') & (df['years in competition'].str.contains(r'^\\d{4}$'))]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 828,
        "statement": "the cat , with 8 premiership , win more premiership than the rest of the team combine",
        "label": 0,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "df[df['nickname'] == 'cats']['no of premierships'].sum() > df[df['nickname'] != 'cats']['no of premierships'].sum()",
        "pandas_eval": "False"
    },
    {
        "id": 829,
        "statement": "more than half the team start in 1983 - 1992 , and none start before then",
        "label": 0,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "(df['years in competition'].str.split(' - | , ').str[0].astype(int).between(1983, 1992)).sum() > len(df)/2 and (df['years in competition'].str.split(' - | , ').str[0].astype(int) >= 1983).all()",
        "pandas_eval": "False"
    },
    {
        "id": 830,
        "statement": "1987 of the team have hold the premiership for more than 3 year in a row",
        "label": 0,
        "table_caption": "farrer football league",
        "table_text": "club#nickname#years in competition#no of premierships#premiership years\nardlethan#stars#1987 - 2003#0#none\nariah park - mirrool#brown bombers#1991 - 2003#0#none\nbarellan united#two blues#1983 - 1992#1#1987\nboree creek#creekers#1982#0#none\ncollingullie - ashmont - kapooka#demons#1982 - 1994 , 1998 - 2010#8#1982 , 1984 , 1999 , 2002 - 03 , 2008 - 09 - 10\ncootamundra#blues#1982 - 2003#0#none\njunee#bulldogs#1987#0#none\njunee - kapooka#bulldogs#1982 - 1986#0#none\nmangoplah - cookardinia united#goannas#1982 - 1994#5#1985 , 1988 - 89 - 90 , 1993\nroyal australian air force#cats#1982 - 1996#0#none\nsouth wagga - tolland#dons#1982 - 1997#0#none\nuranquinty#rosellas#1985 - 1986#0#none\nuranquinty - kapooka#rosellas#1987 - 1998#0#none\nwhitton#tigers#1982 - 1990#0#none\nwhitton - yanco#tigers#1991 - 1994#0#none\n",
        "pandas_code": "any(df['premiership years'].str.contains(r'1987.*1988.*1989|1988.*1989.*1990|1989.*1990.*1991'))",
        "pandas_eval": "False"
    },
    {
        "id": 831,
        "statement": "all 7 candidate in the louisiana house of representative election in 1934 ran unopposed",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1934",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#joachim o fernandez#democratic#1930#re - elected#joachim o fernandez (d) unopposed\nlouisiana 2#paul h maloney#democratic#1930#re - elected#paul h maloney (d) unopposed\nlouisiana 3#numa f montet#democratic#1929#re - elected#numa f montet (d) unopposed\nlouisiana 4#john n sandlin#democratic#1920#re - elected#john n sandlin (d) unopposed\nlouisiana 5#riley joseph wilson#democratic#1914#re - elected#riley joseph wilson (d) unopposed\nlouisiana 6#jared y sanders , jr#democratic#1934#re - elected#jared y sanders , jr (d) unopposed\nlouisiana 7#ren\u00e9 l de rouen#democratic#1927#re - elected#ren\u00e9 l de rouen (d) unopposed\n",
        "pandas_code": "all('unopposed' in candidate for candidate in df['candidates'])",
        "pandas_eval": "True"
    },
    {
        "id": 832,
        "statement": "riley joseph wilson be first elect earliest during the louisiana house of representative election in 1934",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1934",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#joachim o fernandez#democratic#1930#re - elected#joachim o fernandez (d) unopposed\nlouisiana 2#paul h maloney#democratic#1930#re - elected#paul h maloney (d) unopposed\nlouisiana 3#numa f montet#democratic#1929#re - elected#numa f montet (d) unopposed\nlouisiana 4#john n sandlin#democratic#1920#re - elected#john n sandlin (d) unopposed\nlouisiana 5#riley joseph wilson#democratic#1914#re - elected#riley joseph wilson (d) unopposed\nlouisiana 6#jared y sanders , jr#democratic#1934#re - elected#jared y sanders , jr (d) unopposed\nlouisiana 7#ren\u00e9 l de rouen#democratic#1927#re - elected#ren\u00e9 l de rouen (d) unopposed\n",
        "pandas_code": "df[df['incumbent'] == 'riley joseph wilson']['first elected'].min() == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 833,
        "statement": "all 7 candidate in the louisiana house of representative election in 1934 be democrat",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1934",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#joachim o fernandez#democratic#1930#re - elected#joachim o fernandez (d) unopposed\nlouisiana 2#paul h maloney#democratic#1930#re - elected#paul h maloney (d) unopposed\nlouisiana 3#numa f montet#democratic#1929#re - elected#numa f montet (d) unopposed\nlouisiana 4#john n sandlin#democratic#1920#re - elected#john n sandlin (d) unopposed\nlouisiana 5#riley joseph wilson#democratic#1914#re - elected#riley joseph wilson (d) unopposed\nlouisiana 6#jared y sanders , jr#democratic#1934#re - elected#jared y sanders , jr (d) unopposed\nlouisiana 7#ren\u00e9 l de rouen#democratic#1927#re - elected#ren\u00e9 l de rouen (d) unopposed\n",
        "pandas_code": "all(df['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 834,
        "statement": "the average year a candidate be first elect be 1926",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1934",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#joachim o fernandez#democratic#1930#re - elected#joachim o fernandez (d) unopposed\nlouisiana 2#paul h maloney#democratic#1930#re - elected#paul h maloney (d) unopposed\nlouisiana 3#numa f montet#democratic#1929#re - elected#numa f montet (d) unopposed\nlouisiana 4#john n sandlin#democratic#1920#re - elected#john n sandlin (d) unopposed\nlouisiana 5#riley joseph wilson#democratic#1914#re - elected#riley joseph wilson (d) unopposed\nlouisiana 6#jared y sanders , jr#democratic#1934#re - elected#jared y sanders , jr (d) unopposed\nlouisiana 7#ren\u00e9 l de rouen#democratic#1927#re - elected#ren\u00e9 l de rouen (d) unopposed\n",
        "pandas_code": "round(df['first elected'].mean()) == 1926",
        "pandas_eval": "True"
    },
    {
        "id": 835,
        "statement": "all of the candidate in the louisiana house of representative election in 1934 be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1934",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#joachim o fernandez#democratic#1930#re - elected#joachim o fernandez (d) unopposed\nlouisiana 2#paul h maloney#democratic#1930#re - elected#paul h maloney (d) unopposed\nlouisiana 3#numa f montet#democratic#1929#re - elected#numa f montet (d) unopposed\nlouisiana 4#john n sandlin#democratic#1920#re - elected#john n sandlin (d) unopposed\nlouisiana 5#riley joseph wilson#democratic#1914#re - elected#riley joseph wilson (d) unopposed\nlouisiana 6#jared y sanders , jr#democratic#1934#re - elected#jared y sanders , jr (d) unopposed\nlouisiana 7#ren\u00e9 l de rouen#democratic#1927#re - elected#ren\u00e9 l de rouen (d) unopposed\n",
        "pandas_code": "all(df['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 836,
        "statement": "cricket player daniel marsh have participate in the highest number of match with 71 game play",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'daniel marsh', 'matches'].values[0] == df['matches'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 837,
        "statement": "there be 2 player who have bat average in the 30s",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "len(df[(df['average'] >= 30) & (df['average'] < 40)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 838,
        "statement": "out of the player list , adam griffith have not participate in the least number of match",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'adam griffith']['matches'].values[0] != df['matches'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 839,
        "statement": "damien wright have the second lowest bat average of the list player",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'damien wright']['average'].iloc[0] == sorted(df['average'])[1]",
        "pandas_eval": "True"
    },
    {
        "id": 840,
        "statement": "brett geeves participate in fewer match than shaun young",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'brett geeves']['matches'].values[0] < df[df['player'] == 'shaun young']['matches'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 841,
        "statement": "damien wright have 23 more match than brett geeves",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "(df[df['player'] == 'damien wright']['matches'].values[0] - df[df['player'] == 'brett geeves']['matches'].values[0]) == 23",
        "pandas_eval": "True"
    },
    {
        "id": 842,
        "statement": "daniel marsh 's average of 40.60 be the highest of all player",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'daniel marsh', 'average'].values[0] == df['average'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 843,
        "statement": "brett geeves and daniel marsh both have 40 s wicket",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'].isin(['brett geeves', 'daniel marsh']), 's wicket'].eq(40).all()",
        "pandas_eval": "True"
    },
    {
        "id": 844,
        "statement": "brett geeves have the lowest average of all player",
        "label": 1,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'brett geeves', 'average'].iloc[0] == df['average'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 846,
        "statement": "cricket player daniel marsh have participate in the fewest number of match with 71 game play",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'daniel marsh']['matches'].iloc[0] == df['matches'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 847,
        "statement": "all the player have bat average in the 30s",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "all((df['average'] >= 30) & (df['average'] < 40))",
        "pandas_eval": "False"
    },
    {
        "id": 848,
        "statement": "of the player list , adam griffith participate in the second most number of match",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'adam griffith']['matches'].values[0] == df['matches'].nlargest(2).iloc[-1]",
        "pandas_eval": "False"
    },
    {
        "id": 849,
        "statement": "damien wright have the worst batting average of the list player",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'damien wright', 'average'].values[0] == df['average'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 850,
        "statement": "brett geeves participate in only 10 fewer match than shaun young",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "(df[df['player'] == 'brett geeves']['matches'].values[0] == df[df['player'] == 'shaun young']['matches'].values[0] - 10)",
        "pandas_eval": "False"
    },
    {
        "id": 851,
        "statement": "damien wright have 20 match than brett geeves",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'] == 'damien wright']['matches'].values[0] - df[df['player'] == 'brett geeves']['matches'].values[0] == 20",
        "pandas_eval": "False"
    },
    {
        "id": 852,
        "statement": "daniel marsh 's average of 28.42 be the highest of all player",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'daniel marsh', 'average'].values[0] != df['average'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 853,
        "statement": "brett geeves and daniel marsh both have 43 s wicket",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'].isin(['brett geeves', 'daniel marsh'])]['s wicket'].eq(43).all()",
        "pandas_eval": "False"
    },
    {
        "id": 854,
        "statement": "brett geeves have the highest average of all player",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df.loc[df['player'] == 'brett geeves', 'average'].max() == df['average'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 855,
        "statement": "daniel marsh and brett geeves have a tied rank of 3",
        "label": 0,
        "table_caption": "tasmania cricket team list a records",
        "table_text": "rank#s wicket#player#matches#average\n1#63#damien wright#53#28.80\n2#48#adam griffith#38#32.58\n3#43#shaun young#64#33.33\n4#40#brett geeves#30#28.42\n= 4#40#daniel marsh#71#40.60\n",
        "pandas_code": "df[df['player'].isin(['daniel marsh', 'brett geeves'])]['rank'].eq('3').all()",
        "pandas_eval": "False"
    },
    {
        "id": 856,
        "statement": "the episode with segment deal with conga drum and button be show before the episode deal with laminate and detergent",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#netflix#segment a#segment b#segment c#segment d\n4 - 01#40#s02e14#plastic bottles & s jar#mail#s egg#ed handcraft en wood s pen\n4 - 02#41#s02e15#plastic injection moulds#automotive oil filters#filing cabinets#blown glass\n4 - 03#42#s02e16#high - precision cutting tools#stained glass#s semi - trailer#s recorder\n4 - 04#43#s02e17#conga drums#metal plating#s button (part 1)#s button (part 2)\n4 - 05#44#s02e18#grinding wheels#compost#window blinds#milk\n4 - 06#45#s02e19#es brush and push brooms#s blackboard#smoked salmon#s zipper\n4 - 07#46#s02e20#3d commercial signs#hardwood floors#corrugated polyethylene pipe#es mattress\n4 - 08#47#s02e21#ceramic tiles#nuts#steel forgings#s skateboard\n4 - 09#48#s02e22#car engines#flour#s recliner#s envelope\n4 - 10#49#s02e23#plastic cups and cutlery#special effects makeup#gold#s harp\n4 - 11#50#s02e24#laminate#s frozen treat#children 's building blocks#s detergent\n4 - 12#51#s02e25#s decorative moulding#commercial pulleys#industrial rubber hose#sheet vinyl flooring\n",
        "pandas_code": "(df[df['segment a'].str.contains('conga drums') | df['segment c'].str.contains('s button')]['episode'].max() < df[df['segment a'].str.contains('laminate') | df['segment d'].str.contains('s detergent')]['episode'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 857,
        "statement": "segment b have 3 show deal with foodstuff : nut , flour , and frozen treat",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#netflix#segment a#segment b#segment c#segment d\n4 - 01#40#s02e14#plastic bottles & s jar#mail#s egg#ed handcraft en wood s pen\n4 - 02#41#s02e15#plastic injection moulds#automotive oil filters#filing cabinets#blown glass\n4 - 03#42#s02e16#high - precision cutting tools#stained glass#s semi - trailer#s recorder\n4 - 04#43#s02e17#conga drums#metal plating#s button (part 1)#s button (part 2)\n4 - 05#44#s02e18#grinding wheels#compost#window blinds#milk\n4 - 06#45#s02e19#es brush and push brooms#s blackboard#smoked salmon#s zipper\n4 - 07#46#s02e20#3d commercial signs#hardwood floors#corrugated polyethylene pipe#es mattress\n4 - 08#47#s02e21#ceramic tiles#nuts#steel forgings#s skateboard\n4 - 09#48#s02e22#car engines#flour#s recliner#s envelope\n4 - 10#49#s02e23#plastic cups and cutlery#special effects makeup#gold#s harp\n4 - 11#50#s02e24#laminate#s frozen treat#children 's building blocks#s detergent\n4 - 12#51#s02e25#s decorative moulding#commercial pulleys#industrial rubber hose#sheet vinyl flooring\n",
        "pandas_code": "df['segment b'].isin(['nuts', 'flour', 's frozen treat']).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 858,
        "statement": "segment c have 2 show deal with foodstuff : egg and smoke salmon",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#netflix#segment a#segment b#segment c#segment d\n4 - 01#40#s02e14#plastic bottles & s jar#mail#s egg#ed handcraft en wood s pen\n4 - 02#41#s02e15#plastic injection moulds#automotive oil filters#filing cabinets#blown glass\n4 - 03#42#s02e16#high - precision cutting tools#stained glass#s semi - trailer#s recorder\n4 - 04#43#s02e17#conga drums#metal plating#s button (part 1)#s button (part 2)\n4 - 05#44#s02e18#grinding wheels#compost#window blinds#milk\n4 - 06#45#s02e19#es brush and push brooms#s blackboard#smoked salmon#s zipper\n4 - 07#46#s02e20#3d commercial signs#hardwood floors#corrugated polyethylene pipe#es mattress\n4 - 08#47#s02e21#ceramic tiles#nuts#steel forgings#s skateboard\n4 - 09#48#s02e22#car engines#flour#s recliner#s envelope\n4 - 10#49#s02e23#plastic cups and cutlery#special effects makeup#gold#s harp\n4 - 11#50#s02e24#laminate#s frozen treat#children 's building blocks#s detergent\n4 - 12#51#s02e25#s decorative moulding#commercial pulleys#industrial rubber hose#sheet vinyl flooring\n",
        "pandas_code": "len(df[df['segment c'].str.contains('egg|smoked salmon', case=False)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 859,
        "statement": "segment d have 1 show deal with foodstuff : milk",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#netflix#segment a#segment b#segment c#segment d\n4 - 01#40#s02e14#plastic bottles & s jar#mail#s egg#ed handcraft en wood s pen\n4 - 02#41#s02e15#plastic injection moulds#automotive oil filters#filing cabinets#blown glass\n4 - 03#42#s02e16#high - precision cutting tools#stained glass#s semi - trailer#s recorder\n4 - 04#43#s02e17#conga drums#metal plating#s button (part 1)#s button (part 2)\n4 - 05#44#s02e18#grinding wheels#compost#window blinds#milk\n4 - 06#45#s02e19#es brush and push brooms#s blackboard#smoked salmon#s zipper\n4 - 07#46#s02e20#3d commercial signs#hardwood floors#corrugated polyethylene pipe#es mattress\n4 - 08#47#s02e21#ceramic tiles#nuts#steel forgings#s skateboard\n4 - 09#48#s02e22#car engines#flour#s recliner#s envelope\n4 - 10#49#s02e23#plastic cups and cutlery#special effects makeup#gold#s harp\n4 - 11#50#s02e24#laminate#s frozen treat#children 's building blocks#s detergent\n4 - 12#51#s02e25#s decorative moulding#commercial pulleys#industrial rubber hose#sheet vinyl flooring\n",
        "pandas_code": "df[df['segment d'].str.contains('milk', case=False, na=False)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 861,
        "statement": "out of 13 game , 6 of which the buffalo bill win against the opposing team",
        "label": 1,
        "table_caption": "1973 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record\n1#sept 16#new england patriots#win#31#13#23#1 - 0\n2#sept 23#san diego chargers#loss#7#34#16#1 - 1\n3#sept 30#new york jets#win#9#7#15#2 - 1\n4#oct 7#philadelphia eagles#win#27#26#16#3 - 1\n5#oct 14#baltimore colts#win#31#13#18#4 - 1\n6#oct 21#miami dolphins#loss#6#27#8#4 - 2\n7#oct 29#kansas city chiefs#win#23#14#21#5 - 2\n8#nov 4#new orleans saints#loss#0#13#10#5 - 3\n9#nov 11#cincinnati bengals#loss#13#16#10#5 - 4\n10#nov 18#miami dolphins#loss#0#17#15#5 - 5\n11#nov 25#baltimore colts#win#24#17#16#6 - 5\n12#dec 2#atlanta falcons#win#17#6#17#7 - 5\n13#dec 9#new england patriots#win#37#13#13#8 - 5\n",
        "pandas_code": "len(df[df['result'] == 'win']) >= 6",
        "pandas_eval": "True"
    },
    {
        "id": 862,
        "statement": "out of 13 game , 7 of which the buffalo bill lose against the opposing team",
        "label": 1,
        "table_caption": "1973 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record\n1#sept 16#new england patriots#win#31#13#23#1 - 0\n2#sept 23#san diego chargers#loss#7#34#16#1 - 1\n3#sept 30#new york jets#win#9#7#15#2 - 1\n4#oct 7#philadelphia eagles#win#27#26#16#3 - 1\n5#oct 14#baltimore colts#win#31#13#18#4 - 1\n6#oct 21#miami dolphins#loss#6#27#8#4 - 2\n7#oct 29#kansas city chiefs#win#23#14#21#5 - 2\n8#nov 4#new orleans saints#loss#0#13#10#5 - 3\n9#nov 11#cincinnati bengals#loss#13#16#10#5 - 4\n10#nov 18#miami dolphins#loss#0#17#15#5 - 5\n11#nov 25#baltimore colts#win#24#17#16#6 - 5\n12#dec 2#atlanta falcons#win#17#6#17#7 - 5\n13#dec 9#new england patriots#win#37#13#13#8 - 5\n",
        "pandas_code": "len(df[df['result'] == 'loss']) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 863,
        "statement": "out of 13 game , 4 of which be hold during october 1973",
        "label": 1,
        "table_caption": "1973 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record\n1#sept 16#new england patriots#win#31#13#23#1 - 0\n2#sept 23#san diego chargers#loss#7#34#16#1 - 1\n3#sept 30#new york jets#win#9#7#15#2 - 1\n4#oct 7#philadelphia eagles#win#27#26#16#3 - 1\n5#oct 14#baltimore colts#win#31#13#18#4 - 1\n6#oct 21#miami dolphins#loss#6#27#8#4 - 2\n7#oct 29#kansas city chiefs#win#23#14#21#5 - 2\n8#nov 4#new orleans saints#loss#0#13#10#5 - 3\n9#nov 11#cincinnati bengals#loss#13#16#10#5 - 4\n10#nov 18#miami dolphins#loss#0#17#15#5 - 5\n11#nov 25#baltimore colts#win#24#17#16#6 - 5\n12#dec 2#atlanta falcons#win#17#6#17#7 - 5\n13#dec 9#new england patriots#win#37#13#13#8 - 5\n",
        "pandas_code": "len(df[df['date'].str.startswith('oct')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 864,
        "statement": "out of 13 game , 4 of which be hold during november 1973",
        "label": 1,
        "table_caption": "1973 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record\n1#sept 16#new england patriots#win#31#13#23#1 - 0\n2#sept 23#san diego chargers#loss#7#34#16#1 - 1\n3#sept 30#new york jets#win#9#7#15#2 - 1\n4#oct 7#philadelphia eagles#win#27#26#16#3 - 1\n5#oct 14#baltimore colts#win#31#13#18#4 - 1\n6#oct 21#miami dolphins#loss#6#27#8#4 - 2\n7#oct 29#kansas city chiefs#win#23#14#21#5 - 2\n8#nov 4#new orleans saints#loss#0#13#10#5 - 3\n9#nov 11#cincinnati bengals#loss#13#16#10#5 - 4\n10#nov 18#miami dolphins#loss#0#17#15#5 - 5\n11#nov 25#baltimore colts#win#24#17#16#6 - 5\n12#dec 2#atlanta falcons#win#17#6#17#7 - 5\n13#dec 9#new england patriots#win#37#13#13#8 - 5\n",
        "pandas_code": "len(df[df['date'].str.contains('nov')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 865,
        "statement": "out of 13 game , 2 of which be hold during december 1973",
        "label": 1,
        "table_caption": "1973 buffalo bills season",
        "table_text": "game#date#opponent#result#bills points#opponents#bills first downs#record\n1#sept 16#new england patriots#win#31#13#23#1 - 0\n2#sept 23#san diego chargers#loss#7#34#16#1 - 1\n3#sept 30#new york jets#win#9#7#15#2 - 1\n4#oct 7#philadelphia eagles#win#27#26#16#3 - 1\n5#oct 14#baltimore colts#win#31#13#18#4 - 1\n6#oct 21#miami dolphins#loss#6#27#8#4 - 2\n7#oct 29#kansas city chiefs#win#23#14#21#5 - 2\n8#nov 4#new orleans saints#loss#0#13#10#5 - 3\n9#nov 11#cincinnati bengals#loss#13#16#10#5 - 4\n10#nov 18#miami dolphins#loss#0#17#15#5 - 5\n11#nov 25#baltimore colts#win#24#17#16#6 - 5\n12#dec 2#atlanta falcons#win#17#6#17#7 - 5\n13#dec 9#new england patriots#win#37#13#13#8 - 5\n",
        "pandas_code": "len(df[df['date'].str.contains('dec')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 866,
        "statement": "2 of the 5 school location be in mumbai",
        "label": 1,
        "table_caption": "indian institute of technology bombay",
        "table_text": "school#location#pubs 2010#totals 06 - 10#rank 06 - 10#pubs 2011#totals 07 - 11#rank 07 - 11#world ranking\nict , mumbai ( udct )#mumbai#185#960#1#198#1025#1#4\niit bombay#mumbai#72#368#2#75#393#2#na\niit kanpur#kanpur#72#356#3#80#378#3#na\niit kharagpur#kharagpur#68#301#4#59#305#4#na\niit madras#chennai#60#263#5#55#281#5#na\n",
        "pandas_code": "(df['location'].value_counts()['mumbai'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 867,
        "statement": "only 1 of the 5 school be locate in kanpur",
        "label": 1,
        "table_caption": "indian institute of technology bombay",
        "table_text": "school#location#pubs 2010#totals 06 - 10#rank 06 - 10#pubs 2011#totals 07 - 11#rank 07 - 11#world ranking\nict , mumbai ( udct )#mumbai#185#960#1#198#1025#1#4\niit bombay#mumbai#72#368#2#75#393#2#na\niit kanpur#kanpur#72#356#3#80#378#3#na\niit kharagpur#kharagpur#68#301#4#59#305#4#na\niit madras#chennai#60#263#5#55#281#5#na\n",
        "pandas_code": "(df['location'] == 'kanpur').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 868,
        "statement": "only 1 of the 5 school be locate in kharagpur",
        "label": 1,
        "table_caption": "indian institute of technology bombay",
        "table_text": "school#location#pubs 2010#totals 06 - 10#rank 06 - 10#pubs 2011#totals 07 - 11#rank 07 - 11#world ranking\nict , mumbai ( udct )#mumbai#185#960#1#198#1025#1#4\niit bombay#mumbai#72#368#2#75#393#2#na\niit kanpur#kanpur#72#356#3#80#378#3#na\niit kharagpur#kharagpur#68#301#4#59#305#4#na\niit madras#chennai#60#263#5#55#281#5#na\n",
        "pandas_code": "df[df['location'] == 'kharagpur'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 869,
        "statement": "only 1 of the 5 school be locate in chennai",
        "label": 1,
        "table_caption": "indian institute of technology bombay",
        "table_text": "school#location#pubs 2010#totals 06 - 10#rank 06 - 10#pubs 2011#totals 07 - 11#rank 07 - 11#world ranking\nict , mumbai ( udct )#mumbai#185#960#1#198#1025#1#4\niit bombay#mumbai#72#368#2#75#393#2#na\niit kanpur#kanpur#72#356#3#80#378#3#na\niit kharagpur#kharagpur#68#301#4#59#305#4#na\niit madras#chennai#60#263#5#55#281#5#na\n",
        "pandas_code": "df[df['location'] == 'chennai'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 870,
        "statement": "ict , mumbai ( udct ) be the only school with a world ranking",
        "label": 1,
        "table_caption": "indian institute of technology bombay",
        "table_text": "school#location#pubs 2010#totals 06 - 10#rank 06 - 10#pubs 2011#totals 07 - 11#rank 07 - 11#world ranking\nict , mumbai ( udct )#mumbai#185#960#1#198#1025#1#4\niit bombay#mumbai#72#368#2#75#393#2#na\niit kanpur#kanpur#72#356#3#80#378#3#na\niit kharagpur#kharagpur#68#301#4#59#305#4#na\niit madras#chennai#60#263#5#55#281#5#na\n",
        "pandas_code": "df[df['world ranking'] != 'na']['school'].unique().tolist() == ['ict , mumbai ( udct )']",
        "pandas_eval": "True"
    },
    {
        "id": 871,
        "statement": "all of the album be release at least 2 time each",
        "label": 1,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df.groupby('album').size().ge(2).all()",
        "pandas_eval": "True"
    },
    {
        "id": 873,
        "statement": "only the second release of how you luv that vol 2 do not chart",
        "label": 1,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df[(df['album'] == 'how you luv that vol 2') & (df['us'] == '-') & (df['us r&b'] == '-') & (df['us rap'] == '-')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 874,
        "statement": "the longest gap between 2 album release be 2 year",
        "label": 1,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "(df['year'].diff().max() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 875,
        "statement": "big money heavyweight be the only album to be release 3 time",
        "label": 1,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df['album'].value_counts().eq(3).any()",
        "pandas_eval": "True"
    },
    {
        "id": 876,
        "statement": "all of the album be release at least 4 time each",
        "label": 0,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df['album'].value_counts().ge(4).all()",
        "pandas_eval": "False"
    },
    {
        "id": 877,
        "statement": "hood rich have the worst chart performance of any album",
        "label": 0,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df[df['album'] == 'hood rich'][['us', 'us r&b', 'us rap']].replace('-', '0').astype(int).min().min() == df[['us', 'us r&b', 'us rap']].replace('-', '0').astype(int).min().min()",
        "pandas_eval": "False"
    },
    {
        "id": 878,
        "statement": "only the second release of i got that work do not chart",
        "label": 0,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "df[(df['album'] == 'i got that work') & (df['us'] == '-') & (df['us r&b'] == '-') & (df['us rap'] == '-')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 879,
        "statement": "the longest gap between 2 album release be 5 year",
        "label": 0,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "(df['year'].max() - df['year'].min()) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 880,
        "statement": "big money heavyweight be the only album to be release 4 time",
        "label": 0,
        "table_caption": "big tymers discography",
        "table_text": "year#us#us r&b#us rap#album\n1998#-#106#-#how you luv that vol 2\n1998#-#-#-#how you luv that vol 2\n2000#101#24#-#i got that work\n2000#105#24#-#i got that work\n2002#11#4#3#hood rich\n2002#46#23#13#hood rich\n2003#97#53#-#big money heavyweight\n2004#79#38#24#big money heavyweight\n2004#-#95#-#big money heavyweight\n",
        "pandas_code": "len(df[df['album'] == 'big money heavyweight']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 881,
        "statement": "the france feed cup team have year end ranking of 1 for 2 year in a row",
        "label": 1,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year end ranking', ['2003', '2004']].values == '1').all()",
        "pandas_eval": "True"
    },
    {
        "id": 882,
        "statement": "the france feed cup team have their best season of 4 - 0 in 2003",
        "label": 1,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "df.loc[df['tournament'] == 'win - loss', '2003'].values[0] == '4 - 0'",
        "pandas_eval": "True"
    },
    {
        "id": 883,
        "statement": "the france feed cup team do not play in world group ii from 2002 - 2004",
        "label": 1,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "all(df.loc[df['tournament'] == 'world group ii', ['2002', '2003', '2004']].apply(lambda x: x == 'not held'))",
        "pandas_eval": "True"
    },
    {
        "id": 884,
        "statement": "the france feed cup team have year end ranking of 5 or under for 6 out of 11 year",
        "label": 1,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year end ranking'].iloc[:, 1:].astype(int) <= 5).sum(axis=1).values[0] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 885,
        "statement": "the france feed cup team have year end ranking of 3 for 2 year in a row",
        "label": 1,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year end ranking', ['2006', '2007']].values == '3').all()",
        "pandas_eval": "True"
    },
    {
        "id": 886,
        "statement": "the france feed cup team have year end ranking of 20 for 2 year in a row",
        "label": 0,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year end ranking', ['2011', '2012']].astype(int) == 20).all().all()",
        "pandas_eval": "False"
    },
    {
        "id": 887,
        "statement": "the france feed cup team have their best season of 4 - 0 in 2008",
        "label": 0,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'win - loss', '2008'].values[0] == '4 - 0')",
        "pandas_eval": "False"
    },
    {
        "id": 888,
        "statement": "the france feed cup team do not play in world group ii from 2005 - 2006",
        "label": 0,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "not all(df.loc[df['tournament'] == 'world group ii', ['2005', '2006']].isin(['a', 'not held']).all())",
        "pandas_eval": "False"
    },
    {
        "id": 889,
        "statement": "the france feed cup team have year end ranking of 2 or under for 6 out of 11 year",
        "label": 0,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year end ranking'].iloc[:, 1:].astype(int) <= 2).sum().sum() >= 6",
        "pandas_eval": "False"
    },
    {
        "id": 890,
        "statement": "the france feed cup team have year end ranking of 7 for 2 year in a row",
        "label": 0,
        "table_caption": "france fed cup team",
        "table_text": "tournament#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\nfed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup#fed cup\nworld group#qf#w#f#f#qf#sf#qf#qf#qf#qf#a\nworld group play - offs#a#a#a#a#w#a#w#w#w#l#a\nworld group ii#not held#not held#not held#a#a#a#a#a#a#a#l\nworld group ii play - offs#not held#not held#not held#a#a#a#a#a#a#a#w\neurope / africa group i#a#a#a#a#a#a#a#a#a#a#a\nwin - loss#1 - 1#4 - 0#3 - 1#2 - 1#1 - 1#1 - 1#1 - 1#1 - 1#1 - 1#0 - 2#1 - 1\nyear end ranking#6#1#1#2#3#3#5#11#9#15#16\n",
        "pandas_code": "((df.loc[df['tournament'] == 'year end ranking', '2002'] == '7').any() and (df.loc[df['tournament'] == 'year end ranking', '2003'] == '7').any())",
        "pandas_eval": "False"
    },
    {
        "id": 891,
        "statement": "the toronto argonaut be undefeated at the skydome in 1997",
        "label": 1,
        "table_caption": "1997 toronto argonauts season",
        "table_text": "week#date#opponent#location#final score#attendance#record\n1#june 27#blue bombers#skydome#w 38 - 23#16551#1 - 0\n2#july 6#tiger - cats#ivor wynne stadium#w 20 - 15#14033#2 - 0\n3#july 12#roughriders#taylor field#l 27 - 23#22956#2 - 1\n4#july 17#tiger - cats#skydome#w 27 - 20#17222#3 - 1\n5#july 24#lions#bc place stadium#w 34 - 20#23330#4 - 1\n6#july 31#alouettes#skydome#w 46 - 8#16213#5 - 1\n7#august 7#stampeders#mcmahon stadium#l 45 - 35#25311#5 - 2\n8#august 14#eskimos#skydome#w 38 - 14#18031#6 - 2\n9#august 21#roughriders#skydome#w 27 - 1#17330#7 - 2\n10#september 1#tiger - cats#ivor wynne stadium#w 46 - 3#18377#8 - 2\n11#september 7#blue bombers#canad inns stadium#w 66 - 25#21080#9 - 2\n12#september 14#tiger - cats#skydome#w 34 - 9#20234#10 - 2\n13#september 20#eskimos#commonwealth stadium#w 25 - 24#38619#11 - 2\n14#september 28#blue bombers#canad inns stadium#w 41 - 9#20004#12 - 2\n15#october 4#lions#skydome#w 46 - 3#17019#13 - 2\n16#october 11#alouettes#skydome#w 28 - 21#17355#14 - 2\n17#october 18#stampeders#skydome#w 48 - 17#24083#15 - 2\n",
        "pandas_code": "all(df[df['location'] == 'skydome']['final score'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 892,
        "statement": "after loss to the roughrider and the stampeders , the argonaut go on a 10 game win streak to finish the 1997 season",
        "label": 1,
        "table_caption": "1997 toronto argonauts season",
        "table_text": "week#date#opponent#location#final score#attendance#record\n1#june 27#blue bombers#skydome#w 38 - 23#16551#1 - 0\n2#july 6#tiger - cats#ivor wynne stadium#w 20 - 15#14033#2 - 0\n3#july 12#roughriders#taylor field#l 27 - 23#22956#2 - 1\n4#july 17#tiger - cats#skydome#w 27 - 20#17222#3 - 1\n5#july 24#lions#bc place stadium#w 34 - 20#23330#4 - 1\n6#july 31#alouettes#skydome#w 46 - 8#16213#5 - 1\n7#august 7#stampeders#mcmahon stadium#l 45 - 35#25311#5 - 2\n8#august 14#eskimos#skydome#w 38 - 14#18031#6 - 2\n9#august 21#roughriders#skydome#w 27 - 1#17330#7 - 2\n10#september 1#tiger - cats#ivor wynne stadium#w 46 - 3#18377#8 - 2\n11#september 7#blue bombers#canad inns stadium#w 66 - 25#21080#9 - 2\n12#september 14#tiger - cats#skydome#w 34 - 9#20234#10 - 2\n13#september 20#eskimos#commonwealth stadium#w 25 - 24#38619#11 - 2\n14#september 28#blue bombers#canad inns stadium#w 41 - 9#20004#12 - 2\n15#october 4#lions#skydome#w 46 - 3#17019#13 - 2\n16#october 11#alouettes#skydome#w 28 - 21#17355#14 - 2\n17#october 18#stampeders#skydome#w 48 - 17#24083#15 - 2\n",
        "pandas_code": "(df[df['opponent'] == 'roughriders']['final score'].iloc[0].startswith('l')) & (df[df['opponent'] == 'stampeders']['final score'].iloc[0].startswith('l')) & (df['record'].iloc[-1] == '15 - 2')",
        "pandas_eval": "True"
    },
    {
        "id": 893,
        "statement": "the highest attendance for the toronto argonauts in the skydome be 24083 in a game against the stampeders",
        "label": 1,
        "table_caption": "1997 toronto argonauts season",
        "table_text": "week#date#opponent#location#final score#attendance#record\n1#june 27#blue bombers#skydome#w 38 - 23#16551#1 - 0\n2#july 6#tiger - cats#ivor wynne stadium#w 20 - 15#14033#2 - 0\n3#july 12#roughriders#taylor field#l 27 - 23#22956#2 - 1\n4#july 17#tiger - cats#skydome#w 27 - 20#17222#3 - 1\n5#july 24#lions#bc place stadium#w 34 - 20#23330#4 - 1\n6#july 31#alouettes#skydome#w 46 - 8#16213#5 - 1\n7#august 7#stampeders#mcmahon stadium#l 45 - 35#25311#5 - 2\n8#august 14#eskimos#skydome#w 38 - 14#18031#6 - 2\n9#august 21#roughriders#skydome#w 27 - 1#17330#7 - 2\n10#september 1#tiger - cats#ivor wynne stadium#w 46 - 3#18377#8 - 2\n11#september 7#blue bombers#canad inns stadium#w 66 - 25#21080#9 - 2\n12#september 14#tiger - cats#skydome#w 34 - 9#20234#10 - 2\n13#september 20#eskimos#commonwealth stadium#w 25 - 24#38619#11 - 2\n14#september 28#blue bombers#canad inns stadium#w 41 - 9#20004#12 - 2\n15#october 4#lions#skydome#w 46 - 3#17019#13 - 2\n16#october 11#alouettes#skydome#w 28 - 21#17355#14 - 2\n17#october 18#stampeders#skydome#w 48 - 17#24083#15 - 2\n",
        "pandas_code": "df[(df['location'] == 'skydome') & (df['attendance'] == 24083) & (df['opponent'] == 'stampeders')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 894,
        "statement": "the 1997 toronto argonaut score 20 point or more in all of their game",
        "label": 1,
        "table_caption": "1997 toronto argonauts season",
        "table_text": "week#date#opponent#location#final score#attendance#record\n1#june 27#blue bombers#skydome#w 38 - 23#16551#1 - 0\n2#july 6#tiger - cats#ivor wynne stadium#w 20 - 15#14033#2 - 0\n3#july 12#roughriders#taylor field#l 27 - 23#22956#2 - 1\n4#july 17#tiger - cats#skydome#w 27 - 20#17222#3 - 1\n5#july 24#lions#bc place stadium#w 34 - 20#23330#4 - 1\n6#july 31#alouettes#skydome#w 46 - 8#16213#5 - 1\n7#august 7#stampeders#mcmahon stadium#l 45 - 35#25311#5 - 2\n8#august 14#eskimos#skydome#w 38 - 14#18031#6 - 2\n9#august 21#roughriders#skydome#w 27 - 1#17330#7 - 2\n10#september 1#tiger - cats#ivor wynne stadium#w 46 - 3#18377#8 - 2\n11#september 7#blue bombers#canad inns stadium#w 66 - 25#21080#9 - 2\n12#september 14#tiger - cats#skydome#w 34 - 9#20234#10 - 2\n13#september 20#eskimos#commonwealth stadium#w 25 - 24#38619#11 - 2\n14#september 28#blue bombers#canad inns stadium#w 41 - 9#20004#12 - 2\n15#october 4#lions#skydome#w 46 - 3#17019#13 - 2\n16#october 11#alouettes#skydome#w 28 - 21#17355#14 - 2\n17#october 18#stampeders#skydome#w 48 - 17#24083#15 - 2\n",
        "pandas_code": "all(df['final score'].str.extract(r'(\\d+) - \\d+')[0].astype(int) >= 20)",
        "pandas_eval": "True"
    },
    {
        "id": 896,
        "statement": "the captain for the club adelaide unite be eugene galekovi\u0107 and the captain for the club brisbane roar be matt smith",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\n",
        "pandas_code": "(df.loc[df['club'] == 'adelaide united', 'captain'].iloc[0] == 'eugene galekovi\u0107') & (df.loc[df['club'] == 'brisbane roar', 'captain'].iloc[0] == 'matt smith')",
        "pandas_eval": "True"
    },
    {
        "id": 897,
        "statement": "the club adelaide unite , brisbane roar , and central coast mariner do not have a junior marquee player",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\n",
        "pandas_code": "all(df[df['club'].isin(['adelaide united', 'brisbane roar', 'central coast mariners'])]['junior marquee player'] == 'none')",
        "pandas_eval": "True"
    },
    {
        "id": 898,
        "statement": "the club perth glory , sydney fc , and wellington phoenix do not have a junior marquee player",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\n",
        "pandas_code": "all(df[df['club'].isin(['perth glory', 'sydney fc', 'wellington phoenix'])]['junior marquee player'] == 'none')",
        "pandas_eval": "True"
    },
    {
        "id": 899,
        "statement": "the vice - captain for the club melbourne victory and perth glory be leigh broxham and travis dodd , respectively",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\n",
        "pandas_code": "(df.loc[df['club'] == 'melbourne victory', 'vice - captain'].iloc[0] == 'leigh broxham') & (df.loc[df['club'] == 'perth glory', 'vice - captain'].iloc[0] == 'travis dodd')",
        "pandas_eval": "True"
    },
    {
        "id": 900,
        "statement": "the captain for the club sydney fc and newcastle jet be alessandro del piero and ruben zadkovich , respectively",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "club#australian marquee#international marquee#junior marquee player#captain#vice - captain\nadelaide united#none#none#none#eugene galekovi\u0107#c\u00e1ssio\nbrisbane roar#matt mckay#thomas broich#none#matt smith#shane stefanutto\ncentral coast mariners#none#none#none#john hutchinson#michael mcglinchey\nmelbourne heart#none#orlando engelaar#none#harry kewell#tba\nmelbourne victory#archie thompson#pablo contreras#none#mark milligan#leigh broxham\nnewcastle jets#none#emile heskey#none#ruben zadkovich#tba\nperth glory#none#william gallas#none#jacob burns#travis dodd\nsydney fc#brett emerton#alessandro del piero#none#alessandro del piero#brett emerton\nwellington phoenix#none#none#none#andrew durante#ben sigmund\n",
        "pandas_code": "(df.loc[df['club'] == 'sydney fc', 'captain'].values[0] == 'alessandro del piero') & (df.loc[df['club'] == 'newcastle jets', 'captain'].values[0] == 'ruben zadkovich')",
        "pandas_eval": "True"
    },
    {
        "id": 901,
        "statement": "all date occur in the second half of april",
        "label": 1,
        "table_caption": "ana timoti\u0107",
        "table_text": "edition#zone#round#date#against#surface#opponent#outcome#result\n2005#europe / africa group i c#1r#21 april 2005#slovenia#clay#tina pisnik#loss#6 - 0 , 6 - 1\n2005#europe / africa group i c#2r#22 april 2005#great britain#clay#elena baltacha#win#5 - 7 , 6 - 3 , 6 - 0\n2005#europe / africa group play - off#semifinal#23 april 2005#israel#clay#shahar pe'er#loss#6 - 4 , 4 - 6 , 6 - 3\n2006#europe / africa group i b#1r#18 april 2006#slovenia#clay#polona reber\u0161ak#loss#6 - 3 , 6 - 2\n2006#europe / africa group i b#3r#20 april 2006#denmark#clay#eva dyrberg#win#2 - 6 , 6 - 4 , 6 - 4\n2006#europe / africa group i#semifinal#22 april 2006#israel#clay#anna smashnova#win#5 - 7 , 4 - 5 ret\n2007#europe / africa group i c#1r#20 april 2007#slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#loss#6 - 3 , 4 - 6 , 6 - 0\n2007#europe / africa group i c#semifinal#21 april 2007#romania#clay#m\u0103d\u0103lina gojnea#loss#1 - 6 , 6 - 4 , 6 - 3\n",
        "pandas_code": "all(pd.to_datetime(df['date'], format='%d %B %Y').dt.day >= 15)",
        "pandas_eval": "True"
    },
    {
        "id": 902,
        "statement": "slovenia be the against more often than anything else",
        "label": 1,
        "table_caption": "ana timoti\u0107",
        "table_text": "edition#zone#round#date#against#surface#opponent#outcome#result\n2005#europe / africa group i c#1r#21 april 2005#slovenia#clay#tina pisnik#loss#6 - 0 , 6 - 1\n2005#europe / africa group i c#2r#22 april 2005#great britain#clay#elena baltacha#win#5 - 7 , 6 - 3 , 6 - 0\n2005#europe / africa group play - off#semifinal#23 april 2005#israel#clay#shahar pe'er#loss#6 - 4 , 4 - 6 , 6 - 3\n2006#europe / africa group i b#1r#18 april 2006#slovenia#clay#polona reber\u0161ak#loss#6 - 3 , 6 - 2\n2006#europe / africa group i b#3r#20 april 2006#denmark#clay#eva dyrberg#win#2 - 6 , 6 - 4 , 6 - 4\n2006#europe / africa group i#semifinal#22 april 2006#israel#clay#anna smashnova#win#5 - 7 , 4 - 5 ret\n2007#europe / africa group i c#1r#20 april 2007#slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#loss#6 - 3 , 4 - 6 , 6 - 0\n2007#europe / africa group i c#semifinal#21 april 2007#romania#clay#m\u0103d\u0103lina gojnea#loss#1 - 6 , 6 - 4 , 6 - 3\n",
        "pandas_code": "df['against'].value_counts().idxmax() == 'slovenia'",
        "pandas_eval": "True"
    },
    {
        "id": 903,
        "statement": "more game outcome be loss than win",
        "label": 1,
        "table_caption": "ana timoti\u0107",
        "table_text": "edition#zone#round#date#against#surface#opponent#outcome#result\n2005#europe / africa group i c#1r#21 april 2005#slovenia#clay#tina pisnik#loss#6 - 0 , 6 - 1\n2005#europe / africa group i c#2r#22 april 2005#great britain#clay#elena baltacha#win#5 - 7 , 6 - 3 , 6 - 0\n2005#europe / africa group play - off#semifinal#23 april 2005#israel#clay#shahar pe'er#loss#6 - 4 , 4 - 6 , 6 - 3\n2006#europe / africa group i b#1r#18 april 2006#slovenia#clay#polona reber\u0161ak#loss#6 - 3 , 6 - 2\n2006#europe / africa group i b#3r#20 april 2006#denmark#clay#eva dyrberg#win#2 - 6 , 6 - 4 , 6 - 4\n2006#europe / africa group i#semifinal#22 april 2006#israel#clay#anna smashnova#win#5 - 7 , 4 - 5 ret\n2007#europe / africa group i c#1r#20 april 2007#slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#loss#6 - 3 , 4 - 6 , 6 - 0\n2007#europe / africa group i c#semifinal#21 april 2007#romania#clay#m\u0103d\u0103lina gojnea#loss#1 - 6 , 6 - 4 , 6 - 3\n",
        "pandas_code": "df['outcome'].value_counts()['loss'] > df['outcome'].value_counts()['win']",
        "pandas_eval": "True"
    },
    {
        "id": 904,
        "statement": "semi final round be against israel more than romania",
        "label": 1,
        "table_caption": "ana timoti\u0107",
        "table_text": "edition#zone#round#date#against#surface#opponent#outcome#result\n2005#europe / africa group i c#1r#21 april 2005#slovenia#clay#tina pisnik#loss#6 - 0 , 6 - 1\n2005#europe / africa group i c#2r#22 april 2005#great britain#clay#elena baltacha#win#5 - 7 , 6 - 3 , 6 - 0\n2005#europe / africa group play - off#semifinal#23 april 2005#israel#clay#shahar pe'er#loss#6 - 4 , 4 - 6 , 6 - 3\n2006#europe / africa group i b#1r#18 april 2006#slovenia#clay#polona reber\u0161ak#loss#6 - 3 , 6 - 2\n2006#europe / africa group i b#3r#20 april 2006#denmark#clay#eva dyrberg#win#2 - 6 , 6 - 4 , 6 - 4\n2006#europe / africa group i#semifinal#22 april 2006#israel#clay#anna smashnova#win#5 - 7 , 4 - 5 ret\n2007#europe / africa group i c#1r#20 april 2007#slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#loss#6 - 3 , 4 - 6 , 6 - 0\n2007#europe / africa group i c#semifinal#21 april 2007#romania#clay#m\u0103d\u0103lina gojnea#loss#1 - 6 , 6 - 4 , 6 - 3\n",
        "pandas_code": "(df[df['round'] == 'semifinal']['against'].value_counts().get('israel', 0) > df[df['round'] == 'semifinal']['against'].value_counts().get('romania', 0))",
        "pandas_eval": "True"
    },
    {
        "id": 905,
        "statement": "the opponent have never be the same",
        "label": 1,
        "table_caption": "ana timoti\u0107",
        "table_text": "edition#zone#round#date#against#surface#opponent#outcome#result\n2005#europe / africa group i c#1r#21 april 2005#slovenia#clay#tina pisnik#loss#6 - 0 , 6 - 1\n2005#europe / africa group i c#2r#22 april 2005#great britain#clay#elena baltacha#win#5 - 7 , 6 - 3 , 6 - 0\n2005#europe / africa group play - off#semifinal#23 april 2005#israel#clay#shahar pe'er#loss#6 - 4 , 4 - 6 , 6 - 3\n2006#europe / africa group i b#1r#18 april 2006#slovenia#clay#polona reber\u0161ak#loss#6 - 3 , 6 - 2\n2006#europe / africa group i b#3r#20 april 2006#denmark#clay#eva dyrberg#win#2 - 6 , 6 - 4 , 6 - 4\n2006#europe / africa group i#semifinal#22 april 2006#israel#clay#anna smashnova#win#5 - 7 , 4 - 5 ret\n2007#europe / africa group i c#1r#20 april 2007#slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#loss#6 - 3 , 4 - 6 , 6 - 0\n2007#europe / africa group i c#semifinal#21 april 2007#romania#clay#m\u0103d\u0103lina gojnea#loss#1 - 6 , 6 - 4 , 6 - 3\n",
        "pandas_code": "df['opponent'].nunique() == len(df)",
        "pandas_eval": "True"
    },
    {
        "id": 906,
        "statement": "football be the most common sport in the black diamond conference",
        "label": 1,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df['sports'].str.split(' , ').explode().value_counts().idxmax() == 'football'",
        "pandas_eval": "True"
    },
    {
        "id": 907,
        "statement": "the highest enrollment of the 2013 season in the black diamond conference be 549",
        "label": 1,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df['enrollment (2013 / 14)'].max() == 549",
        "pandas_eval": "True"
    },
    {
        "id": 908,
        "statement": "sesser - valler have the lowest enrollment of the 2013 season in the black diamond conference with only 309",
        "label": 1,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df[df['team name'].str.contains('sesser - valier')]['enrollment (2013 / 14)'].min() == 309",
        "pandas_eval": "True"
    },
    {
        "id": 909,
        "statement": "there be 2 out of 7 team total whose mascot be the eagle",
        "label": 1,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "(df['nickname (s)'].str.contains('eagles', case=False).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 910,
        "statement": "there be only 1 wrestling team in the black diamond conference for 2013",
        "label": 1,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df[df['sports'].str.contains('wrestling')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 911,
        "statement": "football be the least common sport in the green diamond conference",
        "label": 0,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df['sports'].str.contains('football').sum() == df['sports'].str.contains('|'.join(df['sports'].explode().value_counts().idxmin())).sum()",
        "pandas_eval": "False"
    },
    {
        "id": 912,
        "statement": "the highest enrollment of the 2011 season in the black diamond conference be 529",
        "label": 0,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df['enrollment (2013 / 14)'].max() == 529",
        "pandas_eval": "False"
    },
    {
        "id": 913,
        "statement": "valler have the highest enrollment of the 2009 season in the black diamond conference with only 309",
        "label": 0,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "df[df['team name'].str.contains('valier')]['enrollment (2013 / 14)'].max() == 309",
        "pandas_eval": "False"
    },
    {
        "id": 914,
        "statement": "there be 4 out of 6 team total whose mascot be the eagle",
        "label": 0,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "sum(df['nickname (s)'].str.contains('eagle', case=False)) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 915,
        "statement": "there be 16 wrestle team in the black diamond conference for 2013",
        "label": 0,
        "table_caption": "black diamond conference",
        "table_text": "team name#schools#sports#host#nickname (s)#colors#enrollment (2013 / 14)\nchristopher - zeigler - royalton#christopher zeigler - royalton#football , track and field#christopher#bearcats lady cats#blue orange#412\neldorado - galatia#eldorado galatia#cross country , golf , track and field#eldorado#eagles#purple gold#461\nelverado - trico#elverado trico#football#elverado#falcons#blue gold#436\nfairfield - cisne#cisne fairfield#wrestling#fairfield#mules lady mules#red black#549\nsesser - valier - waltonville#sesser - valier waltonville#girls' basketball , track and field , volleyball#sesser - valier#red devils#maroon white#309\nsesser - valier - waltonville - woodlawn#sesser - valier waltonville woodlawn#football#sesser - valier#red devils#maroon white#494\nvienna - goreville#goreville vienna#football#vienna#eagles#royal blue orange#503\nzeigler - royalton - christopher#christopher zeigler - royalton#baseball , girls' basketball , golf , softball#zeigler - royalton#tornadoes#navy blue white#412\n",
        "pandas_code": "(df['sports'].str.contains('wrestling').sum()) == 16",
        "pandas_eval": "False"
    },
    {
        "id": 916,
        "statement": "the april 17 score be l 92 - 102 (ot) , follow by a score of l 86 - 96 (ot) on april 20",
        "label": 1,
        "table_caption": "2009 - 10 milwaukee bucks season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 17#atlanta#l 92 - 102 (ot)#b jennings (34)#k thomas (9)#b jennings (3)#philips arena 18729#0 - 1\n2#april 20#atlanta#l 86 - 96 (ot)#j salmons (21)#e ilyasova (15)#c delfino (4) j salmons (4)#philips arena 18938#0 - 2\n3#april 24#atlanta#w 107 - 89 (ot)#j salmons (22)#k thomas (13)#j salmons (7)#bradley center 18717#1 - 2\n4#april 26#atlanta#w 111 - 104 (ot)#b jennings (23)#k thomas (9)#b jennings (6)#bradley center 18717#2 - 2\n5#april 28#atlanta#w 91 - 87 (ot)#b jennings (25)#e ilyasova (7)#j salmons (5)#philips arena 19304#3 - 2\n6#april 30#atlanta#l 69 - 83 (ot)#c delfino 20#k thomas 9#j salmons 4#bradley center 18717#3 - 3\n",
        "pandas_code": "(df.loc[df['date'] == 'april 17', 'score'].values[0] == 'l 92 - 102 (ot)') & (df.loc[df['date'] == 'april 20', 'score'].values[0] == 'l 86 - 96 (ot)')",
        "pandas_eval": "True"
    },
    {
        "id": 918,
        "statement": "k thomas have the high rebound in 4 of the 6 game play",
        "label": 1,
        "table_caption": "2009 - 10 milwaukee bucks season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 17#atlanta#l 92 - 102 (ot)#b jennings (34)#k thomas (9)#b jennings (3)#philips arena 18729#0 - 1\n2#april 20#atlanta#l 86 - 96 (ot)#j salmons (21)#e ilyasova (15)#c delfino (4) j salmons (4)#philips arena 18938#0 - 2\n3#april 24#atlanta#w 107 - 89 (ot)#j salmons (22)#k thomas (13)#j salmons (7)#bradley center 18717#1 - 2\n4#april 26#atlanta#w 111 - 104 (ot)#b jennings (23)#k thomas (9)#b jennings (6)#bradley center 18717#2 - 2\n5#april 28#atlanta#w 91 - 87 (ot)#b jennings (25)#e ilyasova (7)#j salmons (5)#philips arena 19304#3 - 2\n6#april 30#atlanta#l 69 - 83 (ot)#c delfino 20#k thomas 9#j salmons 4#bradley center 18717#3 - 3\n",
        "pandas_code": "(df['high rebounds'].str.contains('k thomas').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 919,
        "statement": "the highest attendance be in philips arena , with 19304 watch the game",
        "label": 1,
        "table_caption": "2009 - 10 milwaukee bucks season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 17#atlanta#l 92 - 102 (ot)#b jennings (34)#k thomas (9)#b jennings (3)#philips arena 18729#0 - 1\n2#april 20#atlanta#l 86 - 96 (ot)#j salmons (21)#e ilyasova (15)#c delfino (4) j salmons (4)#philips arena 18938#0 - 2\n3#april 24#atlanta#w 107 - 89 (ot)#j salmons (22)#k thomas (13)#j salmons (7)#bradley center 18717#1 - 2\n4#april 26#atlanta#w 111 - 104 (ot)#b jennings (23)#k thomas (9)#b jennings (6)#bradley center 18717#2 - 2\n5#april 28#atlanta#w 91 - 87 (ot)#b jennings (25)#e ilyasova (7)#j salmons (5)#philips arena 19304#3 - 2\n6#april 30#atlanta#l 69 - 83 (ot)#c delfino 20#k thomas 9#j salmons 4#bradley center 18717#3 - 3\n",
        "pandas_code": "df[df['location attendance'].str.contains('19304')]['location attendance'].str.contains('philips arena').any()",
        "pandas_eval": "True"
    },
    {
        "id": 920,
        "statement": "3 game be play in philips arena and 3 in bradley center from april 17 thru the 30th",
        "label": 1,
        "table_caption": "2009 - 10 milwaukee bucks season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 17#atlanta#l 92 - 102 (ot)#b jennings (34)#k thomas (9)#b jennings (3)#philips arena 18729#0 - 1\n2#april 20#atlanta#l 86 - 96 (ot)#j salmons (21)#e ilyasova (15)#c delfino (4) j salmons (4)#philips arena 18938#0 - 2\n3#april 24#atlanta#w 107 - 89 (ot)#j salmons (22)#k thomas (13)#j salmons (7)#bradley center 18717#1 - 2\n4#april 26#atlanta#w 111 - 104 (ot)#b jennings (23)#k thomas (9)#b jennings (6)#bradley center 18717#2 - 2\n5#april 28#atlanta#w 91 - 87 (ot)#b jennings (25)#e ilyasova (7)#j salmons (5)#philips arena 19304#3 - 2\n6#april 30#atlanta#l 69 - 83 (ot)#c delfino 20#k thomas 9#j salmons 4#bradley center 18717#3 - 3\n",
        "pandas_code": "(df['location attendance'].str.contains('philips arena').sum() == 3) & (df['location attendance'].str.contains('bradley center').sum() == 3) & (df['date'].min() == 'april 17') & (df['date'].max() == 'april 30')",
        "pandas_eval": "True"
    },
    {
        "id": 921,
        "statement": "tour de santa catarina be not hold in ecuador , but in brazil",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[df['race name'] == 'tour de santa catarina']['location'].values[0] != 'ecuador') & (df[df['race name'] == 'tour de santa catarina']['location'].values[0] == 'brazil')",
        "pandas_eval": "True"
    },
    {
        "id": 922,
        "statement": "alex diniz ( bra ) win in brazil after \u00f3scar soliz ( bol ) win in bolivia",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df.loc[df['winner'] == 'alex diniz ( bra )', 'location'].values[0] == 'brazil') & (df.loc[df['winner'] == '\u00f3scar soliz ( bol )', 'location'].values[0] == 'bolivia') & (pd.to_datetime(df.loc[df['winner'] == 'alex diniz ( bra )', 'date'].values[0].split(' - ')[1], format='%d %B') > pd.to_datetime(df.loc[df['winner'] == '\u00f3scar soliz ( bol )', 'date'].values[0].split(' - ')[1], format='%d %B'))",
        "pandas_eval": "True"
    },
    {
        "id": 923,
        "statement": "alex atapuma ( col ) be from team indernari\u00f1o and henry raabe ( crc ) from bcr - pizza hut",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[df['winner'] == 'alex atapuma ( col )']['team'].values[0] == 'indernari\u00f1o') & (df[df['winner'] == 'henry raabe ( crc )']['team'].values[0] == 'bcr - pizza hut')",
        "pandas_eval": "True"
    },
    {
        "id": 924,
        "statement": "the 2 race in the 2007 - 08 uci america tour in 7 - 14 october be in venezuela and mexico",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[df['date'] == '7 - 14 october']['location'].isin(['venezuela', 'mexico']).sum()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 925,
        "statement": "there be 7 race in october nov and all have a 2.2 uci rating",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df['date'].str.contains('october|november', case=False, regex=True).sum() == 6) & (df['uci rating'] == 2.2).all()",
        "pandas_eval": "True"
    },
    {
        "id": 926,
        "statement": "the team that race on 14 - 28 december be bcr - pizza hut and the team that race on 17 - 25 november be indernari\u00f1o",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[df['date'] == '14 - 28 december']['team'].values[0] == 'bcr - pizza hut') & (df[df['date'] == '17 - 25 november']['team'].values[0] == 'indernari\u00f1o')",
        "pandas_eval": "True"
    },
    {
        "id": 927,
        "statement": "when the race name be vuelta a ecuador the location be ecuador with uci rating of 2.2",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "df[(df['race name'] == 'vuelta a ecuador') & (df['location'] == 'ecuador') & (df['uci rating'] == 2.2)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 928,
        "statement": "carlos lopex from mexico win on 20 october - 1 november and \u00f3scar soliz from bolivia win on 6 - 11 november",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "((df['winner'].str.contains('carlos l\u00f3pez')) & (df['location'] == 'guatemala') & (df['date'] == '20 october - 1 november')).any() and ((df['winner'].str.contains('\u00f3scar soliz')) & (df['location'] == 'bolivia') & (df['date'] == '6 - 11 november')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 929,
        "statement": "relax - gam race on 7 - 14 october with francisco mancebo (esp) as the winner in race name vuelta chihuahua internacional",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "((df['team'] == 'relax - gam') & (df['date'] == '7 - 14 october') & (df['winner'] == 'francisco mancebo ( esp )') & (df['race name'] == 'vuelta chihuahua internacional')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 930,
        "statement": "the uci rating be 2.2 for a race on 15 - 25 november when the winner be alex atapuma from colombia with team indernari\u00f1o",
        "label": 1,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "df[(df['date'] == '15 - 25 november') & (df['winner'] == 'alex atapuma ( col )') & (df['team'] == 'indernari\u00f1o')]['uci rating'].eq(2.2).all()",
        "pandas_eval": "True"
    },
    {
        "id": 931,
        "statement": "ecuador hold vuelta a ecuador while brazil hold tour de santa catarina",
        "label": 0,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[df['race name'] == 'vuelta a ecuador']['location'].iloc[0] != 'ecuador') | (df[df['race name'] == 'tour de santa catarina']['location'].iloc[0] != 'brazil')",
        "pandas_eval": "False"
    },
    {
        "id": 932,
        "statement": "alex diniz be the winner in brazil and \u00f3scar soliz be the winner in bolivia",
        "label": 0,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "not (((df['winner'] == 'alex diniz ( bra )') & (df['location'] == 'brazil')).any() and ((df['winner'] == '\u00f3scar soliz ( bol )') & (df['location'] == 'bolivia')).any())",
        "pandas_eval": "False"
    },
    {
        "id": 933,
        "statement": "alex atampuma be the winner from indernari\u00f1o in ecuador while henry raabe be the winner from bar - pizza hut in costa rica",
        "label": 0,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "(df[(df['winner'] == 'alex atapuma ( col )') & (df['team'] == 'indernari\u00f1o') & (df['location'] == 'ecuador')].shape[0] > 0) & (df[(df['winner'] == 'henry raabe ( crc )') & (df['team'] == 'bar - pizza hut') & (df['location'] == 'costa rica')].shape[0] > 0)",
        "pandas_eval": "False"
    },
    {
        "id": 934,
        "statement": "venezuela and mexico hold the 2 race in the 2007 - 08 uci america tour during 7 - 14 october in mexico",
        "label": 0,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "((df['location'] == 'mexico') & (df['date'] == '7 - 14 october')).sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 935,
        "statement": "the 2.2 uci rating hold in mexico have 7 race in october nov",
        "label": 0,
        "table_caption": "2007 - 08 uci america tour",
        "table_text": "date#race name#location#uci rating#winner#team\n7 - 14 october#clasico ciclistico banfoandes#venezuela#2.2#sergio luis henao ( col )#colombia \u00e9s pasi\u00f3n coldeportes\n7 - 14 october#vuelta chihuahua internacional#mexico#2.2#francisco mancebo ( esp )#relax - gam\n20 october - 1 november#vuelta a guatemala#guatemala#2.2#carlos l\u00f3pez ( mex )#canel 's - turbo - mayordomo\n6 - 11 november#doble copacabana gp fides#bolivia#2.2#\u00f3scar soliz ( bol )#coordinadora ebsa\n15 - 25 november#tour de santa catarina#brazil#2.2#alex diniz ( bra )#scott - marcondes cesar - s\u00e3o jos\u00e9 dos campos\n17 - 25 november#vuelta a ecuador#ecuador#2.2#alex atapuma ( col )#indernari\u00f1o\n14 - 28 december#vuelta ciclista a costa rica#costa rica#2.2#henry raabe ( crc )#bcr - pizza hut\n",
        "pandas_code": "((df['location'] == 'mexico') & (df['uci rating'] == 2.2) & (df['date'].str.contains('october|november'))).sum() == 7",
        "pandas_eval": "False"
    },
    {
        "id": 936,
        "statement": "in the 2009 volta a catalunya , thor hushovd win 2 stage",
        "label": 1,
        "table_caption": "2009 volta a catalunya",
        "table_text": "stage#winner#general classification#mountains classification#sprint classification#team classification\n1#thor hushovd#thor hushovd#no award#no award#cerv\u00e9lo testteam\n2#matti breschel#alejandro valverde#lloyd mondory#samuel dumoulin#cerv\u00e9lo testteam\n3#alejandro valverde#alejandro valverde#xavier tond\u00f3#samuel dumoulin#euskaltel - euskadi\n4#juli\u00e1n s\u00e1nchez#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n5#nikolay trusov#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n6#thor hushovd#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n7#greg henderson#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n",
        "pandas_code": "df[df['winner'] == 'thor hushovd'].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 937,
        "statement": "in the 2009 volta a catalunya , member of astana win more stage than any other team",
        "label": 1,
        "table_caption": "2009 volta a catalunya",
        "table_text": "stage#winner#general classification#mountains classification#sprint classification#team classification\n1#thor hushovd#thor hushovd#no award#no award#cerv\u00e9lo testteam\n2#matti breschel#alejandro valverde#lloyd mondory#samuel dumoulin#cerv\u00e9lo testteam\n3#alejandro valverde#alejandro valverde#xavier tond\u00f3#samuel dumoulin#euskaltel - euskadi\n4#juli\u00e1n s\u00e1nchez#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n5#nikolay trusov#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n6#thor hushovd#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n7#greg henderson#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n",
        "pandas_code": "df[df['team classification'] == 'astana']['stage'].nunique() > df[df['team classification'] != 'astana'].groupby('team classification')['stage'].nunique().max()",
        "pandas_eval": "True"
    },
    {
        "id": 938,
        "statement": "samuel dumoulin win all of the sprint classification",
        "label": 1,
        "table_caption": "2009 volta a catalunya",
        "table_text": "stage#winner#general classification#mountains classification#sprint classification#team classification\n1#thor hushovd#thor hushovd#no award#no award#cerv\u00e9lo testteam\n2#matti breschel#alejandro valverde#lloyd mondory#samuel dumoulin#cerv\u00e9lo testteam\n3#alejandro valverde#alejandro valverde#xavier tond\u00f3#samuel dumoulin#euskaltel - euskadi\n4#juli\u00e1n s\u00e1nchez#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n5#nikolay trusov#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n6#thor hushovd#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n7#greg henderson#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n",
        "pandas_code": "all(df[df['sprint classification'] != 'no award']['sprint classification'] == 'samuel dumoulin')",
        "pandas_eval": "True"
    },
    {
        "id": 939,
        "statement": "xavier tond\u00f3 win 3 of the 7 stage of the mountain classification",
        "label": 1,
        "table_caption": "2009 volta a catalunya",
        "table_text": "stage#winner#general classification#mountains classification#sprint classification#team classification\n1#thor hushovd#thor hushovd#no award#no award#cerv\u00e9lo testteam\n2#matti breschel#alejandro valverde#lloyd mondory#samuel dumoulin#cerv\u00e9lo testteam\n3#alejandro valverde#alejandro valverde#xavier tond\u00f3#samuel dumoulin#euskaltel - euskadi\n4#juli\u00e1n s\u00e1nchez#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n5#nikolay trusov#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n6#thor hushovd#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n7#greg henderson#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n",
        "pandas_code": "(df['mountains classification'].value_counts()['xavier tond\u00f3'] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 940,
        "statement": "alejandro valverde win all but 1 stage of the general classification",
        "label": 1,
        "table_caption": "2009 volta a catalunya",
        "table_text": "stage#winner#general classification#mountains classification#sprint classification#team classification\n1#thor hushovd#thor hushovd#no award#no award#cerv\u00e9lo testteam\n2#matti breschel#alejandro valverde#lloyd mondory#samuel dumoulin#cerv\u00e9lo testteam\n3#alejandro valverde#alejandro valverde#xavier tond\u00f3#samuel dumoulin#euskaltel - euskadi\n4#juli\u00e1n s\u00e1nchez#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n5#nikolay trusov#alejandro valverde#xavier tond\u00f3#samuel dumoulin#astana\n6#thor hushovd#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n7#greg henderson#alejandro valverde#juli\u00e1n s\u00e1nchez#samuel dumoulin#astana\n",
        "pandas_code": "(df['general classification'].value_counts()['alejandro valverde'] == (len(df) - 1))",
        "pandas_eval": "True"
    },
    {
        "id": 941,
        "statement": "the point of joris hendrickx / kaspars liepins be 2 less than that of marko happich / meinrad schelbert",
        "label": 1,
        "table_caption": "sidecarcross world championship",
        "table_text": "position#driver / passenger#equipment#bike no#points\n1#dani\u00e3l willemsen / sven verbrugge 1#zabel - wsp#1#487\n2#janis daiders / lauris daiders#zabel - vmc#8#478\n3#jan hendrickx / tim smeuninx#zabel - vmc#3#405\n4#maris rupeiks / kaspars stupelis 2#zabel - wsp#5#349\n5#etienne bax / ben van den bogaart#zabel - vmc#4#347\n6#ben adriaenssen / guennady auvray#ktm - vmc#6#346\n7#ewgeny scherbinin / haralds kurpnieks#zabel - wsp#20#321\n8#marko happich / meinrad schelbert#zabel - vmc#15#317\n9#joris hendrickx / kaspars liepins#ktm - vmc#2#315\n",
        "pandas_code": "df[df['driver / passenger'] == 'joris hendrickx / kaspars liepins']['points'].values[0] == df[df['driver / passenger'] == 'marko happich / meinrad schelbert']['points'].values[0] - 2",
        "pandas_eval": "True"
    },
    {
        "id": 942,
        "statement": "there be 2 driver / passenger (s) with less than 320 point",
        "label": 1,
        "table_caption": "sidecarcross world championship",
        "table_text": "position#driver / passenger#equipment#bike no#points\n1#dani\u00e3l willemsen / sven verbrugge 1#zabel - wsp#1#487\n2#janis daiders / lauris daiders#zabel - vmc#8#478\n3#jan hendrickx / tim smeuninx#zabel - vmc#3#405\n4#maris rupeiks / kaspars stupelis 2#zabel - wsp#5#349\n5#etienne bax / ben van den bogaart#zabel - vmc#4#347\n6#ben adriaenssen / guennady auvray#ktm - vmc#6#346\n7#ewgeny scherbinin / haralds kurpnieks#zabel - wsp#20#321\n8#marko happich / meinrad schelbert#zabel - vmc#15#317\n9#joris hendrickx / kaspars liepins#ktm - vmc#2#315\n",
        "pandas_code": "len(df[df['points'] < 320]['driver / passenger'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 943,
        "statement": "in driver / passenger , janis daiders / lauris daiders achieve the second highest point",
        "label": 1,
        "table_caption": "sidecarcross world championship",
        "table_text": "position#driver / passenger#equipment#bike no#points\n1#dani\u00e3l willemsen / sven verbrugge 1#zabel - wsp#1#487\n2#janis daiders / lauris daiders#zabel - vmc#8#478\n3#jan hendrickx / tim smeuninx#zabel - vmc#3#405\n4#maris rupeiks / kaspars stupelis 2#zabel - wsp#5#349\n5#etienne bax / ben van den bogaart#zabel - vmc#4#347\n6#ben adriaenssen / guennady auvray#ktm - vmc#6#346\n7#ewgeny scherbinin / haralds kurpnieks#zabel - wsp#20#321\n8#marko happich / meinrad schelbert#zabel - vmc#15#317\n9#joris hendrickx / kaspars liepins#ktm - vmc#2#315\n",
        "pandas_code": "df[df['driver / passenger'] == 'janis daiders / lauris daiders']['points'].iloc[0] == df['points'].nlargest(2).iloc[1]",
        "pandas_eval": "True"
    },
    {
        "id": 944,
        "statement": "the point ofetienne bax / ben van bogaart be 1 more than that of ben adriaenssen / guennady auvray",
        "label": 1,
        "table_caption": "sidecarcross world championship",
        "table_text": "position#driver / passenger#equipment#bike no#points\n1#dani\u00e3l willemsen / sven verbrugge 1#zabel - wsp#1#487\n2#janis daiders / lauris daiders#zabel - vmc#8#478\n3#jan hendrickx / tim smeuninx#zabel - vmc#3#405\n4#maris rupeiks / kaspars stupelis 2#zabel - wsp#5#349\n5#etienne bax / ben van den bogaart#zabel - vmc#4#347\n6#ben adriaenssen / guennady auvray#ktm - vmc#6#346\n7#ewgeny scherbinin / haralds kurpnieks#zabel - wsp#20#321\n8#marko happich / meinrad schelbert#zabel - vmc#15#317\n9#joris hendrickx / kaspars liepins#ktm - vmc#2#315\n",
        "pandas_code": "(df[df['driver / passenger'] == 'etienne bax / ben van den bogaart']['points'].values[0] - df[df['driver / passenger'] == 'ben adriaenssen / guennady auvray']['points'].values[0]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 945,
        "statement": "there be 3 driver / passenger (s) with more than 400 point",
        "label": 1,
        "table_caption": "sidecarcross world championship",
        "table_text": "position#driver / passenger#equipment#bike no#points\n1#dani\u00e3l willemsen / sven verbrugge 1#zabel - wsp#1#487\n2#janis daiders / lauris daiders#zabel - vmc#8#478\n3#jan hendrickx / tim smeuninx#zabel - vmc#3#405\n4#maris rupeiks / kaspars stupelis 2#zabel - wsp#5#349\n5#etienne bax / ben van den bogaart#zabel - vmc#4#347\n6#ben adriaenssen / guennady auvray#ktm - vmc#6#346\n7#ewgeny scherbinin / haralds kurpnieks#zabel - wsp#20#321\n8#marko happich / meinrad schelbert#zabel - vmc#15#317\n9#joris hendrickx / kaspars liepins#ktm - vmc#2#315\n",
        "pandas_code": "len(df[df['points'] > 400]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 946,
        "statement": "michael ruffin attend a different school than delaney rudd",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\naleksandar radojevi\u00e4\u2021#51#serbia#center#2004 - 05#barton college\nrick roberson#20#united states#forward#1974 - 75#cincinnati\nfred roberts#33#united states#forward#1984 - 86#byu\ntruck robinson#21#united states#power forward#1977 - 79#tennessee state\nbill robinzine#52#united states#power forward#1981 - 82#depaul\nscott roth#33#united states#forward#1987 - 89#wisconsin\ndelaney rudd#11#united states#guard#1989 - 92#wake forest\nmichael ruffin#51#united states#forward - center#2003 - 04#tulsa\n",
        "pandas_code": "df[df['player'] == 'michael ruffin']['school / club team'].values[0] != df[df['player'] == 'delaney rudd']['school / club team'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 947,
        "statement": "delaney rudd attend a different school than scott roth",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\naleksandar radojevi\u00e4\u2021#51#serbia#center#2004 - 05#barton college\nrick roberson#20#united states#forward#1974 - 75#cincinnati\nfred roberts#33#united states#forward#1984 - 86#byu\ntruck robinson#21#united states#power forward#1977 - 79#tennessee state\nbill robinzine#52#united states#power forward#1981 - 82#depaul\nscott roth#33#united states#forward#1987 - 89#wisconsin\ndelaney rudd#11#united states#guard#1989 - 92#wake forest\nmichael ruffin#51#united states#forward - center#2003 - 04#tulsa\n",
        "pandas_code": "df.loc[df['player'] == 'delaney rudd', 'school / club team'].values[0] != df.loc[df['player'] == 'scott roth', 'school / club team'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 948,
        "statement": "bill robinzine attend a different school than truck robinson",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\naleksandar radojevi\u00e4\u2021#51#serbia#center#2004 - 05#barton college\nrick roberson#20#united states#forward#1974 - 75#cincinnati\nfred roberts#33#united states#forward#1984 - 86#byu\ntruck robinson#21#united states#power forward#1977 - 79#tennessee state\nbill robinzine#52#united states#power forward#1981 - 82#depaul\nscott roth#33#united states#forward#1987 - 89#wisconsin\ndelaney rudd#11#united states#guard#1989 - 92#wake forest\nmichael ruffin#51#united states#forward - center#2003 - 04#tulsa\n",
        "pandas_code": "df[df['player'] == 'bill robinzine']['school / club team'].values[0] != df[df['player'] == 'truck robinson']['school / club team'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 949,
        "statement": "fred robert attend a different school than rick roberson",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\naleksandar radojevi\u00e4\u2021#51#serbia#center#2004 - 05#barton college\nrick roberson#20#united states#forward#1974 - 75#cincinnati\nfred roberts#33#united states#forward#1984 - 86#byu\ntruck robinson#21#united states#power forward#1977 - 79#tennessee state\nbill robinzine#52#united states#power forward#1981 - 82#depaul\nscott roth#33#united states#forward#1987 - 89#wisconsin\ndelaney rudd#11#united states#guard#1989 - 92#wake forest\nmichael ruffin#51#united states#forward - center#2003 - 04#tulsa\n",
        "pandas_code": "df[df['player'] == 'fred roberts']['school / club team'].values[0] != df[df['player'] == 'rick roberson']['school / club team'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 950,
        "statement": "bill robinzine attend a different school than aleksandar radojevi\u00e4",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\naleksandar radojevi\u00e4\u2021#51#serbia#center#2004 - 05#barton college\nrick roberson#20#united states#forward#1974 - 75#cincinnati\nfred roberts#33#united states#forward#1984 - 86#byu\ntruck robinson#21#united states#power forward#1977 - 79#tennessee state\nbill robinzine#52#united states#power forward#1981 - 82#depaul\nscott roth#33#united states#forward#1987 - 89#wisconsin\ndelaney rudd#11#united states#guard#1989 - 92#wake forest\nmichael ruffin#51#united states#forward - center#2003 - 04#tulsa\n",
        "pandas_code": "df.loc[df['player'] == 'bill robinzine', 'school / club team'].values[0] != df.loc[df['player'] == 'aleksandar radojevi\u00e4\u2021', 'school / club team'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 951,
        "statement": "elisabeta lip\u0103 from romania (rou) win the highest amount of metal with a total of 8",
        "label": 1,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "df.loc[df['athlete (nation)'].str.contains('elisabeta lip\u0103 romania \\\\(rou\\\\)'), 'total'].values[0] == 8",
        "pandas_eval": "True"
    },
    {
        "id": 952,
        "statement": "viorica susanu from romania (rou) be 1 of 4 athlete that win 4 gold metal",
        "label": 1,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "df[(df['athlete (nation)'].str.contains('viorica susanu romania')) & (df['gold'] == 4)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 953,
        "statement": "jack beresford from great britain (gbr) win 1 more silver metal than constan\u0163a burcic\u0103 romania (rou)",
        "label": 1,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "(df[df['athlete (nation)'].str.contains('jack beresford great britain \\(gbr\\)')]['silver'].values[0] - df[df['athlete (nation)'].str.contains('constan\u0163a burcic\u0103 romania \\(rou\\)')]['silver'].values[0]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 954,
        "statement": "together steve redgrave from great britain (gbr) jack beresford from great britain (gbr) win an average of 4 gold metal",
        "label": 1,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "(df[df['athlete (nation)'].str.contains('steve redgrave great britain \\(gbr\\)')]['gold'].values[0] + df[df['athlete (nation)'].str.contains('jack beresford great britain \\(gbr\\)')]['gold'].values[0]) / 2 == 4",
        "pandas_eval": "True"
    },
    {
        "id": 955,
        "statement": "vyacheslav ivanov soviet union (urs) win 3 gold metal and viorica susanu from romania (rou) win 4 gold metal",
        "label": 1,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "(df[df['athlete (nation)'] == 'vyacheslav ivanov soviet union (urs)']['gold'].values[0] == 3) & (df[df['athlete (nation)'] == 'viorica susanu romania (rou)']['gold'].values[0] == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 956,
        "statement": "elisabeta lip\u0103 do not win any bronze medal",
        "label": 0,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "df[df['athlete (nation)'].str.contains('elisabeta lip\u0103')]['bronze'].values[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 957,
        "statement": "viorica susanu win more than 1 gold medal",
        "label": 0,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "df[df['athlete (nation)'].str.contains('viorica susanu')]['gold'].values[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 958,
        "statement": "jack beresford win no silver medal and 1 bronze medal",
        "label": 0,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "(df['athlete (nation)'].str.contains('jack beresford') & (df['silver'] == 0) & (df['bronze'] == 1)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 959,
        "statement": "jack beresford win more gold medal than steve redgrave , who win 4",
        "label": 0,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "(df[df['athlete (nation)'].str.contains('jack beresford')]['gold'].values[0] > df[df['athlete (nation)'].str.contains('steve redgrave')]['gold'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 960,
        "statement": "vyacheslav ivanov be the only player who win no gold medal , while viorica susanu win 2",
        "label": 0,
        "table_caption": "rowing at the summer olympics",
        "table_text": "athlete (nation)#olympics#gold#silver#bronze#total\nelisabeta lip\u0103 romania (rou)#1984 , 1988 , 1992 , 1996 , 2000 , 2004#5#2#1#8\nsteve redgrave great britain (gbr)#1984 , 1988 , 1992 , 1996 , 2000#5#0#1#6\ngeorgeta damian romania (rou)#2000 , 2004 , 2008#5#0#1#6\ndoina ignat romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#4#1#1#6\nkathrin boron germany (ger)#1992 , 1996 , 2000 , 2004 , 2008#4#0#1#5\nviorica susanu romania (rou)#1996 , 2000 , 2004 , 2008#4#0#1#5\nmatthew pinsent great britain (gbr)#1992 , 1996 , 2000 , 2004#4#0#0#4\njack beresford great britain (gbr)#1920 , 1924 , 1928 , 1932 , 1936#3#2#0#5\nconstan\u0163a burcic\u0103 romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\nelena georgescu romania (rou)#1992 , 1996 , 2000 , 2004 , 2008#3#1#1#5\ndrew ginn australia (aus)#1996 , 2004 , 2008 , 2012#3#1#0#4\neskild ebbesen denmark (den)#1996 , 2000 , 2004 , 2008 , 2012#3#0#2#5\nmarnie mcbean canada (can)#1992 , 1996#3#0#1#4\nkathleen heddle canada (can)#1992 , 1996#3#0#1#4\njames tomkins australia (aus)#1992 , 1996 , 2000 , 2004#3#0#1#4\njohn b kelly , sr united states (usa)#1920 , 1924#3#0#0#3\npaul costello united states (usa)#1920 , 1924 , 1928#3#0#0#3\nvyacheslav ivanov soviet union (urs)#1956 , 1960 , 1964#3#0#0#3\nsiegfried brietzke east germany (gdr)#1972 , 1976 , 1980#3#0#0#3\npertti karppinen finland (fin)#1976 , 1980 , 1984#3#0#0#3\nagostino abbagnale italy (ita)#1988 , 1996 , 2000#3#0#0#3\nliliana gafencu romania (rou)#1996 , 2000 , 2004#3#0#0#3\n",
        "pandas_code": "((df['athlete (nation)'].str.contains('vyacheslav ivanov')) & (df['gold'] == 0)).any() & ((df['athlete (nation)'].str.contains('viorica susanu')) & (df['gold'] == 2)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 961,
        "statement": "princeton tiger have an online format which the humor magazine private eye do not",
        "label": 1,
        "table_caption": "humor magazine",
        "table_text": "title#language#country#years published#frequency#medium#classification\nfish rap live!#english#usa#1985 - 1988 , 1990 - present#9 per year#paper#satire , absurdism , theme issues\nharvard lampoon#english#usa#1876 - present#five per year#paper#satire\nkrokodil#russian#russia#1922 - 1991 , 2005 - present#weekly#paper#satire\nle canard encha\u00een\u00e9#french#france#1915 - present#weekly#paper#satire\nmad#english#usa#1952 - present#monthly (1952 - 2009) , quarterly (2009 - )#paper#satire , comics\nprinceton tiger#english#usa#1882 - present#quarterly#paper (1882 - ) online (2009 - )#satire\nprivate eye#english#uk#1961 - present#biweekly#paper#satire\nrumpus#english#usa#1992 - present#six per year#paper#satire / tabloid\nthe wittenburg door#english#usa#1971 - present#bimonthly#paper#christian satire\nthe yale record#english#usa#1872 - present#8 per year#paper#satire , theme issues\njester of columbia#english#usa#1991 - 1997 , 2005 - present#quarterly#paper#satire , absurdism\n",
        "pandas_code": "df.loc[df['title'] == 'princeton tiger', 'medium'].str.contains('online').any() and not df.loc[df['title'] == 'private eye', 'medium'].str.contains('online').any()",
        "pandas_eval": "True"
    },
    {
        "id": 962,
        "statement": "the humor magazine mad be publish in english and not russian like krokodil",
        "label": 1,
        "table_caption": "humor magazine",
        "table_text": "title#language#country#years published#frequency#medium#classification\nfish rap live!#english#usa#1985 - 1988 , 1990 - present#9 per year#paper#satire , absurdism , theme issues\nharvard lampoon#english#usa#1876 - present#five per year#paper#satire\nkrokodil#russian#russia#1922 - 1991 , 2005 - present#weekly#paper#satire\nle canard encha\u00een\u00e9#french#france#1915 - present#weekly#paper#satire\nmad#english#usa#1952 - present#monthly (1952 - 2009) , quarterly (2009 - )#paper#satire , comics\nprinceton tiger#english#usa#1882 - present#quarterly#paper (1882 - ) online (2009 - )#satire\nprivate eye#english#uk#1961 - present#biweekly#paper#satire\nrumpus#english#usa#1992 - present#six per year#paper#satire / tabloid\nthe wittenburg door#english#usa#1971 - present#bimonthly#paper#christian satire\nthe yale record#english#usa#1872 - present#8 per year#paper#satire , theme issues\njester of columbia#english#usa#1991 - 1997 , 2005 - present#quarterly#paper#satire , absurdism\n",
        "pandas_code": "df[(df['title'] == 'mad') & (df['language'] == 'english') & (df['language'] != 'russian')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 963,
        "statement": "fish rap live! have an exclamation mark in its name while mad do not",
        "label": 1,
        "table_caption": "humor magazine",
        "table_text": "title#language#country#years published#frequency#medium#classification\nfish rap live!#english#usa#1985 - 1988 , 1990 - present#9 per year#paper#satire , absurdism , theme issues\nharvard lampoon#english#usa#1876 - present#five per year#paper#satire\nkrokodil#russian#russia#1922 - 1991 , 2005 - present#weekly#paper#satire\nle canard encha\u00een\u00e9#french#france#1915 - present#weekly#paper#satire\nmad#english#usa#1952 - present#monthly (1952 - 2009) , quarterly (2009 - )#paper#satire , comics\nprinceton tiger#english#usa#1882 - present#quarterly#paper (1882 - ) online (2009 - )#satire\nprivate eye#english#uk#1961 - present#biweekly#paper#satire\nrumpus#english#usa#1992 - present#six per year#paper#satire / tabloid\nthe wittenburg door#english#usa#1971 - present#bimonthly#paper#christian satire\nthe yale record#english#usa#1872 - present#8 per year#paper#satire , theme issues\njester of columbia#english#usa#1991 - 1997 , 2005 - present#quarterly#paper#satire , absurdism\n",
        "pandas_code": "(df[df['title'] == 'fish rap live!']['title'].str.contains('!').any()) & (~df[df['title'] == 'mad']['title'].str.contains('!').any())",
        "pandas_eval": "True"
    },
    {
        "id": 964,
        "statement": "mad be release quarterly while private eye be release bi weekly",
        "label": 1,
        "table_caption": "humor magazine",
        "table_text": "title#language#country#years published#frequency#medium#classification\nfish rap live!#english#usa#1985 - 1988 , 1990 - present#9 per year#paper#satire , absurdism , theme issues\nharvard lampoon#english#usa#1876 - present#five per year#paper#satire\nkrokodil#russian#russia#1922 - 1991 , 2005 - present#weekly#paper#satire\nle canard encha\u00een\u00e9#french#france#1915 - present#weekly#paper#satire\nmad#english#usa#1952 - present#monthly (1952 - 2009) , quarterly (2009 - )#paper#satire , comics\nprinceton tiger#english#usa#1882 - present#quarterly#paper (1882 - ) online (2009 - )#satire\nprivate eye#english#uk#1961 - present#biweekly#paper#satire\nrumpus#english#usa#1992 - present#six per year#paper#satire / tabloid\nthe wittenburg door#english#usa#1971 - present#bimonthly#paper#christian satire\nthe yale record#english#usa#1872 - present#8 per year#paper#satire , theme issues\njester of columbia#english#usa#1991 - 1997 , 2005 - present#quarterly#paper#satire , absurdism\n",
        "pandas_code": "('quarterly' in df[df['title'] == 'mad']['frequency'].iloc[0]) & (df[df['title'] == 'private eye']['frequency'].iloc[0] == 'biweekly')",
        "pandas_eval": "True"
    },
    {
        "id": 965,
        "statement": "harvard lampoon release five per year while rumpus release 6 per year",
        "label": 1,
        "table_caption": "humor magazine",
        "table_text": "title#language#country#years published#frequency#medium#classification\nfish rap live!#english#usa#1985 - 1988 , 1990 - present#9 per year#paper#satire , absurdism , theme issues\nharvard lampoon#english#usa#1876 - present#five per year#paper#satire\nkrokodil#russian#russia#1922 - 1991 , 2005 - present#weekly#paper#satire\nle canard encha\u00een\u00e9#french#france#1915 - present#weekly#paper#satire\nmad#english#usa#1952 - present#monthly (1952 - 2009) , quarterly (2009 - )#paper#satire , comics\nprinceton tiger#english#usa#1882 - present#quarterly#paper (1882 - ) online (2009 - )#satire\nprivate eye#english#uk#1961 - present#biweekly#paper#satire\nrumpus#english#usa#1992 - present#six per year#paper#satire / tabloid\nthe wittenburg door#english#usa#1971 - present#bimonthly#paper#christian satire\nthe yale record#english#usa#1872 - present#8 per year#paper#satire , theme issues\njester of columbia#english#usa#1991 - 1997 , 2005 - present#quarterly#paper#satire , absurdism\n",
        "pandas_code": "(df[df['title'] == 'harvard lampoon']['frequency'].values[0] == 'five per year') & (df[df['title'] == 'rumpus']['frequency'].values[0] == 'six per year')",
        "pandas_eval": "True"
    },
    {
        "id": 966,
        "statement": "out of 25 season all of them be direct by richard l bare",
        "label": 1,
        "table_caption": "list of green acres episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n119#1#lisa 's mudder comes for a visit#richard l bare#jay sommers and dick chevillat#september 27 , 1969#122\n120#2#everybody tries to love a countess#richard l bare#jay sommers and dick chevillat#october 4 , 1969#123\n121#3#where there 's a will#richard l bare#jay sommers and dick chevillat#october 11 , 1969#119\n122#4#a tale of a tail#richard l bare#jay sommers and dick chevillat#october 18 , 1969#120\n123#5#you and your big shrunken head#richard l bare#jay sommers and dick chevillat#october 25 , 1969#121\n124#6#the road#richard l bare#jay sommers and john l greene#november 1 , 1969#128\n125#7#four of spades#richard l bare#jay sommers & john l greene#november 8 , 1969#127\n126#8#the youth center#richard l bare#jay sommers and john l greene#november 15 , 1969#129\n128#10#oliver 's schoolgirl crush#richard l bare#jay sommers and dick chevillat#november 29 , 1969#126\n129#11#ralph 's nuptials#richard l bare#jay sommers and dick chevillat#december 13 , 1969#133\n130#12#oliver and the cornstalk#richard l bare#jay sommers and dick chevillat#december 20 , 1969#124\n131#13#beauty is skin deep#richard l bare#jay sommers and dick chevillat#december 27 , 1969#125\n132#14#the wish - book#richard l bare#jay sommers and dick chevillat#january 3 , 1970#131\n133#15#rest and relaxation#richard l bare#jay sommers and dick chevillat#january 10 , 1970#135\n134#16#trapped#richard l bare#jay sommers and dick chevillat#january 17 , 1970#136\n136#18#the ex - con#richard l bare#jay sommers and dick chevillat#january 31 , 1970#130\n138#20#the confrontation#richard l bare#jay sommers and dick chevillat#february 14 , 1970#138\n140#22#the picnic#richard l bare#jay sommers and dick chevillat#march 7 , 1970#140\n141#23#the beeping rock#richard l bare#jay sommers and dick chevillat#march 21 , 1970#141\n142#24#uncle fedor#richard l bare#jay sommers and dick chevillat#march 28 , 1970#143\n143#25#the wealthy landowner#richard l bare#jay sommers and dick chevillat#april 11 , 1970#142\n",
        "pandas_code": "df['directed by'].eq('richard l bare').all()",
        "pandas_eval": "True"
    },
    {
        "id": 967,
        "statement": "the average episode per season range from 119 - 143 episode",
        "label": 1,
        "table_caption": "list of green acres episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n119#1#lisa 's mudder comes for a visit#richard l bare#jay sommers and dick chevillat#september 27 , 1969#122\n120#2#everybody tries to love a countess#richard l bare#jay sommers and dick chevillat#october 4 , 1969#123\n121#3#where there 's a will#richard l bare#jay sommers and dick chevillat#october 11 , 1969#119\n122#4#a tale of a tail#richard l bare#jay sommers and dick chevillat#october 18 , 1969#120\n123#5#you and your big shrunken head#richard l bare#jay sommers and dick chevillat#october 25 , 1969#121\n124#6#the road#richard l bare#jay sommers and john l greene#november 1 , 1969#128\n125#7#four of spades#richard l bare#jay sommers & john l greene#november 8 , 1969#127\n126#8#the youth center#richard l bare#jay sommers and john l greene#november 15 , 1969#129\n128#10#oliver 's schoolgirl crush#richard l bare#jay sommers and dick chevillat#november 29 , 1969#126\n129#11#ralph 's nuptials#richard l bare#jay sommers and dick chevillat#december 13 , 1969#133\n130#12#oliver and the cornstalk#richard l bare#jay sommers and dick chevillat#december 20 , 1969#124\n131#13#beauty is skin deep#richard l bare#jay sommers and dick chevillat#december 27 , 1969#125\n132#14#the wish - book#richard l bare#jay sommers and dick chevillat#january 3 , 1970#131\n133#15#rest and relaxation#richard l bare#jay sommers and dick chevillat#january 10 , 1970#135\n134#16#trapped#richard l bare#jay sommers and dick chevillat#january 17 , 1970#136\n136#18#the ex - con#richard l bare#jay sommers and dick chevillat#january 31 , 1970#130\n138#20#the confrontation#richard l bare#jay sommers and dick chevillat#february 14 , 1970#138\n140#22#the picnic#richard l bare#jay sommers and dick chevillat#march 7 , 1970#140\n141#23#the beeping rock#richard l bare#jay sommers and dick chevillat#march 21 , 1970#141\n142#24#uncle fedor#richard l bare#jay sommers and dick chevillat#march 28 , 1970#143\n143#25#the wealthy landowner#richard l bare#jay sommers and dick chevillat#april 11 , 1970#142\n",
        "pandas_code": "df['no in series'].between(119, 143).all()",
        "pandas_eval": "True"
    },
    {
        "id": 968,
        "statement": "out of 25 season , 4 of which air in the month of january , 1970",
        "label": 1,
        "table_caption": "list of green acres episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n119#1#lisa 's mudder comes for a visit#richard l bare#jay sommers and dick chevillat#september 27 , 1969#122\n120#2#everybody tries to love a countess#richard l bare#jay sommers and dick chevillat#october 4 , 1969#123\n121#3#where there 's a will#richard l bare#jay sommers and dick chevillat#october 11 , 1969#119\n122#4#a tale of a tail#richard l bare#jay sommers and dick chevillat#october 18 , 1969#120\n123#5#you and your big shrunken head#richard l bare#jay sommers and dick chevillat#october 25 , 1969#121\n124#6#the road#richard l bare#jay sommers and john l greene#november 1 , 1969#128\n125#7#four of spades#richard l bare#jay sommers & john l greene#november 8 , 1969#127\n126#8#the youth center#richard l bare#jay sommers and john l greene#november 15 , 1969#129\n128#10#oliver 's schoolgirl crush#richard l bare#jay sommers and dick chevillat#november 29 , 1969#126\n129#11#ralph 's nuptials#richard l bare#jay sommers and dick chevillat#december 13 , 1969#133\n130#12#oliver and the cornstalk#richard l bare#jay sommers and dick chevillat#december 20 , 1969#124\n131#13#beauty is skin deep#richard l bare#jay sommers and dick chevillat#december 27 , 1969#125\n132#14#the wish - book#richard l bare#jay sommers and dick chevillat#january 3 , 1970#131\n133#15#rest and relaxation#richard l bare#jay sommers and dick chevillat#january 10 , 1970#135\n134#16#trapped#richard l bare#jay sommers and dick chevillat#january 17 , 1970#136\n136#18#the ex - con#richard l bare#jay sommers and dick chevillat#january 31 , 1970#130\n138#20#the confrontation#richard l bare#jay sommers and dick chevillat#february 14 , 1970#138\n140#22#the picnic#richard l bare#jay sommers and dick chevillat#march 7 , 1970#140\n141#23#the beeping rock#richard l bare#jay sommers and dick chevillat#march 21 , 1970#141\n142#24#uncle fedor#richard l bare#jay sommers and dick chevillat#march 28 , 1970#143\n143#25#the wealthy landowner#richard l bare#jay sommers and dick chevillat#april 11 , 1970#142\n",
        "pandas_code": "len(df[df['original air date'].str.contains('january .* , 1970')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 970,
        "statement": "there be 12 out of 25 season that be air in the year 1969",
        "label": 1,
        "table_caption": "list of green acres episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n119#1#lisa 's mudder comes for a visit#richard l bare#jay sommers and dick chevillat#september 27 , 1969#122\n120#2#everybody tries to love a countess#richard l bare#jay sommers and dick chevillat#october 4 , 1969#123\n121#3#where there 's a will#richard l bare#jay sommers and dick chevillat#october 11 , 1969#119\n122#4#a tale of a tail#richard l bare#jay sommers and dick chevillat#october 18 , 1969#120\n123#5#you and your big shrunken head#richard l bare#jay sommers and dick chevillat#october 25 , 1969#121\n124#6#the road#richard l bare#jay sommers and john l greene#november 1 , 1969#128\n125#7#four of spades#richard l bare#jay sommers & john l greene#november 8 , 1969#127\n126#8#the youth center#richard l bare#jay sommers and john l greene#november 15 , 1969#129\n128#10#oliver 's schoolgirl crush#richard l bare#jay sommers and dick chevillat#november 29 , 1969#126\n129#11#ralph 's nuptials#richard l bare#jay sommers and dick chevillat#december 13 , 1969#133\n130#12#oliver and the cornstalk#richard l bare#jay sommers and dick chevillat#december 20 , 1969#124\n131#13#beauty is skin deep#richard l bare#jay sommers and dick chevillat#december 27 , 1969#125\n132#14#the wish - book#richard l bare#jay sommers and dick chevillat#january 3 , 1970#131\n133#15#rest and relaxation#richard l bare#jay sommers and dick chevillat#january 10 , 1970#135\n134#16#trapped#richard l bare#jay sommers and dick chevillat#january 17 , 1970#136\n136#18#the ex - con#richard l bare#jay sommers and dick chevillat#january 31 , 1970#130\n138#20#the confrontation#richard l bare#jay sommers and dick chevillat#february 14 , 1970#138\n140#22#the picnic#richard l bare#jay sommers and dick chevillat#march 7 , 1970#140\n141#23#the beeping rock#richard l bare#jay sommers and dick chevillat#march 21 , 1970#141\n142#24#uncle fedor#richard l bare#jay sommers and dick chevillat#march 28 , 1970#143\n143#25#the wealthy landowner#richard l bare#jay sommers and dick chevillat#april 11 , 1970#142\n",
        "pandas_code": "len(df[df['original air date'].str.contains('1969')]) == 12",
        "pandas_eval": "True"
    },
    {
        "id": 971,
        "statement": "william j driver be the earliest elected representative in this table",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\narkansas 1#william j driver#democratic#1920#re - elected#william j driver (d) unopposed\narkansas 2#john e miller#democratic#1930#re - elected#john e miller (d) unopposed\narkansas 3#claude fuller#democratic#1928#re - elected#claude fuller (d) unopposed\narkansas 4#william b cravens#democratic#1932#re - elected#william b cravens (d) unopposed\narkansas 5#david d terry#democratic#1933#re - elected#david d terry (d) unopposed\narkansas 6#john little mcclellan#democratic#1934#re - elected#john little mcclellan (d) unopposed\n",
        "pandas_code": "df.loc[df['first elected'].idxmin(), 'incumbent'] == 'william j driver'",
        "pandas_eval": "True"
    },
    {
        "id": 972,
        "statement": "john little mcclellan be the latest elected representative in this table",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\narkansas 1#william j driver#democratic#1920#re - elected#william j driver (d) unopposed\narkansas 2#john e miller#democratic#1930#re - elected#john e miller (d) unopposed\narkansas 3#claude fuller#democratic#1928#re - elected#claude fuller (d) unopposed\narkansas 4#william b cravens#democratic#1932#re - elected#william b cravens (d) unopposed\narkansas 5#david d terry#democratic#1933#re - elected#david d terry (d) unopposed\narkansas 6#john little mcclellan#democratic#1934#re - elected#john little mcclellan (d) unopposed\n",
        "pandas_code": "df[df['incumbent'] == 'john little mcclellan']['first elected'].max() == df['first elected'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 973,
        "statement": "william b craven be re - elect 1 year before david d terry",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\narkansas 1#william j driver#democratic#1920#re - elected#william j driver (d) unopposed\narkansas 2#john e miller#democratic#1930#re - elected#john e miller (d) unopposed\narkansas 3#claude fuller#democratic#1928#re - elected#claude fuller (d) unopposed\narkansas 4#william b cravens#democratic#1932#re - elected#william b cravens (d) unopposed\narkansas 5#david d terry#democratic#1933#re - elected#david d terry (d) unopposed\narkansas 6#john little mcclellan#democratic#1934#re - elected#john little mcclellan (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'] == 'william b cravens']['first elected'].values[0] == df[df['incumbent'] == 'david d terry']['first elected'].values[0] - 1)",
        "pandas_eval": "True"
    },
    {
        "id": 974,
        "statement": "william j driver be first elect 14 year before john little mcclellan",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\narkansas 1#william j driver#democratic#1920#re - elected#william j driver (d) unopposed\narkansas 2#john e miller#democratic#1930#re - elected#john e miller (d) unopposed\narkansas 3#claude fuller#democratic#1928#re - elected#claude fuller (d) unopposed\narkansas 4#william b cravens#democratic#1932#re - elected#william b cravens (d) unopposed\narkansas 5#david d terry#democratic#1933#re - elected#david d terry (d) unopposed\narkansas 6#john little mcclellan#democratic#1934#re - elected#john little mcclellan (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'] == 'william j driver']['first elected'].values[0] + 14) == df[df['incumbent'] == 'john little mcclellan']['first elected'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 975,
        "statement": "john e miller be elect 2 year after claude fuller",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1936",
        "table_text": "district#incumbent#party#first elected#result#candidates\narkansas 1#william j driver#democratic#1920#re - elected#william j driver (d) unopposed\narkansas 2#john e miller#democratic#1930#re - elected#john e miller (d) unopposed\narkansas 3#claude fuller#democratic#1928#re - elected#claude fuller (d) unopposed\narkansas 4#william b cravens#democratic#1932#re - elected#william b cravens (d) unopposed\narkansas 5#david d terry#democratic#1933#re - elected#david d terry (d) unopposed\narkansas 6#john little mcclellan#democratic#1934#re - elected#john little mcclellan (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'] == 'john e miller']['first elected'].values[0] - df[df['incumbent'] == 'claude fuller']['first elected'].values[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 976,
        "statement": "the result show more win than loss and draw",
        "label": 1,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "(df['res'].value_counts().get('win', 0) > df['res'].value_counts().get('loss', 0) + df['res'].value_counts().get('draw', 0))",
        "pandas_eval": "True"
    },
    {
        "id": 977,
        "statement": "all except 1 have 1 round in the mixed martial art competition",
        "label": 1,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "(df['round'] == 1).sum() == len(df) - 1",
        "pandas_eval": "True"
    },
    {
        "id": 978,
        "statement": "2 artist have an event of mfc 1 - midwest fight 1 , jamie schell and dave kirshman",
        "label": 1,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "len(df[(df['event'] == 'mfc 1 - midwest fighting 1') & (df['opponent'].isin(['jamie schell', 'dave kirshman']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 979,
        "statement": "2 opponent have a tko method , joe campanella and jamie schell 2 time",
        "label": 1,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "(df[df['method'].str.contains('tko', case=False, na=False)]['opponent'].value_counts().loc[['joe campanella', 'jamie schell']] >= [1, 2]).all()",
        "pandas_eval": "True"
    },
    {
        "id": 980,
        "statement": "only opponent ruben villareal have 3 round and a method of draw",
        "label": 1,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "((df['opponent'] == 'ruben villareal') & (df['round'] == 3) & (df['method'] == 'draw') & (df['res'] == 'draw')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 981,
        "statement": "the result show 14 more win than loss or draw",
        "label": 0,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "(df['res'].value_counts().get('win', 0) - df['res'].value_counts().get('loss', 0) - df['res'].value_counts().get('draw', 0)) == 14",
        "pandas_eval": "False"
    },
    {
        "id": 982,
        "statement": "all except 2 have 1 round in the mixed martial art competition",
        "label": 0,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "(df['round'] == 1).sum() == (len(df) - 2)",
        "pandas_eval": "False"
    },
    {
        "id": 983,
        "statement": "2 artist have an event of mfc 1 - midwest fight 1 , jamie schell and sam adkins",
        "label": 0,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "len(df[(df['event'] == 'mfc 1 - midwest fighting 1') & (df['opponent'].isin(['jamie schell', 'sam adkins']))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 984,
        "statement": "3 opponent have a tko method , jim axtell , joe campanella and jamie schell 2 time",
        "label": 0,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "len(df[(df['opponent'].isin(['jim axtell', 'joe campanella', 'jamie schell'])) & (df['method'] == 'tko')]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 985,
        "statement": "only opponent ruben villareal have 3 round and a submission",
        "label": 0,
        "table_caption": "andre roberts (mixed martial artist)",
        "table_text": "res#record#opponent#method#event#round#time\nloss#14 - 2 - 1#dan christison#submission (armbar)#wec 13 - heavyweight explosion#1#3:26\ndraw#14 - 1 - 1#ruben villareal#draw#sb 38 - superbrawl 38#3#5:00\nwin#14 - 1#gabe beauperthy#submission (kimura)#ec 57 - extreme challenge 57#1#3:34\nwin#13 - 1#johnathan ivey#submission (bad position)#sb 30 - collision course#1#1:38\nwin#12 - 1#ray seraille#submission (neck crank)#sb 28 - superbrawl 28#1#2:49\nwin#11 - 1#joe campanella#tko#ec 27 - extreme challenge 27#1#2:07\nwin#10 - 1#ron waterman#ko#ufc 21#1#2:51\nloss#9 - 1#gary goodridge#submission (punches)#ufc 19#1#0:43\nwin#9 - 0#jamie schell#tko#icf 1 - iowa cage fighting 1#1#1:25\nwin#8 - 0#jamie schell#tko#mfc 1 - midwest fighting 1#1#1:35\nwin#7 - 0#dave kirshman#submission#mfc 1 - midwest fighting 1#1#0:10\nwin#6 - 0#phil breecher#n / a#ec 19 - extreme challenge 19#1#0:35\nwin#5 - 0#harry moskowitz#ko#ufc 17#1#3:15\nwin#4 - 0#jason brewer#submission (strikes)#ec 15 - extreme challenge 15#1#0:39\nwin#3 - 0#sam adkins#submission#ec 11 - extreme challenge 11#1#4:02\nwin#2 - 0#jim axtell#submission#ec 4 - extreme challenge 4#1#5:41\nwin#1 - 0#trevor thrasher#submission#ec 2 - extreme challenge 2#1#3:59\n",
        "pandas_code": "((df['opponent'] == 'ruben villareal') & (df['round'] == 3) & (df['method'].str.contains('submission'))).any()",
        "pandas_eval": "False"
    },
    {
        "id": 986,
        "statement": "dna sequencer pacbio have the lowest accuracy at 90%",
        "label": 1,
        "table_caption": "dna sequencer",
        "table_text": "sequencer#ion torrent pgm#454 gs flx#hiseq 2000#solidv4#pacbio#sanger 3730xl\nmanufacturer#ion torrent (life technologies)#454 life sciences (roche)#illumina#applied biosystems (life technologies)#pacific biosciences#applied biosystems (life technologies)\nsequencing chemistry#ion semiconductor sequencing#pyrosequencing#polymerase - based sequence - by - synthesis#ligation - based sequencing#phospholinked fluorescent nucleotides#dideoxy chain termination\namplification approach#emulsion pcr#emulsion pcr#bridge amplification#emulsion pcr#single - molecule , no amplification#pcr\ndata output per run#100 - 200 mb#0.7 gb#600 gb#120 gb#100 - 500 mb#1.9\u223c84 kb\naccuracy#99%#99.9%#99.9%#99.94%#90%#99.999%\ntime per run#2 hours#24 hours#3 - 10 days#7 - 14 days#2 hours#20 minutes - 3 hours\nread length#200 - 400 bp#700 bp#100x100 bp paired end#50x50 bp paired end#1500 - 5000 bp (avg)#400 - 900 bp\ncost per run#350 usd#7000 usd#6000 usd (30x human genome)#4000 usd#300 usd#4 usd (single read / reaction)\ncost per mb#1.00 usd#10 usd#0.07 usd#0.13 usd#2.00 usd#2400 usd\n",
        "pandas_code": "df.loc[df['sequencer'] == 'accuracy', 'pacbio'].values[0] == '90%'",
        "pandas_eval": "True"
    },
    {
        "id": 990,
        "statement": "solidv4 be the cheapest cost per mb at 0.13usd",
        "label": 1,
        "table_caption": "dna sequencer",
        "table_text": "sequencer#ion torrent pgm#454 gs flx#hiseq 2000#solidv4#pacbio#sanger 3730xl\nmanufacturer#ion torrent (life technologies)#454 life sciences (roche)#illumina#applied biosystems (life technologies)#pacific biosciences#applied biosystems (life technologies)\nsequencing chemistry#ion semiconductor sequencing#pyrosequencing#polymerase - based sequence - by - synthesis#ligation - based sequencing#phospholinked fluorescent nucleotides#dideoxy chain termination\namplification approach#emulsion pcr#emulsion pcr#bridge amplification#emulsion pcr#single - molecule , no amplification#pcr\ndata output per run#100 - 200 mb#0.7 gb#600 gb#120 gb#100 - 500 mb#1.9\u223c84 kb\naccuracy#99%#99.9%#99.9%#99.94%#90%#99.999%\ntime per run#2 hours#24 hours#3 - 10 days#7 - 14 days#2 hours#20 minutes - 3 hours\nread length#200 - 400 bp#700 bp#100x100 bp paired end#50x50 bp paired end#1500 - 5000 bp (avg)#400 - 900 bp\ncost per run#350 usd#7000 usd#6000 usd (30x human genome)#4000 usd#300 usd#4 usd (single read / reaction)\ncost per mb#1.00 usd#10 usd#0.07 usd#0.13 usd#2.00 usd#2400 usd\n",
        "pandas_code": "df.loc[df['sequencer'] == 'cost per mb', 'solidv4'].values[0] == '0.13 usd'",
        "pandas_eval": "True"
    },
    {
        "id": 991,
        "statement": "subiaco oval be the venue for 10 straight year",
        "label": 1,
        "table_caption": "list of west australian football league premiers",
        "table_text": "season#premiers#runners up#score#captain#coach#simpson medal#venue#attendance#umpires\n2001#east perth#south fremantle#17.18 (120) - 5.8 (38)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#23842#c hendrie , m fussell , d corcoran\n2002#east perth#west perth#15.14 (104) - 5.14 (44)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#31382#l farmer , c hendrie , r worthington\n2003#west perth#subiaco#13.9 (87) - 9.10 (64)#steve trewhella#darren harris#brent lecras (wp)#subiaco oval#17775#c hendrie , d corcoran , r worthington\n2004#subiaco#claremont#15.9 (99) - 7.9 (51)#richard maloney#peter german#paul vines (s)#subiaco oval#21507#d corcoran , l farmer , m fussell\n2005#south fremantle#claremont#17.8 (110) - 7.12 (54)#david gault#john dimmer#toby mcgrath (sf)#subiaco oval#22570#l farmer , m fussell , g bandy\n2006#subiaco#south fremantle#24.9 (153) - 10.10 (70)#marc webb#peter german#marc webb (s)#subiaco oval#21291#g bandy , g parker , t keating\n2007#subiaco#claremont#15.13 (103) - 9.8 (62)#marc webb#scott watters#brad smith (s)#subiaco oval#19541#g statham , g bandy , t keating\n2008#subiaco#swan districts#22.16 (148) - 14.7 (91)#marc webb#scott watters#chris hall (s)#subiaco oval#23199#g statham , s parry , d margetts\n2009#south fremantle#subiaco#17.11 (113) - 13.17 (95)#toby mcgrath#john dimmer#ashton hams (sf)#subiaco oval#22727#s parry , c hendrie , g statham\n2010#swan districts#claremont#14.16 (100) - 14.15 (99)#josh roberts#brian dawson#andrew krakouer (sd)#subiaco oval#24600#s parry , c hendrie , g statham\n2011#claremont#subiaco#19.13 (127) - 10.11 (71)#clancy rudeforth#simon mcphee#beau wilkes (c)#patersons stadium#15459#s parry , c hendrie , s mcphee\n2012#claremont#east fremantle#18.16 (124) - 15.8 (98)#luke blackwell#marc webb#paul medhurst (c)#patersons stadium#18612#s mcphee , t keating , j orr\n",
        "pandas_code": "all(df['venue'].iloc[:10] == 'subiaco oval')",
        "pandas_eval": "True"
    },
    {
        "id": 992,
        "statement": "subiaco be the premier more than any other group",
        "label": 1,
        "table_caption": "list of west australian football league premiers",
        "table_text": "season#premiers#runners up#score#captain#coach#simpson medal#venue#attendance#umpires\n2001#east perth#south fremantle#17.18 (120) - 5.8 (38)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#23842#c hendrie , m fussell , d corcoran\n2002#east perth#west perth#15.14 (104) - 5.14 (44)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#31382#l farmer , c hendrie , r worthington\n2003#west perth#subiaco#13.9 (87) - 9.10 (64)#steve trewhella#darren harris#brent lecras (wp)#subiaco oval#17775#c hendrie , d corcoran , r worthington\n2004#subiaco#claremont#15.9 (99) - 7.9 (51)#richard maloney#peter german#paul vines (s)#subiaco oval#21507#d corcoran , l farmer , m fussell\n2005#south fremantle#claremont#17.8 (110) - 7.12 (54)#david gault#john dimmer#toby mcgrath (sf)#subiaco oval#22570#l farmer , m fussell , g bandy\n2006#subiaco#south fremantle#24.9 (153) - 10.10 (70)#marc webb#peter german#marc webb (s)#subiaco oval#21291#g bandy , g parker , t keating\n2007#subiaco#claremont#15.13 (103) - 9.8 (62)#marc webb#scott watters#brad smith (s)#subiaco oval#19541#g statham , g bandy , t keating\n2008#subiaco#swan districts#22.16 (148) - 14.7 (91)#marc webb#scott watters#chris hall (s)#subiaco oval#23199#g statham , s parry , d margetts\n2009#south fremantle#subiaco#17.11 (113) - 13.17 (95)#toby mcgrath#john dimmer#ashton hams (sf)#subiaco oval#22727#s parry , c hendrie , g statham\n2010#swan districts#claremont#14.16 (100) - 14.15 (99)#josh roberts#brian dawson#andrew krakouer (sd)#subiaco oval#24600#s parry , c hendrie , g statham\n2011#claremont#subiaco#19.13 (127) - 10.11 (71)#clancy rudeforth#simon mcphee#beau wilkes (c)#patersons stadium#15459#s parry , c hendrie , s mcphee\n2012#claremont#east fremantle#18.16 (124) - 15.8 (98)#luke blackwell#marc webb#paul medhurst (c)#patersons stadium#18612#s mcphee , t keating , j orr\n",
        "pandas_code": "df['premiers'].value_counts().idxmax() == 'subiaco'",
        "pandas_eval": "True"
    },
    {
        "id": 993,
        "statement": "pair as captain and coach , jeremy barnard and tony micale win back - to - back for east perth in 2001 and 2002",
        "label": 1,
        "table_caption": "list of west australian football league premiers",
        "table_text": "season#premiers#runners up#score#captain#coach#simpson medal#venue#attendance#umpires\n2001#east perth#south fremantle#17.18 (120) - 5.8 (38)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#23842#c hendrie , m fussell , d corcoran\n2002#east perth#west perth#15.14 (104) - 5.14 (44)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#31382#l farmer , c hendrie , r worthington\n2003#west perth#subiaco#13.9 (87) - 9.10 (64)#steve trewhella#darren harris#brent lecras (wp)#subiaco oval#17775#c hendrie , d corcoran , r worthington\n2004#subiaco#claremont#15.9 (99) - 7.9 (51)#richard maloney#peter german#paul vines (s)#subiaco oval#21507#d corcoran , l farmer , m fussell\n2005#south fremantle#claremont#17.8 (110) - 7.12 (54)#david gault#john dimmer#toby mcgrath (sf)#subiaco oval#22570#l farmer , m fussell , g bandy\n2006#subiaco#south fremantle#24.9 (153) - 10.10 (70)#marc webb#peter german#marc webb (s)#subiaco oval#21291#g bandy , g parker , t keating\n2007#subiaco#claremont#15.13 (103) - 9.8 (62)#marc webb#scott watters#brad smith (s)#subiaco oval#19541#g statham , g bandy , t keating\n2008#subiaco#swan districts#22.16 (148) - 14.7 (91)#marc webb#scott watters#chris hall (s)#subiaco oval#23199#g statham , s parry , d margetts\n2009#south fremantle#subiaco#17.11 (113) - 13.17 (95)#toby mcgrath#john dimmer#ashton hams (sf)#subiaco oval#22727#s parry , c hendrie , g statham\n2010#swan districts#claremont#14.16 (100) - 14.15 (99)#josh roberts#brian dawson#andrew krakouer (sd)#subiaco oval#24600#s parry , c hendrie , g statham\n2011#claremont#subiaco#19.13 (127) - 10.11 (71)#clancy rudeforth#simon mcphee#beau wilkes (c)#patersons stadium#15459#s parry , c hendrie , s mcphee\n2012#claremont#east fremantle#18.16 (124) - 15.8 (98)#luke blackwell#marc webb#paul medhurst (c)#patersons stadium#18612#s mcphee , t keating , j orr\n",
        "pandas_code": "all(df[(df['season'].isin([2001, 2002])) & (df['premiers'] == 'east perth')][['captain', 'coach']].apply(lambda x: x['captain'] == 'jeremy barnard' and x['coach'] == 'tony micale', axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 994,
        "statement": "marc webb be the winning captain for 2 different coach : peter german and scott watters",
        "label": 1,
        "table_caption": "list of west australian football league premiers",
        "table_text": "season#premiers#runners up#score#captain#coach#simpson medal#venue#attendance#umpires\n2001#east perth#south fremantle#17.18 (120) - 5.8 (38)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#23842#c hendrie , m fussell , d corcoran\n2002#east perth#west perth#15.14 (104) - 5.14 (44)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#31382#l farmer , c hendrie , r worthington\n2003#west perth#subiaco#13.9 (87) - 9.10 (64)#steve trewhella#darren harris#brent lecras (wp)#subiaco oval#17775#c hendrie , d corcoran , r worthington\n2004#subiaco#claremont#15.9 (99) - 7.9 (51)#richard maloney#peter german#paul vines (s)#subiaco oval#21507#d corcoran , l farmer , m fussell\n2005#south fremantle#claremont#17.8 (110) - 7.12 (54)#david gault#john dimmer#toby mcgrath (sf)#subiaco oval#22570#l farmer , m fussell , g bandy\n2006#subiaco#south fremantle#24.9 (153) - 10.10 (70)#marc webb#peter german#marc webb (s)#subiaco oval#21291#g bandy , g parker , t keating\n2007#subiaco#claremont#15.13 (103) - 9.8 (62)#marc webb#scott watters#brad smith (s)#subiaco oval#19541#g statham , g bandy , t keating\n2008#subiaco#swan districts#22.16 (148) - 14.7 (91)#marc webb#scott watters#chris hall (s)#subiaco oval#23199#g statham , s parry , d margetts\n2009#south fremantle#subiaco#17.11 (113) - 13.17 (95)#toby mcgrath#john dimmer#ashton hams (sf)#subiaco oval#22727#s parry , c hendrie , g statham\n2010#swan districts#claremont#14.16 (100) - 14.15 (99)#josh roberts#brian dawson#andrew krakouer (sd)#subiaco oval#24600#s parry , c hendrie , g statham\n2011#claremont#subiaco#19.13 (127) - 10.11 (71)#clancy rudeforth#simon mcphee#beau wilkes (c)#patersons stadium#15459#s parry , c hendrie , s mcphee\n2012#claremont#east fremantle#18.16 (124) - 15.8 (98)#luke blackwell#marc webb#paul medhurst (c)#patersons stadium#18612#s mcphee , t keating , j orr\n",
        "pandas_code": "df[df['captain'] == 'marc webb']['coach'].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 995,
        "statement": "2002 be the year with the highest attendance",
        "label": 1,
        "table_caption": "list of west australian football league premiers",
        "table_text": "season#premiers#runners up#score#captain#coach#simpson medal#venue#attendance#umpires\n2001#east perth#south fremantle#17.18 (120) - 5.8 (38)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#23842#c hendrie , m fussell , d corcoran\n2002#east perth#west perth#15.14 (104) - 5.14 (44)#jeremy barnard#tony micale#ryan turnbull (ep)#subiaco oval#31382#l farmer , c hendrie , r worthington\n2003#west perth#subiaco#13.9 (87) - 9.10 (64)#steve trewhella#darren harris#brent lecras (wp)#subiaco oval#17775#c hendrie , d corcoran , r worthington\n2004#subiaco#claremont#15.9 (99) - 7.9 (51)#richard maloney#peter german#paul vines (s)#subiaco oval#21507#d corcoran , l farmer , m fussell\n2005#south fremantle#claremont#17.8 (110) - 7.12 (54)#david gault#john dimmer#toby mcgrath (sf)#subiaco oval#22570#l farmer , m fussell , g bandy\n2006#subiaco#south fremantle#24.9 (153) - 10.10 (70)#marc webb#peter german#marc webb (s)#subiaco oval#21291#g bandy , g parker , t keating\n2007#subiaco#claremont#15.13 (103) - 9.8 (62)#marc webb#scott watters#brad smith (s)#subiaco oval#19541#g statham , g bandy , t keating\n2008#subiaco#swan districts#22.16 (148) - 14.7 (91)#marc webb#scott watters#chris hall (s)#subiaco oval#23199#g statham , s parry , d margetts\n2009#south fremantle#subiaco#17.11 (113) - 13.17 (95)#toby mcgrath#john dimmer#ashton hams (sf)#subiaco oval#22727#s parry , c hendrie , g statham\n2010#swan districts#claremont#14.16 (100) - 14.15 (99)#josh roberts#brian dawson#andrew krakouer (sd)#subiaco oval#24600#s parry , c hendrie , g statham\n2011#claremont#subiaco#19.13 (127) - 10.11 (71)#clancy rudeforth#simon mcphee#beau wilkes (c)#patersons stadium#15459#s parry , c hendrie , s mcphee\n2012#claremont#east fremantle#18.16 (124) - 15.8 (98)#luke blackwell#marc webb#paul medhurst (c)#patersons stadium#18612#s mcphee , t keating , j orr\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'season'] == 2002",
        "pandas_eval": "True"
    },
    {
        "id": 997,
        "statement": "all of the game during round 10 of the 1926 vfl season be play on 3 july 1926",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "all(df['date'] == '3 july 1926')",
        "pandas_eval": "True"
    },
    {
        "id": 998,
        "statement": "the highest scoring home team during round 10 of the 1926 vfl season be north melbourne with 8.5 (53)",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "df[(df['date'] == '3 july 1926') & (df['home team'] == 'north melbourne') & (df['home team score'] == '8.5 (53)')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 999,
        "statement": "the lowest scoring home team during round 10 of the 1926 vfl season be fitzroy with 6.13 (49)",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "df[df['date'].str.contains('3 july 1926') & (df['home team score'] == '6.13 (49)')]['home team'].eq('fitzroy').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1001,
        "statement": "south melbourne 's away team score be not the highest away team score but follow but consecutively by essendon 's away team score",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "(df['away team score'].iloc[df['away team score'].argsort()[::-1]].iloc[0] != df[df['away team'] == 'south melbourne']['away team score'].values[0]) & (df['away team score'].iloc[df['away team score'].argsort()[::-1]].iloc[1] == df[df['away team'] == 'essendon']['away team score'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1002,
        "statement": "during round the 1926 vfl season , not all game be hold in the month of july 1926",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "not all(pd.to_datetime(df['date']).dt.month == 7)",
        "pandas_eval": "False"
    },
    {
        "id": 1003,
        "statement": "during round the 1926 vfl season , north melbourne 's home team score be higher than the melbourne away team score",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "(df[df['home team'] == 'north melbourne']['home team score'].str.extract(r'(\\\\d+)').astype(float).iloc[0] > df[df['away team'] == 'melbourne']['away team score'].str.extract(r'(\\\\d+)').astype(float).iloc[0]).item()",
        "pandas_eval": "False"
    },
    {
        "id": 1004,
        "statement": "during round the 1926 vfl season , fitzroy 's home team score be not the lowest home team score",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "df[df['home team'] == 'fitzroy']['home team score'].iloc[0] != df['home team score'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 1005,
        "statement": "during round the 1926 vfl season , south melbourn 's away team score be not the highest away team score",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#8.10 (58)#south melbourne#11.16 (82)#glenferrie oval#10000#3 july 1926\ngeelong#8.19 (67)#melbourne#8.5 (53)#corio oval#21500#3 july 1926\nst kilda#7.17 (59)#footscray#10.7 (67)#junction oval#12500#3 july 1926\nfitzroy#6.13 (49)#essendon#11.12 (78)#brunswick street oval#13000#3 july 1926\nnorth melbourne#8.5 (53)#collingwood#10.16 (76)#arden street oval#5000#3 july 1926\nrichmond#7.17 (59)#carlton#8.6 (54)#punt road oval#32000#3 july 1926\n",
        "pandas_code": "df[df['away team'] == 'south melbourne']['away team score'].max() == df['away team score'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1006,
        "statement": "7 player have not play for the vancouver canuck",
        "label": 1,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "len(df[df['reg gp'] == 0]) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 1007,
        "statement": "dixon ward have play the second - highest number of regular - season game with vancouver , and also the second - highest number of playoff game",
        "label": 1,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df['reg gp'].nlargest(2).iloc[-1] == df[df['player'] == 'dixon ward']['reg gp'].values[0]) and (df['pl gp'].nlargest(2).iloc[-1] == df[df['player'] == 'dixon ward']['pl gp'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1008,
        "statement": "leif rohlin have play 79 more game with vancouver than dane jackson",
        "label": 1,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df[df['player'] == 'leif rohlin']['reg gp'].values[0] - df[df['player'] == 'dane jackson']['reg gp'].values[0]) == 80",
        "pandas_eval": "True"
    },
    {
        "id": 1009,
        "statement": "4 of the draft pick come from ncaa school",
        "label": 1,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "df['team (league)'].str.contains('ncaa', case=False).sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1010,
        "statement": "trevor linden play in the most game for vancouver",
        "label": 1,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "df.loc[df['reg gp'].idxmax(), 'player'] == 'trevor linden'",
        "pandas_eval": "True"
    },
    {
        "id": 1011,
        "statement": "only 3 player have not play for the vancouver canuck",
        "label": 0,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df['reg gp'] == 0).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1012,
        "statement": "dixon ward be not in the top 3 for the number of regular - season game play nor playoff game play with vancouver",
        "label": 0,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df[df['player'] == 'dixon ward']['reg gp'].values[0] not in sorted(df['reg gp'].unique(), reverse=True)[:3]) and (df[df['player'] == 'dixon ward']['pl gp'].values[0] not in sorted(df['pl gp'].unique(), reverse=True)[:3])",
        "pandas_eval": "False"
    },
    {
        "id": 1013,
        "statement": "leif rohlin have play 5 time more game with vancouver than dane jackson",
        "label": 0,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df[df['player'] == 'leif rohlin']['reg gp'].values[0] == 5 * df[df['player'] == 'dane jackson']['reg gp'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1014,
        "statement": "only dixon ward play more game than trevor linden for vancouver",
        "label": 0,
        "table_caption": "list of vancouver canucks draft picks",
        "table_text": "rd#pick#player#team (league)#reg gp#pl gp\n1#2#trevor linden#medicine hat tigers ( whl )#1140#118\n2#33#leif rohlin#vik v\ucc44ster\ucc45s hk ( swe )#95#5\n3#44#dane jackson#vernon lakers (bcjhl)#15#6\n6#107#corrie d'alessio#cornell university ( ncaa )#0#0\n6#122#phil von stefenelli#boston university ( ncaa )#0#0\n7#128#dixon ward#university of north dakota ( ncaa )#103#9\n8#149#greg geldart#st albert saints ( ajhl )#0#0\n9#170#roger akerstrom#lule\ucc45 hf ( swe )#0#0\n10#191#paul constantin#lake superior state university ( ncaa )#0#0\n11#212#chris wolanin#university of illinois ( ncaa )#0#0\n12#233#stefan nilsson#f\ucc44rjestad bk ( swe )#0#0\n",
        "pandas_code": "(df[df['player'] == 'dixon ward']['reg gp'].values[0] > df[df['player'] == 'trevor linden']['reg gp'].values[0]) and (df['reg gp'].max() == df[df['player'] == 'dixon ward']['reg gp'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1015,
        "statement": "gloriavale christian community school be the only school in the west coast region that be under private authority",
        "label": 1,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "(df[df['name'] == 'gloriavale christian community school']['authority'].values[0] == 'private') and (df[df['authority'] == 'private'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1016,
        "statement": "greymouth high school and john paul ii high school each exist for 9 - 15 year",
        "label": 1,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "all(df[df['name'].isin(['greymouth high school', 'john paul ii high school'])]['years'] == '9 - 15')",
        "pandas_eval": "True"
    },
    {
        "id": 1017,
        "statement": "11 of the 14 school have be in the west coast region for 1 - 8 year",
        "label": 1,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "(df['years'] == '1 - 8').sum() == 11",
        "pandas_eval": "True"
    },
    {
        "id": 1018,
        "statement": "4 of the 14 school be in the greymouth area",
        "label": 1,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "len(df[df['area'] == 'greymouth']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1019,
        "statement": "paparoa range school do not have state integrate authority",
        "label": 1,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "df[df['name'] == 'paparoa range school']['authority'].values[0] != 'state integrated'",
        "pandas_eval": "True"
    },
    {
        "id": 1020,
        "statement": "greymouht high school be the only school in the west coast region that be under private authority",
        "label": 0,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "df[(df['name'] == 'greymouth high school') & (df['authority'] == 'private')].shape[0] == 1 and df[df['authority'] == 'private'].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1021,
        "statement": "greymouth high school and st patrick 's school each exist for 9 - 15 year",
        "label": 0,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "all(df[df['name'].isin(['greymouth high school', \"st patrick 's school\"])]['years'] == '9 - 15')",
        "pandas_eval": "False"
    },
    {
        "id": 1022,
        "statement": "2 of the 14 school have be in the west coast region for 1 - 8 year",
        "label": 0,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "df[df['years'] == '1 - 8'].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1023,
        "statement": "4 of the 14 school be in the karoro area",
        "label": 0,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "(df['area'] == 'karoro').sum() == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1024,
        "statement": "paparoa range school have state intergrated authority",
        "label": 0,
        "table_caption": "list of schools in the west coast region",
        "table_text": "name#years#area#authority#decile#roll\nawahono school - grey valley#1 - 8#ahaura#state#4#84\nbarrytown school#1 - 8#barrytown#state#4#19\nblaketown school#1 - 8#blaketown#state#2#70\ncobden school#1 - 8#cobden#state#2#150\ngloriavale christian community school#1 - 15#haupiri#private#n / a#n / a\ngreymouth high school#9 - 15#greymouth#state#4#637\ngreymouth main school#1 - 8#greymouth#state#5#363\njohn paul ii high school#9 - 15#greymouth#state integrated#4#157\nkaroro school#1 - 8#karoro#state#8#158\nlake brunner school#1 - 8#moana#state#7#52\npaparoa range school#1 - 8#dobson#state#3#124\nparoa school#1 - 8#paroa#state#6#156\nrunanga school#1 - 8#runanga#state#3#98\nst patrick 's school#1 - 8#greymouth#state integrated#5#115\n",
        "pandas_code": "df[df['name'] == 'paparoa range school']['authority'].iloc[0] == 'state integrated'",
        "pandas_eval": "False"
    },
    {
        "id": 1025,
        "statement": "istanbul be the host city 11 of the past 21 year",
        "label": 1,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "(df['host city'].value_counts().get('istanbul', 0) == 11)",
        "pandas_eval": "True"
    },
    {
        "id": 1026,
        "statement": "none of the winner win 2 time",
        "label": 1,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "df['winner'].value_counts().max() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1027,
        "statement": "1991 be the only year from 1988 to 2010 where a competition be not host",
        "label": 1,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "len(df[df['year'] == 1991]) == 0 and len(df[(df['year'] >= 1988) & (df['year'] <= 2010) & (df['year'] != 1991)]) == 21",
        "pandas_eval": "True"
    },
    {
        "id": 1028,
        "statement": "shenzhen have host 6 fewer competions than istanbul",
        "label": 1,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "(df['host city'].value_counts()['istanbul'] - df['host city'].value_counts()['shenzhen']) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1029,
        "statement": "taipei , turku , and beijing only host 1 year each",
        "label": 1,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "df[df['host city'].isin(['taipei', 'turku', 'beijing'])].groupby('host city')['year'].nunique().eq(1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1030,
        "statement": "istanbul be the host city 15 of the past 21 year",
        "label": 0,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "(df['host city'].value_counts().get('istanbul', 0) == 15)",
        "pandas_eval": "False"
    },
    {
        "id": 1031,
        "statement": "3 of the winner win 2 time",
        "label": 0,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "df['winner'].value_counts().eq(2).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1032,
        "statement": "1995 be the only year other than 1990 to 2010 where a competition be not host",
        "label": 0,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "len(df[(df['year'] != 1990) & (df['year'] != 1995) & (df['year'] != 2010)]['host city'].unique()) == 0",
        "pandas_eval": "False"
    },
    {
        "id": 1033,
        "statement": "shenzhen have host more than 2 more competition than instanbul",
        "label": 0,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "(df['host city'].value_counts()['shenzhen'] - df['host city'].value_counts()['istanbul']) > 2",
        "pandas_eval": "False"
    },
    {
        "id": 1034,
        "statement": "taipei , turku , and beijing each host 2 year",
        "label": 0,
        "table_caption": "miss model of the world",
        "table_text": "host city#year#winner#1st runner up#2nd runner up\nistanbul#1988#neneng robiah#helena kirsi#sibel tan\nistanbul#1989#kao arden#marisabel valdes#aneta rusewitz\ntaipei#1990#sharon luengo#nany naegele#eva pedraza\nturku#1992#celine cassagnes#meike swartz#sonia bermudez\nistanbul#1993#gemith gemparo#natalia martinez#dimitri kostaki\nistanbul#1994#isabelle de silva#irini alexiou#alexandra koukoulyka\nistanbul#1995#laura marlen cabrera#tugba ozay#jakki denell aherne\nistanbul#1996#carla paneca fernandez#mercedes mwajas#yenni vaca paz\nistanbul#1997#caroline lubrez#mari carmen#assel isabaeva\nistanbul#1998#abby essien#veronica laskaeva#woon yeow\nantalya#1999#michaella walker#rosana pelaz#thanyaluk worapimrat\nistanbul#2000#malgorzata rozniecka#alexandra aguilera#feza gursoy\nistanbul#2001#laura keranen#ksenia saifutdinova#oksana dahan\nistanbul#2002#yan wei#marta gracia#dimitra alexandraki\nbeijing#2004#khemanit jamikorn#ma li#ling zhang\nshenzhen#2005#catherine abboud#chien - an lin#elena tihomirova\nshenzhen#2006#di song#eleonora masalab#ana maria ortiz rodal\nyuncheng#2007#iveta lutovsk\u00e1#stephanie thomas#ria antoniou\nshenzhen#2008#mariia iakimuk#hommy king#serap tun\u00e7\nshenzhen#2009#emene nyame#deyra cimen#iuliia galichenko\nshenzhen#2010#amanda delgado#ema masters#qing qing cao\n",
        "pandas_code": "df[df['host city'].isin(['taipei', 'turku', 'beijing'])].groupby('host city').size().eq(2).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1035,
        "statement": "the toyoto center be the location for at least 4 game",
        "label": 1,
        "table_caption": "2008 - 09 houston rockets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n34#january 2#toronto#l 73 - 94 (ot)#von wafer (18)#luis scola (13)#rafer alston (4)#air canada centre 19800#21 - 13\n35#january 3#atlanta#l 100 - 103 (ot)#carl landry (18)#yao ming (15)#rafer alston (9)#philips arena 16740#21 - 14\n36#january 6#philadelphia#l 96 - 104 (ot)#luis scola (18)#luis scola (17)#tracy mcgrady (9)#wachovia center 14858#21 - 15\n37#january 7#boston#w 89 - 85 (ot)#yao ming (26)#yao ming , chuck hayes (8)#yao ming , aaron brooks , ron artest (4)#td banknorth garden 18624#22 - 15\n38#january 9#oklahoma city#w 98 - 96 (ot)#tracy mcgrady (26)#yao ming (7)#rafer alston (6)#ford center 19136#23 - 15\n39#january 10#new york#w 96 - 76 (ot)#luis scola (18)#luis scola (11)#rafer alston (6)#toyota center 18280#24 - 15\n40#january 13#la lakers#l 100 - 105 (ot)#von wafer (23)#yao ming (17)#rafer alston (6)#toyota center 18557#24 - 16\n41#january 17#miami#w 93 - 86 (ot)#yao ming (26)#yao ming (10)#shane battier (6)#toyota center 18369#25 - 16\n42#january 19#denver#w 115 - 113 (ot)#yao ming (31)#luis scola (8)#rafer alston (11)#toyota center 18199#26 - 16\n43#january 21#utah#w 108 - 99 (ot)#rafer alston (23)#yao ming (12)#rafer alston (8)#toyota center 17037#27 - 16\n44#january 23#indiana#l 102 - 107 (ot)#luis scola (25)#shane battier (12)#rafer alston (8)#conseco fieldhouse 14486#27 - 17\n45#january 25#detroit#w 108 - 105 (ot)#ron artest (24)#ron artest (9)#rafer alston (10)#the palace of auburn hills 22076#28 - 17\n46#january 26#new york#l 98 - 104 (ot)#tracy mcgrady (20)#luis scola (14)#tracy mcgrady (6)#madison square garden 19155#28 - 18\n47#january 28#philadelphia#l 93 - 95 (ot)#tracy mcgrady (24)#luis scola (10)#rafer alston (9)#toyota center 15544#28 - 19\n",
        "pandas_code": "df['location attendance'].str.contains('toyota center').sum() >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 1036,
        "statement": "the palace of auburn hill have a greater attendance than the madison square garden",
        "label": 1,
        "table_caption": "2008 - 09 houston rockets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n34#january 2#toronto#l 73 - 94 (ot)#von wafer (18)#luis scola (13)#rafer alston (4)#air canada centre 19800#21 - 13\n35#january 3#atlanta#l 100 - 103 (ot)#carl landry (18)#yao ming (15)#rafer alston (9)#philips arena 16740#21 - 14\n36#january 6#philadelphia#l 96 - 104 (ot)#luis scola (18)#luis scola (17)#tracy mcgrady (9)#wachovia center 14858#21 - 15\n37#january 7#boston#w 89 - 85 (ot)#yao ming (26)#yao ming , chuck hayes (8)#yao ming , aaron brooks , ron artest (4)#td banknorth garden 18624#22 - 15\n38#january 9#oklahoma city#w 98 - 96 (ot)#tracy mcgrady (26)#yao ming (7)#rafer alston (6)#ford center 19136#23 - 15\n39#january 10#new york#w 96 - 76 (ot)#luis scola (18)#luis scola (11)#rafer alston (6)#toyota center 18280#24 - 15\n40#january 13#la lakers#l 100 - 105 (ot)#von wafer (23)#yao ming (17)#rafer alston (6)#toyota center 18557#24 - 16\n41#january 17#miami#w 93 - 86 (ot)#yao ming (26)#yao ming (10)#shane battier (6)#toyota center 18369#25 - 16\n42#january 19#denver#w 115 - 113 (ot)#yao ming (31)#luis scola (8)#rafer alston (11)#toyota center 18199#26 - 16\n43#january 21#utah#w 108 - 99 (ot)#rafer alston (23)#yao ming (12)#rafer alston (8)#toyota center 17037#27 - 16\n44#january 23#indiana#l 102 - 107 (ot)#luis scola (25)#shane battier (12)#rafer alston (8)#conseco fieldhouse 14486#27 - 17\n45#january 25#detroit#w 108 - 105 (ot)#ron artest (24)#ron artest (9)#rafer alston (10)#the palace of auburn hills 22076#28 - 17\n46#january 26#new york#l 98 - 104 (ot)#tracy mcgrady (20)#luis scola (14)#tracy mcgrady (6)#madison square garden 19155#28 - 18\n47#january 28#philadelphia#l 93 - 95 (ot)#tracy mcgrady (24)#luis scola (10)#rafer alston (9)#toyota center 15544#28 - 19\n",
        "pandas_code": "df[df['location attendance'].str.contains('the palace of auburn hills')]['location attendance'].str.extract('(\\d+)')[0].astype(int).values[0] > df[df['location attendance'].str.contains('madison square garden')]['location attendance'].str.extract('(\\d+)')[0].astype(int).values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1037,
        "statement": "yao ming have the high rebound during 6 game",
        "label": 1,
        "table_caption": "2008 - 09 houston rockets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n34#january 2#toronto#l 73 - 94 (ot)#von wafer (18)#luis scola (13)#rafer alston (4)#air canada centre 19800#21 - 13\n35#january 3#atlanta#l 100 - 103 (ot)#carl landry (18)#yao ming (15)#rafer alston (9)#philips arena 16740#21 - 14\n36#january 6#philadelphia#l 96 - 104 (ot)#luis scola (18)#luis scola (17)#tracy mcgrady (9)#wachovia center 14858#21 - 15\n37#january 7#boston#w 89 - 85 (ot)#yao ming (26)#yao ming , chuck hayes (8)#yao ming , aaron brooks , ron artest (4)#td banknorth garden 18624#22 - 15\n38#january 9#oklahoma city#w 98 - 96 (ot)#tracy mcgrady (26)#yao ming (7)#rafer alston (6)#ford center 19136#23 - 15\n39#january 10#new york#w 96 - 76 (ot)#luis scola (18)#luis scola (11)#rafer alston (6)#toyota center 18280#24 - 15\n40#january 13#la lakers#l 100 - 105 (ot)#von wafer (23)#yao ming (17)#rafer alston (6)#toyota center 18557#24 - 16\n41#january 17#miami#w 93 - 86 (ot)#yao ming (26)#yao ming (10)#shane battier (6)#toyota center 18369#25 - 16\n42#january 19#denver#w 115 - 113 (ot)#yao ming (31)#luis scola (8)#rafer alston (11)#toyota center 18199#26 - 16\n43#january 21#utah#w 108 - 99 (ot)#rafer alston (23)#yao ming (12)#rafer alston (8)#toyota center 17037#27 - 16\n44#january 23#indiana#l 102 - 107 (ot)#luis scola (25)#shane battier (12)#rafer alston (8)#conseco fieldhouse 14486#27 - 17\n45#january 25#detroit#w 108 - 105 (ot)#ron artest (24)#ron artest (9)#rafer alston (10)#the palace of auburn hills 22076#28 - 17\n46#january 26#new york#l 98 - 104 (ot)#tracy mcgrady (20)#luis scola (14)#tracy mcgrady (6)#madison square garden 19155#28 - 18\n47#january 28#philadelphia#l 93 - 95 (ot)#tracy mcgrady (24)#luis scola (10)#rafer alston (9)#toyota center 15544#28 - 19\n",
        "pandas_code": "df[df['high rebounds'].str.contains('yao ming')].shape[0] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1038,
        "statement": "the first 3 game - on january 2 , 3 , and 6 - be all loss",
        "label": 1,
        "table_caption": "2008 - 09 houston rockets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n34#january 2#toronto#l 73 - 94 (ot)#von wafer (18)#luis scola (13)#rafer alston (4)#air canada centre 19800#21 - 13\n35#january 3#atlanta#l 100 - 103 (ot)#carl landry (18)#yao ming (15)#rafer alston (9)#philips arena 16740#21 - 14\n36#january 6#philadelphia#l 96 - 104 (ot)#luis scola (18)#luis scola (17)#tracy mcgrady (9)#wachovia center 14858#21 - 15\n37#january 7#boston#w 89 - 85 (ot)#yao ming (26)#yao ming , chuck hayes (8)#yao ming , aaron brooks , ron artest (4)#td banknorth garden 18624#22 - 15\n38#january 9#oklahoma city#w 98 - 96 (ot)#tracy mcgrady (26)#yao ming (7)#rafer alston (6)#ford center 19136#23 - 15\n39#january 10#new york#w 96 - 76 (ot)#luis scola (18)#luis scola (11)#rafer alston (6)#toyota center 18280#24 - 15\n40#january 13#la lakers#l 100 - 105 (ot)#von wafer (23)#yao ming (17)#rafer alston (6)#toyota center 18557#24 - 16\n41#january 17#miami#w 93 - 86 (ot)#yao ming (26)#yao ming (10)#shane battier (6)#toyota center 18369#25 - 16\n42#january 19#denver#w 115 - 113 (ot)#yao ming (31)#luis scola (8)#rafer alston (11)#toyota center 18199#26 - 16\n43#january 21#utah#w 108 - 99 (ot)#rafer alston (23)#yao ming (12)#rafer alston (8)#toyota center 17037#27 - 16\n44#january 23#indiana#l 102 - 107 (ot)#luis scola (25)#shane battier (12)#rafer alston (8)#conseco fieldhouse 14486#27 - 17\n45#january 25#detroit#w 108 - 105 (ot)#ron artest (24)#ron artest (9)#rafer alston (10)#the palace of auburn hills 22076#28 - 17\n46#january 26#new york#l 98 - 104 (ot)#tracy mcgrady (20)#luis scola (14)#tracy mcgrady (6)#madison square garden 19155#28 - 18\n47#january 28#philadelphia#l 93 - 95 (ot)#tracy mcgrady (24)#luis scola (10)#rafer alston (9)#toyota center 15544#28 - 19\n",
        "pandas_code": "all(df[df['date'].isin(['january 2', 'january 3', 'january 6'])]['score'].str.startswith('l'))",
        "pandas_eval": "True"
    },
    {
        "id": 1039,
        "statement": "the last 2 game - on january 26 and 28 - be both loss",
        "label": 1,
        "table_caption": "2008 - 09 houston rockets season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n34#january 2#toronto#l 73 - 94 (ot)#von wafer (18)#luis scola (13)#rafer alston (4)#air canada centre 19800#21 - 13\n35#january 3#atlanta#l 100 - 103 (ot)#carl landry (18)#yao ming (15)#rafer alston (9)#philips arena 16740#21 - 14\n36#january 6#philadelphia#l 96 - 104 (ot)#luis scola (18)#luis scola (17)#tracy mcgrady (9)#wachovia center 14858#21 - 15\n37#january 7#boston#w 89 - 85 (ot)#yao ming (26)#yao ming , chuck hayes (8)#yao ming , aaron brooks , ron artest (4)#td banknorth garden 18624#22 - 15\n38#january 9#oklahoma city#w 98 - 96 (ot)#tracy mcgrady (26)#yao ming (7)#rafer alston (6)#ford center 19136#23 - 15\n39#january 10#new york#w 96 - 76 (ot)#luis scola (18)#luis scola (11)#rafer alston (6)#toyota center 18280#24 - 15\n40#january 13#la lakers#l 100 - 105 (ot)#von wafer (23)#yao ming (17)#rafer alston (6)#toyota center 18557#24 - 16\n41#january 17#miami#w 93 - 86 (ot)#yao ming (26)#yao ming (10)#shane battier (6)#toyota center 18369#25 - 16\n42#january 19#denver#w 115 - 113 (ot)#yao ming (31)#luis scola (8)#rafer alston (11)#toyota center 18199#26 - 16\n43#january 21#utah#w 108 - 99 (ot)#rafer alston (23)#yao ming (12)#rafer alston (8)#toyota center 17037#27 - 16\n44#january 23#indiana#l 102 - 107 (ot)#luis scola (25)#shane battier (12)#rafer alston (8)#conseco fieldhouse 14486#27 - 17\n45#january 25#detroit#w 108 - 105 (ot)#ron artest (24)#ron artest (9)#rafer alston (10)#the palace of auburn hills 22076#28 - 17\n46#january 26#new york#l 98 - 104 (ot)#tracy mcgrady (20)#luis scola (14)#tracy mcgrady (6)#madison square garden 19155#28 - 18\n47#january 28#philadelphia#l 93 - 95 (ot)#tracy mcgrady (24)#luis scola (10)#rafer alston (9)#toyota center 15544#28 - 19\n",
        "pandas_code": "(df[df['date'].isin(['january 26', 'january 28'])]['score'].str.startswith('l')).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1040,
        "statement": "saudi arabia have the same number of club as uae",
        "label": 1,
        "table_caption": "2010 afc champions league",
        "table_text": "pos#member association#points (total 500)#clubs#group stage#play - off#afc cup\n4#saudi arabia#365#12#4#0#0\n5#uae#356#12#3#1#0\n7#iran#340#18#4#0#0\n9#uzbekistan#289#16#2#0#1\n10#qatar#270#10#2#0#0\n",
        "pandas_code": "df[df['member association'] == 'saudi arabia']['clubs'].values[0] == df[df['member association'] == 'uae']['clubs'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1041,
        "statement": "saudi arabia have the highest point but no play - off and no afc cup",
        "label": 1,
        "table_caption": "2010 afc champions league",
        "table_text": "pos#member association#points (total 500)#clubs#group stage#play - off#afc cup\n4#saudi arabia#365#12#4#0#0\n5#uae#356#12#3#1#0\n7#iran#340#18#4#0#0\n9#uzbekistan#289#16#2#0#1\n10#qatar#270#10#2#0#0\n",
        "pandas_code": "(df[df['member association'] == 'saudi arabia']['points (total 500)'].max() == df['points (total 500)'].max()) & (df[df['member association'] == 'saudi arabia']['play - off'].sum() == 0) & (df[df['member association'] == 'saudi arabia']['afc cup'].sum() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1042,
        "statement": "the member association with fewest club have the fewest point",
        "label": 1,
        "table_caption": "2010 afc champions league",
        "table_text": "pos#member association#points (total 500)#clubs#group stage#play - off#afc cup\n4#saudi arabia#365#12#4#0#0\n5#uae#356#12#3#1#0\n7#iran#340#18#4#0#0\n9#uzbekistan#289#16#2#0#1\n10#qatar#270#10#2#0#0\n",
        "pandas_code": "df.loc[df['clubs'].idxmin(), 'points (total 500)'] == df['points (total 500)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1043,
        "statement": "the member association with the highest and lowest point don't have a play - off or afc cup",
        "label": 1,
        "table_caption": "2010 afc champions league",
        "table_text": "pos#member association#points (total 500)#clubs#group stage#play - off#afc cup\n4#saudi arabia#365#12#4#0#0\n5#uae#356#12#3#1#0\n7#iran#340#18#4#0#0\n9#uzbekistan#289#16#2#0#1\n10#qatar#270#10#2#0#0\n",
        "pandas_code": "(df.loc[df['points (total 500)'].idxmax(), ['play - off', 'afc cup']].sum() == 0) and (df.loc[df['points (total 500)'].idxmin(), ['play - off', 'afc cup']].sum() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1044,
        "statement": "the team with the highest club have the same number of group stage as another team",
        "label": 1,
        "table_caption": "2010 afc champions league",
        "table_text": "pos#member association#points (total 500)#clubs#group stage#play - off#afc cup\n4#saudi arabia#365#12#4#0#0\n5#uae#356#12#3#1#0\n7#iran#340#18#4#0#0\n9#uzbekistan#289#16#2#0#1\n10#qatar#270#10#2#0#0\n",
        "pandas_code": "df.loc[df['clubs'].idxmax(), 'group stage'] in df['group stage'].value_counts()[df['group stage'].value_counts() > 1].index",
        "pandas_eval": "True"
    },
    {
        "id": 1046,
        "statement": "out if the 7 engine type 1 of which be a 5.0 l hybrid v8 that have a 2ur - fse in the region of asia and europe",
        "label": 1,
        "table_caption": "lexus ls (xf40)",
        "table_text": "chassis code#model no#production years#drivetrain#transmission#engine type#engine code#region (s)\nusf40 (japanese)#ls 460#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america , asia , europe , oceania\nusf40 (japanese)#ls 460#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fe#middle east\nusf41#ls 460 l#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america , asia , europe\nusf41#ls 460 l#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fe#middle east\nusf45#ls 460 awd#2007 -#awd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america\nusf46#ls 460 l awd#2007 -#awd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america\nuvf45 (japanese)#ls 600h#2007 -#awd#l110f cvt#5.0 l hybrid v8#2ur - fse#asia , europe\n",
        "pandas_code": "df[(df['engine type'] == '5.0 l hybrid v8') & (df['engine code'] == '2ur - fse') & (df['region (s)'].str.contains('asia') & df['region (s)'].str.contains('europe'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1049,
        "statement": "there be 3 chassis code that have the same awd drivetrain",
        "label": 1,
        "table_caption": "lexus ls (xf40)",
        "table_text": "chassis code#model no#production years#drivetrain#transmission#engine type#engine code#region (s)\nusf40 (japanese)#ls 460#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america , asia , europe , oceania\nusf40 (japanese)#ls 460#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fe#middle east\nusf41#ls 460 l#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america , asia , europe\nusf41#ls 460 l#2006 -#rwd#8 - speed aa80e at#4.6 l petrol v8#1ur - fe#middle east\nusf45#ls 460 awd#2007 -#awd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america\nusf46#ls 460 l awd#2007 -#awd#8 - speed aa80e at#4.6 l petrol v8#1ur - fse#n america\nuvf45 (japanese)#ls 600h#2007 -#awd#l110f cvt#5.0 l hybrid v8#2ur - fse#asia , europe\n",
        "pandas_code": "df[df['drivetrain'] == 'awd']['chassis code'].nunique() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1050,
        "statement": "abdullah alhaddad and ariq algallaf be both in class cat a",
        "label": 1,
        "table_caption": "kuwait at the 2008 summer paralympics",
        "table_text": "athlete#class#event#bout 1#bout 2#bout 3#bout 4#bout 5#bout 6#rank#1 / 8 finals#quarterfinals#semifinals\nabdullah alhaddad#cat a#foil#pender ( pol ) l 3 - 5#maillard ( fra ) l 1 - 5#mato ( hun ) l 1 - 5#pellegrini ( ita ) l 4 - 5#andreev ( rus ) w 5 - 2#n / a#5 q#pender ( pol ) l 6 - 15#did not advance#did not advance\nabdullah alhaddad#cat a#\u00e9p\u00e9e#pylarinos ( gre ) w 5 - 3#davydenko ( ukr ) l 1 - 5#serafini ( ita ) w 5 - 1#maillard ( fra ) l 4 - 5#saengsawang ( tha ) w 5 - 4#sanchez ( esp ) w 5 - 0#3 q#saengsawang ( tha ) l 9 - 15#did not advance#did not advance\ntariq alqallaf#cat a#foil#saengsawang ( tha ) w 5 - 1#zhang ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#horvath ( hun ) w 5 - 4#granell ( esp ) w 5 - 0#andree ( ger ) w 5 - 1#3 q#bazhukov ( ukr ) w 15 - 9#ye ( chn ) l 6 - 15#did not advance\ntariq alqallaf#cat a#\u00e9p\u00e9e#horvath ( hun ) w 5 - 1#stanczuk ( pol ) w 5 - 3#wong ( hkg ) l 3 - 5#tian ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#n / a#5 q#maillard ( fra ) l 7 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#foil#fawcett ( gbr ) w 5 - 2#francois ( fra ) l 3 - 5#rodgers ( usa ) l 4 - 5#datsko ( ukr ) l 4 - 5#czop ( pol ) l 2 - 5#n / a#5 q#hui ( hkg ) l 3 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#\u00e9p\u00e9e#williams ( usa ) l 4 - 5#bogdos ( gre ) l 4 - 5#poleshchuk ( rus ) l 1 - 5#latreche ( fra ) l 2 - 5#komar ( ukr ) l 2 - 5#n / a#6#did not advance#did not advance#did not advance\n",
        "pandas_code": "all(df[df['athlete'].isin(['abdullah alhaddad', 'tariq alqallaf'])]['class'] == 'cat a')",
        "pandas_eval": "True"
    },
    {
        "id": 1051,
        "statement": "abdulwahab alsaedi be in class cat b 2 time",
        "label": 1,
        "table_caption": "kuwait at the 2008 summer paralympics",
        "table_text": "athlete#class#event#bout 1#bout 2#bout 3#bout 4#bout 5#bout 6#rank#1 / 8 finals#quarterfinals#semifinals\nabdullah alhaddad#cat a#foil#pender ( pol ) l 3 - 5#maillard ( fra ) l 1 - 5#mato ( hun ) l 1 - 5#pellegrini ( ita ) l 4 - 5#andreev ( rus ) w 5 - 2#n / a#5 q#pender ( pol ) l 6 - 15#did not advance#did not advance\nabdullah alhaddad#cat a#\u00e9p\u00e9e#pylarinos ( gre ) w 5 - 3#davydenko ( ukr ) l 1 - 5#serafini ( ita ) w 5 - 1#maillard ( fra ) l 4 - 5#saengsawang ( tha ) w 5 - 4#sanchez ( esp ) w 5 - 0#3 q#saengsawang ( tha ) l 9 - 15#did not advance#did not advance\ntariq alqallaf#cat a#foil#saengsawang ( tha ) w 5 - 1#zhang ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#horvath ( hun ) w 5 - 4#granell ( esp ) w 5 - 0#andree ( ger ) w 5 - 1#3 q#bazhukov ( ukr ) w 15 - 9#ye ( chn ) l 6 - 15#did not advance\ntariq alqallaf#cat a#\u00e9p\u00e9e#horvath ( hun ) w 5 - 1#stanczuk ( pol ) w 5 - 3#wong ( hkg ) l 3 - 5#tian ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#n / a#5 q#maillard ( fra ) l 7 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#foil#fawcett ( gbr ) w 5 - 2#francois ( fra ) l 3 - 5#rodgers ( usa ) l 4 - 5#datsko ( ukr ) l 4 - 5#czop ( pol ) l 2 - 5#n / a#5 q#hui ( hkg ) l 3 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#\u00e9p\u00e9e#williams ( usa ) l 4 - 5#bogdos ( gre ) l 4 - 5#poleshchuk ( rus ) l 1 - 5#latreche ( fra ) l 2 - 5#komar ( ukr ) l 2 - 5#n / a#6#did not advance#did not advance#did not advance\n",
        "pandas_code": "df[df['athlete'] == 'abdulwahab alsaedi']['class'].value_counts().get('cat b', 0) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1052,
        "statement": "there be 2 event list - foil and \u00e9p\u00e9e",
        "label": 1,
        "table_caption": "kuwait at the 2008 summer paralympics",
        "table_text": "athlete#class#event#bout 1#bout 2#bout 3#bout 4#bout 5#bout 6#rank#1 / 8 finals#quarterfinals#semifinals\nabdullah alhaddad#cat a#foil#pender ( pol ) l 3 - 5#maillard ( fra ) l 1 - 5#mato ( hun ) l 1 - 5#pellegrini ( ita ) l 4 - 5#andreev ( rus ) w 5 - 2#n / a#5 q#pender ( pol ) l 6 - 15#did not advance#did not advance\nabdullah alhaddad#cat a#\u00e9p\u00e9e#pylarinos ( gre ) w 5 - 3#davydenko ( ukr ) l 1 - 5#serafini ( ita ) w 5 - 1#maillard ( fra ) l 4 - 5#saengsawang ( tha ) w 5 - 4#sanchez ( esp ) w 5 - 0#3 q#saengsawang ( tha ) l 9 - 15#did not advance#did not advance\ntariq alqallaf#cat a#foil#saengsawang ( tha ) w 5 - 1#zhang ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#horvath ( hun ) w 5 - 4#granell ( esp ) w 5 - 0#andree ( ger ) w 5 - 1#3 q#bazhukov ( ukr ) w 15 - 9#ye ( chn ) l 6 - 15#did not advance\ntariq alqallaf#cat a#\u00e9p\u00e9e#horvath ( hun ) w 5 - 1#stanczuk ( pol ) w 5 - 3#wong ( hkg ) l 3 - 5#tian ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#n / a#5 q#maillard ( fra ) l 7 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#foil#fawcett ( gbr ) w 5 - 2#francois ( fra ) l 3 - 5#rodgers ( usa ) l 4 - 5#datsko ( ukr ) l 4 - 5#czop ( pol ) l 2 - 5#n / a#5 q#hui ( hkg ) l 3 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#\u00e9p\u00e9e#williams ( usa ) l 4 - 5#bogdos ( gre ) l 4 - 5#poleshchuk ( rus ) l 1 - 5#latreche ( fra ) l 2 - 5#komar ( ukr ) l 2 - 5#n / a#6#did not advance#did not advance#did not advance\n",
        "pandas_code": "len(df['event'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1053,
        "statement": "only tariq algallaf advance to the quarterfinal",
        "label": 1,
        "table_caption": "kuwait at the 2008 summer paralympics",
        "table_text": "athlete#class#event#bout 1#bout 2#bout 3#bout 4#bout 5#bout 6#rank#1 / 8 finals#quarterfinals#semifinals\nabdullah alhaddad#cat a#foil#pender ( pol ) l 3 - 5#maillard ( fra ) l 1 - 5#mato ( hun ) l 1 - 5#pellegrini ( ita ) l 4 - 5#andreev ( rus ) w 5 - 2#n / a#5 q#pender ( pol ) l 6 - 15#did not advance#did not advance\nabdullah alhaddad#cat a#\u00e9p\u00e9e#pylarinos ( gre ) w 5 - 3#davydenko ( ukr ) l 1 - 5#serafini ( ita ) w 5 - 1#maillard ( fra ) l 4 - 5#saengsawang ( tha ) w 5 - 4#sanchez ( esp ) w 5 - 0#3 q#saengsawang ( tha ) l 9 - 15#did not advance#did not advance\ntariq alqallaf#cat a#foil#saengsawang ( tha ) w 5 - 1#zhang ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#horvath ( hun ) w 5 - 4#granell ( esp ) w 5 - 0#andree ( ger ) w 5 - 1#3 q#bazhukov ( ukr ) w 15 - 9#ye ( chn ) l 6 - 15#did not advance\ntariq alqallaf#cat a#\u00e9p\u00e9e#horvath ( hun ) w 5 - 1#stanczuk ( pol ) w 5 - 3#wong ( hkg ) l 3 - 5#tian ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#n / a#5 q#maillard ( fra ) l 7 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#foil#fawcett ( gbr ) w 5 - 2#francois ( fra ) l 3 - 5#rodgers ( usa ) l 4 - 5#datsko ( ukr ) l 4 - 5#czop ( pol ) l 2 - 5#n / a#5 q#hui ( hkg ) l 3 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#\u00e9p\u00e9e#williams ( usa ) l 4 - 5#bogdos ( gre ) l 4 - 5#poleshchuk ( rus ) l 1 - 5#latreche ( fra ) l 2 - 5#komar ( ukr ) l 2 - 5#n / a#6#did not advance#did not advance#did not advance\n",
        "pandas_code": "df[(df['athlete'] == 'tariq alqallaf') & (df['quarterfinals'] != 'did not advance')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1054,
        "statement": "nobody advance to the semifinal",
        "label": 1,
        "table_caption": "kuwait at the 2008 summer paralympics",
        "table_text": "athlete#class#event#bout 1#bout 2#bout 3#bout 4#bout 5#bout 6#rank#1 / 8 finals#quarterfinals#semifinals\nabdullah alhaddad#cat a#foil#pender ( pol ) l 3 - 5#maillard ( fra ) l 1 - 5#mato ( hun ) l 1 - 5#pellegrini ( ita ) l 4 - 5#andreev ( rus ) w 5 - 2#n / a#5 q#pender ( pol ) l 6 - 15#did not advance#did not advance\nabdullah alhaddad#cat a#\u00e9p\u00e9e#pylarinos ( gre ) w 5 - 3#davydenko ( ukr ) l 1 - 5#serafini ( ita ) w 5 - 1#maillard ( fra ) l 4 - 5#saengsawang ( tha ) w 5 - 4#sanchez ( esp ) w 5 - 0#3 q#saengsawang ( tha ) l 9 - 15#did not advance#did not advance\ntariq alqallaf#cat a#foil#saengsawang ( tha ) w 5 - 1#zhang ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#horvath ( hun ) w 5 - 4#granell ( esp ) w 5 - 0#andree ( ger ) w 5 - 1#3 q#bazhukov ( ukr ) w 15 - 9#ye ( chn ) l 6 - 15#did not advance\ntariq alqallaf#cat a#\u00e9p\u00e9e#horvath ( hun ) w 5 - 1#stanczuk ( pol ) w 5 - 3#wong ( hkg ) l 3 - 5#tian ( chn ) l 0 - 5#betti ( ita ) l 0 - 5#n / a#5 q#maillard ( fra ) l 7 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#foil#fawcett ( gbr ) w 5 - 2#francois ( fra ) l 3 - 5#rodgers ( usa ) l 4 - 5#datsko ( ukr ) l 4 - 5#czop ( pol ) l 2 - 5#n / a#5 q#hui ( hkg ) l 3 - 15#did not advance#did not advance\nabdulwahab alsaedi#cat b#\u00e9p\u00e9e#williams ( usa ) l 4 - 5#bogdos ( gre ) l 4 - 5#poleshchuk ( rus ) l 1 - 5#latreche ( fra ) l 2 - 5#komar ( ukr ) l 2 - 5#n / a#6#did not advance#did not advance#did not advance\n",
        "pandas_code": "all(df['semifinals'] == 'did not advance')",
        "pandas_eval": "True"
    },
    {
        "id": 1055,
        "statement": "shri kunja bihari meher receive the award in a different category from smt shantha sinha",
        "label": 1,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "df[df['name'].isin(['shri kunja bihari meher', 'smt shantha sinha'])].drop_duplicates('name')['field'].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1057,
        "statement": "dr mammootty be from the same state as prof ottaplakkal neelakanta velu kurup",
        "label": 1,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "df[df['name'] == 'dr mammootty']['state'].values[0] == df[df['name'] == 'prof ottaplakkal neelakanta velu kurup']['state'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1059,
        "statement": "shri krishnarao ganpatrao sable be not the only winner from maharashtra",
        "label": 1,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "len(df[(df['state'] == 'maharashtra') & (df['name'] != 'shri krishnarao ganpatrao sable')]) > 0",
        "pandas_eval": "True"
    },
    {
        "id": 1060,
        "statement": "smt shantha sinha and shri kunja bihari meher both receive the award in the art field",
        "label": 0,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "((df['name'] == 'smt shantha sinha') & (df['field'] == 'arts')).any() & ((df['name'] == 'shri kunja bihari meher') & (df['field'] == 'arts')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1061,
        "statement": "dr mammootty be the only recipient from kerala",
        "label": 0,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "(df[df['state'] == 'kerala']['name'].nunique() == 1) & (df[df['state'] == 'kerala']['name'].iloc[0] == 'dr mammootty')",
        "pandas_eval": "False"
    },
    {
        "id": 1062,
        "statement": "shri narayan gangaram surve and shri krishnarao ganpatrao sable be the only winner from maharashtra",
        "label": 0,
        "table_caption": "padma shri awards (1990 - 99)",
        "table_text": "year#name#field#state#country\n1998#cardinal antony padiyara#social work#kerala#india\n1998#dr manmohan attavar#science & engineering#karnataka#india\n1998#shri lila ram sangwan#sports#haryana#india\n1998#smt kanta tyagi#social work#madhya pradesh#india\n1998#prof aditya naraian purohit#science & engineering#uttarakhand#india\n1998#prof brijinder nath goswamy#literature & education#chandigarh#india\n1998#prof gurdial singh#literature & education#punjab#india\n1998#prof priyambada mohanty hejmadi#science & engineering#orissa#india\n1998#prof ranjit roy chaudhury#medicine#delhi#india\n1998#shri chewang phunsog#civil service#delhi#india\n1998#shri kongbrailatpam ibomcha sharma#arts#manipur#india\n1998#shri krishnarao ganpatrao sable#arts#maharashtra#india\n1998#shri kunja bihari meher#arts#orissa#india\n1998#shri narayan gangaram surve#literature & education#maharashtra#india\n1998#shri naushad ismail padamsee#trade & industry#maharashtra#india\n1998#prof ottaplakkal neelakanta velu kurup#literature & education#kerala#india\n1998#dr mammootty#arts#kerala#india\n1998#shri pargat singh#sports#punjab#india\n1998#shri pradhan shambu saran#science & engineering#delhi#india\n1998#shri ralte vanlawma#social work#mizoram#india\n1998#shri ramesh krishnan#sports#tamil nadu#india\n1998#shri shambu nath khajuria#social work#jammu and kashmir#india\n1998#shri suryadevara ramachandra rao#civil service#gujarat#india\n1998#shri uppalapu srinivas#arts#tamil nadu#india\n1998#shri v k saraswat#science & engineering#andhra pradesh#india\n1998#sister leonarda angela casiraghi#social work#karnataka#india\n1998#smt dipali borthakur#arts#assam#india\n1998#smt lalsangzuali sailo#literature & education#mizoram#india\n1998#smt shantha sinha#social work#andhra pradesh#india\n1998#smt shiny wilson#sports#kerala#india\n1998#smt zohra segal#arts#delhi#india\n",
        "pandas_code": "(df[df['state'] == 'maharashtra']['name'].nunique() == 2) and (df[df['state'] == 'maharashtra']['name'].isin(['shri narayan gangaram surve', 'shri krishnarao ganpatrao sable']).all())",
        "pandas_eval": "False"
    },
    {
        "id": 1063,
        "statement": "season no 5 and 6 have the highest number of episode in the list",
        "label": 1,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "(df.loc[df['season no'].isin([5, 6]), 'episodes'] == df['episodes'].max()).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1064,
        "statement": "alan kohler be the host in all the season number",
        "label": 1,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "all(df[df['season no'] == 1]['host'] == 'alan kohler')",
        "pandas_eval": "True"
    },
    {
        "id": 1065,
        "statement": "season no 4 have 1 episode more than season no 3",
        "label": 1,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "df.loc[df['season no'] == 4, 'episodes'].values[0] == df.loc[df['season no'] == 3, 'episodes'].values[0] + 1",
        "pandas_eval": "True"
    },
    {
        "id": 1066,
        "statement": "season no 1 have the least number of episode",
        "label": 1,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "df.loc[df['season no'] == 1, 'episodes'].values[0] == df['episodes'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1067,
        "statement": "all the season except 1 be start on the month of february",
        "label": 1,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "all(df[df['season no'] != 1]['season start'].str.contains('february', case=False))",
        "pandas_eval": "True"
    },
    {
        "id": 1068,
        "statement": "season 4 and 5 have the highest number of episode in the list",
        "label": 0,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "df.loc[df['season no'].isin([4, 5]), 'episodes'].max() < df['episodes'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1069,
        "statement": "alan kohler host only the first 5 season",
        "label": 0,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "(df['host'].iloc[:5] == 'alan kohler').all() and (df['host'].iloc[5:] != 'alan kohler').all()",
        "pandas_eval": "False"
    },
    {
        "id": 1070,
        "statement": "season 4 have fewer episode than season 3",
        "label": 0,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "df.loc[df['season no'] == 4, 'episodes'].values[0] < df.loc[df['season no'] == 3, 'episodes'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1071,
        "statement": "season 1 have the same amount of episode as season 2",
        "label": 0,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "df.loc[df['season no'] == 1, 'episodes'].values[0] == df.loc[df['season no'] == 2, 'episodes'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1072,
        "statement": "all the season start in february of their respective year and end in december",
        "label": 0,
        "table_caption": "inside business",
        "table_text": "season no#season start#season end#episodes#host\n1#4 august 2002#8 december 2002#19#alan kohler\n2#9 february 2003#30 november 2003#41#alan kohler\n3#15 february 2004#5 december 2004#41#alan kohler\n4#13 february 2005#4 december 2005#42#alan kohler\n5#12 february 2006#10 december 2006#43#alan kohler\n6#11 february 2007#9 december 2007#43#alan kohler\n",
        "pandas_code": "all(df['season start'].str.contains('february')) and all(df['season end'].str.contains('december'))",
        "pandas_eval": "False"
    },
    {
        "id": 1073,
        "statement": "carlos reutemann have the fewest entries",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#3rd places#percentage\n1#rubens barrichello#1993 - 2011#326#28#8.58%\n1#kimi r\u00e4ikk\u00f6nen#2001 - 2013#194#28#14.43%\n3#fernando alonso#2001 , 2003 - 2013#215#26#12.09%\n4#david coulthard#1994 - 2008#247#23#9.31%\n5#gerhard berger#1984 - 1997#210#21#10.00%\n5#michael schumacher#1991 - 2006 , 2010 - 2012#308#21#6.81%\n7#carlos reutemann#1972 - 1982#146#20#13.69%\n7#alain prost#1980 - 1991 , 1993#202#20#9.90%\n9#jenson button#2000 - 2013#247#19#7.69%\n",
        "pandas_code": "df.loc[df['entries'].idxmin(), 'driver'] == 'carlos reutemann'",
        "pandas_eval": "True"
    },
    {
        "id": 1074,
        "statement": "jenson button have the fewest 3rd place",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#3rd places#percentage\n1#rubens barrichello#1993 - 2011#326#28#8.58%\n1#kimi r\u00e4ikk\u00f6nen#2001 - 2013#194#28#14.43%\n3#fernando alonso#2001 , 2003 - 2013#215#26#12.09%\n4#david coulthard#1994 - 2008#247#23#9.31%\n5#gerhard berger#1984 - 1997#210#21#10.00%\n5#michael schumacher#1991 - 2006 , 2010 - 2012#308#21#6.81%\n7#carlos reutemann#1972 - 1982#146#20#13.69%\n7#alain prost#1980 - 1991 , 1993#202#20#9.90%\n9#jenson button#2000 - 2013#247#19#7.69%\n",
        "pandas_code": "df.loc[df['driver'] == 'jenson button', '3rd places'].values[0] == df['3rd places'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1075,
        "statement": "fernando alonso have 2 less 3rd place than kimi r\u00e4ikk\u00f6nen",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#3rd places#percentage\n1#rubens barrichello#1993 - 2011#326#28#8.58%\n1#kimi r\u00e4ikk\u00f6nen#2001 - 2013#194#28#14.43%\n3#fernando alonso#2001 , 2003 - 2013#215#26#12.09%\n4#david coulthard#1994 - 2008#247#23#9.31%\n5#gerhard berger#1984 - 1997#210#21#10.00%\n5#michael schumacher#1991 - 2006 , 2010 - 2012#308#21#6.81%\n7#carlos reutemann#1972 - 1982#146#20#13.69%\n7#alain prost#1980 - 1991 , 1993#202#20#9.90%\n9#jenson button#2000 - 2013#247#19#7.69%\n",
        "pandas_code": "(df[df['driver'] == 'fernando alonso']['3rd places'].values[0] == df[df['driver'] == 'kimi r\u00e4ikk\u00f6nen']['3rd places'].values[0] - 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1076,
        "statement": "carlos reutermann have the 2nd highest percentage",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#3rd places#percentage\n1#rubens barrichello#1993 - 2011#326#28#8.58%\n1#kimi r\u00e4ikk\u00f6nen#2001 - 2013#194#28#14.43%\n3#fernando alonso#2001 , 2003 - 2013#215#26#12.09%\n4#david coulthard#1994 - 2008#247#23#9.31%\n5#gerhard berger#1984 - 1997#210#21#10.00%\n5#michael schumacher#1991 - 2006 , 2010 - 2012#308#21#6.81%\n7#carlos reutemann#1972 - 1982#146#20#13.69%\n7#alain prost#1980 - 1991 , 1993#202#20#9.90%\n9#jenson button#2000 - 2013#247#19#7.69%\n",
        "pandas_code": "df.sort_values('percentage', key=lambda x: x.str.rstrip('%').astype(float), ascending=False).iloc[1]['driver'] == 'carlos reutemann'",
        "pandas_eval": "True"
    },
    {
        "id": 1077,
        "statement": "jenson button have 1 less 3rd place than alain prost",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#3rd places#percentage\n1#rubens barrichello#1993 - 2011#326#28#8.58%\n1#kimi r\u00e4ikk\u00f6nen#2001 - 2013#194#28#14.43%\n3#fernando alonso#2001 , 2003 - 2013#215#26#12.09%\n4#david coulthard#1994 - 2008#247#23#9.31%\n5#gerhard berger#1984 - 1997#210#21#10.00%\n5#michael schumacher#1991 - 2006 , 2010 - 2012#308#21#6.81%\n7#carlos reutemann#1972 - 1982#146#20#13.69%\n7#alain prost#1980 - 1991 , 1993#202#20#9.90%\n9#jenson button#2000 - 2013#247#19#7.69%\n",
        "pandas_code": "df[df['driver'] == 'jenson button']['3rd places'].values[0] == df[df['driver'] == 'alain prost']['3rd places'].values[0] - 1",
        "pandas_eval": "True"
    },
    {
        "id": 1078,
        "statement": "feliks glapka , pozna\u0144 win the polish national badminton championship men single before przemys\u0142aw wacha",
        "label": 1,
        "table_caption": "polish national badminton championships",
        "table_text": "year#mens singles#womens singles#mens doubles#womens doubles#mixed doubles\n1964#feliks glapka , pozna\u0144#teresa mas\u0142owska , warszawa#feliks glapka marian grys , pozna\u0144#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1965#aleksander koczur , krak\u00f3w#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1966#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak irena j\u00f3zefowicz , \u0142\u00f3d\u017a\n1967#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#barbara rojewska , olsztyn#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#krzysztof englander bo\u017cena basi\u0144ska , wroc\u0142aw\n1968#krzysztof englander , wroc\u0142aw#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#krzysztof englander irena karolczak , wroc\u0142aw\n1969#andrzej domaga\u0142a , wroc\u0142aw#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#bogus\u0142aw \u017co\u0142\u0105dkowski teresa mas\u0142owska , warszawa\n1970#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#jan makarus jolanta proch , szczecin\n1971#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#lidia baczy\u0144ska , wroc\u0142aw#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak ewa astasiewicz , \u0142\u00f3d\u017a\n1972#wies\u0142aw danielski#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#lidia baczy\u0144ska irena karolczak#leszek nowakowski hana snochowska\n1973#andrzej domaga\u0142a#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#no competition#s\u0142awomir wloszczynski irena karolczak\n1974#stanis\u0142aw rosko#irena karolczak#ryszard borek stanis\u0142aw rosko#irena karolczak hana snochowska#leszek nowakowski hana snochowska\n1975#zygmunt skrzypczy\u0144ski#irena karolczak#andrzej domaga\u0142a wies\u0142aw \u015bwi\u0105tczak#irena karolczak hana snochowska#leslaw markowicz irena karolczak\n1976#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#krzysztof englander janusz labisko#irena karolczak wanda czama\u0144ska#leslaw markowicz irena karolczak\n1978#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska el\u017cbieta utecht#janusz labisko anna zy\u015bk\n1979#brunon rduch#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska maria bahryj#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1980#zygmunt skrzypczy\u0144ski#bo\u017cena wojtkowska#zygmunt skrzypczy\u0144ski janusz labisko#bo\u017cena wojtkowska ewa rusznica#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1981#brunon rduch#bo\u017cena wojtkowska#brunon rduch norbert w\u0119grzyn#bo\u017cena wojtkowska zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa rusznica\n1982#stanis\u0142aw rosko#bo\u017cena wojtkowska#stanis\u0142aw rosko kazimierz ciurys#bo\u017cena wojtkowska ewa rusznica#jerzy do\u0142han bo\u017cena wojtkowska\n1983#stanis\u0142aw rosko#ewa rusznica#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska bo\u017cena siemieniec#kazimierz ciurys bo\u017cena wojtkowska\n1984#stanis\u0142aw rosko#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska ewa wilman#kazimierz ciurys bo\u017cena wojtkowska\n1985#grzegorz olchowik#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1986#grzegorz olchowik#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1987#jerzy do\u0142han#bo\u017cena haracz#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1988#jerzy do\u0142han#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1989#jacek hankiewicz#bo\u017cena siemieniec#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1990#jacek hankiewicz#beata syta#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz beata syta#jerzy do\u0142han bo\u017cena haracz\n1991#jacek hankiewicz#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1992#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1993#jacek hankiewicz#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1994#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#monika lipi\u0144ska sylwia rutkiewicz#damian p\u0142awecki dorota borek\n1995#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#jerzy do\u0142han bo\u017cena haracz\n1996#dariusz zi\u0119ba#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#monika bienkowska katarzyna boczek#robert mateusiak sylwia rutkiewicz\n1997#jacek nied\u017awiedzki#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#damian p\u0142awecki dorota borek\n1998#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz damian p\u0142awecki#bo\u017cena haracz katarzyna krasowska#damian p\u0142awecki dorota grzejdak\n1999#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz joanna szleszy\u0144ska#robert mateusiak monika bienkowska\n2000#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz katarzyna krasowska#robert mateusiak barbara kulanty\n2001#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#barbara kulanty joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2002#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2003#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn paulina matusewicz#robert mateusiak barbara kulanty\n2004#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2005#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2006#przemys\u0142aw wacha#angelika w\u0119grzyn#rafa\u0142 hawel przemys\u0142aw wacha#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2007#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2008#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2009#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#ma\u0142gorzata kurdelska agnieszka wojtkowska#robert mateusiak kamila augustyn\n2010#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n",
        "pandas_code": "df.loc[df['mens singles'].str.contains('feliks glapka , pozna\u0144'), 'year'].iloc[0] < df.loc[df['mens singles'].str.contains('przemys\u0142aw wacha'), 'year'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1079,
        "statement": "feliks glapka , pozna\u0144 win the polish national badminton championship men single before jacek nied\u017awiedzki",
        "label": 1,
        "table_caption": "polish national badminton championships",
        "table_text": "year#mens singles#womens singles#mens doubles#womens doubles#mixed doubles\n1964#feliks glapka , pozna\u0144#teresa mas\u0142owska , warszawa#feliks glapka marian grys , pozna\u0144#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1965#aleksander koczur , krak\u00f3w#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1966#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak irena j\u00f3zefowicz , \u0142\u00f3d\u017a\n1967#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#barbara rojewska , olsztyn#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#krzysztof englander bo\u017cena basi\u0144ska , wroc\u0142aw\n1968#krzysztof englander , wroc\u0142aw#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#krzysztof englander irena karolczak , wroc\u0142aw\n1969#andrzej domaga\u0142a , wroc\u0142aw#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#bogus\u0142aw \u017co\u0142\u0105dkowski teresa mas\u0142owska , warszawa\n1970#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#jan makarus jolanta proch , szczecin\n1971#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#lidia baczy\u0144ska , wroc\u0142aw#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak ewa astasiewicz , \u0142\u00f3d\u017a\n1972#wies\u0142aw danielski#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#lidia baczy\u0144ska irena karolczak#leszek nowakowski hana snochowska\n1973#andrzej domaga\u0142a#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#no competition#s\u0142awomir wloszczynski irena karolczak\n1974#stanis\u0142aw rosko#irena karolczak#ryszard borek stanis\u0142aw rosko#irena karolczak hana snochowska#leszek nowakowski hana snochowska\n1975#zygmunt skrzypczy\u0144ski#irena karolczak#andrzej domaga\u0142a wies\u0142aw \u015bwi\u0105tczak#irena karolczak hana snochowska#leslaw markowicz irena karolczak\n1976#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#krzysztof englander janusz labisko#irena karolczak wanda czama\u0144ska#leslaw markowicz irena karolczak\n1978#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska el\u017cbieta utecht#janusz labisko anna zy\u015bk\n1979#brunon rduch#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska maria bahryj#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1980#zygmunt skrzypczy\u0144ski#bo\u017cena wojtkowska#zygmunt skrzypczy\u0144ski janusz labisko#bo\u017cena wojtkowska ewa rusznica#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1981#brunon rduch#bo\u017cena wojtkowska#brunon rduch norbert w\u0119grzyn#bo\u017cena wojtkowska zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa rusznica\n1982#stanis\u0142aw rosko#bo\u017cena wojtkowska#stanis\u0142aw rosko kazimierz ciurys#bo\u017cena wojtkowska ewa rusznica#jerzy do\u0142han bo\u017cena wojtkowska\n1983#stanis\u0142aw rosko#ewa rusznica#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska bo\u017cena siemieniec#kazimierz ciurys bo\u017cena wojtkowska\n1984#stanis\u0142aw rosko#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska ewa wilman#kazimierz ciurys bo\u017cena wojtkowska\n1985#grzegorz olchowik#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1986#grzegorz olchowik#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1987#jerzy do\u0142han#bo\u017cena haracz#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1988#jerzy do\u0142han#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1989#jacek hankiewicz#bo\u017cena siemieniec#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1990#jacek hankiewicz#beata syta#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz beata syta#jerzy do\u0142han bo\u017cena haracz\n1991#jacek hankiewicz#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1992#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1993#jacek hankiewicz#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1994#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#monika lipi\u0144ska sylwia rutkiewicz#damian p\u0142awecki dorota borek\n1995#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#jerzy do\u0142han bo\u017cena haracz\n1996#dariusz zi\u0119ba#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#monika bienkowska katarzyna boczek#robert mateusiak sylwia rutkiewicz\n1997#jacek nied\u017awiedzki#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#damian p\u0142awecki dorota borek\n1998#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz damian p\u0142awecki#bo\u017cena haracz katarzyna krasowska#damian p\u0142awecki dorota grzejdak\n1999#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz joanna szleszy\u0144ska#robert mateusiak monika bienkowska\n2000#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz katarzyna krasowska#robert mateusiak barbara kulanty\n2001#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#barbara kulanty joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2002#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2003#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn paulina matusewicz#robert mateusiak barbara kulanty\n2004#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2005#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2006#przemys\u0142aw wacha#angelika w\u0119grzyn#rafa\u0142 hawel przemys\u0142aw wacha#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2007#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2008#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2009#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#ma\u0142gorzata kurdelska agnieszka wojtkowska#robert mateusiak kamila augustyn\n2010#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n",
        "pandas_code": "df[(df['mens singles'] == 'feliks glapka , pozna\u0144') & (df['year'] < df[df['mens singles'].str.contains('jacek nied\u017awiedzki')]['year'].min())].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1080,
        "statement": "feliks glapka , pozna\u0144 win the polish national badminton championship mens single beforedariusz zi\u0119ba",
        "label": 1,
        "table_caption": "polish national badminton championships",
        "table_text": "year#mens singles#womens singles#mens doubles#womens doubles#mixed doubles\n1964#feliks glapka , pozna\u0144#teresa mas\u0142owska , warszawa#feliks glapka marian grys , pozna\u0144#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1965#aleksander koczur , krak\u00f3w#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1966#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak irena j\u00f3zefowicz , \u0142\u00f3d\u017a\n1967#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#barbara rojewska , olsztyn#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#krzysztof englander bo\u017cena basi\u0144ska , wroc\u0142aw\n1968#krzysztof englander , wroc\u0142aw#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#krzysztof englander irena karolczak , wroc\u0142aw\n1969#andrzej domaga\u0142a , wroc\u0142aw#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#bogus\u0142aw \u017co\u0142\u0105dkowski teresa mas\u0142owska , warszawa\n1970#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#jan makarus jolanta proch , szczecin\n1971#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#lidia baczy\u0144ska , wroc\u0142aw#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak ewa astasiewicz , \u0142\u00f3d\u017a\n1972#wies\u0142aw danielski#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#lidia baczy\u0144ska irena karolczak#leszek nowakowski hana snochowska\n1973#andrzej domaga\u0142a#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#no competition#s\u0142awomir wloszczynski irena karolczak\n1974#stanis\u0142aw rosko#irena karolczak#ryszard borek stanis\u0142aw rosko#irena karolczak hana snochowska#leszek nowakowski hana snochowska\n1975#zygmunt skrzypczy\u0144ski#irena karolczak#andrzej domaga\u0142a wies\u0142aw \u015bwi\u0105tczak#irena karolczak hana snochowska#leslaw markowicz irena karolczak\n1976#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#krzysztof englander janusz labisko#irena karolczak wanda czama\u0144ska#leslaw markowicz irena karolczak\n1978#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska el\u017cbieta utecht#janusz labisko anna zy\u015bk\n1979#brunon rduch#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska maria bahryj#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1980#zygmunt skrzypczy\u0144ski#bo\u017cena wojtkowska#zygmunt skrzypczy\u0144ski janusz labisko#bo\u017cena wojtkowska ewa rusznica#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1981#brunon rduch#bo\u017cena wojtkowska#brunon rduch norbert w\u0119grzyn#bo\u017cena wojtkowska zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa rusznica\n1982#stanis\u0142aw rosko#bo\u017cena wojtkowska#stanis\u0142aw rosko kazimierz ciurys#bo\u017cena wojtkowska ewa rusznica#jerzy do\u0142han bo\u017cena wojtkowska\n1983#stanis\u0142aw rosko#ewa rusznica#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska bo\u017cena siemieniec#kazimierz ciurys bo\u017cena wojtkowska\n1984#stanis\u0142aw rosko#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska ewa wilman#kazimierz ciurys bo\u017cena wojtkowska\n1985#grzegorz olchowik#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1986#grzegorz olchowik#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1987#jerzy do\u0142han#bo\u017cena haracz#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1988#jerzy do\u0142han#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1989#jacek hankiewicz#bo\u017cena siemieniec#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1990#jacek hankiewicz#beata syta#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz beata syta#jerzy do\u0142han bo\u017cena haracz\n1991#jacek hankiewicz#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1992#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1993#jacek hankiewicz#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1994#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#monika lipi\u0144ska sylwia rutkiewicz#damian p\u0142awecki dorota borek\n1995#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#jerzy do\u0142han bo\u017cena haracz\n1996#dariusz zi\u0119ba#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#monika bienkowska katarzyna boczek#robert mateusiak sylwia rutkiewicz\n1997#jacek nied\u017awiedzki#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#damian p\u0142awecki dorota borek\n1998#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz damian p\u0142awecki#bo\u017cena haracz katarzyna krasowska#damian p\u0142awecki dorota grzejdak\n1999#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz joanna szleszy\u0144ska#robert mateusiak monika bienkowska\n2000#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz katarzyna krasowska#robert mateusiak barbara kulanty\n2001#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#barbara kulanty joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2002#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2003#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn paulina matusewicz#robert mateusiak barbara kulanty\n2004#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2005#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2006#przemys\u0142aw wacha#angelika w\u0119grzyn#rafa\u0142 hawel przemys\u0142aw wacha#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2007#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2008#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2009#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#ma\u0142gorzata kurdelska agnieszka wojtkowska#robert mateusiak kamila augustyn\n2010#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n",
        "pandas_code": "df.loc[df['mens singles'].str.contains('feliks glapka , pozna\u0144'), 'year'].iloc[0] < df.loc[df['mens singles'].str.contains('dariusz zi\u0119ba'), 'year'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1081,
        "statement": "teresa mas\u0142owska , warszawa win the polish national badminton championship womens single before kamila augustyn",
        "label": 1,
        "table_caption": "polish national badminton championships",
        "table_text": "year#mens singles#womens singles#mens doubles#womens doubles#mixed doubles\n1964#feliks glapka , pozna\u0144#teresa mas\u0142owska , warszawa#feliks glapka marian grys , pozna\u0144#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1965#aleksander koczur , krak\u00f3w#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1966#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak irena j\u00f3zefowicz , \u0142\u00f3d\u017a\n1967#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#barbara rojewska , olsztyn#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#krzysztof englander bo\u017cena basi\u0144ska , wroc\u0142aw\n1968#krzysztof englander , wroc\u0142aw#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#krzysztof englander irena karolczak , wroc\u0142aw\n1969#andrzej domaga\u0142a , wroc\u0142aw#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#bogus\u0142aw \u017co\u0142\u0105dkowski teresa mas\u0142owska , warszawa\n1970#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#jan makarus jolanta proch , szczecin\n1971#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#lidia baczy\u0144ska , wroc\u0142aw#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak ewa astasiewicz , \u0142\u00f3d\u017a\n1972#wies\u0142aw danielski#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#lidia baczy\u0144ska irena karolczak#leszek nowakowski hana snochowska\n1973#andrzej domaga\u0142a#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#no competition#s\u0142awomir wloszczynski irena karolczak\n1974#stanis\u0142aw rosko#irena karolczak#ryszard borek stanis\u0142aw rosko#irena karolczak hana snochowska#leszek nowakowski hana snochowska\n1975#zygmunt skrzypczy\u0144ski#irena karolczak#andrzej domaga\u0142a wies\u0142aw \u015bwi\u0105tczak#irena karolczak hana snochowska#leslaw markowicz irena karolczak\n1976#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#krzysztof englander janusz labisko#irena karolczak wanda czama\u0144ska#leslaw markowicz irena karolczak\n1978#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska el\u017cbieta utecht#janusz labisko anna zy\u015bk\n1979#brunon rduch#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska maria bahryj#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1980#zygmunt skrzypczy\u0144ski#bo\u017cena wojtkowska#zygmunt skrzypczy\u0144ski janusz labisko#bo\u017cena wojtkowska ewa rusznica#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1981#brunon rduch#bo\u017cena wojtkowska#brunon rduch norbert w\u0119grzyn#bo\u017cena wojtkowska zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa rusznica\n1982#stanis\u0142aw rosko#bo\u017cena wojtkowska#stanis\u0142aw rosko kazimierz ciurys#bo\u017cena wojtkowska ewa rusznica#jerzy do\u0142han bo\u017cena wojtkowska\n1983#stanis\u0142aw rosko#ewa rusznica#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska bo\u017cena siemieniec#kazimierz ciurys bo\u017cena wojtkowska\n1984#stanis\u0142aw rosko#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska ewa wilman#kazimierz ciurys bo\u017cena wojtkowska\n1985#grzegorz olchowik#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1986#grzegorz olchowik#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1987#jerzy do\u0142han#bo\u017cena haracz#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1988#jerzy do\u0142han#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1989#jacek hankiewicz#bo\u017cena siemieniec#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1990#jacek hankiewicz#beata syta#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz beata syta#jerzy do\u0142han bo\u017cena haracz\n1991#jacek hankiewicz#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1992#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1993#jacek hankiewicz#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1994#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#monika lipi\u0144ska sylwia rutkiewicz#damian p\u0142awecki dorota borek\n1995#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#jerzy do\u0142han bo\u017cena haracz\n1996#dariusz zi\u0119ba#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#monika bienkowska katarzyna boczek#robert mateusiak sylwia rutkiewicz\n1997#jacek nied\u017awiedzki#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#damian p\u0142awecki dorota borek\n1998#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz damian p\u0142awecki#bo\u017cena haracz katarzyna krasowska#damian p\u0142awecki dorota grzejdak\n1999#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz joanna szleszy\u0144ska#robert mateusiak monika bienkowska\n2000#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz katarzyna krasowska#robert mateusiak barbara kulanty\n2001#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#barbara kulanty joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2002#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2003#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn paulina matusewicz#robert mateusiak barbara kulanty\n2004#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2005#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2006#przemys\u0142aw wacha#angelika w\u0119grzyn#rafa\u0142 hawel przemys\u0142aw wacha#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2007#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2008#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2009#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#ma\u0142gorzata kurdelska agnieszka wojtkowska#robert mateusiak kamila augustyn\n2010#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n",
        "pandas_code": "df.loc[df['womens singles'].str.contains('teresa mas\u0142owska , warszawa', na=False), 'year'].min() < df.loc[df['womens singles'].str.contains('kamila augustyn', na=False), 'year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1082,
        "statement": "teresa mas\u0142owska , warszawa win the polish national badminton championship womens single before angelika w\u0119grzyn",
        "label": 1,
        "table_caption": "polish national badminton championships",
        "table_text": "year#mens singles#womens singles#mens doubles#womens doubles#mixed doubles\n1964#feliks glapka , pozna\u0144#teresa mas\u0142owska , warszawa#feliks glapka marian grys , pozna\u0144#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1965#aleksander koczur , krak\u00f3w#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#boles\u0142aw suterski stanis\u0142awa suterska , pozna\u0144\n1966#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak irena j\u00f3zefowicz , \u0142\u00f3d\u017a\n1967#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#barbara rojewska , olsztyn#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#krzysztof englander bo\u017cena basi\u0144ska , wroc\u0142aw\n1968#krzysztof englander , wroc\u0142aw#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#krzysztof englander irena karolczak , wroc\u0142aw\n1969#andrzej domaga\u0142a , wroc\u0142aw#teresa mas\u0142owska , warszawa#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#bogus\u0142aw \u017co\u0142\u0105dkowski teresa mas\u0142owska , warszawa\n1970#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#irena karolczak , wroc\u0142aw#jerzy przybylski lech wo\u017any , pozna\u0144#no competition#jan makarus jolanta proch , szczecin\n1971#wies\u0142aw \u015bwi\u0105tczak , \u0142\u00f3d\u017a#lidia baczy\u0144ska , wroc\u0142aw#andrzej domaga\u0142a krzysztof englander , wroc\u0142aw#no competition#wies\u0142aw \u015bwi\u0105tczak ewa astasiewicz , \u0142\u00f3d\u017a\n1972#wies\u0142aw danielski#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#lidia baczy\u0144ska irena karolczak#leszek nowakowski hana snochowska\n1973#andrzej domaga\u0142a#irena karolczak#wies\u0142aw danielski zygmunt skrzypczy\u0144ski#no competition#s\u0142awomir wloszczynski irena karolczak\n1974#stanis\u0142aw rosko#irena karolczak#ryszard borek stanis\u0142aw rosko#irena karolczak hana snochowska#leszek nowakowski hana snochowska\n1975#zygmunt skrzypczy\u0144ski#irena karolczak#andrzej domaga\u0142a wies\u0142aw \u015bwi\u0105tczak#irena karolczak hana snochowska#leslaw markowicz irena karolczak\n1976#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#krzysztof englander janusz labisko#irena karolczak wanda czama\u0144ska#leslaw markowicz irena karolczak\n1978#zygmunt skrzypczy\u0144ski#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska el\u017cbieta utecht#janusz labisko anna zy\u015bk\n1979#brunon rduch#el\u017cbieta utecht#zygmunt skrzypczy\u0144ski s\u0142awomir w\u0142oszy\u0144ski#bo\u017cena wojtkowska maria bahryj#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1980#zygmunt skrzypczy\u0144ski#bo\u017cena wojtkowska#zygmunt skrzypczy\u0144ski janusz labisko#bo\u017cena wojtkowska ewa rusznica#zygmunt skrzypczy\u0144ski el\u017cbieta utecht\n1981#brunon rduch#bo\u017cena wojtkowska#brunon rduch norbert w\u0119grzyn#bo\u017cena wojtkowska zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa rusznica\n1982#stanis\u0142aw rosko#bo\u017cena wojtkowska#stanis\u0142aw rosko kazimierz ciurys#bo\u017cena wojtkowska ewa rusznica#jerzy do\u0142han bo\u017cena wojtkowska\n1983#stanis\u0142aw rosko#ewa rusznica#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska bo\u017cena siemieniec#kazimierz ciurys bo\u017cena wojtkowska\n1984#stanis\u0142aw rosko#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena wojtkowska ewa wilman#kazimierz ciurys bo\u017cena wojtkowska\n1985#grzegorz olchowik#bo\u017cena wojtkowska#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1986#grzegorz olchowik#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena siemieniec zofia \u017c\u00f3\u0142ta\u0144ska#jerzy do\u0142han ewa wilman\n1987#jerzy do\u0142han#bo\u017cena haracz#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1988#jerzy do\u0142han#bo\u017cena siemieniec#jerzy do\u0142han grzegorz olchowik#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1989#jacek hankiewicz#bo\u017cena siemieniec#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1990#jacek hankiewicz#beata syta#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz beata syta#jerzy do\u0142han bo\u017cena haracz\n1991#jacek hankiewicz#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena siemieniec#jerzy do\u0142han bo\u017cena haracz\n1992#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1993#jacek hankiewicz#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#bo\u017cena haracz bo\u017cena b\u0105k#jerzy do\u0142han bo\u017cena haracz\n1994#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#monika lipi\u0144ska sylwia rutkiewicz#damian p\u0142awecki dorota borek\n1995#dariusz zi\u0119ba#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#jerzy do\u0142han bo\u017cena haracz\n1996#dariusz zi\u0119ba#katarzyna krasowska#dariusz zi\u0119ba jacek hankiewicz#monika bienkowska katarzyna boczek#robert mateusiak sylwia rutkiewicz\n1997#jacek nied\u017awiedzki#katarzyna krasowska#jerzy do\u0142han damian p\u0142awecki#dorota borek katarzyna krasowska#damian p\u0142awecki dorota borek\n1998#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz damian p\u0142awecki#bo\u017cena haracz katarzyna krasowska#damian p\u0142awecki dorota grzejdak\n1999#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz joanna szleszy\u0144ska#robert mateusiak monika bienkowska\n2000#jacek nied\u017awiedzki#katarzyna krasowska#micha\u0142 \u0142ogosz robert mateusiak#bo\u017cena haracz katarzyna krasowska#robert mateusiak barbara kulanty\n2001#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#barbara kulanty joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2002#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn joanna szleszy\u0144ska#robert mateusiak barbara kulanty\n2003#jacek nied\u017awiedzki#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn paulina matusewicz#robert mateusiak barbara kulanty\n2004#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2005#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak barbara kulanty\n2006#przemys\u0142aw wacha#angelika w\u0119grzyn#rafa\u0142 hawel przemys\u0142aw wacha#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2007#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2008#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n2009#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#ma\u0142gorzata kurdelska agnieszka wojtkowska#robert mateusiak kamila augustyn\n2010#przemys\u0142aw wacha#kamila augustyn#micha\u0142 \u0142ogosz robert mateusiak#kamila augustyn nadie\u017cda kostiuczyk#robert mateusiak nadie\u017cda kostiuczyk\n",
        "pandas_code": "df.loc[df['womens singles'].str.contains('teresa mas\u0142owska , warszawa', na=False), 'year'].min() < df.loc[df['womens singles'].str.contains('angelika w\u0119grzyn', na=False), 'year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1083,
        "statement": "all episode be release in the same month",
        "label": 1,
        "table_caption": "list of whose line is it anyway? uk episodes",
        "table_text": "date#episode#performer 1#performer 2#performer 3#performer 4\n2 january 1988#1#john sessions#stephen fry#dawn french#lenny henry\n9 january 1988#2#john sessions#stephen fry#hugh laurie#enn reitel\n16 january 1988#3#john sessions#stephen fry#nonny williams#jimmy mulville\n23 january 1988#4#john sessions#stephen fry#kate robbins#griff rhys jones\n30 january 1988#5#john sessions#stephen fry#jimmy mulville#john bird\n",
        "pandas_code": "df['date'].apply(lambda x: x.split()[1]).nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1084,
        "statement": "steven fry appear in each of the episode",
        "label": 1,
        "table_caption": "list of whose line is it anyway? uk episodes",
        "table_text": "date#episode#performer 1#performer 2#performer 3#performer 4\n2 january 1988#1#john sessions#stephen fry#dawn french#lenny henry\n9 january 1988#2#john sessions#stephen fry#hugh laurie#enn reitel\n16 january 1988#3#john sessions#stephen fry#nonny williams#jimmy mulville\n23 january 1988#4#john sessions#stephen fry#kate robbins#griff rhys jones\n30 january 1988#5#john sessions#stephen fry#jimmy mulville#john bird\n",
        "pandas_code": "all(df['performer 2'] == 'stephen fry')",
        "pandas_eval": "True"
    },
    {
        "id": 1085,
        "statement": "john session appear in each of the episode",
        "label": 1,
        "table_caption": "list of whose line is it anyway? uk episodes",
        "table_text": "date#episode#performer 1#performer 2#performer 3#performer 4\n2 january 1988#1#john sessions#stephen fry#dawn french#lenny henry\n9 january 1988#2#john sessions#stephen fry#hugh laurie#enn reitel\n16 january 1988#3#john sessions#stephen fry#nonny williams#jimmy mulville\n23 january 1988#4#john sessions#stephen fry#kate robbins#griff rhys jones\n30 january 1988#5#john sessions#stephen fry#jimmy mulville#john bird\n",
        "pandas_code": "all(df['performer 1'] == 'john sessions')",
        "pandas_eval": "True"
    },
    {
        "id": 1086,
        "statement": "jimmy mulville appear in more episode than kate robbins",
        "label": 1,
        "table_caption": "list of whose line is it anyway? uk episodes",
        "table_text": "date#episode#performer 1#performer 2#performer 3#performer 4\n2 january 1988#1#john sessions#stephen fry#dawn french#lenny henry\n9 january 1988#2#john sessions#stephen fry#hugh laurie#enn reitel\n16 january 1988#3#john sessions#stephen fry#nonny williams#jimmy mulville\n23 january 1988#4#john sessions#stephen fry#kate robbins#griff rhys jones\n30 january 1988#5#john sessions#stephen fry#jimmy mulville#john bird\n",
        "pandas_code": "(df[df['performer 3'] == 'jimmy mulville'].shape[0] + df[df['performer 4'] == 'jimmy mulville'].shape[0]) > df[df['performer 3'] == 'kate robbins'].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1087,
        "statement": "john bird and hugh laurie have the same number of appearance",
        "label": 1,
        "table_caption": "list of whose line is it anyway? uk episodes",
        "table_text": "date#episode#performer 1#performer 2#performer 3#performer 4\n2 january 1988#1#john sessions#stephen fry#dawn french#lenny henry\n9 january 1988#2#john sessions#stephen fry#hugh laurie#enn reitel\n16 january 1988#3#john sessions#stephen fry#nonny williams#jimmy mulville\n23 january 1988#4#john sessions#stephen fry#kate robbins#griff rhys jones\n30 january 1988#5#john sessions#stephen fry#jimmy mulville#john bird\n",
        "pandas_code": "(df[df == 'john bird'].count().sum() == df[df == 'hugh laurie'].count().sum())",
        "pandas_eval": "True"
    },
    {
        "id": 1088,
        "statement": "jan fr\u00f6hlich win the most men 's single match",
        "label": 1,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df[\"men 's singles\"].value_counts().idxmax() == 'jan fr\u00f6hlich'",
        "pandas_eval": "True"
    },
    {
        "id": 1089,
        "statement": "mark\u00e9ta koudelkov\u00e1 win the most women 's single match",
        "label": 1,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df['women \\'s singles'].value_counts().idxmax() == 'mark\u00e9ta koudelkov\u00e1'",
        "pandas_eval": "True"
    },
    {
        "id": 1090,
        "statement": "daniel gaspar win in the mixed double 4 time",
        "label": 1,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df[df['mixed doubles'].str.contains('daniel gaspar')].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1091,
        "statement": "mark\u00e9ta koudelkov\u00e1 win the women 's single 9 time",
        "label": 1,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df[df[\"women 's singles\"] == 'mark\u00e9ta koudelkov\u00e1'].shape[0] == 9",
        "pandas_eval": "True"
    },
    {
        "id": 1092,
        "statement": "krist\u00edna lud\u00edkov\u00e1 win in the women 's double 3 time",
        "label": 1,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df[df['women \\'s doubles'].str.contains('krist\u00edna lud\u00edkov\u00e1')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1093,
        "statement": "petr koukal win the most men 's single match",
        "label": 0,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "(df[\"men 's singles\"].value_counts().idxmax() == 'petr koukal')",
        "pandas_eval": "False"
    },
    {
        "id": 1094,
        "statement": "mark\u00e9ta koudelkov\u00e1 win the most women 's single match from 2004 - 2011",
        "label": 0,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "df[(df['year'] >= 2004) & (df['year'] <= 2011)]['women \\'s singles'].value_counts().idxmax() == 'mark\u00e9ta koudelkov\u00e1'",
        "pandas_eval": "False"
    },
    {
        "id": 1095,
        "statement": "daniel gaspar win in the mixed double 4 time with jitka lacinov\u00e1",
        "label": 0,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "len(df[df['mixed doubles'].str.contains('daniel gaspar jitka lacinov\u00e1', na=False)]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1096,
        "statement": "mark\u00e9ta koudelkov\u00e1 be a 7 - time champion of the women 's single",
        "label": 0,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "(df[\"women 's singles\"].value_counts()['mark\u00e9ta koudelkov\u00e1'] == 7)",
        "pandas_eval": "False"
    },
    {
        "id": 1097,
        "statement": "krist\u00edna lud\u00edkov\u00e1 win in the women 's double for 3 consecutive year",
        "label": 0,
        "table_caption": "czech national badminton championships",
        "table_text": "year#men 's singles#women 's singles#men 's doubles#women 's doubles#mixed doubles\n1993#tomasz mendrek#eva lacinov\u00e1#petr janda daniel gaspar#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1994#tomasz mendrek#jitka lacinov\u00e1#tomasz mendrek richard hobzik#eva lacinov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1995#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek ond\u0159ej lubas#eva lacinov\u00e1 jitka lacinov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n1996#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#petr janda daniel gaspar#jarka nov\u00e1kov\u00e1 jitka lacinov\u00e1#daniel gaspar jitka lacinov\u00e1\n1997#tomasz mendrek#mark\u00e9ta koudelkov\u00e1#tomasz mendrek petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#jan jurka dana kykalov\u00e1\n1998#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#daniel gaspar jana da\u0148helkov\u00e1\n1999#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#zden\u011bk musil petr b\u00e1\u0161a#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#michal koudelka mark\u00e9ta koudelkov\u00e1\n2000#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#mark\u00e9ta koudelkov\u00e1 ludmila b\u00e1\u0161ov\u00e1#pavel kubi\u0161 eva melounov\u00e1\n2001#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#jan fr\u00f6hlich petr martinec#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2002#jan fr\u00f6hlich#martina bene\u0161ov\u00e1#ren\u00e9 ned\u011bla ji\u0159\u00ed sko\u010ddopole#ivana vilimkov\u00e1 hana proch\u00e1zkov\u00e1#martin herout eva bro\u017eov\u00e1\n2003#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#ji\u0159\u00ed provaznik adam hobzik#eva bro\u017eov\u00e1 hana milisov\u00e1#petr koukal mark\u00e9ta koudelkov\u00e1\n2004#jan fr\u00f6hlich#mark\u00e9ta koudelkov\u00e1#filip st\u00e1dn\u00edk michal svoboda#mark\u00e9ta koudelkov\u00e1 krist\u00edna lud\u00edkov\u00e1#ji\u0159\u00ed sko\u010ddopole hana proch\u00e1zkov\u00e1\n2005#jan fr\u00f6hlich#hana proch\u00e1zkov\u00e1#jan fr\u00f6hlich jan vondra#eva bro\u017eov\u00e1 hana milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2006#jan fr\u00f6hlich#eva bro\u017eov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva bro\u017eov\u00e1 hanka milisov\u00e1#martin herout eva bro\u017eov\u00e1\n2007#petr koukal#eva tit\u011brov\u00e1#stanislav kohoutek pavel flori\u00e1n#eva tit\u011brov\u00e1 hana milisov\u00e1#martin herout eva tit\u011brov\u00e1\n2008#petr koukal#krist\u00edna lud\u00edkov\u00e1#stanislav kohoutek pavel flori\u00e1n#hana proch\u00e1zkov\u00e1 krist\u00edna lud\u00edkov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2009#petr koukal#martina bene\u0161ov\u00e1#jakub bitman pavel dran\u010d\u00e1k#eva tit\u011brov\u00e1 hana milisov\u00e1#pavel flori\u00e1n martina bene\u0161ov\u00e1\n2010#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#hana kollarov\u00e1 martina bene\u0161ov\u00e1#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n2011#petr koukal#krist\u00edna lud\u00edkov\u00e1#jakub bitman pavel dran\u010d\u00e1k#krist\u00edna lud\u00edkov\u00e1 mark\u00e9ta mouritsen#jakub bitman al\u017eb\u011bta b\u00e1\u0161ov\u00e1\n",
        "pandas_code": "((df['women \\'s doubles'].shift(1) == 'krist\u00edna lud\u00edkov\u00e1') & (df['women \\'s doubles'].shift(2) == 'krist\u00edna lud\u00edkov\u00e1') & (df['women \\'s doubles'] == 'krist\u00edna lud\u00edkov\u00e1')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1098,
        "statement": "7 different driver win race during the 1961 formula one season",
        "label": 1,
        "table_caption": "1961 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nii lombank trophy#snetterton#26 march#jack brabham#cooper - climax#report\nix glover trophy#goodwood#3 april#john surtees#cooper - climax#report\nxxi pau grand prix#pau#3 april#jim clark#lotus - climax#report\niii brussels grand prix#heysel#9 april#jack brabham#cooper - climax#report\nii vienna grand prix#aspern aerodrome#16 april#stirling moss#lotus - climax#report\nvi aintree 200#aintree#22 april#jack brabham#cooper - climax#report\nxi syracuse grand prix#syracuse#25 april#giancarlo baghetti#ferrari#report\nxix naples grand prix#posillipo#14 may#giancarlo baghetti#ferrari#report\nix london trophy#crystal palace#23 may#roy salvadori#cooper - climax#report\nvi silver city trophy#brands hatch#3 june#stirling moss#lotus - climax#report\nsolitude grand prix#solitudering#23 july#innes ireland#lotus - climax#report\nvii kanonloppet#karlskoga#20 august#stirling moss#lotus - climax#report\nii danish grand prix#roskildering#2627 august#stirling moss#lotus - climax#report\nxv modena grand prix#modena#3 september#stirling moss#lotus - climax#report\niii flugplatzrennen#zeltweg airfield#17 september#innes ireland#lotus - climax#report\nviii gold cup#oulton park#23 september#stirling moss#ferguson - climax#report\nv lewis - evans trophy#brands hatch#1 october#tony marsh#brm - climax#report\ni coppa italia#vallelunga#12 october#giancarlo baghetti#porsche#report\nv rand grand prix#kyalami#9 december#jim clark#lotus - climax#report\ni natal grand prix#westmead#17 december#jim clark#lotus - climax#report\n",
        "pandas_code": "len(df['winning driver'].unique()) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1099,
        "statement": "the lotus - climax win the most race during the 1961 formula one season",
        "label": 1,
        "table_caption": "1961 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nii lombank trophy#snetterton#26 march#jack brabham#cooper - climax#report\nix glover trophy#goodwood#3 april#john surtees#cooper - climax#report\nxxi pau grand prix#pau#3 april#jim clark#lotus - climax#report\niii brussels grand prix#heysel#9 april#jack brabham#cooper - climax#report\nii vienna grand prix#aspern aerodrome#16 april#stirling moss#lotus - climax#report\nvi aintree 200#aintree#22 april#jack brabham#cooper - climax#report\nxi syracuse grand prix#syracuse#25 april#giancarlo baghetti#ferrari#report\nxix naples grand prix#posillipo#14 may#giancarlo baghetti#ferrari#report\nix london trophy#crystal palace#23 may#roy salvadori#cooper - climax#report\nvi silver city trophy#brands hatch#3 june#stirling moss#lotus - climax#report\nsolitude grand prix#solitudering#23 july#innes ireland#lotus - climax#report\nvii kanonloppet#karlskoga#20 august#stirling moss#lotus - climax#report\nii danish grand prix#roskildering#2627 august#stirling moss#lotus - climax#report\nxv modena grand prix#modena#3 september#stirling moss#lotus - climax#report\niii flugplatzrennen#zeltweg airfield#17 september#innes ireland#lotus - climax#report\nviii gold cup#oulton park#23 september#stirling moss#ferguson - climax#report\nv lewis - evans trophy#brands hatch#1 october#tony marsh#brm - climax#report\ni coppa italia#vallelunga#12 october#giancarlo baghetti#porsche#report\nv rand grand prix#kyalami#9 december#jim clark#lotus - climax#report\ni natal grand prix#westmead#17 december#jim clark#lotus - climax#report\n",
        "pandas_code": "df[df['constructor'] == 'lotus - climax'].shape[0] > df[df['constructor'] != 'lotus - climax'].groupby('constructor').size().max()",
        "pandas_eval": "True"
    },
    {
        "id": 1101,
        "statement": "stirling moss win the most race with 6 total win in the season",
        "label": 1,
        "table_caption": "1961 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nii lombank trophy#snetterton#26 march#jack brabham#cooper - climax#report\nix glover trophy#goodwood#3 april#john surtees#cooper - climax#report\nxxi pau grand prix#pau#3 april#jim clark#lotus - climax#report\niii brussels grand prix#heysel#9 april#jack brabham#cooper - climax#report\nii vienna grand prix#aspern aerodrome#16 april#stirling moss#lotus - climax#report\nvi aintree 200#aintree#22 april#jack brabham#cooper - climax#report\nxi syracuse grand prix#syracuse#25 april#giancarlo baghetti#ferrari#report\nxix naples grand prix#posillipo#14 may#giancarlo baghetti#ferrari#report\nix london trophy#crystal palace#23 may#roy salvadori#cooper - climax#report\nvi silver city trophy#brands hatch#3 june#stirling moss#lotus - climax#report\nsolitude grand prix#solitudering#23 july#innes ireland#lotus - climax#report\nvii kanonloppet#karlskoga#20 august#stirling moss#lotus - climax#report\nii danish grand prix#roskildering#2627 august#stirling moss#lotus - climax#report\nxv modena grand prix#modena#3 september#stirling moss#lotus - climax#report\niii flugplatzrennen#zeltweg airfield#17 september#innes ireland#lotus - climax#report\nviii gold cup#oulton park#23 september#stirling moss#ferguson - climax#report\nv lewis - evans trophy#brands hatch#1 october#tony marsh#brm - climax#report\ni coppa italia#vallelunga#12 october#giancarlo baghetti#porsche#report\nv rand grand prix#kyalami#9 december#jim clark#lotus - climax#report\ni natal grand prix#westmead#17 december#jim clark#lotus - climax#report\n",
        "pandas_code": "(df['winning driver'].value_counts().max() == 6) and (df['winning driver'].value_counts().idxmax() == 'stirling moss')",
        "pandas_eval": "True"
    },
    {
        "id": 1102,
        "statement": "6 different car constructor win race during the 1961 season",
        "label": 1,
        "table_caption": "1961 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\nii lombank trophy#snetterton#26 march#jack brabham#cooper - climax#report\nix glover trophy#goodwood#3 april#john surtees#cooper - climax#report\nxxi pau grand prix#pau#3 april#jim clark#lotus - climax#report\niii brussels grand prix#heysel#9 april#jack brabham#cooper - climax#report\nii vienna grand prix#aspern aerodrome#16 april#stirling moss#lotus - climax#report\nvi aintree 200#aintree#22 april#jack brabham#cooper - climax#report\nxi syracuse grand prix#syracuse#25 april#giancarlo baghetti#ferrari#report\nxix naples grand prix#posillipo#14 may#giancarlo baghetti#ferrari#report\nix london trophy#crystal palace#23 may#roy salvadori#cooper - climax#report\nvi silver city trophy#brands hatch#3 june#stirling moss#lotus - climax#report\nsolitude grand prix#solitudering#23 july#innes ireland#lotus - climax#report\nvii kanonloppet#karlskoga#20 august#stirling moss#lotus - climax#report\nii danish grand prix#roskildering#2627 august#stirling moss#lotus - climax#report\nxv modena grand prix#modena#3 september#stirling moss#lotus - climax#report\niii flugplatzrennen#zeltweg airfield#17 september#innes ireland#lotus - climax#report\nviii gold cup#oulton park#23 september#stirling moss#ferguson - climax#report\nv lewis - evans trophy#brands hatch#1 october#tony marsh#brm - climax#report\ni coppa italia#vallelunga#12 october#giancarlo baghetti#porsche#report\nv rand grand prix#kyalami#9 december#jim clark#lotus - climax#report\ni natal grand prix#westmead#17 december#jim clark#lotus - climax#report\n",
        "pandas_code": "len(df['constructor'].unique()) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1103,
        "statement": "danny granger be list 8 time under high point",
        "label": 1,
        "table_caption": "2008 - 09 indiana pacers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n17#december 2#la lakers#w 118 - 117 (ot)#danny granger (32)#troy murphy (17)#t j ford (8)#conseco fieldhouse 16412#7 - 10\n18#december 3#boston#l 96 - 114 (ot)#danny granger (20)#troy murphy (10)#t j ford (8)#td banknorth garden 18624#7 - 11\n19#december 5#cleveland#l 73 - 97 (ot)#troy murphy (15)#jeff foster (7)#danny granger (5)#quicken loans arena 20562#7 - 12\n20#december 7#boston#l 117 - 122 (ot)#marquis daniels (26)#marquis daniels , jeff foster (7)#danny granger (6)#conseco fieldhouse 16102#7 - 13\n21#december 10#toronto#l 88 - 101 (ot)#danny granger (22)#troy murphy (20)#troy murphy (6)#air canada centre 17877#7 - 14\n22#december 12#detroit#l 110 - 114 (ot)#danny granger (42)#troy murphy (11)#t j ford (10)#the palace of auburn hills 22076#7 - 15\n23#december 13#milwaukee#l 103 - 121 (ot)#t j ford (27)#troy murphy (11)#t j ford (6)#bradley center 14921#7 - 16\n24#december 15#washington#w 118 - 98 (ot)#danny granger (27)#troy murphy (12)#marquis daniels (7)#verizon center 14502#8 - 16\n25#december 17#golden state#w 127 - 120 (ot)#danny granger (41)#danny granger , brandon rush , jeff foster (11)#danny granger (6)#conseco fieldhouse 11151#9 - 16\n26#december 19#la clippers#l 109 - 117 (2ot)#jarrett jack (27)#jeff foster (11)#jarrett jack (7)#conseco fieldhouse 12653#9 - 17\n27#december 20#philadelphia#w 95 - 94 (ot)#t j ford (25)#jeff foster (10)#jarrett jack (8)#wachovia center 14599#10 - 17\n28#december 23#new jersey#l 107 - 108 (ot)#danny granger (26)#jeff foster (14)#jarrett jack (8)#conseco fieldhouse 11272#10 - 18\n29#december 26#memphis#l 105 - 108 (ot)#marquis daniels (28)#troy murphy (12)#jarrett jack , danny granger (5)#fedexforum 12346#10 - 19\n30#december 28#new orleans#l 103 - 105 (ot)#danny granger (34)#troy murphy (16)#jarrett jack , marquis daniels (6)#conseco fieldhouse 14374#10 - 20\n",
        "pandas_code": "df['high points'].str.contains('danny granger').sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1104,
        "statement": "game date be from dec 2 to dec 28",
        "label": 1,
        "table_caption": "2008 - 09 indiana pacers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n17#december 2#la lakers#w 118 - 117 (ot)#danny granger (32)#troy murphy (17)#t j ford (8)#conseco fieldhouse 16412#7 - 10\n18#december 3#boston#l 96 - 114 (ot)#danny granger (20)#troy murphy (10)#t j ford (8)#td banknorth garden 18624#7 - 11\n19#december 5#cleveland#l 73 - 97 (ot)#troy murphy (15)#jeff foster (7)#danny granger (5)#quicken loans arena 20562#7 - 12\n20#december 7#boston#l 117 - 122 (ot)#marquis daniels (26)#marquis daniels , jeff foster (7)#danny granger (6)#conseco fieldhouse 16102#7 - 13\n21#december 10#toronto#l 88 - 101 (ot)#danny granger (22)#troy murphy (20)#troy murphy (6)#air canada centre 17877#7 - 14\n22#december 12#detroit#l 110 - 114 (ot)#danny granger (42)#troy murphy (11)#t j ford (10)#the palace of auburn hills 22076#7 - 15\n23#december 13#milwaukee#l 103 - 121 (ot)#t j ford (27)#troy murphy (11)#t j ford (6)#bradley center 14921#7 - 16\n24#december 15#washington#w 118 - 98 (ot)#danny granger (27)#troy murphy (12)#marquis daniels (7)#verizon center 14502#8 - 16\n25#december 17#golden state#w 127 - 120 (ot)#danny granger (41)#danny granger , brandon rush , jeff foster (11)#danny granger (6)#conseco fieldhouse 11151#9 - 16\n26#december 19#la clippers#l 109 - 117 (2ot)#jarrett jack (27)#jeff foster (11)#jarrett jack (7)#conseco fieldhouse 12653#9 - 17\n27#december 20#philadelphia#w 95 - 94 (ot)#t j ford (25)#jeff foster (10)#jarrett jack (8)#wachovia center 14599#10 - 17\n28#december 23#new jersey#l 107 - 108 (ot)#danny granger (26)#jeff foster (14)#jarrett jack (8)#conseco fieldhouse 11272#10 - 18\n29#december 26#memphis#l 105 - 108 (ot)#marquis daniels (28)#troy murphy (12)#jarrett jack , danny granger (5)#fedexforum 12346#10 - 19\n30#december 28#new orleans#l 103 - 105 (ot)#danny granger (34)#troy murphy (16)#jarrett jack , marquis daniels (6)#conseco fieldhouse 14374#10 - 20\n",
        "pandas_code": "all(df['date'].isin(['december 2', 'december 3', 'december 5', 'december 7', 'december 10', 'december 12', 'december 13', 'december 15', 'december 17', 'december 19', 'december 20', 'december 23', 'december 26', 'december 28']))",
        "pandas_eval": "True"
    },
    {
        "id": 1105,
        "statement": "troy murphy have the most high rebound",
        "label": 1,
        "table_caption": "2008 - 09 indiana pacers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n17#december 2#la lakers#w 118 - 117 (ot)#danny granger (32)#troy murphy (17)#t j ford (8)#conseco fieldhouse 16412#7 - 10\n18#december 3#boston#l 96 - 114 (ot)#danny granger (20)#troy murphy (10)#t j ford (8)#td banknorth garden 18624#7 - 11\n19#december 5#cleveland#l 73 - 97 (ot)#troy murphy (15)#jeff foster (7)#danny granger (5)#quicken loans arena 20562#7 - 12\n20#december 7#boston#l 117 - 122 (ot)#marquis daniels (26)#marquis daniels , jeff foster (7)#danny granger (6)#conseco fieldhouse 16102#7 - 13\n21#december 10#toronto#l 88 - 101 (ot)#danny granger (22)#troy murphy (20)#troy murphy (6)#air canada centre 17877#7 - 14\n22#december 12#detroit#l 110 - 114 (ot)#danny granger (42)#troy murphy (11)#t j ford (10)#the palace of auburn hills 22076#7 - 15\n23#december 13#milwaukee#l 103 - 121 (ot)#t j ford (27)#troy murphy (11)#t j ford (6)#bradley center 14921#7 - 16\n24#december 15#washington#w 118 - 98 (ot)#danny granger (27)#troy murphy (12)#marquis daniels (7)#verizon center 14502#8 - 16\n25#december 17#golden state#w 127 - 120 (ot)#danny granger (41)#danny granger , brandon rush , jeff foster (11)#danny granger (6)#conseco fieldhouse 11151#9 - 16\n26#december 19#la clippers#l 109 - 117 (2ot)#jarrett jack (27)#jeff foster (11)#jarrett jack (7)#conseco fieldhouse 12653#9 - 17\n27#december 20#philadelphia#w 95 - 94 (ot)#t j ford (25)#jeff foster (10)#jarrett jack (8)#wachovia center 14599#10 - 17\n28#december 23#new jersey#l 107 - 108 (ot)#danny granger (26)#jeff foster (14)#jarrett jack (8)#conseco fieldhouse 11272#10 - 18\n29#december 26#memphis#l 105 - 108 (ot)#marquis daniels (28)#troy murphy (12)#jarrett jack , danny granger (5)#fedexforum 12346#10 - 19\n30#december 28#new orleans#l 103 - 105 (ot)#danny granger (34)#troy murphy (16)#jarrett jack , marquis daniels (6)#conseco fieldhouse 14374#10 - 20\n",
        "pandas_code": "df['high rebounds'].apply(lambda x: 'troy murphy' in x.lower()).sum() > df['high rebounds'].apply(lambda x: any(name in x.lower() for name in ['danny granger', 'jeff foster', 'marquis daniels', 'brandon rush', 'jarrett jack', 't j ford'])).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 1107,
        "statement": "the longest gap between game be 3 day",
        "label": 1,
        "table_caption": "2008 - 09 indiana pacers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n17#december 2#la lakers#w 118 - 117 (ot)#danny granger (32)#troy murphy (17)#t j ford (8)#conseco fieldhouse 16412#7 - 10\n18#december 3#boston#l 96 - 114 (ot)#danny granger (20)#troy murphy (10)#t j ford (8)#td banknorth garden 18624#7 - 11\n19#december 5#cleveland#l 73 - 97 (ot)#troy murphy (15)#jeff foster (7)#danny granger (5)#quicken loans arena 20562#7 - 12\n20#december 7#boston#l 117 - 122 (ot)#marquis daniels (26)#marquis daniels , jeff foster (7)#danny granger (6)#conseco fieldhouse 16102#7 - 13\n21#december 10#toronto#l 88 - 101 (ot)#danny granger (22)#troy murphy (20)#troy murphy (6)#air canada centre 17877#7 - 14\n22#december 12#detroit#l 110 - 114 (ot)#danny granger (42)#troy murphy (11)#t j ford (10)#the palace of auburn hills 22076#7 - 15\n23#december 13#milwaukee#l 103 - 121 (ot)#t j ford (27)#troy murphy (11)#t j ford (6)#bradley center 14921#7 - 16\n24#december 15#washington#w 118 - 98 (ot)#danny granger (27)#troy murphy (12)#marquis daniels (7)#verizon center 14502#8 - 16\n25#december 17#golden state#w 127 - 120 (ot)#danny granger (41)#danny granger , brandon rush , jeff foster (11)#danny granger (6)#conseco fieldhouse 11151#9 - 16\n26#december 19#la clippers#l 109 - 117 (2ot)#jarrett jack (27)#jeff foster (11)#jarrett jack (7)#conseco fieldhouse 12653#9 - 17\n27#december 20#philadelphia#w 95 - 94 (ot)#t j ford (25)#jeff foster (10)#jarrett jack (8)#wachovia center 14599#10 - 17\n28#december 23#new jersey#l 107 - 108 (ot)#danny granger (26)#jeff foster (14)#jarrett jack (8)#conseco fieldhouse 11272#10 - 18\n29#december 26#memphis#l 105 - 108 (ot)#marquis daniels (28)#troy murphy (12)#jarrett jack , danny granger (5)#fedexforum 12346#10 - 19\n30#december 28#new orleans#l 103 - 105 (ot)#danny granger (34)#troy murphy (16)#jarrett jack , marquis daniels (6)#conseco fieldhouse 14374#10 - 20\n",
        "pandas_code": "(df['date'].apply(lambda x: pd.to_datetime(x + ' 2008')).diff().max() == pd.Timedelta('3 days'))",
        "pandas_eval": "True"
    },
    {
        "id": 1108,
        "statement": "6 license be list under proprietary , include 1 for the team foundation server",
        "label": 1,
        "table_caption": "comparison of continuous integration software",
        "table_text": "name#platform#license#windows builders#java builders#other builders#scm system#notification#ide integration#other integration\napache gump#python#apache 2.0#unknown#ant , maven 1#unknown#cvs , subversion#e - mail#unknown#unknown\nappveyor ci#hosted#proprietary#visual studio , msbuild , psake#no#custom script , powershell#git , mercurial#e - mail#none#github , bitbucket , kiln , windows azure\nautode#python#proprietary#msbuild , visual studio#ant , maven 1 & 2#yes (common command - line builders)#cvs , git , mercurial , subversion#e - mail#none (standalone)#unknown\ncabie#lamp#gpl2#unknown#unknown#unknown#cvs , subversion , perforce#web#unknown#unknown\ncircleci#hosted#proprietary#unknown#unknown#ruby , python , nodejs , php , java#git#e - mail , campfire , hipchat , cctray#unknown#heroku , github\ndraconet#windows#bsd - style#nant , visual studio#unknown#unknown#cvs , visual source safe , pvcs , subversion#e - mail#unknown#unknown\neasycis#windows#proprietary#msbuild , nant , visual studio#unknown#unknown#git , mercurial , subversion , tfs , file system#systray , rss , e - mail#none#none\ngo#cross - platform#proprietary#yes#yes#cross - platform command - line#subversion , perforce , mercurial , git , tfs#e - mail , cctray#none#restful api\nintegrity#ruby#mit#yes (command line)#yes (command line)#command line#git#e - mail , web#unknown#unknown\nteam foundation server#windows , vstm#proprietary#msbuild#ant , maven#custom script , command line#team foundation server#e - mail , soap#visual studio , eclipse#unknown\ntinderbox#cross - platform#mpl#unknown#unknown#unknown#unknown#e - mail#unknown#unknown\n",
        "pandas_code": "(df['license'].value_counts()['proprietary'] == 6) & (df[df['name'] == 'team foundation server']['license'].iloc[0] == 'proprietary')",
        "pandas_eval": "True"
    },
    {
        "id": 1109,
        "statement": "5 of the java builder be unknown , include 1 for tinderbox and draconet",
        "label": 1,
        "table_caption": "comparison of continuous integration software",
        "table_text": "name#platform#license#windows builders#java builders#other builders#scm system#notification#ide integration#other integration\napache gump#python#apache 2.0#unknown#ant , maven 1#unknown#cvs , subversion#e - mail#unknown#unknown\nappveyor ci#hosted#proprietary#visual studio , msbuild , psake#no#custom script , powershell#git , mercurial#e - mail#none#github , bitbucket , kiln , windows azure\nautode#python#proprietary#msbuild , visual studio#ant , maven 1 & 2#yes (common command - line builders)#cvs , git , mercurial , subversion#e - mail#none (standalone)#unknown\ncabie#lamp#gpl2#unknown#unknown#unknown#cvs , subversion , perforce#web#unknown#unknown\ncircleci#hosted#proprietary#unknown#unknown#ruby , python , nodejs , php , java#git#e - mail , campfire , hipchat , cctray#unknown#heroku , github\ndraconet#windows#bsd - style#nant , visual studio#unknown#unknown#cvs , visual source safe , pvcs , subversion#e - mail#unknown#unknown\neasycis#windows#proprietary#msbuild , nant , visual studio#unknown#unknown#git , mercurial , subversion , tfs , file system#systray , rss , e - mail#none#none\ngo#cross - platform#proprietary#yes#yes#cross - platform command - line#subversion , perforce , mercurial , git , tfs#e - mail , cctray#none#restful api\nintegrity#ruby#mit#yes (command line)#yes (command line)#command line#git#e - mail , web#unknown#unknown\nteam foundation server#windows , vstm#proprietary#msbuild#ant , maven#custom script , command line#team foundation server#e - mail , soap#visual studio , eclipse#unknown\ntinderbox#cross - platform#mpl#unknown#unknown#unknown#unknown#e - mail#unknown#unknown\n",
        "pandas_code": "sum(df['java builders'] == 'unknown') == 5 and 'tinderbox' in df[df['java builders'] == 'unknown']['name'].values and 'draconet' in df[df['java builders'] == 'unknown']['name'].values",
        "pandas_eval": "True"
    },
    {
        "id": 1110,
        "statement": "both the apache group and autode be use python as their platform",
        "label": 1,
        "table_caption": "comparison of continuous integration software",
        "table_text": "name#platform#license#windows builders#java builders#other builders#scm system#notification#ide integration#other integration\napache gump#python#apache 2.0#unknown#ant , maven 1#unknown#cvs , subversion#e - mail#unknown#unknown\nappveyor ci#hosted#proprietary#visual studio , msbuild , psake#no#custom script , powershell#git , mercurial#e - mail#none#github , bitbucket , kiln , windows azure\nautode#python#proprietary#msbuild , visual studio#ant , maven 1 & 2#yes (common command - line builders)#cvs , git , mercurial , subversion#e - mail#none (standalone)#unknown\ncabie#lamp#gpl2#unknown#unknown#unknown#cvs , subversion , perforce#web#unknown#unknown\ncircleci#hosted#proprietary#unknown#unknown#ruby , python , nodejs , php , java#git#e - mail , campfire , hipchat , cctray#unknown#heroku , github\ndraconet#windows#bsd - style#nant , visual studio#unknown#unknown#cvs , visual source safe , pvcs , subversion#e - mail#unknown#unknown\neasycis#windows#proprietary#msbuild , nant , visual studio#unknown#unknown#git , mercurial , subversion , tfs , file system#systray , rss , e - mail#none#none\ngo#cross - platform#proprietary#yes#yes#cross - platform command - line#subversion , perforce , mercurial , git , tfs#e - mail , cctray#none#restful api\nintegrity#ruby#mit#yes (command line)#yes (command line)#command line#git#e - mail , web#unknown#unknown\nteam foundation server#windows , vstm#proprietary#msbuild#ant , maven#custom script , command line#team foundation server#e - mail , soap#visual studio , eclipse#unknown\ntinderbox#cross - platform#mpl#unknown#unknown#unknown#unknown#e - mail#unknown#unknown\n",
        "pandas_code": "all(df[df['name'].isin(['apache gump', 'autode'])]['platform'] == 'python')",
        "pandas_eval": "True"
    },
    {
        "id": 1111,
        "statement": "the team foundation server be the only software that be use an scm system name after itself",
        "label": 1,
        "table_caption": "comparison of continuous integration software",
        "table_text": "name#platform#license#windows builders#java builders#other builders#scm system#notification#ide integration#other integration\napache gump#python#apache 2.0#unknown#ant , maven 1#unknown#cvs , subversion#e - mail#unknown#unknown\nappveyor ci#hosted#proprietary#visual studio , msbuild , psake#no#custom script , powershell#git , mercurial#e - mail#none#github , bitbucket , kiln , windows azure\nautode#python#proprietary#msbuild , visual studio#ant , maven 1 & 2#yes (common command - line builders)#cvs , git , mercurial , subversion#e - mail#none (standalone)#unknown\ncabie#lamp#gpl2#unknown#unknown#unknown#cvs , subversion , perforce#web#unknown#unknown\ncircleci#hosted#proprietary#unknown#unknown#ruby , python , nodejs , php , java#git#e - mail , campfire , hipchat , cctray#unknown#heroku , github\ndraconet#windows#bsd - style#nant , visual studio#unknown#unknown#cvs , visual source safe , pvcs , subversion#e - mail#unknown#unknown\neasycis#windows#proprietary#msbuild , nant , visual studio#unknown#unknown#git , mercurial , subversion , tfs , file system#systray , rss , e - mail#none#none\ngo#cross - platform#proprietary#yes#yes#cross - platform command - line#subversion , perforce , mercurial , git , tfs#e - mail , cctray#none#restful api\nintegrity#ruby#mit#yes (command line)#yes (command line)#command line#git#e - mail , web#unknown#unknown\nteam foundation server#windows , vstm#proprietary#msbuild#ant , maven#custom script , command line#team foundation server#e - mail , soap#visual studio , eclipse#unknown\ntinderbox#cross - platform#mpl#unknown#unknown#unknown#unknown#e - mail#unknown#unknown\n",
        "pandas_code": "df[df['name'] == 'team foundation server']['scm system'].iloc[0] == 'team foundation server' and df[df['name'] != 'team foundation server']['scm system'].str.contains('team foundation server').sum() == 0",
        "pandas_eval": "True"
    },
    {
        "id": 1112,
        "statement": "every software but cabie us e - mail as a possible option for notification",
        "label": 1,
        "table_caption": "comparison of continuous integration software",
        "table_text": "name#platform#license#windows builders#java builders#other builders#scm system#notification#ide integration#other integration\napache gump#python#apache 2.0#unknown#ant , maven 1#unknown#cvs , subversion#e - mail#unknown#unknown\nappveyor ci#hosted#proprietary#visual studio , msbuild , psake#no#custom script , powershell#git , mercurial#e - mail#none#github , bitbucket , kiln , windows azure\nautode#python#proprietary#msbuild , visual studio#ant , maven 1 & 2#yes (common command - line builders)#cvs , git , mercurial , subversion#e - mail#none (standalone)#unknown\ncabie#lamp#gpl2#unknown#unknown#unknown#cvs , subversion , perforce#web#unknown#unknown\ncircleci#hosted#proprietary#unknown#unknown#ruby , python , nodejs , php , java#git#e - mail , campfire , hipchat , cctray#unknown#heroku , github\ndraconet#windows#bsd - style#nant , visual studio#unknown#unknown#cvs , visual source safe , pvcs , subversion#e - mail#unknown#unknown\neasycis#windows#proprietary#msbuild , nant , visual studio#unknown#unknown#git , mercurial , subversion , tfs , file system#systray , rss , e - mail#none#none\ngo#cross - platform#proprietary#yes#yes#cross - platform command - line#subversion , perforce , mercurial , git , tfs#e - mail , cctray#none#restful api\nintegrity#ruby#mit#yes (command line)#yes (command line)#command line#git#e - mail , web#unknown#unknown\nteam foundation server#windows , vstm#proprietary#msbuild#ant , maven#custom script , command line#team foundation server#e - mail , soap#visual studio , eclipse#unknown\ntinderbox#cross - platform#mpl#unknown#unknown#unknown#unknown#e - mail#unknown#unknown\n",
        "pandas_code": "all(df[df['name'] != 'cabie']['notification'].str.contains('e - mail'))",
        "pandas_eval": "True"
    },
    {
        "id": 1113,
        "statement": "hungary have the highest equivalent daily inflation rate of any country",
        "label": 1,
        "table_caption": "hyperinflation",
        "table_text": "country#currency name#month with highest inflation rate#highest monthly inflation rate#equivalent daily inflation rate#time required for prices to double\nhungary#hungarian peng\u0151#july 1946#4190000#207.19%#15 hours\nzimbabwe#zimbabwe dollar#november 2008#796000000#98.01%#24.7 hours\nyugoslavia#yugoslav dinar#january 1994#31130000#64.63%#1.4 days\nrepublika srpska#republika srpska dinar#january 1994#2970000#64.3%#1.4 days\ngermany#german papiermark#october 1923#29500#20.87%#3.7 days\n",
        "pandas_code": "df.loc[df['country'] == 'hungary', 'equivalent daily inflation rate'].str.rstrip('%').astype(float).iloc[0] == df['equivalent daily inflation rate'].str.rstrip('%').astype(float).max()",
        "pandas_eval": "True"
    },
    {
        "id": 1114,
        "statement": "the average number of hour it take for price to double in each country be 39.14",
        "label": 1,
        "table_caption": "hyperinflation",
        "table_text": "country#currency name#month with highest inflation rate#highest monthly inflation rate#equivalent daily inflation rate#time required for prices to double\nhungary#hungarian peng\u0151#july 1946#4190000#207.19%#15 hours\nzimbabwe#zimbabwe dollar#november 2008#796000000#98.01%#24.7 hours\nyugoslavia#yugoslav dinar#january 1994#31130000#64.63%#1.4 days\nrepublika srpska#republika srpska dinar#january 1994#2970000#64.3%#1.4 days\ngermany#german papiermark#october 1923#29500#20.87%#3.7 days\n",
        "pandas_code": "abs(df['time required for prices to double'].apply(lambda x: float(x.split()[0]) * 24 if 'days' in x else float(x.split()[0])).mean() - 39.14) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 1115,
        "statement": "zimbabwe have a more recent month with the highest inflation rate than any other country",
        "label": 1,
        "table_caption": "hyperinflation",
        "table_text": "country#currency name#month with highest inflation rate#highest monthly inflation rate#equivalent daily inflation rate#time required for prices to double\nhungary#hungarian peng\u0151#july 1946#4190000#207.19%#15 hours\nzimbabwe#zimbabwe dollar#november 2008#796000000#98.01%#24.7 hours\nyugoslavia#yugoslav dinar#january 1994#31130000#64.63%#1.4 days\nrepublika srpska#republika srpska dinar#january 1994#2970000#64.3%#1.4 days\ngermany#german papiermark#october 1923#29500#20.87%#3.7 days\n",
        "pandas_code": "pd.to_datetime(df[df['country'] == 'zimbabwe']['month with highest inflation rate'].iloc[0], format='%B %Y') > pd.to_datetime(df[df['country'] != 'zimbabwe']['month with highest inflation rate'], format='%B %Y').max()",
        "pandas_eval": "True"
    },
    {
        "id": 1116,
        "statement": "2 country have jan 1994 as the month with the highest inflation rate",
        "label": 1,
        "table_caption": "hyperinflation",
        "table_text": "country#currency name#month with highest inflation rate#highest monthly inflation rate#equivalent daily inflation rate#time required for prices to double\nhungary#hungarian peng\u0151#july 1946#4190000#207.19%#15 hours\nzimbabwe#zimbabwe dollar#november 2008#796000000#98.01%#24.7 hours\nyugoslavia#yugoslav dinar#january 1994#31130000#64.63%#1.4 days\nrepublika srpska#republika srpska dinar#january 1994#2970000#64.3%#1.4 days\ngermany#german papiermark#october 1923#29500#20.87%#3.7 days\n",
        "pandas_code": "(df['month with highest inflation rate'].str.contains('january 1994').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1118,
        "statement": "the kennedy space center locate in florida launch 4 spacecraft",
        "label": 1,
        "table_caption": "spaceport",
        "table_text": "spaceport#launch complex#launcher#spacecraft#flights#years\nbaikonur cosmodrome , kazakhstan#site 1#vostok (r)#vostok 1 - 6#6 orbital#1961 - 1963\nbaikonur cosmodrome , kazakhstan#site 1#voskhod (r)#voskhod 1 - 2#2 orbital#1964 - 1965\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz 1 - 40#37 orbital#1967 - 1981\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz - t 2 - 15#14 orbital#1980 - 1986\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tm 2 - 34#33 orbital#1987 - 2002\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tma 1 - 22#22 orbital#2002 - 2011\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz tma - m 1 - 9#9 orbital#2010 -\ncape canaveral afs , florida , usa#lc5#redstone#mercury 3 - 4#2 sub - o#1961 - 1961\ncape canaveral afs , florida , usa#lc14#atlas#mercury 6 - 9#4 orbital#1962 - 1963\ncape canaveral afs , florida , usa#lc19#titan ii#gemini 3 - 12#10 orbital#1965 - 1966\ncape canaveral afs , florida , usa#lc34#saturn ib#apollo 7#1 orbital#1968 - 1968\nkennedy space center , florida , usa#lc39#saturn v#apollo 8 - 17#10 lun / or#1968 - 1970\nkennedy space center , florida , usa#lc39#saturn ib#skylab 2 - 4#3 orbital#1973 - 1974\nkennedy space center , florida , usa#lc39#saturn ib#apollo - soyuz#1 orbital#1975 - 1975\nkennedy space center , florida , usa#lc39#sts 1 - 135#space shuttle#134 orbital#1981 - 2011\n",
        "pandas_code": "df[(df['spaceport'] == 'kennedy space center , florida , usa') & (df['spacecraft'].str.contains('4'))].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 1119,
        "statement": "the cape canaveral afs locate in florida launch 4 spacecraft",
        "label": 1,
        "table_caption": "spaceport",
        "table_text": "spaceport#launch complex#launcher#spacecraft#flights#years\nbaikonur cosmodrome , kazakhstan#site 1#vostok (r)#vostok 1 - 6#6 orbital#1961 - 1963\nbaikonur cosmodrome , kazakhstan#site 1#voskhod (r)#voskhod 1 - 2#2 orbital#1964 - 1965\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz 1 - 40#37 orbital#1967 - 1981\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz - t 2 - 15#14 orbital#1980 - 1986\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tm 2 - 34#33 orbital#1987 - 2002\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tma 1 - 22#22 orbital#2002 - 2011\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz tma - m 1 - 9#9 orbital#2010 -\ncape canaveral afs , florida , usa#lc5#redstone#mercury 3 - 4#2 sub - o#1961 - 1961\ncape canaveral afs , florida , usa#lc14#atlas#mercury 6 - 9#4 orbital#1962 - 1963\ncape canaveral afs , florida , usa#lc19#titan ii#gemini 3 - 12#10 orbital#1965 - 1966\ncape canaveral afs , florida , usa#lc34#saturn ib#apollo 7#1 orbital#1968 - 1968\nkennedy space center , florida , usa#lc39#saturn v#apollo 8 - 17#10 lun / or#1968 - 1970\nkennedy space center , florida , usa#lc39#saturn ib#skylab 2 - 4#3 orbital#1973 - 1974\nkennedy space center , florida , usa#lc39#saturn ib#apollo - soyuz#1 orbital#1975 - 1975\nkennedy space center , florida , usa#lc39#sts 1 - 135#space shuttle#134 orbital#1981 - 2011\n",
        "pandas_code": "len(df[(df['spaceport'] == 'cape canaveral afs , florida , usa') & (df['spacecraft'].str.contains('4'))]) > 0",
        "pandas_eval": "True"
    },
    {
        "id": 1120,
        "statement": "the balkonur cosmodrone locate in kazakhstan launch 7 spacecraft",
        "label": 1,
        "table_caption": "spaceport",
        "table_text": "spaceport#launch complex#launcher#spacecraft#flights#years\nbaikonur cosmodrome , kazakhstan#site 1#vostok (r)#vostok 1 - 6#6 orbital#1961 - 1963\nbaikonur cosmodrome , kazakhstan#site 1#voskhod (r)#voskhod 1 - 2#2 orbital#1964 - 1965\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz 1 - 40#37 orbital#1967 - 1981\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz - t 2 - 15#14 orbital#1980 - 1986\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tm 2 - 34#33 orbital#1987 - 2002\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tma 1 - 22#22 orbital#2002 - 2011\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz tma - m 1 - 9#9 orbital#2010 -\ncape canaveral afs , florida , usa#lc5#redstone#mercury 3 - 4#2 sub - o#1961 - 1961\ncape canaveral afs , florida , usa#lc14#atlas#mercury 6 - 9#4 orbital#1962 - 1963\ncape canaveral afs , florida , usa#lc19#titan ii#gemini 3 - 12#10 orbital#1965 - 1966\ncape canaveral afs , florida , usa#lc34#saturn ib#apollo 7#1 orbital#1968 - 1968\nkennedy space center , florida , usa#lc39#saturn v#apollo 8 - 17#10 lun / or#1968 - 1970\nkennedy space center , florida , usa#lc39#saturn ib#skylab 2 - 4#3 orbital#1973 - 1974\nkennedy space center , florida , usa#lc39#saturn ib#apollo - soyuz#1 orbital#1975 - 1975\nkennedy space center , florida , usa#lc39#sts 1 - 135#space shuttle#134 orbital#1981 - 2011\n",
        "pandas_code": "len(df[(df['spaceport'].str.contains('baikonur cosmodrome', case=False)) & (df['spaceport'].str.contains('kazakhstan', case=False))]['spacecraft'].unique()) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 1121,
        "statement": "the soyuz tma - m 1 - 9 be the only spacecraft that start operation in 2010",
        "label": 1,
        "table_caption": "spaceport",
        "table_text": "spaceport#launch complex#launcher#spacecraft#flights#years\nbaikonur cosmodrome , kazakhstan#site 1#vostok (r)#vostok 1 - 6#6 orbital#1961 - 1963\nbaikonur cosmodrome , kazakhstan#site 1#voskhod (r)#voskhod 1 - 2#2 orbital#1964 - 1965\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz 1 - 40#37 orbital#1967 - 1981\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz - t 2 - 15#14 orbital#1980 - 1986\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tm 2 - 34#33 orbital#1987 - 2002\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tma 1 - 22#22 orbital#2002 - 2011\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz tma - m 1 - 9#9 orbital#2010 -\ncape canaveral afs , florida , usa#lc5#redstone#mercury 3 - 4#2 sub - o#1961 - 1961\ncape canaveral afs , florida , usa#lc14#atlas#mercury 6 - 9#4 orbital#1962 - 1963\ncape canaveral afs , florida , usa#lc19#titan ii#gemini 3 - 12#10 orbital#1965 - 1966\ncape canaveral afs , florida , usa#lc34#saturn ib#apollo 7#1 orbital#1968 - 1968\nkennedy space center , florida , usa#lc39#saturn v#apollo 8 - 17#10 lun / or#1968 - 1970\nkennedy space center , florida , usa#lc39#saturn ib#skylab 2 - 4#3 orbital#1973 - 1974\nkennedy space center , florida , usa#lc39#saturn ib#apollo - soyuz#1 orbital#1975 - 1975\nkennedy space center , florida , usa#lc39#sts 1 - 135#space shuttle#134 orbital#1981 - 2011\n",
        "pandas_code": "df[(df['spacecraft'] == 'soyuz tma - m 1 - 9') & (df['years'].str.startswith('2010'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1122,
        "statement": "the soyuz tm 1 - 22 and the space shuttle both ended operation in 2011",
        "label": 1,
        "table_caption": "spaceport",
        "table_text": "spaceport#launch complex#launcher#spacecraft#flights#years\nbaikonur cosmodrome , kazakhstan#site 1#vostok (r)#vostok 1 - 6#6 orbital#1961 - 1963\nbaikonur cosmodrome , kazakhstan#site 1#voskhod (r)#voskhod 1 - 2#2 orbital#1964 - 1965\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz 1 - 40#37 orbital#1967 - 1981\nbaikonur cosmodrome , kazakhstan#site 1 , 31#soyuz (r)#soyuz - t 2 - 15#14 orbital#1980 - 1986\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tm 2 - 34#33 orbital#1987 - 2002\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz - tma 1 - 22#22 orbital#2002 - 2011\nbaikonur cosmodrome , kazakhstan#site 1#soyuz (r)#soyuz tma - m 1 - 9#9 orbital#2010 -\ncape canaveral afs , florida , usa#lc5#redstone#mercury 3 - 4#2 sub - o#1961 - 1961\ncape canaveral afs , florida , usa#lc14#atlas#mercury 6 - 9#4 orbital#1962 - 1963\ncape canaveral afs , florida , usa#lc19#titan ii#gemini 3 - 12#10 orbital#1965 - 1966\ncape canaveral afs , florida , usa#lc34#saturn ib#apollo 7#1 orbital#1968 - 1968\nkennedy space center , florida , usa#lc39#saturn v#apollo 8 - 17#10 lun / or#1968 - 1970\nkennedy space center , florida , usa#lc39#saturn ib#skylab 2 - 4#3 orbital#1973 - 1974\nkennedy space center , florida , usa#lc39#saturn ib#apollo - soyuz#1 orbital#1975 - 1975\nkennedy space center , florida , usa#lc39#sts 1 - 135#space shuttle#134 orbital#1981 - 2011\n",
        "pandas_code": "(df[df['spacecraft'].str.contains('soyuz - tma 1 - 22') & df['years'].str.contains('2011')].shape[0] > 0) & (df[df['spacecraft'].str.contains('space shuttle') & df['years'].str.contains('2011')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1123,
        "statement": "the ghost of mortis have a higher number of us viewer (million) than the citadel",
        "label": 1,
        "table_caption": "list of star wars : the clone wars episodes",
        "table_text": "no#-#title#directed by#written by#original air date#production code#us viewers (million)\n45#1#clone cadets#dave filoni#cameron litvack#september 17 , 2010#3.01#2.42\n46#2#arc troopers#kyle dunlevy#cameron litvack#september 17 , 2010#3.02#2.42\n47#3#supply lines#brian kalin o'connell#steven melching & eoghan mahony#september 24 , 2010#2.24#1.69\n48#4#sphere of influence#kyle dunlevy#katie lucas & steven melching#october 1 , 2010#2.25#1.88\n49#5#corruption#giancarlo volpe#cameron litvack#october 8 , 2010#3.04#1.78\n50#6#the academy#giancarlo volpe#katie lucas & steven melching#october 15 , 2010#2.26#1.79\n51#7#assassin#kyle dunlevy#katie lucas#october 22 , 2010#2.21#1.85\n52#8#evil plans#brian kalin o'connell#steve mitchell & craig van sickle#november 5 , 2010#3.03#1.84\n53#9#hunt for ziro#steward lee#steve mitchell & craig van sickle#november 12 , 2010#3.05#1.76\n54#10#heroes on both sides#kyle dunlevy#daniel arkin#november 19 , 2010#3.06#1.75\n55#11#pursuit of peace#duwayne dunham#daniel arkin#december 3 , 2010#3.07#1.61\n56#12#nightsisters#giancarlo volpe#katie lucas#january 7 , 2011#3.08#1.86\n57#13#monster#kyle dunlevy#katie lucas#january 14 , 2011#3.10#n / a\n58#14#witches of the mist#giancarlo volpe#katie lucas#january 21 , 2011#3.12#2.21\n59#15#overlords#steward lee#christian taylor#january 28 , 2011#3.09#1.74\n60#16#altar of mortis#brian kalin o'connell#christian taylor#february 4 , 2011#3.11#2.29\n61#17#ghosts of mortis#steward lee#christian taylor#february 11 , 2011#3.13#2.24\n62#18#the citadel#kyle dunlevy#matt michnovetz#february 18 , 2011#3.14#1.84\n63#19#counter attack#brian kalin o'connell#matt michnovetz#march 4 , 2011#3.15#1.87\n64#20#citadel rescue#steward lee#matt michnovetz#march 11 , 2011#3.17#1.55\n65#21#padawan lost#dave filoni#bonnie mark#march 19 , 2011 (uk) april 1 , 2011 (usa)#3.16#2.31\n",
        "pandas_code": "df[df['title'] == 'ghosts of mortis']['us viewers (million)'].values[0] > df[df['title'] == 'the citadel']['us viewers (million)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1124,
        "statement": "cameron litvack write 3 of the episode",
        "label": 1,
        "table_caption": "list of star wars : the clone wars episodes",
        "table_text": "no#-#title#directed by#written by#original air date#production code#us viewers (million)\n45#1#clone cadets#dave filoni#cameron litvack#september 17 , 2010#3.01#2.42\n46#2#arc troopers#kyle dunlevy#cameron litvack#september 17 , 2010#3.02#2.42\n47#3#supply lines#brian kalin o'connell#steven melching & eoghan mahony#september 24 , 2010#2.24#1.69\n48#4#sphere of influence#kyle dunlevy#katie lucas & steven melching#october 1 , 2010#2.25#1.88\n49#5#corruption#giancarlo volpe#cameron litvack#october 8 , 2010#3.04#1.78\n50#6#the academy#giancarlo volpe#katie lucas & steven melching#october 15 , 2010#2.26#1.79\n51#7#assassin#kyle dunlevy#katie lucas#october 22 , 2010#2.21#1.85\n52#8#evil plans#brian kalin o'connell#steve mitchell & craig van sickle#november 5 , 2010#3.03#1.84\n53#9#hunt for ziro#steward lee#steve mitchell & craig van sickle#november 12 , 2010#3.05#1.76\n54#10#heroes on both sides#kyle dunlevy#daniel arkin#november 19 , 2010#3.06#1.75\n55#11#pursuit of peace#duwayne dunham#daniel arkin#december 3 , 2010#3.07#1.61\n56#12#nightsisters#giancarlo volpe#katie lucas#january 7 , 2011#3.08#1.86\n57#13#monster#kyle dunlevy#katie lucas#january 14 , 2011#3.10#n / a\n58#14#witches of the mist#giancarlo volpe#katie lucas#january 21 , 2011#3.12#2.21\n59#15#overlords#steward lee#christian taylor#january 28 , 2011#3.09#1.74\n60#16#altar of mortis#brian kalin o'connell#christian taylor#february 4 , 2011#3.11#2.29\n61#17#ghosts of mortis#steward lee#christian taylor#february 11 , 2011#3.13#2.24\n62#18#the citadel#kyle dunlevy#matt michnovetz#february 18 , 2011#3.14#1.84\n63#19#counter attack#brian kalin o'connell#matt michnovetz#march 4 , 2011#3.15#1.87\n64#20#citadel rescue#steward lee#matt michnovetz#march 11 , 2011#3.17#1.55\n65#21#padawan lost#dave filoni#bonnie mark#march 19 , 2011 (uk) april 1 , 2011 (usa)#3.16#2.31\n",
        "pandas_code": "df[df['written by'].str.contains('cameron litvack')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1126,
        "statement": "the average number of us viewer (million) for the first 2 episode be 2.42",
        "label": 1,
        "table_caption": "list of star wars : the clone wars episodes",
        "table_text": "no#-#title#directed by#written by#original air date#production code#us viewers (million)\n45#1#clone cadets#dave filoni#cameron litvack#september 17 , 2010#3.01#2.42\n46#2#arc troopers#kyle dunlevy#cameron litvack#september 17 , 2010#3.02#2.42\n47#3#supply lines#brian kalin o'connell#steven melching & eoghan mahony#september 24 , 2010#2.24#1.69\n48#4#sphere of influence#kyle dunlevy#katie lucas & steven melching#october 1 , 2010#2.25#1.88\n49#5#corruption#giancarlo volpe#cameron litvack#october 8 , 2010#3.04#1.78\n50#6#the academy#giancarlo volpe#katie lucas & steven melching#october 15 , 2010#2.26#1.79\n51#7#assassin#kyle dunlevy#katie lucas#october 22 , 2010#2.21#1.85\n52#8#evil plans#brian kalin o'connell#steve mitchell & craig van sickle#november 5 , 2010#3.03#1.84\n53#9#hunt for ziro#steward lee#steve mitchell & craig van sickle#november 12 , 2010#3.05#1.76\n54#10#heroes on both sides#kyle dunlevy#daniel arkin#november 19 , 2010#3.06#1.75\n55#11#pursuit of peace#duwayne dunham#daniel arkin#december 3 , 2010#3.07#1.61\n56#12#nightsisters#giancarlo volpe#katie lucas#january 7 , 2011#3.08#1.86\n57#13#monster#kyle dunlevy#katie lucas#january 14 , 2011#3.10#n / a\n58#14#witches of the mist#giancarlo volpe#katie lucas#january 21 , 2011#3.12#2.21\n59#15#overlords#steward lee#christian taylor#january 28 , 2011#3.09#1.74\n60#16#altar of mortis#brian kalin o'connell#christian taylor#february 4 , 2011#3.11#2.29\n61#17#ghosts of mortis#steward lee#christian taylor#february 11 , 2011#3.13#2.24\n62#18#the citadel#kyle dunlevy#matt michnovetz#february 18 , 2011#3.14#1.84\n63#19#counter attack#brian kalin o'connell#matt michnovetz#march 4 , 2011#3.15#1.87\n64#20#citadel rescue#steward lee#matt michnovetz#march 11 , 2011#3.17#1.55\n65#21#padawan lost#dave filoni#bonnie mark#march 19 , 2011 (uk) april 1 , 2011 (usa)#3.16#2.31\n",
        "pandas_code": "df[df['no'].isin([45, 46])]['us viewers (million)'].astype(float).mean() == 2.42",
        "pandas_eval": "True"
    },
    {
        "id": 1127,
        "statement": "the average number of us viewer (million) for the first 5 episode be 2.038",
        "label": 1,
        "table_caption": "list of star wars : the clone wars episodes",
        "table_text": "no#-#title#directed by#written by#original air date#production code#us viewers (million)\n45#1#clone cadets#dave filoni#cameron litvack#september 17 , 2010#3.01#2.42\n46#2#arc troopers#kyle dunlevy#cameron litvack#september 17 , 2010#3.02#2.42\n47#3#supply lines#brian kalin o'connell#steven melching & eoghan mahony#september 24 , 2010#2.24#1.69\n48#4#sphere of influence#kyle dunlevy#katie lucas & steven melching#october 1 , 2010#2.25#1.88\n49#5#corruption#giancarlo volpe#cameron litvack#october 8 , 2010#3.04#1.78\n50#6#the academy#giancarlo volpe#katie lucas & steven melching#october 15 , 2010#2.26#1.79\n51#7#assassin#kyle dunlevy#katie lucas#october 22 , 2010#2.21#1.85\n52#8#evil plans#brian kalin o'connell#steve mitchell & craig van sickle#november 5 , 2010#3.03#1.84\n53#9#hunt for ziro#steward lee#steve mitchell & craig van sickle#november 12 , 2010#3.05#1.76\n54#10#heroes on both sides#kyle dunlevy#daniel arkin#november 19 , 2010#3.06#1.75\n55#11#pursuit of peace#duwayne dunham#daniel arkin#december 3 , 2010#3.07#1.61\n56#12#nightsisters#giancarlo volpe#katie lucas#january 7 , 2011#3.08#1.86\n57#13#monster#kyle dunlevy#katie lucas#january 14 , 2011#3.10#n / a\n58#14#witches of the mist#giancarlo volpe#katie lucas#january 21 , 2011#3.12#2.21\n59#15#overlords#steward lee#christian taylor#january 28 , 2011#3.09#1.74\n60#16#altar of mortis#brian kalin o'connell#christian taylor#february 4 , 2011#3.11#2.29\n61#17#ghosts of mortis#steward lee#christian taylor#february 11 , 2011#3.13#2.24\n62#18#the citadel#kyle dunlevy#matt michnovetz#february 18 , 2011#3.14#1.84\n63#19#counter attack#brian kalin o'connell#matt michnovetz#march 4 , 2011#3.15#1.87\n64#20#citadel rescue#steward lee#matt michnovetz#march 11 , 2011#3.17#1.55\n65#21#padawan lost#dave filoni#bonnie mark#march 19 , 2011 (uk) april 1 , 2011 (usa)#3.16#2.31\n",
        "pandas_code": "round(df['us viewers (million)'].iloc[:5].astype(float).mean(), 3) == 2.038",
        "pandas_eval": "True"
    },
    {
        "id": 1128,
        "statement": "the 1993 colt have 2 extend losing streak , 4 game long each",
        "label": 1,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "(df['result'].str.startswith('l').rolling(4).sum().eq(4).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1130,
        "statement": "the worst loss of the season be be shut out by the patriots , 38 - 0",
        "label": 1,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "df[df['result'] == 'l 0 - 38']['opponent'].eq('new england patriots').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1132,
        "statement": "the colt be shut out 2 time (38 - 0 and 31 - 0) , and also hold to just 3 point 1 time , in a very low - scoring season",
        "label": 1,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "(df['result'].str.contains('0 - 38').sum() == 1) & (df['result'].str.contains('0 - 31').sum() == 1) & (df['result'].str.contains('3 - 27').sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1133,
        "statement": "the 1993 colt have 2 extend losing streak , both be shorter than 3 game long",
        "label": 0,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "len(df[(df['result'].str.startswith('l')) & (df['result'].shift(1).str.startswith('l')) & (df['result'].shift(2).str.startswith('l'))]) == 0",
        "pandas_eval": "False"
    },
    {
        "id": 1134,
        "statement": "the worst loss of the season be by less than 35 point",
        "label": 0,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "df['result'].str.extract(r'([Ll]) (\\d+) - (\\d+)').dropna().astype({1: int, 2: int}).apply(lambda x: x[2] - x[1], axis=1).max() < 35",
        "pandas_eval": "False"
    },
    {
        "id": 1135,
        "statement": "the colt never score more than 10 point in a victory",
        "label": 0,
        "table_caption": "1993 indianapolis colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 5 , 1993#miami dolphins#l 20 - 24#0 - 1#hoosier dome#51858\n2#september 12 , 1993#cincinnati bengals#w 9 - 6#1 - 1#riverfront stadium#50299\n3#-#-#-#-#-#\n4#september 26 , 1993#cleveland browns#w 23 - 10#2 - 1#hoosier dome#59654\n5#october 3 , 1993#denver broncos#l 13 - 35#2 - 2#mile high stadium#74953\n6#october 10 , 1993#dallas cowboys#l 3 - 27#2 - 3#hoosier dome#60453\n7#-#-#-#-#-#\n8#october 24 , 1993#miami dolphins#l 27 - 41#2 - 4#joe robbie stadium#57301\n9#october 31 , 1993#new england patriots#w 9 - 6#3 - 4#hoosier dome#46522\n10#november 7 , 1993#washington redskins#l 24 - 30#3 - 5#rfk stadium#50523\n11#november 14 , 1993#new york jets#l 17 - 31#3 - 6#hoosier dome#47351\n12#november 21 , 1993#buffalo bills#l 9 - 23#3 - 7#rich stadium#79101\n13#november 29 , 1993#san diego chargers#l 0 - 31#3 - 8#hoosier dome#54110\n14#december 5 , 1993#new york jets#w 9 - 6#4 - 8#the meadowlands#45799\n15#december 12 , 1993#new york giants#l 6 - 20#4 - 9#giants stadium#70411\n16#december 19 , 1993#philadelphia eagles#l 10 - 20#4 - 10#hoosier dome#44952\n17#december 26 , 1993#new england patriots#l 0 - 38#4 - 11#foxboro stadium#26571\n18#january 2 , 1994#buffalo bills#l 10 - 30#4 - 12#hoosier dome#43028\n",
        "pandas_code": "all(df[df['result'].str.startswith('w')]['result'].str.extract(r'(\\d+) - \\d+')[0].astype(int) <= 10)",
        "pandas_eval": "False"
    },
    {
        "id": 1136,
        "statement": "most of the geological feature be name in 1985",
        "label": 1,
        "table_caption": "list of geological features on ganymede",
        "table_text": "name#latitude#longitude#diameter#year named#namesake\nakitu sulcus#38.9n#194.3w#365.0#1997#where marduk 's statue was carried each year\napsu sulci#39.4s#234.7w#1950.0#1979#sumero - akkadian , primordial ocean\narbela sulcus#21.1s#349.8w#1940.0#1985#assyrian town where ishtar was worshipped\nbubastis sulci#72.3s#282.9w#2651.0#1988#town in egypt where bast was worshipped\ndukug sulcus#83.5n#3.8w#385.0#1985#sumerian holy cosmic chamber of the gods\nerech sulcus#7.3s#179.2w#953.0#1985#akkadian town that was built by marduk\nharpagia sulcus#11.7s#318.7w#1792.0#1985#greek , where ganymede was abducted an eagle\nhursag sulcus#9.7s#233.1w#750.0#1985#sumerian mountain where winds dwell\nlagash sulcus#10.9s#163.2w#1575.0#1985#early babylonian town\nlarsa sulcus#3.8n#248.7w#1000.0#2000#sumerian town\nmysia sulci#7.0s#7.9w#5066.0#1979#greek , where ganymede was abducted by an eagle\nnineveh sulcus#23.5n#53.1w#1700.0#1997#city where ishtar was worshipped\nnippur sulcus#36.9n#185.0w#1425.0#1985#sumerian city\nphilae sulcus#65.5n#169.0w#900.0#1997#temple that was the chief sanctuary of isis\nsippar sulcus#15.4s#189.3w#1508.0#1985#ancient babylonian town\numma sulcus#4.1n#250.0w#1270.0#2000#sumerian town\nur sulcus#49.8n#177.5w#1145.0#1985#ancient sumerian seat of moon worship\n",
        "pandas_code": "df['year named'].value_counts().idxmax() == 1985",
        "pandas_eval": "True"
    },
    {
        "id": 1137,
        "statement": "akitu sulcus have the smallest diameter",
        "label": 1,
        "table_caption": "list of geological features on ganymede",
        "table_text": "name#latitude#longitude#diameter#year named#namesake\nakitu sulcus#38.9n#194.3w#365.0#1997#where marduk 's statue was carried each year\napsu sulci#39.4s#234.7w#1950.0#1979#sumero - akkadian , primordial ocean\narbela sulcus#21.1s#349.8w#1940.0#1985#assyrian town where ishtar was worshipped\nbubastis sulci#72.3s#282.9w#2651.0#1988#town in egypt where bast was worshipped\ndukug sulcus#83.5n#3.8w#385.0#1985#sumerian holy cosmic chamber of the gods\nerech sulcus#7.3s#179.2w#953.0#1985#akkadian town that was built by marduk\nharpagia sulcus#11.7s#318.7w#1792.0#1985#greek , where ganymede was abducted an eagle\nhursag sulcus#9.7s#233.1w#750.0#1985#sumerian mountain where winds dwell\nlagash sulcus#10.9s#163.2w#1575.0#1985#early babylonian town\nlarsa sulcus#3.8n#248.7w#1000.0#2000#sumerian town\nmysia sulci#7.0s#7.9w#5066.0#1979#greek , where ganymede was abducted by an eagle\nnineveh sulcus#23.5n#53.1w#1700.0#1997#city where ishtar was worshipped\nnippur sulcus#36.9n#185.0w#1425.0#1985#sumerian city\nphilae sulcus#65.5n#169.0w#900.0#1997#temple that was the chief sanctuary of isis\nsippar sulcus#15.4s#189.3w#1508.0#1985#ancient babylonian town\numma sulcus#4.1n#250.0w#1270.0#2000#sumerian town\nur sulcus#49.8n#177.5w#1145.0#1985#ancient sumerian seat of moon worship\n",
        "pandas_code": "df.loc[df['name'] == 'akitu sulcus', 'diameter'].iloc[0] == df['diameter'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1138,
        "statement": "akitu sulcus be further north than apsu sulci",
        "label": 1,
        "table_caption": "list of geological features on ganymede",
        "table_text": "name#latitude#longitude#diameter#year named#namesake\nakitu sulcus#38.9n#194.3w#365.0#1997#where marduk 's statue was carried each year\napsu sulci#39.4s#234.7w#1950.0#1979#sumero - akkadian , primordial ocean\narbela sulcus#21.1s#349.8w#1940.0#1985#assyrian town where ishtar was worshipped\nbubastis sulci#72.3s#282.9w#2651.0#1988#town in egypt where bast was worshipped\ndukug sulcus#83.5n#3.8w#385.0#1985#sumerian holy cosmic chamber of the gods\nerech sulcus#7.3s#179.2w#953.0#1985#akkadian town that was built by marduk\nharpagia sulcus#11.7s#318.7w#1792.0#1985#greek , where ganymede was abducted an eagle\nhursag sulcus#9.7s#233.1w#750.0#1985#sumerian mountain where winds dwell\nlagash sulcus#10.9s#163.2w#1575.0#1985#early babylonian town\nlarsa sulcus#3.8n#248.7w#1000.0#2000#sumerian town\nmysia sulci#7.0s#7.9w#5066.0#1979#greek , where ganymede was abducted by an eagle\nnineveh sulcus#23.5n#53.1w#1700.0#1997#city where ishtar was worshipped\nnippur sulcus#36.9n#185.0w#1425.0#1985#sumerian city\nphilae sulcus#65.5n#169.0w#900.0#1997#temple that was the chief sanctuary of isis\nsippar sulcus#15.4s#189.3w#1508.0#1985#ancient babylonian town\numma sulcus#4.1n#250.0w#1270.0#2000#sumerian town\nur sulcus#49.8n#177.5w#1145.0#1985#ancient sumerian seat of moon worship\n",
        "pandas_code": "float(df[df['name'] == 'akitu sulcus']['latitude'].str[:-1].values[0]) * (1 if 'n' in df[df['name'] == 'akitu sulcus']['latitude'].values[0] else -1) > float(df[df['name'] == 'apsu sulci']['latitude'].str[:-1].values[0]) * (1 if 'n' in df[df['name'] == 'apsu sulci']['latitude'].values[0] else -1)",
        "pandas_eval": "True"
    },
    {
        "id": 1139,
        "statement": "only 2 feature be name in 2000",
        "label": 1,
        "table_caption": "list of geological features on ganymede",
        "table_text": "name#latitude#longitude#diameter#year named#namesake\nakitu sulcus#38.9n#194.3w#365.0#1997#where marduk 's statue was carried each year\napsu sulci#39.4s#234.7w#1950.0#1979#sumero - akkadian , primordial ocean\narbela sulcus#21.1s#349.8w#1940.0#1985#assyrian town where ishtar was worshipped\nbubastis sulci#72.3s#282.9w#2651.0#1988#town in egypt where bast was worshipped\ndukug sulcus#83.5n#3.8w#385.0#1985#sumerian holy cosmic chamber of the gods\nerech sulcus#7.3s#179.2w#953.0#1985#akkadian town that was built by marduk\nharpagia sulcus#11.7s#318.7w#1792.0#1985#greek , where ganymede was abducted an eagle\nhursag sulcus#9.7s#233.1w#750.0#1985#sumerian mountain where winds dwell\nlagash sulcus#10.9s#163.2w#1575.0#1985#early babylonian town\nlarsa sulcus#3.8n#248.7w#1000.0#2000#sumerian town\nmysia sulci#7.0s#7.9w#5066.0#1979#greek , where ganymede was abducted by an eagle\nnineveh sulcus#23.5n#53.1w#1700.0#1997#city where ishtar was worshipped\nnippur sulcus#36.9n#185.0w#1425.0#1985#sumerian city\nphilae sulcus#65.5n#169.0w#900.0#1997#temple that was the chief sanctuary of isis\nsippar sulcus#15.4s#189.3w#1508.0#1985#ancient babylonian town\numma sulcus#4.1n#250.0w#1270.0#2000#sumerian town\nur sulcus#49.8n#177.5w#1145.0#1985#ancient sumerian seat of moon worship\n",
        "pandas_code": "len(df[df['year named'] == 2000]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1141,
        "statement": "moore lead the team in point for the most total game",
        "label": 1,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "df['high points'].str.contains('moore').sum() > df['high points'].str.contains('moore').sum() - 1",
        "pandas_eval": "True"
    },
    {
        "id": 1142,
        "statement": "allen lead the team in rebound for 5 game",
        "label": 1,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "(df['high rebounds'].str.contains('allen').sum() == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 1143,
        "statement": "the most total assist by an owl player in 1 game be 9",
        "label": 1,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "(df['high assists'].str.extract('(\\\\d+)').astype(int).max().item() == 9)",
        "pandas_eval": "True"
    },
    {
        "id": 1144,
        "statement": "the owl do not lose a game in february of 2011",
        "label": 1,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "all(df['score'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 1146,
        "statement": "wyatt lead the team in point for the most total game",
        "label": 0,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "df['high points'].str.contains('wyatt').sum() > df['high points'].str.contains('moore').sum()",
        "pandas_eval": "False"
    },
    {
        "id": 1147,
        "statement": "allen lead the team in rebound for every game",
        "label": 0,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "all(df['high rebounds'].str.contains('allen'))",
        "pandas_eval": "False"
    },
    {
        "id": 1148,
        "statement": "the most total assist by an owl player in 1 game be 7",
        "label": 0,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "(df['high assists'].str.extract(r'(\\\\d+)').astype(float).max() == 7).item()",
        "pandas_eval": "False"
    },
    {
        "id": 1149,
        "statement": "the owl only lose 1 game in february of 2011",
        "label": 0,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "df[df['date'].str.contains('february')]['score'].str.startswith('L').sum() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1150,
        "statement": "the lowest attendance figure for a game be on february 17",
        "label": 0,
        "table_caption": "2010 - 11 temple owls men 's basketball team",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n21#february 2#la salle#w 71 - 67#wyatt - 18#allen - 7#moore - 5#tom gola arena , philadelphia , pa (3121)#16 - 5 (6 - 2)\n22#february 5#rhode island#w 80 - 67#randall - 27#allen - 10#moore / fernandez - 6#liacouras center , philadelphia , pa (8679)#17 - 5 (7 - 2)\n23#february 9#fordham#w 77 - 66#moore - 22#allen - 14#allen / moore / wyatt - 4#liacouras center , philadelphia , pa (3858)#18 - 5 (8 - 2)\n24#february 12#dayton#w 75 - 63#moore - 26#moore / eric - 9#fernandez - 9#university of dayton arena , dayton , oh (13117)#19 - 5 (9 - 2)\n25#february 17#richmond#w 73 - 53#moore - 24#allen / jefferson - 7#allen - 4#liacouras center , philadelphia , pa (6078)#20 - 5 (10 - 2)\n26#february 20#saint joseph 's#w 66 - 52#moore - 17#allen - 12#fernandez - 6#liacouras center , philadelphia , pa (10206)#21 - 5 (11 - 2)\n",
        "pandas_code": "(df[df['date'] == 'february 17']['location attendance'].str.extract(r'\\\\((\\\\d+)\\\\)').astype(float).iloc[0] == df['location attendance'].str.extract(r'\\\\((\\\\d+)\\\\)').astype(float).min()).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1151,
        "statement": "black tara team only win 3 of the 12 round in asia season",
        "label": 1,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "(df['winning team'] == 'black tara').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1152,
        "statement": "earl bamber win the most round compare to all other driver in asia season",
        "label": 1,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "df['winning driver'].value_counts().idxmax() == 'earl bamber'",
        "pandas_eval": "True"
    },
    {
        "id": 1153,
        "statement": "armaan ebrahim win round 2 seperate date , may 16 - 18 and june 20 - 22",
        "label": 1,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "(df['winning driver'].eq('armaan ebrahim') & df['date'].isin(['may 16 - 18', 'june 20 - 22'])).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1154,
        "statement": "earl bamber be the only driving to win anything at sentul international circuit and okayama international circuit",
        "label": 1,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "all(df[(df['circuit'].isin(['sentul international circuit', 'okayama international circuit']))]['winning driver'] == 'earl bamber')",
        "pandas_eval": "True"
    },
    {
        "id": 1155,
        "statement": "in sepang , malaysia 3 different driver win on 4 seperate date",
        "label": 1,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "len(df[df['location'] == 'sepang , malaysia']['winning driver'].unique()) == 3 and len(df[df['location'] == 'sepang , malaysia']['date'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1156,
        "statement": "black tara team only win 4 of the 12 round in asia season",
        "label": 0,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "len(df[df['winning team'] == 'black tara']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1157,
        "statement": "earl bamber win the least round compare to all other driver in asia season",
        "label": 0,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "(df['winning driver'].value_counts().idxmin() == 'earl bamber')",
        "pandas_eval": "False"
    },
    {
        "id": 1158,
        "statement": "armaan ebrahim lose round 2 seperate date , may 16 - 18 and june 20 - 22",
        "label": 0,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "df[(df['round'] == 2) & (df['winning driver'] != 'armaan ebrahim') & (df['date'].isin(['may 16 - 18', 'june 20 - 22']))].empty",
        "pandas_eval": "False"
    },
    {
        "id": 1159,
        "statement": "james grunwell be the only driver to win anything at sentul international circuit and okayama international circuit",
        "label": 0,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "(df[df['circuit'].isin(['sentul international circuit', 'okayama international circuit'])]['winning driver'].unique() == ['james grunwell']).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1160,
        "statement": "in sepang , malaysia 3 different driver win on 5 seperate date",
        "label": 0,
        "table_caption": "2008 formula v6 asia season",
        "table_text": "round#location#circuit#date#winning driver#winning team\n1#sepang , malaysia#sepang international circuit#may 16 - 18#armaan ebrahim#black tara\n2#sepang , malaysia#sepang international circuit#may 16 - 18#earl bamber#tpc team qi - meritus\n3#sepang , malaysia#sepang international circuit#june 20 - 22#james grunwell#tpc team qi - meritus\n4#sepang , malaysia#sepang international circuit#june 20 - 22#armaan ebrahim#black tara\n5#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n6#sentul , indonesia#sentul international circuit#july 18 - 20#earl bamber#tpc team qi - meritus\n7#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n8#okayama , japan#okayama international circuit#october 24 - 26#earl bamber#tpc team qi - meritus\n9#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n10#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n11#shanghai , china#shanghai international circuit#november 21 - 23#james grunwell#tpc team qi - meritus\n12#shanghai , china#shanghai international circuit#november 21 - 23#isaiah - ro charlez#black tara\n",
        "pandas_code": "len(df[df['location'] == 'sepang , malaysia']['winning driver'].unique()) == 3 and len(df[df['location'] == 'sepang , malaysia']['date'].unique()) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1161,
        "statement": "benjamin harris and walter pilliet be the only member of parliament who be the incumbent and the winner",
        "label": 1,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df[(df['incumbent'] == df['winner']) & ~df['incumbent'].isin(['benjamin harris', 'walter pilliet'])].empty",
        "pandas_eval": "True"
    },
    {
        "id": 1163,
        "statement": "there be no woman elect into parliament during 1882 to 1884",
        "label": 1,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df[(df['by - election'] >= 1882) & (df['by - election'] <= 1884) & (df['winner'].str.contains('woman', case=False))].empty",
        "pandas_eval": "True"
    },
    {
        "id": 1164,
        "statement": "selwyn be the only electorate area to have someone elect into parliament 2 time",
        "label": 1,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df[df['electorate'] == 'selwyn']['by - election'].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1165,
        "statement": "resignation occur more often than any other reason for a member of parliament to end their occupation",
        "label": 1,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df['reason'].value_counts().idxmax() == 'resignation'",
        "pandas_eval": "True"
    },
    {
        "id": 1166,
        "statement": "benjamin harris and walter pilliet be 2 of 4 member of parliament who be the incumbent and the winner",
        "label": 0,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "len(df[df['incumbent'] == df['winner']]) == 4 and all(name in df[df['incumbent'] == df['winner']]['incumbent'].values for name in ['benjamin harris', 'walter pilliet'])",
        "pandas_eval": "False"
    },
    {
        "id": 1167,
        "statement": "edward james lee be 1 of 6 member of parliament who be elect winner who then become incumbent due to their death",
        "label": 0,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df[(df['winner'] == 'edward james lee') & (df['reason'] == 'death')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1168,
        "statement": "there be 1 woman elect into parliament during 1882 to 1884",
        "label": 0,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df[(df['by - election'] >= 1882) & (df['by - election'] <= 1884) & (df['winner'].str.contains('woman', case=False))].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1169,
        "statement": "selwyn be the only electorate area to have have 1 by - election",
        "label": 0,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "len(df[df['electorate'] == 'selwyn']) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1170,
        "statement": "death occur more often than any other reason for a member of parliament to end their occupation",
        "label": 0,
        "table_caption": "8th new zealand parliament",
        "table_text": "by - election#electorate#date#incumbent#reason#winner\n1882#franklin north#9 june#benjamin harris#election declared void#benjamin harris\n1882#wakanui#16 june#cathcart wason#election declared void#joseph ivess\n1882#stanmore#11 july#walter pilliet#election declared void#walter pilliet\n1883#peninsula#22 january#james seaton#death#william larnach\n1883#selwyn#6 april#john hall#resignation#edward james lee\n1883#inangahua#14 may#thomas shailer weston#resignation#edward shaw\n1883#bruce#29 june#james rutherford#death#james mcdonald\n1884#selwyn#15 february#edward james lee#death#edward wakefield\n1884#thorndon#13 may#william levin#resignation#alfred newman\n1884#kaiapoi#16 may#isaac wilson#resignation#edward richardson\n",
        "pandas_code": "df['reason'].value_counts().idxmax() == 'death'",
        "pandas_eval": "False"
    },
    {
        "id": 1171,
        "statement": "all participant at the 2008 master france be male",
        "label": 1,
        "table_caption": "2008 masters france",
        "table_text": "seed#player#marseille (32 draw)#metz (32)#lyon (32)#paris (48 - byes)\n1#paul - henri mathieu#semi - finals#final#round 2#round 1\n2#gilles simon#quarter - finals#round 1#semi - finals#round 3\n3#julien benneteau#round 2#round 1#final#dnp\n4#adrian mannarino#dnq#semi - finals#dnq#round 1\n5#marc gicquel#round 1#quarter - finals#round 1#round 1\n6#micha\u00e3l llodra#round 2#round 1#round 1#dnp\n7#josselin ouanna#dnp#dnp#quarter - finals#round 1\n",
        "pandas_code": "all(df['player'].str.contains(r'\\b(?:paul|henri|gilles|julien|adrian|marc|micha\u00e3l|josselin)\\b', case=False, na=False))",
        "pandas_eval": "True"
    },
    {
        "id": 1172,
        "statement": "paul - henri mathieu be a higher seed player than gilles simon",
        "label": 1,
        "table_caption": "2008 masters france",
        "table_text": "seed#player#marseille (32 draw)#metz (32)#lyon (32)#paris (48 - byes)\n1#paul - henri mathieu#semi - finals#final#round 2#round 1\n2#gilles simon#quarter - finals#round 1#semi - finals#round 3\n3#julien benneteau#round 2#round 1#final#dnp\n4#adrian mannarino#dnq#semi - finals#dnq#round 1\n5#marc gicquel#round 1#quarter - finals#round 1#round 1\n6#micha\u00e3l llodra#round 2#round 1#round 1#dnp\n7#josselin ouanna#dnp#dnp#quarter - finals#round 1\n",
        "pandas_code": "df[df['player'] == 'paul - henri mathieu']['seed'].values[0] < df[df['player'] == 'gilles simon']['seed'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1173,
        "statement": "gilles simon be a higher seed player than julien benneteau",
        "label": 1,
        "table_caption": "2008 masters france",
        "table_text": "seed#player#marseille (32 draw)#metz (32)#lyon (32)#paris (48 - byes)\n1#paul - henri mathieu#semi - finals#final#round 2#round 1\n2#gilles simon#quarter - finals#round 1#semi - finals#round 3\n3#julien benneteau#round 2#round 1#final#dnp\n4#adrian mannarino#dnq#semi - finals#dnq#round 1\n5#marc gicquel#round 1#quarter - finals#round 1#round 1\n6#micha\u00e3l llodra#round 2#round 1#round 1#dnp\n7#josselin ouanna#dnp#dnp#quarter - finals#round 1\n",
        "pandas_code": "df[df['player'] == 'gilles simon']['seed'].values[0] < df[df['player'] == 'julien benneteau']['seed'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1174,
        "statement": "julien benneteau be a higher seed player than adrian mannarino",
        "label": 1,
        "table_caption": "2008 masters france",
        "table_text": "seed#player#marseille (32 draw)#metz (32)#lyon (32)#paris (48 - byes)\n1#paul - henri mathieu#semi - finals#final#round 2#round 1\n2#gilles simon#quarter - finals#round 1#semi - finals#round 3\n3#julien benneteau#round 2#round 1#final#dnp\n4#adrian mannarino#dnq#semi - finals#dnq#round 1\n5#marc gicquel#round 1#quarter - finals#round 1#round 1\n6#micha\u00e3l llodra#round 2#round 1#round 1#dnp\n7#josselin ouanna#dnp#dnp#quarter - finals#round 1\n",
        "pandas_code": "df[df['player'] == 'julien benneteau']['seed'].values[0] < df[df['player'] == 'adrian mannarino']['seed'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1175,
        "statement": "adrian mannarino be a higher seed player than marc gicquel",
        "label": 1,
        "table_caption": "2008 masters france",
        "table_text": "seed#player#marseille (32 draw)#metz (32)#lyon (32)#paris (48 - byes)\n1#paul - henri mathieu#semi - finals#final#round 2#round 1\n2#gilles simon#quarter - finals#round 1#semi - finals#round 3\n3#julien benneteau#round 2#round 1#final#dnp\n4#adrian mannarino#dnq#semi - finals#dnq#round 1\n5#marc gicquel#round 1#quarter - finals#round 1#round 1\n6#micha\u00e3l llodra#round 2#round 1#round 1#dnp\n7#josselin ouanna#dnp#dnp#quarter - finals#round 1\n",
        "pandas_code": "df[df['player'] == 'adrian mannarino']['seed'].values[0] < df[df['player'] == 'marc gicquel']['seed'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1176,
        "statement": "there be 2 driver that complete 61 lap , while only 1 driver complete 60 lap",
        "label": 1,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "(df[df['laps'] == 61]['driver'].count() == 2) & (df[df['laps'] == 60]['driver'].count() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1177,
        "statement": "during the portuguese grand prix , the most laps any driver complete be 62",
        "label": 1,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "df['laps'].max() == 62",
        "pandas_eval": "True"
    },
    {
        "id": 1178,
        "statement": "stirling moss complete 57 more lap that graham hill during the race",
        "label": 1,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "df[df['driver'] == 'stirling moss']['laps'].values[0] - df[df['driver'] == 'graham hill']['laps'].values[0] == 57",
        "pandas_eval": "True"
    },
    {
        "id": 1179,
        "statement": "there be 2 driver that have to retire from the race due to transmission issue",
        "label": 1,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "len(df[df['time / retired'].str.contains('transmission', case=False, na=False)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1180,
        "statement": "ferrari be the constructor of choice for 3 driver in the race",
        "label": 1,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "(df['constructor'] == 'ferrari').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1181,
        "statement": "there be 8 driver that complete 61 lap , while only 1 driver complete 59",
        "label": 0,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "(df[df['laps'] == 61].shape[0] == 8) & (df[df['laps'] == 59].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 1182,
        "statement": "during the portugese grand prix , the most laps any driver complete be 10",
        "label": 0,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "df['laps'].max() == 10",
        "pandas_eval": "False"
    },
    {
        "id": 1183,
        "statement": "innes ireland complete 57 more lap than graham hill during that race",
        "label": 0,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "(df[df['driver'] == 'innes ireland']['laps'].values[0] - df[df['driver'] == 'graham hill']['laps'].values[0]) == 57",
        "pandas_eval": "False"
    },
    {
        "id": 1184,
        "statement": "there be only 1 driver that have to retire from the race due to tramsmission issue",
        "label": 0,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "len(df[df['time / retired'].str.contains('transmission', case=False, na=False)]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1185,
        "statement": "lotus - climax be the constructor of choice for 3 driver in the race",
        "label": 0,
        "table_caption": "1959 portuguese grand prix",
        "table_text": "driver#constructor#laps#time / retired#grid\nstirling moss#cooper - climax#62#2:11:55.41#1\nmasten gregory#cooper - climax#61#+ 1 lap#3\ndan gurney#ferrari#61#+ 1 lap#6\nmaurice trintignant#cooper - climax#60#+ 2 laps#4\nharry schell#brm#59#+ 3 laps#9\nroy salvadori#aston martin#59#+ 3 laps#12\nron flockhart#brm#59#+ 3 laps#11\ncarroll shelby#aston martin#58#+ 4 laps#13\ntony brooks#ferrari#57#+ 5 laps#10\nm\u00e1rio de ara\u00fajo cabral#cooper - maserati#56#+ 6 laps#14\nbruce mclaren#cooper - climax#38#transmission#8\njack brabham#cooper - climax#23#transmission#2\njo bonnier#brm#10#engine#5\nphil hill#ferrari#5#accident#7\ngraham hill#lotus - climax#5#accident#15\ninnes ireland#lotus - climax#3#gearbox#16\n",
        "pandas_code": "len(df[df['constructor'] == 'lotus - climax']) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1186,
        "statement": "norway have 10 gold and 10 silver , the highest of all",
        "label": 1,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "(df[df['nation'] == 'norway']['gold'].values[0] == 10) & (df[df['nation'] == 'norway']['silver'].values[0] == 10)",
        "pandas_eval": "True"
    },
    {
        "id": 1187,
        "statement": "the least in silver be 1 which be by japan",
        "label": 1,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df.loc[df['silver'].idxmin(), 'nation'] == 'japan'",
        "pandas_eval": "True"
    },
    {
        "id": 1188,
        "statement": "10 nation participate in the olympics",
        "label": 1,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "len(df['nation'].unique()) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 1189,
        "statement": "the highest bronze be 9 which on rank 8",
        "label": 1,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df.loc[df['bronze'].idxmax(), 'rank'] == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1190,
        "statement": "germany have the highest total in the winter olympics",
        "label": 1,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df[df['nation'] == 'germany']['total'].max() == df['total'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1191,
        "statement": "united state have 10 gold and 10 silver , the highest of all",
        "label": 0,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "(df[df['nation'] == 'united states']['gold'].values[0] == 10) & (df[df['nation'] == 'united states']['silver'].values[0] == 10) & (df['gold'].max() == 10) & (df['silver'].max() == 10)",
        "pandas_eval": "False"
    },
    {
        "id": 1192,
        "statement": "the least in silver be 1 which be by germany",
        "label": 0,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df.loc[df['silver'].idxmin(), 'nation'] == 'germany'",
        "pandas_eval": "False"
    },
    {
        "id": 1193,
        "statement": "15 nation participate in the olympics",
        "label": 0,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "len(df['nation'].unique()) == 15",
        "pandas_eval": "False"
    },
    {
        "id": 1194,
        "statement": "the highest bronze be 9 which on rank 4",
        "label": 0,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df.loc[df['bronze'].idxmax(), 'rank'] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1195,
        "statement": "italy have the highest total in the winter olympics",
        "label": 0,
        "table_caption": "1998 winter olympics",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#12#9#8#29\n2#norway#10#10#5#25\n3#russia#9#6#3#18\n4#canada#6#5#4#15\n5#united states#6#3#4#13\n6#netherlands#5#4#2#11\n7#japan#5#1#4#10\n8#austria#3#5#9#17\n9#south korea#3#1#2#6\n10#italy#2#6#2#10\n",
        "pandas_code": "df[df['nation'] == 'italy']['total'].max() == df['total'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1196,
        "statement": "jack nicklaus have the most money and rank number 1 in the tournament",
        "label": 1,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "(df[df['player'] == 'jack nicklaus']['money'].iloc[0] == df['money'].max()) & (df[df['player'] == 'jack nicklaus']['place'].iloc[0] == '1')",
        "pandas_eval": "True"
    },
    {
        "id": 1197,
        "statement": "all the player be from the same country in the tournament",
        "label": 1,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "df['country'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1198,
        "statement": "there be 3 player that place t10 in the tournament",
        "label": 1,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "len(df[df['place'] == 't10']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1199,
        "statement": "there be 3 player that have 3600 in the tournament",
        "label": 1,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "(df[df['money'] == 3600].shape[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1200,
        "statement": "tom watson be place before tom kite in the tournament",
        "label": 1,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "df[df['player'].isin(['tom watson', 'tom kite'])].assign(place_num=df['place'].str.replace('t', '').astype(int)).sort_values('place_num').iloc[0]['player'] == 'tom watson'",
        "pandas_eval": "True"
    },
    {
        "id": 1201,
        "statement": "tom weiskopf have the most money , but tom kite rank number 1 in the tournament",
        "label": 0,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "(df[df['player'] == 'tom weiskopf']['money'].max() > df['money'].max()) & (df[df['player'] == 'tom kite']['place'].iloc[0] == '1')",
        "pandas_eval": "False"
    },
    {
        "id": 1202,
        "statement": "3 of the player be from the same country in the tournament",
        "label": 0,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "df['country'].value_counts().max() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1203,
        "statement": "there be 5 player that place t8 in the tournament",
        "label": 0,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "len(df[df['place'] == 't8']) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1204,
        "statement": "there be 2 player that have 7500 in the tournament",
        "label": 0,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "(df['money'] == 7500).sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1205,
        "statement": "hale irwin be place after tom kite in the tournament",
        "label": 0,
        "table_caption": "1975 masters tournament",
        "table_text": "place#player#country#score#to par#money\n1#jack nicklaus#united states#68 + 67 + 73 + 68 = 276#- 12#40000\nt2#johnny miller#united states#75 + 71 + 65 + 66 = 277#- 11#21250\nt2#tom weiskopf#united states#69 + 72 + 66 + 70 = 277#- 11#21250\nt4#hale irwin#united states#73 + 74 + 71 + 64 = 282#- 6#12500\nt4#bobby nichols#united states#67 + 74 + 72 + 69 = 282#- 6#12500\n6#billy casper#united states#70 + 70 + 73 + 70 = 283#- 5#7500\n7#dave hill#united states#75 + 71 + 70 + 68 = 284#- 4#6000\nt8#hubert green#united states#74 + 71 + 70 + 70 = 285#- 3#4500\nt8#tom watson#united states#70 + 70 + 72 + 73 = 285#- 3#4500\nt10#tom kite#united states#72 + 74 + 71 + 69 = 286#- 2#3600\nt10#j c snead#united states#69 + 72 + 75 + 70 = 286#- 2#3600\nt10#lee trevino#united states#71 + 70 + 74 + 71 = 286#- 2#3600\n",
        "pandas_code": "int(df[df['player'] == 'hale irwin']['place'].str.extract('(\\\\d+)')[0].values[0]) > int(df[df['player'] == 'tom kite']['place'].str.extract('(\\\\d+)')[0].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1206,
        "statement": "adrienne curry 's cover come before candice michelle 's cover",
        "label": 1,
        "table_caption": "list of people in playboy 2000 - 09",
        "table_text": "date#cover model#centerfold model#interview subject#20 questions#pictorials\n1 - 06#lisa guerrero#athena lundberg#mark cuban#kate beckinsale#lisa guerrero\n2 - 06#adrianne curry#cassandra lynn#al franken#hugh laurie#adrianne curry , girls of tuscany\n3 - 06#jessica alba#monica leigh#kanye west#franz ferdinand#willa ford\n4 - 06#candice michelle#holley ann dorrough#keanu reeves#craig ferguson#candice michelle , cyber girls in print\n5 - 06#alison waite#alison waite#ozzie guill\u00e3n#rebecca romijn#girls of the top 10 party schools , rachel sterling\n6 - 06#kara monaco#stephanie larimore#shepard smith#jason lee#pmoy - kara monaco , girls of myspace\n7 - 06#vida guerra#sara jean underwood#jerry bruckheimer#dana white#vida guerra\n8 - 06#monica leigh#nicole voss#denis leary#luke wilson#girls of orange county , stacey dash\n10 - 06#tamara witmer#jordan monroe#ludacris#johnny knoxville#girls of the big 12 , christine dolce\n11 - 06#mercedes mcnab#sarah elizabeth#arianna huffington#tenacious d#mercedes mcnab , girls of hawaiian tropic\n",
        "pandas_code": "df[df['cover model'] == 'adrianne curry'].index[0] < df[df['cover model'] == 'candice michelle'].index[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1207,
        "statement": "monica leigh 's cover come after vida guerra 's cover",
        "label": 1,
        "table_caption": "list of people in playboy 2000 - 09",
        "table_text": "date#cover model#centerfold model#interview subject#20 questions#pictorials\n1 - 06#lisa guerrero#athena lundberg#mark cuban#kate beckinsale#lisa guerrero\n2 - 06#adrianne curry#cassandra lynn#al franken#hugh laurie#adrianne curry , girls of tuscany\n3 - 06#jessica alba#monica leigh#kanye west#franz ferdinand#willa ford\n4 - 06#candice michelle#holley ann dorrough#keanu reeves#craig ferguson#candice michelle , cyber girls in print\n5 - 06#alison waite#alison waite#ozzie guill\u00e3n#rebecca romijn#girls of the top 10 party schools , rachel sterling\n6 - 06#kara monaco#stephanie larimore#shepard smith#jason lee#pmoy - kara monaco , girls of myspace\n7 - 06#vida guerra#sara jean underwood#jerry bruckheimer#dana white#vida guerra\n8 - 06#monica leigh#nicole voss#denis leary#luke wilson#girls of orange county , stacey dash\n10 - 06#tamara witmer#jordan monroe#ludacris#johnny knoxville#girls of the big 12 , christine dolce\n11 - 06#mercedes mcnab#sarah elizabeth#arianna huffington#tenacious d#mercedes mcnab , girls of hawaiian tropic\n",
        "pandas_code": "df[df['cover model'] == 'monica leigh'].index[0] > df[df['cover model'] == 'vida guerra'].index[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1208,
        "statement": "lisa guerrero 's cover be first of the year",
        "label": 1,
        "table_caption": "list of people in playboy 2000 - 09",
        "table_text": "date#cover model#centerfold model#interview subject#20 questions#pictorials\n1 - 06#lisa guerrero#athena lundberg#mark cuban#kate beckinsale#lisa guerrero\n2 - 06#adrianne curry#cassandra lynn#al franken#hugh laurie#adrianne curry , girls of tuscany\n3 - 06#jessica alba#monica leigh#kanye west#franz ferdinand#willa ford\n4 - 06#candice michelle#holley ann dorrough#keanu reeves#craig ferguson#candice michelle , cyber girls in print\n5 - 06#alison waite#alison waite#ozzie guill\u00e3n#rebecca romijn#girls of the top 10 party schools , rachel sterling\n6 - 06#kara monaco#stephanie larimore#shepard smith#jason lee#pmoy - kara monaco , girls of myspace\n7 - 06#vida guerra#sara jean underwood#jerry bruckheimer#dana white#vida guerra\n8 - 06#monica leigh#nicole voss#denis leary#luke wilson#girls of orange county , stacey dash\n10 - 06#tamara witmer#jordan monroe#ludacris#johnny knoxville#girls of the big 12 , christine dolce\n11 - 06#mercedes mcnab#sarah elizabeth#arianna huffington#tenacious d#mercedes mcnab , girls of hawaiian tropic\n",
        "pandas_code": "df.loc[df['date'] == '1 - 06', 'cover model'].values[0] == 'lisa guerrero'",
        "pandas_eval": "True"
    },
    {
        "id": 1209,
        "statement": "mercedes mcnab 's cover be last of the year",
        "label": 1,
        "table_caption": "list of people in playboy 2000 - 09",
        "table_text": "date#cover model#centerfold model#interview subject#20 questions#pictorials\n1 - 06#lisa guerrero#athena lundberg#mark cuban#kate beckinsale#lisa guerrero\n2 - 06#adrianne curry#cassandra lynn#al franken#hugh laurie#adrianne curry , girls of tuscany\n3 - 06#jessica alba#monica leigh#kanye west#franz ferdinand#willa ford\n4 - 06#candice michelle#holley ann dorrough#keanu reeves#craig ferguson#candice michelle , cyber girls in print\n5 - 06#alison waite#alison waite#ozzie guill\u00e3n#rebecca romijn#girls of the top 10 party schools , rachel sterling\n6 - 06#kara monaco#stephanie larimore#shepard smith#jason lee#pmoy - kara monaco , girls of myspace\n7 - 06#vida guerra#sara jean underwood#jerry bruckheimer#dana white#vida guerra\n8 - 06#monica leigh#nicole voss#denis leary#luke wilson#girls of orange county , stacey dash\n10 - 06#tamara witmer#jordan monroe#ludacris#johnny knoxville#girls of the big 12 , christine dolce\n11 - 06#mercedes mcnab#sarah elizabeth#arianna huffington#tenacious d#mercedes mcnab , girls of hawaiian tropic\n",
        "pandas_code": "df[df['cover model'] == 'mercedes mcnab'].index[-1] == df.index[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 1210,
        "statement": "kara monaco 's cover be sixth in the year",
        "label": 1,
        "table_caption": "list of people in playboy 2000 - 09",
        "table_text": "date#cover model#centerfold model#interview subject#20 questions#pictorials\n1 - 06#lisa guerrero#athena lundberg#mark cuban#kate beckinsale#lisa guerrero\n2 - 06#adrianne curry#cassandra lynn#al franken#hugh laurie#adrianne curry , girls of tuscany\n3 - 06#jessica alba#monica leigh#kanye west#franz ferdinand#willa ford\n4 - 06#candice michelle#holley ann dorrough#keanu reeves#craig ferguson#candice michelle , cyber girls in print\n5 - 06#alison waite#alison waite#ozzie guill\u00e3n#rebecca romijn#girls of the top 10 party schools , rachel sterling\n6 - 06#kara monaco#stephanie larimore#shepard smith#jason lee#pmoy - kara monaco , girls of myspace\n7 - 06#vida guerra#sara jean underwood#jerry bruckheimer#dana white#vida guerra\n8 - 06#monica leigh#nicole voss#denis leary#luke wilson#girls of orange county , stacey dash\n10 - 06#tamara witmer#jordan monroe#ludacris#johnny knoxville#girls of the big 12 , christine dolce\n11 - 06#mercedes mcnab#sarah elizabeth#arianna huffington#tenacious d#mercedes mcnab , girls of hawaiian tropic\n",
        "pandas_code": "df.loc[df['cover model'] == 'kara monaco'].index[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1211,
        "statement": "jawann oldham have the highest no",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nvictor oladipo#5#united states#guard#2013 - present#indiana\njawann oldham#55#united states#center#1989 - 1990#seattle\nkevin ollie#3#united states#guard#1998#connecticut\nshaquille o'neal#32#united states#center#1992 - 1996#louisiana state\ndaniel orton#21#united states#center#2010 - 2012#kentucky\nbo outlaw#45#united states#forward - center#1997 - 2001#houston\nbo outlaw#45#united states#forward - center#2005 - 2008#houston\ndoug overton#11#united states#guard#1998 - 1999#la salle\n",
        "pandas_code": "df.loc[df['no'].idxmax(), 'player'] == 'jawann oldham'",
        "pandas_eval": "True"
    },
    {
        "id": 1213,
        "statement": "bo outlaw leave the orlando magic and then return later",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nvictor oladipo#5#united states#guard#2013 - present#indiana\njawann oldham#55#united states#center#1989 - 1990#seattle\nkevin ollie#3#united states#guard#1998#connecticut\nshaquille o'neal#32#united states#center#1992 - 1996#louisiana state\ndaniel orton#21#united states#center#2010 - 2012#kentucky\nbo outlaw#45#united states#forward - center#1997 - 2001#houston\nbo outlaw#45#united states#forward - center#2005 - 2008#houston\ndoug overton#11#united states#guard#1998 - 1999#la salle\n",
        "pandas_code": "len(df[(df['player'] == 'bo outlaw') & (df['years in orlando'].str.contains('1997 - 2001|2005 - 2008'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1214,
        "statement": "all player be of the same nationality",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nvictor oladipo#5#united states#guard#2013 - present#indiana\njawann oldham#55#united states#center#1989 - 1990#seattle\nkevin ollie#3#united states#guard#1998#connecticut\nshaquille o'neal#32#united states#center#1992 - 1996#louisiana state\ndaniel orton#21#united states#center#2010 - 2012#kentucky\nbo outlaw#45#united states#forward - center#1997 - 2001#houston\nbo outlaw#45#united states#forward - center#2005 - 2008#houston\ndoug overton#11#united states#guard#1998 - 1999#la salle\n",
        "pandas_code": "df['nationality'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1215,
        "statement": "kevin ollie have a lower number than doug overton",
        "label": 1,
        "table_caption": "orlando magic all - time roster",
        "table_text": "player#no#nationality#position#years in orlando#school / club team\nvictor oladipo#5#united states#guard#2013 - present#indiana\njawann oldham#55#united states#center#1989 - 1990#seattle\nkevin ollie#3#united states#guard#1998#connecticut\nshaquille o'neal#32#united states#center#1992 - 1996#louisiana state\ndaniel orton#21#united states#center#2010 - 2012#kentucky\nbo outlaw#45#united states#forward - center#1997 - 2001#houston\nbo outlaw#45#united states#forward - center#2005 - 2008#houston\ndoug overton#11#united states#guard#1998 - 1999#la salle\n",
        "pandas_code": "df[df['player'] == 'kevin ollie']['no'].values[0] < df[df['player'] == 'doug overton']['no'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1216,
        "statement": "binevanagh be scrap and sell before knocklayd",
        "label": 1,
        "table_caption": "ncc class a1",
        "table_text": "number#builder#built#rebuilt#name as rebuilt#scrapped / sold\n33#york rd#1902#1928#binevanagh#1949\n34#york rd#1901#1928#knocklayd#1950\n58#york rd#1907#1934#lurigethan#1954\n62#york rd#1903#1928#slemish#1954\n64#derby#1905#1929#trostan#1954\n65#derby#1905#1929#knockagh#1950\n66#derby#1905#1930#ben madigan#1954\n68#derby#1908#1927#slieve gallion#1947\n",
        "pandas_code": "df[df['name as rebuilt'] == 'binevanagh']['scrapped / sold'].values[0] < df[df['name as rebuilt'] == 'knocklayd']['scrapped / sold'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1217,
        "statement": "knocklayd be scrap and sell before lurigethan",
        "label": 1,
        "table_caption": "ncc class a1",
        "table_text": "number#builder#built#rebuilt#name as rebuilt#scrapped / sold\n33#york rd#1902#1928#binevanagh#1949\n34#york rd#1901#1928#knocklayd#1950\n58#york rd#1907#1934#lurigethan#1954\n62#york rd#1903#1928#slemish#1954\n64#derby#1905#1929#trostan#1954\n65#derby#1905#1929#knockagh#1950\n66#derby#1905#1930#ben madigan#1954\n68#derby#1908#1927#slieve gallion#1947\n",
        "pandas_code": "(df[df['name as rebuilt'] == 'knocklayd']['scrapped / sold'].values[0] < df[df['name as rebuilt'] == 'lurigethan']['scrapped / sold'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1218,
        "statement": "knockagh be scrap and sell before slemish",
        "label": 1,
        "table_caption": "ncc class a1",
        "table_text": "number#builder#built#rebuilt#name as rebuilt#scrapped / sold\n33#york rd#1902#1928#binevanagh#1949\n34#york rd#1901#1928#knocklayd#1950\n58#york rd#1907#1934#lurigethan#1954\n62#york rd#1903#1928#slemish#1954\n64#derby#1905#1929#trostan#1954\n65#derby#1905#1929#knockagh#1950\n66#derby#1905#1930#ben madigan#1954\n68#derby#1908#1927#slieve gallion#1947\n",
        "pandas_code": "(df[df['name as rebuilt'] == 'knockagh']['scrapped / sold'].values[0] < df[df['name as rebuilt'] == 'slemish']['scrapped / sold'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1219,
        "statement": "knockagh be scrap and sell before trostan",
        "label": 1,
        "table_caption": "ncc class a1",
        "table_text": "number#builder#built#rebuilt#name as rebuilt#scrapped / sold\n33#york rd#1902#1928#binevanagh#1949\n34#york rd#1901#1928#knocklayd#1950\n58#york rd#1907#1934#lurigethan#1954\n62#york rd#1903#1928#slemish#1954\n64#derby#1905#1929#trostan#1954\n65#derby#1905#1929#knockagh#1950\n66#derby#1905#1930#ben madigan#1954\n68#derby#1908#1927#slieve gallion#1947\n",
        "pandas_code": "df.loc[df['name as rebuilt'] == 'knockagh', 'scrapped / sold'].values[0] < df.loc[df['name as rebuilt'] == 'trostan', 'scrapped / sold'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1220,
        "statement": "slieve gallion be scrap and sell before knockagh",
        "label": 1,
        "table_caption": "ncc class a1",
        "table_text": "number#builder#built#rebuilt#name as rebuilt#scrapped / sold\n33#york rd#1902#1928#binevanagh#1949\n34#york rd#1901#1928#knocklayd#1950\n58#york rd#1907#1934#lurigethan#1954\n62#york rd#1903#1928#slemish#1954\n64#derby#1905#1929#trostan#1954\n65#derby#1905#1929#knockagh#1950\n66#derby#1905#1930#ben madigan#1954\n68#derby#1908#1927#slieve gallion#1947\n",
        "pandas_code": "df.loc[df['name as rebuilt'] == 'slieve gallion', 'scrapped / sold'].values[0] < df.loc[df['name as rebuilt'] == 'knockagh', 'scrapped / sold'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1221,
        "statement": "noam pitlik direct episode 30 - 50",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n30#1#the thief#noam pitlik#jeffrey ferro & fredric weiss#september 26 , 1986#5a03\n31#2#grandma#noam pitlik#frank dungan & jeff stein & tony sheehan#october 03 , 1986#5a04\n32#3#debut#noam pitlik#fredric weiss & jeffrey ferro#october 17 , 1986#5a05\n33#4#kevin 's date#noam pitlik#tony sheehan#october 24 , 1986#5a07\n34#5#halloween#noam pitlik#jeffrey ferro & fredric weiss#october 31 , 1986#5a08\n35#6#deportation : part 1#noam pitlik#frank dungan & jeff stein & tony sheehan#november 07 , 1986#5a01\n36#7#deportation : part 2#noam pitlik#frank dungan & jeff stein & tony sheehan#november 14 , 1986#5a02\n37#8#reunion#noam pitlik#frank dungan & jeff stein#november 21 , 1986#5a10\n38#9#the spelling bee#noam pitlik#fredric weiss & jeffrey ferro#december 05 , 1986#5a11\n39#10#pills#noam pitlik#gene braunstein & bob perlow#december 12 , 1986#5a09\n40#11#the ticket#noam pitlik#tony sheehan#january 30 , 1987#5a12\n41#12#college bound#noam pitlik#jeffrey ferro & fredric weiss#january 09 , 1987#5a13\n42#13#inky#noam pitlik#frank dungan & jeff stein#january 16 , 1987#5a14\n43#14#jobless#noam pitlik#frank dungan & jeff stein & tony sheehan#january 23 , 1987#5a15\n44#15#the crush#noam pitlik#fredric weiss & jeffrey ferro#february 06 , 1987#5a16\n46#17#the cadet#noam pitlik#jeffrey ferro & fredric weiss#february 20 , 1987#5a18\n47#18#kevin 's older woman#noam pitlik#frank dungan & jeff stein & tony sheehan#february 27 , 1987#5a19\n48#19#baby#noam pitlik#lisa albert#march 06 , 1987#5a06\n49#20#separation#noam pitlik#frank dungan & jeff stein#may 01 , 1987#5a21\n50#21#the mogul#noam pitlik#frank dungan & jeff stein & tony sheehan#may 08 , 1987#5a22\n",
        "pandas_code": "all(df[(df['ep'] >= 30) & (df['ep'] <= 50)]['directed by'] == 'noam pitlik')",
        "pandas_eval": "True"
    },
    {
        "id": 1222,
        "statement": "tony sheehan write more episode than lisa albert",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n30#1#the thief#noam pitlik#jeffrey ferro & fredric weiss#september 26 , 1986#5a03\n31#2#grandma#noam pitlik#frank dungan & jeff stein & tony sheehan#october 03 , 1986#5a04\n32#3#debut#noam pitlik#fredric weiss & jeffrey ferro#october 17 , 1986#5a05\n33#4#kevin 's date#noam pitlik#tony sheehan#october 24 , 1986#5a07\n34#5#halloween#noam pitlik#jeffrey ferro & fredric weiss#october 31 , 1986#5a08\n35#6#deportation : part 1#noam pitlik#frank dungan & jeff stein & tony sheehan#november 07 , 1986#5a01\n36#7#deportation : part 2#noam pitlik#frank dungan & jeff stein & tony sheehan#november 14 , 1986#5a02\n37#8#reunion#noam pitlik#frank dungan & jeff stein#november 21 , 1986#5a10\n38#9#the spelling bee#noam pitlik#fredric weiss & jeffrey ferro#december 05 , 1986#5a11\n39#10#pills#noam pitlik#gene braunstein & bob perlow#december 12 , 1986#5a09\n40#11#the ticket#noam pitlik#tony sheehan#january 30 , 1987#5a12\n41#12#college bound#noam pitlik#jeffrey ferro & fredric weiss#january 09 , 1987#5a13\n42#13#inky#noam pitlik#frank dungan & jeff stein#january 16 , 1987#5a14\n43#14#jobless#noam pitlik#frank dungan & jeff stein & tony sheehan#january 23 , 1987#5a15\n44#15#the crush#noam pitlik#fredric weiss & jeffrey ferro#february 06 , 1987#5a16\n46#17#the cadet#noam pitlik#jeffrey ferro & fredric weiss#february 20 , 1987#5a18\n47#18#kevin 's older woman#noam pitlik#frank dungan & jeff stein & tony sheehan#february 27 , 1987#5a19\n48#19#baby#noam pitlik#lisa albert#march 06 , 1987#5a06\n49#20#separation#noam pitlik#frank dungan & jeff stein#may 01 , 1987#5a21\n50#21#the mogul#noam pitlik#frank dungan & jeff stein & tony sheehan#may 08 , 1987#5a22\n",
        "pandas_code": "(df['written by'].str.contains('tony sheehan').sum() > df['written by'].str.contains('lisa albert').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 1223,
        "statement": "2 episode title begin with kevin 's",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n30#1#the thief#noam pitlik#jeffrey ferro & fredric weiss#september 26 , 1986#5a03\n31#2#grandma#noam pitlik#frank dungan & jeff stein & tony sheehan#october 03 , 1986#5a04\n32#3#debut#noam pitlik#fredric weiss & jeffrey ferro#october 17 , 1986#5a05\n33#4#kevin 's date#noam pitlik#tony sheehan#october 24 , 1986#5a07\n34#5#halloween#noam pitlik#jeffrey ferro & fredric weiss#october 31 , 1986#5a08\n35#6#deportation : part 1#noam pitlik#frank dungan & jeff stein & tony sheehan#november 07 , 1986#5a01\n36#7#deportation : part 2#noam pitlik#frank dungan & jeff stein & tony sheehan#november 14 , 1986#5a02\n37#8#reunion#noam pitlik#frank dungan & jeff stein#november 21 , 1986#5a10\n38#9#the spelling bee#noam pitlik#fredric weiss & jeffrey ferro#december 05 , 1986#5a11\n39#10#pills#noam pitlik#gene braunstein & bob perlow#december 12 , 1986#5a09\n40#11#the ticket#noam pitlik#tony sheehan#january 30 , 1987#5a12\n41#12#college bound#noam pitlik#jeffrey ferro & fredric weiss#january 09 , 1987#5a13\n42#13#inky#noam pitlik#frank dungan & jeff stein#january 16 , 1987#5a14\n43#14#jobless#noam pitlik#frank dungan & jeff stein & tony sheehan#january 23 , 1987#5a15\n44#15#the crush#noam pitlik#fredric weiss & jeffrey ferro#february 06 , 1987#5a16\n46#17#the cadet#noam pitlik#jeffrey ferro & fredric weiss#february 20 , 1987#5a18\n47#18#kevin 's older woman#noam pitlik#frank dungan & jeff stein & tony sheehan#february 27 , 1987#5a19\n48#19#baby#noam pitlik#lisa albert#march 06 , 1987#5a06\n49#20#separation#noam pitlik#frank dungan & jeff stein#may 01 , 1987#5a21\n50#21#the mogul#noam pitlik#frank dungan & jeff stein & tony sheehan#may 08 , 1987#5a22\n",
        "pandas_code": "df['title'].str.startswith(\"kevin 's\").sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1225,
        "statement": "frank dungan participate in write 9 episode",
        "label": 1,
        "table_caption": "list of mr. belvedere episodes",
        "table_text": "ep#season#title#directed by#written by#original air date#prod code\n30#1#the thief#noam pitlik#jeffrey ferro & fredric weiss#september 26 , 1986#5a03\n31#2#grandma#noam pitlik#frank dungan & jeff stein & tony sheehan#october 03 , 1986#5a04\n32#3#debut#noam pitlik#fredric weiss & jeffrey ferro#october 17 , 1986#5a05\n33#4#kevin 's date#noam pitlik#tony sheehan#october 24 , 1986#5a07\n34#5#halloween#noam pitlik#jeffrey ferro & fredric weiss#october 31 , 1986#5a08\n35#6#deportation : part 1#noam pitlik#frank dungan & jeff stein & tony sheehan#november 07 , 1986#5a01\n36#7#deportation : part 2#noam pitlik#frank dungan & jeff stein & tony sheehan#november 14 , 1986#5a02\n37#8#reunion#noam pitlik#frank dungan & jeff stein#november 21 , 1986#5a10\n38#9#the spelling bee#noam pitlik#fredric weiss & jeffrey ferro#december 05 , 1986#5a11\n39#10#pills#noam pitlik#gene braunstein & bob perlow#december 12 , 1986#5a09\n40#11#the ticket#noam pitlik#tony sheehan#january 30 , 1987#5a12\n41#12#college bound#noam pitlik#jeffrey ferro & fredric weiss#january 09 , 1987#5a13\n42#13#inky#noam pitlik#frank dungan & jeff stein#january 16 , 1987#5a14\n43#14#jobless#noam pitlik#frank dungan & jeff stein & tony sheehan#january 23 , 1987#5a15\n44#15#the crush#noam pitlik#fredric weiss & jeffrey ferro#february 06 , 1987#5a16\n46#17#the cadet#noam pitlik#jeffrey ferro & fredric weiss#february 20 , 1987#5a18\n47#18#kevin 's older woman#noam pitlik#frank dungan & jeff stein & tony sheehan#february 27 , 1987#5a19\n48#19#baby#noam pitlik#lisa albert#march 06 , 1987#5a06\n49#20#separation#noam pitlik#frank dungan & jeff stein#may 01 , 1987#5a21\n50#21#the mogul#noam pitlik#frank dungan & jeff stein & tony sheehan#may 08 , 1987#5a22\n",
        "pandas_code": "df['written by'].str.contains('frank dungan').sum() == 9",
        "pandas_eval": "True"
    },
    {
        "id": 1226,
        "statement": "there be 4 different time less than 200 lap be complete and 2 of those record belong to the same manufacturer",
        "label": 1,
        "table_caption": "quicken loans 400",
        "table_text": "year#date#driver#team#manufacturer#laps#-#race time#average speed (mph)#report\n1969#june 15#cale yarborough#wood brothers racing#mercury#250#500 (804.672)#3:35:26#139.254#report\n1970#june 7#cale yarborough#wood brothers racing#mercury#200#400 (643.737)#2:53:02#138.302#report\n1971#june 13#bobby allison#holman - moody#mercury#197#401.88 (646.763)#2:41:13#149.567#report\n1972#june 11#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:43:40#146.639#report\n1973#june 24#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:36:22#153.485#report\n1974#june 16#richard petty#petty enterprises#dodge#180#360 (579.363)#2:48:46#127.098#report\n1975#june 15#david pearson#wood brothers racing#mercury#200#400 (643.737)#3:02:39#131.398#report\n1976#june 20#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:50:02#141.148#report\n1977#june 19#cale yarborough#junior johnson & associates#chevrolet#200#400 (643.737)#2:57:44#135.033#report\n1978#june 18#cale yarborough#junior johnson & associates#oldsmobile#200#400 (643.737)#2:40:28#149.563#report\n1979#june 17#buddy baker#ranier - lundy#chevrolet#200#400 (643.737)#2:56:44#135.798#report\n1980#june 15#benny parsons#mc anderson racing#chevrolet#200#400 (643.737)#3:02:05#131.808#report\n1981#june 21#bobby allison#ranier - lundy#buick#200#400 (643.737)#3:03:47#130.589#report\n1982#june 20#cale yarborough#mc anderson racing#buick#200#400 (643.737)#3:23:13#118.101#report\n1983#june 19#cale yarborough#ranier - lundy#chevrolet#200#400 (643.737)#2:53:00#138.728#report\n1984#june 17#bill elliott#melling racing#ford#200#400 (643.737)#2:58:10#134.705#report\n1985#june 16#bill elliott#melling racing#ford#200#400 (643.737)#2:45:48#144.724#report\n1986#june 15#bill elliott#melling racing#ford#200#400 (643.737)#2:53:21#138.851#report\n1987#june 28#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:41:40#148.454#report\n1988#june 26#rusty wallace#blue max racing#pontiac#200#400 (643.737)#2:36:18#153.551#report\n1989#june 25#bill elliott#melling racing#ford#200#400 (643.737)#2:52:38#139.023#report\n1990#june 24#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:39:46#150.219#report\n1991#june 23#davey allison#robert yates racing#ford#200#400 (643.737)#2:29:09#160.912#report\n1992#june 21#davey allison#robert yates racing#ford#200#400 (643.737)#2:37:12#152.672#report\n1993#june 20#ricky rudd#hendrick motorsports#chevrolet#200#400 (643.737)#2:41:38#148.484#report\n1994#june 19#rusty wallace#penske racing#ford#200#400 (643.737)#3:11:58#125.022#report\n1995#june 18#bobby labonte#joe gibbs racing#chevrolet#200#400 (643.737)#2:58:58#134.141#report\n1996#june 23#rusty wallace#penske racing#ford#200#400 (643.737)#2:24:23#166.033#report\n1997#june 15#ernie irvan#robert yates racing#ford#200#400 (643.737)#2:36:31#153.338#report\n1998#june 14#mark martin#roush racing#ford#200#400 (643.737)#2:31:14#158.695#report\n1999#june 13#dale jarrett#robert yates racing#ford#200#400 (643.737)#2:17:56#173.997#report\n2000#june 11#tony stewart#joe gibbs racing#pontiac#194#388 (624.425)#2:41:45#143.926#report\n2001#june 10#jeff gordon#hendrick motorsports#chevrolet#200#400 (643.737)#2:58:50#134.203#report\n2002#june 16#matt kenseth#roush racing#ford#200#400 (643.737)#2:35:01#154.822#report\n2003#june 15#kurt busch#roush racing#ford#200#400 (643.737)#3:02:54#131.219#report\n2004#june 20#ryan newman#penske racing#dodge#200#400 (643.737)#2:52:18#139.292#report\n2005#june 19#greg biffle#roush racing#ford#200#400 (643.737)#2:39:22#150.596#report\n2006#june 18#kasey kahne#evernham motorsports#dodge#129#258 (415.210)#2:10:19#118.788#report\n2007#june 17#carl edwards#roush fenway racing#ford#200#400 (643.737)#2:42:05#148.07#report\n2008#june 15#dale earnhardt , jr#hendrick motorsports#chevrolet#203#406 (653.393)#2:47:34#145.375#report\n2009#june 14#mark martin#hendrick motorsports#chevrolet#200#400 (643.737)#2:34:21#155.491#report\n2010#june 13#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:33:28#156.386#report\n2011#june 19#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:36:50#153.029#report\n2012#june 17#dale earnhardt , jr#hendrick motorsports#chevrolet#200#400 (643.737)#2:52:29#139.144#report\n",
        "pandas_code": "len(df[df['laps'] < 200]) == 4 and df[df['laps'] < 200]['manufacturer'].duplicated(keep=False).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1227,
        "statement": "ford be 1 of the manufacturer who compete before toyota do",
        "label": 1,
        "table_caption": "quicken loans 400",
        "table_text": "year#date#driver#team#manufacturer#laps#-#race time#average speed (mph)#report\n1969#june 15#cale yarborough#wood brothers racing#mercury#250#500 (804.672)#3:35:26#139.254#report\n1970#june 7#cale yarborough#wood brothers racing#mercury#200#400 (643.737)#2:53:02#138.302#report\n1971#june 13#bobby allison#holman - moody#mercury#197#401.88 (646.763)#2:41:13#149.567#report\n1972#june 11#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:43:40#146.639#report\n1973#june 24#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:36:22#153.485#report\n1974#june 16#richard petty#petty enterprises#dodge#180#360 (579.363)#2:48:46#127.098#report\n1975#june 15#david pearson#wood brothers racing#mercury#200#400 (643.737)#3:02:39#131.398#report\n1976#june 20#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:50:02#141.148#report\n1977#june 19#cale yarborough#junior johnson & associates#chevrolet#200#400 (643.737)#2:57:44#135.033#report\n1978#june 18#cale yarborough#junior johnson & associates#oldsmobile#200#400 (643.737)#2:40:28#149.563#report\n1979#june 17#buddy baker#ranier - lundy#chevrolet#200#400 (643.737)#2:56:44#135.798#report\n1980#june 15#benny parsons#mc anderson racing#chevrolet#200#400 (643.737)#3:02:05#131.808#report\n1981#june 21#bobby allison#ranier - lundy#buick#200#400 (643.737)#3:03:47#130.589#report\n1982#june 20#cale yarborough#mc anderson racing#buick#200#400 (643.737)#3:23:13#118.101#report\n1983#june 19#cale yarborough#ranier - lundy#chevrolet#200#400 (643.737)#2:53:00#138.728#report\n1984#june 17#bill elliott#melling racing#ford#200#400 (643.737)#2:58:10#134.705#report\n1985#june 16#bill elliott#melling racing#ford#200#400 (643.737)#2:45:48#144.724#report\n1986#june 15#bill elliott#melling racing#ford#200#400 (643.737)#2:53:21#138.851#report\n1987#june 28#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:41:40#148.454#report\n1988#june 26#rusty wallace#blue max racing#pontiac#200#400 (643.737)#2:36:18#153.551#report\n1989#june 25#bill elliott#melling racing#ford#200#400 (643.737)#2:52:38#139.023#report\n1990#june 24#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:39:46#150.219#report\n1991#june 23#davey allison#robert yates racing#ford#200#400 (643.737)#2:29:09#160.912#report\n1992#june 21#davey allison#robert yates racing#ford#200#400 (643.737)#2:37:12#152.672#report\n1993#june 20#ricky rudd#hendrick motorsports#chevrolet#200#400 (643.737)#2:41:38#148.484#report\n1994#june 19#rusty wallace#penske racing#ford#200#400 (643.737)#3:11:58#125.022#report\n1995#june 18#bobby labonte#joe gibbs racing#chevrolet#200#400 (643.737)#2:58:58#134.141#report\n1996#june 23#rusty wallace#penske racing#ford#200#400 (643.737)#2:24:23#166.033#report\n1997#june 15#ernie irvan#robert yates racing#ford#200#400 (643.737)#2:36:31#153.338#report\n1998#june 14#mark martin#roush racing#ford#200#400 (643.737)#2:31:14#158.695#report\n1999#june 13#dale jarrett#robert yates racing#ford#200#400 (643.737)#2:17:56#173.997#report\n2000#june 11#tony stewart#joe gibbs racing#pontiac#194#388 (624.425)#2:41:45#143.926#report\n2001#june 10#jeff gordon#hendrick motorsports#chevrolet#200#400 (643.737)#2:58:50#134.203#report\n2002#june 16#matt kenseth#roush racing#ford#200#400 (643.737)#2:35:01#154.822#report\n2003#june 15#kurt busch#roush racing#ford#200#400 (643.737)#3:02:54#131.219#report\n2004#june 20#ryan newman#penske racing#dodge#200#400 (643.737)#2:52:18#139.292#report\n2005#june 19#greg biffle#roush racing#ford#200#400 (643.737)#2:39:22#150.596#report\n2006#june 18#kasey kahne#evernham motorsports#dodge#129#258 (415.210)#2:10:19#118.788#report\n2007#june 17#carl edwards#roush fenway racing#ford#200#400 (643.737)#2:42:05#148.07#report\n2008#june 15#dale earnhardt , jr#hendrick motorsports#chevrolet#203#406 (653.393)#2:47:34#145.375#report\n2009#june 14#mark martin#hendrick motorsports#chevrolet#200#400 (643.737)#2:34:21#155.491#report\n2010#june 13#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:33:28#156.386#report\n2011#june 19#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:36:50#153.029#report\n2012#june 17#dale earnhardt , jr#hendrick motorsports#chevrolet#200#400 (643.737)#2:52:29#139.144#report\n",
        "pandas_code": "df[df['manufacturer'] == 'ford']['year'].min() < df[df['manufacturer'] == 'toyota']['year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1228,
        "statement": "cale yarborough have drive for 4 different team",
        "label": 1,
        "table_caption": "quicken loans 400",
        "table_text": "year#date#driver#team#manufacturer#laps#-#race time#average speed (mph)#report\n1969#june 15#cale yarborough#wood brothers racing#mercury#250#500 (804.672)#3:35:26#139.254#report\n1970#june 7#cale yarborough#wood brothers racing#mercury#200#400 (643.737)#2:53:02#138.302#report\n1971#june 13#bobby allison#holman - moody#mercury#197#401.88 (646.763)#2:41:13#149.567#report\n1972#june 11#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:43:40#146.639#report\n1973#june 24#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:36:22#153.485#report\n1974#june 16#richard petty#petty enterprises#dodge#180#360 (579.363)#2:48:46#127.098#report\n1975#june 15#david pearson#wood brothers racing#mercury#200#400 (643.737)#3:02:39#131.398#report\n1976#june 20#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:50:02#141.148#report\n1977#june 19#cale yarborough#junior johnson & associates#chevrolet#200#400 (643.737)#2:57:44#135.033#report\n1978#june 18#cale yarborough#junior johnson & associates#oldsmobile#200#400 (643.737)#2:40:28#149.563#report\n1979#june 17#buddy baker#ranier - lundy#chevrolet#200#400 (643.737)#2:56:44#135.798#report\n1980#june 15#benny parsons#mc anderson racing#chevrolet#200#400 (643.737)#3:02:05#131.808#report\n1981#june 21#bobby allison#ranier - lundy#buick#200#400 (643.737)#3:03:47#130.589#report\n1982#june 20#cale yarborough#mc anderson racing#buick#200#400 (643.737)#3:23:13#118.101#report\n1983#june 19#cale yarborough#ranier - lundy#chevrolet#200#400 (643.737)#2:53:00#138.728#report\n1984#june 17#bill elliott#melling racing#ford#200#400 (643.737)#2:58:10#134.705#report\n1985#june 16#bill elliott#melling racing#ford#200#400 (643.737)#2:45:48#144.724#report\n1986#june 15#bill elliott#melling racing#ford#200#400 (643.737)#2:53:21#138.851#report\n1987#june 28#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:41:40#148.454#report\n1988#june 26#rusty wallace#blue max racing#pontiac#200#400 (643.737)#2:36:18#153.551#report\n1989#june 25#bill elliott#melling racing#ford#200#400 (643.737)#2:52:38#139.023#report\n1990#june 24#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:39:46#150.219#report\n1991#june 23#davey allison#robert yates racing#ford#200#400 (643.737)#2:29:09#160.912#report\n1992#june 21#davey allison#robert yates racing#ford#200#400 (643.737)#2:37:12#152.672#report\n1993#june 20#ricky rudd#hendrick motorsports#chevrolet#200#400 (643.737)#2:41:38#148.484#report\n1994#june 19#rusty wallace#penske racing#ford#200#400 (643.737)#3:11:58#125.022#report\n1995#june 18#bobby labonte#joe gibbs racing#chevrolet#200#400 (643.737)#2:58:58#134.141#report\n1996#june 23#rusty wallace#penske racing#ford#200#400 (643.737)#2:24:23#166.033#report\n1997#june 15#ernie irvan#robert yates racing#ford#200#400 (643.737)#2:36:31#153.338#report\n1998#june 14#mark martin#roush racing#ford#200#400 (643.737)#2:31:14#158.695#report\n1999#june 13#dale jarrett#robert yates racing#ford#200#400 (643.737)#2:17:56#173.997#report\n2000#june 11#tony stewart#joe gibbs racing#pontiac#194#388 (624.425)#2:41:45#143.926#report\n2001#june 10#jeff gordon#hendrick motorsports#chevrolet#200#400 (643.737)#2:58:50#134.203#report\n2002#june 16#matt kenseth#roush racing#ford#200#400 (643.737)#2:35:01#154.822#report\n2003#june 15#kurt busch#roush racing#ford#200#400 (643.737)#3:02:54#131.219#report\n2004#june 20#ryan newman#penske racing#dodge#200#400 (643.737)#2:52:18#139.292#report\n2005#june 19#greg biffle#roush racing#ford#200#400 (643.737)#2:39:22#150.596#report\n2006#june 18#kasey kahne#evernham motorsports#dodge#129#258 (415.210)#2:10:19#118.788#report\n2007#june 17#carl edwards#roush fenway racing#ford#200#400 (643.737)#2:42:05#148.07#report\n2008#june 15#dale earnhardt , jr#hendrick motorsports#chevrolet#203#406 (653.393)#2:47:34#145.375#report\n2009#june 14#mark martin#hendrick motorsports#chevrolet#200#400 (643.737)#2:34:21#155.491#report\n2010#june 13#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:33:28#156.386#report\n2011#june 19#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:36:50#153.029#report\n2012#june 17#dale earnhardt , jr#hendrick motorsports#chevrolet#200#400 (643.737)#2:52:29#139.144#report\n",
        "pandas_code": "len(df[df['driver'] == 'cale yarborough']['team'].unique()) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1229,
        "statement": "cale yarborough have complete the most lap for a single race",
        "label": 1,
        "table_caption": "quicken loans 400",
        "table_text": "year#date#driver#team#manufacturer#laps#-#race time#average speed (mph)#report\n1969#june 15#cale yarborough#wood brothers racing#mercury#250#500 (804.672)#3:35:26#139.254#report\n1970#june 7#cale yarborough#wood brothers racing#mercury#200#400 (643.737)#2:53:02#138.302#report\n1971#june 13#bobby allison#holman - moody#mercury#197#401.88 (646.763)#2:41:13#149.567#report\n1972#june 11#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:43:40#146.639#report\n1973#june 24#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:36:22#153.485#report\n1974#june 16#richard petty#petty enterprises#dodge#180#360 (579.363)#2:48:46#127.098#report\n1975#june 15#david pearson#wood brothers racing#mercury#200#400 (643.737)#3:02:39#131.398#report\n1976#june 20#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:50:02#141.148#report\n1977#june 19#cale yarborough#junior johnson & associates#chevrolet#200#400 (643.737)#2:57:44#135.033#report\n1978#june 18#cale yarborough#junior johnson & associates#oldsmobile#200#400 (643.737)#2:40:28#149.563#report\n1979#june 17#buddy baker#ranier - lundy#chevrolet#200#400 (643.737)#2:56:44#135.798#report\n1980#june 15#benny parsons#mc anderson racing#chevrolet#200#400 (643.737)#3:02:05#131.808#report\n1981#june 21#bobby allison#ranier - lundy#buick#200#400 (643.737)#3:03:47#130.589#report\n1982#june 20#cale yarborough#mc anderson racing#buick#200#400 (643.737)#3:23:13#118.101#report\n1983#june 19#cale yarborough#ranier - lundy#chevrolet#200#400 (643.737)#2:53:00#138.728#report\n1984#june 17#bill elliott#melling racing#ford#200#400 (643.737)#2:58:10#134.705#report\n1985#june 16#bill elliott#melling racing#ford#200#400 (643.737)#2:45:48#144.724#report\n1986#june 15#bill elliott#melling racing#ford#200#400 (643.737)#2:53:21#138.851#report\n1987#june 28#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:41:40#148.454#report\n1988#june 26#rusty wallace#blue max racing#pontiac#200#400 (643.737)#2:36:18#153.551#report\n1989#june 25#bill elliott#melling racing#ford#200#400 (643.737)#2:52:38#139.023#report\n1990#june 24#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:39:46#150.219#report\n1991#june 23#davey allison#robert yates racing#ford#200#400 (643.737)#2:29:09#160.912#report\n1992#june 21#davey allison#robert yates racing#ford#200#400 (643.737)#2:37:12#152.672#report\n1993#june 20#ricky rudd#hendrick motorsports#chevrolet#200#400 (643.737)#2:41:38#148.484#report\n1994#june 19#rusty wallace#penske racing#ford#200#400 (643.737)#3:11:58#125.022#report\n1995#june 18#bobby labonte#joe gibbs racing#chevrolet#200#400 (643.737)#2:58:58#134.141#report\n1996#june 23#rusty wallace#penske racing#ford#200#400 (643.737)#2:24:23#166.033#report\n1997#june 15#ernie irvan#robert yates racing#ford#200#400 (643.737)#2:36:31#153.338#report\n1998#june 14#mark martin#roush racing#ford#200#400 (643.737)#2:31:14#158.695#report\n1999#june 13#dale jarrett#robert yates racing#ford#200#400 (643.737)#2:17:56#173.997#report\n2000#june 11#tony stewart#joe gibbs racing#pontiac#194#388 (624.425)#2:41:45#143.926#report\n2001#june 10#jeff gordon#hendrick motorsports#chevrolet#200#400 (643.737)#2:58:50#134.203#report\n2002#june 16#matt kenseth#roush racing#ford#200#400 (643.737)#2:35:01#154.822#report\n2003#june 15#kurt busch#roush racing#ford#200#400 (643.737)#3:02:54#131.219#report\n2004#june 20#ryan newman#penske racing#dodge#200#400 (643.737)#2:52:18#139.292#report\n2005#june 19#greg biffle#roush racing#ford#200#400 (643.737)#2:39:22#150.596#report\n2006#june 18#kasey kahne#evernham motorsports#dodge#129#258 (415.210)#2:10:19#118.788#report\n2007#june 17#carl edwards#roush fenway racing#ford#200#400 (643.737)#2:42:05#148.07#report\n2008#june 15#dale earnhardt , jr#hendrick motorsports#chevrolet#203#406 (653.393)#2:47:34#145.375#report\n2009#june 14#mark martin#hendrick motorsports#chevrolet#200#400 (643.737)#2:34:21#155.491#report\n2010#june 13#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:33:28#156.386#report\n2011#june 19#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:36:50#153.029#report\n2012#june 17#dale earnhardt , jr#hendrick motorsports#chevrolet#200#400 (643.737)#2:52:29#139.144#report\n",
        "pandas_code": "df[df['driver'] == 'cale yarborough']['laps'].max() == df['laps'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1230,
        "statement": "ford be the manufacturer that have appear most for the quicken loan 400",
        "label": 1,
        "table_caption": "quicken loans 400",
        "table_text": "year#date#driver#team#manufacturer#laps#-#race time#average speed (mph)#report\n1969#june 15#cale yarborough#wood brothers racing#mercury#250#500 (804.672)#3:35:26#139.254#report\n1970#june 7#cale yarborough#wood brothers racing#mercury#200#400 (643.737)#2:53:02#138.302#report\n1971#june 13#bobby allison#holman - moody#mercury#197#401.88 (646.763)#2:41:13#149.567#report\n1972#june 11#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:43:40#146.639#report\n1973#june 24#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:36:22#153.485#report\n1974#june 16#richard petty#petty enterprises#dodge#180#360 (579.363)#2:48:46#127.098#report\n1975#june 15#david pearson#wood brothers racing#mercury#200#400 (643.737)#3:02:39#131.398#report\n1976#june 20#david pearson#wood brothers racing#mercury#200#400 (643.737)#2:50:02#141.148#report\n1977#june 19#cale yarborough#junior johnson & associates#chevrolet#200#400 (643.737)#2:57:44#135.033#report\n1978#june 18#cale yarborough#junior johnson & associates#oldsmobile#200#400 (643.737)#2:40:28#149.563#report\n1979#june 17#buddy baker#ranier - lundy#chevrolet#200#400 (643.737)#2:56:44#135.798#report\n1980#june 15#benny parsons#mc anderson racing#chevrolet#200#400 (643.737)#3:02:05#131.808#report\n1981#june 21#bobby allison#ranier - lundy#buick#200#400 (643.737)#3:03:47#130.589#report\n1982#june 20#cale yarborough#mc anderson racing#buick#200#400 (643.737)#3:23:13#118.101#report\n1983#june 19#cale yarborough#ranier - lundy#chevrolet#200#400 (643.737)#2:53:00#138.728#report\n1984#june 17#bill elliott#melling racing#ford#200#400 (643.737)#2:58:10#134.705#report\n1985#june 16#bill elliott#melling racing#ford#200#400 (643.737)#2:45:48#144.724#report\n1986#june 15#bill elliott#melling racing#ford#200#400 (643.737)#2:53:21#138.851#report\n1987#june 28#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:41:40#148.454#report\n1988#june 26#rusty wallace#blue max racing#pontiac#200#400 (643.737)#2:36:18#153.551#report\n1989#june 25#bill elliott#melling racing#ford#200#400 (643.737)#2:52:38#139.023#report\n1990#june 24#dale earnhardt#richard childress racing#chevrolet#200#400 (643.737)#2:39:46#150.219#report\n1991#june 23#davey allison#robert yates racing#ford#200#400 (643.737)#2:29:09#160.912#report\n1992#june 21#davey allison#robert yates racing#ford#200#400 (643.737)#2:37:12#152.672#report\n1993#june 20#ricky rudd#hendrick motorsports#chevrolet#200#400 (643.737)#2:41:38#148.484#report\n1994#june 19#rusty wallace#penske racing#ford#200#400 (643.737)#3:11:58#125.022#report\n1995#june 18#bobby labonte#joe gibbs racing#chevrolet#200#400 (643.737)#2:58:58#134.141#report\n1996#june 23#rusty wallace#penske racing#ford#200#400 (643.737)#2:24:23#166.033#report\n1997#june 15#ernie irvan#robert yates racing#ford#200#400 (643.737)#2:36:31#153.338#report\n1998#june 14#mark martin#roush racing#ford#200#400 (643.737)#2:31:14#158.695#report\n1999#june 13#dale jarrett#robert yates racing#ford#200#400 (643.737)#2:17:56#173.997#report\n2000#june 11#tony stewart#joe gibbs racing#pontiac#194#388 (624.425)#2:41:45#143.926#report\n2001#june 10#jeff gordon#hendrick motorsports#chevrolet#200#400 (643.737)#2:58:50#134.203#report\n2002#june 16#matt kenseth#roush racing#ford#200#400 (643.737)#2:35:01#154.822#report\n2003#june 15#kurt busch#roush racing#ford#200#400 (643.737)#3:02:54#131.219#report\n2004#june 20#ryan newman#penske racing#dodge#200#400 (643.737)#2:52:18#139.292#report\n2005#june 19#greg biffle#roush racing#ford#200#400 (643.737)#2:39:22#150.596#report\n2006#june 18#kasey kahne#evernham motorsports#dodge#129#258 (415.210)#2:10:19#118.788#report\n2007#june 17#carl edwards#roush fenway racing#ford#200#400 (643.737)#2:42:05#148.07#report\n2008#june 15#dale earnhardt , jr#hendrick motorsports#chevrolet#203#406 (653.393)#2:47:34#145.375#report\n2009#june 14#mark martin#hendrick motorsports#chevrolet#200#400 (643.737)#2:34:21#155.491#report\n2010#june 13#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:33:28#156.386#report\n2011#june 19#denny hamlin#joe gibbs racing#toyota#200#400 (643.737)#2:36:50#153.029#report\n2012#june 17#dale earnhardt , jr#hendrick motorsports#chevrolet#200#400 (643.737)#2:52:29#139.144#report\n",
        "pandas_code": "df['manufacturer'].value_counts().idxmax() == 'ford'",
        "pandas_eval": "True"
    },
    {
        "id": 1231,
        "statement": "ian baker - finch 's overall score be 1 greater than 2 of the other competitor",
        "label": 1,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "(df[df['player'] == 'ian baker - finch']['score'].str.split(' = ').str[1].astype(int).iloc[0] - df[df['player'] != 'ian baker - finch']['score'].str.split(' = ').str[1].astype(int)).eq(1).sum() >= 2",
        "pandas_eval": "True"
    },
    {
        "id": 1232,
        "statement": "payne stewart represent a different country from ian woosnam",
        "label": 1,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "df[df['player'] == 'payne stewart']['country'].values[0] != df[df['player'] == 'ian woosnam']['country'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1233,
        "statement": "nick faldo make more than 2 time as much money as jodie mudd",
        "label": 1,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "df[df['player'] == 'nick faldo']['money'].values[0] > 2 * df[df['player'] == 'jodie mudd']['money'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1235,
        "statement": "greg norman score 66 more time than david graham do",
        "label": 1,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "(df[df['player'] == 'greg norman']['score'].str.count('66').values[0] > df[df['player'] == 'david graham']['score'].str.count('66').values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1236,
        "statement": "ian baker - finch score worse than all but 1 competitor",
        "label": 0,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "int(df[df['player'] == 'ian baker - finch']['score'].iloc[0].split(' = ')[1]) > df[df['player'] != 'ian baker - finch']['score'].apply(lambda x: int(x.split(' = ')[1])).sort_values().iloc[-2]",
        "pandas_eval": "False"
    },
    {
        "id": 1237,
        "statement": "payne stewart represent zimbabwe",
        "label": 0,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "df[df['player'] == 'payne stewart']['country'].iloc[0] == 'zimbabwe'",
        "pandas_eval": "False"
    },
    {
        "id": 1238,
        "statement": "nick faldo make a third of the amount of money as mark mcnulty",
        "label": 0,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "df[df['player'] == 'nick faldo']['money'].values[0] == df[df['player'] == 'mark mcnulty']['money'].values[0] / 3",
        "pandas_eval": "False"
    },
    {
        "id": 1239,
        "statement": "steve pate have the same score in his third round as corey pavin have in his second round",
        "label": 0,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "df[df['player'] == 'steve pate']['score'].str.split(' + ').str[2].iloc[0] == df[df['player'] == 'corey pavin']['score'].str.split(' + ').str[1].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1240,
        "statement": "greg norman score 30 fewer time than ian woosman",
        "label": 0,
        "table_caption": "1990 open championship",
        "table_text": "place#player#country#score#to par#money\n1#nick faldo#england#67 + 65 + 67 + 71 = 270#- 18#85000\nt2#mark mcnulty#zimbabwe#74 + 68 + 68 + 65 = 275#- 13#60000\nt2#payne stewart#united states#68 + 68 + 68 + 71 = 275#- 13#60000\nt4#jodie mudd#united states#72 + 66 + 72 + 66 = 276#- 12#40000\nt4#ian woosnam#wales#68 + 69 + 70 + 69 = 276#- 12#40000\nt6#ian baker - finch#australia#68 + 72 + 64 + 73 = 277#- 11#28500\nt6#greg norman#australia#66 + 66 + 76 + 69 = 277#- 11#28500\nt8#david graham#australia#72 + 71 + 70 + 66 = 279#- 9#22000\nt8#donnie hammond#united states#70 + 71 + 68 + 70 = 279#- 9#22000\nt8#steve pate#united states#70 + 68 + 72 + 69 = 279#- 9#22000\nt8#corey pavin#united states#71 + 69 + 68 + 71 = 279#- 9#22000\n",
        "pandas_code": "int(df[df['player'] == 'greg norman']['score'].str.split('=').str[1].str.strip().astype(int).iloc[0]) == int(df[df['player'] == 'ian woosnam']['score'].str.split('=').str[1].str.strip().astype(int).iloc[0]) - 30",
        "pandas_eval": "False"
    },
    {
        "id": 1241,
        "statement": "only 3 kind of software hold a gpl license",
        "label": 1,
        "table_caption": "comparison of brainwave entrainment software",
        "table_text": "software#version#operating systems#developer#license\nbeeone smod / hms#3.1#windows#hemi - synths explorers#proprietary\nbrainwave generator#3.1#windows#noromaa solutions oy#proprietary\ngnaural#1.0.20100707#freebsd , linux , mac os x , windows#gnaural#gpl\nbrainigniter player#6.0#windows#volition#proprietary\nneuro - programmer 3#3.0.9.0#windows#transparent corp#proprietary\nmind workstation#1.2.2.0#windows#transparent corp#proprietary\nsbagen#1.4.4#dos , freebsd , linux , mac os x , windows , wince#uazu#gpl\nbrainwave studio#1.5#mac os x , ios#rcs software#proprietary\ndiscord#3.2.1#linux#stan lysiak#gpl\n",
        "pandas_code": "df[df['license'] == 'gpl'].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1243,
        "statement": "sbagen have the widest range of availability , over 6 operating system",
        "label": 1,
        "table_caption": "comparison of brainwave entrainment software",
        "table_text": "software#version#operating systems#developer#license\nbeeone smod / hms#3.1#windows#hemi - synths explorers#proprietary\nbrainwave generator#3.1#windows#noromaa solutions oy#proprietary\ngnaural#1.0.20100707#freebsd , linux , mac os x , windows#gnaural#gpl\nbrainigniter player#6.0#windows#volition#proprietary\nneuro - programmer 3#3.0.9.0#windows#transparent corp#proprietary\nmind workstation#1.2.2.0#windows#transparent corp#proprietary\nsbagen#1.4.4#dos , freebsd , linux , mac os x , windows , wince#uazu#gpl\nbrainwave studio#1.5#mac os x , ios#rcs software#proprietary\ndiscord#3.2.1#linux#stan lysiak#gpl\n",
        "pandas_code": "len(df[df['software'] == 'sbagen']['operating systems'].str.split(' , ').iloc[0]) >= 6",
        "pandas_eval": "True"
    },
    {
        "id": 1244,
        "statement": "transparent corp be the only developer to make more than 1 software",
        "label": 1,
        "table_caption": "comparison of brainwave entrainment software",
        "table_text": "software#version#operating systems#developer#license\nbeeone smod / hms#3.1#windows#hemi - synths explorers#proprietary\nbrainwave generator#3.1#windows#noromaa solutions oy#proprietary\ngnaural#1.0.20100707#freebsd , linux , mac os x , windows#gnaural#gpl\nbrainigniter player#6.0#windows#volition#proprietary\nneuro - programmer 3#3.0.9.0#windows#transparent corp#proprietary\nmind workstation#1.2.2.0#windows#transparent corp#proprietary\nsbagen#1.4.4#dos , freebsd , linux , mac os x , windows , wince#uazu#gpl\nbrainwave studio#1.5#mac os x , ios#rcs software#proprietary\ndiscord#3.2.1#linux#stan lysiak#gpl\n",
        "pandas_code": "df[df['developer'] == 'transparent corp']['software'].count() > 1",
        "pandas_eval": "True"
    },
    {
        "id": 1245,
        "statement": "window be the most represent operating system on the table",
        "label": 1,
        "table_caption": "comparison of brainwave entrainment software",
        "table_text": "software#version#operating systems#developer#license\nbeeone smod / hms#3.1#windows#hemi - synths explorers#proprietary\nbrainwave generator#3.1#windows#noromaa solutions oy#proprietary\ngnaural#1.0.20100707#freebsd , linux , mac os x , windows#gnaural#gpl\nbrainigniter player#6.0#windows#volition#proprietary\nneuro - programmer 3#3.0.9.0#windows#transparent corp#proprietary\nmind workstation#1.2.2.0#windows#transparent corp#proprietary\nsbagen#1.4.4#dos , freebsd , linux , mac os x , windows , wince#uazu#gpl\nbrainwave studio#1.5#mac os x , ios#rcs software#proprietary\ndiscord#3.2.1#linux#stan lysiak#gpl\n",
        "pandas_code": "df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('linux').sum() and df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('mac os x').sum() and df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('freebsd').sum() and df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('ios').sum() and df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('dos').sum() and df['operating systems'].str.contains('windows').sum() > df['operating systems'].str.contains('wince').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 1246,
        "statement": "there be 4 game on the 2nd of august , a saturday",
        "label": 1,
        "table_caption": "2008 afl season",
        "table_text": "home team#home team score#away team#away team score#ground#crowd#date#report\ncollingwood#8.14 (62)#hawthorn#17.14 (116)#mcg#58307#friday , 1 august#aflcomau\nessendon#19.10 (124)#melbourne#17.6 (108)#mcg#46334#saturday , 2 august#aflcomau\nadelaide#13.16 (94)#carlton#12.14 (86)#aami stadium#40730#saturday , 2 august#aflcomau\ngeelong#20.14 (134)#richmond#10.11 (71)#telstra dome#42238#saturday , 2 august#aflcomau\nnorth melbourne#13.14 (92)#brisbane lions#11.18 (84)#gold coast stadium#10037#saturday , 2 august#aflcomau\nwestern bulldogs#17.11 (113)#sydney#14.13 (97)#manuka oval#13550#sunday , 3 august#aflcomau\nst kilda#14.17 (101)#port adelaide#14.9 (93)#telstra dome#22878#sunday , 3 august#aflcomau\n",
        "pandas_code": "len(df[df['date'].str.contains('2 august')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1247,
        "statement": "there be 2 game on the 3rd of august , a sunday",
        "label": 1,
        "table_caption": "2008 afl season",
        "table_text": "home team#home team score#away team#away team score#ground#crowd#date#report\ncollingwood#8.14 (62)#hawthorn#17.14 (116)#mcg#58307#friday , 1 august#aflcomau\nessendon#19.10 (124)#melbourne#17.6 (108)#mcg#46334#saturday , 2 august#aflcomau\nadelaide#13.16 (94)#carlton#12.14 (86)#aami stadium#40730#saturday , 2 august#aflcomau\ngeelong#20.14 (134)#richmond#10.11 (71)#telstra dome#42238#saturday , 2 august#aflcomau\nnorth melbourne#13.14 (92)#brisbane lions#11.18 (84)#gold coast stadium#10037#saturday , 2 august#aflcomau\nwestern bulldogs#17.11 (113)#sydney#14.13 (97)#manuka oval#13550#sunday , 3 august#aflcomau\nst kilda#14.17 (101)#port adelaide#14.9 (93)#telstra dome#22878#sunday , 3 august#aflcomau\n",
        "pandas_code": "len(df[df['date'].str.contains('3 august') & df['date'].str.contains('sunday')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1248,
        "statement": "richmond and the brisbane lion both have away score less than 12",
        "label": 1,
        "table_caption": "2008 afl season",
        "table_text": "home team#home team score#away team#away team score#ground#crowd#date#report\ncollingwood#8.14 (62)#hawthorn#17.14 (116)#mcg#58307#friday , 1 august#aflcomau\nessendon#19.10 (124)#melbourne#17.6 (108)#mcg#46334#saturday , 2 august#aflcomau\nadelaide#13.16 (94)#carlton#12.14 (86)#aami stadium#40730#saturday , 2 august#aflcomau\ngeelong#20.14 (134)#richmond#10.11 (71)#telstra dome#42238#saturday , 2 august#aflcomau\nnorth melbourne#13.14 (92)#brisbane lions#11.18 (84)#gold coast stadium#10037#saturday , 2 august#aflcomau\nwestern bulldogs#17.11 (113)#sydney#14.13 (97)#manuka oval#13550#sunday , 3 august#aflcomau\nst kilda#14.17 (101)#port adelaide#14.9 (93)#telstra dome#22878#sunday , 3 august#aflcomau\n",
        "pandas_code": "all(df[df['away team'].isin(['richmond', 'brisbane lions'])]['away team score'].apply(lambda x: float(x.split()[0]) < 12))",
        "pandas_eval": "True"
    },
    {
        "id": 1249,
        "statement": "no game list have a crowd less than 10000",
        "label": 1,
        "table_caption": "2008 afl season",
        "table_text": "home team#home team score#away team#away team score#ground#crowd#date#report\ncollingwood#8.14 (62)#hawthorn#17.14 (116)#mcg#58307#friday , 1 august#aflcomau\nessendon#19.10 (124)#melbourne#17.6 (108)#mcg#46334#saturday , 2 august#aflcomau\nadelaide#13.16 (94)#carlton#12.14 (86)#aami stadium#40730#saturday , 2 august#aflcomau\ngeelong#20.14 (134)#richmond#10.11 (71)#telstra dome#42238#saturday , 2 august#aflcomau\nnorth melbourne#13.14 (92)#brisbane lions#11.18 (84)#gold coast stadium#10037#saturday , 2 august#aflcomau\nwestern bulldogs#17.11 (113)#sydney#14.13 (97)#manuka oval#13550#sunday , 3 august#aflcomau\nst kilda#14.17 (101)#port adelaide#14.9 (93)#telstra dome#22878#sunday , 3 august#aflcomau\n",
        "pandas_code": "all(df['crowd'] >= 10000)",
        "pandas_eval": "True"
    },
    {
        "id": 1250,
        "statement": "only 1 game be play on the 1st of august , a friday",
        "label": 1,
        "table_caption": "2008 afl season",
        "table_text": "home team#home team score#away team#away team score#ground#crowd#date#report\ncollingwood#8.14 (62)#hawthorn#17.14 (116)#mcg#58307#friday , 1 august#aflcomau\nessendon#19.10 (124)#melbourne#17.6 (108)#mcg#46334#saturday , 2 august#aflcomau\nadelaide#13.16 (94)#carlton#12.14 (86)#aami stadium#40730#saturday , 2 august#aflcomau\ngeelong#20.14 (134)#richmond#10.11 (71)#telstra dome#42238#saturday , 2 august#aflcomau\nnorth melbourne#13.14 (92)#brisbane lions#11.18 (84)#gold coast stadium#10037#saturday , 2 august#aflcomau\nwestern bulldogs#17.11 (113)#sydney#14.13 (97)#manuka oval#13550#sunday , 3 august#aflcomau\nst kilda#14.17 (101)#port adelaide#14.9 (93)#telstra dome#22878#sunday , 3 august#aflcomau\n",
        "pandas_code": "len(df[df['date'].str.contains('1 august')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1251,
        "statement": "4 team did not play in the 2008 suruga bank championship",
        "label": 1,
        "table_caption": "2008 - 09 in argentine football",
        "table_text": "team / competition#2008 suruga bank championship#2008 recopa sudamericana#2008 copa sudamericana#2009 copa libertadores\nargentinos juniors#did not play#did not play#semifinals eliminated by estudiantes#did not qualify\narsenal de sarand\u00ed#champions defeated gamba osaka#runner up lost to boca juniors#round of 16 eliminated by estudiantes#did not qualify\nboca juniors#did not play#champions defeated arsenal de sarand\u00ed#quarterfinals eliminated by internacional#round of 16 eliminated by defensor sporting\nestudiantes de la plata#did not play#did not play#runner up lost to internacional#champions defeated cruzeiro\nindependiente#did not play#did not play#first round eliminated by estudiantes#did not qualify\n",
        "pandas_code": "(df['2008 suruga bank championship'] == 'did not play').sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1252,
        "statement": "3 team did not play in the 1008 recopa sudamericana",
        "label": 1,
        "table_caption": "2008 - 09 in argentine football",
        "table_text": "team / competition#2008 suruga bank championship#2008 recopa sudamericana#2008 copa sudamericana#2009 copa libertadores\nargentinos juniors#did not play#did not play#semifinals eliminated by estudiantes#did not qualify\narsenal de sarand\u00ed#champions defeated gamba osaka#runner up lost to boca juniors#round of 16 eliminated by estudiantes#did not qualify\nboca juniors#did not play#champions defeated arsenal de sarand\u00ed#quarterfinals eliminated by internacional#round of 16 eliminated by defensor sporting\nestudiantes de la plata#did not play#did not play#runner up lost to internacional#champions defeated cruzeiro\nindependiente#did not play#did not play#first round eliminated by estudiantes#did not qualify\n",
        "pandas_code": "len(df[df['2008 recopa sudamericana'] == 'did not play']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1253,
        "statement": "3 team did not qualify for the 2009 copa libertadores",
        "label": 1,
        "table_caption": "2008 - 09 in argentine football",
        "table_text": "team / competition#2008 suruga bank championship#2008 recopa sudamericana#2008 copa sudamericana#2009 copa libertadores\nargentinos juniors#did not play#did not play#semifinals eliminated by estudiantes#did not qualify\narsenal de sarand\u00ed#champions defeated gamba osaka#runner up lost to boca juniors#round of 16 eliminated by estudiantes#did not qualify\nboca juniors#did not play#champions defeated arsenal de sarand\u00ed#quarterfinals eliminated by internacional#round of 16 eliminated by defensor sporting\nestudiantes de la plata#did not play#did not play#runner up lost to internacional#champions defeated cruzeiro\nindependiente#did not play#did not play#first round eliminated by estudiantes#did not qualify\n",
        "pandas_code": "len(df[df['2009 copa libertadores'] == 'did not qualify']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1254,
        "statement": "boca junior defeat arsenal de sarand\u00ed , after arsenal de sarand\u00ed defeat gamba osaka",
        "label": 1,
        "table_caption": "2008 - 09 in argentine football",
        "table_text": "team / competition#2008 suruga bank championship#2008 recopa sudamericana#2008 copa sudamericana#2009 copa libertadores\nargentinos juniors#did not play#did not play#semifinals eliminated by estudiantes#did not qualify\narsenal de sarand\u00ed#champions defeated gamba osaka#runner up lost to boca juniors#round of 16 eliminated by estudiantes#did not qualify\nboca juniors#did not play#champions defeated arsenal de sarand\u00ed#quarterfinals eliminated by internacional#round of 16 eliminated by defensor sporting\nestudiantes de la plata#did not play#did not play#runner up lost to internacional#champions defeated cruzeiro\nindependiente#did not play#did not play#first round eliminated by estudiantes#did not qualify\n",
        "pandas_code": "(df[df['team / competition'] == 'boca juniors']['2008 recopa sudamericana'].values[0] == 'champions defeated arsenal de sarand\u00ed') & (df[df['team / competition'] == 'arsenal de sarand\u00ed']['2008 suruga bank championship'].values[0] == 'champions defeated gamba osaka')",
        "pandas_eval": "True"
    },
    {
        "id": 1255,
        "statement": "argentinos junior only play 1 game",
        "label": 1,
        "table_caption": "2008 - 09 in argentine football",
        "table_text": "team / competition#2008 suruga bank championship#2008 recopa sudamericana#2008 copa sudamericana#2009 copa libertadores\nargentinos juniors#did not play#did not play#semifinals eliminated by estudiantes#did not qualify\narsenal de sarand\u00ed#champions defeated gamba osaka#runner up lost to boca juniors#round of 16 eliminated by estudiantes#did not qualify\nboca juniors#did not play#champions defeated arsenal de sarand\u00ed#quarterfinals eliminated by internacional#round of 16 eliminated by defensor sporting\nestudiantes de la plata#did not play#did not play#runner up lost to internacional#champions defeated cruzeiro\nindependiente#did not play#did not play#first round eliminated by estudiantes#did not qualify\n",
        "pandas_code": "df[df['team / competition'] == 'argentinos juniors'].apply(lambda x: x.str.contains('did not play|did not qualify').sum() == 3, axis=1).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1256,
        "statement": "game 27 feature the vancouver canucks as the opponent and a final score of 4 - 3",
        "label": 1,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "df[(df['game'] == 27) & (df['opponent'] == 'vancouver canucks') & (df['score'] == '4 - 3')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1257,
        "statement": "when the point become 51 , it be after game 39 on december 31 with a score of 5 - 3",
        "label": 1,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "((df['points'] == 51) & (df['game'] == 39) & (df['december'] == 31) & (df['score'] == '5 - 3')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1258,
        "statement": "in game 32 the philadelphia flyer play the hartford whaler and the final score be 4 - 0",
        "label": 1,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "(df.loc[df['game'] == 32, 'opponent'].iloc[0] == 'hartford whalers') & (df.loc[df['game'] == 32, 'score'].iloc[0] == '4 - 0')",
        "pandas_eval": "True"
    },
    {
        "id": 1259,
        "statement": "the largest score be 6 - 4 on december 27 and the smallest score be 1 - 1 ot on december 4",
        "label": 1,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "(df.loc[df['december'] == 27, 'score'].values[0] == '6 - 4') & (df.loc[df['december'] == 4, 'score'].values[0] == '1 - 1 ot')",
        "pandas_eval": "True"
    },
    {
        "id": 1260,
        "statement": "after game 27 against the edmonton oilers the flyer record go to 22 - 12 - 3",
        "label": 1,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "df.loc[df['game'] > 27, 'record'].eq('22 - 12 - 3').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1261,
        "statement": "game 27 feature the vancouver canucks as the defender and the final score of 4 - 3",
        "label": 0,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "((df['game'] == 27) & (df['opponent'] == 'vancouver canucks') & (df['score'] == '4 - 3')).any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 1262,
        "statement": "in game 32 the florida panther play the hartford whaler and the final score be 4 - 0",
        "label": 0,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "df[(df['game'] == 32) & (df['opponent'] == 'florida panthers') & (df['score'] == '4 - 0')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1263,
        "statement": "the largest score be 6 - 4 on december 29 and the smallest score be 1 - 1 on cedember 4",
        "label": 0,
        "table_caption": "1996 - 97 philadelphia flyers season",
        "table_text": "game#december#opponent#score#record#points\n27#1#vancouver canucks#4 - 3#14 - 12 - 1#29\n28#4#new york rangers#1 - 1 ot#14 - 12 - 2#30\n29#6#dallas stars#6 - 3#15 - 12 - 2#32\n30#10#florida panthers#5 - 4#16 - 12 - 2#34\n31#12#hartford whalers#3 - 2#17 - 12 - 2#36\n32#14#hartford whalers#4 - 0#18 - 12 - 2#38\n33#15#boston bruins#6 - 0#19 - 12 - 2#40\n34#19#new york islanders#5 - 0#20 - 12 - 2#42\n35#21#st louis blues#4 - 0#21 - 12 - 2#44\n36#22#chicago blackhawks#2 - 2 ot#21 - 12 - 3#45\n37#27#edmonton oilers#6 - 4#22 - 12 - 3#47\n38#29#calgary flames#4 - 2#23 - 12 - 3#49\n39#31#vancouver canucks#5 - 3#24 - 12 - 3#51\n",
        "pandas_code": "(df.loc[df['december'] == 29, 'score'].values[0] == '6 - 4') & (df.loc[df['december'] == 4, 'score'].values[0] == '1 - 1 ot')",
        "pandas_eval": "False"
    },
    {
        "id": 1264,
        "statement": "the circuit charterhall be race on 2 time during the season",
        "label": 1,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "len(df[df['circuit'] == 'charterhall']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1265,
        "statement": "racer bob gerard win the last 2 race of the season",
        "label": 1,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "all(df[df['winning driver'] == 'bob gerard'].tail(2)['race name'] == df.tail(2)['race name'])",
        "pandas_eval": "True"
    },
    {
        "id": 1266,
        "statement": "the race on the circuit snetterton be hold on 17 october",
        "label": 1,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "((df['circuit'] == 'snetterton') & (df['date'] == '17 october')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1267,
        "statement": "toulo de graffenried win the first race of the season",
        "label": 1,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "df.loc[0, 'winning driver'] == 'toulo de graffenried'",
        "pandas_eval": "True"
    },
    {
        "id": 1268,
        "statement": "on 3 october , the race be hold on the castle combe cicruit",
        "label": 1,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "df[(df['date'] == '3 october') & (df['circuit'] == 'castle combe')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1269,
        "statement": "the circuit charterhall be race on thrice during the season",
        "label": 0,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "(df['circuit'] == 'charterhall').sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1270,
        "statement": "racer tony rolt win the last 2 race of the season",
        "label": 0,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "df[df['winning driver'] == 'tony rolt'].tail(2).index.equals(df.tail(2).index)",
        "pandas_eval": "False"
    },
    {
        "id": 1271,
        "statement": "the race on the circuit snetterton be hold on 9 august",
        "label": 0,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "((df['circuit'] == 'snetterton') & (df['date'] == '9 august')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1272,
        "statement": "maurice trintignant win the first race of the season",
        "label": 0,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "df[df['date'] == df['date'].min()]['winning driver'].iloc[0] == 'maurice trintignant'",
        "pandas_eval": "False"
    },
    {
        "id": 1273,
        "statement": "on 3 october , the race be hold on the bernau cicruit",
        "label": 0,
        "table_caption": "1953 formula one season",
        "table_text": "race name#circuit#date#winning driver#constructor#report\niii gran premio di siracusa#syracuse#22 march#toulo de graffenried#maserati#report\nxiv pau grand prix#pau#6 april#alberto ascari#ferrari#report\nv lavant cup#goodwood#6 april#toulo de graffenried#maserati#report\nii aston martin owners club f2 race#snetterton#18 april#eric thompson#connaught - lea francis#report\niii grand prix de bordeaux#bordeaux#3 may#alberto ascari#ferrari#report\ni strassen - rennen karl - marx - stadt#karl - marx - stadt#3 may#rudolf krause#bmw - reif#report\nv brdc international trophy#silverstone#9 may#mike hawthorn#ferrari#report\nxv el\u00e4intarhanajot#el\u00e4intarharata#10 may#rodney nuckey#cooper - bristol#report\nvi gran premio di napoli#posillipo#10 may#giuseppe farina#ferrari#report\nvii ulster trophy#dundrod#16 may#mike hawthorn#ferrari#report\ni winfield jc formula 2 race#charterhall#23 may#ken wharton#cooper - climax#report\niii coronation trophy#crystal palace#23 may#tony rolt#connaught - lea francis#report\nxxiii grand prix des fronti\u00e8res#chimay#24 may#maurice trintignant#gordini#report\ni snetterton coronation trophy#snetterton#30 may#tony rolt#connaught - lea francis#report\nxvii internationales adac eifelrennen#n\u00fcrburgring#31 may#toulo de graffenried#maserati#report\nxv grand prix de l'albigeois#albi#31 may#louis rosier#ferrari#report\ni coupe de printemps#montlh\u00e9ry#31 may#marcel balsa#bmw#report\ni paul greifzu gedachtnisrennen#dessau#7 june#edgar barth#emw#report\nii west essex cc race#snetterton#27 june#kenneth mcalpine#connaught - lea francis#report\ni midlands mecc race#silverstone#27 june#tony crook#cooper - alta#report\niii grand prix de rouen - les - essarts#rouen - les - essarts#28 june#giuseppe farina#ferrari#report\niv strassen - rennen halle - saale - schleife#halle - saale - schleife#5 july#edgar barth#emw#report\ni crystal palace trophy#crystal palace#11 july#tony rolt#connaught - lea francis#report\nix internationales avusrennen#avus#12 july#jacques swaters#ferrari#report\nii united states air force trophy#snetterton#25 july#tony rolt#connaught - lea francis#report\nv circuit du lac#aix - les - bains#26 july#\u00e9lie bayol#osca#report\ni dresden autobahnspinne#dresden - hellerau#26 july#edgar barth#emw#report\ni bristol mc & lcc race#thruxton#3 august#tony rolt#connaught - lea francis#report\ni mid - cheshire mc race#oulton park#8 august#tony rolt#connaught - lea francis#report\niii grand prix de sables d'olonne#les sables - d'olonne#9 august#louis rosier#ferrari#report\nii newcastle journal trophy#charterhall#15 august#ken wharton#cooper - bristol#report\nv circuit de cadours#cadours#30 august#maurice trintignant#gordini#report\nv sachsenringrennen#sachsenring#6 september#edgar barth#emw - bmw#report\ni redex trophy#snetterton#12 september#eric thompson#connaught - lea francis#report\niii skarpn\u00e4cksloppet#skarpn\u00e4ck#13 september#erik lundgren#ford#report\ni london trophy#crystal palace#19 september#stirling moss#cooper - alta#report\niv gran premio di modena#modena#20 september#juan manuel fangio#maserati#report\nvi madgwick cup#goodwood#26 september#roy salvadori#connaught - lea francis#report\nii bernau autobahnschleife#bernau#27 september#artur rosenhammer#emw#report\nii joe fry memorial trophy#castle combe#3 october#bob gerard#cooper - bristol#report\ni curtis trophy#snetterton#17 october#bob gerard#cooper - bristol#report\n",
        "pandas_code": "df[(df['date'] == '3 october') & (df['circuit'] == 'bernau')].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 1274,
        "statement": "all member of the wisconsin house of representative be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2000",
        "table_text": "district#incumbent#party#first elected#results#candidates\nwisconsin 1#paul ryan#republican#1998#re - elected#paul ryan (r) 66% jeffrey thomas (d) 34%\nwisconsin 2#tammy baldwin#democratic#1998#re - elected#tammy baldwin (d) 51% john sharpless (r) 49%\nwisconsin 3#ron kind#democratic#1996#re - elected#ron kind (d) 64% susan tully (r) 36%\nwisconsin 5#tom barrett#democratic#1992#re - elected#tom barrett (d) 78% jonathan smith (r) 22%\nwisconsin 6#tom petri#republican#1979#re - elected#tom petri (r) 65% dan flaherty (d) 35%\nwisconsin 7#dave obey#democratic#1969#re - elected#dave obey (d) 63% sean cronin (r) 37%\nwisconsin 8#mark green#republican#1998#re - elected#mark green (r) 75% dean reich (d) 25%\n",
        "pandas_code": "all(df[df['district'].str.contains('wisconsin')]['results'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 1276,
        "statement": "there be more democratic candidate elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2000",
        "table_text": "district#incumbent#party#first elected#results#candidates\nwisconsin 1#paul ryan#republican#1998#re - elected#paul ryan (r) 66% jeffrey thomas (d) 34%\nwisconsin 2#tammy baldwin#democratic#1998#re - elected#tammy baldwin (d) 51% john sharpless (r) 49%\nwisconsin 3#ron kind#democratic#1996#re - elected#ron kind (d) 64% susan tully (r) 36%\nwisconsin 5#tom barrett#democratic#1992#re - elected#tom barrett (d) 78% jonathan smith (r) 22%\nwisconsin 6#tom petri#republican#1979#re - elected#tom petri (r) 65% dan flaherty (d) 35%\nwisconsin 7#dave obey#democratic#1969#re - elected#dave obey (d) 63% sean cronin (r) 37%\nwisconsin 8#mark green#republican#1998#re - elected#mark green (r) 75% dean reich (d) 25%\n",
        "pandas_code": "(df['party'].value_counts()['democratic'] > df['party'].value_counts()['republican'])",
        "pandas_eval": "True"
    },
    {
        "id": 1277,
        "statement": "dave obey be the longest tenured member",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2000",
        "table_text": "district#incumbent#party#first elected#results#candidates\nwisconsin 1#paul ryan#republican#1998#re - elected#paul ryan (r) 66% jeffrey thomas (d) 34%\nwisconsin 2#tammy baldwin#democratic#1998#re - elected#tammy baldwin (d) 51% john sharpless (r) 49%\nwisconsin 3#ron kind#democratic#1996#re - elected#ron kind (d) 64% susan tully (r) 36%\nwisconsin 5#tom barrett#democratic#1992#re - elected#tom barrett (d) 78% jonathan smith (r) 22%\nwisconsin 6#tom petri#republican#1979#re - elected#tom petri (r) 65% dan flaherty (d) 35%\nwisconsin 7#dave obey#democratic#1969#re - elected#dave obey (d) 63% sean cronin (r) 37%\nwisconsin 8#mark green#republican#1998#re - elected#mark green (r) 75% dean reich (d) 25%\n",
        "pandas_code": "df.loc[df['first elected'].idxmin(), 'incumbent'] == 'dave obey'",
        "pandas_eval": "True"
    },
    {
        "id": 1278,
        "statement": "tammy baldwin have the lowest margin of victory",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2000",
        "table_text": "district#incumbent#party#first elected#results#candidates\nwisconsin 1#paul ryan#republican#1998#re - elected#paul ryan (r) 66% jeffrey thomas (d) 34%\nwisconsin 2#tammy baldwin#democratic#1998#re - elected#tammy baldwin (d) 51% john sharpless (r) 49%\nwisconsin 3#ron kind#democratic#1996#re - elected#ron kind (d) 64% susan tully (r) 36%\nwisconsin 5#tom barrett#democratic#1992#re - elected#tom barrett (d) 78% jonathan smith (r) 22%\nwisconsin 6#tom petri#republican#1979#re - elected#tom petri (r) 65% dan flaherty (d) 35%\nwisconsin 7#dave obey#democratic#1969#re - elected#dave obey (d) 63% sean cronin (r) 37%\nwisconsin 8#mark green#republican#1998#re - elected#mark green (r) 75% dean reich (d) 25%\n",
        "pandas_code": "(df[df['incumbent'] == 'tammy baldwin']['candidates'].str.extract(r'(\\d+)%')[0].astype(int).min() == df['candidates'].str.extract(r'(\\d+)%')[0].astype(int).min())",
        "pandas_eval": "True"
    },
    {
        "id": 1279,
        "statement": "bob charles' total be 149 and lee trevino 's total be 152",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "(df[df['player'] == 'bob charles']['total'].values[0] == 149) & (df[df['player'] == 'lee trevino']['total'].values[0] == 152)",
        "pandas_eval": "True"
    },
    {
        "id": 1280,
        "statement": "3 player represent the united state",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "len(df[df['country'] == 'united states']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1281,
        "statement": "lee trevino win more year than bob charles",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "len(df[df['player'] == 'lee trevino']['year (s) won'].str.split(',').iloc[0]) > len(df[df['player'] == 'bob charles']['year (s) won'].str.split(',').iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1282,
        "statement": "ian baker - finch 's total be 2 more point than tom weiskopf",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "df.loc[df['player'] == 'ian baker - finch', 'total'].values[0] == df.loc[df['player'] == 'tom weiskopf', 'total'].values[0] + 2",
        "pandas_eval": "True"
    },
    {
        "id": 1283,
        "statement": "1 player have a total under 150",
        "label": 1,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "(df['total'] < 150).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1284,
        "statement": "bob charles 's total be less than 100",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "df.loc[df['player'] == 'bob charles', 'total'].values[0] < 100",
        "pandas_eval": "False"
    },
    {
        "id": 1285,
        "statement": "all player be from australia",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "all(df['country'] == 'australia')",
        "pandas_eval": "False"
    },
    {
        "id": 1286,
        "statement": "lee trevino do not win any of the year",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "df[df['player'] == 'lee trevino']['year (s) won'].iloc[0] == ''",
        "pandas_eval": "False"
    },
    {
        "id": 1287,
        "statement": "ian baker - finch have a total higher that arnold palmer",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "df[df['player'] == 'ian baker - finch']['total'].values[0] > df[df['player'] == 'arnold palmer']['total'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1288,
        "statement": "all player total be even number",
        "label": 0,
        "table_caption": "1995 open championship",
        "table_text": "player#country#year (s) won#total#to par\nbob charles#new zealand#1963#149#+ 5\ntom weiskopf#united states#1973#151#+ 7\nlee trevino#united states#1971 , 1972#152#+ 8\nian baker - finch#australia#1991#153#+ 9\narnold palmer#united states#1961 , 1962#158#+ 14\n",
        "pandas_code": "all(df['total'] % 2 == 0)",
        "pandas_eval": "False"
    },
    {
        "id": 1290,
        "statement": "simon ammann score 8.7 more overall nt point than takanobu okabe",
        "label": 1,
        "table_caption": "2008 - 09 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall nt points#overall wc points (rank)\n1#takanobu okabe#japan#123.5#123.0#241.7#467.7 (2)#214 (24)\n2#simon ammann#switzerland#119.5#126.0#240.4#476.4 (1)#1578 (2)\n3#adam ma\u00e5\u201aysz#poland#119.5#127.5#239.3#443.3 (8)#307 (18)\n4#harri olli#finland#130.5#115.0#234.9#467.4 (3)#706 (6)\n5#noriaki kasai#japan#124.5#117.0#231.2#330.7 (19)#301 (19)\n",
        "pandas_code": "abs(float(df[df['name'] == 'simon ammann']['overall nt points'].str.split().str[0]) - float(df[df['name'] == 'takanobu okabe']['overall nt points'].str.split().str[0]) - 8.7) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 1292,
        "statement": "simon ammann and adam ma\u00e5\u201aysz each make a first jump of 119.5 meter",
        "label": 1,
        "table_caption": "2008 - 09 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall nt points#overall wc points (rank)\n1#takanobu okabe#japan#123.5#123.0#241.7#467.7 (2)#214 (24)\n2#simon ammann#switzerland#119.5#126.0#240.4#476.4 (1)#1578 (2)\n3#adam ma\u00e5\u201aysz#poland#119.5#127.5#239.3#443.3 (8)#307 (18)\n4#harri olli#finland#130.5#115.0#234.9#467.4 (3)#706 (6)\n5#noriaki kasai#japan#124.5#117.0#231.2#330.7 (19)#301 (19)\n",
        "pandas_code": "all(df.loc[df['name'].isin(['simon ammann', 'adam ma\u00e5\u201aysz']), '1st (m)'] == 119.5)",
        "pandas_eval": "True"
    },
    {
        "id": 1293,
        "statement": "fourth rank harri olli of finland make the longest first jump , of 130.5 m",
        "label": 1,
        "table_caption": "2008 - 09 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall nt points#overall wc points (rank)\n1#takanobu okabe#japan#123.5#123.0#241.7#467.7 (2)#214 (24)\n2#simon ammann#switzerland#119.5#126.0#240.4#476.4 (1)#1578 (2)\n3#adam ma\u00e5\u201aysz#poland#119.5#127.5#239.3#443.3 (8)#307 (18)\n4#harri olli#finland#130.5#115.0#234.9#467.4 (3)#706 (6)\n5#noriaki kasai#japan#124.5#117.0#231.2#330.7 (19)#301 (19)\n",
        "pandas_code": "df[(df['rank'] == 4) & (df['name'] == 'harri olli') & (df['nationality'] == 'finland') & (df['1st (m)'] == 130.5)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1294,
        "statement": "the most commonly listed language in the table be english",
        "label": 1,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['language'].str.contains('english').sum() > (len(df) / 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1295,
        "statement": "all of the artist listing in the table be unique , non be list more than 1 time",
        "label": 1,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['artist'].is_unique",
        "pandas_eval": "True"
    },
    {
        "id": 1296,
        "statement": "the number of point award range from 14 up to 272",
        "label": 1,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "(df['points'].min() == 14) & (df['points'].max() == 272)",
        "pandas_eval": "True"
    },
    {
        "id": 1297,
        "statement": "the majority of the song list in the table have more than 1 word in the title",
        "label": 1,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['song'].apply(lambda x: len(x.split()) > 1).sum() > len(df) / 2",
        "pandas_eval": "True"
    },
    {
        "id": 1298,
        "statement": "other than english , none of the language list in the table be list more than 1 time",
        "label": 1,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['language'].apply(lambda x: len([lang for lang in x.split(' , ') if lang != 'english']) == len(set([lang for lang in x.split(' , ') if lang != 'english']))).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1299,
        "statement": "the most commonly listed language in the table be french",
        "label": 0,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['language'].str.split(', ').explode().value_counts().idxmax() == 'french'",
        "pandas_eval": "False"
    },
    {
        "id": 1300,
        "statement": "5 be the artist be list more than 1 time , the other 20 be unique",
        "label": 0,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "len(df['artist'].value_counts()[df['artist'].value_counts() > 1]) == 5 and len(df['artist'].value_counts()[df['artist'].value_counts() == 1]) == 20",
        "pandas_eval": "False"
    },
    {
        "id": 1301,
        "statement": "the number of point award range from 12 up to 280",
        "label": 0,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "(df['points'].min() < 12) | (df['points'].max() > 280)",
        "pandas_eval": "False"
    },
    {
        "id": 1302,
        "statement": "the majority of the song list in the table have more than 4 word in the title",
        "label": 0,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "(df['song'].str.split().str.len() > 4).sum() > len(df) / 2",
        "pandas_eval": "False"
    },
    {
        "id": 1303,
        "statement": "other than spanish , none of the language list in the table be list more than 1 time",
        "label": 0,
        "table_caption": "eurovision song contest 2008",
        "table_text": "draw#language#artist#song#place#points\n01#romanian , italian#nico and vlad#pe - o margine de lume#20#45\n02#english#andy abraham#even if#25#14\n03#albanian#olta boka#zemr\u00ebn e lam\u00eb peng#17#55\n04#english#no angels#disappear#23#14\n05#english , armenian#sirusho#q\u00e9l\u00e9 , q\u00e9l\u00e9 (\u0584\u0565\u056c\u0565 \u0584\u0565\u056c\u0565)#04#199\n06#bosnian#laka#poku\u0161aj#10#110\n07#hebrew , english#boaz ma'uda#the fire in your eyes#09#124\n08#finnish#ter\u00e4sbetoni#miss\u00e4 miehet ratsastaa#22#35\n09#croatian#kraljevi ulice and 75 cents#romanca#21#44\n10#english#isis gee#for life#24#14\n11#english#euroband#this is my life#14#64\n12#turkish#mor ve \u00f6tesi#deli#07#138\n13#portuguese#vnia fernandes#senhora do mar (negras \u00e1guas)#13#69\n14#english#pirates of the sea#wolves of the sea#12#83\n15#english#charlotte perrelli#hero#18#47\n16#english#simon mathew#all night long#15#60\n17#english#diana gurtskaya#peace will come#11#83\n18#english#ani lorak#shady lady#02#230\n19#english , french#s\u00e9bastien tellier#divine#19#47\n20#english#elnur and samir#day after day#08#132\n21#english#kalomira#secret combination#03#218\n22#spanish , english#rodolfo chikilicuatre#baila el chiki - chiki#16#55\n23#serbian#jelena toma\u0161evi\u0107 feat bora dugi\u0107#oro (\u043e\u0440\u043e)#06#160\n24#english#dima bilan#believe#01#272\n25#english#maria haukaas storeng#hold on be strong#05#182\n",
        "pandas_code": "df['language'].apply(lambda x: len(x.split(' , '))).max() == 1 or df[df['language'].str.contains('spanish')]['language'].apply(lambda x: len(x.split(' , '))).max() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1304,
        "statement": "the television movie appearance happen the year after he co - host music bank",
        "label": 1,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "(df.loc[df['title'] == 'music bank', 'year'].values[0] + 1) in df.loc[df['notes'] == 'television movie', 'year'].values",
        "pandas_eval": "True"
    },
    {
        "id": 1305,
        "statement": "he have a main role season 5 in nonstop and play a teacher in 2007",
        "label": 1,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "((df['title'] == 'nonstop') & (df['notes'] == 'main role , season 5')).any() & ((df['title'] == 'high kick!') & (df['role'] == 'teacher') & (df['year'] == 2007)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1306,
        "statement": "he be a series regular , play himself for mnet 1 time and for kbs2 2 time",
        "label": 1,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "(df[(df['role'] == 'himself') & (df['notes'].str.contains('series regular'))].groupby('network').size().get('mnet', 0) == 1) & (df[(df['role'] == 'himself') & (df['notes'].str.contains('series regular'))].groupby('network').size().get('kbs2', 0) == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1307,
        "statement": "table have 2 cameo appearance in 2 consecutive year and both be for the same network",
        "label": 1,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "(df['notes'].str.contains('cameo appearance').sum() == 2) and (df[df['notes'].str.contains('cameo appearance')]['year'].diff().abs().iloc[-1] == 1) and (df[df['notes'].str.contains('cameo appearance')]['network'].nunique() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1308,
        "statement": "in 2012 tablo be a celebrity guest for 2 show for the network sb",
        "label": 1,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "len(df[(df['year'] == 2012) & (df['role'] == 'himself') & (df['network'] == 'sbs')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1309,
        "statement": "series regular music bank",
        "label": 0,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "any((df['title'] == 'music bank') & (df['notes'] == 'series regular'))",
        "pandas_eval": "False"
    },
    {
        "id": 1311,
        "statement": "main role , season 5 kbs2",
        "label": 0,
        "table_caption": "tablo",
        "table_text": "year#title#role#network#notes\n2004#epik high 's love and delusion#himself#mnet#series regular\n2005#nonstop#tablo#mbc#main role , season 5\n2007#high kick!#teacher#mbc#cameo appearance (episode 150)\n2008#woman of matchless beauty#himself#mbc#cameo appearance (episodes 7 & 8)\n2008#music bank#co - host#kbs2#alongside kim sung - eun and min seo - hyun\n2009#mnet director 's cut#himself#mnet#television movie\n2010#happy together#himself#kbs#celebrity guest (season 3 , episode 144)\n2010#happy birthday#himself#kbs2#series regular\n2012#strong heart#himself#sbs#celebrity guest alongside yg family\n2012#healing camp , aren't you happy#himself#sbs#celebrity guest (november 5th)\n2013#superman is back#himself#kbs2#series regular\n",
        "pandas_code": "((df['notes'] == 'main role , season 5') & (df['network'] == 'kbs2')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1312,
        "statement": "libray be the zodiac sign after september and before november",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#abbr#transcription#sanskrit word#zodiac sign\njanuary#\u0e21\u0e01\u0e23\u0e32\u0e04\u0e21#\u0e21\u0e04#makarakhom , mokkarakhom#makara sea - monster#capricorn\nfebruary#\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c#\u0e01\u0e1e#kumphaphan#kumbha pitcher , water - pot#aquarius\nmarch#\u0e21\u0e35\u0e19\u0e32\u0e04\u0e21#\u0e21\u0e35\u0e04#minakhom#m\u012bna (a specific kind of) fish#pisces\napril#\u0e40\u0e21\u0e29\u0e32\u0e22\u0e19#\u0e40\u0e21\u0e22#mesayon#me\u1e63a ram#aries\nmay#\u0e1e\u0e24\u0e29\u0e20\u0e32\u0e04\u0e21#\u0e1e\u0e04#phruetsaphakhom#v\u1e5b\u1e63abha bull#taurus\njune#\u0e21\u0e34\u0e16\u0e38\u0e19\u0e32\u0e22\u0e19#\u0e21\u0e34\u0e22#mithunayon#mithuna a pair#gemini\njuly#\u0e01\u0e23\u0e01\u0e0e\u0e32\u0e04\u0e21#\u0e01\u0e04#karakadakhom#karka\u1e6da crab#cancer\naugust#\u0e2a\u0e34\u0e07\u0e2b\u0e32\u0e04\u0e21#\u0e2a\u0e04#singhakhom#sinha lion#leo\nseptember#\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19#\u0e01\u0e22#kanyayon#kany\u0101 girl#virgo\noctober#\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21#\u0e15\u0e04#tulakhom#tul\u0101 balance#libra\nnovember#\u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19#\u0e1e\u0e22#phruetsachikayon#v\u1e5b\u015bcika scorpion#scorpio\n",
        "pandas_code": "df[(df['english name'] == 'september') | (df['english name'] == 'october') | (df['english name'] == 'november')]['zodiac sign'].tolist() == ['virgo', 'libra', 'scorpio']",
        "pandas_eval": "True"
    },
    {
        "id": 1313,
        "statement": "aries be the zodiac sign for \u0e40\u0e21\u0e29\u0e32\u0e22\u0e19 , the fourth month in the year",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#abbr#transcription#sanskrit word#zodiac sign\njanuary#\u0e21\u0e01\u0e23\u0e32\u0e04\u0e21#\u0e21\u0e04#makarakhom , mokkarakhom#makara sea - monster#capricorn\nfebruary#\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c#\u0e01\u0e1e#kumphaphan#kumbha pitcher , water - pot#aquarius\nmarch#\u0e21\u0e35\u0e19\u0e32\u0e04\u0e21#\u0e21\u0e35\u0e04#minakhom#m\u012bna (a specific kind of) fish#pisces\napril#\u0e40\u0e21\u0e29\u0e32\u0e22\u0e19#\u0e40\u0e21\u0e22#mesayon#me\u1e63a ram#aries\nmay#\u0e1e\u0e24\u0e29\u0e20\u0e32\u0e04\u0e21#\u0e1e\u0e04#phruetsaphakhom#v\u1e5b\u1e63abha bull#taurus\njune#\u0e21\u0e34\u0e16\u0e38\u0e19\u0e32\u0e22\u0e19#\u0e21\u0e34\u0e22#mithunayon#mithuna a pair#gemini\njuly#\u0e01\u0e23\u0e01\u0e0e\u0e32\u0e04\u0e21#\u0e01\u0e04#karakadakhom#karka\u1e6da crab#cancer\naugust#\u0e2a\u0e34\u0e07\u0e2b\u0e32\u0e04\u0e21#\u0e2a\u0e04#singhakhom#sinha lion#leo\nseptember#\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19#\u0e01\u0e22#kanyayon#kany\u0101 girl#virgo\noctober#\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21#\u0e15\u0e04#tulakhom#tul\u0101 balance#libra\nnovember#\u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19#\u0e1e\u0e22#phruetsachikayon#v\u1e5b\u015bcika scorpion#scorpio\n",
        "pandas_code": "df.loc[df['english name'] == 'april', 'zodiac sign'].values[0] == 'aries'",
        "pandas_eval": "True"
    },
    {
        "id": 1314,
        "statement": "the transcription for a month before october be kanyayon",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#abbr#transcription#sanskrit word#zodiac sign\njanuary#\u0e21\u0e01\u0e23\u0e32\u0e04\u0e21#\u0e21\u0e04#makarakhom , mokkarakhom#makara sea - monster#capricorn\nfebruary#\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c#\u0e01\u0e1e#kumphaphan#kumbha pitcher , water - pot#aquarius\nmarch#\u0e21\u0e35\u0e19\u0e32\u0e04\u0e21#\u0e21\u0e35\u0e04#minakhom#m\u012bna (a specific kind of) fish#pisces\napril#\u0e40\u0e21\u0e29\u0e32\u0e22\u0e19#\u0e40\u0e21\u0e22#mesayon#me\u1e63a ram#aries\nmay#\u0e1e\u0e24\u0e29\u0e20\u0e32\u0e04\u0e21#\u0e1e\u0e04#phruetsaphakhom#v\u1e5b\u1e63abha bull#taurus\njune#\u0e21\u0e34\u0e16\u0e38\u0e19\u0e32\u0e22\u0e19#\u0e21\u0e34\u0e22#mithunayon#mithuna a pair#gemini\njuly#\u0e01\u0e23\u0e01\u0e0e\u0e32\u0e04\u0e21#\u0e01\u0e04#karakadakhom#karka\u1e6da crab#cancer\naugust#\u0e2a\u0e34\u0e07\u0e2b\u0e32\u0e04\u0e21#\u0e2a\u0e04#singhakhom#sinha lion#leo\nseptember#\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19#\u0e01\u0e22#kanyayon#kany\u0101 girl#virgo\noctober#\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21#\u0e15\u0e04#tulakhom#tul\u0101 balance#libra\nnovember#\u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19#\u0e1e\u0e22#phruetsachikayon#v\u1e5b\u015bcika scorpion#scorpio\n",
        "pandas_code": "df.loc[df['english name'] == 'september', 'transcription'].values[0] == 'kanyayon'",
        "pandas_eval": "True"
    },
    {
        "id": 1315,
        "statement": "the sanskrit word for a month before september be sinha lion",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#abbr#transcription#sanskrit word#zodiac sign\njanuary#\u0e21\u0e01\u0e23\u0e32\u0e04\u0e21#\u0e21\u0e04#makarakhom , mokkarakhom#makara sea - monster#capricorn\nfebruary#\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c#\u0e01\u0e1e#kumphaphan#kumbha pitcher , water - pot#aquarius\nmarch#\u0e21\u0e35\u0e19\u0e32\u0e04\u0e21#\u0e21\u0e35\u0e04#minakhom#m\u012bna (a specific kind of) fish#pisces\napril#\u0e40\u0e21\u0e29\u0e32\u0e22\u0e19#\u0e40\u0e21\u0e22#mesayon#me\u1e63a ram#aries\nmay#\u0e1e\u0e24\u0e29\u0e20\u0e32\u0e04\u0e21#\u0e1e\u0e04#phruetsaphakhom#v\u1e5b\u1e63abha bull#taurus\njune#\u0e21\u0e34\u0e16\u0e38\u0e19\u0e32\u0e22\u0e19#\u0e21\u0e34\u0e22#mithunayon#mithuna a pair#gemini\njuly#\u0e01\u0e23\u0e01\u0e0e\u0e32\u0e04\u0e21#\u0e01\u0e04#karakadakhom#karka\u1e6da crab#cancer\naugust#\u0e2a\u0e34\u0e07\u0e2b\u0e32\u0e04\u0e21#\u0e2a\u0e04#singhakhom#sinha lion#leo\nseptember#\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19#\u0e01\u0e22#kanyayon#kany\u0101 girl#virgo\noctober#\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21#\u0e15\u0e04#tulakhom#tul\u0101 balance#libra\nnovember#\u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19#\u0e1e\u0e22#phruetsachikayon#v\u1e5b\u015bcika scorpion#scorpio\n",
        "pandas_code": "df.loc[df['english name'] == 'august', 'sanskrit word'].iloc[0] == 'sinha lion'",
        "pandas_eval": "True"
    },
    {
        "id": 1316,
        "statement": "leo be not the zodiac sign for \u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c",
        "label": 1,
        "table_caption": "thai solar calendar",
        "table_text": "english name#thai name#abbr#transcription#sanskrit word#zodiac sign\njanuary#\u0e21\u0e01\u0e23\u0e32\u0e04\u0e21#\u0e21\u0e04#makarakhom , mokkarakhom#makara sea - monster#capricorn\nfebruary#\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c#\u0e01\u0e1e#kumphaphan#kumbha pitcher , water - pot#aquarius\nmarch#\u0e21\u0e35\u0e19\u0e32\u0e04\u0e21#\u0e21\u0e35\u0e04#minakhom#m\u012bna (a specific kind of) fish#pisces\napril#\u0e40\u0e21\u0e29\u0e32\u0e22\u0e19#\u0e40\u0e21\u0e22#mesayon#me\u1e63a ram#aries\nmay#\u0e1e\u0e24\u0e29\u0e20\u0e32\u0e04\u0e21#\u0e1e\u0e04#phruetsaphakhom#v\u1e5b\u1e63abha bull#taurus\njune#\u0e21\u0e34\u0e16\u0e38\u0e19\u0e32\u0e22\u0e19#\u0e21\u0e34\u0e22#mithunayon#mithuna a pair#gemini\njuly#\u0e01\u0e23\u0e01\u0e0e\u0e32\u0e04\u0e21#\u0e01\u0e04#karakadakhom#karka\u1e6da crab#cancer\naugust#\u0e2a\u0e34\u0e07\u0e2b\u0e32\u0e04\u0e21#\u0e2a\u0e04#singhakhom#sinha lion#leo\nseptember#\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19#\u0e01\u0e22#kanyayon#kany\u0101 girl#virgo\noctober#\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21#\u0e15\u0e04#tulakhom#tul\u0101 balance#libra\nnovember#\u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19#\u0e1e\u0e22#phruetsachikayon#v\u1e5b\u015bcika scorpion#scorpio\n",
        "pandas_code": "df.loc[df['thai name'] == '\u0e01\u0e38\u0e21\u0e20\u0e32\u0e1e\u0e31\u0e19\u0e18\u0e4c', 'zodiac sign'].values[0] != 'leo'",
        "pandas_eval": "True"
    },
    {
        "id": 1317,
        "statement": "season 3 be the highest viewed season",
        "label": 1,
        "table_caption": "law & order : special victims unit",
        "table_text": "season#episodes#timeslot (est)#season premiere#season finale#tv season#ranking#viewers (in millions)\n1#22#monday 9:00pm (1999) friday 10:00pm (2000)#september 20 , 1999#may 19 , 2000#1999 - 2000#33rd#12.18\n2#21#friday 10:00pm#october 20 , 2000#may 11 , 2001#2000 - 01#29th#13.1\n3#23#friday 10:00pm#september 28 , 2001#may 17 , 2002#2001 - 02#14th#15.2\n4#25#friday 10:00pm#september 27 , 2002#may 16 , 2003#2002 - 03#16th#14.83\n5#25#tuesday 10:00pm#september 23 , 2003#may 18 , 2004#2003 - 04#21st#12.72\n6#23#tuesday 10:00pm#september 21 , 2004#may 24 , 2005#2004 - 05#23rd#13.46\n7#22#tuesday 10:00pm#september 20 , 2005#may 16 , 2006#2005 - 06#24th#13.78\n8#22#tuesday 10:00pm#september 19 , 2006#may 22 , 2007#2006 - 07#38th#11.94\n9#19#tuesday 10:00pm#september 25 , 2007#may 13 , 2008#2007 - 08#30th#11.33\n10#22#tuesday 10:00pm#september 23 , 2008#june 2 , 2009#2008 - 09#39th#10.11\n11#24#wednesday 9:00pm wednesday 10:00pm#september 23 , 2009#may 19 , 2010#2009 - 10#44th#8.81\n13#23#wednesday 10:00pm#september 21 , 2011#may 23 , 2012#2011 - 12#67th#7.59\n14#24#wednesday 9:00pm#september 26 , 2012#may 22 , 2013#2012 - 13#56th#7.30\n",
        "pandas_code": "df.loc[df['season'] == 3, 'viewers (in millions)'].values[0] == df['viewers (in millions)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1318,
        "statement": "season 2 be the only 1 with a premiere in october",
        "label": 1,
        "table_caption": "law & order : special victims unit",
        "table_text": "season#episodes#timeslot (est)#season premiere#season finale#tv season#ranking#viewers (in millions)\n1#22#monday 9:00pm (1999) friday 10:00pm (2000)#september 20 , 1999#may 19 , 2000#1999 - 2000#33rd#12.18\n2#21#friday 10:00pm#october 20 , 2000#may 11 , 2001#2000 - 01#29th#13.1\n3#23#friday 10:00pm#september 28 , 2001#may 17 , 2002#2001 - 02#14th#15.2\n4#25#friday 10:00pm#september 27 , 2002#may 16 , 2003#2002 - 03#16th#14.83\n5#25#tuesday 10:00pm#september 23 , 2003#may 18 , 2004#2003 - 04#21st#12.72\n6#23#tuesday 10:00pm#september 21 , 2004#may 24 , 2005#2004 - 05#23rd#13.46\n7#22#tuesday 10:00pm#september 20 , 2005#may 16 , 2006#2005 - 06#24th#13.78\n8#22#tuesday 10:00pm#september 19 , 2006#may 22 , 2007#2006 - 07#38th#11.94\n9#19#tuesday 10:00pm#september 25 , 2007#may 13 , 2008#2007 - 08#30th#11.33\n10#22#tuesday 10:00pm#september 23 , 2008#june 2 , 2009#2008 - 09#39th#10.11\n11#24#wednesday 9:00pm wednesday 10:00pm#september 23 , 2009#may 19 , 2010#2009 - 10#44th#8.81\n13#23#wednesday 10:00pm#september 21 , 2011#may 23 , 2012#2011 - 12#67th#7.59\n14#24#wednesday 9:00pm#september 26 , 2012#may 22 , 2013#2012 - 13#56th#7.30\n",
        "pandas_code": "df[df['season'] == 2]['season premiere'].str.contains('october').all() and not df[df['season'] != 2]['season premiere'].str.contains('october').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1319,
        "statement": "there be 3 september 23 season premiere",
        "label": 1,
        "table_caption": "law & order : special victims unit",
        "table_text": "season#episodes#timeslot (est)#season premiere#season finale#tv season#ranking#viewers (in millions)\n1#22#monday 9:00pm (1999) friday 10:00pm (2000)#september 20 , 1999#may 19 , 2000#1999 - 2000#33rd#12.18\n2#21#friday 10:00pm#october 20 , 2000#may 11 , 2001#2000 - 01#29th#13.1\n3#23#friday 10:00pm#september 28 , 2001#may 17 , 2002#2001 - 02#14th#15.2\n4#25#friday 10:00pm#september 27 , 2002#may 16 , 2003#2002 - 03#16th#14.83\n5#25#tuesday 10:00pm#september 23 , 2003#may 18 , 2004#2003 - 04#21st#12.72\n6#23#tuesday 10:00pm#september 21 , 2004#may 24 , 2005#2004 - 05#23rd#13.46\n7#22#tuesday 10:00pm#september 20 , 2005#may 16 , 2006#2005 - 06#24th#13.78\n8#22#tuesday 10:00pm#september 19 , 2006#may 22 , 2007#2006 - 07#38th#11.94\n9#19#tuesday 10:00pm#september 25 , 2007#may 13 , 2008#2007 - 08#30th#11.33\n10#22#tuesday 10:00pm#september 23 , 2008#june 2 , 2009#2008 - 09#39th#10.11\n11#24#wednesday 9:00pm wednesday 10:00pm#september 23 , 2009#may 19 , 2010#2009 - 10#44th#8.81\n13#23#wednesday 10:00pm#september 21 , 2011#may 23 , 2012#2011 - 12#67th#7.59\n14#24#wednesday 9:00pm#september 26 , 2012#may 22 , 2013#2012 - 13#56th#7.30\n",
        "pandas_code": "len(df[df['season premiere'].str.contains('september 23')]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1320,
        "statement": "there be 2 season finale on may 22nd",
        "label": 1,
        "table_caption": "law & order : special victims unit",
        "table_text": "season#episodes#timeslot (est)#season premiere#season finale#tv season#ranking#viewers (in millions)\n1#22#monday 9:00pm (1999) friday 10:00pm (2000)#september 20 , 1999#may 19 , 2000#1999 - 2000#33rd#12.18\n2#21#friday 10:00pm#october 20 , 2000#may 11 , 2001#2000 - 01#29th#13.1\n3#23#friday 10:00pm#september 28 , 2001#may 17 , 2002#2001 - 02#14th#15.2\n4#25#friday 10:00pm#september 27 , 2002#may 16 , 2003#2002 - 03#16th#14.83\n5#25#tuesday 10:00pm#september 23 , 2003#may 18 , 2004#2003 - 04#21st#12.72\n6#23#tuesday 10:00pm#september 21 , 2004#may 24 , 2005#2004 - 05#23rd#13.46\n7#22#tuesday 10:00pm#september 20 , 2005#may 16 , 2006#2005 - 06#24th#13.78\n8#22#tuesday 10:00pm#september 19 , 2006#may 22 , 2007#2006 - 07#38th#11.94\n9#19#tuesday 10:00pm#september 25 , 2007#may 13 , 2008#2007 - 08#30th#11.33\n10#22#tuesday 10:00pm#september 23 , 2008#june 2 , 2009#2008 - 09#39th#10.11\n11#24#wednesday 9:00pm wednesday 10:00pm#september 23 , 2009#may 19 , 2010#2009 - 10#44th#8.81\n13#23#wednesday 10:00pm#september 21 , 2011#may 23 , 2012#2011 - 12#67th#7.59\n14#24#wednesday 9:00pm#september 26 , 2012#may 22 , 2013#2012 - 13#56th#7.30\n",
        "pandas_code": "len(df[df['season finale'].str.contains('may 22')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1321,
        "statement": "tuesday timeslots outnumber any other day of the week",
        "label": 1,
        "table_caption": "law & order : special victims unit",
        "table_text": "season#episodes#timeslot (est)#season premiere#season finale#tv season#ranking#viewers (in millions)\n1#22#monday 9:00pm (1999) friday 10:00pm (2000)#september 20 , 1999#may 19 , 2000#1999 - 2000#33rd#12.18\n2#21#friday 10:00pm#october 20 , 2000#may 11 , 2001#2000 - 01#29th#13.1\n3#23#friday 10:00pm#september 28 , 2001#may 17 , 2002#2001 - 02#14th#15.2\n4#25#friday 10:00pm#september 27 , 2002#may 16 , 2003#2002 - 03#16th#14.83\n5#25#tuesday 10:00pm#september 23 , 2003#may 18 , 2004#2003 - 04#21st#12.72\n6#23#tuesday 10:00pm#september 21 , 2004#may 24 , 2005#2004 - 05#23rd#13.46\n7#22#tuesday 10:00pm#september 20 , 2005#may 16 , 2006#2005 - 06#24th#13.78\n8#22#tuesday 10:00pm#september 19 , 2006#may 22 , 2007#2006 - 07#38th#11.94\n9#19#tuesday 10:00pm#september 25 , 2007#may 13 , 2008#2007 - 08#30th#11.33\n10#22#tuesday 10:00pm#september 23 , 2008#june 2 , 2009#2008 - 09#39th#10.11\n11#24#wednesday 9:00pm wednesday 10:00pm#september 23 , 2009#may 19 , 2010#2009 - 10#44th#8.81\n13#23#wednesday 10:00pm#september 21 , 2011#may 23 , 2012#2011 - 12#67th#7.59\n14#24#wednesday 9:00pm#september 26 , 2012#may 22 , 2013#2012 - 13#56th#7.30\n",
        "pandas_code": "df['timeslot (est)'].str.contains('tuesday').sum() > max(df['timeslot (est)'].str.contains('monday').sum(), df['timeslot (est)'].str.contains('friday').sum(), df['timeslot (est)'].str.contains('wednesday').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 1322,
        "statement": "the truck chevrolet silverado be use in 11 of the race , and ford f - 150 thrice inthe 2009 nascar",
        "label": 1,
        "table_caption": "2009 nascar camping world truck series",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#listed owner (s)#crew chief\nbilly ballew motorsports#toyota tundra#15#shane sieg#phoenix construction#billy ballew#richie wauters\nbilly ballew motorsports#toyota tundra#51#kyle busch#miccosukee / nos energy drink#billy ballew#doug george\ncircle bar racing#ford f - 150#10#james buescher (r)#international maxxforce diesel#tom mitchell#jamie jones\ncircle bar racing#ford f - 150#14#rick crawford#circle bar truck corral#tom mitchell#kevin starland\nfast track racing enterprises#chevrolet silverado#47#brandon knupp#ft - 11 degreaser#dave mcclure#dave mcclure\nfast track racing enterprises#chevrolet silverado#48#bryan silas#rockingham speedway#andy hillenburg#jonathan mostafa\ngermain racing#toyota tundra#30#todd bodine#germaincom#stephen germain#mike hillman , jr\ngunbroker racing#dodge ram#21#marc mitchell#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#22#wayne edwards#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#23#jason white#gunbrokercom#dwayne gaulding#doug howe\nht motorsports#toyota tundra#24#david starr#zachry holdings#jim harris#jason miller\nht motorsports#toyota tundra#25#terry cook#harris trucking#sharon harris#danny rollins\nkevin harvick incorporated#chevrolet silverado#4#ricky carmichael (r)#monster energy#delana harvick#billy wilburn\nkevin harvick incorporated#chevrolet silverado#33#ron hornaday#longhorn smokeless / vfw#delana harvick#rick ren\nlafferty motorsports#chevrolet silverado#89#mike harmon#blue ox automotive#chris lafferty#chris lafferty\nmrd motorsports#chevrolet silverado#8#dennis setzer#malcomson construction#dave malcomson#randy dean\nnorm benning racing#chevrolet silverado#57#norm benning#d3 outdoors#norm benning#ken causey\npremier racing#toyota tundra#17#timothy peters#hayes iron & metal#steve stallings#chad hendrick\nrandy moss motorsports#toyota tundra#5#mike skinner#exide batteries / bad boy mowers#david dollar#eric phillips\nrandy moss motorsports#toyota tundra#81#tayler malsam (r)#one eighty#randy moss#doug wolcott\nred horse racing#toyota tundra#1#johnny benson#n / a#tom deloach#trip bruce\nred horse racing#toyota tundra#11#t j bell#n / a#tom deloach#rick gay\nroush fenway racing#ford f - 150#6#colin braun#con - way#jack roush#mike beam\nss - green light racing#chevrolet silverado#7#chad mccumbee#asi limited#ken white#bobby dotter\nss - green light racing#chevrolet silverado#8#butch miller#asi limited#bobby dotter#michael davis\nthorsport / capg racing#chevrolet silverado#13#johnny sauter (r)#fun sand / rodney atkins#mike curb#jason overstreet\nthorsport racing#chevrolet silverado#88#matt crafton#menards#rhonda thorson#bud haefele\nwyler racing#toyota tundra#60#stacy compton#safe auto insurance company#jeff wyler#marcus richmond\n",
        "pandas_code": "(df['truck (s)'].value_counts().get('chevrolet silverado', 0) == 11) & (df['truck (s)'].value_counts().get('ford f - 150', 0) == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1323,
        "statement": "the listed owner (s) rhonda thorson and jeff wyler have bud haefele and marcus richmond as crew chief",
        "label": 1,
        "table_caption": "2009 nascar camping world truck series",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#listed owner (s)#crew chief\nbilly ballew motorsports#toyota tundra#15#shane sieg#phoenix construction#billy ballew#richie wauters\nbilly ballew motorsports#toyota tundra#51#kyle busch#miccosukee / nos energy drink#billy ballew#doug george\ncircle bar racing#ford f - 150#10#james buescher (r)#international maxxforce diesel#tom mitchell#jamie jones\ncircle bar racing#ford f - 150#14#rick crawford#circle bar truck corral#tom mitchell#kevin starland\nfast track racing enterprises#chevrolet silverado#47#brandon knupp#ft - 11 degreaser#dave mcclure#dave mcclure\nfast track racing enterprises#chevrolet silverado#48#bryan silas#rockingham speedway#andy hillenburg#jonathan mostafa\ngermain racing#toyota tundra#30#todd bodine#germaincom#stephen germain#mike hillman , jr\ngunbroker racing#dodge ram#21#marc mitchell#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#22#wayne edwards#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#23#jason white#gunbrokercom#dwayne gaulding#doug howe\nht motorsports#toyota tundra#24#david starr#zachry holdings#jim harris#jason miller\nht motorsports#toyota tundra#25#terry cook#harris trucking#sharon harris#danny rollins\nkevin harvick incorporated#chevrolet silverado#4#ricky carmichael (r)#monster energy#delana harvick#billy wilburn\nkevin harvick incorporated#chevrolet silverado#33#ron hornaday#longhorn smokeless / vfw#delana harvick#rick ren\nlafferty motorsports#chevrolet silverado#89#mike harmon#blue ox automotive#chris lafferty#chris lafferty\nmrd motorsports#chevrolet silverado#8#dennis setzer#malcomson construction#dave malcomson#randy dean\nnorm benning racing#chevrolet silverado#57#norm benning#d3 outdoors#norm benning#ken causey\npremier racing#toyota tundra#17#timothy peters#hayes iron & metal#steve stallings#chad hendrick\nrandy moss motorsports#toyota tundra#5#mike skinner#exide batteries / bad boy mowers#david dollar#eric phillips\nrandy moss motorsports#toyota tundra#81#tayler malsam (r)#one eighty#randy moss#doug wolcott\nred horse racing#toyota tundra#1#johnny benson#n / a#tom deloach#trip bruce\nred horse racing#toyota tundra#11#t j bell#n / a#tom deloach#rick gay\nroush fenway racing#ford f - 150#6#colin braun#con - way#jack roush#mike beam\nss - green light racing#chevrolet silverado#7#chad mccumbee#asi limited#ken white#bobby dotter\nss - green light racing#chevrolet silverado#8#butch miller#asi limited#bobby dotter#michael davis\nthorsport / capg racing#chevrolet silverado#13#johnny sauter (r)#fun sand / rodney atkins#mike curb#jason overstreet\nthorsport racing#chevrolet silverado#88#matt crafton#menards#rhonda thorson#bud haefele\nwyler racing#toyota tundra#60#stacy compton#safe auto insurance company#jeff wyler#marcus richmond\n",
        "pandas_code": "df[df['listed owner (s)'].isin(['rhonda thorson', 'jeff wyler'])]['crew chief'].isin(['bud haefele', 'marcus richmond']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1324,
        "statement": "there be no primary sponsor (s) only for team red horse racing , when the driver be johnny benson and t j bell",
        "label": 1,
        "table_caption": "2009 nascar camping world truck series",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#listed owner (s)#crew chief\nbilly ballew motorsports#toyota tundra#15#shane sieg#phoenix construction#billy ballew#richie wauters\nbilly ballew motorsports#toyota tundra#51#kyle busch#miccosukee / nos energy drink#billy ballew#doug george\ncircle bar racing#ford f - 150#10#james buescher (r)#international maxxforce diesel#tom mitchell#jamie jones\ncircle bar racing#ford f - 150#14#rick crawford#circle bar truck corral#tom mitchell#kevin starland\nfast track racing enterprises#chevrolet silverado#47#brandon knupp#ft - 11 degreaser#dave mcclure#dave mcclure\nfast track racing enterprises#chevrolet silverado#48#bryan silas#rockingham speedway#andy hillenburg#jonathan mostafa\ngermain racing#toyota tundra#30#todd bodine#germaincom#stephen germain#mike hillman , jr\ngunbroker racing#dodge ram#21#marc mitchell#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#22#wayne edwards#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#23#jason white#gunbrokercom#dwayne gaulding#doug howe\nht motorsports#toyota tundra#24#david starr#zachry holdings#jim harris#jason miller\nht motorsports#toyota tundra#25#terry cook#harris trucking#sharon harris#danny rollins\nkevin harvick incorporated#chevrolet silverado#4#ricky carmichael (r)#monster energy#delana harvick#billy wilburn\nkevin harvick incorporated#chevrolet silverado#33#ron hornaday#longhorn smokeless / vfw#delana harvick#rick ren\nlafferty motorsports#chevrolet silverado#89#mike harmon#blue ox automotive#chris lafferty#chris lafferty\nmrd motorsports#chevrolet silverado#8#dennis setzer#malcomson construction#dave malcomson#randy dean\nnorm benning racing#chevrolet silverado#57#norm benning#d3 outdoors#norm benning#ken causey\npremier racing#toyota tundra#17#timothy peters#hayes iron & metal#steve stallings#chad hendrick\nrandy moss motorsports#toyota tundra#5#mike skinner#exide batteries / bad boy mowers#david dollar#eric phillips\nrandy moss motorsports#toyota tundra#81#tayler malsam (r)#one eighty#randy moss#doug wolcott\nred horse racing#toyota tundra#1#johnny benson#n / a#tom deloach#trip bruce\nred horse racing#toyota tundra#11#t j bell#n / a#tom deloach#rick gay\nroush fenway racing#ford f - 150#6#colin braun#con - way#jack roush#mike beam\nss - green light racing#chevrolet silverado#7#chad mccumbee#asi limited#ken white#bobby dotter\nss - green light racing#chevrolet silverado#8#butch miller#asi limited#bobby dotter#michael davis\nthorsport / capg racing#chevrolet silverado#13#johnny sauter (r)#fun sand / rodney atkins#mike curb#jason overstreet\nthorsport racing#chevrolet silverado#88#matt crafton#menards#rhonda thorson#bud haefele\nwyler racing#toyota tundra#60#stacy compton#safe auto insurance company#jeff wyler#marcus richmond\n",
        "pandas_code": "df[(df['team'] == 'red horse racing') & (df['driver (s)'].isin(['johnny benson', 't j bell']))]['primary sponsor (s)'].eq('n / a').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1325,
        "statement": "asi limit be the primary sponsor (s) when the crew chief be bobby dotter and michael davis",
        "label": 1,
        "table_caption": "2009 nascar camping world truck series",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#listed owner (s)#crew chief\nbilly ballew motorsports#toyota tundra#15#shane sieg#phoenix construction#billy ballew#richie wauters\nbilly ballew motorsports#toyota tundra#51#kyle busch#miccosukee / nos energy drink#billy ballew#doug george\ncircle bar racing#ford f - 150#10#james buescher (r)#international maxxforce diesel#tom mitchell#jamie jones\ncircle bar racing#ford f - 150#14#rick crawford#circle bar truck corral#tom mitchell#kevin starland\nfast track racing enterprises#chevrolet silverado#47#brandon knupp#ft - 11 degreaser#dave mcclure#dave mcclure\nfast track racing enterprises#chevrolet silverado#48#bryan silas#rockingham speedway#andy hillenburg#jonathan mostafa\ngermain racing#toyota tundra#30#todd bodine#germaincom#stephen germain#mike hillman , jr\ngunbroker racing#dodge ram#21#marc mitchell#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#22#wayne edwards#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#23#jason white#gunbrokercom#dwayne gaulding#doug howe\nht motorsports#toyota tundra#24#david starr#zachry holdings#jim harris#jason miller\nht motorsports#toyota tundra#25#terry cook#harris trucking#sharon harris#danny rollins\nkevin harvick incorporated#chevrolet silverado#4#ricky carmichael (r)#monster energy#delana harvick#billy wilburn\nkevin harvick incorporated#chevrolet silverado#33#ron hornaday#longhorn smokeless / vfw#delana harvick#rick ren\nlafferty motorsports#chevrolet silverado#89#mike harmon#blue ox automotive#chris lafferty#chris lafferty\nmrd motorsports#chevrolet silverado#8#dennis setzer#malcomson construction#dave malcomson#randy dean\nnorm benning racing#chevrolet silverado#57#norm benning#d3 outdoors#norm benning#ken causey\npremier racing#toyota tundra#17#timothy peters#hayes iron & metal#steve stallings#chad hendrick\nrandy moss motorsports#toyota tundra#5#mike skinner#exide batteries / bad boy mowers#david dollar#eric phillips\nrandy moss motorsports#toyota tundra#81#tayler malsam (r)#one eighty#randy moss#doug wolcott\nred horse racing#toyota tundra#1#johnny benson#n / a#tom deloach#trip bruce\nred horse racing#toyota tundra#11#t j bell#n / a#tom deloach#rick gay\nroush fenway racing#ford f - 150#6#colin braun#con - way#jack roush#mike beam\nss - green light racing#chevrolet silverado#7#chad mccumbee#asi limited#ken white#bobby dotter\nss - green light racing#chevrolet silverado#8#butch miller#asi limited#bobby dotter#michael davis\nthorsport / capg racing#chevrolet silverado#13#johnny sauter (r)#fun sand / rodney atkins#mike curb#jason overstreet\nthorsport racing#chevrolet silverado#88#matt crafton#menards#rhonda thorson#bud haefele\nwyler racing#toyota tundra#60#stacy compton#safe auto insurance company#jeff wyler#marcus richmond\n",
        "pandas_code": "df[(df['primary sponsor (s)'].str.contains('asi limited', case=False)) & (df['crew chief'].isin(['bobby dotter', 'michael davis']))].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 1326,
        "statement": "billy ballew be the listed owner of the toyota tundra drive by shane sieg and kyle busch",
        "label": 1,
        "table_caption": "2009 nascar camping world truck series",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#listed owner (s)#crew chief\nbilly ballew motorsports#toyota tundra#15#shane sieg#phoenix construction#billy ballew#richie wauters\nbilly ballew motorsports#toyota tundra#51#kyle busch#miccosukee / nos energy drink#billy ballew#doug george\ncircle bar racing#ford f - 150#10#james buescher (r)#international maxxforce diesel#tom mitchell#jamie jones\ncircle bar racing#ford f - 150#14#rick crawford#circle bar truck corral#tom mitchell#kevin starland\nfast track racing enterprises#chevrolet silverado#47#brandon knupp#ft - 11 degreaser#dave mcclure#dave mcclure\nfast track racing enterprises#chevrolet silverado#48#bryan silas#rockingham speedway#andy hillenburg#jonathan mostafa\ngermain racing#toyota tundra#30#todd bodine#germaincom#stephen germain#mike hillman , jr\ngunbroker racing#dodge ram#21#marc mitchell#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#22#wayne edwards#gunbrokercom#dwayne gaulding#harold gaulding , jr\ngunbroker racing#dodge ram#23#jason white#gunbrokercom#dwayne gaulding#doug howe\nht motorsports#toyota tundra#24#david starr#zachry holdings#jim harris#jason miller\nht motorsports#toyota tundra#25#terry cook#harris trucking#sharon harris#danny rollins\nkevin harvick incorporated#chevrolet silverado#4#ricky carmichael (r)#monster energy#delana harvick#billy wilburn\nkevin harvick incorporated#chevrolet silverado#33#ron hornaday#longhorn smokeless / vfw#delana harvick#rick ren\nlafferty motorsports#chevrolet silverado#89#mike harmon#blue ox automotive#chris lafferty#chris lafferty\nmrd motorsports#chevrolet silverado#8#dennis setzer#malcomson construction#dave malcomson#randy dean\nnorm benning racing#chevrolet silverado#57#norm benning#d3 outdoors#norm benning#ken causey\npremier racing#toyota tundra#17#timothy peters#hayes iron & metal#steve stallings#chad hendrick\nrandy moss motorsports#toyota tundra#5#mike skinner#exide batteries / bad boy mowers#david dollar#eric phillips\nrandy moss motorsports#toyota tundra#81#tayler malsam (r)#one eighty#randy moss#doug wolcott\nred horse racing#toyota tundra#1#johnny benson#n / a#tom deloach#trip bruce\nred horse racing#toyota tundra#11#t j bell#n / a#tom deloach#rick gay\nroush fenway racing#ford f - 150#6#colin braun#con - way#jack roush#mike beam\nss - green light racing#chevrolet silverado#7#chad mccumbee#asi limited#ken white#bobby dotter\nss - green light racing#chevrolet silverado#8#butch miller#asi limited#bobby dotter#michael davis\nthorsport / capg racing#chevrolet silverado#13#johnny sauter (r)#fun sand / rodney atkins#mike curb#jason overstreet\nthorsport racing#chevrolet silverado#88#matt crafton#menards#rhonda thorson#bud haefele\nwyler racing#toyota tundra#60#stacy compton#safe auto insurance company#jeff wyler#marcus richmond\n",
        "pandas_code": "all(df[(df['driver (s)'].isin(['shane sieg', 'kyle busch'])) & (df['truck (s)'] == 'toyota tundra')]['listed owner (s)'] == 'billy ballew')",
        "pandas_eval": "True"
    },
    {
        "id": 1327,
        "statement": "genndy tartakovsky have direct all of the 12 episode in season 3 of samurai jack",
        "label": 1,
        "table_caption": "list of samurai jack episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date\n27#1#chicken jack#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#august 22 , 2003\n28#2#jack and the rave#randy myers and genndy tartakovsky#chris mitchell and erik wiese#august 29 , 2003\n29#3#the good , the bad and the beautiful#robert alvarez and genndy tartakovsky#paul rudish and charlie bean#september 5 , 2003\n30#4#jack and the zombies#genndy tartakovsky#bryan andrews and brian larsen#september 12 , 2003\n31#5#jack in egypt#chris savino and genndy tartakovsky#bryan andrews and brian larsen#september 19 , 2003\n32#6#jack and the traveling creatures#randy myers and genndy tartakovsky#genndy tartakovsky and erik wiese#september 26 , 2003\n33#7#jack and the creature#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#october 3 , 2003\n34#8#jack and the swamp monster#randy myers and genndy tartakovsky#paul rudish and charlie bean#october 10 , 2003\n35#9#jack and the haunted house#randy myers and genndy tartakovsky#chris reccardi and aaron springer#october 17 , 2003\n36#10#jack , the monks and the ancient master 's son#randy myers and genndy tartakovsky#bryan andrews and brian larsen#october 24 , 2003\n37 / 38#11 / 12#the birth of evil#robert alvarez , randy myers and genndy tartakovsky#don shank and genndy tartakovsky#october 31 , 2003\n",
        "pandas_code": "all('genndy tartakovsky' in x for x in df[df['no in season'].isin(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11 / 12'])]['directed by'])",
        "pandas_eval": "True"
    },
    {
        "id": 1328,
        "statement": "randy myers have work on half of the episode of samurai jack season 3 , while robert alverez have only work on 4",
        "label": 1,
        "table_caption": "list of samurai jack episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date\n27#1#chicken jack#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#august 22 , 2003\n28#2#jack and the rave#randy myers and genndy tartakovsky#chris mitchell and erik wiese#august 29 , 2003\n29#3#the good , the bad and the beautiful#robert alvarez and genndy tartakovsky#paul rudish and charlie bean#september 5 , 2003\n30#4#jack and the zombies#genndy tartakovsky#bryan andrews and brian larsen#september 12 , 2003\n31#5#jack in egypt#chris savino and genndy tartakovsky#bryan andrews and brian larsen#september 19 , 2003\n32#6#jack and the traveling creatures#randy myers and genndy tartakovsky#genndy tartakovsky and erik wiese#september 26 , 2003\n33#7#jack and the creature#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#october 3 , 2003\n34#8#jack and the swamp monster#randy myers and genndy tartakovsky#paul rudish and charlie bean#october 10 , 2003\n35#9#jack and the haunted house#randy myers and genndy tartakovsky#chris reccardi and aaron springer#october 17 , 2003\n36#10#jack , the monks and the ancient master 's son#randy myers and genndy tartakovsky#bryan andrews and brian larsen#october 24 , 2003\n37 / 38#11 / 12#the birth of evil#robert alvarez , randy myers and genndy tartakovsky#don shank and genndy tartakovsky#october 31 , 2003\n",
        "pandas_code": "(df['directed by'].str.contains('randy myers').sum() / len(df) >= 0.5) & (df['directed by'].str.contains('robert alvarez').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 1329,
        "statement": "all of the episode of this season be release exactly a week after each other",
        "label": 1,
        "table_caption": "list of samurai jack episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date\n27#1#chicken jack#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#august 22 , 2003\n28#2#jack and the rave#randy myers and genndy tartakovsky#chris mitchell and erik wiese#august 29 , 2003\n29#3#the good , the bad and the beautiful#robert alvarez and genndy tartakovsky#paul rudish and charlie bean#september 5 , 2003\n30#4#jack and the zombies#genndy tartakovsky#bryan andrews and brian larsen#september 12 , 2003\n31#5#jack in egypt#chris savino and genndy tartakovsky#bryan andrews and brian larsen#september 19 , 2003\n32#6#jack and the traveling creatures#randy myers and genndy tartakovsky#genndy tartakovsky and erik wiese#september 26 , 2003\n33#7#jack and the creature#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#october 3 , 2003\n34#8#jack and the swamp monster#randy myers and genndy tartakovsky#paul rudish and charlie bean#october 10 , 2003\n35#9#jack and the haunted house#randy myers and genndy tartakovsky#chris reccardi and aaron springer#october 17 , 2003\n36#10#jack , the monks and the ancient master 's son#randy myers and genndy tartakovsky#bryan andrews and brian larsen#october 24 , 2003\n37 / 38#11 / 12#the birth of evil#robert alvarez , randy myers and genndy tartakovsky#don shank and genndy tartakovsky#october 31 , 2003\n",
        "pandas_code": "df['original air date'].apply(lambda x: pd.to_datetime(x)).diff().dropna().eq(pd.Timedelta(days=7)).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1331,
        "statement": "all but 2 of the show in this season title include the main character name in it , which be jack",
        "label": 1,
        "table_caption": "list of samurai jack episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date\n27#1#chicken jack#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#august 22 , 2003\n28#2#jack and the rave#randy myers and genndy tartakovsky#chris mitchell and erik wiese#august 29 , 2003\n29#3#the good , the bad and the beautiful#robert alvarez and genndy tartakovsky#paul rudish and charlie bean#september 5 , 2003\n30#4#jack and the zombies#genndy tartakovsky#bryan andrews and brian larsen#september 12 , 2003\n31#5#jack in egypt#chris savino and genndy tartakovsky#bryan andrews and brian larsen#september 19 , 2003\n32#6#jack and the traveling creatures#randy myers and genndy tartakovsky#genndy tartakovsky and erik wiese#september 26 , 2003\n33#7#jack and the creature#robert alvarez and genndy tartakovsky#chris reccardi and aaron springer#october 3 , 2003\n34#8#jack and the swamp monster#randy myers and genndy tartakovsky#paul rudish and charlie bean#october 10 , 2003\n35#9#jack and the haunted house#randy myers and genndy tartakovsky#chris reccardi and aaron springer#october 17 , 2003\n36#10#jack , the monks and the ancient master 's son#randy myers and genndy tartakovsky#bryan andrews and brian larsen#october 24 , 2003\n37 / 38#11 / 12#the birth of evil#robert alvarez , randy myers and genndy tartakovsky#don shank and genndy tartakovsky#october 31 , 2003\n",
        "pandas_code": "len(df[df['title'].str.contains('jack', case=False)]) == len(df) - 2",
        "pandas_eval": "True"
    },
    {
        "id": 1332,
        "statement": "the single ma baker have a combine 51 week on the chart",
        "label": 1,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "df[df['single'] == 'ma baker']['weeks on chart'].sum() == 51",
        "pandas_eval": "True"
    },
    {
        "id": 1333,
        "statement": "the single belfast have a combine 24 week on the chart",
        "label": 1,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "df[df['single'] == 'belfast']['weeks on chart'].sum() == 24",
        "pandas_eval": "True"
    },
    {
        "id": 1334,
        "statement": "ma baker reach number 1 on 2 different chart",
        "label": 1,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "len(df[(df['single'] == 'ma baker') & (df['position'] == 1)]['chart'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1335,
        "statement": "ma baker and belfast both appear on the dutch top 40 and the uk single chart",
        "label": 1,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "all(df[df['single'].isin(['ma baker', 'belfast'])].groupby('single')['chart'].apply(lambda x: {'dutch top 40', 'uk single chart'}.issubset(set(x))))",
        "pandas_eval": "True"
    },
    {
        "id": 1336,
        "statement": "the highest and lowest belfast reach be 3 and 8 , respectively",
        "label": 1,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "(df[df['single'] == 'belfast']['position'].max() == 8) & (df[df['single'] == 'belfast']['position'].min() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1337,
        "statement": "the single ma baker have a combine 23 week on the chart",
        "label": 0,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "df[df['single'] == 'ma baker']['weeks on chart'].sum() == 23",
        "pandas_eval": "False"
    },
    {
        "id": 1338,
        "statement": "the single ma baker have a combine 24 week on the chart",
        "label": 0,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "df[df['single'] == 'ma baker']['weeks on chart'].sum() == 24",
        "pandas_eval": "False"
    },
    {
        "id": 1339,
        "statement": "belfast reach number 1 on 4 different chart",
        "label": 0,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "df[(df['single'] == 'belfast') & (df['position'] == 1)].shape[0] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1340,
        "statement": "ma baker and belfast both appear on the dutch top 40 and the norway 's single chart",
        "label": 0,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "all(df[(df['single'].isin(['ma baker', 'belfast'])) & (df['chart'].isin([\"norway 's single chart\", 'dutch top 40']))].groupby('single')['chart'].nunique() == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1341,
        "statement": "the highest and lowest belfast reach be 1 and 3 , respectively",
        "label": 0,
        "table_caption": "love for sale (boney m. album)",
        "table_text": "year#single#chart#position#weeks on chart\n1977#ma baker#uk single chart#2#13\n1977#ma baker#norway 's single chart#1#23\n1977#ma baker#dutch top 40#1#15\n1977#belfast#uk single chart#8#13\n1977#belfast#dutch top 40#3#11\n",
        "pandas_code": "(df[df['single'] == 'belfast']['position'].max() == 1) & (df[df['single'] == 'belfast']['position'].min() == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 1344,
        "statement": "ruthin rfc have 59 more point for than colwyn bay rfc",
        "label": 1,
        "table_caption": "wru division one north",
        "table_text": "club#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nclub#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nnant conwy rfc#18#17#0#1#578#183#83#19#11#1#80\ncaernarfon rfc#18#17#0#1#570#179#81#21#11#0#79\nmold rfc#18#11#0#7#471#349#63#46#8#3#55\npwllheli rfc#18#10#0#8#479#338#66#42#7#4#51\nbro ffestiniog rfc#18#9#0#9#346#457#52#63#5#2#43\nruthin rfc#18#8#1#9#352#381#49#46#4#1#39\ncolwyn bay rfc#18#5#1#12#293#402#37#55#4#5#31\nllandudno rfc#18#4#2#12#266#536#30#79#2#4#26\nllangefni rfc#18#4#0#14#267#423#27#58#3#5#24\ndenbigh rfc#18#3#0#15#204#578#24#83#1#3#16\n",
        "pandas_code": "(int(df[df['club'] == 'ruthin rfc']['points for'].values[0]) - int(df[df['club'] == 'colwyn bay rfc']['points for'].values[0])) == 59",
        "pandas_eval": "True"
    },
    {
        "id": 1345,
        "statement": "mold rfc have more tries against than caernarfon rfc",
        "label": 1,
        "table_caption": "wru division one north",
        "table_text": "club#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nclub#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nnant conwy rfc#18#17#0#1#578#183#83#19#11#1#80\ncaernarfon rfc#18#17#0#1#570#179#81#21#11#0#79\nmold rfc#18#11#0#7#471#349#63#46#8#3#55\npwllheli rfc#18#10#0#8#479#338#66#42#7#4#51\nbro ffestiniog rfc#18#9#0#9#346#457#52#63#5#2#43\nruthin rfc#18#8#1#9#352#381#49#46#4#1#39\ncolwyn bay rfc#18#5#1#12#293#402#37#55#4#5#31\nllandudno rfc#18#4#2#12#266#536#30#79#2#4#26\nllangefni rfc#18#4#0#14#267#423#27#58#3#5#24\ndenbigh rfc#18#3#0#15#204#578#24#83#1#3#16\n",
        "pandas_code": "df[df['club'] == 'mold rfc']['tries against'].values[0] > df[df['club'] == 'caernarfon rfc']['tries against'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1346,
        "statement": "there be 4 club which achieve more than 50 point",
        "label": 1,
        "table_caption": "wru division one north",
        "table_text": "club#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nclub#played#won#drawn#lost#points for#points against#tries for#tries against#try bonus#losing bonus#points\nnant conwy rfc#18#17#0#1#578#183#83#19#11#1#80\ncaernarfon rfc#18#17#0#1#570#179#81#21#11#0#79\nmold rfc#18#11#0#7#471#349#63#46#8#3#55\npwllheli rfc#18#10#0#8#479#338#66#42#7#4#51\nbro ffestiniog rfc#18#9#0#9#346#457#52#63#5#2#43\nruthin rfc#18#8#1#9#352#381#49#46#4#1#39\ncolwyn bay rfc#18#5#1#12#293#402#37#55#4#5#31\nllandudno rfc#18#4#2#12#266#536#30#79#2#4#26\nllangefni rfc#18#4#0#14#267#423#27#58#3#5#24\ndenbigh rfc#18#3#0#15#204#578#24#83#1#3#16\n",
        "pandas_code": "len(df.iloc[1:][df.iloc[1:]['points'].astype(int) > 50]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1347,
        "statement": "kristy mcpherson have have no first place win throughout the year",
        "label": 1,
        "table_caption": "kristy mcpherson",
        "table_text": "year#tournaments played#cuts made#wins#2nd#3rd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2005#1#0#0#0#0#0#mc#0#n / a#77.00#n / a\n2007#18#11#0#0#0#0#t18#79724#97#73.73#t99\n2008#26#19#0#0#0#6#t4#407237#47#71.86#34\n2009#24#21#0#2#1#6#t2#816182#16#71.25#17\n2010#22#17#0#1#0#4#t2#418217#27#72.26#40\n2011#21#17#0#0#0#0#t18#157025#56#72.65#50\n",
        "pandas_code": "df['wins'].sum() == 0",
        "pandas_eval": "True"
    },
    {
        "id": 1348,
        "statement": "the year in which 1 game be play have the highest scoring average",
        "label": 1,
        "table_caption": "kristy mcpherson",
        "table_text": "year#tournaments played#cuts made#wins#2nd#3rd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2005#1#0#0#0#0#0#mc#0#n / a#77.00#n / a\n2007#18#11#0#0#0#0#t18#79724#97#73.73#t99\n2008#26#19#0#0#0#6#t4#407237#47#71.86#34\n2009#24#21#0#2#1#6#t2#816182#16#71.25#17\n2010#22#17#0#1#0#4#t2#418217#27#72.26#40\n2011#21#17#0#0#0#0#t18#157025#56#72.65#50\n",
        "pandas_code": "df.loc[df['tournaments played'] == 1, 'scoring average'].max() == df['scoring average'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1351,
        "statement": "kristy mcpherson do not make top 10 until 2008",
        "label": 1,
        "table_caption": "kristy mcpherson",
        "table_text": "year#tournaments played#cuts made#wins#2nd#3rd#top 10s#best finish#earnings#money list rank#scoring average#scoring rank\n2005#1#0#0#0#0#0#mc#0#n / a#77.00#n / a\n2007#18#11#0#0#0#0#t18#79724#97#73.73#t99\n2008#26#19#0#0#0#6#t4#407237#47#71.86#34\n2009#24#21#0#2#1#6#t2#816182#16#71.25#17\n2010#22#17#0#1#0#4#t2#418217#27#72.26#40\n2011#21#17#0#0#0#0#t18#157025#56#72.65#50\n",
        "pandas_code": "(df[df['year'] < 2008]['top 10s'] == 0).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1352,
        "statement": "cach\u00e9 be the only film to be disqualified from the award",
        "label": 1,
        "table_caption": "list of austrian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director (s)#result\n1961 (34th)#jedermann#jedermann#gottfried reinhardt#not nominated\n1969 (42nd)#moss on the stones#moos auf den steinen#georg lhotsky#not nominated\n1977 (50th)#i want to live#ich will leben#j\u00f6rg a eggers#not nominated\n1979 (52nd)#tales from the vienna woods#geschichten aus dem wienerwald#maximilian schell#not nominated\n1980 (53rd)#egon schiele#egon schiele - exzesse#herbert vesely#not nominated\n1981 (54th)#der bockerer#der bockerer#franz antel#not nominated\n1983 (56th)#tramps#die letzte runde#peter patzak#not nominated\n1984 (57th)#just behind the door#dicht hinter der t\u00fcr#mansur madavi#not nominated\n1985 (58th)#malambo#malambo#milan dor#not nominated\n1986 (59th)#'38 - vienna before the fall#38 - auch das war wien#wolfgang gl\u00fcck#nominee\n1987 (60th)#welcome in vienna#wohin und zur\u00fcck - welcome in vienna#axel corti#not nominated\n1988 (61st)#undiscovered country#das weite land#luc bondy#not nominated\n1989 (62nd)#the seventh continent#der siebente kontinent#michael haneke#not nominated\n1990 (63rd)#requiem for dominic#requiem f\u00fcr dominik#robert dornhelm#not nominated\n1991 (64th)#i love vienna#i love vienna#houchang allahyari#not nominated\n1992 (65th)#benny 's video#benny 's video#michael haneke#not nominated\n1993 (66th)#india#indien#paul harather#not nominated\n1994 (67th)#i promise#ich gelobe#wolfgang murnberger#not nominated\n1995 (68th)#ant street#die ameisenstra\u00dfe#michael glawogger#not nominated\n1997 (70th)#the unfish#der unfisch#robert dornhelm#not nominated\n1998 (71st)#the inheritors#die siebtelbauern#stefan ruzowitzky#not nominated\n1999 (72nd)#northern skirts#nordrand#barbara albert#not nominated\n2000 (73rd)#the stranger#die fremde#g\u00f6tz spielmann#not nominated\n2001 (74th)#the piano teacher#la pianiste#michael haneke#not nominated\n2003 (76th)#free radicals#b\u00f6se zellen#barbara albert#not nominated\n2004 (77th)#antares#antares#g\u00f6tz spielmann#not nominated\n2005 (78th)#cach\u00e9#cach\u00e9#michael haneke#disqualified\n2006 (79th)#you bet your life#spiele leben#antonin svoboda#not nominated\n2007 (80th)#the counterfeiters#die f\u00e4lscher#stefan ruzowitzky#won academy award\n2008 (81st)#revanche#revanche#g\u00f6tz spielmann#nominee\n2009 (82nd)#for a moment , freedom#ein augenblick freiheit#arash t riahi#not nominated\n2011 (84th)#breathing#atmen#karl markovics#not nominated\n2012 (85th)#amour#amour#michael haneke#won academy award\n",
        "pandas_code": "df[df['film title used in nomination'] == 'cach\u00e9']['result'].eq('disqualified').all() and df['result'].eq('disqualified').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1353,
        "statement": "michael haneke direct 4 film that be in the running to win the award",
        "label": 1,
        "table_caption": "list of austrian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director (s)#result\n1961 (34th)#jedermann#jedermann#gottfried reinhardt#not nominated\n1969 (42nd)#moss on the stones#moos auf den steinen#georg lhotsky#not nominated\n1977 (50th)#i want to live#ich will leben#j\u00f6rg a eggers#not nominated\n1979 (52nd)#tales from the vienna woods#geschichten aus dem wienerwald#maximilian schell#not nominated\n1980 (53rd)#egon schiele#egon schiele - exzesse#herbert vesely#not nominated\n1981 (54th)#der bockerer#der bockerer#franz antel#not nominated\n1983 (56th)#tramps#die letzte runde#peter patzak#not nominated\n1984 (57th)#just behind the door#dicht hinter der t\u00fcr#mansur madavi#not nominated\n1985 (58th)#malambo#malambo#milan dor#not nominated\n1986 (59th)#'38 - vienna before the fall#38 - auch das war wien#wolfgang gl\u00fcck#nominee\n1987 (60th)#welcome in vienna#wohin und zur\u00fcck - welcome in vienna#axel corti#not nominated\n1988 (61st)#undiscovered country#das weite land#luc bondy#not nominated\n1989 (62nd)#the seventh continent#der siebente kontinent#michael haneke#not nominated\n1990 (63rd)#requiem for dominic#requiem f\u00fcr dominik#robert dornhelm#not nominated\n1991 (64th)#i love vienna#i love vienna#houchang allahyari#not nominated\n1992 (65th)#benny 's video#benny 's video#michael haneke#not nominated\n1993 (66th)#india#indien#paul harather#not nominated\n1994 (67th)#i promise#ich gelobe#wolfgang murnberger#not nominated\n1995 (68th)#ant street#die ameisenstra\u00dfe#michael glawogger#not nominated\n1997 (70th)#the unfish#der unfisch#robert dornhelm#not nominated\n1998 (71st)#the inheritors#die siebtelbauern#stefan ruzowitzky#not nominated\n1999 (72nd)#northern skirts#nordrand#barbara albert#not nominated\n2000 (73rd)#the stranger#die fremde#g\u00f6tz spielmann#not nominated\n2001 (74th)#the piano teacher#la pianiste#michael haneke#not nominated\n2003 (76th)#free radicals#b\u00f6se zellen#barbara albert#not nominated\n2004 (77th)#antares#antares#g\u00f6tz spielmann#not nominated\n2005 (78th)#cach\u00e9#cach\u00e9#michael haneke#disqualified\n2006 (79th)#you bet your life#spiele leben#antonin svoboda#not nominated\n2007 (80th)#the counterfeiters#die f\u00e4lscher#stefan ruzowitzky#won academy award\n2008 (81st)#revanche#revanche#g\u00f6tz spielmann#nominee\n2009 (82nd)#for a moment , freedom#ein augenblick freiheit#arash t riahi#not nominated\n2011 (84th)#breathing#atmen#karl markovics#not nominated\n2012 (85th)#amour#amour#michael haneke#won academy award\n",
        "pandas_code": "len(df[df['director (s)'].str.contains('michael haneke')]) >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 1354,
        "statement": "out of michael haneke 's 4 film , only 1 win the academy award",
        "label": 1,
        "table_caption": "list of austrian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director (s)#result\n1961 (34th)#jedermann#jedermann#gottfried reinhardt#not nominated\n1969 (42nd)#moss on the stones#moos auf den steinen#georg lhotsky#not nominated\n1977 (50th)#i want to live#ich will leben#j\u00f6rg a eggers#not nominated\n1979 (52nd)#tales from the vienna woods#geschichten aus dem wienerwald#maximilian schell#not nominated\n1980 (53rd)#egon schiele#egon schiele - exzesse#herbert vesely#not nominated\n1981 (54th)#der bockerer#der bockerer#franz antel#not nominated\n1983 (56th)#tramps#die letzte runde#peter patzak#not nominated\n1984 (57th)#just behind the door#dicht hinter der t\u00fcr#mansur madavi#not nominated\n1985 (58th)#malambo#malambo#milan dor#not nominated\n1986 (59th)#'38 - vienna before the fall#38 - auch das war wien#wolfgang gl\u00fcck#nominee\n1987 (60th)#welcome in vienna#wohin und zur\u00fcck - welcome in vienna#axel corti#not nominated\n1988 (61st)#undiscovered country#das weite land#luc bondy#not nominated\n1989 (62nd)#the seventh continent#der siebente kontinent#michael haneke#not nominated\n1990 (63rd)#requiem for dominic#requiem f\u00fcr dominik#robert dornhelm#not nominated\n1991 (64th)#i love vienna#i love vienna#houchang allahyari#not nominated\n1992 (65th)#benny 's video#benny 's video#michael haneke#not nominated\n1993 (66th)#india#indien#paul harather#not nominated\n1994 (67th)#i promise#ich gelobe#wolfgang murnberger#not nominated\n1995 (68th)#ant street#die ameisenstra\u00dfe#michael glawogger#not nominated\n1997 (70th)#the unfish#der unfisch#robert dornhelm#not nominated\n1998 (71st)#the inheritors#die siebtelbauern#stefan ruzowitzky#not nominated\n1999 (72nd)#northern skirts#nordrand#barbara albert#not nominated\n2000 (73rd)#the stranger#die fremde#g\u00f6tz spielmann#not nominated\n2001 (74th)#the piano teacher#la pianiste#michael haneke#not nominated\n2003 (76th)#free radicals#b\u00f6se zellen#barbara albert#not nominated\n2004 (77th)#antares#antares#g\u00f6tz spielmann#not nominated\n2005 (78th)#cach\u00e9#cach\u00e9#michael haneke#disqualified\n2006 (79th)#you bet your life#spiele leben#antonin svoboda#not nominated\n2007 (80th)#the counterfeiters#die f\u00e4lscher#stefan ruzowitzky#won academy award\n2008 (81st)#revanche#revanche#g\u00f6tz spielmann#nominee\n2009 (82nd)#for a moment , freedom#ein augenblick freiheit#arash t riahi#not nominated\n2011 (84th)#breathing#atmen#karl markovics#not nominated\n2012 (85th)#amour#amour#michael haneke#won academy award\n",
        "pandas_code": "df[df['director (s)'].str.contains('michael haneke')]['result'].value_counts().get('won academy award', 0) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1355,
        "statement": "revanche be a nominee the year after the counterfeiter win the academy award",
        "label": 1,
        "table_caption": "list of austrian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director (s)#result\n1961 (34th)#jedermann#jedermann#gottfried reinhardt#not nominated\n1969 (42nd)#moss on the stones#moos auf den steinen#georg lhotsky#not nominated\n1977 (50th)#i want to live#ich will leben#j\u00f6rg a eggers#not nominated\n1979 (52nd)#tales from the vienna woods#geschichten aus dem wienerwald#maximilian schell#not nominated\n1980 (53rd)#egon schiele#egon schiele - exzesse#herbert vesely#not nominated\n1981 (54th)#der bockerer#der bockerer#franz antel#not nominated\n1983 (56th)#tramps#die letzte runde#peter patzak#not nominated\n1984 (57th)#just behind the door#dicht hinter der t\u00fcr#mansur madavi#not nominated\n1985 (58th)#malambo#malambo#milan dor#not nominated\n1986 (59th)#'38 - vienna before the fall#38 - auch das war wien#wolfgang gl\u00fcck#nominee\n1987 (60th)#welcome in vienna#wohin und zur\u00fcck - welcome in vienna#axel corti#not nominated\n1988 (61st)#undiscovered country#das weite land#luc bondy#not nominated\n1989 (62nd)#the seventh continent#der siebente kontinent#michael haneke#not nominated\n1990 (63rd)#requiem for dominic#requiem f\u00fcr dominik#robert dornhelm#not nominated\n1991 (64th)#i love vienna#i love vienna#houchang allahyari#not nominated\n1992 (65th)#benny 's video#benny 's video#michael haneke#not nominated\n1993 (66th)#india#indien#paul harather#not nominated\n1994 (67th)#i promise#ich gelobe#wolfgang murnberger#not nominated\n1995 (68th)#ant street#die ameisenstra\u00dfe#michael glawogger#not nominated\n1997 (70th)#the unfish#der unfisch#robert dornhelm#not nominated\n1998 (71st)#the inheritors#die siebtelbauern#stefan ruzowitzky#not nominated\n1999 (72nd)#northern skirts#nordrand#barbara albert#not nominated\n2000 (73rd)#the stranger#die fremde#g\u00f6tz spielmann#not nominated\n2001 (74th)#the piano teacher#la pianiste#michael haneke#not nominated\n2003 (76th)#free radicals#b\u00f6se zellen#barbara albert#not nominated\n2004 (77th)#antares#antares#g\u00f6tz spielmann#not nominated\n2005 (78th)#cach\u00e9#cach\u00e9#michael haneke#disqualified\n2006 (79th)#you bet your life#spiele leben#antonin svoboda#not nominated\n2007 (80th)#the counterfeiters#die f\u00e4lscher#stefan ruzowitzky#won academy award\n2008 (81st)#revanche#revanche#g\u00f6tz spielmann#nominee\n2009 (82nd)#for a moment , freedom#ein augenblick freiheit#arash t riahi#not nominated\n2011 (84th)#breathing#atmen#karl markovics#not nominated\n2012 (85th)#amour#amour#michael haneke#won academy award\n",
        "pandas_code": "df[(df['original title'] == 'revanche') & (df['result'] == 'nominee') & (df.index == df[df['original title'] == 'die f\u00e4lscher'].index[0] + 1)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1356,
        "statement": "amour , direct by michael haneke , win the academy award in 2012",
        "label": 1,
        "table_caption": "list of austrian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director (s)#result\n1961 (34th)#jedermann#jedermann#gottfried reinhardt#not nominated\n1969 (42nd)#moss on the stones#moos auf den steinen#georg lhotsky#not nominated\n1977 (50th)#i want to live#ich will leben#j\u00f6rg a eggers#not nominated\n1979 (52nd)#tales from the vienna woods#geschichten aus dem wienerwald#maximilian schell#not nominated\n1980 (53rd)#egon schiele#egon schiele - exzesse#herbert vesely#not nominated\n1981 (54th)#der bockerer#der bockerer#franz antel#not nominated\n1983 (56th)#tramps#die letzte runde#peter patzak#not nominated\n1984 (57th)#just behind the door#dicht hinter der t\u00fcr#mansur madavi#not nominated\n1985 (58th)#malambo#malambo#milan dor#not nominated\n1986 (59th)#'38 - vienna before the fall#38 - auch das war wien#wolfgang gl\u00fcck#nominee\n1987 (60th)#welcome in vienna#wohin und zur\u00fcck - welcome in vienna#axel corti#not nominated\n1988 (61st)#undiscovered country#das weite land#luc bondy#not nominated\n1989 (62nd)#the seventh continent#der siebente kontinent#michael haneke#not nominated\n1990 (63rd)#requiem for dominic#requiem f\u00fcr dominik#robert dornhelm#not nominated\n1991 (64th)#i love vienna#i love vienna#houchang allahyari#not nominated\n1992 (65th)#benny 's video#benny 's video#michael haneke#not nominated\n1993 (66th)#india#indien#paul harather#not nominated\n1994 (67th)#i promise#ich gelobe#wolfgang murnberger#not nominated\n1995 (68th)#ant street#die ameisenstra\u00dfe#michael glawogger#not nominated\n1997 (70th)#the unfish#der unfisch#robert dornhelm#not nominated\n1998 (71st)#the inheritors#die siebtelbauern#stefan ruzowitzky#not nominated\n1999 (72nd)#northern skirts#nordrand#barbara albert#not nominated\n2000 (73rd)#the stranger#die fremde#g\u00f6tz spielmann#not nominated\n2001 (74th)#the piano teacher#la pianiste#michael haneke#not nominated\n2003 (76th)#free radicals#b\u00f6se zellen#barbara albert#not nominated\n2004 (77th)#antares#antares#g\u00f6tz spielmann#not nominated\n2005 (78th)#cach\u00e9#cach\u00e9#michael haneke#disqualified\n2006 (79th)#you bet your life#spiele leben#antonin svoboda#not nominated\n2007 (80th)#the counterfeiters#die f\u00e4lscher#stefan ruzowitzky#won academy award\n2008 (81st)#revanche#revanche#g\u00f6tz spielmann#nominee\n2009 (82nd)#for a moment , freedom#ein augenblick freiheit#arash t riahi#not nominated\n2011 (84th)#breathing#atmen#karl markovics#not nominated\n2012 (85th)#amour#amour#michael haneke#won academy award\n",
        "pandas_code": "df[(df['film title used in nomination'] == 'amour') & (df['director (s)'] == 'michael haneke') & (df['year (ceremony)'].str.contains('2012')) & (df['result'] == 'won academy award')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1357,
        "statement": "there be more player in league 16 than there be in league 30",
        "label": 1,
        "table_caption": "1986 - 87 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\nkim casey#kidderminster harriers#38#4#2#0#44\nmick carter#runcorn#30#2#6#3#41\nsteve butler#maidstone united#24#5#3#2#34\ngary abbott#welling united#23#2#3#0#28\nchris cook#boston united#23#0#2#2#27\nken mckenna#telford united#23#1#0#0#24\nnicky evans#barnet#22#0#4#0#26\nchris townsend#cheltenham town#22#1#0#0#23\nmike doherty#weymouth#16#0#1#1#18\npaul bodin#bath city#16#2#2#1#21\nstewart mell#scarborough#16#0#2#0#18\njohn timmons#altrincham#16#0#0#1#17\n",
        "pandas_code": "len(df[df['league'] == 16]) > len(df[df['league'] == 30])",
        "pandas_eval": "True"
    },
    {
        "id": 1358,
        "statement": "the highest number for fa cup be achieve by the player from the maidstone unite club",
        "label": 1,
        "table_caption": "1986 - 87 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\nkim casey#kidderminster harriers#38#4#2#0#44\nmick carter#runcorn#30#2#6#3#41\nsteve butler#maidstone united#24#5#3#2#34\ngary abbott#welling united#23#2#3#0#28\nchris cook#boston united#23#0#2#2#27\nken mckenna#telford united#23#1#0#0#24\nnicky evans#barnet#22#0#4#0#26\nchris townsend#cheltenham town#22#1#0#0#23\nmike doherty#weymouth#16#0#1#1#18\npaul bodin#bath city#16#2#2#1#21\nstewart mell#scarborough#16#0#2#0#18\njohn timmons#altrincham#16#0#0#1#17\n",
        "pandas_code": "df.loc[df['fa cup'].idxmax(), 'club'] == 'maidstone united'",
        "pandas_eval": "True"
    },
    {
        "id": 1359,
        "statement": "the player with the second highest number of fa trophy be from league 22",
        "label": 1,
        "table_caption": "1986 - 87 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\nkim casey#kidderminster harriers#38#4#2#0#44\nmick carter#runcorn#30#2#6#3#41\nsteve butler#maidstone united#24#5#3#2#34\ngary abbott#welling united#23#2#3#0#28\nchris cook#boston united#23#0#2#2#27\nken mckenna#telford united#23#1#0#0#24\nnicky evans#barnet#22#0#4#0#26\nchris townsend#cheltenham town#22#1#0#0#23\nmike doherty#weymouth#16#0#1#1#18\npaul bodin#bath city#16#2#2#1#21\nstewart mell#scarborough#16#0#2#0#18\njohn timmons#altrincham#16#0#0#1#17\n",
        "pandas_code": "(df[df['fa trophy'] == df['fa trophy'].nlargest(2).iloc[-1]]['league'] == 22).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1360,
        "statement": "the average number of league cup for league 38 and league 22 be the same",
        "label": 1,
        "table_caption": "1986 - 87 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\nkim casey#kidderminster harriers#38#4#2#0#44\nmick carter#runcorn#30#2#6#3#41\nsteve butler#maidstone united#24#5#3#2#34\ngary abbott#welling united#23#2#3#0#28\nchris cook#boston united#23#0#2#2#27\nken mckenna#telford united#23#1#0#0#24\nnicky evans#barnet#22#0#4#0#26\nchris townsend#cheltenham town#22#1#0#0#23\nmike doherty#weymouth#16#0#1#1#18\npaul bodin#bath city#16#2#2#1#21\nstewart mell#scarborough#16#0#2#0#18\njohn timmons#altrincham#16#0#0#1#17\n",
        "pandas_code": "df[df['league'].isin([38, 22])].groupby('league')['league cup'].mean().nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1361,
        "statement": "in league 16 , the only player with any fa cup be from the bath city club",
        "label": 1,
        "table_caption": "1986 - 87 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\nkim casey#kidderminster harriers#38#4#2#0#44\nmick carter#runcorn#30#2#6#3#41\nsteve butler#maidstone united#24#5#3#2#34\ngary abbott#welling united#23#2#3#0#28\nchris cook#boston united#23#0#2#2#27\nken mckenna#telford united#23#1#0#0#24\nnicky evans#barnet#22#0#4#0#26\nchris townsend#cheltenham town#22#1#0#0#23\nmike doherty#weymouth#16#0#1#1#18\npaul bodin#bath city#16#2#2#1#21\nstewart mell#scarborough#16#0#2#0#18\njohn timmons#altrincham#16#0#0#1#17\n",
        "pandas_code": "df[(df['league'] == 16) & (df['fa cup'] > 0)]['club'].eq('bath city').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1362,
        "statement": "12 team race the chevrolet silverado",
        "label": 1,
        "table_caption": "list of nascar teams",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#owner (s)#crew chief#rounds\nadrian carriers racing#chevrolet silverado#97#steve wallace#liz girl logistics#gary adam#nick carlson#4\nben kennedy racing#chevrolet silverado#96#ben kennedy#modspace#robert johnson#chris carrier#2\nbill elliott racing#chevrolet silverado#94#chase elliott#aaron 's dream machine#bill elliott#lance mcgrew#10\nbill martel racing#chevrolet silverado#59#kyle martel#patriot energy group#william martel#william martel#2\nclay greenfield motorsports#ram#68#clay greenfield#1 - 800 - pavement#clay greenfield#danny gill#6\nddk motorsports#toyota tundra#45#nate monteith#highway 126 self storage#k d moore#wade day#1\nhattori racing enterprises#toyota tundra#16#brett moffitt#goodyear of japan#shigeaki hattori#jeroid prince#1\nhenderson motorsports#chevrolet silverado#75#caleb holman#food country usa#charles henderson#butch miller#12\njjc racing#ram#0#chris lafferty#driven2honororg#jennifer jo cobb#scott kazura#10\njim rosenblum racing#chevrolet silverado#28#dominick casola#fdny racing#jim rosenblum#dick rahilly#2\nken schrader racing#toyota tundra#52#ken schrader#federated auto parts#ken schrader#donnie richeson#3\nmake motorsports#chevrolet silverado#50#danny efland#advanced electronic services#mark beaver#perry mitchell#8\nmb motorsports#ford f - 150#63#justin jennings#mittler brothers machine & tool#mike mittler#mike mittler#7\nrbr enterprises#chevrolet silverado#92#scott riggs#fleethq / carquest#ricky benton#michael hester#6\nrss racing#chevrolet silverado#38#j j yeley#n / a#rod sieg#timothy brown#5\nsharp - gallagher racing#chevrolet silverado#6#justin lofton#lofton cattle / j6 ink#eddie sharp#eddie troconis#8\nthorsport racing#toyota tundra#13#brett moffitt#aisin#duke thorson#jeriod prince#10\nturner scott motorsports#chevrolet silverado#30#ben kennedy#modspace#steve turner#mike greci#7\nwauters motorsports#chevrolet silverado#5#tim george , jr#applebee 's#richie wauters#richie wauters#11\n",
        "pandas_code": "(df['truck (s)'] == 'chevrolet silverado').sum() == 12",
        "pandas_eval": "True"
    },
    {
        "id": 1363,
        "statement": "mb motorsports be the only team that race ford f - 150",
        "label": 1,
        "table_caption": "list of nascar teams",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#owner (s)#crew chief#rounds\nadrian carriers racing#chevrolet silverado#97#steve wallace#liz girl logistics#gary adam#nick carlson#4\nben kennedy racing#chevrolet silverado#96#ben kennedy#modspace#robert johnson#chris carrier#2\nbill elliott racing#chevrolet silverado#94#chase elliott#aaron 's dream machine#bill elliott#lance mcgrew#10\nbill martel racing#chevrolet silverado#59#kyle martel#patriot energy group#william martel#william martel#2\nclay greenfield motorsports#ram#68#clay greenfield#1 - 800 - pavement#clay greenfield#danny gill#6\nddk motorsports#toyota tundra#45#nate monteith#highway 126 self storage#k d moore#wade day#1\nhattori racing enterprises#toyota tundra#16#brett moffitt#goodyear of japan#shigeaki hattori#jeroid prince#1\nhenderson motorsports#chevrolet silverado#75#caleb holman#food country usa#charles henderson#butch miller#12\njjc racing#ram#0#chris lafferty#driven2honororg#jennifer jo cobb#scott kazura#10\njim rosenblum racing#chevrolet silverado#28#dominick casola#fdny racing#jim rosenblum#dick rahilly#2\nken schrader racing#toyota tundra#52#ken schrader#federated auto parts#ken schrader#donnie richeson#3\nmake motorsports#chevrolet silverado#50#danny efland#advanced electronic services#mark beaver#perry mitchell#8\nmb motorsports#ford f - 150#63#justin jennings#mittler brothers machine & tool#mike mittler#mike mittler#7\nrbr enterprises#chevrolet silverado#92#scott riggs#fleethq / carquest#ricky benton#michael hester#6\nrss racing#chevrolet silverado#38#j j yeley#n / a#rod sieg#timothy brown#5\nsharp - gallagher racing#chevrolet silverado#6#justin lofton#lofton cattle / j6 ink#eddie sharp#eddie troconis#8\nthorsport racing#toyota tundra#13#brett moffitt#aisin#duke thorson#jeriod prince#10\nturner scott motorsports#chevrolet silverado#30#ben kennedy#modspace#steve turner#mike greci#7\nwauters motorsports#chevrolet silverado#5#tim george , jr#applebee 's#richie wauters#richie wauters#11\n",
        "pandas_code": "(df[df['team'] == 'mb motorsports']['truck (s)'].iloc[0] == 'ford f - 150') and (df['truck (s)'].value_counts()['ford f - 150'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1364,
        "statement": "3 owner be also the crew chief for their team",
        "label": 1,
        "table_caption": "list of nascar teams",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#owner (s)#crew chief#rounds\nadrian carriers racing#chevrolet silverado#97#steve wallace#liz girl logistics#gary adam#nick carlson#4\nben kennedy racing#chevrolet silverado#96#ben kennedy#modspace#robert johnson#chris carrier#2\nbill elliott racing#chevrolet silverado#94#chase elliott#aaron 's dream machine#bill elliott#lance mcgrew#10\nbill martel racing#chevrolet silverado#59#kyle martel#patriot energy group#william martel#william martel#2\nclay greenfield motorsports#ram#68#clay greenfield#1 - 800 - pavement#clay greenfield#danny gill#6\nddk motorsports#toyota tundra#45#nate monteith#highway 126 self storage#k d moore#wade day#1\nhattori racing enterprises#toyota tundra#16#brett moffitt#goodyear of japan#shigeaki hattori#jeroid prince#1\nhenderson motorsports#chevrolet silverado#75#caleb holman#food country usa#charles henderson#butch miller#12\njjc racing#ram#0#chris lafferty#driven2honororg#jennifer jo cobb#scott kazura#10\njim rosenblum racing#chevrolet silverado#28#dominick casola#fdny racing#jim rosenblum#dick rahilly#2\nken schrader racing#toyota tundra#52#ken schrader#federated auto parts#ken schrader#donnie richeson#3\nmake motorsports#chevrolet silverado#50#danny efland#advanced electronic services#mark beaver#perry mitchell#8\nmb motorsports#ford f - 150#63#justin jennings#mittler brothers machine & tool#mike mittler#mike mittler#7\nrbr enterprises#chevrolet silverado#92#scott riggs#fleethq / carquest#ricky benton#michael hester#6\nrss racing#chevrolet silverado#38#j j yeley#n / a#rod sieg#timothy brown#5\nsharp - gallagher racing#chevrolet silverado#6#justin lofton#lofton cattle / j6 ink#eddie sharp#eddie troconis#8\nthorsport racing#toyota tundra#13#brett moffitt#aisin#duke thorson#jeriod prince#10\nturner scott motorsports#chevrolet silverado#30#ben kennedy#modspace#steve turner#mike greci#7\nwauters motorsports#chevrolet silverado#5#tim george , jr#applebee 's#richie wauters#richie wauters#11\n",
        "pandas_code": "df[df['owner (s)'] == df['crew chief']].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1365,
        "statement": "clay greenfield and ken shrader be the only owner who also drive the truck",
        "label": 1,
        "table_caption": "list of nascar teams",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#owner (s)#crew chief#rounds\nadrian carriers racing#chevrolet silverado#97#steve wallace#liz girl logistics#gary adam#nick carlson#4\nben kennedy racing#chevrolet silverado#96#ben kennedy#modspace#robert johnson#chris carrier#2\nbill elliott racing#chevrolet silverado#94#chase elliott#aaron 's dream machine#bill elliott#lance mcgrew#10\nbill martel racing#chevrolet silverado#59#kyle martel#patriot energy group#william martel#william martel#2\nclay greenfield motorsports#ram#68#clay greenfield#1 - 800 - pavement#clay greenfield#danny gill#6\nddk motorsports#toyota tundra#45#nate monteith#highway 126 self storage#k d moore#wade day#1\nhattori racing enterprises#toyota tundra#16#brett moffitt#goodyear of japan#shigeaki hattori#jeroid prince#1\nhenderson motorsports#chevrolet silverado#75#caleb holman#food country usa#charles henderson#butch miller#12\njjc racing#ram#0#chris lafferty#driven2honororg#jennifer jo cobb#scott kazura#10\njim rosenblum racing#chevrolet silverado#28#dominick casola#fdny racing#jim rosenblum#dick rahilly#2\nken schrader racing#toyota tundra#52#ken schrader#federated auto parts#ken schrader#donnie richeson#3\nmake motorsports#chevrolet silverado#50#danny efland#advanced electronic services#mark beaver#perry mitchell#8\nmb motorsports#ford f - 150#63#justin jennings#mittler brothers machine & tool#mike mittler#mike mittler#7\nrbr enterprises#chevrolet silverado#92#scott riggs#fleethq / carquest#ricky benton#michael hester#6\nrss racing#chevrolet silverado#38#j j yeley#n / a#rod sieg#timothy brown#5\nsharp - gallagher racing#chevrolet silverado#6#justin lofton#lofton cattle / j6 ink#eddie sharp#eddie troconis#8\nthorsport racing#toyota tundra#13#brett moffitt#aisin#duke thorson#jeriod prince#10\nturner scott motorsports#chevrolet silverado#30#ben kennedy#modspace#steve turner#mike greci#7\nwauters motorsports#chevrolet silverado#5#tim george , jr#applebee 's#richie wauters#richie wauters#11\n",
        "pandas_code": "df[(df['owner (s)'] == df['driver (s)'])].shape[0] == 2 and set(df[df['owner (s)'] == df['driver (s)']]['owner (s)']) == {'clay greenfield', 'ken schrader'}",
        "pandas_eval": "True"
    },
    {
        "id": 1366,
        "statement": "4 different truck style be use",
        "label": 1,
        "table_caption": "list of nascar teams",
        "table_text": "team#truck (s)#-#driver (s)#primary sponsor (s)#owner (s)#crew chief#rounds\nadrian carriers racing#chevrolet silverado#97#steve wallace#liz girl logistics#gary adam#nick carlson#4\nben kennedy racing#chevrolet silverado#96#ben kennedy#modspace#robert johnson#chris carrier#2\nbill elliott racing#chevrolet silverado#94#chase elliott#aaron 's dream machine#bill elliott#lance mcgrew#10\nbill martel racing#chevrolet silverado#59#kyle martel#patriot energy group#william martel#william martel#2\nclay greenfield motorsports#ram#68#clay greenfield#1 - 800 - pavement#clay greenfield#danny gill#6\nddk motorsports#toyota tundra#45#nate monteith#highway 126 self storage#k d moore#wade day#1\nhattori racing enterprises#toyota tundra#16#brett moffitt#goodyear of japan#shigeaki hattori#jeroid prince#1\nhenderson motorsports#chevrolet silverado#75#caleb holman#food country usa#charles henderson#butch miller#12\njjc racing#ram#0#chris lafferty#driven2honororg#jennifer jo cobb#scott kazura#10\njim rosenblum racing#chevrolet silverado#28#dominick casola#fdny racing#jim rosenblum#dick rahilly#2\nken schrader racing#toyota tundra#52#ken schrader#federated auto parts#ken schrader#donnie richeson#3\nmake motorsports#chevrolet silverado#50#danny efland#advanced electronic services#mark beaver#perry mitchell#8\nmb motorsports#ford f - 150#63#justin jennings#mittler brothers machine & tool#mike mittler#mike mittler#7\nrbr enterprises#chevrolet silverado#92#scott riggs#fleethq / carquest#ricky benton#michael hester#6\nrss racing#chevrolet silverado#38#j j yeley#n / a#rod sieg#timothy brown#5\nsharp - gallagher racing#chevrolet silverado#6#justin lofton#lofton cattle / j6 ink#eddie sharp#eddie troconis#8\nthorsport racing#toyota tundra#13#brett moffitt#aisin#duke thorson#jeriod prince#10\nturner scott motorsports#chevrolet silverado#30#ben kennedy#modspace#steve turner#mike greci#7\nwauters motorsports#chevrolet silverado#5#tim george , jr#applebee 's#richie wauters#richie wauters#11\n",
        "pandas_code": "len(df['truck (s)'].unique()) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1367,
        "statement": "the brewer have have the most first place finish when in the east division",
        "label": 1,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "df[(df['division'] == 'east') & (df['finish'] == '1st')].shape[0] == df[df['division'] == 'east']['finish'].value_counts().get('1st', 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1368,
        "statement": "the brewer be in the american league until 1998 , when they switch to the national league",
        "label": 1,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "all(df[df['mlb season'] < 1998]['league'] == 'al') and all(df[df['mlb season'] >= 1998]['league'] == 'nl')",
        "pandas_eval": "True"
    },
    {
        "id": 1369,
        "statement": "the brewer never win the west division in their time there",
        "label": 1,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "df[(df['division'] == 'west') & (df['finish'] == '1st')].empty",
        "pandas_eval": "True"
    },
    {
        "id": 1370,
        "statement": "the brewer have win out both the central and east division in their history",
        "label": 1,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "any(df[(df['division'] == 'central') | (df['division'] == 'east')]['finish'].str.contains('1st'))",
        "pandas_eval": "True"
    },
    {
        "id": 1371,
        "statement": "the brewer have a higher winning percentage in 1979 when they come in 2nd than in 1981 and 1982 when they come in 1st",
        "label": 1,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "(df[(df['team season'] == 1979) & (df['finish'] == '2nd')]['win %'].values[0] > df[(df['team season'] == 1981) & (df['finish'] == '1st')]['win %'].values[0]) & (df[(df['team season'] == 1979) & (df['finish'] == '2nd')]['win %'].values[0] > df[(df['team season'] == 1982) & (df['finish'] == '1st')]['win %'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1372,
        "statement": "the brewer have have the most first place finish when in the central division",
        "label": 0,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "df[(df['division'] == 'central') & (df['finish'] == '1st')].shape[0] == df[df['division'] == 'central'].groupby('finish').size().max()",
        "pandas_eval": "False"
    },
    {
        "id": 1373,
        "statement": "the brewer be in the national league until 1998 , when they switch to the namerican tional league",
        "label": 0,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "all(df[df['mlb season'] <= 1998]['league'] == 'al') and all(df[df['mlb season'] > 1998]['league'] == 'nl')",
        "pandas_eval": "False"
    },
    {
        "id": 1374,
        "statement": "the brewer have a lower winning percentage in 1979 when they come in 2nd than in 1981 and 1982 when they come in 1st",
        "label": 0,
        "table_caption": "list of milwaukee brewers seasons",
        "table_text": "mlb season#team season#league#division#finish#wins#losses#win %\n1969#1969#al#west#6th#64#98#395\n1970#1970#al#west#4th#65#97#401\n1971#1971#al#west#6th#69#92#429\n1972#1972#al#east#6th#65#91#417\n1973#1973#al#east#5th#74#88#457\n1974#1974#al#east#5th#76#86#469\n1975#1975#al#east#5th#68#94#420\n1976#1976#al#east#6th#66#95#410\n1977#1977#al#east#6th#67#95#414\n1978#1978#al#east#3rd#93#69#574\n1979#1979#al#east#2nd#95#66#590\n1980#1980#al#east#3rd#86#76#531\n1981#1981#al#east#3rd#31#25#554\n1981#1981#al#east#1st#31#22#585\n1982#1982#al#east#1st#95#67#586\n1983#1983#al#east#5th#87#75#537\n1984#1984#al#east#7th#67#94#416\n1985#1985#al#east#6th#71#90#441\n1986#1986#al#east#6th#77#84#478\n1987#1987#al#east#3rd#91#71#562\n1988#1988#al#east#3rd#87#75#537\n1989#1989#al#east#4th#81#81#500\n1990#1990#al#east#6th#74#88#457\n1991#1991#al#east#4th#83#79#512\n1992#1992#al#east#2nd#92#70#568\n1993#1993#al#east#7th#69#93#426\n1994#1994#al#central#5th#53#62#461\n1995#1995#al#central#4th#65#79#451\n1996#1996#al#central#3rd#80#82#494\n1997#1997#al#central#3rd#78#83#484\n1998#1998#nl#central#5th#74#88#457\n1999#1999#nl#central#5th#74#87#460\n2000#2000#nl#central#3rd#73#89#451\n2001#2001#nl#central#4th#68#94#420\n2002#2002#nl#central#6th#56#106#346\n2003#2003#nl#central#6th#68#94#412\n2004#2004#nl#central#6th#67#94#411\n2005#2005#nl#central#3rd#81#81#500\n2006#2006#nl#central#4th#75#87#463\n2007#2007#nl#central#2nd#83#79#512\n2008#2008#nl#central#2nd \u0e22\u0e07#90#72#556\n2009#2009#nl#central#3rd#80#82#494\n2010#2010#nl#central#3rd#77#85#475\n2011#2011#nl#central#1st#96#66#593\n2012#2012#nl#central#3rd#83#79#512\n2013#2013#nl#central#4th#74#88#457\n",
        "pandas_code": "(df[(df['team season'] == 1979) & (df['finish'] == '2nd')]['win %'].values[0] < df[(df['team season'] == 1981) & (df['finish'] == '1st')]['win %'].values[0]) & (df[(df['team season'] == 1979) & (df['finish'] == '2nd')]['win %'].values[0] < df[(df['team season'] == 1982) & (df['finish'] == '1st')]['win %'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1375,
        "statement": "car number 6 have the lowest speed rank",
        "label": 1,
        "table_caption": "jim clark",
        "table_text": "year#car number#start#qual speed#speed rank#finish#laps completed#laps led#race status#chassis\n1963#92#5#149.750#7#2#200#28#running#lotus - ford 29 / 3\n1964#6#1#158.828#1#24#47#14#suspension#lotus - ford 34 / 3\n1965#82#2#160.729#2#1#200#190#running#lotus - ford 38 / 1\n1966#19#2#164.114#2#2#200#66#running#lotus - ford 38 / 4\n1967#31#16#163.213#23#31#35#0#piston#lotus - ford 38 / 7\n",
        "pandas_code": "df[df['car number'] == 6]['speed rank'].min() == df['speed rank'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1376,
        "statement": "car number 82 lead the most lap",
        "label": 1,
        "table_caption": "jim clark",
        "table_text": "year#car number#start#qual speed#speed rank#finish#laps completed#laps led#race status#chassis\n1963#92#5#149.750#7#2#200#28#running#lotus - ford 29 / 3\n1964#6#1#158.828#1#24#47#14#suspension#lotus - ford 34 / 3\n1965#82#2#160.729#2#1#200#190#running#lotus - ford 38 / 1\n1966#19#2#164.114#2#2#200#66#running#lotus - ford 38 / 4\n1967#31#16#163.213#23#31#35#0#piston#lotus - ford 38 / 7\n",
        "pandas_code": "(df[df['car number'] == 82]['laps led'].values[0] == df['laps led'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 1377,
        "statement": "car number 31 have the least lap led",
        "label": 1,
        "table_caption": "jim clark",
        "table_text": "year#car number#start#qual speed#speed rank#finish#laps completed#laps led#race status#chassis\n1963#92#5#149.750#7#2#200#28#running#lotus - ford 29 / 3\n1964#6#1#158.828#1#24#47#14#suspension#lotus - ford 34 / 3\n1965#82#2#160.729#2#1#200#190#running#lotus - ford 38 / 1\n1966#19#2#164.114#2#2#200#66#running#lotus - ford 38 / 4\n1967#31#16#163.213#23#31#35#0#piston#lotus - ford 38 / 7\n",
        "pandas_code": "df[df['car number'] == 31]['laps led'].min() == df['laps led'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1378,
        "statement": "car number 31 finish in last place",
        "label": 1,
        "table_caption": "jim clark",
        "table_text": "year#car number#start#qual speed#speed rank#finish#laps completed#laps led#race status#chassis\n1963#92#5#149.750#7#2#200#28#running#lotus - ford 29 / 3\n1964#6#1#158.828#1#24#47#14#suspension#lotus - ford 34 / 3\n1965#82#2#160.729#2#1#200#190#running#lotus - ford 38 / 1\n1966#19#2#164.114#2#2#200#66#running#lotus - ford 38 / 4\n1967#31#16#163.213#23#31#35#0#piston#lotus - ford 38 / 7\n",
        "pandas_code": "df[df['car number'] == 31]['finish'].max() == df['finish'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1379,
        "statement": "car number 19 have the second most lap led",
        "label": 1,
        "table_caption": "jim clark",
        "table_text": "year#car number#start#qual speed#speed rank#finish#laps completed#laps led#race status#chassis\n1963#92#5#149.750#7#2#200#28#running#lotus - ford 29 / 3\n1964#6#1#158.828#1#24#47#14#suspension#lotus - ford 34 / 3\n1965#82#2#160.729#2#1#200#190#running#lotus - ford 38 / 1\n1966#19#2#164.114#2#2#200#66#running#lotus - ford 38 / 4\n1967#31#16#163.213#23#31#35#0#piston#lotus - ford 38 / 7\n",
        "pandas_code": "df[df['car number'] == 19]['laps led'].values[0] == df['laps led'].nlargest(2).iloc[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 1380,
        "statement": "wade barrett play in the us open cup fewer time than ryan cochrane",
        "label": 1,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "df[df['name'] == 'wade barrett']['us open cup'].values[0] < df[df['name'] == 'ryan cochrane']['us open cup'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1382,
        "statement": "brian ching appear more time in the ml cup than he do in concacaf",
        "label": 1,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "(df[df['name'] == 'brian ching']['mls cup'].values[0] > df[df['name'] == 'brian ching']['concacaf'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1383,
        "statement": "brad davis play more time than stuart holden but fewer time than pat onstad in total",
        "label": 1,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "(df[df['name'] == 'brad davis']['total'].values[0] > df[df['name'] == 'stuart holden']['total'].values[0]) & (df[df['name'] == 'brad davis']['total'].values[0] < df[df['name'] == 'pat onstad']['total'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1384,
        "statement": "the player who be never in concacaf be not the same player who be never in the us open cup",
        "label": 1,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "len(df[(df['concacaf'] == 0) & (df['us open cup'] == 0)]) == 0",
        "pandas_eval": "True"
    },
    {
        "id": 1385,
        "statement": "wade barrett play in the us open cup 3 time , while ryan cochrane only play 2 time",
        "label": 0,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "(df[df['name'] == 'wade barrett']['us open cup'].values[0] == 3) & (df[df['name'] == 'ryan cochrane']['us open cup'].values[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1387,
        "statement": "brian ching appear in concacaf 2 time more than he appear in the ml cup",
        "label": 0,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "(df[df['name'] == 'brian ching']['concacaf'].values[0] - df[df['name'] == 'brian ching']['mls cup'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1388,
        "statement": "stuart holden have play more than brad davis and pat onstad in total",
        "label": 0,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "(df[df['name'] == 'stuart holden']['total'].values[0] > df[df['name'] == 'brad davis']['total'].values[0]) & (df[df['name'] == 'stuart holden']['total'].values[0] > df[df['name'] == 'pat onstad']['total'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1389,
        "statement": "the same player who didn't appear in concacaf , also do not appear in the us open cup",
        "label": 0,
        "table_caption": "houston dynamo records and statistics",
        "table_text": "name#years#mls cup#us open cup#concacaf#other#total\nwade barrett#2006 - present#8#2#5#9#86\npat onstad#2006 - present#9#2#4#8#82\nbrian mullan#2006 - present#8#2#4#7#80\ndwayne de rosario#2006 - present#8#2#5#9#78\neddie robinson#2006 - present#8#3#4#7#72\ncraig waibel#2006 - present#8#1#4#10#72\nryan cochrane#2006 - 2007#7#3#0#6#68\nricardo clark#2006 - present#3#1#5#6#65\nbrian ching#2006 - present#7#1#5#8#62\nbrad davis#2006 - present#8#1#2#6#62\nstuart holden#2006 - present#6#3#0#7#51\nalejandro moreno#2006#4#2#1#6#47\nrichard mulrooney#2007 - present#4#0#4#5#41\n",
        "pandas_code": "df[(df['concacaf'] == 0) & (df['us open cup'] == 0)].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 1390,
        "statement": "type a have the most seat than any other type",
        "label": 1,
        "table_caption": "wismar railbus",
        "table_text": "#type a#type b#type c#type d#type e\ngauge (mm)#1435#1435#1435#1000#1000\nwheelbase (mm)#4400#4000#3500#4000#3500\nlength (mm)#11610#10100#10100#11150#10100\nwidth (mm)#2902#2902#2902#2430#2430\nweight (kg)#6600#6200#6600#6200#5800\nseats#40 + 16#30 + 16#26 + 14#22 + 13#24 + 14\n",
        "pandas_code": "int(df.loc[df['Unnamed: 0'] == 'seats', 'type a'].item().split(' + ')[0]) > max([int(df.loc[df['Unnamed: 0'] == 'seats', col].item().split(' + ')[0]) for col in df.columns if col != 'Unnamed: 0' and col != 'type a'])",
        "pandas_eval": "True"
    },
    {
        "id": 1391,
        "statement": "the length (mm) and width (mm) for type b and c be the same",
        "label": 1,
        "table_caption": "wismar railbus",
        "table_text": "#type a#type b#type c#type d#type e\ngauge (mm)#1435#1435#1435#1000#1000\nwheelbase (mm)#4400#4000#3500#4000#3500\nlength (mm)#11610#10100#10100#11150#10100\nwidth (mm)#2902#2902#2902#2430#2430\nweight (kg)#6600#6200#6600#6200#5800\nseats#40 + 16#30 + 16#26 + 14#22 + 13#24 + 14\n",
        "pandas_code": "(df.loc[df['Unnamed: 0'] == 'length (mm)', 'type b'].values[0] == df.loc[df['Unnamed: 0'] == 'length (mm)', 'type c'].values[0]) & (df.loc[df['Unnamed: 0'] == 'width (mm)', 'type b'].values[0] == df.loc[df['Unnamed: 0'] == 'width (mm)', 'type c'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1392,
        "statement": "the gauge (mm) for type a b and c be higher than type d and e",
        "label": 1,
        "table_caption": "wismar railbus",
        "table_text": "#type a#type b#type c#type d#type e\ngauge (mm)#1435#1435#1435#1000#1000\nwheelbase (mm)#4400#4000#3500#4000#3500\nlength (mm)#11610#10100#10100#11150#10100\nwidth (mm)#2902#2902#2902#2430#2430\nweight (kg)#6600#6200#6600#6200#5800\nseats#40 + 16#30 + 16#26 + 14#22 + 13#24 + 14\n",
        "pandas_code": "all(df.loc[df['Unnamed: 0'] == 'gauge (mm)', ['type a', 'type b', 'type c']].astype(int).values.flatten() > df.loc[df['Unnamed: 0'] == 'gauge (mm)', ['type d', 'type e']].astype(int).values.flatten().max())",
        "pandas_eval": "True"
    },
    {
        "id": 1394,
        "statement": "type a and c be heavier than the others",
        "label": 1,
        "table_caption": "wismar railbus",
        "table_text": "#type a#type b#type c#type d#type e\ngauge (mm)#1435#1435#1435#1000#1000\nwheelbase (mm)#4400#4000#3500#4000#3500\nlength (mm)#11610#10100#10100#11150#10100\nwidth (mm)#2902#2902#2902#2430#2430\nweight (kg)#6600#6200#6600#6200#5800\nseats#40 + 16#30 + 16#26 + 14#22 + 13#24 + 14\n",
        "pandas_code": "all(df.loc[df['Unnamed: 0'] == 'weight (kg)', ['type a', 'type c']].astype(int).values.flatten() > df.loc[df['Unnamed: 0'] == 'weight (kg)', ['type b', 'type d', 'type e']].astype(int).values.flatten().max())",
        "pandas_eval": "True"
    },
    {
        "id": 1395,
        "statement": "no home team score less than 10 or greater than 22",
        "label": 1,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "all((df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) >= 10) & (df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) <= 22))",
        "pandas_eval": "True"
    },
    {
        "id": 1396,
        "statement": "no away team score less than 8 or greater than 20",
        "label": 1,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "all((df['away team score'].str.split('.').str[0].astype(int) >= 8) & (df['away team score'].str.split('.').str[0].astype(int) <= 20))",
        "pandas_eval": "True"
    },
    {
        "id": 1397,
        "statement": "2 of the venue have park in the name and 2 of the venue have oval in the name",
        "label": 1,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "(df['venue'].str.contains('park').sum() == 2) & (df['venue'].str.contains('oval').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1398,
        "statement": "all of the match take place on the 18th of july in the year 1942",
        "label": 1,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "all(df['date'] == '18 july 1942')",
        "pandas_eval": "True"
    },
    {
        "id": 1399,
        "statement": "the smallest crowd be 6000 and the largest be 16000",
        "label": 1,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "(df['crowd'].min() == 4000) & (df['crowd'].max() == 16000)",
        "pandas_eval": "True"
    },
    {
        "id": 1400,
        "statement": "no home team score more than 10 point",
        "label": 0,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "all(df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) <= 10)",
        "pandas_eval": "False"
    },
    {
        "id": 1402,
        "statement": "only 1 of the venue have park or oval in the name",
        "label": 0,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "df['venue'].str.contains('park|oval', case=False, regex=True).sum() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1403,
        "statement": "all of the match list take place before july 1942",
        "label": 0,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "(pd.to_datetime(df['date'], format='%d %B %Y') < pd.to_datetime('1942-07-01')).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1404,
        "statement": "the smallest crowd have more less than 5000 in attendance than the largest",
        "label": 0,
        "table_caption": "1942 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#13.17 (95)#richmond#19.13 (127)#glenferrie oval#6000#18 july 1942\nessendon#10.15 (75)#south melbourne#8.7 (55)#windy hill#16000#18 july 1942\nst kilda#21.9 (135)#north melbourne#9.8 (62)#toorak park#4000#18 july 1942\nmelbourne#14.11 (95)#footscray#19.12 (126)#punt road oval#6000#18 july 1942\ncollingwood#11.11 (77)#carlton#13.14 (92)#victoria park#8000#18 july 1942\n",
        "pandas_code": "(df['crowd'].max() - df['crowd'].min()) < 5000",
        "pandas_eval": "False"
    },
    {
        "id": 1405,
        "statement": "the conservative (h) receive 19.4 % vote while the christian democrat (krf) only receive 6.5 % vote",
        "label": 1,
        "table_caption": "askim",
        "table_text": "party#% votes#% change#seats#change\nlabour (ap)#34.6#+ 11.9#12#+ 4\nconservatives (h)#19.4#- 1.8#7#- 2\nprogress party (frp)#19.4#+ 0.4#7#+ 1\nchristian democrats (krf)#6.5#+ 0.3#2#0\naskimpartiet (askimp)#5.9#- 1.2#2#0\nliberals (v)#5.4#+ 1.2#2#+ 1\ncentre party (sp)#4.7#- 3.7#2#- 1\nsocialist left (sv)#4.1#- 2.9#1#- 2\nothers#0#- 2.9#0#- 1\nturnout#55.0#+ 2.2#35#35\n",
        "pandas_code": "(df[df['party'] == 'conservatives (h)']['% votes'].values[0] == 19.4) & (df[df['party'] == 'christian democrats (krf)']['% votes'].values[0] == 6.5)",
        "pandas_eval": "True"
    },
    {
        "id": 1406,
        "statement": "the labour party (ap) have a larger % change than either the liberal (v) party or the socialist left (sv) party",
        "label": 1,
        "table_caption": "askim",
        "table_text": "party#% votes#% change#seats#change\nlabour (ap)#34.6#+ 11.9#12#+ 4\nconservatives (h)#19.4#- 1.8#7#- 2\nprogress party (frp)#19.4#+ 0.4#7#+ 1\nchristian democrats (krf)#6.5#+ 0.3#2#0\naskimpartiet (askimp)#5.9#- 1.2#2#0\nliberals (v)#5.4#+ 1.2#2#+ 1\ncentre party (sp)#4.7#- 3.7#2#- 1\nsocialist left (sv)#4.1#- 2.9#1#- 2\nothers#0#- 2.9#0#- 1\nturnout#55.0#+ 2.2#35#35\n",
        "pandas_code": "(df.loc[df['party'] == 'labour (ap)', '% change'].item().strip('+').strip().replace(' ', '') > df.loc[df['party'] == 'liberals (v)', '% change'].item().strip('+').strip().replace(' ', '')) and (df.loc[df['party'] == 'labour (ap)', '% change'].item().strip('+').strip().replace(' ', '') > df.loc[df['party'] == 'socialist left (sv)', '% change'].item().strip('+').strip().replace(' ', ''))",
        "pandas_eval": "True"
    },
    {
        "id": 1407,
        "statement": "the askimpartiet (askimp) party have a larger % vote than the liberal (v) party",
        "label": 1,
        "table_caption": "askim",
        "table_text": "party#% votes#% change#seats#change\nlabour (ap)#34.6#+ 11.9#12#+ 4\nconservatives (h)#19.4#- 1.8#7#- 2\nprogress party (frp)#19.4#+ 0.4#7#+ 1\nchristian democrats (krf)#6.5#+ 0.3#2#0\naskimpartiet (askimp)#5.9#- 1.2#2#0\nliberals (v)#5.4#+ 1.2#2#+ 1\ncentre party (sp)#4.7#- 3.7#2#- 1\nsocialist left (sv)#4.1#- 2.9#1#- 2\nothers#0#- 2.9#0#- 1\nturnout#55.0#+ 2.2#35#35\n",
        "pandas_code": "df[df['party'] == 'askimpartiet (askimp)']['% votes'].values[0] > df[df['party'] == 'liberals (v)']['% votes'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1409,
        "statement": "2 party receive between 5 and 6% of the vote : liberal (v) with 5.4 and askimpartiet (askimp) with 5.9",
        "label": 1,
        "table_caption": "askim",
        "table_text": "party#% votes#% change#seats#change\nlabour (ap)#34.6#+ 11.9#12#+ 4\nconservatives (h)#19.4#- 1.8#7#- 2\nprogress party (frp)#19.4#+ 0.4#7#+ 1\nchristian democrats (krf)#6.5#+ 0.3#2#0\naskimpartiet (askimp)#5.9#- 1.2#2#0\nliberals (v)#5.4#+ 1.2#2#+ 1\ncentre party (sp)#4.7#- 3.7#2#- 1\nsocialist left (sv)#4.1#- 2.9#1#- 2\nothers#0#- 2.9#0#- 1\nturnout#55.0#+ 2.2#35#35\n",
        "pandas_code": "(df[df['party'].isin(['liberals (v)', 'askimpartiet (askimp)'])]['% votes'].between(5, 6).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1410,
        "statement": "manchester united and hereford unite win their match",
        "label": 1,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "(df[df['home team'].isin(['manchester united', 'hereford united'])]['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).all()) or (df[df['away team'].isin(['manchester united', 'hereford united'])]['score'].str.split(' - ').apply(lambda x: int(x[1]) > int(x[0])).all())",
        "pandas_eval": "True"
    },
    {
        "id": 1411,
        "statement": "darlington v orient be 1 of 2 match - ups to have 2 replay match",
        "label": 1,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "len(df[(df['home team'].isin(['darlington', 'orient'])) & (df['away team'].isin(['darlington', 'orient'])) & (df['tie no'] == 'replay')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1413,
        "statement": "a total of 5 match end with a 0 - 0 score",
        "label": 1,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "(df['score'] == '0 - 0').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1414,
        "statement": "the most lopsided score be 6 - 0 by nottingham forest over bristol rover",
        "label": 1,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "df[(df['home team'] == 'nottingham forest') & (df['away team'] == 'bristol rovers') & (df['score'] == '6 - 0')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1415,
        "statement": "both manchester unite and hereford unite lose their match",
        "label": 0,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "(df[df['home team'].isin(['manchester united', 'hereford united']) | df['away team'].isin(['manchester united', 'hereford united'])]['score'].apply(lambda x: int(x.split(' - ')[0]) < int(x.split(' - ')[1])).all())",
        "pandas_eval": "False"
    },
    {
        "id": 1416,
        "statement": "none of the match - ups have multiple replay",
        "label": 0,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "df['tie no'].value_counts().eq(1).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1417,
        "statement": "every match have at least 1 team score 1 point",
        "label": 0,
        "table_caption": "1976 - 77 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 0#derby county#8 january 1977\nreplay#derby county#3 - 2#blackpool#19 january 1977\n2#darlington#2 - 2#orient#8 january 1977\nreplay#orient#0 - 0#darlington#11 january 1977\nreplay#darlington#0 - 3#orient#17 january 1977\n3#burnley#2 - 2#lincoln city#8 january 1977\nreplay#lincoln city#0 - 1#burnley#12 january 1977\n4#liverpool#0 - 0#crystal palace#8 january 1977\nreplay#crystal palace#2 - 3#liverpool#11 january 1977\n5#southampton#1 - 1#chelsea#8 january 1977\nreplay#chelsea#0 - 3#southampton#12 january 1977\n6#leicester city#0 - 1#aston villa#8 january 1977\n7#notts county#0 - 1#arsenal#8 january 1977\n8#nottingham forest#1 - 1#bristol rovers#8 january 1977\nreplay#bristol rovers#1 - 1#nottingham forest#11 january 1977\nreplay#nottingham forest#6 - 0#bristol rovers#18 january 1977\n9#northwich victoria#3 - 2#watford#8 january 1977\n10#wolverhampton wanderers#3 - 2#rotherham united#8 january 1977\n11#sunderland#2 - 2#wrexham#8 january 1977\nreplay#wrexham#1 - 0#sunderland#12 january 1977\n12#everton#2 - 0#stoke city#8 january 1977\n13#sheffield united#0 - 0#newcastle united#8 january 1977\nreplay#newcastle united#3 - 1#sheffield united#24 january 1977\n14#ipswich town#4 - 1#bristol city#8 january 1977\n15#manchester city#1 - 1#west bromwich albion#8 january 1977\nreplay#west bromwich albion#0 - 1#manchester city#11 january 1977\n16#queens park rangers#2 - 1#shrewsbury town#8 january 1977\n17#fulham#3 - 3#swindon town#8 january 1977\nreplay#swindon town#5 - 0#fulham#11 january 1977\n18#coventry city#1 - 0#millwall#8 january 1977\n19#west ham united#2 - 1#bolton wanderers#8 january 1977\n20#manchester united#1 - 0#walsall#8 january 1977\n21#hull city#1 - 1#port vale#8 january 1977\nreplay#port vale#3 - 1#hull city#10 january 1977\n22#carlisle united#5 - 1#matlock town#8 january 1977\n23#oldham athletic#3 - 0#plymouth argyle#8 january 1977\n24#wimbledon#0 - 0#middlesbrough#8 january 1977\nreplay#middlesbrough#1 - 0#wimbledon#11 january 1977\n25#southend united#0 - 4#chester#8 january 1977\n26#cardiff city#1 - 0#tottenham hotspur#8 january 1977\n27#halifax town#0 - 1#luton town#8 january 1977\n28#charlton athletic#1 - 1#blackburn rovers#8 january 1977\nreplay#blackburn rovers#2 - 0#charlton athletic#12 january 1977\n29#leeds united#5 - 2#norwich city#8 january 1977\n30#hereford united#1 - 0#reading#8 january 1977\n31#kettering town#2 - 3#colchester united#8 january 1977\n32#birmingham city#1 - 0#portsmouth#8 january 1977\n",
        "pandas_code": "all(df['score'].apply(lambda x: int(x.split(' - ')[0]) >= 1 or int(x.split(' - ')[1]) >= 1))",
        "pandas_eval": "False"
    },
    {
        "id": 1419,
        "statement": "the williams family air after the bates family",
        "label": 1,
        "table_caption": "list of supernanny episodes",
        "table_text": "no overall#no in series#family / families#location (s)#original air date\nuk16#1#the hillhouse - docherty family#ayr ( scotland )#29 august 2006\nuk17#2#the howat family#shenley#5 september 2006\nuk18#3#the brown - smith family#warrington#12 september 2006\nuk19#4#the bates family#evesham#19 september 2006\nuk20#5#the williams family#birmingham#26 september 2006\n",
        "pandas_code": "df[df['family / families'] == 'the williams family']['original air date'].values[0] > df[df['family / families'] == 'the bates family']['original air date'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1420,
        "statement": "the brown - smith family air before the bates family",
        "label": 1,
        "table_caption": "list of supernanny episodes",
        "table_text": "no overall#no in series#family / families#location (s)#original air date\nuk16#1#the hillhouse - docherty family#ayr ( scotland )#29 august 2006\nuk17#2#the howat family#shenley#5 september 2006\nuk18#3#the brown - smith family#warrington#12 september 2006\nuk19#4#the bates family#evesham#19 september 2006\nuk20#5#the williams family#birmingham#26 september 2006\n",
        "pandas_code": "df[df['family / families'] == 'the brown - smith family']['original air date'].iloc[0] < df[df['family / families'] == 'the bates family']['original air date'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1421,
        "statement": "the bates family air after the brown - smith family",
        "label": 1,
        "table_caption": "list of supernanny episodes",
        "table_text": "no overall#no in series#family / families#location (s)#original air date\nuk16#1#the hillhouse - docherty family#ayr ( scotland )#29 august 2006\nuk17#2#the howat family#shenley#5 september 2006\nuk18#3#the brown - smith family#warrington#12 september 2006\nuk19#4#the bates family#evesham#19 september 2006\nuk20#5#the williams family#birmingham#26 september 2006\n",
        "pandas_code": "df[df['family / families'] == 'the bates family']['original air date'].values[0] > df[df['family / families'] == 'the brown - smith family']['original air date'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1422,
        "statement": "the howat family air before the brown - smith family",
        "label": 1,
        "table_caption": "list of supernanny episodes",
        "table_text": "no overall#no in series#family / families#location (s)#original air date\nuk16#1#the hillhouse - docherty family#ayr ( scotland )#29 august 2006\nuk17#2#the howat family#shenley#5 september 2006\nuk18#3#the brown - smith family#warrington#12 september 2006\nuk19#4#the bates family#evesham#19 september 2006\nuk20#5#the williams family#birmingham#26 september 2006\n",
        "pandas_code": "pd.to_datetime(df.loc[df['family / families'] == 'the howat family', 'original air date'].values[0]) < pd.to_datetime(df.loc[df['family / families'] == 'the brown - smith family', 'original air date'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1423,
        "statement": "the brown - smith family air after the howat family",
        "label": 1,
        "table_caption": "list of supernanny episodes",
        "table_text": "no overall#no in series#family / families#location (s)#original air date\nuk16#1#the hillhouse - docherty family#ayr ( scotland )#29 august 2006\nuk17#2#the howat family#shenley#5 september 2006\nuk18#3#the brown - smith family#warrington#12 september 2006\nuk19#4#the bates family#evesham#19 september 2006\nuk20#5#the williams family#birmingham#26 september 2006\n",
        "pandas_code": "pd.to_datetime(df.loc[df['family / families'] == 'the brown - smith family', 'original air date'].values[0]) > pd.to_datetime(df.loc[df['family / families'] == 'the howat family', 'original air date'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1424,
        "statement": "home team richmond , footscray , carlton , and collingwood all play on 17 july 1926 at different venue",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "len(df[(df['home team'].isin(['richmond', 'footscray', 'carlton', 'collingwood'])) & (df['date'] == '17 july 1926')]['venue'].unique()) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1425,
        "statement": "punt road oval be the venue for richmond and south melbourne and prince park be the venue for hawthorn and carlton",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df[(df['venue'] == 'punt road oval') & (df['home team'] == 'richmond') & (df['away team'] == 'south melbourne')].shape[0] > 0) & (df[(df['venue'] == 'princes park') & (df['home team'] == 'carlton') & (df['away team'] == 'hawthorn')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1426,
        "statement": "17 july 1926 be the date for the home team st kilda v away team melbourne game which be the same date that footscray and geelong played",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df[(df['home team'] == 'st kilda') & (df['away team'] == 'melbourne')]['date'].iloc[0] == df[(df['home team'] == 'footscray') & (df['away team'] == 'geelong')]['date'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1427,
        "statement": "the crowd at western oval be larger than the crowd at victoria park which be larger than the crowd at prince park",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df[df['venue'] == 'western oval']['crowd'].values[0] > df[df['venue'] == 'victoria park']['crowd'].values[0]) & (df[df['venue'] == 'victoria park']['crowd'].values[0] > df[df['venue'] == 'princes park']['crowd'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1428,
        "statement": "home team st kilda play in front of a crowd of 14000 while home team carlton play in front of a crowd of 12000",
        "label": 1,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df[(df['home team'] == 'st kilda') & (df['crowd'] == 14000)].shape[0] > 0) & (df[(df['home team'] == 'carlton') & (df['crowd'] == 12000)].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1429,
        "statement": "home team richmond , st kilda , collingwood all play on the 2nd of july 1926 at different venue",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "df[(df['home team'].isin(['richmond', 'st kilda', 'collingwood'])) & (df['date'] == '2 july 1926')]['venue'].nunique() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1430,
        "statement": "punt road oval be the venue for st kilda and prince park be the venue for north melbourne",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df[df['home team'] == 'st kilda']['venue'].iloc[0] == 'punt road oval') & (df[df['home team'] == 'north melbourne']['venue'].iloc[0] == 'princes park')",
        "pandas_eval": "False"
    },
    {
        "id": 1431,
        "statement": "the first of july 1927 be the date for the home team north melbourne v away team st kilda , which be the same date that footscray and geelong played",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df['date'].eq('17 july 1926') & df['home team'].eq('north melbourne') & df['away team'].eq('st kilda')).any() & (df['date'].eq('17 july 1926') & df['home team'].eq('footscray') & df['away team'].eq('geelong')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1432,
        "statement": "the crowd at western oval be half as large than the crowd at victoria park which be the largest than the crowd in the vfl season",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "df[df['venue'] == 'western oval']['crowd'].iloc[0] == df[df['venue'] == 'victoria park']['crowd'].iloc[0] / 2 and df[df['venue'] == 'victoria park']['crowd'].iloc[0] == df['crowd'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1433,
        "statement": "away team st kilda play in front of a crowd of 12350 while home team carlton play in front of a crowd of 6040",
        "label": 0,
        "table_caption": "1926 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nrichmond#10.15 (75)#south melbourne#16.12 (108)#punt road oval#27000#17 july 1926\nfootscray#7.14 (56)#geelong#15.17 (107)#western oval#17000#17 july 1926\ncollingwood#18.16 (124)#fitzroy#11.16 (82)#victoria park#16000#17 july 1926\ncarlton#8.17 (65)#hawthorn#8.9 (57)#princes park#12000#17 july 1926\nst kilda#3.11 (29)#melbourne#17.16 (118)#junction oval#14000#17 july 1926\nnorth melbourne#4.8 (32)#essendon#6.14 (50)#arden street oval#10000#17 july 1926\n",
        "pandas_code": "(df['away team'].eq('st kilda') & df['crowd'].eq(12350)).any() & (df['home team'].eq('carlton') & df['crowd'].eq(6040)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1434,
        "statement": "the path of true love have the greatest number of viewship",
        "label": 1,
        "table_caption": "list of the green green grass episodes",
        "table_text": "episode#original title#directed by#written by#original airdate#duration#viewership\n24#the path of true love#dewi humphreys#jim sullivan#8 january 2009#30 minutes#4.07 million\n25#home brew#dewi humphreys#john sullivan#15 january 2009#30 minutes#4.02 million\n26#calendar boys#dewi humphreys#david cantor#22 january 2009#30 minutes#3.86 million\n27#animal instincts#dewi humphreys#jim sullivan#29 january 2009#30 minutes#3.87 million\n28#your cheating art#dewi humphreys#john sullivan#5 february 2009#30 minutes#3.93 million\n29#the departed#dewi humphreys#keith lindsay#12 february 2009#30 minutes#3.73 million\n30#i done it my way#dewi humphreys#john sullivan#19 february 2009#30 minutes#3.46 million\n31#one man 's junk#dewi humphreys#robert evans#26 february 2009#30 minutes#3.70 million\n",
        "pandas_code": "df.loc[df['original title'] == 'the path of true love', 'viewership'].values[0] == df['viewership'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1435,
        "statement": "one man 's junk and i do it my way have an average viewership of 3.58 million",
        "label": 1,
        "table_caption": "list of the green green grass episodes",
        "table_text": "episode#original title#directed by#written by#original airdate#duration#viewership\n24#the path of true love#dewi humphreys#jim sullivan#8 january 2009#30 minutes#4.07 million\n25#home brew#dewi humphreys#john sullivan#15 january 2009#30 minutes#4.02 million\n26#calendar boys#dewi humphreys#david cantor#22 january 2009#30 minutes#3.86 million\n27#animal instincts#dewi humphreys#jim sullivan#29 january 2009#30 minutes#3.87 million\n28#your cheating art#dewi humphreys#john sullivan#5 february 2009#30 minutes#3.93 million\n29#the departed#dewi humphreys#keith lindsay#12 february 2009#30 minutes#3.73 million\n30#i done it my way#dewi humphreys#john sullivan#19 february 2009#30 minutes#3.46 million\n31#one man 's junk#dewi humphreys#robert evans#26 february 2009#30 minutes#3.70 million\n",
        "pandas_code": "(df[df['original title'].isin([\"one man 's junk\", 'i done it my way'])]['viewership'].str.replace(' million', '').astype(float).mean() == 3.58)",
        "pandas_eval": "True"
    },
    {
        "id": 1436,
        "statement": "the depart have a greater viewership than one man 's junk",
        "label": 1,
        "table_caption": "list of the green green grass episodes",
        "table_text": "episode#original title#directed by#written by#original airdate#duration#viewership\n24#the path of true love#dewi humphreys#jim sullivan#8 january 2009#30 minutes#4.07 million\n25#home brew#dewi humphreys#john sullivan#15 january 2009#30 minutes#4.02 million\n26#calendar boys#dewi humphreys#david cantor#22 january 2009#30 minutes#3.86 million\n27#animal instincts#dewi humphreys#jim sullivan#29 january 2009#30 minutes#3.87 million\n28#your cheating art#dewi humphreys#john sullivan#5 february 2009#30 minutes#3.93 million\n29#the departed#dewi humphreys#keith lindsay#12 february 2009#30 minutes#3.73 million\n30#i done it my way#dewi humphreys#john sullivan#19 february 2009#30 minutes#3.46 million\n31#one man 's junk#dewi humphreys#robert evans#26 february 2009#30 minutes#3.70 million\n",
        "pandas_code": "df[df['original title'] == 'the departed']['viewership'].values[0] > df[df['original title'] == \"one man 's junk\"]['viewership'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1437,
        "statement": "animal instinct have less viewership than your cheat art",
        "label": 1,
        "table_caption": "list of the green green grass episodes",
        "table_text": "episode#original title#directed by#written by#original airdate#duration#viewership\n24#the path of true love#dewi humphreys#jim sullivan#8 january 2009#30 minutes#4.07 million\n25#home brew#dewi humphreys#john sullivan#15 january 2009#30 minutes#4.02 million\n26#calendar boys#dewi humphreys#david cantor#22 january 2009#30 minutes#3.86 million\n27#animal instincts#dewi humphreys#jim sullivan#29 january 2009#30 minutes#3.87 million\n28#your cheating art#dewi humphreys#john sullivan#5 february 2009#30 minutes#3.93 million\n29#the departed#dewi humphreys#keith lindsay#12 february 2009#30 minutes#3.73 million\n30#i done it my way#dewi humphreys#john sullivan#19 february 2009#30 minutes#3.46 million\n31#one man 's junk#dewi humphreys#robert evans#26 february 2009#30 minutes#3.70 million\n",
        "pandas_code": "df[df['original title'] == 'animal instincts']['viewership'].values[0] < df[df['original title'] == 'your cheating art']['viewership'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1438,
        "statement": "there be 3 episode written by john sullivan",
        "label": 1,
        "table_caption": "list of the green green grass episodes",
        "table_text": "episode#original title#directed by#written by#original airdate#duration#viewership\n24#the path of true love#dewi humphreys#jim sullivan#8 january 2009#30 minutes#4.07 million\n25#home brew#dewi humphreys#john sullivan#15 january 2009#30 minutes#4.02 million\n26#calendar boys#dewi humphreys#david cantor#22 january 2009#30 minutes#3.86 million\n27#animal instincts#dewi humphreys#jim sullivan#29 january 2009#30 minutes#3.87 million\n28#your cheating art#dewi humphreys#john sullivan#5 february 2009#30 minutes#3.93 million\n29#the departed#dewi humphreys#keith lindsay#12 february 2009#30 minutes#3.73 million\n30#i done it my way#dewi humphreys#john sullivan#19 february 2009#30 minutes#3.46 million\n31#one man 's junk#dewi humphreys#robert evans#26 february 2009#30 minutes#3.70 million\n",
        "pandas_code": "len(df[df['written by'] == 'john sullivan']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1439,
        "statement": "stephen jackson have the most high assist",
        "label": 1,
        "table_caption": "2008 - 09 golden state warriors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 2#san antonio#l 105 - 110 (ot)#stephen jackson (33)#andris biedri\u0146\u0161 (9)#stephen jackson (11)#oracle arena 18205#15 - 34\n50#february 4#phoenix#w 124 - 112 (ot)#stephen jackson (30)#stephen jackson (11)#stephen jackson (10)#oracle arena 19596#16 - 34\n51#february 6#phoenix#l 105 - 115 (ot)#corey maggette (25)#andris biedri\u0146\u0161 (11)#stephen jackson , jamal crawford (6)#us airways center 18422#16 - 35\n52#february 8#utah#w 116 - 96 (ot)#corey maggette (24)#stephen jackson (10)#stephen jackson (8)#oracle arena 19174#17 - 35\n53#february 10#new york#w 144 - 127 (ot)#stephen jackson (35)#kelenna azubuike (10)#stephen jackson (10)#oracle arena 19098#18 - 35\n54#february 12#portland#w 105 - 98 (ot)#corey maggette (24)#ronny turiaf (11)#stephen jackson (6)#oracle arena 19322#19 - 35\n55#february 18#la lakers#l 121 - 129 (ot)#stephen jackson , corey maggette (24)#anthony randolph (12)#stephen jackson (9)#oracle arena 20007#19 - 36\n56#february 21#oklahoma city#w 133 - 120 (ot)#stephen jackson (26)#corey maggette (8)#stephen jackson (9)#oracle arena 19108#20 - 36\n57#february 23#la clippers#l 105 - 118 (ot)#stephen jackson (28)#corey maggette , ronny turiaf (7)#marco belinelli (5)#staples center 15383#20 - 37\n",
        "pandas_code": "df['high assists'].str.contains('stephen jackson').sum() == df['high assists'].apply(lambda x: 'stephen jackson' in x).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 1440,
        "statement": "stephen jackson 's high assist in game 53 be higher than his high assist in game 54",
        "label": 1,
        "table_caption": "2008 - 09 golden state warriors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 2#san antonio#l 105 - 110 (ot)#stephen jackson (33)#andris biedri\u0146\u0161 (9)#stephen jackson (11)#oracle arena 18205#15 - 34\n50#february 4#phoenix#w 124 - 112 (ot)#stephen jackson (30)#stephen jackson (11)#stephen jackson (10)#oracle arena 19596#16 - 34\n51#february 6#phoenix#l 105 - 115 (ot)#corey maggette (25)#andris biedri\u0146\u0161 (11)#stephen jackson , jamal crawford (6)#us airways center 18422#16 - 35\n52#february 8#utah#w 116 - 96 (ot)#corey maggette (24)#stephen jackson (10)#stephen jackson (8)#oracle arena 19174#17 - 35\n53#february 10#new york#w 144 - 127 (ot)#stephen jackson (35)#kelenna azubuike (10)#stephen jackson (10)#oracle arena 19098#18 - 35\n54#february 12#portland#w 105 - 98 (ot)#corey maggette (24)#ronny turiaf (11)#stephen jackson (6)#oracle arena 19322#19 - 35\n55#february 18#la lakers#l 121 - 129 (ot)#stephen jackson , corey maggette (24)#anthony randolph (12)#stephen jackson (9)#oracle arena 20007#19 - 36\n56#february 21#oklahoma city#w 133 - 120 (ot)#stephen jackson (26)#corey maggette (8)#stephen jackson (9)#oracle arena 19108#20 - 36\n57#february 23#la clippers#l 105 - 118 (ot)#stephen jackson (28)#corey maggette , ronny turiaf (7)#marco belinelli (5)#staples center 15383#20 - 37\n",
        "pandas_code": "int(df.loc[df['game'] == 53, 'high assists'].iloc[0].split('(')[-1].strip(')')) > int(df.loc[df['game'] == 54, 'high assists'].iloc[0].split('(')[-1].strip(')'))",
        "pandas_eval": "True"
    },
    {
        "id": 1441,
        "statement": "they play phoenix more often than they play utah",
        "label": 1,
        "table_caption": "2008 - 09 golden state warriors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 2#san antonio#l 105 - 110 (ot)#stephen jackson (33)#andris biedri\u0146\u0161 (9)#stephen jackson (11)#oracle arena 18205#15 - 34\n50#february 4#phoenix#w 124 - 112 (ot)#stephen jackson (30)#stephen jackson (11)#stephen jackson (10)#oracle arena 19596#16 - 34\n51#february 6#phoenix#l 105 - 115 (ot)#corey maggette (25)#andris biedri\u0146\u0161 (11)#stephen jackson , jamal crawford (6)#us airways center 18422#16 - 35\n52#february 8#utah#w 116 - 96 (ot)#corey maggette (24)#stephen jackson (10)#stephen jackson (8)#oracle arena 19174#17 - 35\n53#february 10#new york#w 144 - 127 (ot)#stephen jackson (35)#kelenna azubuike (10)#stephen jackson (10)#oracle arena 19098#18 - 35\n54#february 12#portland#w 105 - 98 (ot)#corey maggette (24)#ronny turiaf (11)#stephen jackson (6)#oracle arena 19322#19 - 35\n55#february 18#la lakers#l 121 - 129 (ot)#stephen jackson , corey maggette (24)#anthony randolph (12)#stephen jackson (9)#oracle arena 20007#19 - 36\n56#february 21#oklahoma city#w 133 - 120 (ot)#stephen jackson (26)#corey maggette (8)#stephen jackson (9)#oracle arena 19108#20 - 36\n57#february 23#la clippers#l 105 - 118 (ot)#stephen jackson (28)#corey maggette , ronny turiaf (7)#marco belinelli (5)#staples center 15383#20 - 37\n",
        "pandas_code": "(df['team'].value_counts()['phoenix'] > df['team'].value_counts()['utah'])",
        "pandas_eval": "True"
    },
    {
        "id": 1442,
        "statement": "stephen jackson have the more high point than corey maggette",
        "label": 1,
        "table_caption": "2008 - 09 golden state warriors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 2#san antonio#l 105 - 110 (ot)#stephen jackson (33)#andris biedri\u0146\u0161 (9)#stephen jackson (11)#oracle arena 18205#15 - 34\n50#february 4#phoenix#w 124 - 112 (ot)#stephen jackson (30)#stephen jackson (11)#stephen jackson (10)#oracle arena 19596#16 - 34\n51#february 6#phoenix#l 105 - 115 (ot)#corey maggette (25)#andris biedri\u0146\u0161 (11)#stephen jackson , jamal crawford (6)#us airways center 18422#16 - 35\n52#february 8#utah#w 116 - 96 (ot)#corey maggette (24)#stephen jackson (10)#stephen jackson (8)#oracle arena 19174#17 - 35\n53#february 10#new york#w 144 - 127 (ot)#stephen jackson (35)#kelenna azubuike (10)#stephen jackson (10)#oracle arena 19098#18 - 35\n54#february 12#portland#w 105 - 98 (ot)#corey maggette (24)#ronny turiaf (11)#stephen jackson (6)#oracle arena 19322#19 - 35\n55#february 18#la lakers#l 121 - 129 (ot)#stephen jackson , corey maggette (24)#anthony randolph (12)#stephen jackson (9)#oracle arena 20007#19 - 36\n56#february 21#oklahoma city#w 133 - 120 (ot)#stephen jackson (26)#corey maggette (8)#stephen jackson (9)#oracle arena 19108#20 - 36\n57#february 23#la clippers#l 105 - 118 (ot)#stephen jackson (28)#corey maggette , ronny turiaf (7)#marco belinelli (5)#staples center 15383#20 - 37\n",
        "pandas_code": "df['high points'].apply(lambda x: int(x.split('(')[1].split(')')[0]) if 'stephen jackson' in x else 0).sum() > df['high points'].apply(lambda x: int(x.split('(')[1].split(')')[0]) if 'corey maggette' in x else 0).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 1443,
        "statement": "oracle arena be the most common location",
        "label": 1,
        "table_caption": "2008 - 09 golden state warriors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 2#san antonio#l 105 - 110 (ot)#stephen jackson (33)#andris biedri\u0146\u0161 (9)#stephen jackson (11)#oracle arena 18205#15 - 34\n50#february 4#phoenix#w 124 - 112 (ot)#stephen jackson (30)#stephen jackson (11)#stephen jackson (10)#oracle arena 19596#16 - 34\n51#february 6#phoenix#l 105 - 115 (ot)#corey maggette (25)#andris biedri\u0146\u0161 (11)#stephen jackson , jamal crawford (6)#us airways center 18422#16 - 35\n52#february 8#utah#w 116 - 96 (ot)#corey maggette (24)#stephen jackson (10)#stephen jackson (8)#oracle arena 19174#17 - 35\n53#february 10#new york#w 144 - 127 (ot)#stephen jackson (35)#kelenna azubuike (10)#stephen jackson (10)#oracle arena 19098#18 - 35\n54#february 12#portland#w 105 - 98 (ot)#corey maggette (24)#ronny turiaf (11)#stephen jackson (6)#oracle arena 19322#19 - 35\n55#february 18#la lakers#l 121 - 129 (ot)#stephen jackson , corey maggette (24)#anthony randolph (12)#stephen jackson (9)#oracle arena 20007#19 - 36\n56#february 21#oklahoma city#w 133 - 120 (ot)#stephen jackson (26)#corey maggette (8)#stephen jackson (9)#oracle arena 19108#20 - 36\n57#february 23#la clippers#l 105 - 118 (ot)#stephen jackson (28)#corey maggette , ronny turiaf (7)#marco belinelli (5)#staples center 15383#20 - 37\n",
        "pandas_code": "df['location attendance'].str.contains('oracle arena').sum() > (len(df) / 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1444,
        "statement": "the colt only draft 1 quarter back who be from ohio state",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "(df[(df['position'] == 'quarterback') & (df['college'] == 'ohio state')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1446,
        "statement": "the lowest pick number be in round 12 and from texas",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "df[(df['round'] == 12) & (df['college'] == 'texas')]['pick'].min() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1447,
        "statement": "2 of the draft pick be name mike and be select sequentially",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "(df['name'].str.contains('mike', case=False).sum() >= 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1449,
        "statement": "the colt draft 3 quarter back that be from ohio state",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "len(df[(df['position'] == 'quarterback') & (df['college'] == 'ohio state')]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1450,
        "statement": "there be not any draft from college locate in texas",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "not df['college'].str.contains('texas', case=False).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1451,
        "statement": "the lowest pick number be in round 6 and from north carolina",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "df[(df['round'] == 6) & (df['college'] == 'north carolina')]['pick'].min() == df['pick'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 1452,
        "statement": "there be no draft name mike",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "not any(df['name'].str.contains('mike', case=False))",
        "pandas_eval": "False"
    },
    {
        "id": 1453,
        "statement": "the most common name on the draft pick be mike",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#22#22#don mccauley#running back#north carolina\n1#26#26#leonard dunlap#defensive back#north texas state\n2#26#52#bill atessis#defensive end#texas\n3#26#78#karl douglas#quarterback#texas a&i\n5#26#130#john andrews#tight end#indiana\n6#26#156#ken frith#defensive tackle#northeast louisiana\n7#26#182#gordon bowdell#wide receiver#michigan state\n8#25#207#willie bogan#defensive back#dartmouth\n9#26#234#bill burnett#running back#arkansas\n10#26#260#rex kern#quarterback#ohio state\n11#26#286#dave jones#linebacker#baylor\n12#8#294#bob wuensch#offensive tackle#texas\n12#26#312#bill triplett#wide receiver#michigan state\n13#26#338#tom neville#linebacker#yale\n14#26#364#mike mikolayunas#running back#davidson\n15#26#390#mike hogan#linebacker#michigan state\n16#26#416#rich harrington#defensive back#houston\n17#25#441#don nottingham#running back#kent state\n",
        "pandas_code": "df['name'].value_counts().idxmax() == 'mike'",
        "pandas_eval": "False"
    },
    {
        "id": 1454,
        "statement": "gary gait appear before mark steenhuis overall for 2009 nll season",
        "label": 1,
        "table_caption": "2009 nll season",
        "table_text": "month#week#overall#offensive#defensive#transition#rookie\njanuary#1#blaine manning#casey powell#kevin croswell#scott stewart#andrew watt\njanuary#2#gary gait#pat maddalena#ken montour#brodie merrill#sean thomson\njanuary#3#mark steenhuis#mark steenhuis#ken montour#greg peyser#daryl veltman\njanuary#4#dan teat#dan dawson#michael thompson#tyler codron#daryl veltman\njanuary#5#matt disher#mike accursi#matt disher#curtis hodgson#matt danowski\nfebruary#6#gary bining#tracey kelusky#pat campbell#chris driscoll#gary bining\nfebruary#7#mark steenhuis#mark steenhuis#anthony cosmo#jason bloom#tyler crompton\nfebruary#8#dan dawson#mark steenhuis#jon harnett#bobby mcbride#rhys duch\nfebruary#9#shawn evans#shawn evans#matt disher#kyle ross#kevin buchanan\nmarch#10#shawn evans#shawn evans#sandy chapman#pat mccready#kevin buchanan\nmarch#11#bob watson#john tavares#ken montour#paul rabil#tyler crompton\nmarch#12#athan iannucci#andy secore#matt vinc#brodie merrill#rhys duch\nmarch#13#john tavares#colin doyle#tyler richards#brodie merrill#rhys duch\napril#14#anthony cosmo#merrick thomson#matt disher#scott stewart#rhys duch\n",
        "pandas_code": "df[df['overall'] == 'gary gait'].index[0] < df[df['overall'] == 'mark steenhuis'].index[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1455,
        "statement": "rhys duch appear the most on week on rookie for the 2009 nll season",
        "label": 1,
        "table_caption": "2009 nll season",
        "table_text": "month#week#overall#offensive#defensive#transition#rookie\njanuary#1#blaine manning#casey powell#kevin croswell#scott stewart#andrew watt\njanuary#2#gary gait#pat maddalena#ken montour#brodie merrill#sean thomson\njanuary#3#mark steenhuis#mark steenhuis#ken montour#greg peyser#daryl veltman\njanuary#4#dan teat#dan dawson#michael thompson#tyler codron#daryl veltman\njanuary#5#matt disher#mike accursi#matt disher#curtis hodgson#matt danowski\nfebruary#6#gary bining#tracey kelusky#pat campbell#chris driscoll#gary bining\nfebruary#7#mark steenhuis#mark steenhuis#anthony cosmo#jason bloom#tyler crompton\nfebruary#8#dan dawson#mark steenhuis#jon harnett#bobby mcbride#rhys duch\nfebruary#9#shawn evans#shawn evans#matt disher#kyle ross#kevin buchanan\nmarch#10#shawn evans#shawn evans#sandy chapman#pat mccready#kevin buchanan\nmarch#11#bob watson#john tavares#ken montour#paul rabil#tyler crompton\nmarch#12#athan iannucci#andy secore#matt vinc#brodie merrill#rhys duch\nmarch#13#john tavares#colin doyle#tyler richards#brodie merrill#rhys duch\napril#14#anthony cosmo#merrick thomson#matt disher#scott stewart#rhys duch\n",
        "pandas_code": "df['rookie'].value_counts().idxmax() == 'rhys duch'",
        "pandas_eval": "True"
    },
    {
        "id": 1456,
        "statement": "mark steenhuis appear the most on offensive for 2009 nll season",
        "label": 1,
        "table_caption": "2009 nll season",
        "table_text": "month#week#overall#offensive#defensive#transition#rookie\njanuary#1#blaine manning#casey powell#kevin croswell#scott stewart#andrew watt\njanuary#2#gary gait#pat maddalena#ken montour#brodie merrill#sean thomson\njanuary#3#mark steenhuis#mark steenhuis#ken montour#greg peyser#daryl veltman\njanuary#4#dan teat#dan dawson#michael thompson#tyler codron#daryl veltman\njanuary#5#matt disher#mike accursi#matt disher#curtis hodgson#matt danowski\nfebruary#6#gary bining#tracey kelusky#pat campbell#chris driscoll#gary bining\nfebruary#7#mark steenhuis#mark steenhuis#anthony cosmo#jason bloom#tyler crompton\nfebruary#8#dan dawson#mark steenhuis#jon harnett#bobby mcbride#rhys duch\nfebruary#9#shawn evans#shawn evans#matt disher#kyle ross#kevin buchanan\nmarch#10#shawn evans#shawn evans#sandy chapman#pat mccready#kevin buchanan\nmarch#11#bob watson#john tavares#ken montour#paul rabil#tyler crompton\nmarch#12#athan iannucci#andy secore#matt vinc#brodie merrill#rhys duch\nmarch#13#john tavares#colin doyle#tyler richards#brodie merrill#rhys duch\napril#14#anthony cosmo#merrick thomson#matt disher#scott stewart#rhys duch\n",
        "pandas_code": "df['offensive'].value_counts().idxmax() == 'mark steenhuis'",
        "pandas_eval": "True"
    },
    {
        "id": 1457,
        "statement": "mark steenhuis appear 1 more then shawn evans in offensive for 2009 nll season",
        "label": 1,
        "table_caption": "2009 nll season",
        "table_text": "month#week#overall#offensive#defensive#transition#rookie\njanuary#1#blaine manning#casey powell#kevin croswell#scott stewart#andrew watt\njanuary#2#gary gait#pat maddalena#ken montour#brodie merrill#sean thomson\njanuary#3#mark steenhuis#mark steenhuis#ken montour#greg peyser#daryl veltman\njanuary#4#dan teat#dan dawson#michael thompson#tyler codron#daryl veltman\njanuary#5#matt disher#mike accursi#matt disher#curtis hodgson#matt danowski\nfebruary#6#gary bining#tracey kelusky#pat campbell#chris driscoll#gary bining\nfebruary#7#mark steenhuis#mark steenhuis#anthony cosmo#jason bloom#tyler crompton\nfebruary#8#dan dawson#mark steenhuis#jon harnett#bobby mcbride#rhys duch\nfebruary#9#shawn evans#shawn evans#matt disher#kyle ross#kevin buchanan\nmarch#10#shawn evans#shawn evans#sandy chapman#pat mccready#kevin buchanan\nmarch#11#bob watson#john tavares#ken montour#paul rabil#tyler crompton\nmarch#12#athan iannucci#andy secore#matt vinc#brodie merrill#rhys duch\nmarch#13#john tavares#colin doyle#tyler richards#brodie merrill#rhys duch\napril#14#anthony cosmo#merrick thomson#matt disher#scott stewart#rhys duch\n",
        "pandas_code": "(df['offensive'].value_counts()['mark steenhuis'] - df['offensive'].value_counts()['shawn evans']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1458,
        "statement": "mark steenhuis and shawn evan appear the same amount of time on the overall category , with mark steenhuis appear both time before shawn evans",
        "label": 1,
        "table_caption": "2009 nll season",
        "table_text": "month#week#overall#offensive#defensive#transition#rookie\njanuary#1#blaine manning#casey powell#kevin croswell#scott stewart#andrew watt\njanuary#2#gary gait#pat maddalena#ken montour#brodie merrill#sean thomson\njanuary#3#mark steenhuis#mark steenhuis#ken montour#greg peyser#daryl veltman\njanuary#4#dan teat#dan dawson#michael thompson#tyler codron#daryl veltman\njanuary#5#matt disher#mike accursi#matt disher#curtis hodgson#matt danowski\nfebruary#6#gary bining#tracey kelusky#pat campbell#chris driscoll#gary bining\nfebruary#7#mark steenhuis#mark steenhuis#anthony cosmo#jason bloom#tyler crompton\nfebruary#8#dan dawson#mark steenhuis#jon harnett#bobby mcbride#rhys duch\nfebruary#9#shawn evans#shawn evans#matt disher#kyle ross#kevin buchanan\nmarch#10#shawn evans#shawn evans#sandy chapman#pat mccready#kevin buchanan\nmarch#11#bob watson#john tavares#ken montour#paul rabil#tyler crompton\nmarch#12#athan iannucci#andy secore#matt vinc#brodie merrill#rhys duch\nmarch#13#john tavares#colin doyle#tyler richards#brodie merrill#rhys duch\napril#14#anthony cosmo#merrick thomson#matt disher#scott stewart#rhys duch\n",
        "pandas_code": "(df['overall'].value_counts()['mark steenhuis'] == df['overall'].value_counts()['shawn evans']) and (df[df['overall'] == 'mark steenhuis'].index[0] < df[df['overall'] == 'shawn evans'].index[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1459,
        "statement": "there be 8 defence player draft in 1971",
        "label": 1,
        "table_caption": "1971 nhl amateur draft",
        "table_text": "pick#player#position#nationality#nhl team#college / junior / club team\n71#gerry egers#defence#canada#california golden seals#sudbury wolves (noha)\n72#charlie shaw#defence#canada#detroit red wings#toronto marlboros (oha)\n73#tim steeves#defence#canada#vancouver canucks#charlottetown royals (nbshl)\n74#ian williams#right wing#canada#pittsburgh penguins#university of notre dame (ncaa)\n75#pierre duguay#centre#canada#buffalo sabres#quebec remparts (qmjhl)\n76#camille lapierre#centre#canada#los angeles kings#montreal junior canadiens (oha)\n77#alan globensky#defence#canada#minnesota north stars#montreal junior canadiens (oha)\n78#yvon bilodeau#defence#canada#philadelphia flyers#estevan bruins (wchl)\n79#mike ruest#defence#canada#toronto maple leafs#cornwall royals (qmjhl)\n80#bernie doan#defence#canada#st louis blues#calgary centennials (wchl)\n81#ross butler#left wing#canada#montreal canadiens#winnipeg jets (wchl)\n82#jim johnston#centre#canada#chicago black hawks#university of wisconsin (wcha)\n83#wayne wood#goaltender#canada#new york rangers#montreal junior canadiens (oha)\n84#bob mcmahon#defence#canada#boston bruins#st catharines black hawks (oha)\n",
        "pandas_code": "(df['position'] == 'defence').sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1460,
        "statement": "all player draft in round 6 be canadian",
        "label": 1,
        "table_caption": "1971 nhl amateur draft",
        "table_text": "pick#player#position#nationality#nhl team#college / junior / club team\n71#gerry egers#defence#canada#california golden seals#sudbury wolves (noha)\n72#charlie shaw#defence#canada#detroit red wings#toronto marlboros (oha)\n73#tim steeves#defence#canada#vancouver canucks#charlottetown royals (nbshl)\n74#ian williams#right wing#canada#pittsburgh penguins#university of notre dame (ncaa)\n75#pierre duguay#centre#canada#buffalo sabres#quebec remparts (qmjhl)\n76#camille lapierre#centre#canada#los angeles kings#montreal junior canadiens (oha)\n77#alan globensky#defence#canada#minnesota north stars#montreal junior canadiens (oha)\n78#yvon bilodeau#defence#canada#philadelphia flyers#estevan bruins (wchl)\n79#mike ruest#defence#canada#toronto maple leafs#cornwall royals (qmjhl)\n80#bernie doan#defence#canada#st louis blues#calgary centennials (wchl)\n81#ross butler#left wing#canada#montreal canadiens#winnipeg jets (wchl)\n82#jim johnston#centre#canada#chicago black hawks#university of wisconsin (wcha)\n83#wayne wood#goaltender#canada#new york rangers#montreal junior canadiens (oha)\n84#bob mcmahon#defence#canada#boston bruins#st catharines black hawks (oha)\n",
        "pandas_code": "all(df[df['pick'].between(71, 80)]['nationality'] == 'canada')",
        "pandas_eval": "True"
    },
    {
        "id": 1462,
        "statement": "there be 1 right wing player draft in round 6",
        "label": 1,
        "table_caption": "1971 nhl amateur draft",
        "table_text": "pick#player#position#nationality#nhl team#college / junior / club team\n71#gerry egers#defence#canada#california golden seals#sudbury wolves (noha)\n72#charlie shaw#defence#canada#detroit red wings#toronto marlboros (oha)\n73#tim steeves#defence#canada#vancouver canucks#charlottetown royals (nbshl)\n74#ian williams#right wing#canada#pittsburgh penguins#university of notre dame (ncaa)\n75#pierre duguay#centre#canada#buffalo sabres#quebec remparts (qmjhl)\n76#camille lapierre#centre#canada#los angeles kings#montreal junior canadiens (oha)\n77#alan globensky#defence#canada#minnesota north stars#montreal junior canadiens (oha)\n78#yvon bilodeau#defence#canada#philadelphia flyers#estevan bruins (wchl)\n79#mike ruest#defence#canada#toronto maple leafs#cornwall royals (qmjhl)\n80#bernie doan#defence#canada#st louis blues#calgary centennials (wchl)\n81#ross butler#left wing#canada#montreal canadiens#winnipeg jets (wchl)\n82#jim johnston#centre#canada#chicago black hawks#university of wisconsin (wcha)\n83#wayne wood#goaltender#canada#new york rangers#montreal junior canadiens (oha)\n84#bob mcmahon#defence#canada#boston bruins#st catharines black hawks (oha)\n",
        "pandas_code": "(df[(df['position'] == 'right wing') & (df['pick'] >= 71) & (df['pick'] <= 80)].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1463,
        "statement": "there be a total of 14 player draft in round 6",
        "label": 1,
        "table_caption": "1971 nhl amateur draft",
        "table_text": "pick#player#position#nationality#nhl team#college / junior / club team\n71#gerry egers#defence#canada#california golden seals#sudbury wolves (noha)\n72#charlie shaw#defence#canada#detroit red wings#toronto marlboros (oha)\n73#tim steeves#defence#canada#vancouver canucks#charlottetown royals (nbshl)\n74#ian williams#right wing#canada#pittsburgh penguins#university of notre dame (ncaa)\n75#pierre duguay#centre#canada#buffalo sabres#quebec remparts (qmjhl)\n76#camille lapierre#centre#canada#los angeles kings#montreal junior canadiens (oha)\n77#alan globensky#defence#canada#minnesota north stars#montreal junior canadiens (oha)\n78#yvon bilodeau#defence#canada#philadelphia flyers#estevan bruins (wchl)\n79#mike ruest#defence#canada#toronto maple leafs#cornwall royals (qmjhl)\n80#bernie doan#defence#canada#st louis blues#calgary centennials (wchl)\n81#ross butler#left wing#canada#montreal canadiens#winnipeg jets (wchl)\n82#jim johnston#centre#canada#chicago black hawks#university of wisconsin (wcha)\n83#wayne wood#goaltender#canada#new york rangers#montreal junior canadiens (oha)\n84#bob mcmahon#defence#canada#boston bruins#st catharines black hawks (oha)\n",
        "pandas_code": "len(df[df['pick'].between(71, 84)]) == 14",
        "pandas_eval": "True"
    },
    {
        "id": 1464,
        "statement": "for the 2005 - 06 detroit redwing season , detroit play at home 2 more time than away",
        "label": 1,
        "table_caption": "2005 - 06 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\njanuary 3#minnesota#4 - 2#detroit#osgood#20066#26 - 10 - 3\njanuary 5#st louis#0 - 3#detroit#legace#20066#27 - 10 - 3\njanuary 6#detroit#3 - 1#nashville#legace#17113#28 - 10 - 3\njanuary 8#dallas#6 - 3#detroit#osgood#20066#28 - 11 - 3\njanuary 10#detroit#2 - 3#carolina#legace#18730#28 - 12 - 3\njanuary 12#philadelphia#3 - 6#detroit#legace#20066#29 - 12 - 3\njanuary 14#ny rangers#3 - 4#detroit#legace#20066#30 - 12 - 3\njanuary 18#detroit#4 - 0#columbus#osgood#17089#31 - 12 - 3\njanuary 21#detroit#4 - 3#colorado#legace#18007#32 - 12 - 3\njanuary 23#nashville#3 - 2#detroit#legace#20066#32 - 13 - 3\njanuary 24#nashville#2 - 1#detroit#legace#20066#32 - 13 - 4\njanuary 26#vancouver#1 - 2#detroit#legace#20066#33 - 13 - 4\njanuary 28#detroit#1 - 2#dallas#legace#18584#33 - 13 - 5\njanuary 30#detroit#5 - 4#minnesota#legace#18568#34 - 13 - 5\n",
        "pandas_code": "(df['home'].value_counts()['detroit'] - df['visitor'].value_counts()['detroit']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1465,
        "statement": "for the 2005 - 06 detroit redwing season , osgood be the decision january 3 , 8th , and 18th",
        "label": 1,
        "table_caption": "2005 - 06 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\njanuary 3#minnesota#4 - 2#detroit#osgood#20066#26 - 10 - 3\njanuary 5#st louis#0 - 3#detroit#legace#20066#27 - 10 - 3\njanuary 6#detroit#3 - 1#nashville#legace#17113#28 - 10 - 3\njanuary 8#dallas#6 - 3#detroit#osgood#20066#28 - 11 - 3\njanuary 10#detroit#2 - 3#carolina#legace#18730#28 - 12 - 3\njanuary 12#philadelphia#3 - 6#detroit#legace#20066#29 - 12 - 3\njanuary 14#ny rangers#3 - 4#detroit#legace#20066#30 - 12 - 3\njanuary 18#detroit#4 - 0#columbus#osgood#17089#31 - 12 - 3\njanuary 21#detroit#4 - 3#colorado#legace#18007#32 - 12 - 3\njanuary 23#nashville#3 - 2#detroit#legace#20066#32 - 13 - 3\njanuary 24#nashville#2 - 1#detroit#legace#20066#32 - 13 - 4\njanuary 26#vancouver#1 - 2#detroit#legace#20066#33 - 13 - 4\njanuary 28#detroit#1 - 2#dallas#legace#18584#33 - 13 - 5\njanuary 30#detroit#5 - 4#minnesota#legace#18568#34 - 13 - 5\n",
        "pandas_code": "all(df[df['date'].isin(['january 3', 'january 8', 'january 18'])]['decision'] == 'osgood')",
        "pandas_eval": "True"
    },
    {
        "id": 1466,
        "statement": "for the 2005 - 06 detroit redwing season , january 18 game have the lowest attendance of all the game",
        "label": 1,
        "table_caption": "2005 - 06 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\njanuary 3#minnesota#4 - 2#detroit#osgood#20066#26 - 10 - 3\njanuary 5#st louis#0 - 3#detroit#legace#20066#27 - 10 - 3\njanuary 6#detroit#3 - 1#nashville#legace#17113#28 - 10 - 3\njanuary 8#dallas#6 - 3#detroit#osgood#20066#28 - 11 - 3\njanuary 10#detroit#2 - 3#carolina#legace#18730#28 - 12 - 3\njanuary 12#philadelphia#3 - 6#detroit#legace#20066#29 - 12 - 3\njanuary 14#ny rangers#3 - 4#detroit#legace#20066#30 - 12 - 3\njanuary 18#detroit#4 - 0#columbus#osgood#17089#31 - 12 - 3\njanuary 21#detroit#4 - 3#colorado#legace#18007#32 - 12 - 3\njanuary 23#nashville#3 - 2#detroit#legace#20066#32 - 13 - 3\njanuary 24#nashville#2 - 1#detroit#legace#20066#32 - 13 - 4\njanuary 26#vancouver#1 - 2#detroit#legace#20066#33 - 13 - 4\njanuary 28#detroit#1 - 2#dallas#legace#18584#33 - 13 - 5\njanuary 30#detroit#5 - 4#minnesota#legace#18568#34 - 13 - 5\n",
        "pandas_code": "df.loc[df['date'] == 'january 18', 'attendance'].iloc[0] == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1467,
        "statement": "for the 2005 - 06 detroit redwing season , the attendance be 20066 for 8 of the game",
        "label": 1,
        "table_caption": "2005 - 06 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\njanuary 3#minnesota#4 - 2#detroit#osgood#20066#26 - 10 - 3\njanuary 5#st louis#0 - 3#detroit#legace#20066#27 - 10 - 3\njanuary 6#detroit#3 - 1#nashville#legace#17113#28 - 10 - 3\njanuary 8#dallas#6 - 3#detroit#osgood#20066#28 - 11 - 3\njanuary 10#detroit#2 - 3#carolina#legace#18730#28 - 12 - 3\njanuary 12#philadelphia#3 - 6#detroit#legace#20066#29 - 12 - 3\njanuary 14#ny rangers#3 - 4#detroit#legace#20066#30 - 12 - 3\njanuary 18#detroit#4 - 0#columbus#osgood#17089#31 - 12 - 3\njanuary 21#detroit#4 - 3#colorado#legace#18007#32 - 12 - 3\njanuary 23#nashville#3 - 2#detroit#legace#20066#32 - 13 - 3\njanuary 24#nashville#2 - 1#detroit#legace#20066#32 - 13 - 4\njanuary 26#vancouver#1 - 2#detroit#legace#20066#33 - 13 - 4\njanuary 28#detroit#1 - 2#dallas#legace#18584#33 - 13 - 5\njanuary 30#detroit#5 - 4#minnesota#legace#18568#34 - 13 - 5\n",
        "pandas_code": "(df['attendance'] == 20066).sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1468,
        "statement": "for the 2005 - 06 detroit redwing season , the score be the same on january 26 and 28th",
        "label": 1,
        "table_caption": "2005 - 06 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\njanuary 3#minnesota#4 - 2#detroit#osgood#20066#26 - 10 - 3\njanuary 5#st louis#0 - 3#detroit#legace#20066#27 - 10 - 3\njanuary 6#detroit#3 - 1#nashville#legace#17113#28 - 10 - 3\njanuary 8#dallas#6 - 3#detroit#osgood#20066#28 - 11 - 3\njanuary 10#detroit#2 - 3#carolina#legace#18730#28 - 12 - 3\njanuary 12#philadelphia#3 - 6#detroit#legace#20066#29 - 12 - 3\njanuary 14#ny rangers#3 - 4#detroit#legace#20066#30 - 12 - 3\njanuary 18#detroit#4 - 0#columbus#osgood#17089#31 - 12 - 3\njanuary 21#detroit#4 - 3#colorado#legace#18007#32 - 12 - 3\njanuary 23#nashville#3 - 2#detroit#legace#20066#32 - 13 - 3\njanuary 24#nashville#2 - 1#detroit#legace#20066#32 - 13 - 4\njanuary 26#vancouver#1 - 2#detroit#legace#20066#33 - 13 - 4\njanuary 28#detroit#1 - 2#dallas#legace#18584#33 - 13 - 5\njanuary 30#detroit#5 - 4#minnesota#legace#18568#34 - 13 - 5\n",
        "pandas_code": "df.loc[df['date'].isin(['january 26', 'january 28']), 'score'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1469,
        "statement": "6 point separate the first and second place singer in the eurovision song contest 1970",
        "label": 1,
        "table_caption": "eurovision song contest 1970",
        "table_text": "language#artist#song#place#points\ndutch#hearts of soul#waterman#7#7\nfrench#henri d\u00e8s#retour#4#8\nitalian#gianni morandi#occhi di ragazza#8#5\nslovene#eva sr\u0161en#pridi , dala ti bom cvet#11#4\nfrench#jean vall\u00e9e#viens l'oublier#8#5\nfrench#guy bonnet#marie - blanche#4#8\nenglish#mary hopkin#knock , knock who 's there#2#26\nfrench#david alexandre winter#je suis tomb\u00e9 du ciel#12#0\nspanish#julio iglesias#gwendolyne#4#8\nfrench#dominique dussault#marl\u00e8ne#8#5\ngerman#katja ebstein#wunder gibt es immer wieder#3#12\nenglish#dana#all kinds of everything#1#32\n",
        "pandas_code": "abs(df[df['place'] == 1]['points'].values[0] - df[df['place'] == 2]['points'].values[0]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1470,
        "statement": "only 1 singer score less than 4 point in the competition",
        "label": 1,
        "table_caption": "eurovision song contest 1970",
        "table_text": "language#artist#song#place#points\ndutch#hearts of soul#waterman#7#7\nfrench#henri d\u00e8s#retour#4#8\nitalian#gianni morandi#occhi di ragazza#8#5\nslovene#eva sr\u0161en#pridi , dala ti bom cvet#11#4\nfrench#jean vall\u00e9e#viens l'oublier#8#5\nfrench#guy bonnet#marie - blanche#4#8\nenglish#mary hopkin#knock , knock who 's there#2#26\nfrench#david alexandre winter#je suis tomb\u00e9 du ciel#12#0\nspanish#julio iglesias#gwendolyne#4#8\nfrench#dominique dussault#marl\u00e8ne#8#5\ngerman#katja ebstein#wunder gibt es immer wieder#3#12\nenglish#dana#all kinds of everything#1#32\n",
        "pandas_code": "df[df['points'] < 4].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1471,
        "statement": "5 singer perform in french at the eurovision song contest 1970",
        "label": 1,
        "table_caption": "eurovision song contest 1970",
        "table_text": "language#artist#song#place#points\ndutch#hearts of soul#waterman#7#7\nfrench#henri d\u00e8s#retour#4#8\nitalian#gianni morandi#occhi di ragazza#8#5\nslovene#eva sr\u0161en#pridi , dala ti bom cvet#11#4\nfrench#jean vall\u00e9e#viens l'oublier#8#5\nfrench#guy bonnet#marie - blanche#4#8\nenglish#mary hopkin#knock , knock who 's there#2#26\nfrench#david alexandre winter#je suis tomb\u00e9 du ciel#12#0\nspanish#julio iglesias#gwendolyne#4#8\nfrench#dominique dussault#marl\u00e8ne#8#5\ngerman#katja ebstein#wunder gibt es immer wieder#3#12\nenglish#dana#all kinds of everything#1#32\n",
        "pandas_code": "(df['language'] == 'french').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1472,
        "statement": "both the winner and the runner - up perform in english at the eurovision song contest 1970",
        "label": 1,
        "table_caption": "eurovision song contest 1970",
        "table_text": "language#artist#song#place#points\ndutch#hearts of soul#waterman#7#7\nfrench#henri d\u00e8s#retour#4#8\nitalian#gianni morandi#occhi di ragazza#8#5\nslovene#eva sr\u0161en#pridi , dala ti bom cvet#11#4\nfrench#jean vall\u00e9e#viens l'oublier#8#5\nfrench#guy bonnet#marie - blanche#4#8\nenglish#mary hopkin#knock , knock who 's there#2#26\nfrench#david alexandre winter#je suis tomb\u00e9 du ciel#12#0\nspanish#julio iglesias#gwendolyne#4#8\nfrench#dominique dussault#marl\u00e8ne#8#5\ngerman#katja ebstein#wunder gibt es immer wieder#3#12\nenglish#dana#all kinds of everything#1#32\n",
        "pandas_code": "(df[df['place'] == 1]['language'].values[0] == 'english') & (df[df['place'] == 2]['language'].values[0] == 'english')",
        "pandas_eval": "True"
    },
    {
        "id": 1473,
        "statement": "3 performer tie for fourth place in the competition , with 8 point each",
        "label": 1,
        "table_caption": "eurovision song contest 1970",
        "table_text": "language#artist#song#place#points\ndutch#hearts of soul#waterman#7#7\nfrench#henri d\u00e8s#retour#4#8\nitalian#gianni morandi#occhi di ragazza#8#5\nslovene#eva sr\u0161en#pridi , dala ti bom cvet#11#4\nfrench#jean vall\u00e9e#viens l'oublier#8#5\nfrench#guy bonnet#marie - blanche#4#8\nenglish#mary hopkin#knock , knock who 's there#2#26\nfrench#david alexandre winter#je suis tomb\u00e9 du ciel#12#0\nspanish#julio iglesias#gwendolyne#4#8\nfrench#dominique dussault#marl\u00e8ne#8#5\ngerman#katja ebstein#wunder gibt es immer wieder#3#12\nenglish#dana#all kinds of everything#1#32\n",
        "pandas_code": "len(df[df['place'] == 4]) == 3 and all(df[df['place'] == 4]['points'] == 8)",
        "pandas_eval": "True"
    },
    {
        "id": 1474,
        "statement": "corey lee be from north carolina state university while matt mcclendon be from dr phillips hs (orlando , fl)",
        "label": 1,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['player'] == 'corey lee']['school'].values[0] == 'north carolina state university') & (df[df['player'] == 'matt mcclendon']['school'].values[0] == 'dr phillips hs (orlando , fl)')",
        "pandas_eval": "True"
    },
    {
        "id": 1475,
        "statement": "chris reitsma be with team boston red sox , and jason marquis with atlanta brave",
        "label": 1,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['player'] == 'chris reitsma']['team'].iloc[0] == 'boston red sox') & (df[df['player'] == 'jason marquis']['team'].iloc[0] == 'atlanta braves')",
        "pandas_eval": "True"
    },
    {
        "id": 1476,
        "statement": "4 be draft for the p position and only 1 for the 1b , of position",
        "label": 1,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['position'].str.contains('p')].shape[0] == 4) & (df[df['position'].str.contains('1b , of')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1477,
        "statement": "the 1996 major league baseball draft table show pick 31 thru 35 from 5 different school",
        "label": 1,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "len(df['school'].unique()) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1478,
        "statement": "pete tucci be draft for the 1b , of position , and corey lee for the p position",
        "label": 1,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[(df['player'] == 'pete tucci') & (df['position'] == '1b , of')].shape[0] > 0) & (df[(df['player'] == 'corey lee') & (df['position'] == 'p')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1479,
        "statement": "pete tucci be from north carolina state university while matt mcclendon be from dr phillips hs (orlando , fl)",
        "label": 0,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['player'] == 'pete tucci']['school'].values[0] == 'north carolina state university') & (df[df['player'] == 'matt mcclendon']['school'].values[0] == 'dr phillips hs (orlando , fl)')",
        "pandas_eval": "False"
    },
    {
        "id": 1480,
        "statement": "chris reitsma be with team boston red sox , and jason marquis with texas ranger",
        "label": 0,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['player'] == 'chris reitsma']['team'].values[0] == 'boston red sox') & (df[df['player'] == 'jason marquis']['team'].values[0] == 'texas rangers')",
        "pandas_eval": "False"
    },
    {
        "id": 1481,
        "statement": "5 be draft for the p position and only 1 for the 1b , of position",
        "label": 0,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "(df[df['position'].str.contains('p')].shape[0] == 5) & (df[df['position'].str.contains('1b , of')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 1482,
        "statement": "the 1996 major league baseball draft table show pick 31 thru 35 from 4 different school",
        "label": 0,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "len(df['school'].unique()) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1483,
        "statement": "pete tucci be draft for the p position , and corey lee for the p position",
        "label": 0,
        "table_caption": "1996 major league baseball draft",
        "table_text": "pick#player#team#position#school\n31#pete tucci#toronto blue jays#1b , of#providence college\n32#corey lee#texas rangers#p#north carolina state university\n33#matt mcclendon#cincinnati reds#p#dr phillips hs (orlando , fl)\n34#chris reitsma#boston red sox#p#calgary christian hs (calgary , ab)\n35#jason marquis#atlanta braves#p#tottenville hs (staten island , ny)\n",
        "pandas_code": "all(df[(df['player'] == 'pete tucci') | (df['player'] == 'corey lee')]['position'] == 'p')",
        "pandas_eval": "False"
    },
    {
        "id": 1484,
        "statement": "ion be the only channel with 720p video resolution",
        "label": 1,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "df[df['video'] == '720p']['psip short name'].eq('ion').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1485,
        "statement": "with the exception of ion television , all of the channel be in the 4:3 aspect",
        "label": 1,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "df[df['network'] != 'ion television']['aspect'].eq('4:3').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1487,
        "statement": "ion be a part of 3 different network name",
        "label": 1,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "df[df['network'].str.contains('ion')]['network'].nunique() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1488,
        "statement": "the majority of the tv channel be in 480i video resolution",
        "label": 1,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "df['video'].value_counts().idxmax() == '480i'",
        "pandas_eval": "True"
    },
    {
        "id": 1489,
        "statement": "shop be the only channel with 720p video resolution",
        "label": 0,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "df[(df['video'] == '720p') & (df['psip short name'] == 'shop')].shape[0] == 1 and df[df['video'] == '720p'].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1490,
        "statement": "with the exception of ion television , all of the channel be in the 16:9 aspect",
        "label": 0,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "((df['network'] != 'ion television') & (df['aspect'] != '16:9')).any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 1491,
        "statement": "qubo and qvc have different network name and psip short name",
        "label": 0,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "(df[df['psip short name'].isin(['qubo', 'qvc'])].drop_duplicates(subset=['network', 'psip short name']).shape[0] != 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1492,
        "statement": "ion be a part of 4 different network name",
        "label": 0,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "len(df[df['psip short name'].str.contains('ion')]['network'].unique()) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1493,
        "statement": "only 2 of the tv channel be in 480i video resolution",
        "label": 0,
        "table_caption": "wpxn - tv",
        "table_text": "channel#video#aspect#psip short name#network\n31.1#720p#16:9#ion#ion television\n31.2#480i#4:3#qubo#qubo\n31.3#480i#4:3#ionlife#ion life\n31.4#480i#4:3#shop#ion shop\n31.5#480i#4:3#qvc#qvc\n",
        "pandas_code": "(df['video'] == '480i').sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1494,
        "statement": "metalourg bekabad have the higest average squad age out of all team who participate in the 2010 uzbek league",
        "label": 1,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df.loc[df['team'] == 'metalourg bekabad', 'average squad age'].values[0] == df['average squad age'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1495,
        "statement": "adidas manufacture the most kit for the team participate",
        "label": 1,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df['kit manufacturer'].value_counts().idxmax() == 'adidas'",
        "pandas_eval": "True"
    },
    {
        "id": 1496,
        "statement": "nike manufacture a kit for only 1 team : fc bunyodkor",
        "label": 1,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "(df[df['kit manufacturer'] == 'nike']['team'].unique().size == 1) and (df[df['kit manufacturer'] == 'nike']['team'].iloc[0] == 'fc bunyodkor')",
        "pandas_eval": "True"
    },
    {
        "id": 1497,
        "statement": "fk samarqand - dinamo be the only team whose shirt be not sponsor",
        "label": 1,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df[df['shirt sponsor'] == 'unsponsored'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1498,
        "statement": "fc pakhtakor tashkent have the lowest average squad age",
        "label": 1,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df.loc[df['team'] == 'fc pakhtakor tashkent', 'average squad age'].iloc[0] == df['average squad age'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1499,
        "statement": "metalourg bekabad have a lower average squad age than mash'al mubarek",
        "label": 0,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df[df['team'] == 'metalourg bekabad']['average squad age'].values[0] < df[df['team'] == \"mash'al mubarek\"]['average squad age'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1500,
        "statement": "adidas manufacture every kit for the participating team",
        "label": 0,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "all(df['kit manufacturer'] == 'adidas')",
        "pandas_eval": "False"
    },
    {
        "id": 1501,
        "statement": "nike manufacture a kit for only 1 team : fk andijan",
        "label": 0,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df[df['kit manufacturer'] == 'nike']['team'].eq('fk andijan').any()",
        "pandas_eval": "False"
    },
    {
        "id": 1502,
        "statement": "dinamo sponsor fk samarqand and qizilqum zarafshon",
        "label": 0,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df.loc[df['team'].isin(['fk samarqand - dinamo', 'qizilqum zarafshon']), 'shirt sponsor'].eq('unsponsored').all()",
        "pandas_eval": "False"
    },
    {
        "id": 1503,
        "statement": "fc pakhtakor tashkent have the lowest median squad age",
        "label": 0,
        "table_caption": "2010 uzbek league",
        "table_text": "team#manager#captain#kit manufacturer#shirt sponsor#average squad age\nfc bunyodkor#mirjalol qosimov#viktor karpenko#nike#uzgazoil#24.92\nfc pakhtakor tashkent#ravshan khaydarov#odil ahmedov#adidas#kapital bank#23.56\nfc shurtan guzar#edgar gess#asror aliqulov#adidas#sho'rtan gaz mahsulot#26.61\nfk andijan#orif mamatkazin#abdumajid toirov#puma#uzdaewoo#27.21\nfk neftchi farg'ona#yuriy sarkisyan#aziz alijonov#adidas#uzbekneftegaz#26.17\nfk samarqand - dinamo#viktor djalilov#denis romanenco#adidas#unsponsored#26.94\nlokomotiv tashkent#tachmurad agamuradov#aleksandr filimonov#puma#o'zbekiston temir yollari#25.46\nmash'al mubarek#vladimir fomichev#fozil musaev#adidas#uzbekneftegaz#24.60\nmetalourg bekabad#rustam mirsodiqov#odil isaboyev#adidas#uzmk#28.24\nnasaf qarshi#anatoliy demyanenko#hayrulla karimov#puma#sho'rtan gaz mahsulot#27.09\nqizilqum zarafshon#sergei arslanov#shavkat nasibullaev#adidas#ngmk#27.19\n",
        "pandas_code": "df[df['team'] == 'fc pakhtakor tashkent']['average squad age'].iloc[0] != df['average squad age'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 1504,
        "statement": "kentucky play north carolina in game 1 and win but lose in game 2 against baylor",
        "label": 1,
        "table_caption": "1977 kentucky wildcats football team",
        "table_text": "game#date#opponent#result#wildcats points#opponents#record\n1#sept 10#north carolina#win#10#7#1 - 0\n2#sept 17#baylor#loss#6#21#1 - 1\n3#sept 24#17 west virginia#win#28#13#2 - 1\n4#oct 1#4 penn state#win#24#20#3 - 1 , 16\n5#oct 8#mississippi state#win#23#7#4 - 1 , 12\n6#oct 15#16 louisiana state#win#33#13#5 - 1 , 8\n7#oct 22#georgia#win#33#0#6 - 1 , 7\n8#oct 29#virginia tech#win#32#0#7 - 1 , 7\n9#nov 5#vanderbilt#win#28#6#8 - 1 , 7\n10#nov 12#florida#win#14#7#9 - 1 , 7\n",
        "pandas_code": "(df.loc[0, 'opponent'] == 'north carolina' and df.loc[0, 'result'] == 'win') and (df.loc[1, 'opponent'] == 'baylor' and df.loc[1, 'result'] == 'loss')",
        "pandas_eval": "True"
    },
    {
        "id": 1505,
        "statement": "in game 9 and 10 , kentucky won against vanderbilt and florida by a score of 28 - 6 and 14 - 7",
        "label": 1,
        "table_caption": "1977 kentucky wildcats football team",
        "table_text": "game#date#opponent#result#wildcats points#opponents#record\n1#sept 10#north carolina#win#10#7#1 - 0\n2#sept 17#baylor#loss#6#21#1 - 1\n3#sept 24#17 west virginia#win#28#13#2 - 1\n4#oct 1#4 penn state#win#24#20#3 - 1 , 16\n5#oct 8#mississippi state#win#23#7#4 - 1 , 12\n6#oct 15#16 louisiana state#win#33#13#5 - 1 , 8\n7#oct 22#georgia#win#33#0#6 - 1 , 7\n8#oct 29#virginia tech#win#32#0#7 - 1 , 7\n9#nov 5#vanderbilt#win#28#6#8 - 1 , 7\n10#nov 12#florida#win#14#7#9 - 1 , 7\n",
        "pandas_code": "all((df[(df['game'].isin([9, 10])) & (df['opponent'].isin(['vanderbilt', 'florida']))][['wildcats points', 'opponents']].values == [[28, 6], [14, 7]]).all(axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 1506,
        "statement": "on oct 8 , kentucky beat mississippi state 23 - 7 which be a larger margin than their previous victory against 4 penn state , 24 - 20",
        "label": 1,
        "table_caption": "1977 kentucky wildcats football team",
        "table_text": "game#date#opponent#result#wildcats points#opponents#record\n1#sept 10#north carolina#win#10#7#1 - 0\n2#sept 17#baylor#loss#6#21#1 - 1\n3#sept 24#17 west virginia#win#28#13#2 - 1\n4#oct 1#4 penn state#win#24#20#3 - 1 , 16\n5#oct 8#mississippi state#win#23#7#4 - 1 , 12\n6#oct 15#16 louisiana state#win#33#13#5 - 1 , 8\n7#oct 22#georgia#win#33#0#6 - 1 , 7\n8#oct 29#virginia tech#win#32#0#7 - 1 , 7\n9#nov 5#vanderbilt#win#28#6#8 - 1 , 7\n10#nov 12#florida#win#14#7#9 - 1 , 7\n",
        "pandas_code": "(df[(df['date'] == 'oct 8') & (df['opponent'] == 'mississippi state') & (df['wildcats points'] == 23) & (df['opponents'] == 7)].shape[0] > 0) & (df[(df['date'] == 'oct 1') & (df['opponent'] == '4 penn state') & (df['wildcats points'] == 24) & (df['opponents'] == 20)].shape[0] > 0) & (23 - 7 > 24 - 20)",
        "pandas_eval": "True"
    },
    {
        "id": 1507,
        "statement": "the 1977 kentucky wildcat football team have 1 loss on game 2 and win in every other game",
        "label": 1,
        "table_caption": "1977 kentucky wildcats football team",
        "table_text": "game#date#opponent#result#wildcats points#opponents#record\n1#sept 10#north carolina#win#10#7#1 - 0\n2#sept 17#baylor#loss#6#21#1 - 1\n3#sept 24#17 west virginia#win#28#13#2 - 1\n4#oct 1#4 penn state#win#24#20#3 - 1 , 16\n5#oct 8#mississippi state#win#23#7#4 - 1 , 12\n6#oct 15#16 louisiana state#win#33#13#5 - 1 , 8\n7#oct 22#georgia#win#33#0#6 - 1 , 7\n8#oct 29#virginia tech#win#32#0#7 - 1 , 7\n9#nov 5#vanderbilt#win#28#6#8 - 1 , 7\n10#nov 12#florida#win#14#7#9 - 1 , 7\n",
        "pandas_code": "(df.loc[1, 'result'] == 'loss') & (df['result'].iloc[:1].eq('win').all()) & (df['result'].iloc[2:].eq('win').all())",
        "pandas_eval": "True"
    },
    {
        "id": 1508,
        "statement": "after game 6 , kentucky 's record go to 5 - 1 , 8 and after game 7 the record go to 6 - 1 , 7",
        "label": 1,
        "table_caption": "1977 kentucky wildcats football team",
        "table_text": "game#date#opponent#result#wildcats points#opponents#record\n1#sept 10#north carolina#win#10#7#1 - 0\n2#sept 17#baylor#loss#6#21#1 - 1\n3#sept 24#17 west virginia#win#28#13#2 - 1\n4#oct 1#4 penn state#win#24#20#3 - 1 , 16\n5#oct 8#mississippi state#win#23#7#4 - 1 , 12\n6#oct 15#16 louisiana state#win#33#13#5 - 1 , 8\n7#oct 22#georgia#win#33#0#6 - 1 , 7\n8#oct 29#virginia tech#win#32#0#7 - 1 , 7\n9#nov 5#vanderbilt#win#28#6#8 - 1 , 7\n10#nov 12#florida#win#14#7#9 - 1 , 7\n",
        "pandas_code": "(df.loc[df['game'] == 6, 'record'].values[0] == '5 - 1 , 8') and (df.loc[df['game'] == 7, 'record'].values[0] == '6 - 1 , 7')",
        "pandas_eval": "True"
    },
    {
        "id": 1509,
        "statement": "for week 1 , 4 of the girl first name begin with the letter m , a",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#mackenzie ryan#kelli peters#meagan radelat#kimberly holland#\nfebruary#rochelle loewen#rebecca roozrokh#andrea marin#j'tia taylor#\nmarch#jenny gable#claudia costa#crystal lee#meagan rose campbell#natalie taylor\napril#carrie ann beckett#amy sue cooper#amanda renae#summer ehly#\nmay#masha christensen#tiffany logan#cynthia lea#hollie winnard#elizabeth primeaux\njune#mandy ashford#tiffani hollowell#jessica kramer#danielle gamba#\njuly#sheila levell#tanya sue#paula larocca#araya robinson#\naugust#marie thompson#misty dawn#amy robinson#alexis tyler#kate marie\nseptember#brandy canada#christina carson#vivian le brock#amy mccarthy#\noctober#julia marie#jeniffer silva#brenda jones#carrie minter#\nnovember#yumi lee#nicole kelley#trista ashley#christina linehan#alex chanler\n",
        "pandas_code": "df['week 1'].str.startswith(('m', 'a')).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1510,
        "statement": "the first name amy be list 3 time",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#mackenzie ryan#kelli peters#meagan radelat#kimberly holland#\nfebruary#rochelle loewen#rebecca roozrokh#andrea marin#j'tia taylor#\nmarch#jenny gable#claudia costa#crystal lee#meagan rose campbell#natalie taylor\napril#carrie ann beckett#amy sue cooper#amanda renae#summer ehly#\nmay#masha christensen#tiffany logan#cynthia lea#hollie winnard#elizabeth primeaux\njune#mandy ashford#tiffani hollowell#jessica kramer#danielle gamba#\njuly#sheila levell#tanya sue#paula larocca#araya robinson#\naugust#marie thompson#misty dawn#amy robinson#alexis tyler#kate marie\nseptember#brandy canada#christina carson#vivian le brock#amy mccarthy#\noctober#julia marie#jeniffer silva#brenda jones#carrie minter#\nnovember#yumi lee#nicole kelley#trista ashley#christina linehan#alex chanler\n",
        "pandas_code": "df.apply(lambda x: x.str.contains('amy', case=False, na=False)).sum().sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1512,
        "statement": "for april , all of the girl have 2 syllable last name",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#mackenzie ryan#kelli peters#meagan radelat#kimberly holland#\nfebruary#rochelle loewen#rebecca roozrokh#andrea marin#j'tia taylor#\nmarch#jenny gable#claudia costa#crystal lee#meagan rose campbell#natalie taylor\napril#carrie ann beckett#amy sue cooper#amanda renae#summer ehly#\nmay#masha christensen#tiffany logan#cynthia lea#hollie winnard#elizabeth primeaux\njune#mandy ashford#tiffani hollowell#jessica kramer#danielle gamba#\njuly#sheila levell#tanya sue#paula larocca#araya robinson#\naugust#marie thompson#misty dawn#amy robinson#alexis tyler#kate marie\nseptember#brandy canada#christina carson#vivian le brock#amy mccarthy#\noctober#julia marie#jeniffer silva#brenda jones#carrie minter#\nnovember#yumi lee#nicole kelley#trista ashley#christina linehan#alex chanler\n",
        "pandas_code": "all(df.loc[df['Unnamed: 0'] == 'april', ['week 1', 'week 2', 'week 3', 'week 4', 'week 5']].dropna().apply(lambda x: x.str.split().str[-1].str.count('[aeiouAEIOU]') == 2).all())",
        "pandas_eval": "True"
    },
    {
        "id": 1513,
        "statement": "for june , none of the girl have the same letter to begin their first name",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#mackenzie ryan#kelli peters#meagan radelat#kimberly holland#\nfebruary#rochelle loewen#rebecca roozrokh#andrea marin#j'tia taylor#\nmarch#jenny gable#claudia costa#crystal lee#meagan rose campbell#natalie taylor\napril#carrie ann beckett#amy sue cooper#amanda renae#summer ehly#\nmay#masha christensen#tiffany logan#cynthia lea#hollie winnard#elizabeth primeaux\njune#mandy ashford#tiffani hollowell#jessica kramer#danielle gamba#\njuly#sheila levell#tanya sue#paula larocca#araya robinson#\naugust#marie thompson#misty dawn#amy robinson#alexis tyler#kate marie\nseptember#brandy canada#christina carson#vivian le brock#amy mccarthy#\noctober#julia marie#jeniffer silva#brenda jones#carrie minter#\nnovember#yumi lee#nicole kelley#trista ashley#christina linehan#alex chanler\n",
        "pandas_code": "len(set([name[0].lower() for name in df[df['Unnamed: 0'] == 'june'].iloc[:, 1:].dropna().values.flatten()])) == len(df[df['Unnamed: 0'] == 'june'].iloc[:, 1:].dropna().values.flatten())",
        "pandas_eval": "True"
    },
    {
        "id": 1514,
        "statement": ".375 h&h magnum be a heavier bullet than 375 winchester",
        "label": 1,
        "table_caption": ".375 dakota",
        "table_text": "cartridge#bullet weight#muzzle velocity#muzzle energy#source\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 h&h magnum#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 remington ultra magnum#gr (g)#ft / s (m / s)#ft lbf (j)#remington\n.375 ruger#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 winchester#gr (g)#ft / s (m / s)#ft lbf (j)#winchester\n.375 weatherby magnum#gr (g)#ft / s (m / s)#ft lbf (j)#weatherby\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 1515,
        "statement": ".375 weatherby magnum be a heavier bullet than 375 winchester",
        "label": 1,
        "table_caption": ".375 dakota",
        "table_text": "cartridge#bullet weight#muzzle velocity#muzzle energy#source\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 h&h magnum#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 remington ultra magnum#gr (g)#ft / s (m / s)#ft lbf (j)#remington\n.375 ruger#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 winchester#gr (g)#ft / s (m / s)#ft lbf (j)#winchester\n.375 weatherby magnum#gr (g)#ft / s (m / s)#ft lbf (j)#weatherby\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 1516,
        "statement": ".375 dakota be a heavier bullet than 375 winchester",
        "label": 1,
        "table_caption": ".375 dakota",
        "table_text": "cartridge#bullet weight#muzzle velocity#muzzle energy#source\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 h&h magnum#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 remington ultra magnum#gr (g)#ft / s (m / s)#ft lbf (j)#remington\n.375 ruger#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 winchester#gr (g)#ft / s (m / s)#ft lbf (j)#winchester\n.375 weatherby magnum#gr (g)#ft / s (m / s)#ft lbf (j)#weatherby\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 1517,
        "statement": ".375 remington ultra magnum be a heavier bullet than 375 winchester",
        "label": 1,
        "table_caption": ".375 dakota",
        "table_text": "cartridge#bullet weight#muzzle velocity#muzzle energy#source\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 h&h magnum#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 remington ultra magnum#gr (g)#ft / s (m / s)#ft lbf (j)#remington\n.375 ruger#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 winchester#gr (g)#ft / s (m / s)#ft lbf (j)#winchester\n.375 weatherby magnum#gr (g)#ft / s (m / s)#ft lbf (j)#weatherby\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 1518,
        "statement": ".375 ruger be a heavier bullet than 375 winchester",
        "label": 1,
        "table_caption": ".375 dakota",
        "table_text": "cartridge#bullet weight#muzzle velocity#muzzle energy#source\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 dakota#gr (g)#ft / s (m / s)#ft lbf (j)#dakota arms\n.375 h&h magnum#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 remington ultra magnum#gr (g)#ft / s (m / s)#ft lbf (j)#remington\n.375 ruger#gr (g)#ft / s (m / s)#ft lbf (j)#hornady\n.375 winchester#gr (g)#ft / s (m / s)#ft lbf (j)#winchester\n.375 weatherby magnum#gr (g)#ft / s (m / s)#ft lbf (j)#weatherby\n",
        "pandas_code": "df[df['cartridge'] == '.375 ruger']['bullet weight'].iloc[0] == df[df['cartridge'] == '.375 winchester']['bullet weight'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1519,
        "statement": "king , as the liberal leader , be liberal leader 7 time as compare to st laurent with 3 time",
        "label": 1,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "(df['liberal leader'].value_counts()['king'] == 7) & (df['liberal leader'].value_counts()['st laurent'] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1520,
        "statement": "there be 7 seat more in the house in 2004 than in 2000 , and 128 more seat in 2004 than in 1867",
        "label": 1,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "(df[df['year'] == 2004]['seats in house'].values[0] - df[df['year'] == 2000]['seats in house'].values[0] == 7) and (df[df['year'] == 2004]['seats in house'].values[0] - df[df['year'] == 1867]['seats in house'].values[0] == 128)",
        "pandas_eval": "True"
    },
    {
        "id": 1521,
        "statement": "the year with the largest number of seat won be in 1949 with 190 seat won",
        "label": 1,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "df.loc[df['seats won'].idxmax(), 'year'] == 1949",
        "pandas_eval": "True"
    },
    {
        "id": 1522,
        "statement": "the year with the lowest number of seat won be in 2011 with 34 seat won",
        "label": 1,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "df.loc[df['seats won'].idxmin(), 'year'] == 2011",
        "pandas_eval": "True"
    },
    {
        "id": 1524,
        "statement": "pearson , as the liberal leader , be liberal leader 7 time as compare to st laurent with 3 time",
        "label": 0,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "(df[df['liberal leader'] == 'pearson'].shape[0] == 7) & (df[df['liberal leader'] == 'st laurent'].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 1525,
        "statement": "there be 7 seat less in the house in 2004 than in 2000 , and 128 less seat in 2004 than in 1867",
        "label": 0,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "(df.loc[df['year'] == 2004, 'seats in house'].values[0] == df.loc[df['year'] == 2000, 'seats in house'].values[0] - 7) and (df.loc[df['year'] == 2004, 'seats in house'].values[0] == df.loc[df['year'] == 1867, 'seats in house'].values[0] - 128)",
        "pandas_eval": "False"
    },
    {
        "id": 1526,
        "statement": "the year with the lowest number of seat won be in 1949 with 90 seat won",
        "label": 0,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "df.loc[df['seats won'].idxmin(), 'year'] == 1949",
        "pandas_eval": "False"
    },
    {
        "id": 1527,
        "statement": "the year with the highest number of seat won be in 2011 with 34 seat won",
        "label": 0,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "df.loc[df['seats won'].idxmax(), 'year'] == 2011",
        "pandas_eval": "False"
    },
    {
        "id": 1528,
        "statement": "the year with the lowest popular vote be in 1940 when the % of popular vote be 51.3%",
        "label": 0,
        "table_caption": "liberal party of canada",
        "table_text": "year#seats in house#liberal candidates#seats won#seat change#popular vote#% of popular vote#result#liberal leader\n1867#180#65#62#+ 62#60818#22.7%#cons majority#brown\n1872#200#111#95#+ 33#110556#34.7%#cons minority#blake\n1874#206#140#133#+ 38#128059#39.5%#majority gov't#mackenzie\n1878#206#121#63#70#180074#33.1%#cons majority#mackenzie\n1882#211#112#73#+ 10#160547#31.1%#cons majority#blake\n1887#215#184#80#+ 7#312736#43.1%#cons majority#blake\n1891#215#194#90#+ 10#350512#45.2%#cons majority#laurier\n1896#213#190#118#+ 28#401425#41.4%#majority gov't#laurier\n1900#213#209#132#+ 14#477758#50.3%#majority gov't#laurier\n1904#214#208#139#+ 7#521041#50.9%#majority gov't#laurier\n1908#221#213#133#6#570311#48.9%#majority gov't#laurier\n1911#221#214#85#48#596871#45.8%#cons majority#laurier\n1917#235#213#82#3#729756#38.8%#coalition gov't#laurier\n1921#235#204#118#+ 36#1285998#41.2%#majority gov't#king\n1925#245#216#100#18#1252684#39.7%#minority gov't#king\n1926#245#189#116#+ 16#1294072#42.7%#majority gov't#king\n1930#245#226#90#26#1716798#44.0%#cons majority#king\n1935#245#245#173#+ 83#1967839#44.7%#majority gov't#king\n1940#245#242#181#+ 8#2365979#51.3%#majority gov't#king\n1945#245#236#125#56#2086545#39.8%#majority gov't#king\n1949#262#259#190#+ 65#2878097#49.2%#majority gov't#st laurent\n1953#265#263#171#19#2743013#48.6%#majority gov't#st laurent\n1957#265#265#105#66#2703687#40.9%#pc minority#st laurent\n1958#265#265#49#56#2444909#33.5%#pc majority#pearson\n1962#265#264#100#+ 51#2862001#37.2%#pc minority#pearson\n1963#265#265#128#+ 28#3276995#41.5%#minority gov't#pearson\n1965#265#265#131#+ 3#3099521#40.2%#minority gov't#pearson\n1968#264#263#155#+ 24#3686801#45.4%#majority gov't#trudeau\n1972#264#263#109#46#3717804#38.4%#minority gov't#trudeau\n1974#264#264#141#+ 32#4102853#43.2%#majority gov't#trudeau\n1979#282#282#114#27#4595319#40.1%#pc minority#trudeau\n1980#282#282#147#+ 33#4855425#44.4%#majority gov't#trudeau\n1984#282#282#40#107#3516486#28.0%#pc majority#turner\n1988#295#294#83#+ 43#4205072#31.9%#pc majority#turner\n1993#295#295#177#+ 94#5598775#41.2%#majority gov't#chr\u00e9tien\n1997#301#301#155#22#4994377#38.5%#majority gov't#chr\u00e9tien\n2000#301#301#172#+ 17#5251961#40.9%#majority gov't#chr\u00e9tien\n2004#308#308#135#37#4951107#36.7%#minority gov't#martin\n2006#308#308#103#32#4477217#30.1%#cons minority#martin\n2008#308#307#77#26#3629990#26.2%#cons minority#dion\n2011#308#308#34#43#2783175#18.9%#cons majority#ignatieff\n",
        "pandas_code": "df.loc[df['popular vote'].idxmin(), 'year'] == 1940 and df.loc[df['year'] == 1940, '% of popular vote'].values[0] == '51.3%'",
        "pandas_eval": "False"
    },
    {
        "id": 1529,
        "statement": "jos\u00e9 calder\u00f3n score the highest point 2 time",
        "label": 1,
        "table_caption": "2008 - 09 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 1#orlando#l 90 - 113 (ot)#jos\u00e9 calder\u00f3n (16)#joey graham (12)#jos\u00e9 calder\u00f3n , will solomon (5)#air canada centre 19800#19 - 30\n50#february 3#cleveland#l 83 - 101 (ot)#chris bosh (29)#andrea bargnani (10)#anthony parker (8)#quicken loans arena 20562#19 - 31\n51#february 4#la lakers#l 107 - 115 (ot)#joey graham (24)#andrea bargnani , jermaine o'neal (9)#anthony parker (9)#air canada centre 19800#19 - 32\n52#february 6#new orleans#l 92 - 101 (ot)#jermaine o'neal (24)#jamario moon (7)#jos\u00e9 calder\u00f3n (9)#new orleans arena 17319#19 - 33\n53#february 7#memphis#l 70 - 78 (ot)#jos\u00e9 calder\u00f3n (18)#andrea bargnani , jamario moon (9)#jos\u00e9 calder\u00f3n (5)#fedexforum 11498#19 - 34\n54#february 10#minnesota#w 110 - 102 (ot)#joey graham (24)#jamario moon (9)#jos\u00e9 calder\u00f3n (9)#target center 12722#20 - 34\n55#february 11#san antonio#w 91 - 89 (ot)#andrea bargnani (23)#jermaine o'neal (10)#anthony parker (4)#air canada centre 18909#21 - 34\n56#february 18#cleveland#l 76 - 93 (ot)#joey graham (15)#anthony parker (7)#shawn marion (6)#air canada centre 19800#21 - 35\n57#february 20#new york#l 97 - 127 (ot)#joey graham (19)#shawn marion (12)#jos\u00e9 calder\u00f3n (10)#madison square garden 19763#21 - 36\n58#february 22#new york#w 111 - 100 (ot)#andrea bargnani (28)#shawn marion (15)#jos\u00e9 calder\u00f3n (11)#air canada centre 19800#22 - 36\n59#february 24#minnesota#w 118 - 110 (ot)#andrea bargnani , chris bosh (26)#shawn marion (8)#jos\u00e9 calder\u00f3n (13)#air canada centre 17457#23 - 36\n",
        "pandas_code": "(df['high points'].str.contains('jos\u00e9 calder\u00f3n').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1531,
        "statement": "anthony parker have the highest assist 3 time",
        "label": 1,
        "table_caption": "2008 - 09 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 1#orlando#l 90 - 113 (ot)#jos\u00e9 calder\u00f3n (16)#joey graham (12)#jos\u00e9 calder\u00f3n , will solomon (5)#air canada centre 19800#19 - 30\n50#february 3#cleveland#l 83 - 101 (ot)#chris bosh (29)#andrea bargnani (10)#anthony parker (8)#quicken loans arena 20562#19 - 31\n51#february 4#la lakers#l 107 - 115 (ot)#joey graham (24)#andrea bargnani , jermaine o'neal (9)#anthony parker (9)#air canada centre 19800#19 - 32\n52#february 6#new orleans#l 92 - 101 (ot)#jermaine o'neal (24)#jamario moon (7)#jos\u00e9 calder\u00f3n (9)#new orleans arena 17319#19 - 33\n53#february 7#memphis#l 70 - 78 (ot)#jos\u00e9 calder\u00f3n (18)#andrea bargnani , jamario moon (9)#jos\u00e9 calder\u00f3n (5)#fedexforum 11498#19 - 34\n54#february 10#minnesota#w 110 - 102 (ot)#joey graham (24)#jamario moon (9)#jos\u00e9 calder\u00f3n (9)#target center 12722#20 - 34\n55#february 11#san antonio#w 91 - 89 (ot)#andrea bargnani (23)#jermaine o'neal (10)#anthony parker (4)#air canada centre 18909#21 - 34\n56#february 18#cleveland#l 76 - 93 (ot)#joey graham (15)#anthony parker (7)#shawn marion (6)#air canada centre 19800#21 - 35\n57#february 20#new york#l 97 - 127 (ot)#joey graham (19)#shawn marion (12)#jos\u00e9 calder\u00f3n (10)#madison square garden 19763#21 - 36\n58#february 22#new york#w 111 - 100 (ot)#andrea bargnani (28)#shawn marion (15)#jos\u00e9 calder\u00f3n (11)#air canada centre 19800#22 - 36\n59#february 24#minnesota#w 118 - 110 (ot)#andrea bargnani , chris bosh (26)#shawn marion (8)#jos\u00e9 calder\u00f3n (13)#air canada centre 17457#23 - 36\n",
        "pandas_code": "(df['high assists'].str.contains('anthony parker').sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1532,
        "statement": "the quicken loan arena be the only arena to have a game attendance of over 20000 people",
        "label": 1,
        "table_caption": "2008 - 09 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 1#orlando#l 90 - 113 (ot)#jos\u00e9 calder\u00f3n (16)#joey graham (12)#jos\u00e9 calder\u00f3n , will solomon (5)#air canada centre 19800#19 - 30\n50#february 3#cleveland#l 83 - 101 (ot)#chris bosh (29)#andrea bargnani (10)#anthony parker (8)#quicken loans arena 20562#19 - 31\n51#february 4#la lakers#l 107 - 115 (ot)#joey graham (24)#andrea bargnani , jermaine o'neal (9)#anthony parker (9)#air canada centre 19800#19 - 32\n52#february 6#new orleans#l 92 - 101 (ot)#jermaine o'neal (24)#jamario moon (7)#jos\u00e9 calder\u00f3n (9)#new orleans arena 17319#19 - 33\n53#february 7#memphis#l 70 - 78 (ot)#jos\u00e9 calder\u00f3n (18)#andrea bargnani , jamario moon (9)#jos\u00e9 calder\u00f3n (5)#fedexforum 11498#19 - 34\n54#february 10#minnesota#w 110 - 102 (ot)#joey graham (24)#jamario moon (9)#jos\u00e9 calder\u00f3n (9)#target center 12722#20 - 34\n55#february 11#san antonio#w 91 - 89 (ot)#andrea bargnani (23)#jermaine o'neal (10)#anthony parker (4)#air canada centre 18909#21 - 34\n56#february 18#cleveland#l 76 - 93 (ot)#joey graham (15)#anthony parker (7)#shawn marion (6)#air canada centre 19800#21 - 35\n57#february 20#new york#l 97 - 127 (ot)#joey graham (19)#shawn marion (12)#jos\u00e9 calder\u00f3n (10)#madison square garden 19763#21 - 36\n58#february 22#new york#w 111 - 100 (ot)#andrea bargnani (28)#shawn marion (15)#jos\u00e9 calder\u00f3n (11)#air canada centre 19800#22 - 36\n59#february 24#minnesota#w 118 - 110 (ot)#andrea bargnani , chris bosh (26)#shawn marion (8)#jos\u00e9 calder\u00f3n (13)#air canada centre 17457#23 - 36\n",
        "pandas_code": "df[df['location attendance'].str.extract(r'(\\d+)')[0].astype(int) > 20000]['location attendance'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1533,
        "statement": "only 1 game be play in the target center",
        "label": 1,
        "table_caption": "2008 - 09 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n49#february 1#orlando#l 90 - 113 (ot)#jos\u00e9 calder\u00f3n (16)#joey graham (12)#jos\u00e9 calder\u00f3n , will solomon (5)#air canada centre 19800#19 - 30\n50#february 3#cleveland#l 83 - 101 (ot)#chris bosh (29)#andrea bargnani (10)#anthony parker (8)#quicken loans arena 20562#19 - 31\n51#february 4#la lakers#l 107 - 115 (ot)#joey graham (24)#andrea bargnani , jermaine o'neal (9)#anthony parker (9)#air canada centre 19800#19 - 32\n52#february 6#new orleans#l 92 - 101 (ot)#jermaine o'neal (24)#jamario moon (7)#jos\u00e9 calder\u00f3n (9)#new orleans arena 17319#19 - 33\n53#february 7#memphis#l 70 - 78 (ot)#jos\u00e9 calder\u00f3n (18)#andrea bargnani , jamario moon (9)#jos\u00e9 calder\u00f3n (5)#fedexforum 11498#19 - 34\n54#february 10#minnesota#w 110 - 102 (ot)#joey graham (24)#jamario moon (9)#jos\u00e9 calder\u00f3n (9)#target center 12722#20 - 34\n55#february 11#san antonio#w 91 - 89 (ot)#andrea bargnani (23)#jermaine o'neal (10)#anthony parker (4)#air canada centre 18909#21 - 34\n56#february 18#cleveland#l 76 - 93 (ot)#joey graham (15)#anthony parker (7)#shawn marion (6)#air canada centre 19800#21 - 35\n57#february 20#new york#l 97 - 127 (ot)#joey graham (19)#shawn marion (12)#jos\u00e9 calder\u00f3n (10)#madison square garden 19763#21 - 36\n58#february 22#new york#w 111 - 100 (ot)#andrea bargnani (28)#shawn marion (15)#jos\u00e9 calder\u00f3n (11)#air canada centre 19800#22 - 36\n59#february 24#minnesota#w 118 - 110 (ot)#andrea bargnani , chris bosh (26)#shawn marion (8)#jos\u00e9 calder\u00f3n (13)#air canada centre 17457#23 - 36\n",
        "pandas_code": "df[df['location attendance'].str.contains('target center')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1534,
        "statement": "the highest attendance be on december 13 , 1971 at the los angeles ram with 80402 attendee",
        "label": 1,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), ['date', 'opponent', 'attendance']].equals(pd.Series({'date': 'december 13 , 1971', 'opponent': 'los angeles rams', 'attendance': 80402}))",
        "pandas_eval": "True"
    },
    {
        "id": 1537,
        "statement": "the lowest attendance be against and at the kansas city chief , with 51989",
        "label": 1,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "df.loc[df['opponent'] == 'kansas city chiefs', 'attendance'].min() == 51989",
        "pandas_eval": "True"
    },
    {
        "id": 1538,
        "statement": "in the 1971 washington redskins season the team win 9 game while lose only 5",
        "label": 1,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 9) & ((df['result'].str.startswith('l').sum() + df['result'].str.startswith('t').sum()) == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 1539,
        "statement": "the lowest attendance be on december 13 , 1971 at the los angeles ram with 80402 attendee",
        "label": 0,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "df.loc[df['date'] == 'december 13 , 1971', 'attendance'].iloc[0] != 80402",
        "pandas_eval": "False"
    },
    {
        "id": 1540,
        "statement": "all of the home game have the exact same amount of attendee at 5304",
        "label": 0,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "(df[df['result'].str.contains('w|l|t')]['attendance'] == 53041).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1541,
        "statement": "only 2 time do either team score 2 point , october 17 , 1971 november 28 , 1971",
        "label": 0,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "len(df[(df['date'].isin(['october 17 , 1971', 'november 28 , 1971'])) & (df['result'].str.contains(r'\\b2\\b'))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1542,
        "statement": "the lowest attendance be against and at the chicago bear , with 51989",
        "label": 0,
        "table_caption": "1971 washington redskins season",
        "table_text": "week#date#opponent#result#attendance\n1#september 19 , 1971#st louis cardinals#w 24 - 17#46805\n2#september 26 , 1971#new york giants#w 30 - 3#62795\n3#october 3 , 1971#dallas cowboys#w 20 - 16#61554\n4#october 10 , 1971#houston oilers#w 22 - 13#53041\n5#october 17 , 1971#st louis cardinals#w 20 - 0#53041\n6#october 24 , 1971#kansas city chiefs#l 27 - 20#51989\n7#october 31 , 1971#new orleans saints#w 24 - 14#53041\n8#november 7 , 1971#philadelphia eagles#t 7 - 7#53041\n9#november 14 , 1971#chicago bears#l 16 - 15#55049\n10#november 21 , 1971#dallas cowboys#l 13 - 0#53041\n11#november 28 , 1971#philadelphia eagles#w 20 - 13#65358\n12#december 5 , 1971#new york giants#w 23 - 7#53041\n13#december 13 , 1971#los angeles rams#w 38 - 24#80402\n14#december 19 , 1971#cleveland browns#l 20 - 13#53041\n",
        "pandas_code": "df.loc[df['attendance'] == df['attendance'].min(), 'opponent'].values[0] == 'chicago bears' and df['attendance'].min() == 51989",
        "pandas_eval": "False"
    },
    {
        "id": 1543,
        "statement": "john lafalce be incumbent with oldest year of first election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 54.72% nora bredes (d) 45.28%\nnew york 6#floyd flake#democratic#1986#re - elected#floyd flake (d) 84.85% jorawar misir (r) 15.14%\nnew york 7#thomas manton#democratic#1984#re - elected#thomas manton (d) 71.07% rose birtley (r) 28.93%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 91.95% claudette hayle (r) 8.04%\nnew york 22#gerald solomon#republican#1978#re - elected#gerald solomon (r) 60.48% steve james (d) 39.52%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 55.11% marty mack (d) 44.89%\nnew york 27#bill paxon#republican#1988#re - elected#bill paxon (r) 59.88% thomas fricano (d) 40.12%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 61.99% david callard (r) 38.01%\n",
        "pandas_code": "df[df['incumbent'] == 'john lafalce']['first elected'].min() == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1545,
        "statement": "there be equal number of incumbent from republican and democratic party",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 54.72% nora bredes (d) 45.28%\nnew york 6#floyd flake#democratic#1986#re - elected#floyd flake (d) 84.85% jorawar misir (r) 15.14%\nnew york 7#thomas manton#democratic#1984#re - elected#thomas manton (d) 71.07% rose birtley (r) 28.93%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 91.95% claudette hayle (r) 8.04%\nnew york 22#gerald solomon#republican#1978#re - elected#gerald solomon (r) 60.48% steve james (d) 39.52%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 55.11% marty mack (d) 44.89%\nnew york 27#bill paxon#republican#1988#re - elected#bill paxon (r) 59.88% thomas fricano (d) 40.12%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 61.99% david callard (r) 38.01%\n",
        "pandas_code": "df['party'].value_counts().loc['republican'] == df['party'].value_counts().loc['democratic']",
        "pandas_eval": "True"
    },
    {
        "id": 1546,
        "statement": "all incumbent be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1996",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnew york 1#michael forbes#republican#1994#re - elected#michael forbes (r) 54.72% nora bredes (d) 45.28%\nnew york 6#floyd flake#democratic#1986#re - elected#floyd flake (d) 84.85% jorawar misir (r) 15.14%\nnew york 7#thomas manton#democratic#1984#re - elected#thomas manton (d) 71.07% rose birtley (r) 28.93%\nnew york 11#major owens#democratic#1982#re - elected#major owens (d) 91.95% claudette hayle (r) 8.04%\nnew york 22#gerald solomon#republican#1978#re - elected#gerald solomon (r) 60.48% steve james (d) 39.52%\nnew york 25#jim walsh#republican#1988#re - elected#jim walsh (r) 55.11% marty mack (d) 44.89%\nnew york 27#bill paxon#republican#1988#re - elected#bill paxon (r) 59.88% thomas fricano (d) 40.12%\nnew york 29#john lafalce#democratic#1974#re - elected#john lafalce (d) 61.99% david callard (r) 38.01%\n",
        "pandas_code": "all(df['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 1548,
        "statement": "the dover senator have the best overall record at 8 - 3",
        "label": 1,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "df[df['school'] == 'dover']['overall record'].values[0] == '8 - 3'",
        "pandas_eval": "True"
    },
    {
        "id": 1549,
        "statement": "the milford buccaneer and the cap henlopen viking have the worst record at 4 and 6",
        "label": 1,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "(df[df['team'].isin(['buccaneers', 'vikings'])]['overall record'] == '4 - 6').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1550,
        "statement": "4 out of the 6 team fail to make the playoff",
        "label": 1,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "sum(df['season outcome'] == 'failed to make playoffs') == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1551,
        "statement": "the dover senator be the only team that be undefeated in division play",
        "label": 1,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "all(df[df['school'] != 'dover']['division record'] != '5 - 0')",
        "pandas_eval": "True"
    },
    {
        "id": 1552,
        "statement": "both team that make the playoff lose in the first round",
        "label": 1,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "all(df[df['season outcome'].str.contains('loss in first round of div i playoffs')]['team'].isin(df[df['season outcome'].str.contains('playoffs')]['team']))",
        "pandas_eval": "True"
    },
    {
        "id": 1553,
        "statement": "the dover senator have the second best record but fail to make playoff",
        "label": 0,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "(df[df['school'] == 'dover']['division record'].str.split(' - ').apply(lambda x: int(x[0])).iloc[0] == 5) and (df[df['school'] == 'dover']['season outcome'].iloc[0] == 'failed to make playoffs')",
        "pandas_eval": "False"
    },
    {
        "id": 1554,
        "statement": "the milford buccaneer and the cap henlopen viking be the only 1 with record at 4 and 6 and only 1 fail to make playoff",
        "label": 0,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "(df[(df['team'] == 'buccaneers') | (df['team'] == 'vikings')]['overall record'].tolist() == ['4 - 6', '4 - 6']) & (df[df['overall record'] == '4 - 6']['season outcome'].tolist() == ['failed to make playoffs', 'failed to make playoffs'])",
        "pandas_eval": "False"
    },
    {
        "id": 1555,
        "statement": "4 out of the 6 team loss in first round of div i playoff",
        "label": 0,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "df['season outcome'].value_counts()['loss in first round of div i playoffs'] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 1556,
        "statement": "the dover senator score 4 point in the division play",
        "label": 0,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "df.loc[df['school'] == 'dover', 'division record'].iloc[0] == '4 - 0'",
        "pandas_eval": "False"
    },
    {
        "id": 1557,
        "statement": "both team that make the playoff be undefeated in division play",
        "label": 0,
        "table_caption": "henlopen conference",
        "table_text": "school#team#division record#overall record#season outcome\ndover#senators#5 - 0#8 - 3#loss in first round of div i playoffs\ncaesar rodney#riders#3 - 2#4 - 6#failed to make playoffs\nsussex central#golden knights#2 - 3#6 - 5#loss in first round of div i playoffs\nsussex tech#ravens#2 - 3#5 - 5#failed to make playoffs\ncape henlopen#vikings#2 - 3#4 - 6#failed to make playoffs\nmilford#buccaneers#1 - 4#4 - 6#failed to make playoffs\n",
        "pandas_code": "all(df[df['season outcome'].str.contains('playoffs')]['division record'] == '5 - 0')",
        "pandas_eval": "False"
    },
    {
        "id": 1558,
        "statement": "the 2nd , 3rd , and 4th place rank be all hold by repsol honda",
        "label": 1,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "all(df[(df['rank'].isin(['2nd', '3rd', '4th']))]['team'] == 'repsol honda')",
        "pandas_eval": "True"
    },
    {
        "id": 1559,
        "statement": "repsol honda be the only team list with win in 10 year , for both 1989 and 1990",
        "label": 1,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "(df[(df['year'].isin([1989, 1990])) & (df['wins'] > 0)]['team'].unique() == ['repsol honda']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1560,
        "statement": "the team cobas - rotax be 1 of 3 team list only a single time",
        "label": 1,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "df[df['team'] == 'cobas - rotax'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1561,
        "statement": "in 1990 , repsol honda receive 208 point , while cobas - rotax only receive 2 point in 1983",
        "label": 1,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "(df.loc[(df['year'] == 1990) & (df['team'] == 'repsol honda'), 'points'].values[0] == 208) & (df.loc[(df['year'] == 1983) & (df['team'] == 'cobas - rotax'), 'points'].values[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1562,
        "statement": "repsol honda be the only team to be list 4 year consecutively",
        "label": 1,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "len(df[df['team'] == 'repsol honda'].groupby((df['year'] - df['year'].shift() != 1).cumsum()).filter(lambda x: len(x) >= 4)) > 0",
        "pandas_eval": "True"
    },
    {
        "id": 1563,
        "statement": "the 2nd , 3rd , and 4th rank be hold by campsa honda",
        "label": 0,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "df[(df['rank'].isin(['2nd', '3rd', '4th'])) & (df['team'] == 'campsa honda')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1565,
        "statement": "cobas - rotax be the only team list a single time",
        "label": 0,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "len(df[df['team'] == 'cobas - rotax']) != 1",
        "pandas_eval": "False"
    },
    {
        "id": 1566,
        "statement": "in 1990 , repsol honda receive only 2 point more than cobas - rotax do in 1983",
        "label": 0,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "(df.loc[df['year'] == 1990, 'points'].values[0] - df.loc[df['year'] == 1983, 'points'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1567,
        "statement": "repsol honda be tie with campsa honda in the number of mention",
        "label": 0,
        "table_caption": "carlos card\u00fas",
        "table_text": "year#class#team#points#rank#wins\n1983#250cc#cobas - rotax#2#28th#0\n1984#250cc#jj cobas#4#25th#0\n1985#250cc#jj cobas#21#12th#0\n1986#250cc#campsa honda#17#12th#0\n1987#250cc#campsa honda#70#5th#0\n1988#250cc#ducados honda#71#9th#0\n1989#250cc#repsol honda#162#4th#1\n1990#250cc#repsol honda#208#2nd#4\n1991#250cc#repsol honda#205#3rd#0\n1992#250cc#repsol honda#48#8th#0\n1993#250cc#honda#27#18th#0\n",
        "pandas_code": "df[df['team'].isin(['repsol honda', 'campsa honda'])].groupby('team').size().nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1568,
        "statement": "1995 be the most recent year",
        "label": 1,
        "table_caption": "list of birds on stamps of bhutan",
        "table_text": "year#date#type#species#author species#value#scott#yvert#mitchell#sta & gib#order#family\n1968#(20.01)#nor#tragopan satyra#(linnaeus , 1758)#1c#92#143#179#0#galliformes#phasianidae\n1968#(23.04)#nor#lophophorus sclateri#jerdon , 1870#4n#92 g#150#186#0#galliformes#phasianidae\n1968#(07.12)#nor#liocichla phoenicea#(gould , 1837)#2c#99#198#248#187#passeriformes#timaliidae\n1968#(28.12)#nor#liocichla phoenicea#(gould , 1837)#20c#0#0#0#0#passeriformes#timaliidae\n1969#(20.01)#nor#liocichla phoenicea#(gould , 1837)#1.50n#0#0#0#0#passeriformes#timaliidae\n1969#(05.08)#nor#strix aluco#linnaeus , 1758#15c#0#0#0#0#strigiformes#strigidae\n1970#(19.06)#nor#lophophorus sclateri#jerdon , 1870#20c#0#0#0#0#galliformes#phasianidae\n1970#(20.09)#nor#tragopan satyra#(linnaeus , 1758)#85c#0#0#0#0#galliformes#phasianidae\n1970#(02.11)#nor#tragopan satyra#(linnaeus , 1758)#20c#0#0#0#0#galliformes#phasianidae\n1971#(01.07)#nor#lophophorus impejanus#(latham , 1790)#55c#0#0#0#0#galliformes#phasianidae\n1982#(19.04)#nor#chloropsis hardwickii#jardine & selby , 1830#2n#0#0#0#0#passeriformes#irenidae\n1985#(29.11)#nor#lagopus lagopus#(linnaeus , 1758)#1n#0#0#0#0#galliformes#tetraonidae\n1985#(06.12)#nor#anas platyrhynchos#linnaeus , 1758#50c#0#0#0#0#anseriformes#anatidae\n1987#(25.05)#nor#phoenicopterus ruber#linnaeus , 1758#1n#0#0#0#0#phoenicopteriformes#phoenicopteridae\n1989#(22.11)#nor#chrysocolaptes lucidus#(scopoli , 1786)#50c#0#0#0#0#piciformes#picidae\n1990#(21.05)#nor#grus japonensis#(muller , 1776)#25n#0#0#0#0#gruiformes#gruidae\n1993#(01.07)#nor#grus nigricollis#przewalski , 1876#15n#0#0#0#0#gruiformes#gruidae\n1995#(01.09)#nor#megaceryle lugubris#(temminck , 1834)#1n#0#0#0#0#coraciiformes#alcedinidae\n",
        "pandas_code": "df['year'].max() == 1995",
        "pandas_eval": "True"
    },
    {
        "id": 1569,
        "statement": "tragopan satyra be the specie 3 time",
        "label": 1,
        "table_caption": "list of birds on stamps of bhutan",
        "table_text": "year#date#type#species#author species#value#scott#yvert#mitchell#sta & gib#order#family\n1968#(20.01)#nor#tragopan satyra#(linnaeus , 1758)#1c#92#143#179#0#galliformes#phasianidae\n1968#(23.04)#nor#lophophorus sclateri#jerdon , 1870#4n#92 g#150#186#0#galliformes#phasianidae\n1968#(07.12)#nor#liocichla phoenicea#(gould , 1837)#2c#99#198#248#187#passeriformes#timaliidae\n1968#(28.12)#nor#liocichla phoenicea#(gould , 1837)#20c#0#0#0#0#passeriformes#timaliidae\n1969#(20.01)#nor#liocichla phoenicea#(gould , 1837)#1.50n#0#0#0#0#passeriformes#timaliidae\n1969#(05.08)#nor#strix aluco#linnaeus , 1758#15c#0#0#0#0#strigiformes#strigidae\n1970#(19.06)#nor#lophophorus sclateri#jerdon , 1870#20c#0#0#0#0#galliformes#phasianidae\n1970#(20.09)#nor#tragopan satyra#(linnaeus , 1758)#85c#0#0#0#0#galliformes#phasianidae\n1970#(02.11)#nor#tragopan satyra#(linnaeus , 1758)#20c#0#0#0#0#galliformes#phasianidae\n1971#(01.07)#nor#lophophorus impejanus#(latham , 1790)#55c#0#0#0#0#galliformes#phasianidae\n1982#(19.04)#nor#chloropsis hardwickii#jardine & selby , 1830#2n#0#0#0#0#passeriformes#irenidae\n1985#(29.11)#nor#lagopus lagopus#(linnaeus , 1758)#1n#0#0#0#0#galliformes#tetraonidae\n1985#(06.12)#nor#anas platyrhynchos#linnaeus , 1758#50c#0#0#0#0#anseriformes#anatidae\n1987#(25.05)#nor#phoenicopterus ruber#linnaeus , 1758#1n#0#0#0#0#phoenicopteriformes#phoenicopteridae\n1989#(22.11)#nor#chrysocolaptes lucidus#(scopoli , 1786)#50c#0#0#0#0#piciformes#picidae\n1990#(21.05)#nor#grus japonensis#(muller , 1776)#25n#0#0#0#0#gruiformes#gruidae\n1993#(01.07)#nor#grus nigricollis#przewalski , 1876#15n#0#0#0#0#gruiformes#gruidae\n1995#(01.09)#nor#megaceryle lugubris#(temminck , 1834)#1n#0#0#0#0#coraciiformes#alcedinidae\n",
        "pandas_code": "(df['species'] == 'tragopan satyra').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1570,
        "statement": "phasianidae be the most featured family",
        "label": 1,
        "table_caption": "list of birds on stamps of bhutan",
        "table_text": "year#date#type#species#author species#value#scott#yvert#mitchell#sta & gib#order#family\n1968#(20.01)#nor#tragopan satyra#(linnaeus , 1758)#1c#92#143#179#0#galliformes#phasianidae\n1968#(23.04)#nor#lophophorus sclateri#jerdon , 1870#4n#92 g#150#186#0#galliformes#phasianidae\n1968#(07.12)#nor#liocichla phoenicea#(gould , 1837)#2c#99#198#248#187#passeriformes#timaliidae\n1968#(28.12)#nor#liocichla phoenicea#(gould , 1837)#20c#0#0#0#0#passeriformes#timaliidae\n1969#(20.01)#nor#liocichla phoenicea#(gould , 1837)#1.50n#0#0#0#0#passeriformes#timaliidae\n1969#(05.08)#nor#strix aluco#linnaeus , 1758#15c#0#0#0#0#strigiformes#strigidae\n1970#(19.06)#nor#lophophorus sclateri#jerdon , 1870#20c#0#0#0#0#galliformes#phasianidae\n1970#(20.09)#nor#tragopan satyra#(linnaeus , 1758)#85c#0#0#0#0#galliformes#phasianidae\n1970#(02.11)#nor#tragopan satyra#(linnaeus , 1758)#20c#0#0#0#0#galliformes#phasianidae\n1971#(01.07)#nor#lophophorus impejanus#(latham , 1790)#55c#0#0#0#0#galliformes#phasianidae\n1982#(19.04)#nor#chloropsis hardwickii#jardine & selby , 1830#2n#0#0#0#0#passeriformes#irenidae\n1985#(29.11)#nor#lagopus lagopus#(linnaeus , 1758)#1n#0#0#0#0#galliformes#tetraonidae\n1985#(06.12)#nor#anas platyrhynchos#linnaeus , 1758#50c#0#0#0#0#anseriformes#anatidae\n1987#(25.05)#nor#phoenicopterus ruber#linnaeus , 1758#1n#0#0#0#0#phoenicopteriformes#phoenicopteridae\n1989#(22.11)#nor#chrysocolaptes lucidus#(scopoli , 1786)#50c#0#0#0#0#piciformes#picidae\n1990#(21.05)#nor#grus japonensis#(muller , 1776)#25n#0#0#0#0#gruiformes#gruidae\n1993#(01.07)#nor#grus nigricollis#przewalski , 1876#15n#0#0#0#0#gruiformes#gruidae\n1995#(01.09)#nor#megaceryle lugubris#(temminck , 1834)#1n#0#0#0#0#coraciiformes#alcedinidae\n",
        "pandas_code": "df['family'].value_counts().idxmax() == 'phasianidae'",
        "pandas_eval": "True"
    },
    {
        "id": 1571,
        "statement": "the strigiformes order be only featured 1 time",
        "label": 1,
        "table_caption": "list of birds on stamps of bhutan",
        "table_text": "year#date#type#species#author species#value#scott#yvert#mitchell#sta & gib#order#family\n1968#(20.01)#nor#tragopan satyra#(linnaeus , 1758)#1c#92#143#179#0#galliformes#phasianidae\n1968#(23.04)#nor#lophophorus sclateri#jerdon , 1870#4n#92 g#150#186#0#galliformes#phasianidae\n1968#(07.12)#nor#liocichla phoenicea#(gould , 1837)#2c#99#198#248#187#passeriformes#timaliidae\n1968#(28.12)#nor#liocichla phoenicea#(gould , 1837)#20c#0#0#0#0#passeriformes#timaliidae\n1969#(20.01)#nor#liocichla phoenicea#(gould , 1837)#1.50n#0#0#0#0#passeriformes#timaliidae\n1969#(05.08)#nor#strix aluco#linnaeus , 1758#15c#0#0#0#0#strigiformes#strigidae\n1970#(19.06)#nor#lophophorus sclateri#jerdon , 1870#20c#0#0#0#0#galliformes#phasianidae\n1970#(20.09)#nor#tragopan satyra#(linnaeus , 1758)#85c#0#0#0#0#galliformes#phasianidae\n1970#(02.11)#nor#tragopan satyra#(linnaeus , 1758)#20c#0#0#0#0#galliformes#phasianidae\n1971#(01.07)#nor#lophophorus impejanus#(latham , 1790)#55c#0#0#0#0#galliformes#phasianidae\n1982#(19.04)#nor#chloropsis hardwickii#jardine & selby , 1830#2n#0#0#0#0#passeriformes#irenidae\n1985#(29.11)#nor#lagopus lagopus#(linnaeus , 1758)#1n#0#0#0#0#galliformes#tetraonidae\n1985#(06.12)#nor#anas platyrhynchos#linnaeus , 1758#50c#0#0#0#0#anseriformes#anatidae\n1987#(25.05)#nor#phoenicopterus ruber#linnaeus , 1758#1n#0#0#0#0#phoenicopteriformes#phoenicopteridae\n1989#(22.11)#nor#chrysocolaptes lucidus#(scopoli , 1786)#50c#0#0#0#0#piciformes#picidae\n1990#(21.05)#nor#grus japonensis#(muller , 1776)#25n#0#0#0#0#gruiformes#gruidae\n1993#(01.07)#nor#grus nigricollis#przewalski , 1876#15n#0#0#0#0#gruiformes#gruidae\n1995#(01.09)#nor#megaceryle lugubris#(temminck , 1834)#1n#0#0#0#0#coraciiformes#alcedinidae\n",
        "pandas_code": "(df['order'] == 'strigiformes').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1572,
        "statement": "there be 4 stamp from the year 1968",
        "label": 1,
        "table_caption": "list of birds on stamps of bhutan",
        "table_text": "year#date#type#species#author species#value#scott#yvert#mitchell#sta & gib#order#family\n1968#(20.01)#nor#tragopan satyra#(linnaeus , 1758)#1c#92#143#179#0#galliformes#phasianidae\n1968#(23.04)#nor#lophophorus sclateri#jerdon , 1870#4n#92 g#150#186#0#galliformes#phasianidae\n1968#(07.12)#nor#liocichla phoenicea#(gould , 1837)#2c#99#198#248#187#passeriformes#timaliidae\n1968#(28.12)#nor#liocichla phoenicea#(gould , 1837)#20c#0#0#0#0#passeriformes#timaliidae\n1969#(20.01)#nor#liocichla phoenicea#(gould , 1837)#1.50n#0#0#0#0#passeriformes#timaliidae\n1969#(05.08)#nor#strix aluco#linnaeus , 1758#15c#0#0#0#0#strigiformes#strigidae\n1970#(19.06)#nor#lophophorus sclateri#jerdon , 1870#20c#0#0#0#0#galliformes#phasianidae\n1970#(20.09)#nor#tragopan satyra#(linnaeus , 1758)#85c#0#0#0#0#galliformes#phasianidae\n1970#(02.11)#nor#tragopan satyra#(linnaeus , 1758)#20c#0#0#0#0#galliformes#phasianidae\n1971#(01.07)#nor#lophophorus impejanus#(latham , 1790)#55c#0#0#0#0#galliformes#phasianidae\n1982#(19.04)#nor#chloropsis hardwickii#jardine & selby , 1830#2n#0#0#0#0#passeriformes#irenidae\n1985#(29.11)#nor#lagopus lagopus#(linnaeus , 1758)#1n#0#0#0#0#galliformes#tetraonidae\n1985#(06.12)#nor#anas platyrhynchos#linnaeus , 1758#50c#0#0#0#0#anseriformes#anatidae\n1987#(25.05)#nor#phoenicopterus ruber#linnaeus , 1758#1n#0#0#0#0#phoenicopteriformes#phoenicopteridae\n1989#(22.11)#nor#chrysocolaptes lucidus#(scopoli , 1786)#50c#0#0#0#0#piciformes#picidae\n1990#(21.05)#nor#grus japonensis#(muller , 1776)#25n#0#0#0#0#gruiformes#gruidae\n1993#(01.07)#nor#grus nigricollis#przewalski , 1876#15n#0#0#0#0#gruiformes#gruidae\n1995#(01.09)#nor#megaceryle lugubris#(temminck , 1834)#1n#0#0#0#0#coraciiformes#alcedinidae\n",
        "pandas_code": "(df['year'] == 1968).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1573,
        "statement": "3 incumbants fromt the 2006 election be first elect later than 1999",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nindiana 1#pete visclosky#democratic#1984#re - elected\nindiana 2#chris chocola#republican#2002#lost re - election democratic gain\nindiana 3#mark souder#republican#1994#re - elected\nindiana 4#steve buyer#republican#1992#re - elected\nindiana 5#dan burton#republican#1982#re - elected\nindiana 6#mike pence#republican#2000#re - elected\nindiana 7#julia carson#democratic#1996#re - elected\nindiana 8#john hostettler#republican#1994#lost re - election democratic gain\nindiana 9#mike sodrel#republican#2004#lost re - election democratic gain\n",
        "pandas_code": "len(df[df['first elected'] > 1999]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1574,
        "statement": "julia carson and pete visclosky be the only incumbants from the democratic party",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nindiana 1#pete visclosky#democratic#1984#re - elected\nindiana 2#chris chocola#republican#2002#lost re - election democratic gain\nindiana 3#mark souder#republican#1994#re - elected\nindiana 4#steve buyer#republican#1992#re - elected\nindiana 5#dan burton#republican#1982#re - elected\nindiana 6#mike pence#republican#2000#re - elected\nindiana 7#julia carson#democratic#1996#re - elected\nindiana 8#john hostettler#republican#1994#lost re - election democratic gain\nindiana 9#mike sodrel#republican#2004#lost re - election democratic gain\n",
        "pandas_code": "df[(df['party'] == 'democratic') & (df['incumbent'].isin(['julia carson', 'pete visclosky']))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1575,
        "statement": "mike pence be a republican who do not lose the re - election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nindiana 1#pete visclosky#democratic#1984#re - elected\nindiana 2#chris chocola#republican#2002#lost re - election democratic gain\nindiana 3#mark souder#republican#1994#re - elected\nindiana 4#steve buyer#republican#1992#re - elected\nindiana 5#dan burton#republican#1982#re - elected\nindiana 6#mike pence#republican#2000#re - elected\nindiana 7#julia carson#democratic#1996#re - elected\nindiana 8#john hostettler#republican#1994#lost re - election democratic gain\nindiana 9#mike sodrel#republican#2004#lost re - election democratic gain\n",
        "pandas_code": "df[(df['incumbent'] == 'mike pence') & (df['party'] == 'republican') & (~df['results'].str.contains('lost'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1576,
        "statement": "mike sodrel have the most recent first election of all of the incumbants from the 2006 election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nindiana 1#pete visclosky#democratic#1984#re - elected\nindiana 2#chris chocola#republican#2002#lost re - election democratic gain\nindiana 3#mark souder#republican#1994#re - elected\nindiana 4#steve buyer#republican#1992#re - elected\nindiana 5#dan burton#republican#1982#re - elected\nindiana 6#mike pence#republican#2000#re - elected\nindiana 7#julia carson#democratic#1996#re - elected\nindiana 8#john hostettler#republican#1994#lost re - election democratic gain\nindiana 9#mike sodrel#republican#2004#lost re - election democratic gain\n",
        "pandas_code": "df[df['incumbent'] == 'mike sodrel']['first elected'].max() == df['first elected'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1577,
        "statement": "julia carson be first elect 2 year later than john hostettler be",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nindiana 1#pete visclosky#democratic#1984#re - elected\nindiana 2#chris chocola#republican#2002#lost re - election democratic gain\nindiana 3#mark souder#republican#1994#re - elected\nindiana 4#steve buyer#republican#1992#re - elected\nindiana 5#dan burton#republican#1982#re - elected\nindiana 6#mike pence#republican#2000#re - elected\nindiana 7#julia carson#democratic#1996#re - elected\nindiana 8#john hostettler#republican#1994#lost re - election democratic gain\nindiana 9#mike sodrel#republican#2004#lost re - election democratic gain\n",
        "pandas_code": "(df[df['incumbent'] == 'julia carson']['first elected'].values[0] - df[df['incumbent'] == 'john hostettler']['first elected'].values[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1578,
        "statement": "the episode contain the segment on paper umbrella be show before the episode contain the segment on ultralight aircraft",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n21 - 01#261#rubber gloves#soap carvings#aircraft cabinets#motorcycle brake locks\n21 - 02#262#powder horns#handcrafted moulds#perogies#inner tubes\n21 - 03#263#lace#antique frame replicas#orchids#unicycle wheel hubs\n21 - 04#264#external hard drives#frozen shrimp#thai rice boxes#paper towels\n21 - 05#265#tea#roof finials#artificial flowers#alloy wheels\n21 - 06#266#gel caps#playground spring riders#frozen pancakes#natural rubber\n21 - 07#267#paper umbrellas#coal#aircraft seats#s cremation urn\n21 - 08#268#aluminium s canoe#wooden stave bowls#wheelchair accessible vans#s marimba\n21 - 09#269#indy car seats#paper flowers#standby generators (part 1)#standby generators (part 2)\n21 - 10#270#customized knee replacements#leaf springs#lavender essential oil#rivets and rivet tools\n21 - 11#271#cast iron stoves#ultralight aircraft#snow groomers#rubber bands\n21 - 12#272#barber chairs#sewage pumps#bimini boat tops#diesel filters\n",
        "pandas_code": "df[df['segment a'] == 'paper umbrellas'].index[0] < df[df['segment b'] == 'ultralight aircraft'].index[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1579,
        "statement": "segment relate to aircraft appear in 3 different episode",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n21 - 01#261#rubber gloves#soap carvings#aircraft cabinets#motorcycle brake locks\n21 - 02#262#powder horns#handcrafted moulds#perogies#inner tubes\n21 - 03#263#lace#antique frame replicas#orchids#unicycle wheel hubs\n21 - 04#264#external hard drives#frozen shrimp#thai rice boxes#paper towels\n21 - 05#265#tea#roof finials#artificial flowers#alloy wheels\n21 - 06#266#gel caps#playground spring riders#frozen pancakes#natural rubber\n21 - 07#267#paper umbrellas#coal#aircraft seats#s cremation urn\n21 - 08#268#aluminium s canoe#wooden stave bowls#wheelchair accessible vans#s marimba\n21 - 09#269#indy car seats#paper flowers#standby generators (part 1)#standby generators (part 2)\n21 - 10#270#customized knee replacements#leaf springs#lavender essential oil#rivets and rivet tools\n21 - 11#271#cast iron stoves#ultralight aircraft#snow groomers#rubber bands\n21 - 12#272#barber chairs#sewage pumps#bimini boat tops#diesel filters\n",
        "pandas_code": "df[df[['segment a', 'segment b', 'segment c', 'segment d']].apply(lambda x: x.str.contains('aircraft')).any(axis=1)]['episode'].nunique() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1580,
        "statement": "the segment on natural rubber be not in the same episode as the segment on rubber band",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n21 - 01#261#rubber gloves#soap carvings#aircraft cabinets#motorcycle brake locks\n21 - 02#262#powder horns#handcrafted moulds#perogies#inner tubes\n21 - 03#263#lace#antique frame replicas#orchids#unicycle wheel hubs\n21 - 04#264#external hard drives#frozen shrimp#thai rice boxes#paper towels\n21 - 05#265#tea#roof finials#artificial flowers#alloy wheels\n21 - 06#266#gel caps#playground spring riders#frozen pancakes#natural rubber\n21 - 07#267#paper umbrellas#coal#aircraft seats#s cremation urn\n21 - 08#268#aluminium s canoe#wooden stave bowls#wheelchair accessible vans#s marimba\n21 - 09#269#indy car seats#paper flowers#standby generators (part 1)#standby generators (part 2)\n21 - 10#270#customized knee replacements#leaf springs#lavender essential oil#rivets and rivet tools\n21 - 11#271#cast iron stoves#ultralight aircraft#snow groomers#rubber bands\n21 - 12#272#barber chairs#sewage pumps#bimini boat tops#diesel filters\n",
        "pandas_code": "df[(df['segment a'] == 'natural rubber') | (df['segment b'] == 'natural rubber') | (df['segment c'] == 'natural rubber') | (df['segment d'] == 'natural rubber')]['episode'].isin(df[(df['segment a'] == 'rubber bands') | (df['segment b'] == 'rubber bands') | (df['segment c'] == 'rubber bands') | (df['segment d'] == 'rubber bands')]['episode']).any() == False",
        "pandas_eval": "True"
    },
    {
        "id": 1581,
        "statement": "the segment on coal do not appear in the first half of the series",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n21 - 01#261#rubber gloves#soap carvings#aircraft cabinets#motorcycle brake locks\n21 - 02#262#powder horns#handcrafted moulds#perogies#inner tubes\n21 - 03#263#lace#antique frame replicas#orchids#unicycle wheel hubs\n21 - 04#264#external hard drives#frozen shrimp#thai rice boxes#paper towels\n21 - 05#265#tea#roof finials#artificial flowers#alloy wheels\n21 - 06#266#gel caps#playground spring riders#frozen pancakes#natural rubber\n21 - 07#267#paper umbrellas#coal#aircraft seats#s cremation urn\n21 - 08#268#aluminium s canoe#wooden stave bowls#wheelchair accessible vans#s marimba\n21 - 09#269#indy car seats#paper flowers#standby generators (part 1)#standby generators (part 2)\n21 - 10#270#customized knee replacements#leaf springs#lavender essential oil#rivets and rivet tools\n21 - 11#271#cast iron stoves#ultralight aircraft#snow groomers#rubber bands\n21 - 12#272#barber chairs#sewage pumps#bimini boat tops#diesel filters\n",
        "pandas_code": "df[df['segment b'] == 'coal'].index[0] >= len(df) // 2",
        "pandas_eval": "True"
    },
    {
        "id": 1582,
        "statement": "the segment on thai rice box come later in its episode than the segment on antique frame replica",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n21 - 01#261#rubber gloves#soap carvings#aircraft cabinets#motorcycle brake locks\n21 - 02#262#powder horns#handcrafted moulds#perogies#inner tubes\n21 - 03#263#lace#antique frame replicas#orchids#unicycle wheel hubs\n21 - 04#264#external hard drives#frozen shrimp#thai rice boxes#paper towels\n21 - 05#265#tea#roof finials#artificial flowers#alloy wheels\n21 - 06#266#gel caps#playground spring riders#frozen pancakes#natural rubber\n21 - 07#267#paper umbrellas#coal#aircraft seats#s cremation urn\n21 - 08#268#aluminium s canoe#wooden stave bowls#wheelchair accessible vans#s marimba\n21 - 09#269#indy car seats#paper flowers#standby generators (part 1)#standby generators (part 2)\n21 - 10#270#customized knee replacements#leaf springs#lavender essential oil#rivets and rivet tools\n21 - 11#271#cast iron stoves#ultralight aircraft#snow groomers#rubber bands\n21 - 12#272#barber chairs#sewage pumps#bimini boat tops#diesel filters\n",
        "pandas_code": "df.loc[df['segment c'] == 'thai rice boxes', 'episode'].values[0] > df.loc[df['segment b'] == 'antique frame replicas', 'episode'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1583,
        "statement": "in the 6th united state congress there be 3 resignation and 2 death",
        "label": 1,
        "table_caption": "6th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nnew york 1st#jonathan havens (dr)#died october 25 , 1799#john smith (dr)#february 27 , 1800\nconnecticut at - large#jonathan brace (f)#resigned sometime in 1800#john cotton smith (f)#november 17 , 1800\nvirginia 13th#john marshall (f)#resigned june 7 , 1800 to become secretary of state#littleton w tazewell (dr)#november 26 , 1800\nmassachusetts 3rd#samuel lyman (f)#resigned november 6 , 1800#ebenezer mattoon (f)#february 2 , 1801\npennsylvania 8th#thomas hartley (f)#died december 21 , 1800#john stewart (dr)#february 3 , 1801\n",
        "pandas_code": "(df['reason for change'].str.contains('resigned').sum() == 3) & (df['reason for change'].str.contains('died').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1584,
        "statement": "there be 3 democratic - republican successor in the 6th united state congress and 2 federalist",
        "label": 1,
        "table_caption": "6th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nnew york 1st#jonathan havens (dr)#died october 25 , 1799#john smith (dr)#february 27 , 1800\nconnecticut at - large#jonathan brace (f)#resigned sometime in 1800#john cotton smith (f)#november 17 , 1800\nvirginia 13th#john marshall (f)#resigned june 7 , 1800 to become secretary of state#littleton w tazewell (dr)#november 26 , 1800\nmassachusetts 3rd#samuel lyman (f)#resigned november 6 , 1800#ebenezer mattoon (f)#february 2 , 1801\npennsylvania 8th#thomas hartley (f)#died december 21 , 1800#john stewart (dr)#february 3 , 1801\n",
        "pandas_code": "(df['successor'].str.contains('(dr)', case=False).sum() == 3) & (df['successor'].str.contains('(f)', case=False).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1586,
        "statement": "1 vacator of the 6th united state congress resign to become secretary of state in 1800",
        "label": 1,
        "table_caption": "6th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nnew york 1st#jonathan havens (dr)#died october 25 , 1799#john smith (dr)#february 27 , 1800\nconnecticut at - large#jonathan brace (f)#resigned sometime in 1800#john cotton smith (f)#november 17 , 1800\nvirginia 13th#john marshall (f)#resigned june 7 , 1800 to become secretary of state#littleton w tazewell (dr)#november 26 , 1800\nmassachusetts 3rd#samuel lyman (f)#resigned november 6 , 1800#ebenezer mattoon (f)#february 2 , 1801\npennsylvania 8th#thomas hartley (f)#died december 21 , 1800#john stewart (dr)#february 3 , 1801\n",
        "pandas_code": "df['reason for change'].str.contains('resigned.*secretary of state', regex=True).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1587,
        "statement": "there be more federalist vacators , in the 6th united state congress , than democratic - republican",
        "label": 1,
        "table_caption": "6th united states congress",
        "table_text": "district#vacator#reason for change#successor#date successor seated\nnew york 1st#jonathan havens (dr)#died october 25 , 1799#john smith (dr)#february 27 , 1800\nconnecticut at - large#jonathan brace (f)#resigned sometime in 1800#john cotton smith (f)#november 17 , 1800\nvirginia 13th#john marshall (f)#resigned june 7 , 1800 to become secretary of state#littleton w tazewell (dr)#november 26 , 1800\nmassachusetts 3rd#samuel lyman (f)#resigned november 6 , 1800#ebenezer mattoon (f)#february 2 , 1801\npennsylvania 8th#thomas hartley (f)#died december 21 , 1800#john stewart (dr)#february 3 , 1801\n",
        "pandas_code": "(df['vacator'].str.contains('(f)').sum() > df['vacator'].str.contains('(dr)').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 1588,
        "statement": "the bobcat play 1 overtime game",
        "label": 1,
        "table_caption": "2007 - 08 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n48#february 2#denver#101 - 117#jason richardson (38)#emeka okafor (13)#raymond felton (9)#pepsi center 19391#18 - 30\n49#february 4#phoenix#104 - 118#jason richardson (25)#emeka okafor (13)#jeff mcinnis (5)#us airways center 18422#18 - 31\n50#february 8#new jersey#90 - 104#gerald wallace (21)#emeka okafor (17)#nazr mohammed , jason richardson (3)#time warner cable arena 16319#18 - 32\n51#february 10#detroit#87 - 113#jason richardson (27)#emeka okafor (9)#jeff mcinnis , jason richardson (5)#palace of auburn hills 22076#18 - 33\n52#february 11#la lakers#97 - 106#raymond felton (29)#emeka okafor (13)#raymond felton (8)#time warner cable arena 19270#18 - 34\n53#february 13#atlanta#100 - 98 (ot)#raymond felton (22)#emeka okafor (21)#raymond felton (7)#time warner cable arena 11213#19 - 34\n54#february 19#san antonio#65 - 85#raymond felton (19)#emeka okafor (10)#raymond felton , jason richardson (4)#at&t center 18383#19 - 35\n55#february 22#sacramento#115 - 116#jason richardson (29)#emeka okafor (7)#raymond felton (10)#time warner cable arena 16842#19 - 36\n56#february 23#washington#95 - 110#jason richardson (25)#emeka okafor (13)#raymond felton (9)#verizon center 20173#19 - 37\n",
        "pandas_code": "df['score'].str.contains('ot').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1589,
        "statement": "the bobcat only win 1 game",
        "label": 1,
        "table_caption": "2007 - 08 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n48#february 2#denver#101 - 117#jason richardson (38)#emeka okafor (13)#raymond felton (9)#pepsi center 19391#18 - 30\n49#february 4#phoenix#104 - 118#jason richardson (25)#emeka okafor (13)#jeff mcinnis (5)#us airways center 18422#18 - 31\n50#february 8#new jersey#90 - 104#gerald wallace (21)#emeka okafor (17)#nazr mohammed , jason richardson (3)#time warner cable arena 16319#18 - 32\n51#february 10#detroit#87 - 113#jason richardson (27)#emeka okafor (9)#jeff mcinnis , jason richardson (5)#palace of auburn hills 22076#18 - 33\n52#february 11#la lakers#97 - 106#raymond felton (29)#emeka okafor (13)#raymond felton (8)#time warner cable arena 19270#18 - 34\n53#february 13#atlanta#100 - 98 (ot)#raymond felton (22)#emeka okafor (21)#raymond felton (7)#time warner cable arena 11213#19 - 34\n54#february 19#san antonio#65 - 85#raymond felton (19)#emeka okafor (10)#raymond felton , jason richardson (4)#at&t center 18383#19 - 35\n55#february 22#sacramento#115 - 116#jason richardson (29)#emeka okafor (7)#raymond felton (10)#time warner cable arena 16842#19 - 36\n56#february 23#washington#95 - 110#jason richardson (25)#emeka okafor (13)#raymond felton (9)#verizon center 20173#19 - 37\n",
        "pandas_code": "(df['record'].str.split(' - ').str[0].astype(int).max() - df['record'].str.split(' - ').str[0].astype(int).min()) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1590,
        "statement": "the bobcat lose 8 game",
        "label": 1,
        "table_caption": "2007 - 08 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n48#february 2#denver#101 - 117#jason richardson (38)#emeka okafor (13)#raymond felton (9)#pepsi center 19391#18 - 30\n49#february 4#phoenix#104 - 118#jason richardson (25)#emeka okafor (13)#jeff mcinnis (5)#us airways center 18422#18 - 31\n50#february 8#new jersey#90 - 104#gerald wallace (21)#emeka okafor (17)#nazr mohammed , jason richardson (3)#time warner cable arena 16319#18 - 32\n51#february 10#detroit#87 - 113#jason richardson (27)#emeka okafor (9)#jeff mcinnis , jason richardson (5)#palace of auburn hills 22076#18 - 33\n52#february 11#la lakers#97 - 106#raymond felton (29)#emeka okafor (13)#raymond felton (8)#time warner cable arena 19270#18 - 34\n53#february 13#atlanta#100 - 98 (ot)#raymond felton (22)#emeka okafor (21)#raymond felton (7)#time warner cable arena 11213#19 - 34\n54#february 19#san antonio#65 - 85#raymond felton (19)#emeka okafor (10)#raymond felton , jason richardson (4)#at&t center 18383#19 - 35\n55#february 22#sacramento#115 - 116#jason richardson (29)#emeka okafor (7)#raymond felton (10)#time warner cable arena 16842#19 - 36\n56#february 23#washington#95 - 110#jason richardson (25)#emeka okafor (13)#raymond felton (9)#verizon center 20173#19 - 37\n",
        "pandas_code": "len(df[df['score'].str.contains(' - ') & ~df['score'].str.contains('(ot)') & (df['score'].str.split(' - ').str[0].astype(int) < df['score'].str.split(' - ').str[1].str.split(' ').str[0].astype(int))]) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1591,
        "statement": "the biggest arena the bobcat play in be the palace of auburn hill",
        "label": 1,
        "table_caption": "2007 - 08 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n48#february 2#denver#101 - 117#jason richardson (38)#emeka okafor (13)#raymond felton (9)#pepsi center 19391#18 - 30\n49#february 4#phoenix#104 - 118#jason richardson (25)#emeka okafor (13)#jeff mcinnis (5)#us airways center 18422#18 - 31\n50#february 8#new jersey#90 - 104#gerald wallace (21)#emeka okafor (17)#nazr mohammed , jason richardson (3)#time warner cable arena 16319#18 - 32\n51#february 10#detroit#87 - 113#jason richardson (27)#emeka okafor (9)#jeff mcinnis , jason richardson (5)#palace of auburn hills 22076#18 - 33\n52#february 11#la lakers#97 - 106#raymond felton (29)#emeka okafor (13)#raymond felton (8)#time warner cable arena 19270#18 - 34\n53#february 13#atlanta#100 - 98 (ot)#raymond felton (22)#emeka okafor (21)#raymond felton (7)#time warner cable arena 11213#19 - 34\n54#february 19#san antonio#65 - 85#raymond felton (19)#emeka okafor (10)#raymond felton , jason richardson (4)#at&t center 18383#19 - 35\n55#february 22#sacramento#115 - 116#jason richardson (29)#emeka okafor (7)#raymond felton (10)#time warner cable arena 16842#19 - 36\n56#february 23#washington#95 - 110#jason richardson (25)#emeka okafor (13)#raymond felton (9)#verizon center 20173#19 - 37\n",
        "pandas_code": "(df['location attendance'].str.extract('(\\\\d+)').astype(int).max() == int(df[df['location attendance'].str.contains('palace of auburn hills')]['location attendance'].str.extract('(\\\\d+)').iloc[0])).item()",
        "pandas_eval": "True"
    },
    {
        "id": 1592,
        "statement": "the bobcat score 50 more point from game 54 to game 55",
        "label": 1,
        "table_caption": "2007 - 08 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n48#february 2#denver#101 - 117#jason richardson (38)#emeka okafor (13)#raymond felton (9)#pepsi center 19391#18 - 30\n49#february 4#phoenix#104 - 118#jason richardson (25)#emeka okafor (13)#jeff mcinnis (5)#us airways center 18422#18 - 31\n50#february 8#new jersey#90 - 104#gerald wallace (21)#emeka okafor (17)#nazr mohammed , jason richardson (3)#time warner cable arena 16319#18 - 32\n51#february 10#detroit#87 - 113#jason richardson (27)#emeka okafor (9)#jeff mcinnis , jason richardson (5)#palace of auburn hills 22076#18 - 33\n52#february 11#la lakers#97 - 106#raymond felton (29)#emeka okafor (13)#raymond felton (8)#time warner cable arena 19270#18 - 34\n53#february 13#atlanta#100 - 98 (ot)#raymond felton (22)#emeka okafor (21)#raymond felton (7)#time warner cable arena 11213#19 - 34\n54#february 19#san antonio#65 - 85#raymond felton (19)#emeka okafor (10)#raymond felton , jason richardson (4)#at&t center 18383#19 - 35\n55#february 22#sacramento#115 - 116#jason richardson (29)#emeka okafor (7)#raymond felton (10)#time warner cable arena 16842#19 - 36\n56#february 23#washington#95 - 110#jason richardson (25)#emeka okafor (13)#raymond felton (9)#verizon center 20173#19 - 37\n",
        "pandas_code": "abs(int(df.loc[df['game'] == 54, 'score'].str.split(' - ').iloc[0][0]) - int(df.loc[df['game'] == 55, 'score'].str.split(' - ').iloc[0][0])) >= 50",
        "pandas_eval": "True"
    },
    {
        "id": 1593,
        "statement": "garden fork and pewter flask be the segment a of episode 235 and 236 , respectively",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n19 - 01#235#garden forks#english toffee#paint chip cards#bundt s pan\n19 - 02#236#pewter flasks#potato salad#hydrogen s fuel cell#engineered wood siding\n19 - 03#237#canvas wall s tent#s peace pipe#shredded wheat cereal#s cannon\n19 - 04#238#ic robot ing hunt s decoy#canned tomatoes#s scoreboard#s lasso\n19 - 05#239#turf grass#beef jerky#wood chippers#bowling pins\n19 - 06#240#s multi - tool#jojoba oil#s marionette (part 1)#s marionette (part 2)\n19 - 07#241#fish decoys#film digitization#cylinder stoves#concrete light poles\n19 - 08#242#bamboo bicycles#chainsaw art#breath mints#manual motorcycle transmissions\n19 - 09#243#dinnerware#air brake tanks#frosted cereal#s fossil\n19 - 10#244#clay#pitted prunes#s spur#polyurethane tires\n19 - 11#245#s taser#canned soup#jaw harps & mouth bows#s diving board\n19 - 12#246#navajo rugs#crude oil#s kaleidoscope#titanium dental implants\n",
        "pandas_code": "(df.loc[df['episode'] == 235, 'segment a'].values[0] == 'garden forks') & (df.loc[df['episode'] == 236, 'segment a'].values[0] == 'pewter flasks')",
        "pandas_eval": "True"
    },
    {
        "id": 1594,
        "statement": "can soup and crude oil be the segment b of episode 245 and 246 , respectively",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n19 - 01#235#garden forks#english toffee#paint chip cards#bundt s pan\n19 - 02#236#pewter flasks#potato salad#hydrogen s fuel cell#engineered wood siding\n19 - 03#237#canvas wall s tent#s peace pipe#shredded wheat cereal#s cannon\n19 - 04#238#ic robot ing hunt s decoy#canned tomatoes#s scoreboard#s lasso\n19 - 05#239#turf grass#beef jerky#wood chippers#bowling pins\n19 - 06#240#s multi - tool#jojoba oil#s marionette (part 1)#s marionette (part 2)\n19 - 07#241#fish decoys#film digitization#cylinder stoves#concrete light poles\n19 - 08#242#bamboo bicycles#chainsaw art#breath mints#manual motorcycle transmissions\n19 - 09#243#dinnerware#air brake tanks#frosted cereal#s fossil\n19 - 10#244#clay#pitted prunes#s spur#polyurethane tires\n19 - 11#245#s taser#canned soup#jaw harps & mouth bows#s diving board\n19 - 12#246#navajo rugs#crude oil#s kaleidoscope#titanium dental implants\n",
        "pandas_code": "(df.loc[df['episode'] == 245, 'segment b'].values[0] == 'canned soup') & (df.loc[df['episode'] == 246, 'segment b'].values[0] == 'crude oil')",
        "pandas_eval": "True"
    },
    {
        "id": 1595,
        "statement": "episode 240 have s marionette as both its segment c and d , while concrete light pole be episode 241 's segment d",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n19 - 01#235#garden forks#english toffee#paint chip cards#bundt s pan\n19 - 02#236#pewter flasks#potato salad#hydrogen s fuel cell#engineered wood siding\n19 - 03#237#canvas wall s tent#s peace pipe#shredded wheat cereal#s cannon\n19 - 04#238#ic robot ing hunt s decoy#canned tomatoes#s scoreboard#s lasso\n19 - 05#239#turf grass#beef jerky#wood chippers#bowling pins\n19 - 06#240#s multi - tool#jojoba oil#s marionette (part 1)#s marionette (part 2)\n19 - 07#241#fish decoys#film digitization#cylinder stoves#concrete light poles\n19 - 08#242#bamboo bicycles#chainsaw art#breath mints#manual motorcycle transmissions\n19 - 09#243#dinnerware#air brake tanks#frosted cereal#s fossil\n19 - 10#244#clay#pitted prunes#s spur#polyurethane tires\n19 - 11#245#s taser#canned soup#jaw harps & mouth bows#s diving board\n19 - 12#246#navajo rugs#crude oil#s kaleidoscope#titanium dental implants\n",
        "pandas_code": "(df.loc[df['episode'] == 240, ['segment c', 'segment d']].values == ['s marionette (part 1)', 's marionette (part 2)']).all() and df.loc[df['episode'] == 241, 'segment d'].values[0] == 'concrete light poles'",
        "pandas_eval": "True"
    },
    {
        "id": 1596,
        "statement": "manual mortorcyle transmission and polyurethane tire be the segment d of episode 242 and 244 , respectively",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n19 - 01#235#garden forks#english toffee#paint chip cards#bundt s pan\n19 - 02#236#pewter flasks#potato salad#hydrogen s fuel cell#engineered wood siding\n19 - 03#237#canvas wall s tent#s peace pipe#shredded wheat cereal#s cannon\n19 - 04#238#ic robot ing hunt s decoy#canned tomatoes#s scoreboard#s lasso\n19 - 05#239#turf grass#beef jerky#wood chippers#bowling pins\n19 - 06#240#s multi - tool#jojoba oil#s marionette (part 1)#s marionette (part 2)\n19 - 07#241#fish decoys#film digitization#cylinder stoves#concrete light poles\n19 - 08#242#bamboo bicycles#chainsaw art#breath mints#manual motorcycle transmissions\n19 - 09#243#dinnerware#air brake tanks#frosted cereal#s fossil\n19 - 10#244#clay#pitted prunes#s spur#polyurethane tires\n19 - 11#245#s taser#canned soup#jaw harps & mouth bows#s diving board\n19 - 12#246#navajo rugs#crude oil#s kaleidoscope#titanium dental implants\n",
        "pandas_code": "(df.loc[df['episode'] == 242, 'segment d'].values[0] == 'manual motorcycle transmissions') & (df.loc[df['episode'] == 244, 'segment d'].values[0] == 'polyurethane tires')",
        "pandas_eval": "True"
    },
    {
        "id": 1597,
        "statement": "shredded wheat cereal be not episode 235 's segment d , it 's episode 237 's segment c",
        "label": 1,
        "table_caption": "list of how it 's made episodes",
        "table_text": "series ep#episode#segment a#segment b#segment c#segment d\n19 - 01#235#garden forks#english toffee#paint chip cards#bundt s pan\n19 - 02#236#pewter flasks#potato salad#hydrogen s fuel cell#engineered wood siding\n19 - 03#237#canvas wall s tent#s peace pipe#shredded wheat cereal#s cannon\n19 - 04#238#ic robot ing hunt s decoy#canned tomatoes#s scoreboard#s lasso\n19 - 05#239#turf grass#beef jerky#wood chippers#bowling pins\n19 - 06#240#s multi - tool#jojoba oil#s marionette (part 1)#s marionette (part 2)\n19 - 07#241#fish decoys#film digitization#cylinder stoves#concrete light poles\n19 - 08#242#bamboo bicycles#chainsaw art#breath mints#manual motorcycle transmissions\n19 - 09#243#dinnerware#air brake tanks#frosted cereal#s fossil\n19 - 10#244#clay#pitted prunes#s spur#polyurethane tires\n19 - 11#245#s taser#canned soup#jaw harps & mouth bows#s diving board\n19 - 12#246#navajo rugs#crude oil#s kaleidoscope#titanium dental implants\n",
        "pandas_code": "(df.loc[df['episode'] == 235, 'segment d'].values[0] != 'shredded wheat cereal') & (df.loc[df['episode'] == 237, 'segment c'].values[0] == 'shredded wheat cereal')",
        "pandas_eval": "True"
    },
    {
        "id": 1598,
        "statement": "gunnar il professore gren have the highest number of cap at 5 with kjell ros\u00e9n",
        "label": 1,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "(df[df['name'] == 'gunnar il professore gren']['caps'].values[0] == 5) & (df[df['name'] == 'kjell ros\u00e9n']['caps'].values[0] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 1599,
        "statement": "gunnar nordahl score the most goal with 8 in total",
        "label": 1,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "df.loc[df['name'] == 'gunnar nordahl', 'goals'].values[0] == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1600,
        "statement": "only 1 player score more than 3 goal , gunnar nordahl",
        "label": 1,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "(df[df['goals'] > 3]['name'] == 'gunnar nordahl').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1601,
        "statement": "only 4 player come from the club degerfors if",
        "label": 1,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "len(df[df['club'] == 'degerfors if']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1602,
        "statement": "there be 3 goal keeper list in the table",
        "label": 1,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "len(df[df['pos'] == 'gk']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1603,
        "statement": "gunnar il professore gren have the second highest number of cap at 5 with kjell ros\u00e9n",
        "label": 0,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "(df[df['name'].isin(['gunnar il professore gren', 'kjell ros\u00e9n'])]['caps'].eq(5).all()) & (df['caps'].max() > 5)",
        "pandas_eval": "False"
    },
    {
        "id": 1604,
        "statement": "gunnar nordahl score the second most goal with 8 in total",
        "label": 0,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "df[df['name'] == 'gunnar nordahl']['goals'].values[0] == 8 and df['goals'].sort_values(ascending=False).iloc[1] == 8",
        "pandas_eval": "False"
    },
    {
        "id": 1605,
        "statement": "more than 1 player score more than 3 goal , gunnar nordahl among them",
        "label": 0,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "(df['goals'] > 3).sum() > 1 and 'gunnar nordahl' in df[df['goals'] > 3]['name'].values",
        "pandas_eval": "False"
    },
    {
        "id": 1606,
        "statement": "less than 4 player come from the club degerfors if",
        "label": 0,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "len(df[df['club'] == 'degerfors if']) < 4",
        "pandas_eval": "False"
    },
    {
        "id": 1607,
        "statement": "there be more than 3 goal keeper in the table",
        "label": 0,
        "table_caption": "1946 - 47 in swedish football",
        "table_text": "name#pos#caps#goals#club\nolle \u00e5hlund#mf#1#0#degerfors if\nsune mona - lisa andersson#mf#3#1#aik solna\nmagnus skjorta bergstr\u00f6m#gk#1#0#degerfors if\nrune killing emanuelsson#mf#2#0#ifk g\u00f6teborg\ngunnar il professore gren#fw#5#2#ifk g\u00f6teborg\nkjell hjertsson#mf#1#0#malm\u00f6 ff\negon hemliga j\u00f6nsson#fw#1#2#malm\u00f6 ff\nbror karlsson#fw#1#1#h\u00e4lsingborgs if\neric prosten karlsson#fw#1#1#aik solna\nb\u00f6rje leander#df / mf#4#2#aik solna\nnils lidas / greven liedholm#fw#3#1#ifk norrk\u00f6ping\ntorsten lindy lindberg#gk#3#0#ifk norrk\u00f6ping\nlennart lindskog#fw#1#1#degerfors if\nhans malmstr\u00f6m#df#1#0#malm\u00f6 ff\nmalte svarta blixten m\u00e5rtensson#fw#1#1#h\u00e4lsingborgs if\nsture m\u00e5rtensson#mf#1#0#malm\u00f6 ff\nerik nilsson#df#1#0#malm\u00f6 ff\ngustaf nilsson#fw#1#3#malm\u00f6 ff\nharry nilsson#df#5#0#aik solna\nstellan nilsson#fw#2#0#malm\u00f6 ff\nbertil nordahl#mf#2#0#degerfors if\ngunnar nordahl#fw#4#8#ifk norrk\u00f6ping\nknut nordahl#fw / df#5#1#ifk norrk\u00f6ping\narne nyberg#fw#2#1#ifk g\u00f6teborg\nstig nystr\u00f6m#fw#4#1#djurg\u00e5rdens if stockholm\nsven skuggan persson#fw#1#1#ifk norrk\u00f6ping\nkjell ros\u00e9n#mf#5#1#malm\u00f6 ff\ngustav gurra sj\u00f6berg#gk#2#0#aik solna\nb\u00f6rje tapper#fw#1#0#malm\u00f6 ff\njohn nonne wikdahl#df#1#0#h\u00e4lsingborgs if\n",
        "pandas_code": "len(df[df['pos'] == 'gk']) > 3",
        "pandas_eval": "False"
    },
    {
        "id": 1608,
        "statement": "the city of vigo receive the highest amount of sunlight hour per year",
        "label": 1,
        "table_caption": "galicia , spain",
        "table_text": "city / town#july av t#rain#days with rain (year / summer)#days with frost#sunlight hours\nsantiago de compostela#degree#mm (in)#141 / 19#15#1998\na coru\u00f1a#degree#mm (in)#131 / 19#0#1966\nlugo#degree#mm (in)#131 / 18#42#1821\nvigo#degree#mm (in)#130 / 18#5#2212\nourense#degree#mm (in)#97 / 12#30#2043\n",
        "pandas_code": "df.loc[df['city / town'] == 'vigo', 'sunlight hours'].values[0] == df['sunlight hours'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1609,
        "statement": "the city of ourense have the lowest recorded day of rain",
        "label": 1,
        "table_caption": "galicia , spain",
        "table_text": "city / town#july av t#rain#days with rain (year / summer)#days with frost#sunlight hours\nsantiago de compostela#degree#mm (in)#141 / 19#15#1998\na coru\u00f1a#degree#mm (in)#131 / 19#0#1966\nlugo#degree#mm (in)#131 / 18#42#1821\nvigo#degree#mm (in)#130 / 18#5#2212\nourense#degree#mm (in)#97 / 12#30#2043\n",
        "pandas_code": "df[df['city / town'] == 'ourense']['days with rain (year / summer)'].str.split(' / ').str[1].astype(int).min() == df['days with rain (year / summer)'].str.split(' / ').str[1].astype(int).min()",
        "pandas_eval": "True"
    },
    {
        "id": 1610,
        "statement": "the city of lugo have the highest amount of day with frost",
        "label": 1,
        "table_caption": "galicia , spain",
        "table_text": "city / town#july av t#rain#days with rain (year / summer)#days with frost#sunlight hours\nsantiago de compostela#degree#mm (in)#141 / 19#15#1998\na coru\u00f1a#degree#mm (in)#131 / 19#0#1966\nlugo#degree#mm (in)#131 / 18#42#1821\nvigo#degree#mm (in)#130 / 18#5#2212\nourense#degree#mm (in)#97 / 12#30#2043\n",
        "pandas_code": "df.loc[df['city / town'] == 'lugo', 'days with frost'].max() == df['days with frost'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1611,
        "statement": "the city of lugo receive the lowest amount of sunlight hour per year",
        "label": 1,
        "table_caption": "galicia , spain",
        "table_text": "city / town#july av t#rain#days with rain (year / summer)#days with frost#sunlight hours\nsantiago de compostela#degree#mm (in)#141 / 19#15#1998\na coru\u00f1a#degree#mm (in)#131 / 19#0#1966\nlugo#degree#mm (in)#131 / 18#42#1821\nvigo#degree#mm (in)#130 / 18#5#2212\nourense#degree#mm (in)#97 / 12#30#2043\n",
        "pandas_code": "df.loc[df['city / town'] == 'lugo', 'sunlight hours'].iloc[0] == df['sunlight hours'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1612,
        "statement": "santiago de compostela receive the highest level of rainfall annually",
        "label": 1,
        "table_caption": "galicia , spain",
        "table_text": "city / town#july av t#rain#days with rain (year / summer)#days with frost#sunlight hours\nsantiago de compostela#degree#mm (in)#141 / 19#15#1998\na coru\u00f1a#degree#mm (in)#131 / 19#0#1966\nlugo#degree#mm (in)#131 / 18#42#1821\nvigo#degree#mm (in)#130 / 18#5#2212\nourense#degree#mm (in)#97 / 12#30#2043\n",
        "pandas_code": "df.loc[df['city / town'] == 'santiago de compostela', 'days with rain (year / summer)'].str.split(' / ').str[0].astype(int).max() == df['days with rain (year / summer)'].str.split(' / ').str[0].astype(int).max()",
        "pandas_eval": "True"
    },
    {
        "id": 1613,
        "statement": "the only label that do not hit 1 on the billboard peak be giant",
        "label": 1,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df[df['billboard peak'] != 1]['label'].unique().tolist() == ['giant']",
        "pandas_eval": "True"
    },
    {
        "id": 1614,
        "statement": "reprise be credit with 2 platinum album between 2005 - 2008",
        "label": 1,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df[(df['label'] == 'reprise') & (df['riaa cert'] == '2 platinum')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1615,
        "statement": "4 of reprise 's title hit number 1 on the billboard",
        "label": 1,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df[(df['label'] == 'reprise') & (df['billboard peak'] == 1)].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1616,
        "statement": "asylum be release 10 year after the sickness",
        "label": 1,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "(pd.to_datetime(df[df['title'] == 'asylum']['date of release']).dt.year.values[0] - pd.to_datetime(df[df['title'] == 'the sickness']['date of release']).dt.year.values[0]) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 1617,
        "statement": "disturbed 's first release title go 4x platinum",
        "label": 1,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df.loc[df['title'] == 'the sickness', 'riaa cert'].iloc[0] == '4 platinum'",
        "pandas_eval": "True"
    },
    {
        "id": 1618,
        "statement": "the only label that do not hit 1 on the billboard peak be reprise",
        "label": 0,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df[df['billboard peak'] != 1]['label'].unique().tolist() != ['giant']",
        "pandas_eval": "False"
    },
    {
        "id": 1619,
        "statement": "giant be credit with 2 platinum album between 2005 - 2008",
        "label": 0,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df[(df['label'] == 'giant') & (df['riaa cert'] == '2 platinum') & (df['date of release'].str.contains('2005|2006|2007|2008'))].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 1620,
        "statement": "5 of reprise 's title hit number 1 on the billboard",
        "label": 0,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "len(df[(df['label'] == 'reprise') & (df['billboard peak'] == 1)]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1621,
        "statement": "asylum be release 5 year after the sickness",
        "label": 0,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "(df[df['title'] == 'asylum']['date of release'].values[0] == 'august 31 , 2010') & (df[df['title'] == 'the sickness']['date of release'].values[0] == 'march 7 , 2000') & ((pd.to_datetime('august 31 , 2010') - pd.to_datetime('march 7 , 2000')).days // 365 == 5)",
        "pandas_eval": "False"
    },
    {
        "id": 1622,
        "statement": "disturbed 's first release title go 2x platinum",
        "label": 0,
        "table_caption": "disturbed (band)",
        "table_text": "date of release#title#billboard peak#riaa cert#label\nmarch 7 , 2000#the sickness#29#4 platinum#giant\nseptember 17 , 2002#believe#1#2 platinum#reprise\nseptember 20 , 2005#ten thousand fists#1#platinum#reprise\njune 3 , 2008#indestructible#1#platinum#reprise\naugust 31 , 2010#asylum#1#gold#reprise\n",
        "pandas_code": "df.loc[df['date of release'] == 'march 7 , 2000', 'riaa cert'].iloc[0] == '2 platinum'",
        "pandas_eval": "False"
    },
    {
        "id": 1624,
        "statement": "the lowest attendance at a game be 13603 people during the game against indiana",
        "label": 1,
        "table_caption": "2007 - 08 boston celtics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n45#february 5#cleveland#113 - 114#allen (24)#rondo (7)#allen (5)#quicken loans arena 20562#36 - 9\n46#february 6#la clippers#111 - 100#rondo (24)#powe (10)#rondo (8)#td banknorth garden 18624#37 - 9\n47#february 8#minnesota#88 - 86#pierce (18)#powe (8)#pierce (6)#target center 19511#38 - 9\n48#february 10#san antonio#98 - 90#pierce (35)#rondo (11)#rondo (12)#td banknorth garden 18624#39 - 9\n49#february 12#indiana#104 - 97#pierce (28)#pierce (12)#rondo (7)#conseco fieldhouse 13603#40 - 9\n50#february 13#new york#111 - 103#pierce (24)#posey (11)#pierce (7)#td banknorth garden 18624#41 - 9\n51#february 19#denver#118 - 124#pierce (24)#powe (11)#pierce (7)#pepsi center 19894#41 - 10\n52#february 20#golden state#117 - 119#allen (32)#garnett (15)#allen , rondo (6)#oracle arena 20711#41 - 11\n53#february 22#phoenix#77 - 85#garnett (19)#perkins , pierce (6)#garnett (4)#us airways center 18422#41 - 12\n54#february 24#portland#112 - 102#pierce (30)#garnett , pierce (7)#rondo (8)#rose garden 20554#42 - 12\n55#february 25#la clippers#104 - 76#pierce , posey (17)#perkins (9)#allen (7)#staples center 19328#43 - 12\n56#february 27#cleveland#92 - 87#allen (22)#garnett (11)#rondo (8)#td banknorth garden 18624#44 - 12\n",
        "pandas_code": "df[df['team'] == 'indiana']['location attendance'].str.extract('(\\d+)')[0].astype(int).min() == 13603",
        "pandas_eval": "True"
    },
    {
        "id": 1625,
        "statement": "the greatest win margin by the celtic be in the game against the la clipper",
        "label": 1,
        "table_caption": "2007 - 08 boston celtics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n45#february 5#cleveland#113 - 114#allen (24)#rondo (7)#allen (5)#quicken loans arena 20562#36 - 9\n46#february 6#la clippers#111 - 100#rondo (24)#powe (10)#rondo (8)#td banknorth garden 18624#37 - 9\n47#february 8#minnesota#88 - 86#pierce (18)#powe (8)#pierce (6)#target center 19511#38 - 9\n48#february 10#san antonio#98 - 90#pierce (35)#rondo (11)#rondo (12)#td banknorth garden 18624#39 - 9\n49#february 12#indiana#104 - 97#pierce (28)#pierce (12)#rondo (7)#conseco fieldhouse 13603#40 - 9\n50#february 13#new york#111 - 103#pierce (24)#posey (11)#pierce (7)#td banknorth garden 18624#41 - 9\n51#february 19#denver#118 - 124#pierce (24)#powe (11)#pierce (7)#pepsi center 19894#41 - 10\n52#february 20#golden state#117 - 119#allen (32)#garnett (15)#allen , rondo (6)#oracle arena 20711#41 - 11\n53#february 22#phoenix#77 - 85#garnett (19)#perkins , pierce (6)#garnett (4)#us airways center 18422#41 - 12\n54#february 24#portland#112 - 102#pierce (30)#garnett , pierce (7)#rondo (8)#rose garden 20554#42 - 12\n55#february 25#la clippers#104 - 76#pierce , posey (17)#perkins (9)#allen (7)#staples center 19328#43 - 12\n56#february 27#cleveland#92 - 87#allen (22)#garnett (11)#rondo (8)#td banknorth garden 18624#44 - 12\n",
        "pandas_code": "df.loc[df['team'] == 'la clippers', 'score'].apply(lambda x: abs(int(x.split(' - ')[0]) - int(x.split(' - ')[1]))).max() == (df['score'].apply(lambda x: abs(int(x.split(' - ')[0]) - int(x.split(' - ')[1]))).max())",
        "pandas_eval": "True"
    },
    {
        "id": 1626,
        "statement": "pierce have the highest point score in a single game during the game against san antonio",
        "label": 1,
        "table_caption": "2007 - 08 boston celtics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n45#february 5#cleveland#113 - 114#allen (24)#rondo (7)#allen (5)#quicken loans arena 20562#36 - 9\n46#february 6#la clippers#111 - 100#rondo (24)#powe (10)#rondo (8)#td banknorth garden 18624#37 - 9\n47#february 8#minnesota#88 - 86#pierce (18)#powe (8)#pierce (6)#target center 19511#38 - 9\n48#february 10#san antonio#98 - 90#pierce (35)#rondo (11)#rondo (12)#td banknorth garden 18624#39 - 9\n49#february 12#indiana#104 - 97#pierce (28)#pierce (12)#rondo (7)#conseco fieldhouse 13603#40 - 9\n50#february 13#new york#111 - 103#pierce (24)#posey (11)#pierce (7)#td banknorth garden 18624#41 - 9\n51#february 19#denver#118 - 124#pierce (24)#powe (11)#pierce (7)#pepsi center 19894#41 - 10\n52#february 20#golden state#117 - 119#allen (32)#garnett (15)#allen , rondo (6)#oracle arena 20711#41 - 11\n53#february 22#phoenix#77 - 85#garnett (19)#perkins , pierce (6)#garnett (4)#us airways center 18422#41 - 12\n54#february 24#portland#112 - 102#pierce (30)#garnett , pierce (7)#rondo (8)#rose garden 20554#42 - 12\n55#february 25#la clippers#104 - 76#pierce , posey (17)#perkins (9)#allen (7)#staples center 19328#43 - 12\n56#february 27#cleveland#92 - 87#allen (22)#garnett (11)#rondo (8)#td banknorth garden 18624#44 - 12\n",
        "pandas_code": "('pierce' in df[df['team'] == 'san antonio']['high points'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1627,
        "statement": "the highest number of rebound in a single game be 15 by garnett on february 20",
        "label": 1,
        "table_caption": "2007 - 08 boston celtics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n45#february 5#cleveland#113 - 114#allen (24)#rondo (7)#allen (5)#quicken loans arena 20562#36 - 9\n46#february 6#la clippers#111 - 100#rondo (24)#powe (10)#rondo (8)#td banknorth garden 18624#37 - 9\n47#february 8#minnesota#88 - 86#pierce (18)#powe (8)#pierce (6)#target center 19511#38 - 9\n48#february 10#san antonio#98 - 90#pierce (35)#rondo (11)#rondo (12)#td banknorth garden 18624#39 - 9\n49#february 12#indiana#104 - 97#pierce (28)#pierce (12)#rondo (7)#conseco fieldhouse 13603#40 - 9\n50#february 13#new york#111 - 103#pierce (24)#posey (11)#pierce (7)#td banknorth garden 18624#41 - 9\n51#february 19#denver#118 - 124#pierce (24)#powe (11)#pierce (7)#pepsi center 19894#41 - 10\n52#february 20#golden state#117 - 119#allen (32)#garnett (15)#allen , rondo (6)#oracle arena 20711#41 - 11\n53#february 22#phoenix#77 - 85#garnett (19)#perkins , pierce (6)#garnett (4)#us airways center 18422#41 - 12\n54#february 24#portland#112 - 102#pierce (30)#garnett , pierce (7)#rondo (8)#rose garden 20554#42 - 12\n55#february 25#la clippers#104 - 76#pierce , posey (17)#perkins (9)#allen (7)#staples center 19328#43 - 12\n56#february 27#cleveland#92 - 87#allen (22)#garnett (11)#rondo (8)#td banknorth garden 18624#44 - 12\n",
        "pandas_code": "df[(df['high rebounds'].str.contains('garnett')) & (df['high rebounds'].str.contains('15')) & (df['date'] == 'february 20')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1628,
        "statement": "rc celta de vigo have 24 win which be 2 more than sevilla fc with 22 win",
        "label": 1,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "(df.loc[df['club'] == 'rc celta de vigo', 'wins'].values[0] - df.loc[df['club'] == 'sevilla fc', 'wins'].values[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1629,
        "statement": "club ferrol be 1 of 3 club with 10 loss",
        "label": 1,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "len(df[(df['losses'] == 10) & (df['club'] == 'club ferrol')]) == 1 and len(df[df['losses'] == 10]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1630,
        "statement": "real oviedo cf be the only club with a goal difference of 0",
        "label": 1,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "len(df[df['goal difference'] == '0']) == 1 and df[df['goal difference'] == '0']['club'].iloc[0] == 'real oviedo cf'",
        "pandas_eval": "True"
    },
    {
        "id": 1631,
        "statement": "the club with the largest goal difference be jerez industrial cf",
        "label": 1,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "df.loc[df['goal difference'].apply(lambda x: int(x.replace(' ', ''))).idxmin(), 'club'] == 'jerez industrial cf'",
        "pandas_eval": "True"
    },
    {
        "id": 1632,
        "statement": "ad rayo vallecano and cd alcoyano be the only 2 club with the same number of goal for with 51",
        "label": 1,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "len(df[df['goals for'] == 51]['club'].unique()) == 2 and set(df[df['goals for'] == 51]['club']) == {'ad rayo vallecano', 'cd alcoyano'}",
        "pandas_eval": "True"
    },
    {
        "id": 1633,
        "statement": "rc celta de vigo have 24 win which be 2 more than sevilla fc and club ferrol with 22 win",
        "label": 0,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "(df.loc[df['club'] == 'rc celta de vigo', 'wins'].values[0] == 24) & (df.loc[df['club'] == 'sevilla fc', 'wins'].values[0] == 22) & (df.loc[df['club'] == 'club ferrol', 'wins'].values[0] == 22)",
        "pandas_eval": "False"
    },
    {
        "id": 1634,
        "statement": "club ferrol be 1 of 3 club with more than 10 loss",
        "label": 0,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "(df[df['club'] == 'club ferrol']['losses'].values[0] > 10) and (df['losses'] > 10).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1637,
        "statement": "ad rayo vallecano and cd alcoyano be the only 2 club with the same number of goal for with 51 and goal against of less than 54",
        "label": 0,
        "table_caption": "1968 - 69 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#sevilla fc#38#53#22#9#7#56#30#+ 26\n2#rc celta de vigo#38#52#24#4#10#63#28#+ 35\n3#rcd mallorca#38#50#22#6#10#62#37#+ 25\n4#club ferrol#38#47#19#9#10#55#39#+ 16\n5#real gij\u00f3n cf#38#42#16#10#12#60#46#+ 14\n6#cf calvo sotelo#38#42#15#12#11#47#41#+ 6\n7#real betis balompi\u00e9#38#41#14#13#11#69#51#+ 18\n8#real murcia cf#38#40#15#10#13#52#48#+ 4\n9#ad rayo vallecano#38#40#14#12#12#51#44#+ 7\n10#real valladolid#38#39#15#9#14#61#48#+ 13\n11#real oviedo cf#38#38#16#6#16#45#45#0\n12#burgos cf#38#38#14#10#14#47#53#- 6\n13#cd alcoyano#38#35#12#11#15#51#55#- 4\n14#deportivo alav\u00e9s#38#35#15#5#18#47#62#- 15\n15#onteniente cf#38#34#15#4#19#47#54#- 7\n16#cd ilicitano#38#32#12#8#18#50#58#- 8\n17#cd mestalla#38#30#11#8#19#53#62#- 9\n18#c\u00e1diz cf#38#30#13#4#21#44#65#- 21\n19#sd indauchu#38#24#9#6#23#40#79#- 39\n20#jerez industrial cf#38#18#6#6#26#36#91#- 55\n",
        "pandas_code": "(df[(df['goals for'] == 51) & (df['goals against'] < 54)]['club'].tolist() == ['ad rayo vallecano', 'cd alcoyano'])",
        "pandas_eval": "False"
    },
    {
        "id": 1638,
        "statement": "the hoak packer from fresno , ca , be the 1st place team for year 1951 , 1952 , and 1954",
        "label": 1,
        "table_caption": "international softball congress",
        "table_text": "year#1st place team#2nd place team#3rd place team#4th place team#host location\n1951#hoak packers , fresno , ca#nitehawks , long beach , ca#robitaille motors , montreal , qc#wells motors , greeley , co#greeley , co\n1952#hoak packers , fresno , ca#nitehawks , long beach , ca#pointers , barbers point , hi#wyoming angus , johnstown , co#plainview , tx\n1953#nitehawks , long beach , ca#merchants , tampico , il#lions , lorenzo , tx#hoak packers , fresno , ca#selma , ca\n1954#hoak packers , fresno , ca#condors , dinuba , ca#nitehawks , long beach , ca#lions , lorenzo , tx#selma , ca\n1955#nitehawks , long beach , ca#condors , dinuba , ca#elites , new bedford , il#local 1014 chiefs , gary , in#new bedford , il\n1956#nitehawks , long beach , ca#siebren hybrids , geneseo , il#elites , new bedford , il#national cash register , dayton , oh#new bedford , il\n",
        "pandas_code": "all(df[df['year'].isin([1951, 1952, 1954])]['1st place team'] == 'hoak packers , fresno , ca')",
        "pandas_eval": "True"
    },
    {
        "id": 1639,
        "statement": "the nitehawks long beach , ca be either the 1st place team or the 2nd place team for year 1951 , 1952 , 1953 , 1955 , and 1956",
        "label": 1,
        "table_caption": "international softball congress",
        "table_text": "year#1st place team#2nd place team#3rd place team#4th place team#host location\n1951#hoak packers , fresno , ca#nitehawks , long beach , ca#robitaille motors , montreal , qc#wells motors , greeley , co#greeley , co\n1952#hoak packers , fresno , ca#nitehawks , long beach , ca#pointers , barbers point , hi#wyoming angus , johnstown , co#plainview , tx\n1953#nitehawks , long beach , ca#merchants , tampico , il#lions , lorenzo , tx#hoak packers , fresno , ca#selma , ca\n1954#hoak packers , fresno , ca#condors , dinuba , ca#nitehawks , long beach , ca#lions , lorenzo , tx#selma , ca\n1955#nitehawks , long beach , ca#condors , dinuba , ca#elites , new bedford , il#local 1014 chiefs , gary , in#new bedford , il\n1956#nitehawks , long beach , ca#siebren hybrids , geneseo , il#elites , new bedford , il#national cash register , dayton , oh#new bedford , il\n",
        "pandas_code": "df[df['year'].isin([1951, 1952, 1953, 1955, 1956])].apply(lambda row: 'nitehawks , long beach , ca' in [row['1st place team'], row['2nd place team']], axis=1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1640,
        "statement": "the elite , from new bedford , illinois , never place higher than 3rd",
        "label": 1,
        "table_caption": "international softball congress",
        "table_text": "year#1st place team#2nd place team#3rd place team#4th place team#host location\n1951#hoak packers , fresno , ca#nitehawks , long beach , ca#robitaille motors , montreal , qc#wells motors , greeley , co#greeley , co\n1952#hoak packers , fresno , ca#nitehawks , long beach , ca#pointers , barbers point , hi#wyoming angus , johnstown , co#plainview , tx\n1953#nitehawks , long beach , ca#merchants , tampico , il#lions , lorenzo , tx#hoak packers , fresno , ca#selma , ca\n1954#hoak packers , fresno , ca#condors , dinuba , ca#nitehawks , long beach , ca#lions , lorenzo , tx#selma , ca\n1955#nitehawks , long beach , ca#condors , dinuba , ca#elites , new bedford , il#local 1014 chiefs , gary , in#new bedford , il\n1956#nitehawks , long beach , ca#siebren hybrids , geneseo , il#elites , new bedford , il#national cash register , dayton , oh#new bedford , il\n",
        "pandas_code": "all(df[df['3rd place team'].str.contains('elites , new bedford , il')].index.isin(df[df['1st place team'].str.contains('elites , new bedford , il') | df['2nd place team'].str.contains('elites , new bedford , il')].index) == False)",
        "pandas_eval": "True"
    },
    {
        "id": 1641,
        "statement": "the nitehawks , long beach , ca , never place lower than 3rd",
        "label": 1,
        "table_caption": "international softball congress",
        "table_text": "year#1st place team#2nd place team#3rd place team#4th place team#host location\n1951#hoak packers , fresno , ca#nitehawks , long beach , ca#robitaille motors , montreal , qc#wells motors , greeley , co#greeley , co\n1952#hoak packers , fresno , ca#nitehawks , long beach , ca#pointers , barbers point , hi#wyoming angus , johnstown , co#plainview , tx\n1953#nitehawks , long beach , ca#merchants , tampico , il#lions , lorenzo , tx#hoak packers , fresno , ca#selma , ca\n1954#hoak packers , fresno , ca#condors , dinuba , ca#nitehawks , long beach , ca#lions , lorenzo , tx#selma , ca\n1955#nitehawks , long beach , ca#condors , dinuba , ca#elites , new bedford , il#local 1014 chiefs , gary , in#new bedford , il\n1956#nitehawks , long beach , ca#siebren hybrids , geneseo , il#elites , new bedford , il#national cash register , dayton , oh#new bedford , il\n",
        "pandas_code": "all(df[df['1st place team'].str.contains('nitehawks , long beach , ca') | df['2nd place team'].str.contains('nitehawks , long beach , ca') | df['3rd place team'].str.contains('nitehawks , long beach , ca')].index.isin(df.index))",
        "pandas_eval": "True"
    },
    {
        "id": 1642,
        "statement": "the first place team be always from california",
        "label": 1,
        "table_caption": "international softball congress",
        "table_text": "year#1st place team#2nd place team#3rd place team#4th place team#host location\n1951#hoak packers , fresno , ca#nitehawks , long beach , ca#robitaille motors , montreal , qc#wells motors , greeley , co#greeley , co\n1952#hoak packers , fresno , ca#nitehawks , long beach , ca#pointers , barbers point , hi#wyoming angus , johnstown , co#plainview , tx\n1953#nitehawks , long beach , ca#merchants , tampico , il#lions , lorenzo , tx#hoak packers , fresno , ca#selma , ca\n1954#hoak packers , fresno , ca#condors , dinuba , ca#nitehawks , long beach , ca#lions , lorenzo , tx#selma , ca\n1955#nitehawks , long beach , ca#condors , dinuba , ca#elites , new bedford , il#local 1014 chiefs , gary , in#new bedford , il\n1956#nitehawks , long beach , ca#siebren hybrids , geneseo , il#elites , new bedford , il#national cash register , dayton , oh#new bedford , il\n",
        "pandas_code": "all(df['1st place team'].str.contains(', ca'))",
        "pandas_eval": "True"
    },
    {
        "id": 1643,
        "statement": "debian and ubuntu use 2 mandatory access control package : selinux and apparmor",
        "label": 1,
        "table_caption": "comparison of linux distributions",
        "table_text": "distribution#compile time buffer checks#mandatory access control#software executable space protection#grsecurity#rsbac\nalpine linux#unknown#unknown#pax#yes#unknown\ndebian / ubuntu#yes#selinux , apparmor#pax#optional#optional\nfedora#yes#selinux#exec shield#no#no\ngentoo#optional#selinux#pax#optional#optional\nmandriva#unknown#apparmor#unknown#unknown#yes\n",
        "pandas_code": "df[df['distribution'].str.contains('debian / ubuntu')]['mandatory access control'].str.contains('selinux , apparmor').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1644,
        "statement": "debian / ubuntu and fedora require compile time buffer check , on gentoo they be optional",
        "label": 1,
        "table_caption": "comparison of linux distributions",
        "table_text": "distribution#compile time buffer checks#mandatory access control#software executable space protection#grsecurity#rsbac\nalpine linux#unknown#unknown#pax#yes#unknown\ndebian / ubuntu#yes#selinux , apparmor#pax#optional#optional\nfedora#yes#selinux#exec shield#no#no\ngentoo#optional#selinux#pax#optional#optional\nmandriva#unknown#apparmor#unknown#unknown#yes\n",
        "pandas_code": "all(df[df['distribution'].isin(['debian / ubuntu', 'fedora'])]['compile time buffer checks'] == 'yes') and df[df['distribution'] == 'gentoo']['compile time buffer checks'].values[0] == 'optional'",
        "pandas_eval": "True"
    },
    {
        "id": 1645,
        "statement": "3 of the top linux distribution use pax , only 1 use exec shield",
        "label": 1,
        "table_caption": "comparison of linux distributions",
        "table_text": "distribution#compile time buffer checks#mandatory access control#software executable space protection#grsecurity#rsbac\nalpine linux#unknown#unknown#pax#yes#unknown\ndebian / ubuntu#yes#selinux , apparmor#pax#optional#optional\nfedora#yes#selinux#exec shield#no#no\ngentoo#optional#selinux#pax#optional#optional\nmandriva#unknown#apparmor#unknown#unknown#yes\n",
        "pandas_code": "(df['software executable space protection'].value_counts().get('pax', 0) == 3) & (df['software executable space protection'].value_counts().get('exec shield', 0) == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1646,
        "statement": "alpine linux use grsecurity , which be also offer as an option on debian , ubuntu and gentoo , but not on fedora",
        "label": 1,
        "table_caption": "comparison of linux distributions",
        "table_text": "distribution#compile time buffer checks#mandatory access control#software executable space protection#grsecurity#rsbac\nalpine linux#unknown#unknown#pax#yes#unknown\ndebian / ubuntu#yes#selinux , apparmor#pax#optional#optional\nfedora#yes#selinux#exec shield#no#no\ngentoo#optional#selinux#pax#optional#optional\nmandriva#unknown#apparmor#unknown#unknown#yes\n",
        "pandas_code": "(df[df['distribution'] == 'alpine linux']['grsecurity'].values[0] == 'yes') & (df[df['distribution'].isin(['debian / ubuntu', 'gentoo'])]['grsecurity'].values[0] == 'optional') & (df[df['distribution'] == 'fedora']['grsecurity'].values[0] == 'no')",
        "pandas_eval": "True"
    },
    {
        "id": 1647,
        "statement": "the distribution with compile time buffer check also use selinux",
        "label": 1,
        "table_caption": "comparison of linux distributions",
        "table_text": "distribution#compile time buffer checks#mandatory access control#software executable space protection#grsecurity#rsbac\nalpine linux#unknown#unknown#pax#yes#unknown\ndebian / ubuntu#yes#selinux , apparmor#pax#optional#optional\nfedora#yes#selinux#exec shield#no#no\ngentoo#optional#selinux#pax#optional#optional\nmandriva#unknown#apparmor#unknown#unknown#yes\n",
        "pandas_code": "all(df[df['compile time buffer checks'] == 'yes']['mandatory access control'].str.contains('selinux'))",
        "pandas_eval": "True"
    },
    {
        "id": 1648,
        "statement": "lake county have more superfund site than any other county in illinois",
        "label": 1,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "df['county'].value_counts().idxmax() == 'lake'",
        "pandas_eval": "True"
    },
    {
        "id": 1649,
        "statement": "the most recent superfund site in illinois be complete in 2008",
        "label": 1,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "df['construction completed'].str.contains('2008').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1650,
        "statement": "2 of the 12 superfund site in illinois have be delete",
        "label": 1,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "df['deleted'].isin(['01 / 08 / 2001', '02 / 11 / 1991']).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1652,
        "statement": "only 1 superfund site in illinois be list after 1990",
        "label": 1,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "(df['listed'].apply(lambda x: int(x.split('/')[-1].strip()) > 1990).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1653,
        "statement": "lake county have less superfund site than any other county in illinois",
        "label": 0,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "df['county'].value_counts().idxmin() == 'lake'",
        "pandas_eval": "False"
    },
    {
        "id": 1655,
        "statement": "4 of the 12 superfund site in illinois have be delete",
        "label": 0,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "(df['deleted'].value_counts().get('n / a', 0) == 8)",
        "pandas_eval": "False"
    },
    {
        "id": 1656,
        "statement": "the newest superfund site in illinois be all list in the same year",
        "label": 0,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "df['listed'].apply(lambda x: x.split('/')[-1].strip()).nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1657,
        "statement": "only 1 superfund site in illinois be list after 2010",
        "label": 0,
        "table_caption": "list of superfund sites in illinois",
        "table_text": "cerclis id#county#listed#construction completed#partially deleted#deleted\nild980607055#adams#08 / 30 / 1990#03 / 31 / 1999#n / a#n / a\nild980996789#alexander#10 / 04 / 1989#09 / 28 / 1999#n / a#01 / 08 / 2001\nild980397079#cumberland#09 / 08 / 1983#09 / 24 / 1992#n / a#n / a\nil3210020803#jo daviess#03 / 13 / 1989#n / a#n / a#n / a\nild000802827#lake#09 / 08 / 1983#n / a#n / a#n / a\nild003817137#lake#06 / 10 / 1986#09 / 12 / 1989#n / a#02 / 11 / 1991\nild005443544#lake#09 / 08 / 1983#12 / 31 / 1991#n / a#n / a\nild980500102#lake#03 / 31 / 1989#09 / 23 / 2005#n / a#n / a\nild980605836#lake#02 / 21 / 1990#06 / 29 / 2001#n / a#n / a\nild048843809#madison#03 / 04 / 2010#-#-#-\nil0210090049#will#03 / 13 / 1989#09 / 29 / 2008#n / a#n / a\nil7213820460#will#07 / 22 / 1987#09 / 10 / 2008#n / a#n / a\nild053219259#winnebago#09 / 08 / 1983#07 / 13 / 1998#n / a#n / a\n",
        "pandas_code": "(df['listed'] > '12 / 31 / 2010').sum() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1658,
        "statement": "centru have a smaller total than nord - vest",
        "label": 1,
        "table_caption": "development regions of romania",
        "table_text": "region ( nuts 2006)#total (million )#per capita (2005)#ppp (million )#ppp per capita#% of eu average gdp (ppp)\nnord - vest#11575#3499#23164#8500#35.9\ncentru#11439#3742#21892#9100#38.3\nnord - est#10882#2519#21779#5800#24.7\nsud - est#10897#3211#21807#7700#32.5\nsud#12562#3028#25139#7600#32.1\nbucuresti - ilfov#21999.5#7776#37068#19800#83.8\nsud - vest#8226#2920#16463#7200#30.4\n",
        "pandas_code": "df.loc[df['region ( nuts 2006)'] == 'centru', 'total (million )'].values[0] < df.loc[df['region ( nuts 2006)'] == 'nord - vest', 'total (million )'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1659,
        "statement": "per capita , sud - est be greater than sud",
        "label": 1,
        "table_caption": "development regions of romania",
        "table_text": "region ( nuts 2006)#total (million )#per capita (2005)#ppp (million )#ppp per capita#% of eu average gdp (ppp)\nnord - vest#11575#3499#23164#8500#35.9\ncentru#11439#3742#21892#9100#38.3\nnord - est#10882#2519#21779#5800#24.7\nsud - est#10897#3211#21807#7700#32.5\nsud#12562#3028#25139#7600#32.1\nbucuresti - ilfov#21999.5#7776#37068#19800#83.8\nsud - vest#8226#2920#16463#7200#30.4\n",
        "pandas_code": "df.loc[df['region ( nuts 2006)'] == 'sud - est', 'per capita (2005)'].values[0] > df.loc[df['region ( nuts 2006)'] == 'sud', 'per capita (2005)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1660,
        "statement": "the % of eu average gdp be highest in bucuresti - ilfov",
        "label": 1,
        "table_caption": "development regions of romania",
        "table_text": "region ( nuts 2006)#total (million )#per capita (2005)#ppp (million )#ppp per capita#% of eu average gdp (ppp)\nnord - vest#11575#3499#23164#8500#35.9\ncentru#11439#3742#21892#9100#38.3\nnord - est#10882#2519#21779#5800#24.7\nsud - est#10897#3211#21807#7700#32.5\nsud#12562#3028#25139#7600#32.1\nbucuresti - ilfov#21999.5#7776#37068#19800#83.8\nsud - vest#8226#2920#16463#7200#30.4\n",
        "pandas_code": "df.loc[df['region ( nuts 2006)'] == 'bucuresti - ilfov', '% of eu average gdp (ppp)'].iloc[0] == df['% of eu average gdp (ppp)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1661,
        "statement": "the lowest ppp per capita be nord - est",
        "label": 1,
        "table_caption": "development regions of romania",
        "table_text": "region ( nuts 2006)#total (million )#per capita (2005)#ppp (million )#ppp per capita#% of eu average gdp (ppp)\nnord - vest#11575#3499#23164#8500#35.9\ncentru#11439#3742#21892#9100#38.3\nnord - est#10882#2519#21779#5800#24.7\nsud - est#10897#3211#21807#7700#32.5\nsud#12562#3028#25139#7600#32.1\nbucuresti - ilfov#21999.5#7776#37068#19800#83.8\nsud - vest#8226#2920#16463#7200#30.4\n",
        "pandas_code": "df.loc[df['ppp per capita'].idxmin(), 'region ( nuts 2006)'] == 'nord - est'",
        "pandas_eval": "True"
    },
    {
        "id": 1662,
        "statement": "the per capita in 2005 be higher in sud than in sud - vest",
        "label": 1,
        "table_caption": "development regions of romania",
        "table_text": "region ( nuts 2006)#total (million )#per capita (2005)#ppp (million )#ppp per capita#% of eu average gdp (ppp)\nnord - vest#11575#3499#23164#8500#35.9\ncentru#11439#3742#21892#9100#38.3\nnord - est#10882#2519#21779#5800#24.7\nsud - est#10897#3211#21807#7700#32.5\nsud#12562#3028#25139#7600#32.1\nbucuresti - ilfov#21999.5#7776#37068#19800#83.8\nsud - vest#8226#2920#16463#7200#30.4\n",
        "pandas_code": "df[df['region ( nuts 2006)'] == 'sud']['per capita (2005)'].values[0] > df[df['region ( nuts 2006)'] == 'sud - vest']['per capita (2005)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1663,
        "statement": "leo eaton direct the most episode for the secret service",
        "label": 1,
        "table_caption": "the secret service",
        "table_text": "episode no#title#director#writer (s)#original air date (atv)#production no\n1#a case for the bishop#alan perry#gerry and sylvia anderson#21 september 1969#1\n2#a question of miracles#leo eaton#donald james#28 september 1969#2\n3#to catch a spy#brian heard#pat dunlop#5 october 1969#4\n4#the feathered spies#ian spurrier#tony barwick#12 october 1969#3\n5#last train to bufflers halt#alan perry#tony barwick#19 october 1969#5\n6#hole in one#brian heard#shane rimmer#26 october 1969#8\n7#recall to service#peter anderson#pat dunlop#2 november 1969#7\n8#errand of mercy#leo eaton#tony barwick#9 november 1969#6\n9#the deadly whisper#leo eaton#donald james#16 november 1969#11\n10#the cure#leo eaton#pat dunlop#23 november 1969#10\n11#school for spies#ken turner#donald james#30 november 1969#9\n12#may - day , may - day!#alan perry#bob kesten#7 december 1969#12\n",
        "pandas_code": "df['director'].value_counts().idxmax() == 'leo eaton'",
        "pandas_eval": "True"
    },
    {
        "id": 1664,
        "statement": "a hole in one air before errand of mercy",
        "label": 1,
        "table_caption": "the secret service",
        "table_text": "episode no#title#director#writer (s)#original air date (atv)#production no\n1#a case for the bishop#alan perry#gerry and sylvia anderson#21 september 1969#1\n2#a question of miracles#leo eaton#donald james#28 september 1969#2\n3#to catch a spy#brian heard#pat dunlop#5 october 1969#4\n4#the feathered spies#ian spurrier#tony barwick#12 october 1969#3\n5#last train to bufflers halt#alan perry#tony barwick#19 october 1969#5\n6#hole in one#brian heard#shane rimmer#26 october 1969#8\n7#recall to service#peter anderson#pat dunlop#2 november 1969#7\n8#errand of mercy#leo eaton#tony barwick#9 november 1969#6\n9#the deadly whisper#leo eaton#donald james#16 november 1969#11\n10#the cure#leo eaton#pat dunlop#23 november 1969#10\n11#school for spies#ken turner#donald james#30 november 1969#9\n12#may - day , may - day!#alan perry#bob kesten#7 december 1969#12\n",
        "pandas_code": "df[df['title'] == 'hole in one']['original air date (atv)'].values[0] < df[df['title'] == 'errand of mercy']['original air date (atv)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1665,
        "statement": "donald james and tony barwick have write the same number of episode for the secret service",
        "label": 1,
        "table_caption": "the secret service",
        "table_text": "episode no#title#director#writer (s)#original air date (atv)#production no\n1#a case for the bishop#alan perry#gerry and sylvia anderson#21 september 1969#1\n2#a question of miracles#leo eaton#donald james#28 september 1969#2\n3#to catch a spy#brian heard#pat dunlop#5 october 1969#4\n4#the feathered spies#ian spurrier#tony barwick#12 october 1969#3\n5#last train to bufflers halt#alan perry#tony barwick#19 october 1969#5\n6#hole in one#brian heard#shane rimmer#26 october 1969#8\n7#recall to service#peter anderson#pat dunlop#2 november 1969#7\n8#errand of mercy#leo eaton#tony barwick#9 november 1969#6\n9#the deadly whisper#leo eaton#donald james#16 november 1969#11\n10#the cure#leo eaton#pat dunlop#23 november 1969#10\n11#school for spies#ken turner#donald james#30 november 1969#9\n12#may - day , may - day!#alan perry#bob kesten#7 december 1969#12\n",
        "pandas_code": "(df['writer (s)'].value_counts()['donald james'] == df['writer (s)'].value_counts()['tony barwick'])",
        "pandas_eval": "True"
    },
    {
        "id": 1666,
        "statement": "alan perry direct one episode before leo eaton",
        "label": 1,
        "table_caption": "the secret service",
        "table_text": "episode no#title#director#writer (s)#original air date (atv)#production no\n1#a case for the bishop#alan perry#gerry and sylvia anderson#21 september 1969#1\n2#a question of miracles#leo eaton#donald james#28 september 1969#2\n3#to catch a spy#brian heard#pat dunlop#5 october 1969#4\n4#the feathered spies#ian spurrier#tony barwick#12 october 1969#3\n5#last train to bufflers halt#alan perry#tony barwick#19 october 1969#5\n6#hole in one#brian heard#shane rimmer#26 october 1969#8\n7#recall to service#peter anderson#pat dunlop#2 november 1969#7\n8#errand of mercy#leo eaton#tony barwick#9 november 1969#6\n9#the deadly whisper#leo eaton#donald james#16 november 1969#11\n10#the cure#leo eaton#pat dunlop#23 november 1969#10\n11#school for spies#ken turner#donald james#30 november 1969#9\n12#may - day , may - day!#alan perry#bob kesten#7 december 1969#12\n",
        "pandas_code": "(df[df['director'] == 'alan perry']['episode no'].min() + 1) == df[df['director'] == 'leo eaton']['episode no'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1667,
        "statement": "ken turner , peter anderson , and ian spurrier have all direct the least amount of episode",
        "label": 1,
        "table_caption": "the secret service",
        "table_text": "episode no#title#director#writer (s)#original air date (atv)#production no\n1#a case for the bishop#alan perry#gerry and sylvia anderson#21 september 1969#1\n2#a question of miracles#leo eaton#donald james#28 september 1969#2\n3#to catch a spy#brian heard#pat dunlop#5 october 1969#4\n4#the feathered spies#ian spurrier#tony barwick#12 october 1969#3\n5#last train to bufflers halt#alan perry#tony barwick#19 october 1969#5\n6#hole in one#brian heard#shane rimmer#26 october 1969#8\n7#recall to service#peter anderson#pat dunlop#2 november 1969#7\n8#errand of mercy#leo eaton#tony barwick#9 november 1969#6\n9#the deadly whisper#leo eaton#donald james#16 november 1969#11\n10#the cure#leo eaton#pat dunlop#23 november 1969#10\n11#school for spies#ken turner#donald james#30 november 1969#9\n12#may - day , may - day!#alan perry#bob kesten#7 december 1969#12\n",
        "pandas_code": "df[df['director'].isin(['ken turner', 'peter anderson', 'ian spurrier'])].groupby('director').size().min() == df.groupby('director').size().min()",
        "pandas_eval": "True"
    },
    {
        "id": 1668,
        "statement": "game 7 , 12 and 15 be hold in palace of auburn hills , and the highest attendance be against minnesota",
        "label": 1,
        "table_caption": "2008 detroit shock season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n7#june 4#seattle#77 - 67#smith (33)#ford (11)#nolan (8)#palace of auburn hills 8108#6 - 1\n8#june 6#sacramento#84 - 70#smith (30)#braxton , ford , nolan (6)#nolan (9)#arco arena 6663#7 - 1\n9#june 7#seattle#67 - 75#smith (18)#pierson (10)#hornbuckle , pierson (3)#keyarena 7105#7 - 2\n10#june 11#los angeles#73 - 80#smith (16)#pierson , smith (7)#smith (4)#staples center 8520#7 - 3\n11#june 14#phoenix#89 - 79#humphrey (28)#hornbuckle (15)#nolan (8)#us airways center 7696#8 - 3\n12#june 20#minnesota#98 - 93 (ot)#nolan (44)#ford (14)#pierson (4)#palace of auburn hills 8916#9 - 3\n13#june 22#atlanta#97 - 76#ford (20)#ford (12)#nolan (7)#philips arena 7865#10 - 3\n14#june 24#connecticut#68 - 85#nolan (17)#ford (11)#smith (4)#mohegan sun arena 7501#10 - 4\n15#june 26#connecticut#70 - 61#nolan (13)#ford (9)#smith (4)#palace of auburn hills 8636#11 - 4\n16#june 28#chicago#59 - 76#murphy (13)#ford (8)#hornbuckle , sam (4)#uic pavilion 3407#11 - 5\n",
        "pandas_code": "all(df[df['game'].isin([7, 12, 15])]['location / attendance'].str.contains('palace of auburn hills')) and (df.loc[df['opponent'] == 'minnesota', 'location / attendance'].str.extract('(\\\\d+)').astype(int).max() == df['location / attendance'].str.extract('(\\\\d+)').astype(int).max()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 1669,
        "statement": "with seattle as opponent on june 4 and 7 , the record be 6 - 1 and 7 - 2",
        "label": 1,
        "table_caption": "2008 detroit shock season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n7#june 4#seattle#77 - 67#smith (33)#ford (11)#nolan (8)#palace of auburn hills 8108#6 - 1\n8#june 6#sacramento#84 - 70#smith (30)#braxton , ford , nolan (6)#nolan (9)#arco arena 6663#7 - 1\n9#june 7#seattle#67 - 75#smith (18)#pierson (10)#hornbuckle , pierson (3)#keyarena 7105#7 - 2\n10#june 11#los angeles#73 - 80#smith (16)#pierson , smith (7)#smith (4)#staples center 8520#7 - 3\n11#june 14#phoenix#89 - 79#humphrey (28)#hornbuckle (15)#nolan (8)#us airways center 7696#8 - 3\n12#june 20#minnesota#98 - 93 (ot)#nolan (44)#ford (14)#pierson (4)#palace of auburn hills 8916#9 - 3\n13#june 22#atlanta#97 - 76#ford (20)#ford (12)#nolan (7)#philips arena 7865#10 - 3\n14#june 24#connecticut#68 - 85#nolan (17)#ford (11)#smith (4)#mohegan sun arena 7501#10 - 4\n15#june 26#connecticut#70 - 61#nolan (13)#ford (9)#smith (4)#palace of auburn hills 8636#11 - 4\n16#june 28#chicago#59 - 76#murphy (13)#ford (8)#hornbuckle , sam (4)#uic pavilion 3407#11 - 5\n",
        "pandas_code": "all(df[(df['opponent'] == 'seattle') & (df['date'].isin(['june 4', 'june 7']))]['record'] == ['6 - 1', '7 - 2'])",
        "pandas_eval": "True"
    },
    {
        "id": 1670,
        "statement": "sminth have the high point on the 4 game on june 4 , 6 , 7 and 11",
        "label": 1,
        "table_caption": "2008 detroit shock season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n7#june 4#seattle#77 - 67#smith (33)#ford (11)#nolan (8)#palace of auburn hills 8108#6 - 1\n8#june 6#sacramento#84 - 70#smith (30)#braxton , ford , nolan (6)#nolan (9)#arco arena 6663#7 - 1\n9#june 7#seattle#67 - 75#smith (18)#pierson (10)#hornbuckle , pierson (3)#keyarena 7105#7 - 2\n10#june 11#los angeles#73 - 80#smith (16)#pierson , smith (7)#smith (4)#staples center 8520#7 - 3\n11#june 14#phoenix#89 - 79#humphrey (28)#hornbuckle (15)#nolan (8)#us airways center 7696#8 - 3\n12#june 20#minnesota#98 - 93 (ot)#nolan (44)#ford (14)#pierson (4)#palace of auburn hills 8916#9 - 3\n13#june 22#atlanta#97 - 76#ford (20)#ford (12)#nolan (7)#philips arena 7865#10 - 3\n14#june 24#connecticut#68 - 85#nolan (17)#ford (11)#smith (4)#mohegan sun arena 7501#10 - 4\n15#june 26#connecticut#70 - 61#nolan (13)#ford (9)#smith (4)#palace of auburn hills 8636#11 - 4\n16#june 28#chicago#59 - 76#murphy (13)#ford (8)#hornbuckle , sam (4)#uic pavilion 3407#11 - 5\n",
        "pandas_code": "all(df[(df['date'].isin(['june 4', 'june 6', 'june 7', 'june 11'])) & (df['game'] == 4)]['high points'].str.contains('smith'))",
        "pandas_eval": "True"
    },
    {
        "id": 1671,
        "statement": "only the june 20 game against minnesota have an overtime , with a score of 98 - 93 (ot)",
        "label": 1,
        "table_caption": "2008 detroit shock season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n7#june 4#seattle#77 - 67#smith (33)#ford (11)#nolan (8)#palace of auburn hills 8108#6 - 1\n8#june 6#sacramento#84 - 70#smith (30)#braxton , ford , nolan (6)#nolan (9)#arco arena 6663#7 - 1\n9#june 7#seattle#67 - 75#smith (18)#pierson (10)#hornbuckle , pierson (3)#keyarena 7105#7 - 2\n10#june 11#los angeles#73 - 80#smith (16)#pierson , smith (7)#smith (4)#staples center 8520#7 - 3\n11#june 14#phoenix#89 - 79#humphrey (28)#hornbuckle (15)#nolan (8)#us airways center 7696#8 - 3\n12#june 20#minnesota#98 - 93 (ot)#nolan (44)#ford (14)#pierson (4)#palace of auburn hills 8916#9 - 3\n13#june 22#atlanta#97 - 76#ford (20)#ford (12)#nolan (7)#philips arena 7865#10 - 3\n14#june 24#connecticut#68 - 85#nolan (17)#ford (11)#smith (4)#mohegan sun arena 7501#10 - 4\n15#june 26#connecticut#70 - 61#nolan (13)#ford (9)#smith (4)#palace of auburn hills 8636#11 - 4\n16#june 28#chicago#59 - 76#murphy (13)#ford (8)#hornbuckle , sam (4)#uic pavilion 3407#11 - 5\n",
        "pandas_code": "((df['date'] == 'june 20') & (df['opponent'] == 'minnesota') & (df['score'] == '98 - 93 (ot)')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1672,
        "statement": "the highest record be 11 - 5 , on game 16 , follow by 11 - 4 on the previous game",
        "label": 1,
        "table_caption": "2008 detroit shock season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n7#june 4#seattle#77 - 67#smith (33)#ford (11)#nolan (8)#palace of auburn hills 8108#6 - 1\n8#june 6#sacramento#84 - 70#smith (30)#braxton , ford , nolan (6)#nolan (9)#arco arena 6663#7 - 1\n9#june 7#seattle#67 - 75#smith (18)#pierson (10)#hornbuckle , pierson (3)#keyarena 7105#7 - 2\n10#june 11#los angeles#73 - 80#smith (16)#pierson , smith (7)#smith (4)#staples center 8520#7 - 3\n11#june 14#phoenix#89 - 79#humphrey (28)#hornbuckle (15)#nolan (8)#us airways center 7696#8 - 3\n12#june 20#minnesota#98 - 93 (ot)#nolan (44)#ford (14)#pierson (4)#palace of auburn hills 8916#9 - 3\n13#june 22#atlanta#97 - 76#ford (20)#ford (12)#nolan (7)#philips arena 7865#10 - 3\n14#june 24#connecticut#68 - 85#nolan (17)#ford (11)#smith (4)#mohegan sun arena 7501#10 - 4\n15#june 26#connecticut#70 - 61#nolan (13)#ford (9)#smith (4)#palace of auburn hills 8636#11 - 4\n16#june 28#chicago#59 - 76#murphy (13)#ford (8)#hornbuckle , sam (4)#uic pavilion 3407#11 - 5\n",
        "pandas_code": "(df.loc[df['game'] == 16, 'record'].values[0] == '11 - 5') and (df.loc[df['game'] == 15, 'record'].values[0] == '11 - 4')",
        "pandas_eval": "True"
    },
    {
        "id": 1673,
        "statement": "the san diego charger win 5 time during the 1974 season",
        "label": 1,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "df['result'].str.startswith('w').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1674,
        "statement": "the difference in attendance between game in week 1 and week 7 be 9054",
        "label": 1,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "abs(df[df['week'] == 1]['attendance'].values[0] - df[df['week'] == 7]['attendance'].values[0]) == 9054",
        "pandas_eval": "True"
    },
    {
        "id": 1675,
        "statement": "the san diego charger lose all the game they play in october",
        "label": 1,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "all(df[df['date'].str.contains('october')]['result'].str.startswith('l'))",
        "pandas_eval": "True"
    },
    {
        "id": 1676,
        "statement": "the san diego charger play the kansas city chief 2 time during the 1974 season",
        "label": 1,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "len(df[df['opponent'].str.contains('kansas city chiefs', case=False)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1677,
        "statement": "the san diego charger lose both their game against the oakland raider",
        "label": 1,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "(df[df['opponent'] == 'oakland raiders']['result'].str.startswith('l').all())",
        "pandas_eval": "True"
    },
    {
        "id": 1678,
        "statement": "the san diego charger win 6 time during the 1974 season",
        "label": 0,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 1679,
        "statement": "the attendance between game in week 1 and week 7 be 9053",
        "label": 0,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "(df.loc[df['week'] == 7, 'attendance'].values[0] - df.loc[df['week'] == 1, 'attendance'].values[0]) == 9053",
        "pandas_eval": "False"
    },
    {
        "id": 1680,
        "statement": "the san diego charger lose all the game they play in december",
        "label": 0,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "all(df[df['date'].str.contains('december')]['result'].str.startswith('l'))",
        "pandas_eval": "False"
    },
    {
        "id": 1681,
        "statement": "the san diego charger play the kansas city chief thrice during the 1974 season",
        "label": 0,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "len(df[df['opponent'].str.contains('kansas city chiefs')]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1682,
        "statement": "the san diego charger lose both their game against the denver bronco",
        "label": 0,
        "table_caption": "1974 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1974#houston oilers#l 21 - 14#25317\n2#september 22 , 1974#cincinnati bengals#w 20 - 17#51178\n3#september 29 , 1974#miami dolphins#l 28 - 21#44706\n4#october 6 , 1974#philadelphia eagles#l 13 - 7#36124\n5#october 13 , 1974#oakland raiders#l 14 - 10#40539\n6#october 20 , 1974#denver broncos#l 27 - 7#50928\n7#october 27 , 1974#kansas city chiefs#l 24 - 14#34371\n8#november 3 , 1974#cleveland browns#w 36 - 35#35683\n9#november 10 , 1974#kansas city chiefs#w 14 - 7#48551\n10#november 17 , 1974#oakland raiders#l 17 - 10#50178\n11#november 24 , 1974#green bay packers#l 34 - 0#56267\n12#december 1 , 1974#new york jets#l 27 - 14#44888\n13#december 8 , 1974#chicago bears#w 28 - 21#33662\n14#december 15 , 1974#denver broncos#w 17 - 0#36571\n",
        "pandas_code": "all(df[df['opponent'].str.contains('denver broncos')]['result'].str.startswith('l'))",
        "pandas_eval": "False"
    },
    {
        "id": 1683,
        "statement": "the month with the fewest number of date be in march",
        "label": 1,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "df['date'].str.extract(r'(\\w+)')[0].value_counts().idxmin() == 'march'",
        "pandas_eval": "True"
    },
    {
        "id": 1686,
        "statement": "the total number of date where the boston bruin have a score of 0 be 5",
        "label": 1,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "(df[(df['visitor'] == 'boston bruins') & (df['score'].str.startswith('0'))].shape[0] + df[(df['home'] == 'boston bruins') & (df['score'].str.endswith('0'))].shape[0]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1687,
        "statement": "the month with the most date be december february , both with 12",
        "label": 1,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "len(df[df['date'].str.contains('december')]) == 12 and len(df[df['date'].str.contains('february')]) == 12",
        "pandas_eval": "True"
    },
    {
        "id": 1688,
        "statement": "the year with the fewest number of date be in january",
        "label": 0,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "df[df['date'].str.contains('january')].shape[0] == df.groupby(df['date'].str.extract(r'(\\w+)')[0])['date'].count().min()",
        "pandas_eval": "False"
    },
    {
        "id": 1689,
        "statement": "the most point the boston bruins score in a single game be above 9 against the new york ranger",
        "label": 0,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "df[(df['visitor'] == 'boston bruins') & (df['home'] == 'new york rangers')]['score'].apply(lambda x: int(x.split(' - ')[0])).max() > 9",
        "pandas_eval": "False"
    },
    {
        "id": 1690,
        "statement": "the total number of date where the boston bruin have a score of 0 be less than 5",
        "label": 0,
        "table_caption": "1938 - 39 boston bruins season",
        "table_text": "date#visitor#score#home#record\nnovember 3#boston bruins#3 - 2#toronto maple leafs#1 - 0 - 0\nnovember 6#boston bruins#4 - 1#detroit red wings#2 - 0 - 0\nnovember 13#boston bruins#1 - 2#new york americans#2 - 1 - 0\nnovember 15#toronto maple leafs#1 - 1#boston bruins#2 - 1 - 1\nnovember 20#detroit red wings#1 - 4#boston bruins#3 - 1 - 1\nnovember 22#new york rangers#2 - 4#boston bruins#4 - 1 - 1\nnovember 27#new york americans#2 - 8#boston bruins#5 - 1 - 1\ndecember 1#boston bruins#0 - 2#montreal canadiens#5 - 2 - 1\ndecember 4#boston bruins#5 - 0#chicago black hawks#6 - 2 - 1\ndecember 6#chicago black hawks#0 - 2#boston bruins#7 - 2 - 1\ndecember 11#boston bruins#3 - 0#new york rangers#8 - 2 - 1\ndecember 13#montreal canadiens#2 - 3#boston bruins#9 - 2 - 1\ndecember 15#boston bruins#1 - 0#montreal canadiens#10 - 2 - 1\ndecember 18#boston bruins#2 - 0#detroit red wings#11 - 2 - 1\ndecember 20#new york americans#0 - 3#boston bruins#12 - 2 - 1\ndecember 25#new york rangers#1 - 0#boston bruins#12 - 3 - 1\ndecember 27#toronto maple leafs#2 - 8#boston bruins#13 - 3 - 1\ndecember 29#boston bruins#2 - 4#new york americans#13 - 4 - 1\ndecember 31#boston bruins#1 - 2#new york rangers#13 - 5 - 1\njanuary 1#detroit red wings#1 - 4#boston bruins#14 - 5 - 1\njanuary 3#new york americans#1 - 2#boston bruins#15 - 5 - 1\njanuary 5#boston bruins#2 - 1#chicago black hawks#16 - 5 - 1\njanuary 7#boston bruins#0 - 2#toronto maple leafs#16 - 6 - 1\njanuary 10#chicago black hawks#1 - 3#boston bruins#17 - 6 - 1\njanuary 17#toronto maple leafs#1 - 2#boston bruins#18 - 6 - 1\njanuary 19#boston bruins#0 - 1#montreal canadiens#18 - 7 - 1\njanuary 22#boston bruins#5 - 0#detroit red wings#19 - 7 - 1\njanuary 24#montreal canadiens#4 - 6#boston bruins#20 - 7 - 1\njanuary 29#boston bruins#3 - 2#new york americans#21 - 7 - 1\njanuary 31#new york americans#2 - 2#boston bruins#21 - 7 - 2\nfebruary 2#boston bruins#2 - 1#toronto maple leafs#22 - 7 - 2\nfebruary 5#boston bruins#3 - 0#chicago black hawks#23 - 7 - 2\nfebruary 7#toronto maple leafs#0 - 2#boston bruins#24 - 7 - 2\nfebruary 9#boston bruins#4 - 2#new york rangers#25 - 7 - 2\nfebruary 12#new york rangers#3 - 2#boston bruins#25 - 8 - 2\nfebruary 14#detroit red wings#1 - 2#boston bruins#26 - 8 - 2\nfebruary 16#boston bruins#5 - 1#montreal canadiens#27 - 8 - 2\nfebruary 19#boston bruins#1 - 4#detroit red wings#27 - 9 - 2\nfebruary 21#chicago black hawks#2 - 8#boston bruins#28 - 9 - 2\nfebruary 25#boston bruins#0 - 1#toronto maple leafs#28 - 10 - 2\nfebruary 26#boston bruins#5 - 1#chicago black hawks#29 - 10 - 2\nfebruary 28#montreal canadiens#2 - 6#boston bruins#30 - 10 - 2\nmarch 5#new york rangers#3 - 5#boston bruins#31 - 10 - 2\nmarch 7#detroit red wings#0 - 3#boston bruins#32 - 10 - 2\nmarch 9#boston bruins#9 - 6#new york americans#33 - 10 - 2\nmarch 12#boston bruins#4 - 2#new york rangers#34 - 10 - 2\nmarch 14#chicago black hawks#2 - 4#boston bruins#35 - 10 - 2\nmarch 19#montreal canadiens#5 - 7#boston bruins#36 - 10 - 2\n",
        "pandas_code": "(df[(df['visitor'] == 'boston bruins') & (df['score'].str.startswith('0'))].shape[0] + df[(df['home'] == 'boston bruins') & (df['score'].str.endswith('0'))].shape[0]) < 5",
        "pandas_eval": "False"
    },
    {
        "id": 1691,
        "statement": "gil morgan win 6 tournament during the 1998 senior pga tour",
        "label": 1,
        "table_caption": "1998 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 18#mastercard championship#hawaii#1000000#gil morgan (8)#195 ( - 21)#200000\nfeb 1#royal caribbean classic#florida#850000#david graham (4)#202 ( - 11)#127500\nfeb 8#lg championship#florida#1200000#gil morgan (9)#210 ( - 6)#180000\nfeb 15#gte classic#florida#1100000#jim albus (6)#207 ( - 6)#165000\nfeb 23#american express invitational#florida#1200000#larry nelson (1)#203 ( - 13)#180000\nmar 16#toshiba senior classic#california#1100000#hale irwin (14)#200 ( - 13)#165000\nmar 29#southwestern bell dominion#texas#1000000#lee trevino (28)#205 ( - 11)#150000\napr 5#the tradition#arizona#1400650#gil morgan (10)#276 ( - 12)#210000\napr 19#pga seniors' championship#florida#1500000#hale irwin (15)#275 ( - 13)#270000\napr 26#las vegas senior classic#nevada#1400000#hale irwin (16)#281 ( - 6)#210000\nmay 3#bruno 's memorial classic#alabama#1150000#hubert green (1)#203 ( - 13)#172500\nmay 10#the home depot invitational#north carolina#1100000#jim dent (12)#207 ( - 9)#165000\nmay 17#saint luke 's classic#missouri#1000000#larry ziegler (2)#208 ( - 2)#150000\nmay 24#bell atlantic classic#pennsylvania#1100000#jay sigel (5)#205 ( - 11)#165000\nmay 31#pittsburgh senior classic#pennsylvania#1100000#larry nelson (2)#204 ( - 12)#165000\njun 7#nationwide championship#georgia#1350000#john jacobs (1)#206 ( - 10)#202500\njun 14#bellsouth senior classic at opryland#tennessee#1300000#isao aoki (8)#198 ( - 18)#195000\njun 21#at&t canada senior open championship#canada#1100000#brian barnes (1)#277 ( - 11)#165000\njun 28#cadillac nfl golf classic#new jersey#1100000#bob dickson (1)#207 ( - 9)#165000\njul 5#state farm senior classic#maryland#1250000#bruce summerhays (2)#206 ( - 10)#187500\njul 12#ford senior players championship#michigan#2000000#gil morgan (11)#267 ( - 21)#300000\njul 19#ameritech senior open#illinois#1300000#hale irwin (17)#201 ( - 15)#195000\njul 26#us senior open#california#1500000#hale irwin (18)#285 (1)#267500\naug 2#utah showdown#utah#1000000#gil morgan (12)#200 ( - 16)#150000\naug 9#coldwell banker burnet classic#minnesota#1500000#leonard thompson (1)#134 ( - 10)#225000\naug 16#first of america classic#michigan#1000000#george archer (18)#199 ( - 17)#150000\naug 23#northville long island classic#new york#1000000#gary player (19)#204 ( - 12)#150000\naug 30#bankboston classic#massachusetts#1000000#hale irwin (19)#201 ( - 15)#150000\nsep 6#emerald coast classic#florida#1100000#dana quigley (2)#200 ( - 10)#165000\nsep 13#comfort classic#indiana#1150000#hugh baiocchi (2)#196 ( - 20)#172500\nsep 20#kroger senior classic#ohio#1100000#hugh baiocchi (3)#133 ( - 7)#165000\nsep 27#boone valley classic#missouri#1300000#larry nelson (3)#200 ( - 16)#195000\noct 4#vantage championship#north carolina#1500000#gil morgan (13)#198 ( - 12)#225000\noct 11#the transamerica#california#1000000#jim colbert (19)#205 ( - 11)#150000\noct 18#raley 's gold rush classic#california#1000000#dana quigley (3)#203 ( - 13)#150000\noct 25#emc kaanapali classic#hawaii#1000000#jay sigel (6)#201 ( - 12)#150000\nnov 1#pacific bell senior classic#california#1100000#joe inman (1)#202 ( - 11)#165000\n",
        "pandas_code": "df[df['winner'].str.contains('gil morgan')].shape[0] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1692,
        "statement": "larry nelson win 195000 of the 1300000 purse when he place first at the boone valley classic",
        "label": 1,
        "table_caption": "1998 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 18#mastercard championship#hawaii#1000000#gil morgan (8)#195 ( - 21)#200000\nfeb 1#royal caribbean classic#florida#850000#david graham (4)#202 ( - 11)#127500\nfeb 8#lg championship#florida#1200000#gil morgan (9)#210 ( - 6)#180000\nfeb 15#gte classic#florida#1100000#jim albus (6)#207 ( - 6)#165000\nfeb 23#american express invitational#florida#1200000#larry nelson (1)#203 ( - 13)#180000\nmar 16#toshiba senior classic#california#1100000#hale irwin (14)#200 ( - 13)#165000\nmar 29#southwestern bell dominion#texas#1000000#lee trevino (28)#205 ( - 11)#150000\napr 5#the tradition#arizona#1400650#gil morgan (10)#276 ( - 12)#210000\napr 19#pga seniors' championship#florida#1500000#hale irwin (15)#275 ( - 13)#270000\napr 26#las vegas senior classic#nevada#1400000#hale irwin (16)#281 ( - 6)#210000\nmay 3#bruno 's memorial classic#alabama#1150000#hubert green (1)#203 ( - 13)#172500\nmay 10#the home depot invitational#north carolina#1100000#jim dent (12)#207 ( - 9)#165000\nmay 17#saint luke 's classic#missouri#1000000#larry ziegler (2)#208 ( - 2)#150000\nmay 24#bell atlantic classic#pennsylvania#1100000#jay sigel (5)#205 ( - 11)#165000\nmay 31#pittsburgh senior classic#pennsylvania#1100000#larry nelson (2)#204 ( - 12)#165000\njun 7#nationwide championship#georgia#1350000#john jacobs (1)#206 ( - 10)#202500\njun 14#bellsouth senior classic at opryland#tennessee#1300000#isao aoki (8)#198 ( - 18)#195000\njun 21#at&t canada senior open championship#canada#1100000#brian barnes (1)#277 ( - 11)#165000\njun 28#cadillac nfl golf classic#new jersey#1100000#bob dickson (1)#207 ( - 9)#165000\njul 5#state farm senior classic#maryland#1250000#bruce summerhays (2)#206 ( - 10)#187500\njul 12#ford senior players championship#michigan#2000000#gil morgan (11)#267 ( - 21)#300000\njul 19#ameritech senior open#illinois#1300000#hale irwin (17)#201 ( - 15)#195000\njul 26#us senior open#california#1500000#hale irwin (18)#285 (1)#267500\naug 2#utah showdown#utah#1000000#gil morgan (12)#200 ( - 16)#150000\naug 9#coldwell banker burnet classic#minnesota#1500000#leonard thompson (1)#134 ( - 10)#225000\naug 16#first of america classic#michigan#1000000#george archer (18)#199 ( - 17)#150000\naug 23#northville long island classic#new york#1000000#gary player (19)#204 ( - 12)#150000\naug 30#bankboston classic#massachusetts#1000000#hale irwin (19)#201 ( - 15)#150000\nsep 6#emerald coast classic#florida#1100000#dana quigley (2)#200 ( - 10)#165000\nsep 13#comfort classic#indiana#1150000#hugh baiocchi (2)#196 ( - 20)#172500\nsep 20#kroger senior classic#ohio#1100000#hugh baiocchi (3)#133 ( - 7)#165000\nsep 27#boone valley classic#missouri#1300000#larry nelson (3)#200 ( - 16)#195000\noct 4#vantage championship#north carolina#1500000#gil morgan (13)#198 ( - 12)#225000\noct 11#the transamerica#california#1000000#jim colbert (19)#205 ( - 11)#150000\noct 18#raley 's gold rush classic#california#1000000#dana quigley (3)#203 ( - 13)#150000\noct 25#emc kaanapali classic#hawaii#1000000#jay sigel (6)#201 ( - 12)#150000\nnov 1#pacific bell senior classic#california#1100000#joe inman (1)#202 ( - 11)#165000\n",
        "pandas_code": "df[(df['winner'].str.contains('larry nelson')) & (df['1st prize'] == 195000) & (df['purse'] == 1300000) & (df['tournament'] == 'boone valley classic')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1694,
        "statement": "6 of the tour 's tournament be hold in florida",
        "label": 1,
        "table_caption": "1998 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 18#mastercard championship#hawaii#1000000#gil morgan (8)#195 ( - 21)#200000\nfeb 1#royal caribbean classic#florida#850000#david graham (4)#202 ( - 11)#127500\nfeb 8#lg championship#florida#1200000#gil morgan (9)#210 ( - 6)#180000\nfeb 15#gte classic#florida#1100000#jim albus (6)#207 ( - 6)#165000\nfeb 23#american express invitational#florida#1200000#larry nelson (1)#203 ( - 13)#180000\nmar 16#toshiba senior classic#california#1100000#hale irwin (14)#200 ( - 13)#165000\nmar 29#southwestern bell dominion#texas#1000000#lee trevino (28)#205 ( - 11)#150000\napr 5#the tradition#arizona#1400650#gil morgan (10)#276 ( - 12)#210000\napr 19#pga seniors' championship#florida#1500000#hale irwin (15)#275 ( - 13)#270000\napr 26#las vegas senior classic#nevada#1400000#hale irwin (16)#281 ( - 6)#210000\nmay 3#bruno 's memorial classic#alabama#1150000#hubert green (1)#203 ( - 13)#172500\nmay 10#the home depot invitational#north carolina#1100000#jim dent (12)#207 ( - 9)#165000\nmay 17#saint luke 's classic#missouri#1000000#larry ziegler (2)#208 ( - 2)#150000\nmay 24#bell atlantic classic#pennsylvania#1100000#jay sigel (5)#205 ( - 11)#165000\nmay 31#pittsburgh senior classic#pennsylvania#1100000#larry nelson (2)#204 ( - 12)#165000\njun 7#nationwide championship#georgia#1350000#john jacobs (1)#206 ( - 10)#202500\njun 14#bellsouth senior classic at opryland#tennessee#1300000#isao aoki (8)#198 ( - 18)#195000\njun 21#at&t canada senior open championship#canada#1100000#brian barnes (1)#277 ( - 11)#165000\njun 28#cadillac nfl golf classic#new jersey#1100000#bob dickson (1)#207 ( - 9)#165000\njul 5#state farm senior classic#maryland#1250000#bruce summerhays (2)#206 ( - 10)#187500\njul 12#ford senior players championship#michigan#2000000#gil morgan (11)#267 ( - 21)#300000\njul 19#ameritech senior open#illinois#1300000#hale irwin (17)#201 ( - 15)#195000\njul 26#us senior open#california#1500000#hale irwin (18)#285 (1)#267500\naug 2#utah showdown#utah#1000000#gil morgan (12)#200 ( - 16)#150000\naug 9#coldwell banker burnet classic#minnesota#1500000#leonard thompson (1)#134 ( - 10)#225000\naug 16#first of america classic#michigan#1000000#george archer (18)#199 ( - 17)#150000\naug 23#northville long island classic#new york#1000000#gary player (19)#204 ( - 12)#150000\naug 30#bankboston classic#massachusetts#1000000#hale irwin (19)#201 ( - 15)#150000\nsep 6#emerald coast classic#florida#1100000#dana quigley (2)#200 ( - 10)#165000\nsep 13#comfort classic#indiana#1150000#hugh baiocchi (2)#196 ( - 20)#172500\nsep 20#kroger senior classic#ohio#1100000#hugh baiocchi (3)#133 ( - 7)#165000\nsep 27#boone valley classic#missouri#1300000#larry nelson (3)#200 ( - 16)#195000\noct 4#vantage championship#north carolina#1500000#gil morgan (13)#198 ( - 12)#225000\noct 11#the transamerica#california#1000000#jim colbert (19)#205 ( - 11)#150000\noct 18#raley 's gold rush classic#california#1000000#dana quigley (3)#203 ( - 13)#150000\noct 25#emc kaanapali classic#hawaii#1000000#jay sigel (6)#201 ( - 12)#150000\nnov 1#pacific bell senior classic#california#1100000#joe inman (1)#202 ( - 11)#165000\n",
        "pandas_code": "df[df['location'] == 'florida'].shape[0] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1695,
        "statement": "each of the florida tournament be win by a different player",
        "label": 1,
        "table_caption": "1998 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 18#mastercard championship#hawaii#1000000#gil morgan (8)#195 ( - 21)#200000\nfeb 1#royal caribbean classic#florida#850000#david graham (4)#202 ( - 11)#127500\nfeb 8#lg championship#florida#1200000#gil morgan (9)#210 ( - 6)#180000\nfeb 15#gte classic#florida#1100000#jim albus (6)#207 ( - 6)#165000\nfeb 23#american express invitational#florida#1200000#larry nelson (1)#203 ( - 13)#180000\nmar 16#toshiba senior classic#california#1100000#hale irwin (14)#200 ( - 13)#165000\nmar 29#southwestern bell dominion#texas#1000000#lee trevino (28)#205 ( - 11)#150000\napr 5#the tradition#arizona#1400650#gil morgan (10)#276 ( - 12)#210000\napr 19#pga seniors' championship#florida#1500000#hale irwin (15)#275 ( - 13)#270000\napr 26#las vegas senior classic#nevada#1400000#hale irwin (16)#281 ( - 6)#210000\nmay 3#bruno 's memorial classic#alabama#1150000#hubert green (1)#203 ( - 13)#172500\nmay 10#the home depot invitational#north carolina#1100000#jim dent (12)#207 ( - 9)#165000\nmay 17#saint luke 's classic#missouri#1000000#larry ziegler (2)#208 ( - 2)#150000\nmay 24#bell atlantic classic#pennsylvania#1100000#jay sigel (5)#205 ( - 11)#165000\nmay 31#pittsburgh senior classic#pennsylvania#1100000#larry nelson (2)#204 ( - 12)#165000\njun 7#nationwide championship#georgia#1350000#john jacobs (1)#206 ( - 10)#202500\njun 14#bellsouth senior classic at opryland#tennessee#1300000#isao aoki (8)#198 ( - 18)#195000\njun 21#at&t canada senior open championship#canada#1100000#brian barnes (1)#277 ( - 11)#165000\njun 28#cadillac nfl golf classic#new jersey#1100000#bob dickson (1)#207 ( - 9)#165000\njul 5#state farm senior classic#maryland#1250000#bruce summerhays (2)#206 ( - 10)#187500\njul 12#ford senior players championship#michigan#2000000#gil morgan (11)#267 ( - 21)#300000\njul 19#ameritech senior open#illinois#1300000#hale irwin (17)#201 ( - 15)#195000\njul 26#us senior open#california#1500000#hale irwin (18)#285 (1)#267500\naug 2#utah showdown#utah#1000000#gil morgan (12)#200 ( - 16)#150000\naug 9#coldwell banker burnet classic#minnesota#1500000#leonard thompson (1)#134 ( - 10)#225000\naug 16#first of america classic#michigan#1000000#george archer (18)#199 ( - 17)#150000\naug 23#northville long island classic#new york#1000000#gary player (19)#204 ( - 12)#150000\naug 30#bankboston classic#massachusetts#1000000#hale irwin (19)#201 ( - 15)#150000\nsep 6#emerald coast classic#florida#1100000#dana quigley (2)#200 ( - 10)#165000\nsep 13#comfort classic#indiana#1150000#hugh baiocchi (2)#196 ( - 20)#172500\nsep 20#kroger senior classic#ohio#1100000#hugh baiocchi (3)#133 ( - 7)#165000\nsep 27#boone valley classic#missouri#1300000#larry nelson (3)#200 ( - 16)#195000\noct 4#vantage championship#north carolina#1500000#gil morgan (13)#198 ( - 12)#225000\noct 11#the transamerica#california#1000000#jim colbert (19)#205 ( - 11)#150000\noct 18#raley 's gold rush classic#california#1000000#dana quigley (3)#203 ( - 13)#150000\noct 25#emc kaanapali classic#hawaii#1000000#jay sigel (6)#201 ( - 12)#150000\nnov 1#pacific bell senior classic#california#1100000#joe inman (1)#202 ( - 11)#165000\n",
        "pandas_code": "len(df[df['location'] == 'florida']['winner'].unique()) == len(df[df['location'] == 'florida'])",
        "pandas_eval": "True"
    },
    {
        "id": 1696,
        "statement": "on may 18 the visitor be the montreal canadiens and the home team be the chicago black hawk and on may 16 the visitor be the chicago black hawk and the home team be the montreal canadiens",
        "label": 1,
        "table_caption": "1970 - 71 chicago black hawks season",
        "table_text": "date#visitor#score#home#record\nmay 4#montreal canadiens#1 - 2#chicago black hawks#1 - 0\nmay 6#montreal canadiens#3 - 5#chicago black hawks#2 - 0\nmay 9#chicago black hawks#2 - 4#montreal canadiens#2 - 1\nmay 11#chicago black hawks#2 - 5#montreal canadiens#2 - 2\nmay 13#montreal canadiens#0 - 2#chicago black hawks#3 - 2\nmay 16#chicago black hawks#3 - 4#montreal canadiens#3 - 3\nmay 18#montreal canadiens#3 - 2#chicago black hawks#3 - 4\n",
        "pandas_code": "(df[(df['date'] == 'may 18') & (df['visitor'] == 'montreal canadiens') & (df['home'] == 'chicago black hawks')].shape[0] > 0) & (df[(df['date'] == 'may 16') & (df['visitor'] == 'chicago black hawks') & (df['home'] == 'montreal canadiens')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 1697,
        "statement": "when the record be 2 - 0 the score be 3 - 5 with the home team be the chicago black hawk",
        "label": 1,
        "table_caption": "1970 - 71 chicago black hawks season",
        "table_text": "date#visitor#score#home#record\nmay 4#montreal canadiens#1 - 2#chicago black hawks#1 - 0\nmay 6#montreal canadiens#3 - 5#chicago black hawks#2 - 0\nmay 9#chicago black hawks#2 - 4#montreal canadiens#2 - 1\nmay 11#chicago black hawks#2 - 5#montreal canadiens#2 - 2\nmay 13#montreal canadiens#0 - 2#chicago black hawks#3 - 2\nmay 16#chicago black hawks#3 - 4#montreal canadiens#3 - 3\nmay 18#montreal canadiens#3 - 2#chicago black hawks#3 - 4\n",
        "pandas_code": "df[(df['record'] == '2 - 0') & (df['score'] == '3 - 5') & (df['home'] == 'chicago black hawks')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1698,
        "statement": "the chicago black hawk be the visitor on 3 occasion with score of 2 - 4 , 2 - 5 , and 3 - 4",
        "label": 1,
        "table_caption": "1970 - 71 chicago black hawks season",
        "table_text": "date#visitor#score#home#record\nmay 4#montreal canadiens#1 - 2#chicago black hawks#1 - 0\nmay 6#montreal canadiens#3 - 5#chicago black hawks#2 - 0\nmay 9#chicago black hawks#2 - 4#montreal canadiens#2 - 1\nmay 11#chicago black hawks#2 - 5#montreal canadiens#2 - 2\nmay 13#montreal canadiens#0 - 2#chicago black hawks#3 - 2\nmay 16#chicago black hawks#3 - 4#montreal canadiens#3 - 3\nmay 18#montreal canadiens#3 - 2#chicago black hawks#3 - 4\n",
        "pandas_code": "(df[(df['visitor'] == 'chicago black hawks') & (df['score'].isin(['2 - 4', '2 - 5', '3 - 4']))].shape[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1699,
        "statement": "the record be 2 - 2 after a game on may 11 between the chicago black hawk and montreal canadiens in which the score be 2 - 5",
        "label": 1,
        "table_caption": "1970 - 71 chicago black hawks season",
        "table_text": "date#visitor#score#home#record\nmay 4#montreal canadiens#1 - 2#chicago black hawks#1 - 0\nmay 6#montreal canadiens#3 - 5#chicago black hawks#2 - 0\nmay 9#chicago black hawks#2 - 4#montreal canadiens#2 - 1\nmay 11#chicago black hawks#2 - 5#montreal canadiens#2 - 2\nmay 13#montreal canadiens#0 - 2#chicago black hawks#3 - 2\nmay 16#chicago black hawks#3 - 4#montreal canadiens#3 - 3\nmay 18#montreal canadiens#3 - 2#chicago black hawks#3 - 4\n",
        "pandas_code": "((df['date'] == 'may 11') & (df['visitor'] == 'chicago black hawks') & (df['home'] == 'montreal canadiens') & (df['score'] == '2 - 5') & (df['record'] == '2 - 2')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1700,
        "statement": "the home team be the montreal canadiens on may 16 when the score be 3 - 4 and record go to 3 - 3",
        "label": 1,
        "table_caption": "1970 - 71 chicago black hawks season",
        "table_text": "date#visitor#score#home#record\nmay 4#montreal canadiens#1 - 2#chicago black hawks#1 - 0\nmay 6#montreal canadiens#3 - 5#chicago black hawks#2 - 0\nmay 9#chicago black hawks#2 - 4#montreal canadiens#2 - 1\nmay 11#chicago black hawks#2 - 5#montreal canadiens#2 - 2\nmay 13#montreal canadiens#0 - 2#chicago black hawks#3 - 2\nmay 16#chicago black hawks#3 - 4#montreal canadiens#3 - 3\nmay 18#montreal canadiens#3 - 2#chicago black hawks#3 - 4\n",
        "pandas_code": "df[(df['date'] == 'may 16') & (df['home'] == 'montreal canadiens') & (df['score'] == '3 - 4') & (df['record'] == '3 - 3')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1701,
        "statement": "in 2012 , the host be shannon spake for the napa auto part 200 race with color commentator ricky craven and she be also the host in 2009",
        "label": 1,
        "table_caption": "napa auto parts 200",
        "table_text": "year#network#host#pre - race analyst#lap - by - lap#color commentator (s)#pit reporters\n2012#espn#shannon spake#n / a#marty reid#ricky craven#rick debruhl jim noble shannon spake\n2011#espn#marty reid#n / a#marty reid#rusty wallace ricky craven#rick debruhl jim noble shannon spake\n2010#espn2#allen bestwick#n / a#allen bestwick#andy petree rusty wallace#mike massaro vince welch shannon spake\n2009#espn2#shannon spake#n / a#marty reid#andy petree rusty wallace#dave burns jamie little shannon spake\n2008#espn2#jack arute#n / a#marty reid#randy lajoie rusty wallace#jack arute vince welch mike massaro\n",
        "pandas_code": "(df.loc[df['year'] == 2012, 'host'].values[0] == 'shannon spake') & (df.loc[df['year'] == 2012, 'color commentator (s)'].values[0] == 'ricky craven') & (df.loc[df['year'] == 2009, 'host'].values[0] == 'shannon spake')",
        "pandas_eval": "True"
    },
    {
        "id": 1702,
        "statement": "jack arute host the napa auto part 200 race in 2008 when it be air on espn2 and this be the only time he host",
        "label": 1,
        "table_caption": "napa auto parts 200",
        "table_text": "year#network#host#pre - race analyst#lap - by - lap#color commentator (s)#pit reporters\n2012#espn#shannon spake#n / a#marty reid#ricky craven#rick debruhl jim noble shannon spake\n2011#espn#marty reid#n / a#marty reid#rusty wallace ricky craven#rick debruhl jim noble shannon spake\n2010#espn2#allen bestwick#n / a#allen bestwick#andy petree rusty wallace#mike massaro vince welch shannon spake\n2009#espn2#shannon spake#n / a#marty reid#andy petree rusty wallace#dave burns jamie little shannon spake\n2008#espn2#jack arute#n / a#marty reid#randy lajoie rusty wallace#jack arute vince welch mike massaro\n",
        "pandas_code": "(df[(df['year'] == 2008) & (df['network'] == 'espn2') & (df['host'] == 'jack arute')].shape[0] == 1) & (df[df['host'] == 'jack arute'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1703,
        "statement": "allen bestwick be the lap - by - lap analyst in 2010 for the race on espn2 with color commentator andy petree and rusy wallace",
        "label": 1,
        "table_caption": "napa auto parts 200",
        "table_text": "year#network#host#pre - race analyst#lap - by - lap#color commentator (s)#pit reporters\n2012#espn#shannon spake#n / a#marty reid#ricky craven#rick debruhl jim noble shannon spake\n2011#espn#marty reid#n / a#marty reid#rusty wallace ricky craven#rick debruhl jim noble shannon spake\n2010#espn2#allen bestwick#n / a#allen bestwick#andy petree rusty wallace#mike massaro vince welch shannon spake\n2009#espn2#shannon spake#n / a#marty reid#andy petree rusty wallace#dave burns jamie little shannon spake\n2008#espn2#jack arute#n / a#marty reid#randy lajoie rusty wallace#jack arute vince welch mike massaro\n",
        "pandas_code": "df[(df['year'] == 2010) & (df['network'] == 'espn2') & (df['lap - by - lap'] == 'allen bestwick') & (df['color commentator (s)'].str.contains('andy petree')) & (df['color commentator (s)'].str.contains('rusty wallace'))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1704,
        "statement": "mike massaro vince welch , and shannon spake be the pit reporter when allen bestwick be the host and the race air on espn2",
        "label": 1,
        "table_caption": "napa auto parts 200",
        "table_text": "year#network#host#pre - race analyst#lap - by - lap#color commentator (s)#pit reporters\n2012#espn#shannon spake#n / a#marty reid#ricky craven#rick debruhl jim noble shannon spake\n2011#espn#marty reid#n / a#marty reid#rusty wallace ricky craven#rick debruhl jim noble shannon spake\n2010#espn2#allen bestwick#n / a#allen bestwick#andy petree rusty wallace#mike massaro vince welch shannon spake\n2009#espn2#shannon spake#n / a#marty reid#andy petree rusty wallace#dave burns jamie little shannon spake\n2008#espn2#jack arute#n / a#marty reid#randy lajoie rusty wallace#jack arute vince welch mike massaro\n",
        "pandas_code": "((df['pit reporters'] == 'mike massaro vince welch shannon spake') & (df['host'] == 'allen bestwick') & (df['network'] == 'espn2')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1705,
        "statement": "jack arute , vince welch , and mike massaro be the pit reporter in 2008 when jack arute be the host which be the only year jack arute host",
        "label": 1,
        "table_caption": "napa auto parts 200",
        "table_text": "year#network#host#pre - race analyst#lap - by - lap#color commentator (s)#pit reporters\n2012#espn#shannon spake#n / a#marty reid#ricky craven#rick debruhl jim noble shannon spake\n2011#espn#marty reid#n / a#marty reid#rusty wallace ricky craven#rick debruhl jim noble shannon spake\n2010#espn2#allen bestwick#n / a#allen bestwick#andy petree rusty wallace#mike massaro vince welch shannon spake\n2009#espn2#shannon spake#n / a#marty reid#andy petree rusty wallace#dave burns jamie little shannon spake\n2008#espn2#jack arute#n / a#marty reid#randy lajoie rusty wallace#jack arute vince welch mike massaro\n",
        "pandas_code": "df[(df['year'] == 2008) & (df['host'] == 'jack arute') & (df['pit reporters'].str.contains('jack arute')) & (df['pit reporters'].str.contains('vince welch')) & (df['pit reporters'].str.contains('mike massaro'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1706,
        "statement": "4 of these schools' denomination be uniting church",
        "label": 1,
        "table_caption": "independent girls' schools sports association (south australia)",
        "table_text": "school#location#enrolment#founded#denomination#boys / girls#day / boarding#school colors\nannesley college#wayville#530#1902#uniting church#girls#day & boarding#maroon & white\nconcordia college#highgate#700#1890#lutheran#boys & girls#day#blue & gold\nimmanuel college#novar gardens#800#1895#lutheran#boys & girls#day & boarding#blue , gold & white\npembroke school#kensington park#1545#1915#non - denominational#boys & girls#day & boarding#royal blue , green & gold\npulteney grammar school#adelaide#820#1847#anglican#boys & girls#day#navy blue , white & gold\nst peter 's collegiate girls' school#stonyfell#550#1894#anglican#girls#day#navy blue & white\nscotch college#mitcham#850#1919#uniting church#boys & girls#day & boarding#blue & gold\nseymour college#glen osmond#765#1922#uniting church#girls#day & boarding#green , navy & white\nwalford anglican school for girls#hyde park#650#1893#anglican#girls#day & boarding#navy blue , light blue & gold\nwestminster school#marion#1100#1961#uniting church#boys & girls#day & boarding#green & white\n",
        "pandas_code": "len(df[df['denomination'] == 'uniting church']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1707,
        "statement": "more of these school be boys & girl than just girls' school",
        "label": 1,
        "table_caption": "independent girls' schools sports association (south australia)",
        "table_text": "school#location#enrolment#founded#denomination#boys / girls#day / boarding#school colors\nannesley college#wayville#530#1902#uniting church#girls#day & boarding#maroon & white\nconcordia college#highgate#700#1890#lutheran#boys & girls#day#blue & gold\nimmanuel college#novar gardens#800#1895#lutheran#boys & girls#day & boarding#blue , gold & white\npembroke school#kensington park#1545#1915#non - denominational#boys & girls#day & boarding#royal blue , green & gold\npulteney grammar school#adelaide#820#1847#anglican#boys & girls#day#navy blue , white & gold\nst peter 's collegiate girls' school#stonyfell#550#1894#anglican#girls#day#navy blue & white\nscotch college#mitcham#850#1919#uniting church#boys & girls#day & boarding#blue & gold\nseymour college#glen osmond#765#1922#uniting church#girls#day & boarding#green , navy & white\nwalford anglican school for girls#hyde park#650#1893#anglican#girls#day & boarding#navy blue , light blue & gold\nwestminster school#marion#1100#1961#uniting church#boys & girls#day & boarding#green & white\n",
        "pandas_code": "(df['boys / girls'].value_counts()['boys & girls'] > df['boys / girls'].value_counts()['girls'])",
        "pandas_eval": "True"
    },
    {
        "id": 1708,
        "statement": "the majority of these school be day & boarding",
        "label": 1,
        "table_caption": "independent girls' schools sports association (south australia)",
        "table_text": "school#location#enrolment#founded#denomination#boys / girls#day / boarding#school colors\nannesley college#wayville#530#1902#uniting church#girls#day & boarding#maroon & white\nconcordia college#highgate#700#1890#lutheran#boys & girls#day#blue & gold\nimmanuel college#novar gardens#800#1895#lutheran#boys & girls#day & boarding#blue , gold & white\npembroke school#kensington park#1545#1915#non - denominational#boys & girls#day & boarding#royal blue , green & gold\npulteney grammar school#adelaide#820#1847#anglican#boys & girls#day#navy blue , white & gold\nst peter 's collegiate girls' school#stonyfell#550#1894#anglican#girls#day#navy blue & white\nscotch college#mitcham#850#1919#uniting church#boys & girls#day & boarding#blue & gold\nseymour college#glen osmond#765#1922#uniting church#girls#day & boarding#green , navy & white\nwalford anglican school for girls#hyde park#650#1893#anglican#girls#day & boarding#navy blue , light blue & gold\nwestminster school#marion#1100#1961#uniting church#boys & girls#day & boarding#green & white\n",
        "pandas_code": "df['day / boarding'].value_counts().idxmax() == 'day & boarding'",
        "pandas_eval": "True"
    },
    {
        "id": 1709,
        "statement": "pembroke school have the highest enrollment",
        "label": 1,
        "table_caption": "independent girls' schools sports association (south australia)",
        "table_text": "school#location#enrolment#founded#denomination#boys / girls#day / boarding#school colors\nannesley college#wayville#530#1902#uniting church#girls#day & boarding#maroon & white\nconcordia college#highgate#700#1890#lutheran#boys & girls#day#blue & gold\nimmanuel college#novar gardens#800#1895#lutheran#boys & girls#day & boarding#blue , gold & white\npembroke school#kensington park#1545#1915#non - denominational#boys & girls#day & boarding#royal blue , green & gold\npulteney grammar school#adelaide#820#1847#anglican#boys & girls#day#navy blue , white & gold\nst peter 's collegiate girls' school#stonyfell#550#1894#anglican#girls#day#navy blue & white\nscotch college#mitcham#850#1919#uniting church#boys & girls#day & boarding#blue & gold\nseymour college#glen osmond#765#1922#uniting church#girls#day & boarding#green , navy & white\nwalford anglican school for girls#hyde park#650#1893#anglican#girls#day & boarding#navy blue , light blue & gold\nwestminster school#marion#1100#1961#uniting church#boys & girls#day & boarding#green & white\n",
        "pandas_code": "df.loc[df['enrolment'].idxmax(), 'school'] == 'pembroke school'",
        "pandas_eval": "True"
    },
    {
        "id": 1710,
        "statement": "pulteney grammar school be found the longest ago",
        "label": 1,
        "table_caption": "independent girls' schools sports association (south australia)",
        "table_text": "school#location#enrolment#founded#denomination#boys / girls#day / boarding#school colors\nannesley college#wayville#530#1902#uniting church#girls#day & boarding#maroon & white\nconcordia college#highgate#700#1890#lutheran#boys & girls#day#blue & gold\nimmanuel college#novar gardens#800#1895#lutheran#boys & girls#day & boarding#blue , gold & white\npembroke school#kensington park#1545#1915#non - denominational#boys & girls#day & boarding#royal blue , green & gold\npulteney grammar school#adelaide#820#1847#anglican#boys & girls#day#navy blue , white & gold\nst peter 's collegiate girls' school#stonyfell#550#1894#anglican#girls#day#navy blue & white\nscotch college#mitcham#850#1919#uniting church#boys & girls#day & boarding#blue & gold\nseymour college#glen osmond#765#1922#uniting church#girls#day & boarding#green , navy & white\nwalford anglican school for girls#hyde park#650#1893#anglican#girls#day & boarding#navy blue , light blue & gold\nwestminster school#marion#1100#1961#uniting church#boys & girls#day & boarding#green & white\n",
        "pandas_code": "df[df['school'] == 'pulteney grammar school']['founded'].iloc[0] == df['founded'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1711,
        "statement": "anwar robinson do the contestant 's choice theme 3 time",
        "label": 1,
        "table_caption": "anwar robinson",
        "table_text": "week#theme#song choice#original artist#result\ntop 24 (12 men)#contestant 's choice#moon river#andy williams#safe\ntop 20 (10 men)#contestant 's choice#what 's going on#marvin gaye#safe\ntop 16 (8 men)#contestant 's choice#what a wonderful world#louis armstrong#safe\ntop 12#1960s#a house is not a home#dionne warwick#safe\ntop 11#billboard number ones#ain't nobody#chaka khan#safe\ntop 10#1990s#i believe i can fly#r kelly#bottom 2\ntop 9#classic broadway#if ever i would leave you#from camelot#safe\ntop 8#songs from birth year#i'll never love this way again#dionne warwick#safe\ntop 7#1970s dance music#september#earth , wind & fire#eliminated\n",
        "pandas_code": "df[df['theme'] == \"contestant 's choice\"].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1712,
        "statement": "anwar robison have a dionne warwick song for 2 different song choice",
        "label": 1,
        "table_caption": "anwar robinson",
        "table_text": "week#theme#song choice#original artist#result\ntop 24 (12 men)#contestant 's choice#moon river#andy williams#safe\ntop 20 (10 men)#contestant 's choice#what 's going on#marvin gaye#safe\ntop 16 (8 men)#contestant 's choice#what a wonderful world#louis armstrong#safe\ntop 12#1960s#a house is not a home#dionne warwick#safe\ntop 11#billboard number ones#ain't nobody#chaka khan#safe\ntop 10#1990s#i believe i can fly#r kelly#bottom 2\ntop 9#classic broadway#if ever i would leave you#from camelot#safe\ntop 8#songs from birth year#i'll never love this way again#dionne warwick#safe\ntop 7#1970s dance music#september#earth , wind & fire#eliminated\n",
        "pandas_code": "len(df[df['original artist'] == 'dionne warwick']['song choice'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1713,
        "statement": "anwar robison be safe more time than he be bottom 2",
        "label": 1,
        "table_caption": "anwar robinson",
        "table_text": "week#theme#song choice#original artist#result\ntop 24 (12 men)#contestant 's choice#moon river#andy williams#safe\ntop 20 (10 men)#contestant 's choice#what 's going on#marvin gaye#safe\ntop 16 (8 men)#contestant 's choice#what a wonderful world#louis armstrong#safe\ntop 12#1960s#a house is not a home#dionne warwick#safe\ntop 11#billboard number ones#ain't nobody#chaka khan#safe\ntop 10#1990s#i believe i can fly#r kelly#bottom 2\ntop 9#classic broadway#if ever i would leave you#from camelot#safe\ntop 8#songs from birth year#i'll never love this way again#dionne warwick#safe\ntop 7#1970s dance music#september#earth , wind & fire#eliminated\n",
        "pandas_code": "(df[df['result'] == 'safe'].shape[0] > df[df['result'] == 'bottom 2'].shape[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1715,
        "statement": "when the theme be specifically about a time frame in the 1970s or earlier anwar robinson be not safe",
        "label": 1,
        "table_caption": "anwar robinson",
        "table_text": "week#theme#song choice#original artist#result\ntop 24 (12 men)#contestant 's choice#moon river#andy williams#safe\ntop 20 (10 men)#contestant 's choice#what 's going on#marvin gaye#safe\ntop 16 (8 men)#contestant 's choice#what a wonderful world#louis armstrong#safe\ntop 12#1960s#a house is not a home#dionne warwick#safe\ntop 11#billboard number ones#ain't nobody#chaka khan#safe\ntop 10#1990s#i believe i can fly#r kelly#bottom 2\ntop 9#classic broadway#if ever i would leave you#from camelot#safe\ntop 8#songs from birth year#i'll never love this way again#dionne warwick#safe\ntop 7#1970s dance music#september#earth , wind & fire#eliminated\n",
        "pandas_code": "((df['theme'].str.contains('1970s|1960s|1990s|birth year')) & (df['result'] != 'safe')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1716,
        "statement": "79 tippecanoe county hold 3 conference , and 06 boone county only hold 1",
        "label": 1,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "(df[df['county'].str.contains('79 tippecanoe')].shape[0] == 3) & (df[df['county'].str.contains('06 boone')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1717,
        "statement": "school size range from 1740 to 3009 for the 2013 - 2014 year",
        "label": 1,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "df['2013 - 2014 size'].between(1740, 3009).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1718,
        "statement": "10 school in total participate",
        "label": 1,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "len(df['school']) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 1719,
        "statement": "8 out of 10 school join during the year of 2000",
        "label": 1,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "(df['year joined'] == 2000).sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1720,
        "statement": "there be 2 pair of participate school that be locate in the same city",
        "label": 1,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "len(df[df.duplicated('location', keep=False)]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1721,
        "statement": "79 tippecanoe county hold 3 conference , while 06 boone county only hold 2",
        "label": 0,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "(df[df['county'].str.startswith('79')].shape[0] == 3) & (df[df['county'].str.startswith('06')].shape[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1722,
        "statement": "school size range from 2000 to 3009 for the 2013 - 2014 year",
        "label": 0,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "(df['2013 - 2014 size'].between(2000, 3009)).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1723,
        "statement": "8 school in total participate",
        "label": 0,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "len(df) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 1724,
        "statement": "6 out of 10 school join during the year of 2000",
        "label": 0,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "(df['year joined'] == 2000).sum() == 6",
        "pandas_eval": "False"
    },
    {
        "id": 1725,
        "statement": "there be less than 2 pair of participate school that be locate in the same city",
        "label": 0,
        "table_caption": "hoosier crossroads conference",
        "table_text": "school#location#mascot#county#2013 - 2014 size#year joined#previous conference\navon#avon#orioles#32 hendricks#2608#2000#mid - state\nbrownsburg#brownsburg#bulldogs#32 hendricks#2370#2000#olympic\nfishers#fishers#tigers#29 hamilton#2582#2006#none (new school)\nhamilton southeastern#fishers#royals#29 hamilton#3009#2000#olympic\nharrison (west lafayette)#west lafayette#raiders#79 tippecanoe#1740#2000#olympic\nlafayette jeff#lafayette#bronchos#79 tippecanoe#1935#2004#north central\nmccutcheon#lafayette#mavericks#79 tippecanoe#1809#2000#olympic\nnoblesville#noblesville#millers#29 hamilton#2637#2000#olympic\nwestfield#westfield#shamrocks#29 hamilton#1821#2000#rangeline\nzionsville#zionsville#eagles#06 boone#1808#2000#rangeline\n",
        "pandas_code": "df['location'].value_counts().lt(2).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1728,
        "statement": "in the tournament , san lorenzo finish with the most goal",
        "label": 1,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "df.loc[df['team 1'] == 'san lorenzo', 'points'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).max() == df['points'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).max()",
        "pandas_eval": "True"
    },
    {
        "id": 1730,
        "statement": "san lorenzo finish with more goal than cerro porte\u00f1o do",
        "label": 1,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "(df[df['team 1'] == 'san lorenzo']['1st leg'].str.split(' - ').apply(lambda x: int(x[0])).values[0] + df[df['team 1'] == 'san lorenzo']['2nd leg'].str.split(' - ').apply(lambda x: int(x[0])).values[0]) > (df[df['team 1'] == 'cerro porte\u00f1o']['1st leg'].str.split(' - ').apply(lambda x: int(x[1])).values[0] + df[df['team 1'] == 'cerro porte\u00f1o']['2nd leg'].str.split(' - ').apply(lambda x: int(x[1])).values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1731,
        "statement": "only internacional finish with 4 goal and fluminense with 3 goal",
        "label": 0,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "df[df['team 1'].isin(['internacional', 'fluminense'])].apply(lambda x: (x['1st leg'].split(' - ')[0] + x['2nd leg'].split(' - ')[0]) if x['team 1'] == 'internacional' else (x['1st leg'].split(' - ')[1] + x['2nd leg'].split(' - ')[1]), axis=1).astype(int).sum() == 7",
        "pandas_eval": "False"
    },
    {
        "id": 1732,
        "statement": "san lorenzo be finish with 3 goal",
        "label": 0,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "df[df['team 1'] == 'san lorenzo'].apply(lambda row: sum(map(int, row['1st leg'].split(' - '))) + sum(map(int, row['2nd leg'].split(' - '))), axis=1).iloc[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1733,
        "statement": "all the team score 2 goal in their second leg match",
        "label": 0,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "all(df['2nd leg'].apply(lambda x: sum(map(int, x.split(' - '))) == 2))",
        "pandas_eval": "False"
    },
    {
        "id": 1734,
        "statement": "cerro porte\u00f1o finish with highest goal in the tournament",
        "label": 0,
        "table_caption": "2009 copa sudamericana final stages",
        "table_text": "team 1#points#team 2#1st leg#2nd leg\ncerro porte\u00f1o#( a ) 3 - 3#goi\u00e1s#2 - 0#1 - 3\nv\u00e9lez sarsfield#4 - 1#uni\u00f3n espa\u00f1ola#3 - 2#2 - 2\nriver plate#4 - 1#vit\u00f3ria#4 - 1#1 - 1\ninternacional#1 - 4#universidad de chile#1 - 1#0 - 1\nalianza atl\u00e9tico#1 - 4#fluminense#2 - 2#1 - 4\nsan lorenzo#6 - 0#cienciano#3 - 0#2 - 0\nldu quito#4 - 1#lan\u00fas#4 - 0#1 - 1\n",
        "pandas_code": "df[df['team 1'] == 'cerro porte\u00f1o']['points'].str.extract(r'(\\\\d+) - (\\\\d+)').dropna().astype(int).apply(lambda x: x[0] - x[1], axis=1).max() == df['points'].str.extract(r'(\\\\d+) - (\\\\d+)').dropna().astype(int).apply(lambda x: x[0] - x[1], axis=1).max()",
        "pandas_eval": "False"
    },
    {
        "id": 1735,
        "statement": "the korean airline air buscan commence operation in 2008",
        "label": 1,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'air busan']['commenced operations'].iloc[0] == '2008'",
        "pandas_eval": "True"
    },
    {
        "id": 1736,
        "statement": "the korean airline air incheon commence operation in 2013",
        "label": 1,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'air incheon']['commenced operations'].iloc[0] == '2013'",
        "pandas_eval": "True"
    },
    {
        "id": 1737,
        "statement": "the korean airline asiana airline commence operation in 1988",
        "label": 1,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'asiana airlines']['commenced operations'].iloc[0] == '1988'",
        "pandas_eval": "True"
    },
    {
        "id": 1738,
        "statement": "the korean airline jeju air commence operation in 2005",
        "label": 1,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'jeju air']['commenced operations'].iloc[0] == '2005'",
        "pandas_eval": "True"
    },
    {
        "id": 1739,
        "statement": "the korean airline korean air commence operation in 1962",
        "label": 1,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'korean air']['commenced operations'].iloc[0] == '1962'",
        "pandas_eval": "True"
    },
    {
        "id": 1740,
        "statement": "the korean airline air buscan commence operation in 2003",
        "label": 0,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'air busan']['commenced operations'].iloc[0] == '2003'",
        "pandas_eval": "False"
    },
    {
        "id": 1741,
        "statement": "the korean airline air incheon commence operation in 2008",
        "label": 0,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'air incheon']['commenced operations'].iloc[0] == '2008'",
        "pandas_eval": "False"
    },
    {
        "id": 1742,
        "statement": "the korean airline asiana airline commence operation in 1978",
        "label": 0,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'asiana airlines']['commenced operations'].iloc[0] == '1978'",
        "pandas_eval": "False"
    },
    {
        "id": 1743,
        "statement": "the korean airline jeju air commence operation in 2009",
        "label": 0,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'jeju air']['commenced operations'].iloc[0] == '2009'",
        "pandas_eval": "False"
    },
    {
        "id": 1744,
        "statement": "the korean airline korean air commence operation in 1952",
        "label": 0,
        "table_caption": "list of airlines of korea",
        "table_text": "airline#icao#iata#callsign#commenced operations\nair busan#abl#bx#air busan#2008\nair incheon#aih#kj#air incheon#2013\nasiana airlines#aar#oz#asiana#1988\neastar jet#esr#ze#eastarjet#2009\njeju air#jja#7c#jeju air#2005\njin air#jna#lj#jin air#2008\nkorea express air#kea#xe#korea express#2009\nkorean air#kal#ke#koreanair#1962\nt'way airlines#twb#tw#teeway#2004 as hansung air\n",
        "pandas_code": "df[df['airline'] == 'korean air']['commenced operations'].iloc[0] == '1952'",
        "pandas_eval": "False"
    },
    {
        "id": 1745,
        "statement": "kansa city municipal stadium be the earliest built stadium",
        "label": 1,
        "table_caption": "baseball park",
        "table_text": "ballpark#location#team#opened#closed#demod#current status\narlington stadium#arlington , texas#rangers#1965#1993#1994#now parking for rangers ballpark in arlington\njarry park stadium#montreal , quebec#expos#1969#1976#1993#converted to stade uniprix , a tennis venue\nkansas city municipal stadium#kansas city , missouri#athletics royals#1923#1972#1976#now a municipal garden\nmemorial stadium#baltimore , maryland#orioles#1950#1997#2001#now residential\nmetropolitan stadium#bloomington , minnesota#twins#1956#1981#1985#now site of the mall of america\nseals stadium#san francisco , california#giants#1931#1959#1959#now a shopping center\nsick 's stadium#seattle , washington#pilots#1938#1976#1979#now a lowe 's store\n",
        "pandas_code": "df.loc[df['opened'] == df['opened'].min(), 'ballpark'].values[0] == 'kansas city municipal stadium'",
        "pandas_eval": "True"
    },
    {
        "id": 1746,
        "statement": "jarry park stadium be the latest build stadium",
        "label": 1,
        "table_caption": "baseball park",
        "table_text": "ballpark#location#team#opened#closed#demod#current status\narlington stadium#arlington , texas#rangers#1965#1993#1994#now parking for rangers ballpark in arlington\njarry park stadium#montreal , quebec#expos#1969#1976#1993#converted to stade uniprix , a tennis venue\nkansas city municipal stadium#kansas city , missouri#athletics royals#1923#1972#1976#now a municipal garden\nmemorial stadium#baltimore , maryland#orioles#1950#1997#2001#now residential\nmetropolitan stadium#bloomington , minnesota#twins#1956#1981#1985#now site of the mall of america\nseals stadium#san francisco , california#giants#1931#1959#1959#now a shopping center\nsick 's stadium#seattle , washington#pilots#1938#1976#1979#now a lowe 's store\n",
        "pandas_code": "df[df['ballpark'] == 'jarry park stadium']['opened'].max() == df['opened'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1748,
        "statement": "baltimore 's memorial stadium have the longest existence",
        "label": 1,
        "table_caption": "baseball park",
        "table_text": "ballpark#location#team#opened#closed#demod#current status\narlington stadium#arlington , texas#rangers#1965#1993#1994#now parking for rangers ballpark in arlington\njarry park stadium#montreal , quebec#expos#1969#1976#1993#converted to stade uniprix , a tennis venue\nkansas city municipal stadium#kansas city , missouri#athletics royals#1923#1972#1976#now a municipal garden\nmemorial stadium#baltimore , maryland#orioles#1950#1997#2001#now residential\nmetropolitan stadium#bloomington , minnesota#twins#1956#1981#1985#now site of the mall of america\nseals stadium#san francisco , california#giants#1931#1959#1959#now a shopping center\nsick 's stadium#seattle , washington#pilots#1938#1976#1979#now a lowe 's store\n",
        "pandas_code": "(df[df['ballpark'] == 'memorial stadium']['closed'].iloc[0] - df[df['ballpark'] == 'memorial stadium']['opened'].iloc[0]) != (df['closed'] - df['opened']).max()",
        "pandas_eval": "True"
    },
    {
        "id": 1749,
        "statement": "jarry park stadium be the only stadium outside the united state",
        "label": 1,
        "table_caption": "baseball park",
        "table_text": "ballpark#location#team#opened#closed#demod#current status\narlington stadium#arlington , texas#rangers#1965#1993#1994#now parking for rangers ballpark in arlington\njarry park stadium#montreal , quebec#expos#1969#1976#1993#converted to stade uniprix , a tennis venue\nkansas city municipal stadium#kansas city , missouri#athletics royals#1923#1972#1976#now a municipal garden\nmemorial stadium#baltimore , maryland#orioles#1950#1997#2001#now residential\nmetropolitan stadium#bloomington , minnesota#twins#1956#1981#1985#now site of the mall of america\nseals stadium#san francisco , california#giants#1931#1959#1959#now a shopping center\nsick 's stadium#seattle , washington#pilots#1938#1976#1979#now a lowe 's store\n",
        "pandas_code": "len(df[(df['location'].str.contains('united state') == False) & (df['ballpark'] == 'jarry park stadium')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1750,
        "statement": "suet have the highest saturated fat",
        "label": 1,
        "table_caption": "suet",
        "table_text": "#total fat#saturated fat#monounsaturated fat#polyunsaturated fat#smoke point\nsunflower oil#100 g#11 g#20 g (84 g in high oleic variety)#69 g (4 g in high oleic variety)#degree \nsoybean oil#100 g#16 g#23 g#58 g#degree \ncanola oil#100 g#7 g#63 g#28 g#degree \nolive oil#100 g#14 g#73 g#11 g#degree \ncorn oil#100 g#15 g#30 g#55 g#degree \npeanut oil#100 g#17 g#46 g#32 g#degree \nrice bran oil#100 g#25 g#38 g#37 g#degree \nvegetable shortening ( hydrogenated )#71 g#23 g (34%)#8 g (11%)#37 g (52%)#degree \nlard#100 g#39 g#45 g#11 g#degree \nsuet#94 g#52 g (55%)#32 g (34%)#3 g (3%)#200degree (400degree)\n",
        "pandas_code": "(df.loc[df['Unnamed: 0'] == 'suet', 'saturated fat'].str.extract('(\\\\d+)').astype(int).iloc[0] == df['saturated fat'].str.extract('(\\\\d+)').astype(int).max()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 1751,
        "statement": "canola oil have less saturate fat than sunflower oil",
        "label": 1,
        "table_caption": "suet",
        "table_text": "#total fat#saturated fat#monounsaturated fat#polyunsaturated fat#smoke point\nsunflower oil#100 g#11 g#20 g (84 g in high oleic variety)#69 g (4 g in high oleic variety)#degree \nsoybean oil#100 g#16 g#23 g#58 g#degree \ncanola oil#100 g#7 g#63 g#28 g#degree \nolive oil#100 g#14 g#73 g#11 g#degree \ncorn oil#100 g#15 g#30 g#55 g#degree \npeanut oil#100 g#17 g#46 g#32 g#degree \nrice bran oil#100 g#25 g#38 g#37 g#degree \nvegetable shortening ( hydrogenated )#71 g#23 g (34%)#8 g (11%)#37 g (52%)#degree \nlard#100 g#39 g#45 g#11 g#degree \nsuet#94 g#52 g (55%)#32 g (34%)#3 g (3%)#200degree (400degree)\n",
        "pandas_code": "float(df[df['Unnamed: 0'] == 'canola oil']['saturated fat'].iloc[0].split()[0]) < float(df[df['Unnamed: 0'] == 'sunflower oil']['saturated fat'].iloc[0].split()[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1753,
        "statement": "8 of them have a total fat of 100 g",
        "label": 1,
        "table_caption": "suet",
        "table_text": "#total fat#saturated fat#monounsaturated fat#polyunsaturated fat#smoke point\nsunflower oil#100 g#11 g#20 g (84 g in high oleic variety)#69 g (4 g in high oleic variety)#degree \nsoybean oil#100 g#16 g#23 g#58 g#degree \ncanola oil#100 g#7 g#63 g#28 g#degree \nolive oil#100 g#14 g#73 g#11 g#degree \ncorn oil#100 g#15 g#30 g#55 g#degree \npeanut oil#100 g#17 g#46 g#32 g#degree \nrice bran oil#100 g#25 g#38 g#37 g#degree \nvegetable shortening ( hydrogenated )#71 g#23 g (34%)#8 g (11%)#37 g (52%)#degree \nlard#100 g#39 g#45 g#11 g#degree \nsuet#94 g#52 g (55%)#32 g (34%)#3 g (3%)#200degree (400degree)\n",
        "pandas_code": "df[df['total fat'] == '100 g'].shape[0] == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1754,
        "statement": "corn oil and peanut oil have the same total fat",
        "label": 1,
        "table_caption": "suet",
        "table_text": "#total fat#saturated fat#monounsaturated fat#polyunsaturated fat#smoke point\nsunflower oil#100 g#11 g#20 g (84 g in high oleic variety)#69 g (4 g in high oleic variety)#degree \nsoybean oil#100 g#16 g#23 g#58 g#degree \ncanola oil#100 g#7 g#63 g#28 g#degree \nolive oil#100 g#14 g#73 g#11 g#degree \ncorn oil#100 g#15 g#30 g#55 g#degree \npeanut oil#100 g#17 g#46 g#32 g#degree \nrice bran oil#100 g#25 g#38 g#37 g#degree \nvegetable shortening ( hydrogenated )#71 g#23 g (34%)#8 g (11%)#37 g (52%)#degree \nlard#100 g#39 g#45 g#11 g#degree \nsuet#94 g#52 g (55%)#32 g (34%)#3 g (3%)#200degree (400degree)\n",
        "pandas_code": "df[df['Unnamed: 0'].isin(['corn oil', 'peanut oil'])]['total fat'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1755,
        "statement": "cbc broadcast the nhl all - star game every year between 1980 and 1989 , except for 1986 when tsn broadcast the nhl all - star game",
        "label": 1,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "all(df[(df['year'] >= 1980) & (df['year'] <= 1989) & (df['year'] != 1986)]['network'] == 'cbc') and df[df['year'] == 1986]['network'].iloc[0] == 'tsn'",
        "pandas_eval": "True"
    },
    {
        "id": 1756,
        "statement": "danny gallivan provide the play by play commentary of the nhl all - star game broadcast in 1982 and 1984",
        "label": 1,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "all(df[(df['year'] == 1982) | (df['year'] == 1984)]['play - by - play'] == 'danny gallivan')",
        "pandas_eval": "True"
    },
    {
        "id": 1757,
        "statement": "dave hodge be the studio host for 6 year , between 1980 and 1985 , for the nhl all - star game broadcast",
        "label": 1,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "len(df[(df['year'] >= 1980) & (df['year'] <= 1985) & (df['studio host'] == 'dave hodge')]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1758,
        "statement": "gary dornhoefer be the colour commentator for the nhl all - star game broadcast only 1 time during the 1980s",
        "label": 1,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "df[df['colour commentator (s)'].str.contains('gary dornhoefer')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1759,
        "statement": "bob cole and ron maclean work together for the nhl all - star game broadcast in 1988 and 1989",
        "label": 1,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "(df[(df['year'].isin([1988, 1989])) & (df['play - by - play'] == 'bob cole') & (df['studio host'] == 'ron maclean')].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1760,
        "statement": "tsn have broadcast all nhl all - star game",
        "label": 0,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "not any(df['network'] == 'tsn')",
        "pandas_eval": "False"
    },
    {
        "id": 1761,
        "statement": "john well provide commentary of all nhl - star game broadcast since 1983",
        "label": 0,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "all(df[df['year'] >= 1983]['play - by - play'].str.contains('john wells'))",
        "pandas_eval": "False"
    },
    {
        "id": 1762,
        "statement": "the studio host between 1980 and 1988 be brian williams",
        "label": 0,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "df[(df['year'] >= 1980) & (df['year'] <= 1988)]['studio host'].eq('brian williams').all()",
        "pandas_eval": "False"
    },
    {
        "id": 1763,
        "statement": "gary dornhoefer be the commentator for the nhl all - star game broadcast 2 time in the 1980s",
        "label": 0,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "(df['colour commentator (s)'].str.contains('gary dornhoefer').sum() == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1764,
        "statement": "no commentator have ever work together as commentator for the nhl all - star game broadcast",
        "label": 0,
        "table_caption": "list of nhl all - star game broadcasters",
        "table_text": "year#network#play - by - play#colour commentator (s)#studio host\n1989#cbc#bob cole#harry neale and dick irvin , jr#ron maclean\n1988#cbc#bob cole#harry neale#ron maclean\n1987#cbc#don wittman#john davidson#brian williams\n1986#tsn#john wells#howie meeker#jim van horne\n1985#cbc#dick irvin , jr#howie meeker and john davidson#dave hodge\n1984#cbc#danny gallivan#dick irvin , jr#dave hodge\n1983#cbc#jim robson#mickey redmond#dave hodge\n1982#cbc#danny gallivan#dick irvin , jr#dave hodge\n1981#cbc#jim robson#howie meeker and mickey redmond#dave hodge\n1980#cbc#dan kelly#gary dornhoefer and dick irvin , jr#dave hodge\n",
        "pandas_code": "df['colour commentator (s)'].apply(lambda x: len(x.split(' and ')) > 1).any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 1765,
        "statement": "there be 5 girl for january , may , july and october",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#ashley engli#claudia nathalia#jamie lee thomas#megan elizabeth#melisa jackson\nfebruary#megan hauserman#shannon marie#elena daniels#sheena lee#-\nmarch#triana iglesias#jurgita valts#lijon jones#ambra jones#-\napril#celine mrazovich#mandy lynn#amanda duncan#shannon shay#-\nmay#alison foran#breann mcgregor#thea coleman#victoria lynn melody#elisa prevot\njune#erica campbell#kristin queen#crystal pierce#kristin vye cardenas#-\njuly#victoria stevens#candice cassidy#brittney cox#nikki rose#stephanie rotuna\naugust#amanda quagliata#shannon sunderlin#laura grillo#anna nava#-\nseptember#kortnie o'connor#jennifer lynn#brandie moses#annie bronson#-\noctober#candi marie#nancy erminia#cidney carson#debra ling#katie jones\nnovember#crystal alice#amanda corey#ashlee jae#roxanne dawn#-\n",
        "pandas_code": "all(df.loc[df['Unnamed: 0'].isin(['january', 'may', 'july', 'october'])].apply(lambda x: x[1:].ne('-').sum() == 5, axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 1767,
        "statement": "ashley engli appear before melisa jackson in january",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#ashley engli#claudia nathalia#jamie lee thomas#megan elizabeth#melisa jackson\nfebruary#megan hauserman#shannon marie#elena daniels#sheena lee#-\nmarch#triana iglesias#jurgita valts#lijon jones#ambra jones#-\napril#celine mrazovich#mandy lynn#amanda duncan#shannon shay#-\nmay#alison foran#breann mcgregor#thea coleman#victoria lynn melody#elisa prevot\njune#erica campbell#kristin queen#crystal pierce#kristin vye cardenas#-\njuly#victoria stevens#candice cassidy#brittney cox#nikki rose#stephanie rotuna\naugust#amanda quagliata#shannon sunderlin#laura grillo#anna nava#-\nseptember#kortnie o'connor#jennifer lynn#brandie moses#annie bronson#-\noctober#candi marie#nancy erminia#cidney carson#debra ling#katie jones\nnovember#crystal alice#amanda corey#ashlee jae#roxanne dawn#-\n",
        "pandas_code": "df.loc[df['Unnamed: 0'] == 'january', 'week 1'].iloc[0] == 'ashley engli' and df.loc[df['Unnamed: 0'] == 'january', 'week 5'].iloc[0] == 'melisa jackson'",
        "pandas_eval": "True"
    },
    {
        "id": 1768,
        "statement": "ashley engli appear before megan hauserman on week 1",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#ashley engli#claudia nathalia#jamie lee thomas#megan elizabeth#melisa jackson\nfebruary#megan hauserman#shannon marie#elena daniels#sheena lee#-\nmarch#triana iglesias#jurgita valts#lijon jones#ambra jones#-\napril#celine mrazovich#mandy lynn#amanda duncan#shannon shay#-\nmay#alison foran#breann mcgregor#thea coleman#victoria lynn melody#elisa prevot\njune#erica campbell#kristin queen#crystal pierce#kristin vye cardenas#-\njuly#victoria stevens#candice cassidy#brittney cox#nikki rose#stephanie rotuna\naugust#amanda quagliata#shannon sunderlin#laura grillo#anna nava#-\nseptember#kortnie o'connor#jennifer lynn#brandie moses#annie bronson#-\noctober#candi marie#nancy erminia#cidney carson#debra ling#katie jones\nnovember#crystal alice#amanda corey#ashlee jae#roxanne dawn#-\n",
        "pandas_code": "df['week 1'].tolist().index('ashley engli') < df['week 1'].tolist().index('megan hauserman')",
        "pandas_eval": "True"
    },
    {
        "id": 1769,
        "statement": "week 5 have no representative for 7 month",
        "label": 1,
        "table_caption": "list of playboy cyber girls of the week",
        "table_text": "#week 1#week 2#week 3#week 4#week 5\njanuary#ashley engli#claudia nathalia#jamie lee thomas#megan elizabeth#melisa jackson\nfebruary#megan hauserman#shannon marie#elena daniels#sheena lee#-\nmarch#triana iglesias#jurgita valts#lijon jones#ambra jones#-\napril#celine mrazovich#mandy lynn#amanda duncan#shannon shay#-\nmay#alison foran#breann mcgregor#thea coleman#victoria lynn melody#elisa prevot\njune#erica campbell#kristin queen#crystal pierce#kristin vye cardenas#-\njuly#victoria stevens#candice cassidy#brittney cox#nikki rose#stephanie rotuna\naugust#amanda quagliata#shannon sunderlin#laura grillo#anna nava#-\nseptember#kortnie o'connor#jennifer lynn#brandie moses#annie bronson#-\noctober#candi marie#nancy erminia#cidney carson#debra ling#katie jones\nnovember#crystal alice#amanda corey#ashlee jae#roxanne dawn#-\n",
        "pandas_code": "(df['week 5'] == '-').sum() >= 7",
        "pandas_eval": "True"
    },
    {
        "id": 1770,
        "statement": "every region have a start datum of wgs84 except for slovenia etrs89",
        "label": 1,
        "table_caption": "helmert transformation",
        "table_text": "region#start datum#target datum#c_x ( metre )#c_y (metre)#c_z (metre)#s ( ppm )#r x ( arcsecond )#r y ( arcsecond )#r z ( arcsecond )\nslovenia etrs89#d48#d96#409.545#72.164#486.872#17.919665#3.085957#5.469110#11.020289\nengland , scotland , wales#wgs84#osgb36#446.448#125.157#542.06#20.4894#0.1502#0.247#0.8421\nireland#wgs84#ireland 1965#482.53#130.596#564.557#8.15#1.042#0.214#0.631\ngermany#wgs84#dhdn#591.28#81.35#396.39#9.82#1.4770#0.0736#1.4580\ngermany#wgs84#bessel 1841#582#105#414#8.3#1.04#0.35#3.08\ngermany#wgs84#krassovski 1940#24#123#94#1.1#0.02#0.26#0.13\naustria (bev)#wgs84#mgi#577.326#90.129#463.920#2.423#5.137#1.474#5.297\n",
        "pandas_code": "all(df[df['region'] != 'slovenia etrs89']['start datum'] == 'wgs84')",
        "pandas_eval": "True"
    },
    {
        "id": 1771,
        "statement": "germany have 3 target datum - dhdn , bessel 1841 , and krassovski 1940",
        "label": 1,
        "table_caption": "helmert transformation",
        "table_text": "region#start datum#target datum#c_x ( metre )#c_y (metre)#c_z (metre)#s ( ppm )#r x ( arcsecond )#r y ( arcsecond )#r z ( arcsecond )\nslovenia etrs89#d48#d96#409.545#72.164#486.872#17.919665#3.085957#5.469110#11.020289\nengland , scotland , wales#wgs84#osgb36#446.448#125.157#542.06#20.4894#0.1502#0.247#0.8421\nireland#wgs84#ireland 1965#482.53#130.596#564.557#8.15#1.042#0.214#0.631\ngermany#wgs84#dhdn#591.28#81.35#396.39#9.82#1.4770#0.0736#1.4580\ngermany#wgs84#bessel 1841#582#105#414#8.3#1.04#0.35#3.08\ngermany#wgs84#krassovski 1940#24#123#94#1.1#0.02#0.26#0.13\naustria (bev)#wgs84#mgi#577.326#90.129#463.920#2.423#5.137#1.474#5.297\n",
        "pandas_code": "len(df[df['region'] == 'germany']['target datum'].unique()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1772,
        "statement": "ireland do not have a c_x of 81.35 , that be germany 's first c_y",
        "label": 1,
        "table_caption": "helmert transformation",
        "table_text": "region#start datum#target datum#c_x ( metre )#c_y (metre)#c_z (metre)#s ( ppm )#r x ( arcsecond )#r y ( arcsecond )#r z ( arcsecond )\nslovenia etrs89#d48#d96#409.545#72.164#486.872#17.919665#3.085957#5.469110#11.020289\nengland , scotland , wales#wgs84#osgb36#446.448#125.157#542.06#20.4894#0.1502#0.247#0.8421\nireland#wgs84#ireland 1965#482.53#130.596#564.557#8.15#1.042#0.214#0.631\ngermany#wgs84#dhdn#591.28#81.35#396.39#9.82#1.4770#0.0736#1.4580\ngermany#wgs84#bessel 1841#582#105#414#8.3#1.04#0.35#3.08\ngermany#wgs84#krassovski 1940#24#123#94#1.1#0.02#0.26#0.13\naustria (bev)#wgs84#mgi#577.326#90.129#463.920#2.423#5.137#1.474#5.297\n",
        "pandas_code": "not df[(df['region'] == 'ireland') & (df['c_x ( metre )'] == 81.35)].any().any() and df[(df['region'] == 'germany') & (df['c_y (metre)'] == 81.35)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1773,
        "statement": "germany have a c_x of both 582 and 24",
        "label": 1,
        "table_caption": "helmert transformation",
        "table_text": "region#start datum#target datum#c_x ( metre )#c_y (metre)#c_z (metre)#s ( ppm )#r x ( arcsecond )#r y ( arcsecond )#r z ( arcsecond )\nslovenia etrs89#d48#d96#409.545#72.164#486.872#17.919665#3.085957#5.469110#11.020289\nengland , scotland , wales#wgs84#osgb36#446.448#125.157#542.06#20.4894#0.1502#0.247#0.8421\nireland#wgs84#ireland 1965#482.53#130.596#564.557#8.15#1.042#0.214#0.631\ngermany#wgs84#dhdn#591.28#81.35#396.39#9.82#1.4770#0.0736#1.4580\ngermany#wgs84#bessel 1841#582#105#414#8.3#1.04#0.35#3.08\ngermany#wgs84#krassovski 1940#24#123#94#1.1#0.02#0.26#0.13\naustria (bev)#wgs84#mgi#577.326#90.129#463.920#2.423#5.137#1.474#5.297\n",
        "pandas_code": "len(df[(df['region'] == 'germany') & (df['c_x ( metre )'].isin([582, 24]))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1774,
        "statement": "the target datum for austria be mgi and slovenia be d96",
        "label": 1,
        "table_caption": "helmert transformation",
        "table_text": "region#start datum#target datum#c_x ( metre )#c_y (metre)#c_z (metre)#s ( ppm )#r x ( arcsecond )#r y ( arcsecond )#r z ( arcsecond )\nslovenia etrs89#d48#d96#409.545#72.164#486.872#17.919665#3.085957#5.469110#11.020289\nengland , scotland , wales#wgs84#osgb36#446.448#125.157#542.06#20.4894#0.1502#0.247#0.8421\nireland#wgs84#ireland 1965#482.53#130.596#564.557#8.15#1.042#0.214#0.631\ngermany#wgs84#dhdn#591.28#81.35#396.39#9.82#1.4770#0.0736#1.4580\ngermany#wgs84#bessel 1841#582#105#414#8.3#1.04#0.35#3.08\ngermany#wgs84#krassovski 1940#24#123#94#1.1#0.02#0.26#0.13\naustria (bev)#wgs84#mgi#577.326#90.129#463.920#2.423#5.137#1.474#5.297\n",
        "pandas_code": "(df[df['region'] == 'austria (bev)']['target datum'].values[0] == 'mgi') & (df[df['region'] == 'slovenia etrs89']['target datum'].values[0] == 'd96')",
        "pandas_eval": "True"
    },
    {
        "id": 1775,
        "statement": "donlavey racing be the team that be race in 1985 , 1986 , and 1987",
        "label": 1,
        "table_caption": "ken schrader",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1984#5#0#0#0#0#28.8#24.4#16425#53rd#langley racing\n1985#28#0#0#3#0#21.3#18.4#211522#16th#donlavey racing\n1986#29#0#0#4#0#23.5#19.5#235904#16th#donlavey racing\n1987#29#0#1#10#1#11.9#16.2#375918#10th#donlavey racing\n1988#29#1#4#17#2#9.7#11.7#631544#5th#hendrick motorsports\n1989#29#1#10#14#4#7.2#13.2#1037941#5th#hendrick motorsports\n1990#29#0#7#14#3#9.7#15.0#769934#10th#hendrick motorsports\n1991#29#2#10#18#0#10.4#14.3#772434#9th#hendrick motorsports\n1992#29#0#4#11#1#12.3#16.2#639679#17th#hendrick motorsports\n1993#30#0#9#15#6#10.3#15.0#952748#9th#hendrick motorsports\n1994#31#0#9#18#0#14.2#12.4#1171062#4th#hendrick motorsports\n1995#31#0#2#10#1#17.8#20.7#886566#17th#hendrick motorsports\n1996#31#0#3#10#0#16.2#17.2#1089603#12th#hendrick motorsports\n1997#32#0#2#8#2#14.1#17.8#1355292#10th#andy petree racing\n1998#33#0#3#11#2#17.2#17.9#1887399#12th#andy petree racing\n1999#34#0#0#6#1#15.0#20.6#1939147#15th#andy petree racing\n2000#34#0#0#2#0#23.2#21.3#1711476#18th#mb2 motorsports\n2001#36#0#0#5#0#22.8#22.3#2418181#19th#mb2 motorsports\n2002#36#0#0#0#0#24.2#27.0#2460140#30th#mb2 motorsports\n2003#32#0#0#2#0#29.7#28.8#2007424#36th#bam racing\n2004#36#0#0#1#0#29.7#26.6#2666592#31st#bam racing\n2005#36#0#0#3#0#30.1#25.3#3057533#31st#bam racing\n2006#36#0#0#2#0#25.6#26.2#4130883#31st#wood brothers racing\n2007#13#0#0#0#0#27.2#30.7#1519346#49th#wood brothers racing bam racing\n2010#1#0#0#0#0#38.0#18.0#119059#65th#latitude 43 motorsports\n2011#7#0#0#0#0#39.0#28.4#605615#38th#fas lane racing\n2012#13#0#0#0#0#39.5#32.8#1192015#40th#fas lane racing\n",
        "pandas_code": "all(df[df['year'].isin([1985, 1986, 1987])]['team (s)'] == 'donlavey racing')",
        "pandas_eval": "True"
    },
    {
        "id": 1776,
        "statement": "fas lane racing be the team that be race in 2011 and 2012",
        "label": 1,
        "table_caption": "ken schrader",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1984#5#0#0#0#0#28.8#24.4#16425#53rd#langley racing\n1985#28#0#0#3#0#21.3#18.4#211522#16th#donlavey racing\n1986#29#0#0#4#0#23.5#19.5#235904#16th#donlavey racing\n1987#29#0#1#10#1#11.9#16.2#375918#10th#donlavey racing\n1988#29#1#4#17#2#9.7#11.7#631544#5th#hendrick motorsports\n1989#29#1#10#14#4#7.2#13.2#1037941#5th#hendrick motorsports\n1990#29#0#7#14#3#9.7#15.0#769934#10th#hendrick motorsports\n1991#29#2#10#18#0#10.4#14.3#772434#9th#hendrick motorsports\n1992#29#0#4#11#1#12.3#16.2#639679#17th#hendrick motorsports\n1993#30#0#9#15#6#10.3#15.0#952748#9th#hendrick motorsports\n1994#31#0#9#18#0#14.2#12.4#1171062#4th#hendrick motorsports\n1995#31#0#2#10#1#17.8#20.7#886566#17th#hendrick motorsports\n1996#31#0#3#10#0#16.2#17.2#1089603#12th#hendrick motorsports\n1997#32#0#2#8#2#14.1#17.8#1355292#10th#andy petree racing\n1998#33#0#3#11#2#17.2#17.9#1887399#12th#andy petree racing\n1999#34#0#0#6#1#15.0#20.6#1939147#15th#andy petree racing\n2000#34#0#0#2#0#23.2#21.3#1711476#18th#mb2 motorsports\n2001#36#0#0#5#0#22.8#22.3#2418181#19th#mb2 motorsports\n2002#36#0#0#0#0#24.2#27.0#2460140#30th#mb2 motorsports\n2003#32#0#0#2#0#29.7#28.8#2007424#36th#bam racing\n2004#36#0#0#1#0#29.7#26.6#2666592#31st#bam racing\n2005#36#0#0#3#0#30.1#25.3#3057533#31st#bam racing\n2006#36#0#0#2#0#25.6#26.2#4130883#31st#wood brothers racing\n2007#13#0#0#0#0#27.2#30.7#1519346#49th#wood brothers racing bam racing\n2010#1#0#0#0#0#38.0#18.0#119059#65th#latitude 43 motorsports\n2011#7#0#0#0#0#39.0#28.4#605615#38th#fas lane racing\n2012#13#0#0#0#0#39.5#32.8#1192015#40th#fas lane racing\n",
        "pandas_code": "(df[df['year'].isin([2011, 2012])]['team (s)'] == 'fas lane racing').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1777,
        "statement": "bam racing have over 100 start , 32 of them be in 2003",
        "label": 1,
        "table_caption": "ken schrader",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1984#5#0#0#0#0#28.8#24.4#16425#53rd#langley racing\n1985#28#0#0#3#0#21.3#18.4#211522#16th#donlavey racing\n1986#29#0#0#4#0#23.5#19.5#235904#16th#donlavey racing\n1987#29#0#1#10#1#11.9#16.2#375918#10th#donlavey racing\n1988#29#1#4#17#2#9.7#11.7#631544#5th#hendrick motorsports\n1989#29#1#10#14#4#7.2#13.2#1037941#5th#hendrick motorsports\n1990#29#0#7#14#3#9.7#15.0#769934#10th#hendrick motorsports\n1991#29#2#10#18#0#10.4#14.3#772434#9th#hendrick motorsports\n1992#29#0#4#11#1#12.3#16.2#639679#17th#hendrick motorsports\n1993#30#0#9#15#6#10.3#15.0#952748#9th#hendrick motorsports\n1994#31#0#9#18#0#14.2#12.4#1171062#4th#hendrick motorsports\n1995#31#0#2#10#1#17.8#20.7#886566#17th#hendrick motorsports\n1996#31#0#3#10#0#16.2#17.2#1089603#12th#hendrick motorsports\n1997#32#0#2#8#2#14.1#17.8#1355292#10th#andy petree racing\n1998#33#0#3#11#2#17.2#17.9#1887399#12th#andy petree racing\n1999#34#0#0#6#1#15.0#20.6#1939147#15th#andy petree racing\n2000#34#0#0#2#0#23.2#21.3#1711476#18th#mb2 motorsports\n2001#36#0#0#5#0#22.8#22.3#2418181#19th#mb2 motorsports\n2002#36#0#0#0#0#24.2#27.0#2460140#30th#mb2 motorsports\n2003#32#0#0#2#0#29.7#28.8#2007424#36th#bam racing\n2004#36#0#0#1#0#29.7#26.6#2666592#31st#bam racing\n2005#36#0#0#3#0#30.1#25.3#3057533#31st#bam racing\n2006#36#0#0#2#0#25.6#26.2#4130883#31st#wood brothers racing\n2007#13#0#0#0#0#27.2#30.7#1519346#49th#wood brothers racing bam racing\n2010#1#0#0#0#0#38.0#18.0#119059#65th#latitude 43 motorsports\n2011#7#0#0#0#0#39.0#28.4#605615#38th#fas lane racing\n2012#13#0#0#0#0#39.5#32.8#1192015#40th#fas lane racing\n",
        "pandas_code": "(df[df['team (s)'].str.contains('bam racing')]['starts'].sum() > 100) & (df[(df['team (s)'].str.contains('bam racing')) & (df['year'] == 2003)]['starts'].sum() == 32)",
        "pandas_eval": "True"
    },
    {
        "id": 1778,
        "statement": "hendricks motorsports be in fifth place during 1988 and 1989",
        "label": 1,
        "table_caption": "ken schrader",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1984#5#0#0#0#0#28.8#24.4#16425#53rd#langley racing\n1985#28#0#0#3#0#21.3#18.4#211522#16th#donlavey racing\n1986#29#0#0#4#0#23.5#19.5#235904#16th#donlavey racing\n1987#29#0#1#10#1#11.9#16.2#375918#10th#donlavey racing\n1988#29#1#4#17#2#9.7#11.7#631544#5th#hendrick motorsports\n1989#29#1#10#14#4#7.2#13.2#1037941#5th#hendrick motorsports\n1990#29#0#7#14#3#9.7#15.0#769934#10th#hendrick motorsports\n1991#29#2#10#18#0#10.4#14.3#772434#9th#hendrick motorsports\n1992#29#0#4#11#1#12.3#16.2#639679#17th#hendrick motorsports\n1993#30#0#9#15#6#10.3#15.0#952748#9th#hendrick motorsports\n1994#31#0#9#18#0#14.2#12.4#1171062#4th#hendrick motorsports\n1995#31#0#2#10#1#17.8#20.7#886566#17th#hendrick motorsports\n1996#31#0#3#10#0#16.2#17.2#1089603#12th#hendrick motorsports\n1997#32#0#2#8#2#14.1#17.8#1355292#10th#andy petree racing\n1998#33#0#3#11#2#17.2#17.9#1887399#12th#andy petree racing\n1999#34#0#0#6#1#15.0#20.6#1939147#15th#andy petree racing\n2000#34#0#0#2#0#23.2#21.3#1711476#18th#mb2 motorsports\n2001#36#0#0#5#0#22.8#22.3#2418181#19th#mb2 motorsports\n2002#36#0#0#0#0#24.2#27.0#2460140#30th#mb2 motorsports\n2003#32#0#0#2#0#29.7#28.8#2007424#36th#bam racing\n2004#36#0#0#1#0#29.7#26.6#2666592#31st#bam racing\n2005#36#0#0#3#0#30.1#25.3#3057533#31st#bam racing\n2006#36#0#0#2#0#25.6#26.2#4130883#31st#wood brothers racing\n2007#13#0#0#0#0#27.2#30.7#1519346#49th#wood brothers racing bam racing\n2010#1#0#0#0#0#38.0#18.0#119059#65th#latitude 43 motorsports\n2011#7#0#0#0#0#39.0#28.4#605615#38th#fas lane racing\n2012#13#0#0#0#0#39.5#32.8#1192015#40th#fas lane racing\n",
        "pandas_code": "(df[(df['year'] == 1988) | (df['year'] == 1989)]['team (s)'].str.contains('hendrick motorsports') & (df[(df['year'] == 1988) | (df['year'] == 1989)]['position'] == '5th')).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1779,
        "statement": "andy petree racing be show to have be in tenth , twelfth , and fifteenth place",
        "label": 1,
        "table_caption": "ken schrader",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1984#5#0#0#0#0#28.8#24.4#16425#53rd#langley racing\n1985#28#0#0#3#0#21.3#18.4#211522#16th#donlavey racing\n1986#29#0#0#4#0#23.5#19.5#235904#16th#donlavey racing\n1987#29#0#1#10#1#11.9#16.2#375918#10th#donlavey racing\n1988#29#1#4#17#2#9.7#11.7#631544#5th#hendrick motorsports\n1989#29#1#10#14#4#7.2#13.2#1037941#5th#hendrick motorsports\n1990#29#0#7#14#3#9.7#15.0#769934#10th#hendrick motorsports\n1991#29#2#10#18#0#10.4#14.3#772434#9th#hendrick motorsports\n1992#29#0#4#11#1#12.3#16.2#639679#17th#hendrick motorsports\n1993#30#0#9#15#6#10.3#15.0#952748#9th#hendrick motorsports\n1994#31#0#9#18#0#14.2#12.4#1171062#4th#hendrick motorsports\n1995#31#0#2#10#1#17.8#20.7#886566#17th#hendrick motorsports\n1996#31#0#3#10#0#16.2#17.2#1089603#12th#hendrick motorsports\n1997#32#0#2#8#2#14.1#17.8#1355292#10th#andy petree racing\n1998#33#0#3#11#2#17.2#17.9#1887399#12th#andy petree racing\n1999#34#0#0#6#1#15.0#20.6#1939147#15th#andy petree racing\n2000#34#0#0#2#0#23.2#21.3#1711476#18th#mb2 motorsports\n2001#36#0#0#5#0#22.8#22.3#2418181#19th#mb2 motorsports\n2002#36#0#0#0#0#24.2#27.0#2460140#30th#mb2 motorsports\n2003#32#0#0#2#0#29.7#28.8#2007424#36th#bam racing\n2004#36#0#0#1#0#29.7#26.6#2666592#31st#bam racing\n2005#36#0#0#3#0#30.1#25.3#3057533#31st#bam racing\n2006#36#0#0#2#0#25.6#26.2#4130883#31st#wood brothers racing\n2007#13#0#0#0#0#27.2#30.7#1519346#49th#wood brothers racing bam racing\n2010#1#0#0#0#0#38.0#18.0#119059#65th#latitude 43 motorsports\n2011#7#0#0#0#0#39.0#28.4#605615#38th#fas lane racing\n2012#13#0#0#0#0#39.5#32.8#1192015#40th#fas lane racing\n",
        "pandas_code": "df[df['team (s)'].str.contains('andy petree racing')]['position'].isin(['10th', '12th', '15th']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1780,
        "statement": "the spacewalk with an end of may 12 , 2000 15:36 have a duration of 4h , 52 min and the spacewalk with an end of may 22 , 2000 08:32 have a duration of 6h , 44 min",
        "label": 1,
        "table_caption": "list of spacewalks since 2000",
        "table_text": "#spacecraft#spacewalkers#start ( utc )#end (utc)#duration\n182#mir pe - 28 eva 1#sergei zalyotin alexander kaleri#may 12 , 2000 10:44#may 12 , 2000 15:36#4 h , 52 min\n183#sts - 101 eva 1#james voss jeffrey williams#may 22 , 2000 01:48#may 22 , 2000 08:32#6 h , 44 min\n184#sts - 106 eva 1#edward lu yuri malenchenko#september 11 , 2000 04:47#september 11 , 2000 11:01#6 h , 14 min\n185#sts - 92 eva 1#leroy chiao william mcarthur#october 15 , 2000 14:27#october 15 , 2000 20:55#6 h , 28 min\n186#sts - 92 eva 2#michael lopez - alegria peter wisoff#october 16 , 2000 14:15#october 16 , 2000 21:22#7 h , 07 min\n187#sts - 92 eva 3#leroy chiao william mcarthur#october 17 , 2000 14:30#october 17 , 2000 21:18#6 h , 48 min\n188#sts - 92 eva 4#michael lopez - alegria peter wisoff#october 18 , 2000 15:00#october 18 , 2000 21:56#6 h , 56 min\n189#sts - 97 eva 1#joseph tanner carlos i noriega#december 3 , 2000 18:35#december 4 , 2000 02:08#7 h , 33 min\n190#sts - 97 eva 2#joseph tanner carlos noriega#december 5 , 2000 17:21#december 5 , 2000 23:58#6 h , 37 min\n",
        "pandas_code": "(df[df['end (utc)'] == 'may 12 , 2000 15:36']['duration'].iloc[0] == '4 h , 52 min') & (df[df['end (utc)'] == 'may 22 , 2000 08:32']['duration'].iloc[0] == '6 h , 44 min')",
        "pandas_eval": "True"
    },
    {
        "id": 1781,
        "statement": "the spacewalk with the longest durection be 7h , 07 min by michael lopez - alegria peter wisoff",
        "label": 1,
        "table_caption": "list of spacewalks since 2000",
        "table_text": "#spacecraft#spacewalkers#start ( utc )#end (utc)#duration\n182#mir pe - 28 eva 1#sergei zalyotin alexander kaleri#may 12 , 2000 10:44#may 12 , 2000 15:36#4 h , 52 min\n183#sts - 101 eva 1#james voss jeffrey williams#may 22 , 2000 01:48#may 22 , 2000 08:32#6 h , 44 min\n184#sts - 106 eva 1#edward lu yuri malenchenko#september 11 , 2000 04:47#september 11 , 2000 11:01#6 h , 14 min\n185#sts - 92 eva 1#leroy chiao william mcarthur#october 15 , 2000 14:27#october 15 , 2000 20:55#6 h , 28 min\n186#sts - 92 eva 2#michael lopez - alegria peter wisoff#october 16 , 2000 14:15#october 16 , 2000 21:22#7 h , 07 min\n187#sts - 92 eva 3#leroy chiao william mcarthur#october 17 , 2000 14:30#october 17 , 2000 21:18#6 h , 48 min\n188#sts - 92 eva 4#michael lopez - alegria peter wisoff#october 18 , 2000 15:00#october 18 , 2000 21:56#6 h , 56 min\n189#sts - 97 eva 1#joseph tanner carlos i noriega#december 3 , 2000 18:35#december 4 , 2000 02:08#7 h , 33 min\n190#sts - 97 eva 2#joseph tanner carlos noriega#december 5 , 2000 17:21#december 5 , 2000 23:58#6 h , 37 min\n",
        "pandas_code": "df[df['duration'] == '7 h , 07 min']['spacewalkers'].values[0] == 'michael lopez - alegria peter wisoff'",
        "pandas_eval": "True"
    },
    {
        "id": 1782,
        "statement": "when a spacewalk take 6h , 37 min the spacewalker be joseph tanner carlos noriega with a start of december 5 , 2000 17:21",
        "label": 1,
        "table_caption": "list of spacewalks since 2000",
        "table_text": "#spacecraft#spacewalkers#start ( utc )#end (utc)#duration\n182#mir pe - 28 eva 1#sergei zalyotin alexander kaleri#may 12 , 2000 10:44#may 12 , 2000 15:36#4 h , 52 min\n183#sts - 101 eva 1#james voss jeffrey williams#may 22 , 2000 01:48#may 22 , 2000 08:32#6 h , 44 min\n184#sts - 106 eva 1#edward lu yuri malenchenko#september 11 , 2000 04:47#september 11 , 2000 11:01#6 h , 14 min\n185#sts - 92 eva 1#leroy chiao william mcarthur#october 15 , 2000 14:27#october 15 , 2000 20:55#6 h , 28 min\n186#sts - 92 eva 2#michael lopez - alegria peter wisoff#october 16 , 2000 14:15#october 16 , 2000 21:22#7 h , 07 min\n187#sts - 92 eva 3#leroy chiao william mcarthur#october 17 , 2000 14:30#october 17 , 2000 21:18#6 h , 48 min\n188#sts - 92 eva 4#michael lopez - alegria peter wisoff#october 18 , 2000 15:00#october 18 , 2000 21:56#6 h , 56 min\n189#sts - 97 eva 1#joseph tanner carlos i noriega#december 3 , 2000 18:35#december 4 , 2000 02:08#7 h , 33 min\n190#sts - 97 eva 2#joseph tanner carlos noriega#december 5 , 2000 17:21#december 5 , 2000 23:58#6 h , 37 min\n",
        "pandas_code": "df[(df['duration'] == '6 h , 37 min') & (df['spacewalkers'] == 'joseph tanner carlos noriega') & (df['start ( utc )'] == 'december 5 , 2000 17:21')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1783,
        "statement": "the shortest spacewalk be 4h , 52 min by sergei zalyotin alexander kaleri with spacecraft mir pe - 28 eva 1",
        "label": 1,
        "table_caption": "list of spacewalks since 2000",
        "table_text": "#spacecraft#spacewalkers#start ( utc )#end (utc)#duration\n182#mir pe - 28 eva 1#sergei zalyotin alexander kaleri#may 12 , 2000 10:44#may 12 , 2000 15:36#4 h , 52 min\n183#sts - 101 eva 1#james voss jeffrey williams#may 22 , 2000 01:48#may 22 , 2000 08:32#6 h , 44 min\n184#sts - 106 eva 1#edward lu yuri malenchenko#september 11 , 2000 04:47#september 11 , 2000 11:01#6 h , 14 min\n185#sts - 92 eva 1#leroy chiao william mcarthur#october 15 , 2000 14:27#october 15 , 2000 20:55#6 h , 28 min\n186#sts - 92 eva 2#michael lopez - alegria peter wisoff#october 16 , 2000 14:15#october 16 , 2000 21:22#7 h , 07 min\n187#sts - 92 eva 3#leroy chiao william mcarthur#october 17 , 2000 14:30#october 17 , 2000 21:18#6 h , 48 min\n188#sts - 92 eva 4#michael lopez - alegria peter wisoff#october 18 , 2000 15:00#october 18 , 2000 21:56#6 h , 56 min\n189#sts - 97 eva 1#joseph tanner carlos i noriega#december 3 , 2000 18:35#december 4 , 2000 02:08#7 h , 33 min\n190#sts - 97 eva 2#joseph tanner carlos noriega#december 5 , 2000 17:21#december 5 , 2000 23:58#6 h , 37 min\n",
        "pandas_code": "((df['duration'] == '4 h , 52 min') & (df['spacewalkers'] == 'sergei zalyotin alexander kaleri') & (df['spacecraft'] == 'mir pe - 28 eva 1')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1784,
        "statement": "there be 2 spacewalks that be longer than 7 hour and they have an end of october 16 , 2000 21:22 and december 4 , 2000 02:08",
        "label": 1,
        "table_caption": "list of spacewalks since 2000",
        "table_text": "#spacecraft#spacewalkers#start ( utc )#end (utc)#duration\n182#mir pe - 28 eva 1#sergei zalyotin alexander kaleri#may 12 , 2000 10:44#may 12 , 2000 15:36#4 h , 52 min\n183#sts - 101 eva 1#james voss jeffrey williams#may 22 , 2000 01:48#may 22 , 2000 08:32#6 h , 44 min\n184#sts - 106 eva 1#edward lu yuri malenchenko#september 11 , 2000 04:47#september 11 , 2000 11:01#6 h , 14 min\n185#sts - 92 eva 1#leroy chiao william mcarthur#october 15 , 2000 14:27#october 15 , 2000 20:55#6 h , 28 min\n186#sts - 92 eva 2#michael lopez - alegria peter wisoff#october 16 , 2000 14:15#october 16 , 2000 21:22#7 h , 07 min\n187#sts - 92 eva 3#leroy chiao william mcarthur#october 17 , 2000 14:30#october 17 , 2000 21:18#6 h , 48 min\n188#sts - 92 eva 4#michael lopez - alegria peter wisoff#october 18 , 2000 15:00#october 18 , 2000 21:56#6 h , 56 min\n189#sts - 97 eva 1#joseph tanner carlos i noriega#december 3 , 2000 18:35#december 4 , 2000 02:08#7 h , 33 min\n190#sts - 97 eva 2#joseph tanner carlos noriega#december 5 , 2000 17:21#december 5 , 2000 23:58#6 h , 37 min\n",
        "pandas_code": "len(df[(df['duration'].str.contains('7 h')) & (df['end (utc)'].isin(['october 16 , 2000 21:22', 'december 4 , 2000 02:08']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1785,
        "statement": "the colt only tie 1 game",
        "label": 1,
        "table_caption": "1970 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 20 , 1970#san diego chargers#w 16 - 14#1 - 0#san diego stadium#47782\n2#september 28 , 1970#kansas city chiefs#l 24 - 44#1 - 1#memorial stadium#53911\n3#october 4 , 1970#boston patriots#w 14 - 6#2 - 1#harvard stadium#38235\n4#october 11 , 1970#houston oilers#w 24 - 20#3 - 1#astrodome#48050\n5#october 18 , 1970#new york jets#w 29 - 22#4 - 1#shea stadium#63301\n6#october 25 , 1970#boston patriots#w 27 - 3#5 - 1#memorial stadium#60240\n7#november 1 , 1970#miami dolphins#w 35 - 0#6 - 1#memorial stadium#60240\n8#november 9 , 1970#green bay packers#w 13 - 10#7 - 1#milwaukee county stadium#48063\n9#november 15 , 1970#buffalo bills#t 17 - 17#7 - 1 - 1#memorial stadium#60240\n10#november 22 , 1970#miami dolphins#l 17 - 34#7 - 2 - 1#miami orange bowl#67699\n11#november 29 , 1970#chicago bears#w 21 - 20#8 - 2 - 1#memorial stadium#60240\n12#december 6 , 1970#philadelphia eagles#w 29 - 10#9 - 2 - 1#memorial stadium#60240\n13#december 13 , 1970#buffalo bills#w 20 - 14#10 - 2 - 1#war memorial stadium#34346\n",
        "pandas_code": "(df['result'].str.startswith('t').sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 1786,
        "statement": "the colt vs bill game be after the packer game",
        "label": 1,
        "table_caption": "1970 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 20 , 1970#san diego chargers#w 16 - 14#1 - 0#san diego stadium#47782\n2#september 28 , 1970#kansas city chiefs#l 24 - 44#1 - 1#memorial stadium#53911\n3#october 4 , 1970#boston patriots#w 14 - 6#2 - 1#harvard stadium#38235\n4#october 11 , 1970#houston oilers#w 24 - 20#3 - 1#astrodome#48050\n5#october 18 , 1970#new york jets#w 29 - 22#4 - 1#shea stadium#63301\n6#october 25 , 1970#boston patriots#w 27 - 3#5 - 1#memorial stadium#60240\n7#november 1 , 1970#miami dolphins#w 35 - 0#6 - 1#memorial stadium#60240\n8#november 9 , 1970#green bay packers#w 13 - 10#7 - 1#milwaukee county stadium#48063\n9#november 15 , 1970#buffalo bills#t 17 - 17#7 - 1 - 1#memorial stadium#60240\n10#november 22 , 1970#miami dolphins#l 17 - 34#7 - 2 - 1#miami orange bowl#67699\n11#november 29 , 1970#chicago bears#w 21 - 20#8 - 2 - 1#memorial stadium#60240\n12#december 6 , 1970#philadelphia eagles#w 29 - 10#9 - 2 - 1#memorial stadium#60240\n13#december 13 , 1970#buffalo bills#w 20 - 14#10 - 2 - 1#war memorial stadium#34346\n",
        "pandas_code": "df[df['opponent'].isin(['buffalo bills', 'green bay packers'])].sort_values('week').iloc[-1]['opponent'] == 'buffalo bills'",
        "pandas_eval": "True"
    },
    {
        "id": 1787,
        "statement": "the colt vs charger game be before the chief game",
        "label": 1,
        "table_caption": "1970 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 20 , 1970#san diego chargers#w 16 - 14#1 - 0#san diego stadium#47782\n2#september 28 , 1970#kansas city chiefs#l 24 - 44#1 - 1#memorial stadium#53911\n3#october 4 , 1970#boston patriots#w 14 - 6#2 - 1#harvard stadium#38235\n4#october 11 , 1970#houston oilers#w 24 - 20#3 - 1#astrodome#48050\n5#october 18 , 1970#new york jets#w 29 - 22#4 - 1#shea stadium#63301\n6#october 25 , 1970#boston patriots#w 27 - 3#5 - 1#memorial stadium#60240\n7#november 1 , 1970#miami dolphins#w 35 - 0#6 - 1#memorial stadium#60240\n8#november 9 , 1970#green bay packers#w 13 - 10#7 - 1#milwaukee county stadium#48063\n9#november 15 , 1970#buffalo bills#t 17 - 17#7 - 1 - 1#memorial stadium#60240\n10#november 22 , 1970#miami dolphins#l 17 - 34#7 - 2 - 1#miami orange bowl#67699\n11#november 29 , 1970#chicago bears#w 21 - 20#8 - 2 - 1#memorial stadium#60240\n12#december 6 , 1970#philadelphia eagles#w 29 - 10#9 - 2 - 1#memorial stadium#60240\n13#december 13 , 1970#buffalo bills#w 20 - 14#10 - 2 - 1#war memorial stadium#34346\n",
        "pandas_code": "df[df['opponent'].isin(['san diego chargers', 'kansas city chiefs'])].sort_values('date').iloc[0]['opponent'] == 'san diego chargers'",
        "pandas_eval": "True"
    },
    {
        "id": 1788,
        "statement": "the colt vs chiefs game be after the charger game",
        "label": 1,
        "table_caption": "1970 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 20 , 1970#san diego chargers#w 16 - 14#1 - 0#san diego stadium#47782\n2#september 28 , 1970#kansas city chiefs#l 24 - 44#1 - 1#memorial stadium#53911\n3#october 4 , 1970#boston patriots#w 14 - 6#2 - 1#harvard stadium#38235\n4#october 11 , 1970#houston oilers#w 24 - 20#3 - 1#astrodome#48050\n5#october 18 , 1970#new york jets#w 29 - 22#4 - 1#shea stadium#63301\n6#october 25 , 1970#boston patriots#w 27 - 3#5 - 1#memorial stadium#60240\n7#november 1 , 1970#miami dolphins#w 35 - 0#6 - 1#memorial stadium#60240\n8#november 9 , 1970#green bay packers#w 13 - 10#7 - 1#milwaukee county stadium#48063\n9#november 15 , 1970#buffalo bills#t 17 - 17#7 - 1 - 1#memorial stadium#60240\n10#november 22 , 1970#miami dolphins#l 17 - 34#7 - 2 - 1#miami orange bowl#67699\n11#november 29 , 1970#chicago bears#w 21 - 20#8 - 2 - 1#memorial stadium#60240\n12#december 6 , 1970#philadelphia eagles#w 29 - 10#9 - 2 - 1#memorial stadium#60240\n13#december 13 , 1970#buffalo bills#w 20 - 14#10 - 2 - 1#war memorial stadium#34346\n",
        "pandas_code": "(df[df['opponent'] == 'kansas city chiefs']['week'].values[0] > df[df['opponent'] == 'san diego chargers']['week'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1795,
        "statement": "all 4 world record be hold by chinese weight lifter",
        "label": 0,
        "table_caption": "weightlifting at the 2008 summer olympics - men 's 62 kg",
        "table_text": "world record#snatch#shi zhiyong ( chn )#153 kg#izmir , turkey\nclean & jerk#le maosheng ( chn )#182 kg#busan , south korea#2 october 2002\ntotal#zhang jie ( chn )#326 kg#kanazawa , japan#28 april 2008\nolympic record#snatch#shi zhiyong ( chn )#152 kg#athens , greece\nolympic record#clean & jerk#olympic standard#177 kg#-\nolympic record#total#nikolaj pe\u0161alov ( cro )#325 kg#sydney , australia\n",
        "pandas_code": "all(df[df['world record'].isin(['clean & jerk', 'total', 'olympic record'])]['snatch'].str.contains('chn'))",
        "pandas_eval": "False"
    },
    {
        "id": 1796,
        "statement": "nikolaj pe\u0161alov be the only weightlifter to hold both world and olympic record",
        "label": 0,
        "table_caption": "weightlifting at the 2008 summer olympics - men 's 62 kg",
        "table_text": "world record#snatch#shi zhiyong ( chn )#153 kg#izmir , turkey\nclean & jerk#le maosheng ( chn )#182 kg#busan , south korea#2 october 2002\ntotal#zhang jie ( chn )#326 kg#kanazawa , japan#28 april 2008\nolympic record#snatch#shi zhiyong ( chn )#152 kg#athens , greece\nolympic record#clean & jerk#olympic standard#177 kg#-\nolympic record#total#nikolaj pe\u0161alov ( cro )#325 kg#sydney , australia\n",
        "pandas_code": "df[(df['world record'] == 'olympic record') & (df['shi zhiyong ( chn )'] == 'nikolaj pe\u0161alov ( cro )')].shape[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 1797,
        "statement": "shi zhiyong 's olympic record be 5 kg less than his world record",
        "label": 0,
        "table_caption": "weightlifting at the 2008 summer olympics - men 's 62 kg",
        "table_text": "world record#snatch#shi zhiyong ( chn )#153 kg#izmir , turkey\nclean & jerk#le maosheng ( chn )#182 kg#busan , south korea#2 october 2002\ntotal#zhang jie ( chn )#326 kg#kanazawa , japan#28 april 2008\nolympic record#snatch#shi zhiyong ( chn )#152 kg#athens , greece\nolympic record#clean & jerk#olympic standard#177 kg#-\nolympic record#total#nikolaj pe\u0161alov ( cro )#325 kg#sydney , australia\n",
        "pandas_code": "(df.loc[df['world record'] == 'olympic record', 'shi zhiyong ( chn )'].str.extract('(\\\\d+)')[0].astype(float).values + 5 == df.loc[df['world record'] == 'total', 'shi zhiyong ( chn )'].str.extract('(\\\\d+)')[0].astype(float).values).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1798,
        "statement": "the most recent record (28 , april , 2008) be set 5 year after the next most recent (16 , august , 2003)",
        "label": 0,
        "table_caption": "weightlifting at the 2008 summer olympics - men 's 62 kg",
        "table_text": "world record#snatch#shi zhiyong ( chn )#153 kg#izmir , turkey\nclean & jerk#le maosheng ( chn )#182 kg#busan , south korea#2 october 2002\ntotal#zhang jie ( chn )#326 kg#kanazawa , japan#28 april 2008\nolympic record#snatch#shi zhiyong ( chn )#152 kg#athens , greece\nolympic record#clean & jerk#olympic standard#177 kg#-\nolympic record#total#nikolaj pe\u0161alov ( cro )#325 kg#sydney , australia\n",
        "pandas_code": "((pd.to_datetime(df['izmir , turkey'][1]) - pd.to_datetime(df['izmir , turkey'][0])).days / 365) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1799,
        "statement": "nikolaj pe\u0161alov (cro) be the only chinese on this record",
        "label": 0,
        "table_caption": "weightlifting at the 2008 summer olympics - men 's 62 kg",
        "table_text": "world record#snatch#shi zhiyong ( chn )#153 kg#izmir , turkey\nclean & jerk#le maosheng ( chn )#182 kg#busan , south korea#2 october 2002\ntotal#zhang jie ( chn )#326 kg#kanazawa , japan#28 april 2008\nolympic record#snatch#shi zhiyong ( chn )#152 kg#athens , greece\nolympic record#clean & jerk#olympic standard#177 kg#-\nolympic record#total#nikolaj pe\u0161alov ( cro )#325 kg#sydney , australia\n",
        "pandas_code": "df[df['shi zhiyong ( chn )'].str.contains('nikolaj pe\u0161alov ( cro )', na=False)].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 1800,
        "statement": "of the 14 championship 's todd woodbridge win 7 and be runner - up in 7",
        "label": 1,
        "table_caption": "todd woodbridge",
        "table_text": "outcome#year#championship#surface#partner#opponents#score\nwinner#1990#us open#hard#elizabeth sayers smylie#jim pugh natasha zvereva#6 - 4 , 6 - 2\nrunner - up#1992#australian open#hard#arantxa s\u00e1nchez vicario#mark woodforde nicole provis#3 - 6 , 6 - 4 , 9 - 11\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#bryan shelton lori mcneil#6 - 2 , 6 - 3\nwinner#1993#australian open#hard#arantxa s\u00e1nchez vicario#rick leach zina garrison#7 - 5 , 6 - 4\nwinner#1993#us open#hard#helena sukov\u00e1#mark woodforde martina navratilova#6 - 3 , 7 - 6\nrunner - up#1994#australian open#hard#helena sukov\u00e1#andrei olhovskiy larisa savchenko neiland#5 - 7 , 7 - 6 (9 - 7) , 2 - 6\nwinner#1994#wimbledon#grass#helena sukov\u00e1#t j middleton lori mcneil#3 - 6 , 7 - 5 , 6 - 3\nrunner - up#1994#us open#hard#jana novotn\u00e1#patrick galbraith elna reinach#2 - 6 , 4 - 6\nwinner#1995#french open#clay#larisa savchenko#john - laffnie de jager jill hetherington#7 - 6 (10 - 8) , 7 - 6 (7 - 4)\nrunner - up#2000#australian open#hard#arantxa s\u00e1nchez vicario#jared palmer rennae stubbs#5 - 7 , 6 - 7 (3 - 7)\nrunner - up#2000#french open#clay#rennae stubbs#david adams mariaan de swardt#3 - 6 , 6 - 3 , 3 - 6\nwinner#2001#us open#hard#rennae stubbs#leander paes lisa raymond#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#2003#australian open#hard#eleni daniilidou#leander paes martina navr\u00e1tilov\u00e1#4 - 6 , 5 - 7\nrunner - up#2004#wimbledon#grass#alicia molik#wayne black cara black#6 - 3 , 6 - 7 , 4 - 6\n",
        "pandas_code": "(df['outcome'].value_counts() == 7).all()",
        "pandas_eval": "True"
    },
    {
        "id": 1801,
        "statement": "wimbledon be the only championship that be play on a grass surface",
        "label": 1,
        "table_caption": "todd woodbridge",
        "table_text": "outcome#year#championship#surface#partner#opponents#score\nwinner#1990#us open#hard#elizabeth sayers smylie#jim pugh natasha zvereva#6 - 4 , 6 - 2\nrunner - up#1992#australian open#hard#arantxa s\u00e1nchez vicario#mark woodforde nicole provis#3 - 6 , 6 - 4 , 9 - 11\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#bryan shelton lori mcneil#6 - 2 , 6 - 3\nwinner#1993#australian open#hard#arantxa s\u00e1nchez vicario#rick leach zina garrison#7 - 5 , 6 - 4\nwinner#1993#us open#hard#helena sukov\u00e1#mark woodforde martina navratilova#6 - 3 , 7 - 6\nrunner - up#1994#australian open#hard#helena sukov\u00e1#andrei olhovskiy larisa savchenko neiland#5 - 7 , 7 - 6 (9 - 7) , 2 - 6\nwinner#1994#wimbledon#grass#helena sukov\u00e1#t j middleton lori mcneil#3 - 6 , 7 - 5 , 6 - 3\nrunner - up#1994#us open#hard#jana novotn\u00e1#patrick galbraith elna reinach#2 - 6 , 4 - 6\nwinner#1995#french open#clay#larisa savchenko#john - laffnie de jager jill hetherington#7 - 6 (10 - 8) , 7 - 6 (7 - 4)\nrunner - up#2000#australian open#hard#arantxa s\u00e1nchez vicario#jared palmer rennae stubbs#5 - 7 , 6 - 7 (3 - 7)\nrunner - up#2000#french open#clay#rennae stubbs#david adams mariaan de swardt#3 - 6 , 6 - 3 , 3 - 6\nwinner#2001#us open#hard#rennae stubbs#leander paes lisa raymond#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#2003#australian open#hard#eleni daniilidou#leander paes martina navr\u00e1tilov\u00e1#4 - 6 , 5 - 7\nrunner - up#2004#wimbledon#grass#alicia molik#wayne black cara black#6 - 3 , 6 - 7 , 4 - 6\n",
        "pandas_code": "df[df['surface'] == 'grass']['championship'].unique().tolist() == ['wimbledon']",
        "pandas_eval": "True"
    },
    {
        "id": 1802,
        "statement": "todd woodbridge win the first 2 us open championship in 1990 and 1993",
        "label": 1,
        "table_caption": "todd woodbridge",
        "table_text": "outcome#year#championship#surface#partner#opponents#score\nwinner#1990#us open#hard#elizabeth sayers smylie#jim pugh natasha zvereva#6 - 4 , 6 - 2\nrunner - up#1992#australian open#hard#arantxa s\u00e1nchez vicario#mark woodforde nicole provis#3 - 6 , 6 - 4 , 9 - 11\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#bryan shelton lori mcneil#6 - 2 , 6 - 3\nwinner#1993#australian open#hard#arantxa s\u00e1nchez vicario#rick leach zina garrison#7 - 5 , 6 - 4\nwinner#1993#us open#hard#helena sukov\u00e1#mark woodforde martina navratilova#6 - 3 , 7 - 6\nrunner - up#1994#australian open#hard#helena sukov\u00e1#andrei olhovskiy larisa savchenko neiland#5 - 7 , 7 - 6 (9 - 7) , 2 - 6\nwinner#1994#wimbledon#grass#helena sukov\u00e1#t j middleton lori mcneil#3 - 6 , 7 - 5 , 6 - 3\nrunner - up#1994#us open#hard#jana novotn\u00e1#patrick galbraith elna reinach#2 - 6 , 4 - 6\nwinner#1995#french open#clay#larisa savchenko#john - laffnie de jager jill hetherington#7 - 6 (10 - 8) , 7 - 6 (7 - 4)\nrunner - up#2000#australian open#hard#arantxa s\u00e1nchez vicario#jared palmer rennae stubbs#5 - 7 , 6 - 7 (3 - 7)\nrunner - up#2000#french open#clay#rennae stubbs#david adams mariaan de swardt#3 - 6 , 6 - 3 , 3 - 6\nwinner#2001#us open#hard#rennae stubbs#leander paes lisa raymond#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#2003#australian open#hard#eleni daniilidou#leander paes martina navr\u00e1tilov\u00e1#4 - 6 , 5 - 7\nrunner - up#2004#wimbledon#grass#alicia molik#wayne black cara black#6 - 3 , 6 - 7 , 4 - 6\n",
        "pandas_code": "all(df[(df['championship'] == 'us open') & (df['year'].isin([1990, 1993]))]['outcome'] == 'winner')",
        "pandas_eval": "True"
    },
    {
        "id": 1803,
        "statement": "todd woodbridge do not win any championship with jana novotn\u00e1",
        "label": 1,
        "table_caption": "todd woodbridge",
        "table_text": "outcome#year#championship#surface#partner#opponents#score\nwinner#1990#us open#hard#elizabeth sayers smylie#jim pugh natasha zvereva#6 - 4 , 6 - 2\nrunner - up#1992#australian open#hard#arantxa s\u00e1nchez vicario#mark woodforde nicole provis#3 - 6 , 6 - 4 , 9 - 11\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#bryan shelton lori mcneil#6 - 2 , 6 - 3\nwinner#1993#australian open#hard#arantxa s\u00e1nchez vicario#rick leach zina garrison#7 - 5 , 6 - 4\nwinner#1993#us open#hard#helena sukov\u00e1#mark woodforde martina navratilova#6 - 3 , 7 - 6\nrunner - up#1994#australian open#hard#helena sukov\u00e1#andrei olhovskiy larisa savchenko neiland#5 - 7 , 7 - 6 (9 - 7) , 2 - 6\nwinner#1994#wimbledon#grass#helena sukov\u00e1#t j middleton lori mcneil#3 - 6 , 7 - 5 , 6 - 3\nrunner - up#1994#us open#hard#jana novotn\u00e1#patrick galbraith elna reinach#2 - 6 , 4 - 6\nwinner#1995#french open#clay#larisa savchenko#john - laffnie de jager jill hetherington#7 - 6 (10 - 8) , 7 - 6 (7 - 4)\nrunner - up#2000#australian open#hard#arantxa s\u00e1nchez vicario#jared palmer rennae stubbs#5 - 7 , 6 - 7 (3 - 7)\nrunner - up#2000#french open#clay#rennae stubbs#david adams mariaan de swardt#3 - 6 , 6 - 3 , 3 - 6\nwinner#2001#us open#hard#rennae stubbs#leander paes lisa raymond#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#2003#australian open#hard#eleni daniilidou#leander paes martina navr\u00e1tilov\u00e1#4 - 6 , 5 - 7\nrunner - up#2004#wimbledon#grass#alicia molik#wayne black cara black#6 - 3 , 6 - 7 , 4 - 6\n",
        "pandas_code": "not any((df['partner'] == 'jana novotn\u00e1') & (df['outcome'] == 'winner'))",
        "pandas_eval": "True"
    },
    {
        "id": 1804,
        "statement": "both championship todd woodbridge compete in for the year 2000 he be runner - up",
        "label": 1,
        "table_caption": "todd woodbridge",
        "table_text": "outcome#year#championship#surface#partner#opponents#score\nwinner#1990#us open#hard#elizabeth sayers smylie#jim pugh natasha zvereva#6 - 4 , 6 - 2\nrunner - up#1992#australian open#hard#arantxa s\u00e1nchez vicario#mark woodforde nicole provis#3 - 6 , 6 - 4 , 9 - 11\nwinner#1992#french open#clay#arantxa s\u00e1nchez vicario#bryan shelton lori mcneil#6 - 2 , 6 - 3\nwinner#1993#australian open#hard#arantxa s\u00e1nchez vicario#rick leach zina garrison#7 - 5 , 6 - 4\nwinner#1993#us open#hard#helena sukov\u00e1#mark woodforde martina navratilova#6 - 3 , 7 - 6\nrunner - up#1994#australian open#hard#helena sukov\u00e1#andrei olhovskiy larisa savchenko neiland#5 - 7 , 7 - 6 (9 - 7) , 2 - 6\nwinner#1994#wimbledon#grass#helena sukov\u00e1#t j middleton lori mcneil#3 - 6 , 7 - 5 , 6 - 3\nrunner - up#1994#us open#hard#jana novotn\u00e1#patrick galbraith elna reinach#2 - 6 , 4 - 6\nwinner#1995#french open#clay#larisa savchenko#john - laffnie de jager jill hetherington#7 - 6 (10 - 8) , 7 - 6 (7 - 4)\nrunner - up#2000#australian open#hard#arantxa s\u00e1nchez vicario#jared palmer rennae stubbs#5 - 7 , 6 - 7 (3 - 7)\nrunner - up#2000#french open#clay#rennae stubbs#david adams mariaan de swardt#3 - 6 , 6 - 3 , 3 - 6\nwinner#2001#us open#hard#rennae stubbs#leander paes lisa raymond#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#2003#australian open#hard#eleni daniilidou#leander paes martina navr\u00e1tilov\u00e1#4 - 6 , 5 - 7\nrunner - up#2004#wimbledon#grass#alicia molik#wayne black cara black#6 - 3 , 6 - 7 , 4 - 6\n",
        "pandas_code": "all(df[(df['year'] == 2000) & (df['outcome'] == 'runner - up')]['championship'].isin(['australian open', 'french open']))",
        "pandas_eval": "True"
    },
    {
        "id": 1805,
        "statement": "university of east anglia , have the largest number of student",
        "label": 1,
        "table_caption": "1994 group",
        "table_text": "institution#location#established#gained university status#vice - chancellor#total number of students#research funding (000)\nbirkbeck , university of london#london#1823#1920#professor david latchman#19020#9985\nuniversity of east anglia#norwich#1963#1963#professor edward acton#19585#16482\nuniversity of essex#colchester#1964#1964#professor anthony forster#11690#9967\ngoldsmiths , university of london#london#1891#1904#dr pat loughrey#7615#8539\ninstitute of education , university of london#london#1902#1932#professor chris husbands#7215#7734\nuniversity of lancaster#lancaster#1964#1964#professor mark smith#12695#18640\nuniversity of leicester#leicester#1921#1957#professor robert burgess#16160#22225\nloughborough university#loughborough#1909#1966#professor robert allison#17825#22398\nroyal holloway , university of london#egham#1849#1900#professor paul layzell (principal)#7620#13699\nsoas , university of london#london#1916#1916#professor paul webley#4525#7238\n",
        "pandas_code": "df.loc[df['institution'] == 'university of east anglia', 'total number of students'].values[0] == df['total number of students'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1806,
        "statement": "soas , university of london , have th least amount of student",
        "label": 1,
        "table_caption": "1994 group",
        "table_text": "institution#location#established#gained university status#vice - chancellor#total number of students#research funding (000)\nbirkbeck , university of london#london#1823#1920#professor david latchman#19020#9985\nuniversity of east anglia#norwich#1963#1963#professor edward acton#19585#16482\nuniversity of essex#colchester#1964#1964#professor anthony forster#11690#9967\ngoldsmiths , university of london#london#1891#1904#dr pat loughrey#7615#8539\ninstitute of education , university of london#london#1902#1932#professor chris husbands#7215#7734\nuniversity of lancaster#lancaster#1964#1964#professor mark smith#12695#18640\nuniversity of leicester#leicester#1921#1957#professor robert burgess#16160#22225\nloughborough university#loughborough#1909#1966#professor robert allison#17825#22398\nroyal holloway , university of london#egham#1849#1900#professor paul layzell (principal)#7620#13699\nsoas , university of london#london#1916#1916#professor paul webley#4525#7238\n",
        "pandas_code": "df[df['institution'] == 'soas , university of london']['total number of students'].min() == df['total number of students'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1807,
        "statement": "london have the highest number of university at 4",
        "label": 1,
        "table_caption": "1994 group",
        "table_text": "institution#location#established#gained university status#vice - chancellor#total number of students#research funding (000)\nbirkbeck , university of london#london#1823#1920#professor david latchman#19020#9985\nuniversity of east anglia#norwich#1963#1963#professor edward acton#19585#16482\nuniversity of essex#colchester#1964#1964#professor anthony forster#11690#9967\ngoldsmiths , university of london#london#1891#1904#dr pat loughrey#7615#8539\ninstitute of education , university of london#london#1902#1932#professor chris husbands#7215#7734\nuniversity of lancaster#lancaster#1964#1964#professor mark smith#12695#18640\nuniversity of leicester#leicester#1921#1957#professor robert burgess#16160#22225\nloughborough university#loughborough#1909#1966#professor robert allison#17825#22398\nroyal holloway , university of london#egham#1849#1900#professor paul layzell (principal)#7620#13699\nsoas , university of london#london#1916#1916#professor paul webley#4525#7238\n",
        "pandas_code": "df[df['location'] == 'london'].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1808,
        "statement": "university of east anglia , soas , university of london , of lancaster , and university of east anglia , be the only university to gain university status in the year they be establish",
        "label": 1,
        "table_caption": "1994 group",
        "table_text": "institution#location#established#gained university status#vice - chancellor#total number of students#research funding (000)\nbirkbeck , university of london#london#1823#1920#professor david latchman#19020#9985\nuniversity of east anglia#norwich#1963#1963#professor edward acton#19585#16482\nuniversity of essex#colchester#1964#1964#professor anthony forster#11690#9967\ngoldsmiths , university of london#london#1891#1904#dr pat loughrey#7615#8539\ninstitute of education , university of london#london#1902#1932#professor chris husbands#7215#7734\nuniversity of lancaster#lancaster#1964#1964#professor mark smith#12695#18640\nuniversity of leicester#leicester#1921#1957#professor robert burgess#16160#22225\nloughborough university#loughborough#1909#1966#professor robert allison#17825#22398\nroyal holloway , university of london#egham#1849#1900#professor paul layzell (principal)#7620#13699\nsoas , university of london#london#1916#1916#professor paul webley#4525#7238\n",
        "pandas_code": "df[df['institution'].isin(['university of east anglia', 'soas , university of london', 'university of lancaster']) & (df['gained university status'] == df['established'])].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1809,
        "statement": "birkbeck , university of london , be establish 97 year before gain university status",
        "label": 1,
        "table_caption": "1994 group",
        "table_text": "institution#location#established#gained university status#vice - chancellor#total number of students#research funding (000)\nbirkbeck , university of london#london#1823#1920#professor david latchman#19020#9985\nuniversity of east anglia#norwich#1963#1963#professor edward acton#19585#16482\nuniversity of essex#colchester#1964#1964#professor anthony forster#11690#9967\ngoldsmiths , university of london#london#1891#1904#dr pat loughrey#7615#8539\ninstitute of education , university of london#london#1902#1932#professor chris husbands#7215#7734\nuniversity of lancaster#lancaster#1964#1964#professor mark smith#12695#18640\nuniversity of leicester#leicester#1921#1957#professor robert burgess#16160#22225\nloughborough university#loughborough#1909#1966#professor robert allison#17825#22398\nroyal holloway , university of london#egham#1849#1900#professor paul layzell (principal)#7620#13699\nsoas , university of london#london#1916#1916#professor paul webley#4525#7238\n",
        "pandas_code": "(df[df['institution'] == 'birkbeck , university of london']['established'].values[0] + 97) == df[df['institution'] == 'birkbeck , university of london']['gained university status'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1810,
        "statement": "2 new district be create",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 2#clair engle#democratic#1943#re - elected#clair engle (d) unopposed\ncalifornia 5#john shelley#democratic#1949#re - elected#john shelley (d) unopposed\ncalifornia 8#george paul miller redistricted from 6th#democratic#1944#re - elected#george paul miller (d) unopposed\ncalifornia 12#allan o hunter redistricted from 9th#republican#1950#re - elected#allan o hunter (r) unopposed\ncalifornia 13#ernest k bramblett redistricted from 11th#republican#1946#re - elected#ernest k bramblett (r) 51.0% will hays (d) 49.0%\ncalifornia 14#thomas h werdel redistricted from 10th#republican#1948#lost re - election democratic gain#harlan hagen (d) 51.0% thomas h werdel (r) 49.0%\ncalifornia 15#gordon l mcdonough#republican#1944#re - elected#gordon l mcdonough (r) unopposed\ncalifornia 18#none (district created)#none (district created)#none (district created)#new seat republican gain#craig hosmer (r) 55.5% joseph m kennick (d) 44.5%\ncalifornia 20#john carl hinshaw#republican#1938#re - elected#john carl hinshaw (r) unopposed\ncalifornia 22#none (district created)#none (district created)#none (district created)#new seat republican gain#joseph f holt (r) 60.5% dean e mchenry (d) 39.5%\ncalifornia 29#john j phillips redistricted from 22nd#republican#1942#re - elected#john j phillips (r) unopposed\n",
        "pandas_code": "df[df['result'].str.contains('new seat')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1811,
        "statement": "3 incumbent be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 2#clair engle#democratic#1943#re - elected#clair engle (d) unopposed\ncalifornia 5#john shelley#democratic#1949#re - elected#john shelley (d) unopposed\ncalifornia 8#george paul miller redistricted from 6th#democratic#1944#re - elected#george paul miller (d) unopposed\ncalifornia 12#allan o hunter redistricted from 9th#republican#1950#re - elected#allan o hunter (r) unopposed\ncalifornia 13#ernest k bramblett redistricted from 11th#republican#1946#re - elected#ernest k bramblett (r) 51.0% will hays (d) 49.0%\ncalifornia 14#thomas h werdel redistricted from 10th#republican#1948#lost re - election democratic gain#harlan hagen (d) 51.0% thomas h werdel (r) 49.0%\ncalifornia 15#gordon l mcdonough#republican#1944#re - elected#gordon l mcdonough (r) unopposed\ncalifornia 18#none (district created)#none (district created)#none (district created)#new seat republican gain#craig hosmer (r) 55.5% joseph m kennick (d) 44.5%\ncalifornia 20#john carl hinshaw#republican#1938#re - elected#john carl hinshaw (r) unopposed\ncalifornia 22#none (district created)#none (district created)#none (district created)#new seat republican gain#joseph f holt (r) 60.5% dean e mchenry (d) 39.5%\ncalifornia 29#john j phillips redistricted from 22nd#republican#1942#re - elected#john j phillips (r) unopposed\n",
        "pandas_code": "(df['party'] == 'democratic').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1812,
        "statement": "6 incumbent be republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 2#clair engle#democratic#1943#re - elected#clair engle (d) unopposed\ncalifornia 5#john shelley#democratic#1949#re - elected#john shelley (d) unopposed\ncalifornia 8#george paul miller redistricted from 6th#democratic#1944#re - elected#george paul miller (d) unopposed\ncalifornia 12#allan o hunter redistricted from 9th#republican#1950#re - elected#allan o hunter (r) unopposed\ncalifornia 13#ernest k bramblett redistricted from 11th#republican#1946#re - elected#ernest k bramblett (r) 51.0% will hays (d) 49.0%\ncalifornia 14#thomas h werdel redistricted from 10th#republican#1948#lost re - election democratic gain#harlan hagen (d) 51.0% thomas h werdel (r) 49.0%\ncalifornia 15#gordon l mcdonough#republican#1944#re - elected#gordon l mcdonough (r) unopposed\ncalifornia 18#none (district created)#none (district created)#none (district created)#new seat republican gain#craig hosmer (r) 55.5% joseph m kennick (d) 44.5%\ncalifornia 20#john carl hinshaw#republican#1938#re - elected#john carl hinshaw (r) unopposed\ncalifornia 22#none (district created)#none (district created)#none (district created)#new seat republican gain#joseph f holt (r) 60.5% dean e mchenry (d) 39.5%\ncalifornia 29#john j phillips redistricted from 22nd#republican#1942#re - elected#john j phillips (r) unopposed\n",
        "pandas_code": "(df['party'] == 'republican').sum() == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1813,
        "statement": "8 incumbent be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 2#clair engle#democratic#1943#re - elected#clair engle (d) unopposed\ncalifornia 5#john shelley#democratic#1949#re - elected#john shelley (d) unopposed\ncalifornia 8#george paul miller redistricted from 6th#democratic#1944#re - elected#george paul miller (d) unopposed\ncalifornia 12#allan o hunter redistricted from 9th#republican#1950#re - elected#allan o hunter (r) unopposed\ncalifornia 13#ernest k bramblett redistricted from 11th#republican#1946#re - elected#ernest k bramblett (r) 51.0% will hays (d) 49.0%\ncalifornia 14#thomas h werdel redistricted from 10th#republican#1948#lost re - election democratic gain#harlan hagen (d) 51.0% thomas h werdel (r) 49.0%\ncalifornia 15#gordon l mcdonough#republican#1944#re - elected#gordon l mcdonough (r) unopposed\ncalifornia 18#none (district created)#none (district created)#none (district created)#new seat republican gain#craig hosmer (r) 55.5% joseph m kennick (d) 44.5%\ncalifornia 20#john carl hinshaw#republican#1938#re - elected#john carl hinshaw (r) unopposed\ncalifornia 22#none (district created)#none (district created)#none (district created)#new seat republican gain#joseph f holt (r) 60.5% dean e mchenry (d) 39.5%\ncalifornia 29#john j phillips redistricted from 22nd#republican#1942#re - elected#john j phillips (r) unopposed\n",
        "pandas_code": "len(df[df['result'].str.contains('re - elected')]) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 1814,
        "statement": "1 incumbent lost re - election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 2#clair engle#democratic#1943#re - elected#clair engle (d) unopposed\ncalifornia 5#john shelley#democratic#1949#re - elected#john shelley (d) unopposed\ncalifornia 8#george paul miller redistricted from 6th#democratic#1944#re - elected#george paul miller (d) unopposed\ncalifornia 12#allan o hunter redistricted from 9th#republican#1950#re - elected#allan o hunter (r) unopposed\ncalifornia 13#ernest k bramblett redistricted from 11th#republican#1946#re - elected#ernest k bramblett (r) 51.0% will hays (d) 49.0%\ncalifornia 14#thomas h werdel redistricted from 10th#republican#1948#lost re - election democratic gain#harlan hagen (d) 51.0% thomas h werdel (r) 49.0%\ncalifornia 15#gordon l mcdonough#republican#1944#re - elected#gordon l mcdonough (r) unopposed\ncalifornia 18#none (district created)#none (district created)#none (district created)#new seat republican gain#craig hosmer (r) 55.5% joseph m kennick (d) 44.5%\ncalifornia 20#john carl hinshaw#republican#1938#re - elected#john carl hinshaw (r) unopposed\ncalifornia 22#none (district created)#none (district created)#none (district created)#new seat republican gain#joseph f holt (r) 60.5% dean e mchenry (d) 39.5%\ncalifornia 29#john j phillips redistricted from 22nd#republican#1942#re - elected#john j phillips (r) unopposed\n",
        "pandas_code": "df[df['result'] == 'lost re - election democratic gain'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1815,
        "statement": "5 different player have a total goal higher than 100",
        "label": 1,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "len(df[df['total goals'] > 100]['name'].unique()) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1817,
        "statement": "ko jeong - woon and kim hyun - seok both achieve the 40 - 40 club before the year 2000",
        "label": 1,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "((df['name'] == 'ko jeong - woon') & (df['date'] < '2000-01-01') & (df['goals'] >= 40) & (df['assists'] >= 40)).any() and ((df['name'] == 'kim hyun - seok') & (df['date'] < '2000-01-01') & (df['goals'] >= 40) & (df['assists'] >= 40)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1818,
        "statement": "3 player be list from the team seongnam ilhwa chunma",
        "label": 1,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "len(df[df['team'] == 'seongnam ilhwa chunma']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1819,
        "statement": "the venue pohang appear 3 time , and the venue jeonju appear 2 time",
        "label": 1,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "(df['venue'].value_counts()['pohang'] == 3) & (df['venue'].value_counts()['jeonju'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1820,
        "statement": "5 different player have a total goal fewer than 100",
        "label": 0,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "len(df[df['total goals'] < 100]['name'].unique()) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1821,
        "statement": "6 different player have a total assist fewer than 50",
        "label": 0,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "len(df[df['total assists'] < 50]['name'].unique()) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 1822,
        "statement": "ko jeong - woon and kim hyun - seok both achieve the 40 - 40 club after the year 2000",
        "label": 0,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "(df[(df['name'] == 'ko jeong - woon') | (df['name'] == 'kim hyun - seok')]['date'].apply(lambda x: int(x.split(' - ')[0]) > 2000).all())",
        "pandas_eval": "False"
    },
    {
        "id": 1823,
        "statement": "more than 3 player be list from the team seongnam ilhwa chunma",
        "label": 0,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "len(df[df['team'] == 'seongnam ilhwa chunma']) > 3",
        "pandas_eval": "False"
    },
    {
        "id": 1824,
        "statement": "the venue pohang appear more than 3 time , and the venue jeonju appear 1 time",
        "label": 0,
        "table_caption": "list of k league 40 - 40 club members",
        "table_text": "name#date#goals#assists#team#venue#opponent#competition#total goals#total assists\nko jeong - woon#1998 - 09 - 05#42#40#pohang steelers#pohang#chunnam dragons#k - league#55#48\nkim hyun - seok#1998 - 10 - 10#89#40#ulsan hyundai horang - i#pohang#pohang steelers#k - league#110#54\nshin tae - yong#2000 - 09 - 03#72#40#seongnam ilhwa chunma#mokdong#bucheon sk#k - league#99#68\nlee sang - yoon#2001 - 04 - 29#70#40#bucheon sk#bucheon#daejeon citizen#k - league cup#71#43\nroh sang - rae#2003 - 04 - 27#72#40#daegu fc#daegu#busan i'cons#k - league#76#40\ndenis laktionov#2003 - 08 - 27#45#40#seongnam ilhwa chunma#busan#busan i'cons#k - league#57#59\nkim do - hoon#2005 - 08 - 31#113#40#seongnam ilhwa chunma#seongnam#incheon united#k - league#114#41\nwoo sung - yong#2007 - 10 - 22#109#40#ulsan hyundai horang - i#ulsan#daejeon citizen#k - league#116#43\nkim dae - eui#2010 - 05 - 29#51#40#suwon samsung bluewings#suwon#gangwon fc#k - league cup#51#41\nkim eun - jung#2010 - 08 - 28#91#40#jeju united#daejeon#daejeon citizen#k - league#119#54\neninho#2010 - 11 - 20#51#40#jeonbuk hyundai motors#jeonju#gyeongnam fc#championship#80#60\nlee dong - gook#2011 - 07 - 03#109#40#jeonbuk hyundai motors#jeonju#fc seoul#k - league#141#53\nmauricio molina#2012 - 11 - 15#49#40#fc seoul#seoul#ulsan hyundai#k - league#50#42\nhwang jin - sung#2012 - 11 - 29#40#51#pohang steelers#pohang#fc seoul#k - league#41#51\n",
        "pandas_code": "(df['venue'].value_counts()['pohang'] > 3) & (df['venue'].value_counts()['jeonju'] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 1826,
        "statement": "the earliest any player end be in 2009 , with 2012 as the latest",
        "label": 1,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "(df['ends'].min() == 2009) & (df['ends'].max() == 2012)",
        "pandas_eval": "True"
    },
    {
        "id": 1827,
        "statement": "the player streit , z\u00e9 roberto and s\u00e1nchez be since 2008 (winter)",
        "label": 1,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "all(df[df['name'].isin(['streit', 'z\u00e9 roberto', 's\u00e1nchez'])]['since'] == '2008 (winter)')",
        "pandas_eval": "True"
    },
    {
        "id": 1828,
        "statement": "f\u00e4hrmann , krstaji\u0107 , varela , heppke and l\u00f8venkrands end in year 2009",
        "label": 1,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "all(df[df['name'].isin(['f\u00e4hrmann', 'krstaji\u0107', 'varela', 'heppke', 'l\u00f8venkrands'])]['ends'] == 2009)",
        "pandas_eval": "True"
    },
    {
        "id": 1829,
        "statement": "asamoah of germany have the highest goal of 40 , follow by kur\u00e1nyi",
        "label": 1,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "(df['goals'].max() == df[df['name'] == 'asamoah']['goals'].values[0]) and (df[df['name'] == 'kur\u00e1nyi']['goals'].values[0] == df[df['name'] != 'asamoah']['goals'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 1830,
        "statement": "bordon ( captain ) and rafinha be the only player from the usa",
        "label": 0,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "((df['name'] == 'bordon ( captain )') | (df['name'] == 'rafinha')).sum() == 2 and (df[df['name'].isin(['bordon ( captain )', 'rafinha'])]['nat'] == 'usa').all()",
        "pandas_eval": "False"
    },
    {
        "id": 1831,
        "statement": "the earliest any player end be in 2010 , with 2011 as the latest",
        "label": 0,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "(df['ends'].min() == 2010) & (df['ends'].max() == 2011)",
        "pandas_eval": "False"
    },
    {
        "id": 1832,
        "statement": "the player streit , z\u00e9 roberto and s\u00e1nchez be since 2010 (summer)",
        "label": 0,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "all(df[df['name'].isin(['streit', 'z\u00e9 roberto', 's\u00e1nchez'])]['since'] == '2010 (summer)')",
        "pandas_eval": "False"
    },
    {
        "id": 1833,
        "statement": "farf\u00e1n , alt\u0131ntop , s\u00e1nchez , grossm\u00fcller and kenia end in year 2009",
        "label": 0,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "all(df[df['name'].isin(['farf\u00e1n', 'alt\u0131ntop', 's\u00e1nchez', 'grossm\u00fcller', 'kenia'])]['ends'] == 2009)",
        "pandas_eval": "False"
    },
    {
        "id": 1834,
        "statement": "asamoah of germany have the highest goal of 4 , follow by engelaar",
        "label": 0,
        "table_caption": "2008 - 09 fc schalke 04 season",
        "table_text": "nat#name#since#goals#ends\nger#neuer#2005#0#2012\nger#f\u00e4hrmann#2006#0#2009\nger#schober#2007#0#2011\nmar#amsif#2008#0#2010\nger#westermann#2007#4#2011\nbra#bordon ( captain )#2004#12#2011\nbra#rafinha#2005#4#2011\nser#krstaji\u0107#2004#6#2009\nger#h\u00f6wedes#2007#0#2010\nger#pander#2004#4#2011\nper#zambrano#2008#0#2012\ngeo#kobiashvili#2003#8#2010\nger#streit#2008 (winter)#0#2012\nuru#varela#2002#8#2009\nger#ernst#2005#1#2010\ncro#rakiti\u0107#2007#3#2011\nger#jones#2007#1#2011\nbra#z\u00e9 roberto#2008 (winter)#1#2011\nuru#grossm\u00fcller#2007#1#2011\nger#heppke#2006#0#2009\ngeo#kenia#2008#0#2012\nned#engelaar#2008#0#2011\nden#l\u00f8venkrands#2006#6#2009\nger#asamoah#1999#41#2011\nper#farf\u00e1n#2008#0#2012\ntur#alt\u0131ntop#2006#12#2010\nger#kur\u00e1nyi#2005#40#2010\nuru#s\u00e1nchez#2008 (winter)#1#2011\n",
        "pandas_code": "(df[df['name'] == 'asamoah']['goals'].values[0] == 4) & (df[df['name'] == 'engelaar']['goals'].values[0] < 4)",
        "pandas_eval": "False"
    },
    {
        "id": 1835,
        "statement": "the most winning be 690321",
        "label": 1,
        "table_caption": "chad little",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1992#1#0#0#0#0#29.0#29.0#1400#120th#37 little racing\n1993#12#0#2#3#0#22.1#22.6#56508#32nd#23 mark rypien motorsports\n1994#28#0#10#14#0#21.0#11.9#234022#3rd#23 mark rypien motorsports\n1995#26#6#11#13#0#15.5#14.5#529056#2nd#23 mark rypien motorsports\n1996#26#0#2#7#1#15.3#16.5#317394#5th#23 mark rypien motorsports\n1998#1#0#0#0#0#6.0#30.0#4380#108th#9 roush racing\n2001#33#0#2#6#0#24.8#16.0#690321#9th#74 bace motorsports\n",
        "pandas_code": "df['winnings'].max() == 690321",
        "pandas_eval": "True"
    },
    {
        "id": 1836,
        "statement": "only 1 race have a pole of more than 0 and that be in 1996",
        "label": 1,
        "table_caption": "chad little",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1992#1#0#0#0#0#29.0#29.0#1400#120th#37 little racing\n1993#12#0#2#3#0#22.1#22.6#56508#32nd#23 mark rypien motorsports\n1994#28#0#10#14#0#21.0#11.9#234022#3rd#23 mark rypien motorsports\n1995#26#6#11#13#0#15.5#14.5#529056#2nd#23 mark rypien motorsports\n1996#26#0#2#7#1#15.3#16.5#317394#5th#23 mark rypien motorsports\n1998#1#0#0#0#0#6.0#30.0#4380#108th#9 roush racing\n2001#33#0#2#6#0#24.8#16.0#690321#9th#74 bace motorsports\n",
        "pandas_code": "(df['poles'] > 0).sum() == 1 and df.loc[df['poles'] > 0, 'year'].iloc[0] == 1996",
        "pandas_eval": "True"
    },
    {
        "id": 1837,
        "statement": "the earliest race be in 1992",
        "label": 1,
        "table_caption": "chad little",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1992#1#0#0#0#0#29.0#29.0#1400#120th#37 little racing\n1993#12#0#2#3#0#22.1#22.6#56508#32nd#23 mark rypien motorsports\n1994#28#0#10#14#0#21.0#11.9#234022#3rd#23 mark rypien motorsports\n1995#26#6#11#13#0#15.5#14.5#529056#2nd#23 mark rypien motorsports\n1996#26#0#2#7#1#15.3#16.5#317394#5th#23 mark rypien motorsports\n1998#1#0#0#0#0#6.0#30.0#4380#108th#9 roush racing\n2001#33#0#2#6#0#24.8#16.0#690321#9th#74 bace motorsports\n",
        "pandas_code": "df['year'].min() == 1992",
        "pandas_eval": "True"
    },
    {
        "id": 1838,
        "statement": "the race transpire last in 2001",
        "label": 1,
        "table_caption": "chad little",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1992#1#0#0#0#0#29.0#29.0#1400#120th#37 little racing\n1993#12#0#2#3#0#22.1#22.6#56508#32nd#23 mark rypien motorsports\n1994#28#0#10#14#0#21.0#11.9#234022#3rd#23 mark rypien motorsports\n1995#26#6#11#13#0#15.5#14.5#529056#2nd#23 mark rypien motorsports\n1996#26#0#2#7#1#15.3#16.5#317394#5th#23 mark rypien motorsports\n1998#1#0#0#0#0#6.0#30.0#4380#108th#9 roush racing\n2001#33#0#2#6#0#24.8#16.0#690321#9th#74 bace motorsports\n",
        "pandas_code": "df[df['year'] == 2001].index[-1] == df.index[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 1839,
        "statement": "team 23 mark rypien motorsports have the most participation",
        "label": 1,
        "table_caption": "chad little",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position#team (s)\n1992#1#0#0#0#0#29.0#29.0#1400#120th#37 little racing\n1993#12#0#2#3#0#22.1#22.6#56508#32nd#23 mark rypien motorsports\n1994#28#0#10#14#0#21.0#11.9#234022#3rd#23 mark rypien motorsports\n1995#26#6#11#13#0#15.5#14.5#529056#2nd#23 mark rypien motorsports\n1996#26#0#2#7#1#15.3#16.5#317394#5th#23 mark rypien motorsports\n1998#1#0#0#0#0#6.0#30.0#4380#108th#9 roush racing\n2001#33#0#2#6#0#24.8#16.0#690321#9th#74 bace motorsports\n",
        "pandas_code": "df['team (s)'].value_counts().idxmax() == '23 mark rypien motorsports'",
        "pandas_eval": "True"
    },
    {
        "id": 1840,
        "statement": "axis & allies (nova game edition) have the most piece of any version",
        "label": 1,
        "table_caption": "axis & allies",
        "table_text": "release#title#start#pieces#board (inches)#board (cm)#type#new units new units when compared to the original a&a : classic version of the game#playable powers\n1981#axis & allies (nova games edition)#1942#415#37 19\u00bd#93 50#global#same as classic plus nuke pieces were cardboard#5: germany , japan , ussr , uk , usa\n1999#axis & allies : europe#1941#373#30 20#75 50#theater#destroyer , artillery#4: germany , ussr , uk , usa\n2004#axis & allies : d - day#1944#241#30 20#75 50#local#artillery , blockhouse#3: germany , uk , usa\n2006#axis & allies : battle of the bulge#1944#157#30 20#75 50#local#artillery , truck#3: germany , uk , usa\n2007#axis & allies : guadalcanal#1942#172#30 20#75 50#local#destroyer , cruiser , artillery#2: japan , usa\n",
        "pandas_code": "df.loc[df['title'] == 'axis & allies (nova games edition)', 'pieces'].values[0] == df['pieces'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1842,
        "statement": "axis & allies : europe have 132 more piece than axis & allies : d - day",
        "label": 1,
        "table_caption": "axis & allies",
        "table_text": "release#title#start#pieces#board (inches)#board (cm)#type#new units new units when compared to the original a&a : classic version of the game#playable powers\n1981#axis & allies (nova games edition)#1942#415#37 19\u00bd#93 50#global#same as classic plus nuke pieces were cardboard#5: germany , japan , ussr , uk , usa\n1999#axis & allies : europe#1941#373#30 20#75 50#theater#destroyer , artillery#4: germany , ussr , uk , usa\n2004#axis & allies : d - day#1944#241#30 20#75 50#local#artillery , blockhouse#3: germany , uk , usa\n2006#axis & allies : battle of the bulge#1944#157#30 20#75 50#local#artillery , truck#3: germany , uk , usa\n2007#axis & allies : guadalcanal#1942#172#30 20#75 50#local#destroyer , cruiser , artillery#2: japan , usa\n",
        "pandas_code": "(df[df['title'] == 'axis & allies : europe']['pieces'].values[0] - df[df['title'] == 'axis & allies : d - day']['pieces'].values[0]) == 132",
        "pandas_eval": "True"
    },
    {
        "id": 1843,
        "statement": "2 version of axis & ally have 3 playable power",
        "label": 1,
        "table_caption": "axis & allies",
        "table_text": "release#title#start#pieces#board (inches)#board (cm)#type#new units new units when compared to the original a&a : classic version of the game#playable powers\n1981#axis & allies (nova games edition)#1942#415#37 19\u00bd#93 50#global#same as classic plus nuke pieces were cardboard#5: germany , japan , ussr , uk , usa\n1999#axis & allies : europe#1941#373#30 20#75 50#theater#destroyer , artillery#4: germany , ussr , uk , usa\n2004#axis & allies : d - day#1944#241#30 20#75 50#local#artillery , blockhouse#3: germany , uk , usa\n2006#axis & allies : battle of the bulge#1944#157#30 20#75 50#local#artillery , truck#3: germany , uk , usa\n2007#axis & allies : guadalcanal#1942#172#30 20#75 50#local#destroyer , cruiser , artillery#2: japan , usa\n",
        "pandas_code": "(df['playable powers'].str.startswith('3:').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1844,
        "statement": "axis & allies : guadalcanal start earlier than axis & allies : battle of the bulge",
        "label": 1,
        "table_caption": "axis & allies",
        "table_text": "release#title#start#pieces#board (inches)#board (cm)#type#new units new units when compared to the original a&a : classic version of the game#playable powers\n1981#axis & allies (nova games edition)#1942#415#37 19\u00bd#93 50#global#same as classic plus nuke pieces were cardboard#5: germany , japan , ussr , uk , usa\n1999#axis & allies : europe#1941#373#30 20#75 50#theater#destroyer , artillery#4: germany , ussr , uk , usa\n2004#axis & allies : d - day#1944#241#30 20#75 50#local#artillery , blockhouse#3: germany , uk , usa\n2006#axis & allies : battle of the bulge#1944#157#30 20#75 50#local#artillery , truck#3: germany , uk , usa\n2007#axis & allies : guadalcanal#1942#172#30 20#75 50#local#destroyer , cruiser , artillery#2: japan , usa\n",
        "pandas_code": "df[df['title'] == 'axis & allies : guadalcanal']['start'].values[0] < df[df['title'] == 'axis & allies : battle of the bulge']['start'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1845,
        "statement": "joshua bailey be a center",
        "label": 1,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['player'] == 'joshua bailey']['position'].eq('c').all()",
        "pandas_eval": "True"
    },
    {
        "id": 1846,
        "statement": "aaron ness be from the united state",
        "label": 1,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['player'] == 'aaron ness']['nationality'].iloc[0] == 'united states'",
        "pandas_eval": "True"
    },
    {
        "id": 1847,
        "statement": "kirill petrov be russian",
        "label": 1,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['player'] == 'kirill petrov']['nationality'].iloc[0] == 'russia'",
        "pandas_eval": "True"
    },
    {
        "id": 1848,
        "statement": "jared spurgeon be canadian",
        "label": 1,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['player'] == 'jared spurgeon']['nationality'].values[0] == 'canada'",
        "pandas_eval": "True"
    },
    {
        "id": 1849,
        "statement": "matt donovan play for the cedar rapid",
        "label": 1,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['player'] == 'matt donovan']['college / junior / club team (league)'].iloc[0] == 'cedar rapids (ushl)'",
        "pandas_eval": "True"
    },
    {
        "id": 1850,
        "statement": "joshua bailey be a center from the united state",
        "label": 0,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "((df['player'] == 'joshua bailey') & (df['position'] == 'c') & (df['nationality'] == 'united states')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1851,
        "statement": "aaron ness in center position be from the united state",
        "label": 0,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "((df['player'] == 'aaron ness') & (df['position'] == 'c') & (df['nationality'] == 'united states')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1852,
        "statement": "kirill petrov be 1 of the 3 russian in the season",
        "label": 0,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "df[df['nationality'] == 'russia'].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1853,
        "statement": "jared spurgeon be canadian from sarnia sting (ohl)",
        "label": 0,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "((df['player'] == 'jared spurgeon') & (df['nationality'] == 'canada') & (df['college / junior / club team (league)'] == 'sarnia sting (ohl)')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1854,
        "statement": "matt donovan from russia play for the cedar rapid",
        "label": 0,
        "table_caption": "2008 - 09 new york islanders season",
        "table_text": "round#player#position#nationality#college / junior / club team (league)\n1#joshua bailey#c#canada#windsor spitfires (ohl)\n2#corey trevino#c#canada#stouffville spirit (opjhl)\n2#aaron ness#d#united states#roseau high school (high - mn)\n2#travis hamonic#d#canada#moose jaw warriors (whl)\n3#david toews#c#canada#shattuck - st mary 's (midget major aaa)\n3#jyri niemi#d#finland#saskatoon blades (whl)\n3#kirill petrov#rw#russia#kazan (russia)\n4#matt donovan#d#united states#cedar rapids (ushl)\n4#david ullstrom#w#sweden#hv71 (swe - jr)\n5#kevin poulin#g#canada#victoriaville tigres (qmjhl)\n5#matthew martin#lw#canada#sarnia sting (ohl)\n6#jared spurgeon#d#canada#spokane chiefs (whl)\n6#justin dibenedetto#c#canada#sarnia sting (ohl)\n",
        "pandas_code": "((df['player'] == 'matt donovan') & (df['nationality'] == 'russia') & (df['college / junior / club team (league)'] == 'cedar rapids (ushl)')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1855,
        "statement": "there be 2 team that have a total of 6 goal",
        "label": 1,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "len(df[df['goals'] == 6]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1857,
        "statement": "there be a total of 120 goal that be score",
        "label": 1,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "df['goals'].sum() == 120",
        "pandas_eval": "True"
    },
    {
        "id": 1858,
        "statement": "adrian elrick be not the only player to score a single goal",
        "label": 1,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "len(df[df['goals'] == 1]) > 1",
        "pandas_eval": "True"
    },
    {
        "id": 1859,
        "statement": "duncan cole score 4 goal while steve sumner score 22 goal",
        "label": 1,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "(df[df['name'] == 'duncan cole']['goals'].values[0] == 4) & (df[df['name'] == 'steve sumner']['goals'].values[0] == 22)",
        "pandas_eval": "True"
    },
    {
        "id": 1860,
        "statement": "there be 2 team that have a total of 10 goal",
        "label": 0,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "(df['goals'].sum() == 10)",
        "pandas_eval": "False"
    },
    {
        "id": 1862,
        "statement": "there be a total of 20 goal that be score",
        "label": 0,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "df['goals'].sum() == 20",
        "pandas_eval": "False"
    },
    {
        "id": 1863,
        "statement": "adrian elrick be the only player to score a single goal",
        "label": 0,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "(df['goals'] == 1).sum() == 1 and df.loc[df['goals'] == 1, 'name'].iloc[0] == 'adrian elrick'",
        "pandas_eval": "False"
    },
    {
        "id": 1864,
        "statement": "duncan cole score 14 goal while steve sumner score 12 goal",
        "label": 0,
        "table_caption": "new zealand national football team",
        "table_text": "name#career#caps#goals#first cap#recent cap\nivan vicelich#1995 -#87#6#25 june 1995#09 september 2013\nsimon elliott#1995 - 2011#69#6#21 february 1995#1 june 2011\nvaughan coveny#1992 - 2006#64#28#7 june 1992#4 june 2006\nricki herbert#1980 - 1989#61#7#20 august 1980#9 april 1989\nchris jackson#1995 - 2003#60#10#21 february 1995#22 june 2003\nbrian turner#1967 - 1982#59#21#australia 5 november 1967#23 june 1982\nduncan cole#1978 - 1988#58#4#1 october 1978#27 march 1988\nsteve sumner#1976 - 1988#58#22#burma 13 september 1976#23 june 1988\nchris zoricich#1988 - 2003#57#1#23 march 1988#22 june 2003\nceri evans#1980 - 1993#56#2#16 october 1980#australia 6 june 1993\nmichael mcgarry#1986 - 1997#54#12#17 september 1986#australia 6 july 1997\nleo bertos#2003 -#54#0#13 october 2003#15 october 2013\nadrian elrick#1975 - 1984#53#1#china pr 26 july 1975#24 april 1984\n",
        "pandas_code": "(df[df['name'] == 'duncan cole']['goals'].values[0] == 14) & (df[df['name'] == 'steve sumner']['goals'].values[0] == 12)",
        "pandas_eval": "False"
    },
    {
        "id": 1865,
        "statement": "3 candidate run unopposed",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#silvio conte#republican#1958#re - elected#silvio conte (r) 82.7% john r arden (d) 17.3%\nmassachusetts 2#edward boland#democratic#1952#retired democratic hold#richard neal (d) 80.3% louis r godena (i) 19.7%\nmassachusetts 3#joseph d early#democratic#1974#re - elected#joseph d early (d) unopposed\nmassachusetts 4#barney frank#democratic#1980#re - elected#barney frank (d) 70.3% debra r tucker (r) 29.7%\nmassachusetts 7#ed markey#democratic#1976#re - elected#ed markey (d) unopposed\nmassachusetts 9#joe moakley#democratic#1972#re - elected#joe moakley (d) unopposed\n",
        "pandas_code": "df['candidates'].str.contains('unopposed').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1866,
        "statement": "5 incumbent be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#silvio conte#republican#1958#re - elected#silvio conte (r) 82.7% john r arden (d) 17.3%\nmassachusetts 2#edward boland#democratic#1952#retired democratic hold#richard neal (d) 80.3% louis r godena (i) 19.7%\nmassachusetts 3#joseph d early#democratic#1974#re - elected#joseph d early (d) unopposed\nmassachusetts 4#barney frank#democratic#1980#re - elected#barney frank (d) 70.3% debra r tucker (r) 29.7%\nmassachusetts 7#ed markey#democratic#1976#re - elected#ed markey (d) unopposed\nmassachusetts 9#joe moakley#democratic#1972#re - elected#joe moakley (d) unopposed\n",
        "pandas_code": "df[df['result'].str.contains('re - elected')].shape[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1867,
        "statement": "barney frank be first elect the most recently",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#silvio conte#republican#1958#re - elected#silvio conte (r) 82.7% john r arden (d) 17.3%\nmassachusetts 2#edward boland#democratic#1952#retired democratic hold#richard neal (d) 80.3% louis r godena (i) 19.7%\nmassachusetts 3#joseph d early#democratic#1974#re - elected#joseph d early (d) unopposed\nmassachusetts 4#barney frank#democratic#1980#re - elected#barney frank (d) 70.3% debra r tucker (r) 29.7%\nmassachusetts 7#ed markey#democratic#1976#re - elected#ed markey (d) unopposed\nmassachusetts 9#joe moakley#democratic#1972#re - elected#joe moakley (d) unopposed\n",
        "pandas_code": "df[df['incumbent'] == 'barney frank']['first elected'].max() == df['first elected'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1868,
        "statement": "5 out of 6 incumbent be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#silvio conte#republican#1958#re - elected#silvio conte (r) 82.7% john r arden (d) 17.3%\nmassachusetts 2#edward boland#democratic#1952#retired democratic hold#richard neal (d) 80.3% louis r godena (i) 19.7%\nmassachusetts 3#joseph d early#democratic#1974#re - elected#joseph d early (d) unopposed\nmassachusetts 4#barney frank#democratic#1980#re - elected#barney frank (d) 70.3% debra r tucker (r) 29.7%\nmassachusetts 7#ed markey#democratic#1976#re - elected#ed markey (d) unopposed\nmassachusetts 9#joe moakley#democratic#1972#re - elected#joe moakley (d) unopposed\n",
        "pandas_code": "df[df['party'] == 'democratic'].shape[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1869,
        "statement": "edward boland be first elect 6 year before silvio conte",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1988",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmassachusetts 1#silvio conte#republican#1958#re - elected#silvio conte (r) 82.7% john r arden (d) 17.3%\nmassachusetts 2#edward boland#democratic#1952#retired democratic hold#richard neal (d) 80.3% louis r godena (i) 19.7%\nmassachusetts 3#joseph d early#democratic#1974#re - elected#joseph d early (d) unopposed\nmassachusetts 4#barney frank#democratic#1980#re - elected#barney frank (d) 70.3% debra r tucker (r) 29.7%\nmassachusetts 7#ed markey#democratic#1976#re - elected#ed markey (d) unopposed\nmassachusetts 9#joe moakley#democratic#1972#re - elected#joe moakley (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'] == 'silvio conte']['first elected'].values[0] - df[df['incumbent'] == 'edward boland']['first elected'].values[0]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 1870,
        "statement": "there be 2 golfer with a final score of 206 at the 2006 pga championship",
        "label": 1,
        "table_caption": "2006 pga championship",
        "table_text": "place#player#country#score#to par\nt1#tiger woods#united states#69 + 68 + 65 = 202#- 14\nt1#luke donald#england#68 + 68 + 66 = 202#- 14\n3#mike weir#canada#72 + 67 + 65 = 204#- 12\n4#geoff ogilvy#australia#69 + 68 + 68 = 205#- 11\nt5#shaun micheel#united states#69 + 70 + 67 = 206#- 10\nt5#sergio garc\u00eda#spain#69 + 70 + 67 = 206#- 10\n7#k j choi#south korea#73 + 67 + 67 = 207#- 9\nt8#chris dimarco#united states#71 + 70 + 67 = 208#- 8\nt8#tim herron#united states#69 + 67 + 72 = 208#- 8\nt8#phil mickelson#united states#69 + 71 + 68 = 208#- 8\nt8#ian poulter#england#70 + 70 + 68 = 208#- 8\n",
        "pandas_code": "len(df[df['score'].str.contains('206')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1871,
        "statement": "there be be 1 golfer from south korea that finish in the top 11 at the 2006 pga championship",
        "label": 1,
        "table_caption": "2006 pga championship",
        "table_text": "place#player#country#score#to par\nt1#tiger woods#united states#69 + 68 + 65 = 202#- 14\nt1#luke donald#england#68 + 68 + 66 = 202#- 14\n3#mike weir#canada#72 + 67 + 65 = 204#- 12\n4#geoff ogilvy#australia#69 + 68 + 68 = 205#- 11\nt5#shaun micheel#united states#69 + 70 + 67 = 206#- 10\nt5#sergio garc\u00eda#spain#69 + 70 + 67 = 206#- 10\n7#k j choi#south korea#73 + 67 + 67 = 207#- 9\nt8#chris dimarco#united states#71 + 70 + 67 = 208#- 8\nt8#tim herron#united states#69 + 67 + 72 = 208#- 8\nt8#phil mickelson#united states#69 + 71 + 68 = 208#- 8\nt8#ian poulter#england#70 + 70 + 68 = 208#- 8\n",
        "pandas_code": "any((df['country'] == 'south korea') & (df['place'].str.extract(r'(\\d+)', expand=False).astype(float) <= 11))",
        "pandas_eval": "True"
    },
    {
        "id": 1872,
        "statement": "tiger wood score 6 point less than phil mickelson at the 2006 pga championship",
        "label": 1,
        "table_caption": "2006 pga championship",
        "table_text": "place#player#country#score#to par\nt1#tiger woods#united states#69 + 68 + 65 = 202#- 14\nt1#luke donald#england#68 + 68 + 66 = 202#- 14\n3#mike weir#canada#72 + 67 + 65 = 204#- 12\n4#geoff ogilvy#australia#69 + 68 + 68 = 205#- 11\nt5#shaun micheel#united states#69 + 70 + 67 = 206#- 10\nt5#sergio garc\u00eda#spain#69 + 70 + 67 = 206#- 10\n7#k j choi#south korea#73 + 67 + 67 = 207#- 9\nt8#chris dimarco#united states#71 + 70 + 67 = 208#- 8\nt8#tim herron#united states#69 + 67 + 72 = 208#- 8\nt8#phil mickelson#united states#69 + 71 + 68 = 208#- 8\nt8#ian poulter#england#70 + 70 + 68 = 208#- 8\n",
        "pandas_code": "int(df[df['player'] == 'tiger woods']['score'].str.split('=').str[-1].str.strip().astype(int).iloc[0]) == int(df[df['player'] == 'phil mickelson']['score'].str.split('=').str[-1].str.strip().astype(int).iloc[0]) - 6",
        "pandas_eval": "True"
    },
    {
        "id": 1873,
        "statement": "2 out of the top 3 finisher at the 2006 pga championship come from north america",
        "label": 1,
        "table_caption": "2006 pga championship",
        "table_text": "place#player#country#score#to par\nt1#tiger woods#united states#69 + 68 + 65 = 202#- 14\nt1#luke donald#england#68 + 68 + 66 = 202#- 14\n3#mike weir#canada#72 + 67 + 65 = 204#- 12\n4#geoff ogilvy#australia#69 + 68 + 68 = 205#- 11\nt5#shaun micheel#united states#69 + 70 + 67 = 206#- 10\nt5#sergio garc\u00eda#spain#69 + 70 + 67 = 206#- 10\n7#k j choi#south korea#73 + 67 + 67 = 207#- 9\nt8#chris dimarco#united states#71 + 70 + 67 = 208#- 8\nt8#tim herron#united states#69 + 67 + 72 = 208#- 8\nt8#phil mickelson#united states#69 + 71 + 68 = 208#- 8\nt8#ian poulter#england#70 + 70 + 68 = 208#- 8\n",
        "pandas_code": "sum(df['country'].iloc[:3].isin(['united states', 'canada'])) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1874,
        "statement": "there be 4 golfer tie for eighth place at the 2006 pga championship",
        "label": 1,
        "table_caption": "2006 pga championship",
        "table_text": "place#player#country#score#to par\nt1#tiger woods#united states#69 + 68 + 65 = 202#- 14\nt1#luke donald#england#68 + 68 + 66 = 202#- 14\n3#mike weir#canada#72 + 67 + 65 = 204#- 12\n4#geoff ogilvy#australia#69 + 68 + 68 = 205#- 11\nt5#shaun micheel#united states#69 + 70 + 67 = 206#- 10\nt5#sergio garc\u00eda#spain#69 + 70 + 67 = 206#- 10\n7#k j choi#south korea#73 + 67 + 67 = 207#- 9\nt8#chris dimarco#united states#71 + 70 + 67 = 208#- 8\nt8#tim herron#united states#69 + 67 + 72 = 208#- 8\nt8#phil mickelson#united states#69 + 71 + 68 = 208#- 8\nt8#ian poulter#england#70 + 70 + 68 = 208#- 8\n",
        "pandas_code": "len(df[df['place'] == 't8']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1875,
        "statement": "in the 1997 - 98 fa cup season , there be 5 replay tie no",
        "label": 1,
        "table_caption": "1997 - 98 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#aston villa#4 - 0#west bromwich albion#24 january 1998\n2#sheffield wednesday#0 - 3#blackburn rovers#26 january 1998\n3#middlesbrough#1 - 2#arsenal#24 january 1998\n4#ipswich town#1 - 1#sheffield united#24 january 1998\nreplay#sheffield united#1 - 0#ipswich town#3 february 1998\n5#tranmere rovers#1 - 0#sunderland#24 january 1998\n6#tottenham hotspur#1 - 1#barnsley#24 january 1998\nreplay#barnsley#3 - 1#tottenham hotspur#4 february 1998\n7#manchester city#1 - 2#west ham united#25 january 1998\n8#coventry city#2 - 0#derby county#24 january 1998\n9#manchester united#5 - 1#walsall#24 january 1998\n10#crystal palace#3 - 0#leicester city#24 january 1998\n11#huddersfield town#0 - 1#wimbledon#24 january 1998\n12#cardiff city#1 - 1#reading#24 january 1998\nreplay#reading#1 - 1#cardiff city#3 february 1998\nreading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties\n13#charlton athletic#1 - 1#wolverhampton wanderers#24 january 1998\nreplay#wolverhampton wanderers#3 - 0#charlton athletic#3 february 1998\n14#leeds united#2 - 0#grimsby town#24 january 1998\n15#birmingham city#2 - 1#stockport county#24 january 1998\n16#stevenage borough#1 - 1#newcastle united#25 january 1998\nreplay#newcastle united#2 - 1#stevenage borough#4 february 1998\n",
        "pandas_code": "len(df[df['tie no'].str.contains('replay', case=False)]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1876,
        "statement": "manchester unite score the most point in the season when they play against walsall",
        "label": 1,
        "table_caption": "1997 - 98 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#aston villa#4 - 0#west bromwich albion#24 january 1998\n2#sheffield wednesday#0 - 3#blackburn rovers#26 january 1998\n3#middlesbrough#1 - 2#arsenal#24 january 1998\n4#ipswich town#1 - 1#sheffield united#24 january 1998\nreplay#sheffield united#1 - 0#ipswich town#3 february 1998\n5#tranmere rovers#1 - 0#sunderland#24 january 1998\n6#tottenham hotspur#1 - 1#barnsley#24 january 1998\nreplay#barnsley#3 - 1#tottenham hotspur#4 february 1998\n7#manchester city#1 - 2#west ham united#25 january 1998\n8#coventry city#2 - 0#derby county#24 january 1998\n9#manchester united#5 - 1#walsall#24 january 1998\n10#crystal palace#3 - 0#leicester city#24 january 1998\n11#huddersfield town#0 - 1#wimbledon#24 january 1998\n12#cardiff city#1 - 1#reading#24 january 1998\nreplay#reading#1 - 1#cardiff city#3 february 1998\nreading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties\n13#charlton athletic#1 - 1#wolverhampton wanderers#24 january 1998\nreplay#wolverhampton wanderers#3 - 0#charlton athletic#3 february 1998\n14#leeds united#2 - 0#grimsby town#24 january 1998\n15#birmingham city#2 - 1#stockport county#24 january 1998\n16#stevenage borough#1 - 1#newcastle united#25 january 1998\nreplay#newcastle united#2 - 1#stevenage borough#4 february 1998\n",
        "pandas_code": "df[(df['home team'] == 'manchester united') & (df['away team'] == 'walsall')]['score'].str.split(' - ').apply(lambda x: int(x[0])).max() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1877,
        "statement": "in the 1997 - 98 fa cup season most of the game be play in jan",
        "label": 1,
        "table_caption": "1997 - 98 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#aston villa#4 - 0#west bromwich albion#24 january 1998\n2#sheffield wednesday#0 - 3#blackburn rovers#26 january 1998\n3#middlesbrough#1 - 2#arsenal#24 january 1998\n4#ipswich town#1 - 1#sheffield united#24 january 1998\nreplay#sheffield united#1 - 0#ipswich town#3 february 1998\n5#tranmere rovers#1 - 0#sunderland#24 january 1998\n6#tottenham hotspur#1 - 1#barnsley#24 january 1998\nreplay#barnsley#3 - 1#tottenham hotspur#4 february 1998\n7#manchester city#1 - 2#west ham united#25 january 1998\n8#coventry city#2 - 0#derby county#24 january 1998\n9#manchester united#5 - 1#walsall#24 january 1998\n10#crystal palace#3 - 0#leicester city#24 january 1998\n11#huddersfield town#0 - 1#wimbledon#24 january 1998\n12#cardiff city#1 - 1#reading#24 january 1998\nreplay#reading#1 - 1#cardiff city#3 february 1998\nreading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties\n13#charlton athletic#1 - 1#wolverhampton wanderers#24 january 1998\nreplay#wolverhampton wanderers#3 - 0#charlton athletic#3 february 1998\n14#leeds united#2 - 0#grimsby town#24 january 1998\n15#birmingham city#2 - 1#stockport county#24 january 1998\n16#stevenage borough#1 - 1#newcastle united#25 january 1998\nreplay#newcastle united#2 - 1#stevenage borough#4 february 1998\n",
        "pandas_code": "df['attendance'].str.contains('january').sum() > len(df) / 2",
        "pandas_eval": "True"
    },
    {
        "id": 1879,
        "statement": "the lowest scoring game be play on february 3rd jan 24th",
        "label": 1,
        "table_caption": "1997 - 98 fa cup",
        "table_text": "tie no#home team#score#away team#attendance\n1#aston villa#4 - 0#west bromwich albion#24 january 1998\n2#sheffield wednesday#0 - 3#blackburn rovers#26 january 1998\n3#middlesbrough#1 - 2#arsenal#24 january 1998\n4#ipswich town#1 - 1#sheffield united#24 january 1998\nreplay#sheffield united#1 - 0#ipswich town#3 february 1998\n5#tranmere rovers#1 - 0#sunderland#24 january 1998\n6#tottenham hotspur#1 - 1#barnsley#24 january 1998\nreplay#barnsley#3 - 1#tottenham hotspur#4 february 1998\n7#manchester city#1 - 2#west ham united#25 january 1998\n8#coventry city#2 - 0#derby county#24 january 1998\n9#manchester united#5 - 1#walsall#24 january 1998\n10#crystal palace#3 - 0#leicester city#24 january 1998\n11#huddersfield town#0 - 1#wimbledon#24 january 1998\n12#cardiff city#1 - 1#reading#24 january 1998\nreplay#reading#1 - 1#cardiff city#3 february 1998\nreading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties#reading won 4 - 3 on penalties\n13#charlton athletic#1 - 1#wolverhampton wanderers#24 january 1998\nreplay#wolverhampton wanderers#3 - 0#charlton athletic#3 february 1998\n14#leeds united#2 - 0#grimsby town#24 january 1998\n15#birmingham city#2 - 1#stockport county#24 january 1998\n16#stevenage borough#1 - 1#newcastle united#25 january 1998\nreplay#newcastle united#2 - 1#stevenage borough#4 february 1998\n",
        "pandas_code": "df[df['attendance'].isin(['3 february 1998', '24 january 1998'])]['score'].apply(lambda x: int(x.split(' - ')[0]) + int(x.split(' - ')[1])).min() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1880,
        "statement": "5 school be in the gray division",
        "label": 1,
        "table_caption": "miami valley conference",
        "table_text": "school#location#founded#affiliation#mascot#division\ncincinnati country day school#cincinnati , ohio#1926#private#indians#gray\ncincinnati christian schools#fairfield , ohio#1989#private christian#cougars#gray\ncincinnati hills christian academy#cincinnati , ohio#1989#private christian#eagles#scarlet\nlockland high school#cincinnati , ohio#1851#public / open enrollment#panthers#scarlet\nclark montessori high school#cincinnati , ohio#1994#public#cougars#gray\nnorth college hill high school#cincinnati , ohio#1901#public#trojans#scarlet\nnew miami high school#new miami , ohio#1972#public / open enrollment#vikings#gray\nseven hills school#cincinnati , ohio#1906#private#stingers#scarlet\nst bernard - elmwood place high school#cincinnati , ohio#1900#public / open enrollment#titans#gray\n",
        "pandas_code": "len(df[df['division'] == 'gray']) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 1881,
        "statement": "lockland high school be found before st bernard - elmwood place high school",
        "label": 1,
        "table_caption": "miami valley conference",
        "table_text": "school#location#founded#affiliation#mascot#division\ncincinnati country day school#cincinnati , ohio#1926#private#indians#gray\ncincinnati christian schools#fairfield , ohio#1989#private christian#cougars#gray\ncincinnati hills christian academy#cincinnati , ohio#1989#private christian#eagles#scarlet\nlockland high school#cincinnati , ohio#1851#public / open enrollment#panthers#scarlet\nclark montessori high school#cincinnati , ohio#1994#public#cougars#gray\nnorth college hill high school#cincinnati , ohio#1901#public#trojans#scarlet\nnew miami high school#new miami , ohio#1972#public / open enrollment#vikings#gray\nseven hills school#cincinnati , ohio#1906#private#stingers#scarlet\nst bernard - elmwood place high school#cincinnati , ohio#1900#public / open enrollment#titans#gray\n",
        "pandas_code": "df[df['school'] == 'lockland high school']['founded'].values[0] < df[df['school'] == 'st bernard - elmwood place high school']['founded'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1882,
        "statement": "3 school have cougar as their mascot",
        "label": 1,
        "table_caption": "miami valley conference",
        "table_text": "school#location#founded#affiliation#mascot#division\ncincinnati country day school#cincinnati , ohio#1926#private#indians#gray\ncincinnati christian schools#fairfield , ohio#1989#private christian#cougars#gray\ncincinnati hills christian academy#cincinnati , ohio#1989#private christian#eagles#scarlet\nlockland high school#cincinnati , ohio#1851#public / open enrollment#panthers#scarlet\nclark montessori high school#cincinnati , ohio#1994#public#cougars#gray\nnorth college hill high school#cincinnati , ohio#1901#public#trojans#scarlet\nnew miami high school#new miami , ohio#1972#public / open enrollment#vikings#gray\nseven hills school#cincinnati , ohio#1906#private#stingers#scarlet\nst bernard - elmwood place high school#cincinnati , ohio#1900#public / open enrollment#titans#gray\n",
        "pandas_code": "(df['mascot'] == 'cougars').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1883,
        "statement": "all except 2 be in cincinnati , ohio",
        "label": 1,
        "table_caption": "miami valley conference",
        "table_text": "school#location#founded#affiliation#mascot#division\ncincinnati country day school#cincinnati , ohio#1926#private#indians#gray\ncincinnati christian schools#fairfield , ohio#1989#private christian#cougars#gray\ncincinnati hills christian academy#cincinnati , ohio#1989#private christian#eagles#scarlet\nlockland high school#cincinnati , ohio#1851#public / open enrollment#panthers#scarlet\nclark montessori high school#cincinnati , ohio#1994#public#cougars#gray\nnorth college hill high school#cincinnati , ohio#1901#public#trojans#scarlet\nnew miami high school#new miami , ohio#1972#public / open enrollment#vikings#gray\nseven hills school#cincinnati , ohio#1906#private#stingers#scarlet\nst bernard - elmwood place high school#cincinnati , ohio#1900#public / open enrollment#titans#gray\n",
        "pandas_code": "df[df['location'] != 'cincinnati , ohio'].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1884,
        "statement": "lockland high school be the first 1 found",
        "label": 1,
        "table_caption": "miami valley conference",
        "table_text": "school#location#founded#affiliation#mascot#division\ncincinnati country day school#cincinnati , ohio#1926#private#indians#gray\ncincinnati christian schools#fairfield , ohio#1989#private christian#cougars#gray\ncincinnati hills christian academy#cincinnati , ohio#1989#private christian#eagles#scarlet\nlockland high school#cincinnati , ohio#1851#public / open enrollment#panthers#scarlet\nclark montessori high school#cincinnati , ohio#1994#public#cougars#gray\nnorth college hill high school#cincinnati , ohio#1901#public#trojans#scarlet\nnew miami high school#new miami , ohio#1972#public / open enrollment#vikings#gray\nseven hills school#cincinnati , ohio#1906#private#stingers#scarlet\nst bernard - elmwood place high school#cincinnati , ohio#1900#public / open enrollment#titans#gray\n",
        "pandas_code": "df[df['school'] == 'lockland high school']['founded'].iloc[0] == min(df['founded'])",
        "pandas_eval": "True"
    },
    {
        "id": 1885,
        "statement": "the chart be list by the busiest us airports to the least busy",
        "label": 1,
        "table_caption": "list of the busiest airports in the united states",
        "table_text": "rank#airport name#location#iata code#passengers#% chg 2009 / 10\n1#john f kennedy international airport#queens , new york#jfk#22702882#5.8%\n2#miami international airport#miami , florida#mia#16207353#5.3%\n3#los angeles international airport#los angeles , california#lax#15505566#4.7%\n4#newark liberty international airport#newark , new jersey#ewr#11372186#6.6%\n5#o'hare international airport#chicago , illinois#ord#10426287#2.5%\n6#hartsfield - jackson atlanta international airport#atlanta , georgia#atl#9120974#4.1%\n7#san francisco international airport#san mateo county , california#sfo#8476172#7.4%\n8#george bush intercontinental airport#houston , texas#iah#8279513#9.5%\n9#washington dulles international airport#dulles , virginia#iad#6167501#9.5%\n",
        "pandas_code": "df['rank'].is_monotonic_increasing",
        "pandas_eval": "True"
    },
    {
        "id": 1886,
        "statement": "washington dull international airport be the least busiest of all",
        "label": 1,
        "table_caption": "list of the busiest airports in the united states",
        "table_text": "rank#airport name#location#iata code#passengers#% chg 2009 / 10\n1#john f kennedy international airport#queens , new york#jfk#22702882#5.8%\n2#miami international airport#miami , florida#mia#16207353#5.3%\n3#los angeles international airport#los angeles , california#lax#15505566#4.7%\n4#newark liberty international airport#newark , new jersey#ewr#11372186#6.6%\n5#o'hare international airport#chicago , illinois#ord#10426287#2.5%\n6#hartsfield - jackson atlanta international airport#atlanta , georgia#atl#9120974#4.1%\n7#san francisco international airport#san mateo county , california#sfo#8476172#7.4%\n8#george bush intercontinental airport#houston , texas#iah#8279513#9.5%\n9#washington dulles international airport#dulles , virginia#iad#6167501#9.5%\n",
        "pandas_code": "df[df['airport name'] == 'washington dulles international airport']['passengers'].iloc[0] == df['passengers'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1887,
        "statement": "john f kennedy airport be the busiest of all the airport on the list",
        "label": 1,
        "table_caption": "list of the busiest airports in the united states",
        "table_text": "rank#airport name#location#iata code#passengers#% chg 2009 / 10\n1#john f kennedy international airport#queens , new york#jfk#22702882#5.8%\n2#miami international airport#miami , florida#mia#16207353#5.3%\n3#los angeles international airport#los angeles , california#lax#15505566#4.7%\n4#newark liberty international airport#newark , new jersey#ewr#11372186#6.6%\n5#o'hare international airport#chicago , illinois#ord#10426287#2.5%\n6#hartsfield - jackson atlanta international airport#atlanta , georgia#atl#9120974#4.1%\n7#san francisco international airport#san mateo county , california#sfo#8476172#7.4%\n8#george bush intercontinental airport#houston , texas#iah#8279513#9.5%\n9#washington dulles international airport#dulles , virginia#iad#6167501#9.5%\n",
        "pandas_code": "df.loc[df['rank'] == 1, 'airport name'].values[0] == 'john f kennedy international airport'",
        "pandas_eval": "True"
    },
    {
        "id": 1889,
        "statement": "john f kennedy airport have 3 time as many passenger as washington dull international airport",
        "label": 1,
        "table_caption": "list of the busiest airports in the united states",
        "table_text": "rank#airport name#location#iata code#passengers#% chg 2009 / 10\n1#john f kennedy international airport#queens , new york#jfk#22702882#5.8%\n2#miami international airport#miami , florida#mia#16207353#5.3%\n3#los angeles international airport#los angeles , california#lax#15505566#4.7%\n4#newark liberty international airport#newark , new jersey#ewr#11372186#6.6%\n5#o'hare international airport#chicago , illinois#ord#10426287#2.5%\n6#hartsfield - jackson atlanta international airport#atlanta , georgia#atl#9120974#4.1%\n7#san francisco international airport#san mateo county , california#sfo#8476172#7.4%\n8#george bush intercontinental airport#houston , texas#iah#8279513#9.5%\n9#washington dulles international airport#dulles , virginia#iad#6167501#9.5%\n",
        "pandas_code": "df[df['airport name'] == 'john f kennedy international airport']['passengers'].values[0] >= 3 * df[df['airport name'] == 'washington dulles international airport']['passengers'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1890,
        "statement": "4 of the top 10 highest gross bollywood film be release in 2012",
        "label": 1,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "len(df[df['year'] == 2012]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1891,
        "statement": "2 of the top 10 highest gross bollywood film be release in 2013",
        "label": 1,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "df[df['year'] == 2013].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1892,
        "statement": "2 of the top 10 highest gross bollywood film be release in 2010",
        "label": 1,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "(df[df['year'] == 2010].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 1893,
        "statement": "3 of the top 10 highest gross bollywood film be sequel",
        "label": 1,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "df['movie'].str.contains('2|3 idiots', case=False, regex=True).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1894,
        "statement": "2 of the top 10 highest gross bollywood film be from studio arbaaz khan production",
        "label": 1,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "len(df[df['studio (s)'].str.contains('arbaaz khan productions')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1895,
        "statement": "half of the top 10 highest gross bollywood film be release in 2012",
        "label": 0,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "sum(df['year'] == 2012) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 1896,
        "statement": "none of the top 10 highest gross bollywood film be release in 2013",
        "label": 0,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "not any(df[df['rank'] <= 10]['year'] == 2013)",
        "pandas_eval": "False"
    },
    {
        "id": 1897,
        "statement": "both highest grossing bollywood film release in 2010 be in the last 2 place in the top 10",
        "label": 0,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "((df[df['year'] == 2010]['rank'] >= 9).all())",
        "pandas_eval": "False"
    },
    {
        "id": 1898,
        "statement": "none of the top 10 highest gross bollywood film be sequel , all be first entry",
        "label": 0,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "all(df['movie'].apply(lambda x: '2' not in x and '3' not in x))",
        "pandas_eval": "False"
    },
    {
        "id": 1899,
        "statement": "more than half of the top 10 highest gross bollywood film be from studio arbaaz khan production",
        "label": 0,
        "table_caption": "list of highest - grossing bollywood films",
        "table_text": "rank#movie#year#studio (s)#second week nett gross\n1#3 idiots#2009#vinod chopra productions#56 , 24 , 00000\n2#yeh jawaani hai deewani#2013#dharma productions#44 , 78 , 00000\n3#chennai express#2013#red chillies entertainment#43 , 85 , 00000\n4#dabangg 2#2012#arbaaz khan productions#36 , 51 , 00000\n5#dabangg#2010#arbaaz khan productions#35 , 97 , 00000\n6#rowdy rathore#2012#utv motion pictures#33 , 15 , 00000\n7#ready#2008#t - series#32 , 52 , 00000\n8#golmaal 3#2010#shree ashtavinayak cine vision ltd#31 , 92 , 00000\n9#housefull 2#2012#nadiadwala grandson entertainment#31 , 24 , 00000\n10#ek tha tiger#2012#yash raj films#29 , 49 , 00000\n",
        "pandas_code": "df[df['studio (s)'].str.contains('arbaaz khan productions')].shape[0] > 5",
        "pandas_eval": "False"
    },
    {
        "id": 1900,
        "statement": "there be 3 game that have an attendance of over 60000",
        "label": 1,
        "table_caption": "1963 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1963#new york giants#l 28 - 37#0 - 1#memorial stadium#60029\n2#september 22 , 1963#san francisco 49ers#w 20 - 14#1 - 1#kezar stadium#31006\n3#september 29 , 1963#green bay packers#l 20 - 31#1 - 2#lambeau field#42327\n4#october 6 , 1963#chicago bears#l 3 - 10#1 - 3#wrigley field#48998\n5#october 13 , 1963#san francisco 49ers#w 20 - 3#2 - 3#memorial stadium#56962\n6#october 20 , 1963#detroit lions#w 25 - 21#3 - 3#tiger stadium#51901\n7#october 27 , 1963#green bay packers#l 20 - 34#3 - 4#memorial stadium#60065\n8#november 3 , 1963#chicago bears#l 7 - 17#3 - 5#memorial stadium#60065\n9#november 10 , 1963#detroit lions#w 24 - 21#4 - 5#memorial stadium#59758\n10#november 17 , 1963#minnesota vikings#w 37 - 34#5 - 5#metropolitan stadium#33136\n11#november 24 , 1963#los angeles rams#l 16 - 17#5 - 6#los angeles memorial coliseum#48555\n12#december 1 , 1963#washington redskins#w 36 - 20#6 - 6#rfk stadium#44006\n13#december 8 , 1963#minnesota vikings#w 41 - 10#7 - 6#memorial stadium#54122\n",
        "pandas_code": "len(df[df['attendance'] > 60000]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1901,
        "statement": "the game against the detroit lion have a higher attendance than the game against the san fransisco 49ers",
        "label": 1,
        "table_caption": "1963 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1963#new york giants#l 28 - 37#0 - 1#memorial stadium#60029\n2#september 22 , 1963#san francisco 49ers#w 20 - 14#1 - 1#kezar stadium#31006\n3#september 29 , 1963#green bay packers#l 20 - 31#1 - 2#lambeau field#42327\n4#october 6 , 1963#chicago bears#l 3 - 10#1 - 3#wrigley field#48998\n5#october 13 , 1963#san francisco 49ers#w 20 - 3#2 - 3#memorial stadium#56962\n6#october 20 , 1963#detroit lions#w 25 - 21#3 - 3#tiger stadium#51901\n7#october 27 , 1963#green bay packers#l 20 - 34#3 - 4#memorial stadium#60065\n8#november 3 , 1963#chicago bears#l 7 - 17#3 - 5#memorial stadium#60065\n9#november 10 , 1963#detroit lions#w 24 - 21#4 - 5#memorial stadium#59758\n10#november 17 , 1963#minnesota vikings#w 37 - 34#5 - 5#metropolitan stadium#33136\n11#november 24 , 1963#los angeles rams#l 16 - 17#5 - 6#los angeles memorial coliseum#48555\n12#december 1 , 1963#washington redskins#w 36 - 20#6 - 6#rfk stadium#44006\n13#december 8 , 1963#minnesota vikings#w 41 - 10#7 - 6#memorial stadium#54122\n",
        "pandas_code": "df[df['opponent'] == 'detroit lions']['attendance'].max() > df[df['opponent'] == 'san francisco 49ers']['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1902,
        "statement": "the baltimore colt finish off the 1963 season with a 7 - 6 record",
        "label": 1,
        "table_caption": "1963 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1963#new york giants#l 28 - 37#0 - 1#memorial stadium#60029\n2#september 22 , 1963#san francisco 49ers#w 20 - 14#1 - 1#kezar stadium#31006\n3#september 29 , 1963#green bay packers#l 20 - 31#1 - 2#lambeau field#42327\n4#october 6 , 1963#chicago bears#l 3 - 10#1 - 3#wrigley field#48998\n5#october 13 , 1963#san francisco 49ers#w 20 - 3#2 - 3#memorial stadium#56962\n6#october 20 , 1963#detroit lions#w 25 - 21#3 - 3#tiger stadium#51901\n7#october 27 , 1963#green bay packers#l 20 - 34#3 - 4#memorial stadium#60065\n8#november 3 , 1963#chicago bears#l 7 - 17#3 - 5#memorial stadium#60065\n9#november 10 , 1963#detroit lions#w 24 - 21#4 - 5#memorial stadium#59758\n10#november 17 , 1963#minnesota vikings#w 37 - 34#5 - 5#metropolitan stadium#33136\n11#november 24 , 1963#los angeles rams#l 16 - 17#5 - 6#los angeles memorial coliseum#48555\n12#december 1 , 1963#washington redskins#w 36 - 20#6 - 6#rfk stadium#44006\n13#december 8 , 1963#minnesota vikings#w 41 - 10#7 - 6#memorial stadium#54122\n",
        "pandas_code": "df['record'].iloc[-1] == '7 - 6'",
        "pandas_eval": "True"
    },
    {
        "id": 1903,
        "statement": "the game at the memorial stadium have the highest attendance out of all of the location",
        "label": 1,
        "table_caption": "1963 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1963#new york giants#l 28 - 37#0 - 1#memorial stadium#60029\n2#september 22 , 1963#san francisco 49ers#w 20 - 14#1 - 1#kezar stadium#31006\n3#september 29 , 1963#green bay packers#l 20 - 31#1 - 2#lambeau field#42327\n4#october 6 , 1963#chicago bears#l 3 - 10#1 - 3#wrigley field#48998\n5#october 13 , 1963#san francisco 49ers#w 20 - 3#2 - 3#memorial stadium#56962\n6#october 20 , 1963#detroit lions#w 25 - 21#3 - 3#tiger stadium#51901\n7#october 27 , 1963#green bay packers#l 20 - 34#3 - 4#memorial stadium#60065\n8#november 3 , 1963#chicago bears#l 7 - 17#3 - 5#memorial stadium#60065\n9#november 10 , 1963#detroit lions#w 24 - 21#4 - 5#memorial stadium#59758\n10#november 17 , 1963#minnesota vikings#w 37 - 34#5 - 5#metropolitan stadium#33136\n11#november 24 , 1963#los angeles rams#l 16 - 17#5 - 6#los angeles memorial coliseum#48555\n12#december 1 , 1963#washington redskins#w 36 - 20#6 - 6#rfk stadium#44006\n13#december 8 , 1963#minnesota vikings#w 41 - 10#7 - 6#memorial stadium#54122\n",
        "pandas_code": "df[df['game site'] == 'memorial stadium']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1904,
        "statement": "the baltimore colt beat the san fransisco 49ers every time they play them",
        "label": 1,
        "table_caption": "1963 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1963#new york giants#l 28 - 37#0 - 1#memorial stadium#60029\n2#september 22 , 1963#san francisco 49ers#w 20 - 14#1 - 1#kezar stadium#31006\n3#september 29 , 1963#green bay packers#l 20 - 31#1 - 2#lambeau field#42327\n4#october 6 , 1963#chicago bears#l 3 - 10#1 - 3#wrigley field#48998\n5#october 13 , 1963#san francisco 49ers#w 20 - 3#2 - 3#memorial stadium#56962\n6#october 20 , 1963#detroit lions#w 25 - 21#3 - 3#tiger stadium#51901\n7#october 27 , 1963#green bay packers#l 20 - 34#3 - 4#memorial stadium#60065\n8#november 3 , 1963#chicago bears#l 7 - 17#3 - 5#memorial stadium#60065\n9#november 10 , 1963#detroit lions#w 24 - 21#4 - 5#memorial stadium#59758\n10#november 17 , 1963#minnesota vikings#w 37 - 34#5 - 5#metropolitan stadium#33136\n11#november 24 , 1963#los angeles rams#l 16 - 17#5 - 6#los angeles memorial coliseum#48555\n12#december 1 , 1963#washington redskins#w 36 - 20#6 - 6#rfk stadium#44006\n13#december 8 , 1963#minnesota vikings#w 41 - 10#7 - 6#memorial stadium#54122\n",
        "pandas_code": "all(df[df['opponent'] == 'san francisco 49ers']['result'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 1905,
        "statement": "the racer chris hoy and craig maclean be both from the united kingdom",
        "label": 1,
        "table_caption": "cycling at the 2004 summer olympics - men 's track time trial",
        "table_text": "rank#race number#name#noc#- 250 m#- 500 m#- 750 m#- 1000 m\n1#58#chris hoy#united kingdom#17.984#31.414#45.505#1:00.711\n2#54#arnaud tournant#france#18.057#31.555#45.576#1:00.896\n3#73#stefan nimke#germany#18.487#31.919#46.027#1:01.186\n4#7#shane kelly#australia#18.351#31.861#46.057#1:01.224\n5#101#theo bos#netherlands#18.697#32.356#46.684#1:01.986\n6#52#fran\u00e7ois pervis#france#18.353#32.087#46.570#1:02.328\n7#59#craig maclean#united kingdom#18.445#32.367#46.611#1:02.369\n8#68#carsten bergemann#germany#18.901#32.850#47.272#1:02.551\n9#28#ahmed lopez naranjo#cuba#18.272#31.855#46.436#1:02.739\n10#30#alois ka\u0148kovsk\u00fd#czech republic#18.903#32.625#47.152#1:03.038\n11#105#teun mulder#netherlands#18.591#32.446#47.084#1:03.165\n12#35#ruben donet#spain#18.683#32.725#47.391#1:03.505\n13#24#wilson meneses guitierrez#colombia#18.713#32.700#47.505#1:03.614\n14#120#grzegorz krejner#poland#19.083#33.112#47.929#1:03.923\n15#76#dimitrios georgalis#greece#18.852#32.943#48.002#1:04.204\n16#137#lin chih hsun#chinese taipei#19.716#34.503#49.800#1:06.240\n",
        "pandas_code": "all(df[df['name'].isin(['chris hoy', 'craig maclean'])]['noc'] == 'united kingdom')",
        "pandas_eval": "True"
    },
    {
        "id": 1906,
        "statement": "the racer stefan nimke and carsten bergemann be both from germany",
        "label": 1,
        "table_caption": "cycling at the 2004 summer olympics - men 's track time trial",
        "table_text": "rank#race number#name#noc#- 250 m#- 500 m#- 750 m#- 1000 m\n1#58#chris hoy#united kingdom#17.984#31.414#45.505#1:00.711\n2#54#arnaud tournant#france#18.057#31.555#45.576#1:00.896\n3#73#stefan nimke#germany#18.487#31.919#46.027#1:01.186\n4#7#shane kelly#australia#18.351#31.861#46.057#1:01.224\n5#101#theo bos#netherlands#18.697#32.356#46.684#1:01.986\n6#52#fran\u00e7ois pervis#france#18.353#32.087#46.570#1:02.328\n7#59#craig maclean#united kingdom#18.445#32.367#46.611#1:02.369\n8#68#carsten bergemann#germany#18.901#32.850#47.272#1:02.551\n9#28#ahmed lopez naranjo#cuba#18.272#31.855#46.436#1:02.739\n10#30#alois ka\u0148kovsk\u00fd#czech republic#18.903#32.625#47.152#1:03.038\n11#105#teun mulder#netherlands#18.591#32.446#47.084#1:03.165\n12#35#ruben donet#spain#18.683#32.725#47.391#1:03.505\n13#24#wilson meneses guitierrez#colombia#18.713#32.700#47.505#1:03.614\n14#120#grzegorz krejner#poland#19.083#33.112#47.929#1:03.923\n15#76#dimitrios georgalis#greece#18.852#32.943#48.002#1:04.204\n16#137#lin chih hsun#chinese taipei#19.716#34.503#49.800#1:06.240\n",
        "pandas_code": "all(df[df['name'].isin(['stefan nimke', 'carsten bergemann'])]['noc'] == 'germany')",
        "pandas_eval": "True"
    },
    {
        "id": 1907,
        "statement": "grzegorz krejner be the only racer who come from poland",
        "label": 1,
        "table_caption": "cycling at the 2004 summer olympics - men 's track time trial",
        "table_text": "rank#race number#name#noc#- 250 m#- 500 m#- 750 m#- 1000 m\n1#58#chris hoy#united kingdom#17.984#31.414#45.505#1:00.711\n2#54#arnaud tournant#france#18.057#31.555#45.576#1:00.896\n3#73#stefan nimke#germany#18.487#31.919#46.027#1:01.186\n4#7#shane kelly#australia#18.351#31.861#46.057#1:01.224\n5#101#theo bos#netherlands#18.697#32.356#46.684#1:01.986\n6#52#fran\u00e7ois pervis#france#18.353#32.087#46.570#1:02.328\n7#59#craig maclean#united kingdom#18.445#32.367#46.611#1:02.369\n8#68#carsten bergemann#germany#18.901#32.850#47.272#1:02.551\n9#28#ahmed lopez naranjo#cuba#18.272#31.855#46.436#1:02.739\n10#30#alois ka\u0148kovsk\u00fd#czech republic#18.903#32.625#47.152#1:03.038\n11#105#teun mulder#netherlands#18.591#32.446#47.084#1:03.165\n12#35#ruben donet#spain#18.683#32.725#47.391#1:03.505\n13#24#wilson meneses guitierrez#colombia#18.713#32.700#47.505#1:03.614\n14#120#grzegorz krejner#poland#19.083#33.112#47.929#1:03.923\n15#76#dimitrios georgalis#greece#18.852#32.943#48.002#1:04.204\n16#137#lin chih hsun#chinese taipei#19.716#34.503#49.800#1:06.240\n",
        "pandas_code": "len(df[df['noc'] == 'poland']) == 1 and df[df['noc'] == 'poland']['name'].iloc[0] == 'grzegorz krejner'",
        "pandas_eval": "True"
    },
    {
        "id": 1908,
        "statement": "dimitrios georgalis be the only racer who come from greece",
        "label": 1,
        "table_caption": "cycling at the 2004 summer olympics - men 's track time trial",
        "table_text": "rank#race number#name#noc#- 250 m#- 500 m#- 750 m#- 1000 m\n1#58#chris hoy#united kingdom#17.984#31.414#45.505#1:00.711\n2#54#arnaud tournant#france#18.057#31.555#45.576#1:00.896\n3#73#stefan nimke#germany#18.487#31.919#46.027#1:01.186\n4#7#shane kelly#australia#18.351#31.861#46.057#1:01.224\n5#101#theo bos#netherlands#18.697#32.356#46.684#1:01.986\n6#52#fran\u00e7ois pervis#france#18.353#32.087#46.570#1:02.328\n7#59#craig maclean#united kingdom#18.445#32.367#46.611#1:02.369\n8#68#carsten bergemann#germany#18.901#32.850#47.272#1:02.551\n9#28#ahmed lopez naranjo#cuba#18.272#31.855#46.436#1:02.739\n10#30#alois ka\u0148kovsk\u00fd#czech republic#18.903#32.625#47.152#1:03.038\n11#105#teun mulder#netherlands#18.591#32.446#47.084#1:03.165\n12#35#ruben donet#spain#18.683#32.725#47.391#1:03.505\n13#24#wilson meneses guitierrez#colombia#18.713#32.700#47.505#1:03.614\n14#120#grzegorz krejner#poland#19.083#33.112#47.929#1:03.923\n15#76#dimitrios georgalis#greece#18.852#32.943#48.002#1:04.204\n16#137#lin chih hsun#chinese taipei#19.716#34.503#49.800#1:06.240\n",
        "pandas_code": "len(df[df['noc'] == 'greece']) == 1 and df[df['noc'] == 'greece']['name'].iloc[0] == 'dimitrios georgalis'",
        "pandas_eval": "True"
    },
    {
        "id": 1909,
        "statement": "the racer theo bos and teun mulder be both from the netherlands",
        "label": 1,
        "table_caption": "cycling at the 2004 summer olympics - men 's track time trial",
        "table_text": "rank#race number#name#noc#- 250 m#- 500 m#- 750 m#- 1000 m\n1#58#chris hoy#united kingdom#17.984#31.414#45.505#1:00.711\n2#54#arnaud tournant#france#18.057#31.555#45.576#1:00.896\n3#73#stefan nimke#germany#18.487#31.919#46.027#1:01.186\n4#7#shane kelly#australia#18.351#31.861#46.057#1:01.224\n5#101#theo bos#netherlands#18.697#32.356#46.684#1:01.986\n6#52#fran\u00e7ois pervis#france#18.353#32.087#46.570#1:02.328\n7#59#craig maclean#united kingdom#18.445#32.367#46.611#1:02.369\n8#68#carsten bergemann#germany#18.901#32.850#47.272#1:02.551\n9#28#ahmed lopez naranjo#cuba#18.272#31.855#46.436#1:02.739\n10#30#alois ka\u0148kovsk\u00fd#czech republic#18.903#32.625#47.152#1:03.038\n11#105#teun mulder#netherlands#18.591#32.446#47.084#1:03.165\n12#35#ruben donet#spain#18.683#32.725#47.391#1:03.505\n13#24#wilson meneses guitierrez#colombia#18.713#32.700#47.505#1:03.614\n14#120#grzegorz krejner#poland#19.083#33.112#47.929#1:03.923\n15#76#dimitrios georgalis#greece#18.852#32.943#48.002#1:04.204\n16#137#lin chih hsun#chinese taipei#19.716#34.503#49.800#1:06.240\n",
        "pandas_code": "df[df['name'].isin(['theo bos', 'teun mulder'])]['noc'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1910,
        "statement": "the boston celtic didn't lose a game from game 44 until game 52 against the phoenix sun",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "all(df[(df['game'] > 44) & (df['game'] < 52)]['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])))",
        "pandas_eval": "True"
    },
    {
        "id": 1912,
        "statement": "the record for the boston celtic advance to 39 - 12 after beat the denver nugget on february , 17",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "df[(df['date'] == 'sun feb 17') & (df['opponent'] == 'denver nuggets') & (df['record'] == '39 - 12')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1913,
        "statement": "the boston celtic play the charlotte hornet 2 time in february",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "df[(df['opponent'] == 'charlotte hornets') & (df['date'].str.contains('feb'))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1914,
        "statement": "the boston celtic win every game include in the chart that be play at boston garden",
        "label": 1,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "all(df[df['location'] == 'boston garden']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])))",
        "pandas_eval": "True"
    },
    {
        "id": 1915,
        "statement": "of the 3 game with the sun , the boston celtic didn't lose a game from game 44 until game 52 against the phoenix sun",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "df[(df['opponent'].str.contains('suns', case=False)) & (df['game'] >= 44) & (df['game'] <= 52)]['score'].apply(lambda x: int(x.split(' - ')[0]) > int(x.split(' - ')[1])).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1916,
        "statement": "the boston celtic lose a total of 3 game against the golden state warrior in the chart",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "len(df[(df['opponent'] == 'golden state warriors') & (df['score'].apply(lambda x: int(x.split(' - ')[0]) < int(x.split(' - ')[1])))]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1917,
        "statement": "the record for the boston celtic advance to 39 - 12 after beat the denver nugget on february , 17 , with a score of 105 - 109",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "(df[(df['date'] == 'sun feb 17') & (df['opponent'] == 'denver nuggets') & (df['score'] == '105 - 109') & (df['record'] == '39 - 12')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 1918,
        "statement": "the boston celtic play the charlotte hornet 2 time in february in madison square garden",
        "label": 0,
        "table_caption": "1990 - 91 boston celtics season",
        "table_text": "game#date#opponent#score#location#record\n44#fri feb 1#charlotte hornets#91 - 92#charlotte coliseum#32 - 12\n45#sun feb 3#washington bullets#119 - 101#boston garden#33 - 12\n46#wed feb 6#charlotte hornets#133 - 117#boston garden#34 - 12\n47#thu feb 7#new york knicks#117 - 101#madison square garden#35 - 12\n48#tue feb 12#seattle supersonics#114 - 111#seattle center coliseum#36 - 12\n49#thu feb 14#golden state warriors#128 - 112#oakland coliseum#37 - 12\n50#fri feb 15#los angeles lakers#98 - 85#the forum#38 - 12\n51#sun feb 17#denver nuggets#126 - 108#mcnichols sports arena#39 - 12\n52#tue feb 19#phoenix suns#105 - 109#arizona veterans memorial coliseum#39 - 13\n53#fri feb 22#new jersey nets#111 - 99#hartford civic center#40 - 13\n54#sun feb 24#indiana pacers#109 - 115#market square arena#40 - 14\n55#tue feb 26#chicago bulls#99 - 129#chicago stadium#40 - 15\n56#wed feb 27#minnesota timberwolves#116 - 111#boston garden#41 - 15\n",
        "pandas_code": "df[(df['opponent'] == 'charlotte hornets') & (df['date'].str.contains('feb')) & (df['location'] == 'madison square garden')].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1920,
        "statement": "all incumbent be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\nindiana 1#ray j madden#democratic#1942#re - elected#ray j madden (d) 58.3% albert harrigan (r) 41.7%\nindiana 3#john brademas#democratic#1958#re - elected#john brademas (d) 55.8% robert a ehlers (r) 44.2%\nindiana 4#e ross adair#republican#1950#re - elected#e ross adair (r) 63.5% j byron hayes (d) 36.5%\nindiana 5#j edward roush#democratic#1958#re - elected#j edward roush (d) 51.1% kenneth bowman (r) 48.9%\nindiana 7#none (district created)#none (district created)#none (district created)#new seat republican gain#john t myers (r) 54.3% elden c tipton (d) 45.7%\n",
        "pandas_code": "all(df[df['incumbent'] != 'none (district created)']['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 1921,
        "statement": "3 incumbent be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\nindiana 1#ray j madden#democratic#1942#re - elected#ray j madden (d) 58.3% albert harrigan (r) 41.7%\nindiana 3#john brademas#democratic#1958#re - elected#john brademas (d) 55.8% robert a ehlers (r) 44.2%\nindiana 4#e ross adair#republican#1950#re - elected#e ross adair (r) 63.5% j byron hayes (d) 36.5%\nindiana 5#j edward roush#democratic#1958#re - elected#j edward roush (d) 51.1% kenneth bowman (r) 48.9%\nindiana 7#none (district created)#none (district created)#none (district created)#new seat republican gain#john t myers (r) 54.3% elden c tipton (d) 45.7%\n",
        "pandas_code": "(df['party'] == 'democratic').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1922,
        "statement": "only 1 new district be create",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\nindiana 1#ray j madden#democratic#1942#re - elected#ray j madden (d) 58.3% albert harrigan (r) 41.7%\nindiana 3#john brademas#democratic#1958#re - elected#john brademas (d) 55.8% robert a ehlers (r) 44.2%\nindiana 4#e ross adair#republican#1950#re - elected#e ross adair (r) 63.5% j byron hayes (d) 36.5%\nindiana 5#j edward roush#democratic#1958#re - elected#j edward roush (d) 51.1% kenneth bowman (r) 48.9%\nindiana 7#none (district created)#none (district created)#none (district created)#new seat republican gain#john t myers (r) 54.3% elden c tipton (d) 45.7%\n",
        "pandas_code": "df[df['result'] == 'new seat republican gain'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1923,
        "statement": "1 incumbent be republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\nindiana 1#ray j madden#democratic#1942#re - elected#ray j madden (d) 58.3% albert harrigan (r) 41.7%\nindiana 3#john brademas#democratic#1958#re - elected#john brademas (d) 55.8% robert a ehlers (r) 44.2%\nindiana 4#e ross adair#republican#1950#re - elected#e ross adair (r) 63.5% j byron hayes (d) 36.5%\nindiana 5#j edward roush#democratic#1958#re - elected#j edward roush (d) 51.1% kenneth bowman (r) 48.9%\nindiana 7#none (district created)#none (district created)#none (district created)#new seat republican gain#john t myers (r) 54.3% elden c tipton (d) 45.7%\n",
        "pandas_code": "(df['party'] == 'republican').any()",
        "pandas_eval": "True"
    },
    {
        "id": 1924,
        "statement": "2 incumbent be both first elect in 1958",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\nindiana 1#ray j madden#democratic#1942#re - elected#ray j madden (d) 58.3% albert harrigan (r) 41.7%\nindiana 3#john brademas#democratic#1958#re - elected#john brademas (d) 55.8% robert a ehlers (r) 44.2%\nindiana 4#e ross adair#republican#1950#re - elected#e ross adair (r) 63.5% j byron hayes (d) 36.5%\nindiana 5#j edward roush#democratic#1958#re - elected#j edward roush (d) 51.1% kenneth bowman (r) 48.9%\nindiana 7#none (district created)#none (district created)#none (district created)#new seat republican gain#john t myers (r) 54.3% elden c tipton (d) 45.7%\n",
        "pandas_code": "len(df[(df['first elected'] == '1958') & (df['incumbent'] != 'none (district created)')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1925,
        "statement": "cleveland brown lose to detroit lion on august 1962",
        "label": 1,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'] == 'detroit lions') & (df['result'].str.startswith('l')) & (df['date'].str.contains('august'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1926,
        "statement": "cleveland brown score 34 point against the san francisco 49ers",
        "label": 1,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'].str.contains('san francisco 49ers')) & (df['result'].str.contains('34'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1927,
        "statement": "77683 crowd attend the cleveland brown v pittsburgh steelers",
        "label": 1,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'] == 'pittsburgh steelers') & (df['attendance'] == 77683)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1928,
        "statement": "cleveland brown beat chicago bear 28 - 24 on september 7 1962",
        "label": 1,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'] == 'chicago bears') & (df['result'] == 'w 28 - 24') & (df['date'] == 'september 7 , 1962')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 1929,
        "statement": "cleveland brown win 4 out of their 5 game in their exhibition schedule in 1962",
        "label": 1,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 1930,
        "statement": "detroit lion lose to the clevland brown on august 11 , 1962",
        "label": 0,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'] == 'detroit lions') & (df['date'] == 'august 11 , 1962') & (df['result'].str.startswith('L'))].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1931,
        "statement": "49ers score 34 point against the cleveland brown",
        "label": 0,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "not any(df[df['opponent'].str.contains('49ers')]['result'].str.contains('34 - 27'))",
        "pandas_eval": "False"
    },
    {
        "id": 1932,
        "statement": "77683 crowd attend the cleveland brown vs the chicago bear game /",
        "label": 0,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "((df['attendance'] == 77683) & (df['opponent'] == 'chicago bears')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1933,
        "statement": "the chicago bear beat cleaveland brown on september 7 , 1962",
        "label": 0,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "df[(df['opponent'] == 'chicago bears') & (df['date'] == 'september 7 , 1962') & (df['result'].str.startswith('w'))].empty",
        "pandas_eval": "False"
    },
    {
        "id": 1934,
        "statement": "in 1962 , the cleveland brows win 2 out of 5 game in their schedule",
        "label": 0,
        "table_caption": "1962 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 11 , 1962#detroit lions#l 17 - 14#34241\n2#august 18 , 1962#pittsburgh steelers#w 33 - 10#77683\n3#august 25 , 1962#san francisco 49ers at portland , or#w 34 - 27#27161\n4#september 1 , 1962#los angeles rams#w 26 - 24#43118\n5#september 7 , 1962#chicago bears#w 28 - 24#57878\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 1936,
        "statement": "only in 2000 - 2001 be there more than 1 winner , martin brodeur and patrik elia share the award",
        "label": 1,
        "table_caption": "list of new jersey devils award winners",
        "table_text": "2011 - 12#zach parise#2001 - 02#martin brodeur#1992 - 93#alexander semak\n2010 - 11#patrik elias#2000 - 01#martin brodeur patrik elias#1991 - 92#claude lemieux\n2009 - 10#zach parise#1999 - 2000#martin brodeur#1990 - 91#chris terreri\n2008 - 09#zach parise#1998 - 99#martin brodeur#1989 - 90#john maclean\n2007 - 08#martin brodeur#1997 - 98#martin brodeur#1988 - 89#patrik sundstrom\n2006 - 07#martin brodeur#1996 - 97#martin brodeur#1987 - 88#kirk muller\n2005 - 06#brian gionta#1995 - 96#martin brodeur#1986 - 87#kirk muller\n2003 - 04#scott niedermayer#1994 - 95#stephane richer#1985 - 86#greg adams\n",
        "pandas_code": "df['2001 - 02'].apply(lambda x: len(x.split()) > 1).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1938,
        "statement": "the first multiple award winner be kirk muller in 1986 - 87 and 1987 - 88",
        "label": 1,
        "table_caption": "list of new jersey devils award winners",
        "table_text": "2011 - 12#zach parise#2001 - 02#martin brodeur#1992 - 93#alexander semak\n2010 - 11#patrik elias#2000 - 01#martin brodeur patrik elias#1991 - 92#claude lemieux\n2009 - 10#zach parise#1999 - 2000#martin brodeur#1990 - 91#chris terreri\n2008 - 09#zach parise#1998 - 99#martin brodeur#1989 - 90#john maclean\n2007 - 08#martin brodeur#1997 - 98#martin brodeur#1988 - 89#patrik sundstrom\n2006 - 07#martin brodeur#1996 - 97#martin brodeur#1987 - 88#kirk muller\n2005 - 06#brian gionta#1995 - 96#martin brodeur#1986 - 87#kirk muller\n2003 - 04#scott niedermayer#1994 - 95#stephane richer#1985 - 86#greg adams\n",
        "pandas_code": "df['alexander semak'].apply(lambda x: 'kirk muller' in x).any()",
        "pandas_eval": "True"
    },
    {
        "id": 1939,
        "statement": "there be no award list for 1993 - 94 , 2002 - 03 or 2004 - 05",
        "label": 1,
        "table_caption": "list of new jersey devils award winners",
        "table_text": "2011 - 12#zach parise#2001 - 02#martin brodeur#1992 - 93#alexander semak\n2010 - 11#patrik elias#2000 - 01#martin brodeur patrik elias#1991 - 92#claude lemieux\n2009 - 10#zach parise#1999 - 2000#martin brodeur#1990 - 91#chris terreri\n2008 - 09#zach parise#1998 - 99#martin brodeur#1989 - 90#john maclean\n2007 - 08#martin brodeur#1997 - 98#martin brodeur#1988 - 89#patrik sundstrom\n2006 - 07#martin brodeur#1996 - 97#martin brodeur#1987 - 88#kirk muller\n2005 - 06#brian gionta#1995 - 96#martin brodeur#1986 - 87#kirk muller\n2003 - 04#scott niedermayer#1994 - 95#stephane richer#1985 - 86#greg adams\n",
        "pandas_code": "all(year not in df.columns for year in ['1993 - 94', '2002 - 03', '2004 - 05'])",
        "pandas_eval": "True"
    },
    {
        "id": 1940,
        "statement": "in the history of the boston marathon , kenya have more men 's open winner than canada",
        "label": 1,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'] == 'kenya', 'men \\'s open'].values[0] > df.loc[df['country'] == 'canada', 'men \\'s open'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1941,
        "statement": "in the history of the boston marathon , canada have more men 's wheelchair winner than kenya",
        "label": 1,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "(df[df['country'] == 'canada']['men \\'s wheelchair'].values[0] > df[df['country'] == 'kenya']['men \\'s wheelchair'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1942,
        "statement": "in the history of the boston marathon , finland and germany have the same amount of women 's wheelchair winner",
        "label": 1,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'].isin(['finland', 'germany']), 'women \\'s wheelchair'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 1943,
        "statement": "in the history of the boston marathon , new zealand have more women 's open winner than france",
        "label": 1,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'] == 'new zealand', 'women \\'s open'].values[0] > df.loc[df['country'] == 'france', 'women \\'s open'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1944,
        "statement": "in the history of the boston marathon , sweden have more men 's open winner than poland",
        "label": 1,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "(df[df['country'] == 'sweden'][\"men 's open\"].values[0] > df[df['country'] == 'poland'][\"men 's open\"].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 1945,
        "statement": "canada have the most men 's open winner",
        "label": 0,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'] == 'canada', 'men \\'s open'].values[0] == df['men \\'s open'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1946,
        "statement": "kenya have the most men 's wheelchair winner",
        "label": 0,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'] == 'kenya', 'men \\'s wheelchair'].values[0] == df['men \\'s wheelchair'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1947,
        "statement": "finland have more women 's wheelchair winner than germany",
        "label": 0,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df[df['country'] == 'finland']['women \\'s wheelchair'].values[0] > df[df['country'] == 'germany']['women \\'s wheelchair'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1948,
        "statement": "france and new zealand have win the same number of women 's open winner",
        "label": 0,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "df.loc[df['country'].isin(['france', 'new zealand']), 'women \\'s open'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1949,
        "statement": "poland have win more men 's open winner than sweden",
        "label": 0,
        "table_caption": "list of winners of the boston marathon",
        "table_text": "country#men 's open#women 's open#men 's wheelchair#women 's wheelchair#total\nunited states#44#15#12#25#96\nkenya#20#10#0#0#30\ncanada#16#1#4#0#21\njapan#8#0#3#5#16\nswitzerland#0#0#7#2#9\nsouth africa#0#0#9#0#9\nethiopia#4#4#0#0#8\nfinland#7#0#0#0#7\ngermany#1#5#0#0#6\naustralia#1#0#0#4#5\nrussia#0#4#0#0#4\nfrance#0#0#3#0#3\nnew zealand#1#2#0#0#3\nportugal#0#3#0#0#3\nsouth korea#3#0#0#0#3\nunited kingdom#3#0#0#0#3\nbelgium#2#0#0#0#2\nnorway#0#2#0#0#2\ncolombia#1#0#0#0#1\ndenmark#0#0#0#1#1\ngreece#1#0#0#0#1\nguatemala#1#0#0#0#1\nireland#1#0#0#0#1\nitaly#1#0#0#0#1\npoland#0#1#0#0#1\nsweden#1#0#0#0#1\nyugoslavia#1#0#0#0#1\n",
        "pandas_code": "(df[df['country'] == 'poland'][\"men 's open\"].values[0] > df[df['country'] == 'sweden'][\"men 's open\"].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1950,
        "statement": "the buffalo bill have 3 third - round pick in the 1984 nfl draft",
        "label": 1,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "len(df[df['round'] == 3]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1951,
        "statement": "the buffalo bill select 2 player from miami university in the 1984 nfl draft",
        "label": 1,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "len(df[df['college'].str.contains('miami', case=False)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1952,
        "statement": "rodney bellinger be take 2 pick before sean mcnanie in the 1984 nfl draft",
        "label": 1,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "df[df['player'].isin(['rodney bellinger', 'sean mcnanie'])].sort_values('pick').iloc[0]['player'] == 'rodney bellinger'",
        "pandas_eval": "True"
    },
    {
        "id": 1953,
        "statement": "john kidd be the only punter draft by the buffalo bill 1984",
        "label": 1,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "(df[df['position'] == 'punter']['player'].tolist() == ['john kidd'])",
        "pandas_eval": "True"
    },
    {
        "id": 1954,
        "statement": "the buffalo bill draft 2 more defensive back than tight end in 1984",
        "label": 1,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "(df[df['position'] == 'defensive back'].shape[0] - df[df['position'] == 'tight end'].shape[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1955,
        "statement": "the speedy neals have 3 third - round pick in the 1984 nfl draft",
        "label": 0,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "len(df[(df['round'] == 3) & (df['player'].str.contains('speedy neal'))]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1956,
        "statement": "the buffalo bill select 2 player from miami university in the 1987 nfl draft",
        "label": 0,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "len(df[(df['college'].str.contains('miami', case=False)) & (df['round'] == 2)]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 1957,
        "statement": "craig white be take 2 pick before sean mcnanie in the 1984 nfl draft",
        "label": 0,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "df[df['player'] == 'craig white']['pick'].values[0] < df[df['player'] == 'sean mcnanie']['pick'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 1958,
        "statement": "stan david be the only punter draft by the buffalo bill 1984",
        "label": 0,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "(df[df['player'] == 'stan david']['position'].eq('punter').any()) & (df['position'].eq('punter').sum() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 1959,
        "statement": "the buffalo bill draft 6 more defensive back than tight end in 1984",
        "label": 0,
        "table_caption": "1984 buffalo bills season",
        "table_text": "round#pick#player#position#college\n1#26#greg bell#running back#notre dame\n2#41#eric richardson#wide receiver#san jose state\n3#77#rodney bellinger#defensive back#miami (fl)\n3#79#sean mcnanie#defensive end#san diego state\n3#82#speedy neal#running back#miami (fl)\n4#95#mitchell brookins#wide receiver#illinois\n5#128#john kidd#punter#northwestern\n6#155#tony slaton#center#usc\n7#182#stan david#defensive back#texas tech\n8#209#stacy rayfield#defensive back#texas - arlington\n9#236#leroy howell#defensive end#appalachian state\n10#236#joe azelby#linebacker#harvard\n11#296#craig white#wide receiver#missouri\n12#323#russell davis#tight end#maryland\n",
        "pandas_code": "(df[df['position'] == 'defensive back'].shape[0] - df[df['position'] == 'tight end'].shape[0]) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 1960,
        "statement": "deran sarafian direct 3 episode this season",
        "label": 1,
        "table_caption": "house (season 4)",
        "table_text": "no#-#title#directed by#written by#us viewers (million)#rank (week)#original air date\n72#2#the right stuff#deran sarafian#doris egan & leonard dick#17.44#6#october 2 , 2007\n73#3#97 seconds#david platt#russel friend & garrett lerner#18.03#5#october 9 , 2007\n74#4#guardian angels#deran sarafian#david hoselton#18.10#5#october 23 , 2007\n75#5#mirror mirror#david platt#david foster#17.29#7#october 30 , 2007\n77#7#ugly#david straiton#sean whitesell#16.95#6#november 13 , 2007\n78#8#you don't want to know#lesli linka glatter#sara hess#16.88#6#november 20 , 2007\n79#9#games#deran sarafian#eli attie#16.96#7#november 27 , 2007\n80#10#it 's a wonderful lie#matt shakman#pamela davis#22.56#6#january 29 , 2008\n81#11#frozen#david straiton#liz friedman#29.04#3#february 3 , 2008\n82#12#don't ever change#deran sarafian#doris egan & leonard dick#23.15#3#february 5 , 2008\n83#13#no more mr nice guy#deran sarafian#david hoselton & david shore#14.51#n / a#april 28 , 2008\n",
        "pandas_code": "df[df['directed by'] == 'deran sarafian'].shape[0] >= 3",
        "pandas_eval": "True"
    },
    {
        "id": 1961,
        "statement": "the episode frozen have more viewer than the episode game",
        "label": 1,
        "table_caption": "house (season 4)",
        "table_text": "no#-#title#directed by#written by#us viewers (million)#rank (week)#original air date\n72#2#the right stuff#deran sarafian#doris egan & leonard dick#17.44#6#october 2 , 2007\n73#3#97 seconds#david platt#russel friend & garrett lerner#18.03#5#october 9 , 2007\n74#4#guardian angels#deran sarafian#david hoselton#18.10#5#october 23 , 2007\n75#5#mirror mirror#david platt#david foster#17.29#7#october 30 , 2007\n77#7#ugly#david straiton#sean whitesell#16.95#6#november 13 , 2007\n78#8#you don't want to know#lesli linka glatter#sara hess#16.88#6#november 20 , 2007\n79#9#games#deran sarafian#eli attie#16.96#7#november 27 , 2007\n80#10#it 's a wonderful lie#matt shakman#pamela davis#22.56#6#january 29 , 2008\n81#11#frozen#david straiton#liz friedman#29.04#3#february 3 , 2008\n82#12#don't ever change#deran sarafian#doris egan & leonard dick#23.15#3#february 5 , 2008\n83#13#no more mr nice guy#deran sarafian#david hoselton & david shore#14.51#n / a#april 28 , 2008\n",
        "pandas_code": "df[df['title'] == 'frozen']['us viewers (million)'].values[0] > df[df['title'] == 'games']['us viewers (million)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 1962,
        "statement": "episode 81 and 82 air within 2 day of each other",
        "label": 1,
        "table_caption": "house (season 4)",
        "table_text": "no#-#title#directed by#written by#us viewers (million)#rank (week)#original air date\n72#2#the right stuff#deran sarafian#doris egan & leonard dick#17.44#6#october 2 , 2007\n73#3#97 seconds#david platt#russel friend & garrett lerner#18.03#5#october 9 , 2007\n74#4#guardian angels#deran sarafian#david hoselton#18.10#5#october 23 , 2007\n75#5#mirror mirror#david platt#david foster#17.29#7#october 30 , 2007\n77#7#ugly#david straiton#sean whitesell#16.95#6#november 13 , 2007\n78#8#you don't want to know#lesli linka glatter#sara hess#16.88#6#november 20 , 2007\n79#9#games#deran sarafian#eli attie#16.96#7#november 27 , 2007\n80#10#it 's a wonderful lie#matt shakman#pamela davis#22.56#6#january 29 , 2008\n81#11#frozen#david straiton#liz friedman#29.04#3#february 3 , 2008\n82#12#don't ever change#deran sarafian#doris egan & leonard dick#23.15#3#february 5 , 2008\n83#13#no more mr nice guy#deran sarafian#david hoselton & david shore#14.51#n / a#april 28 , 2008\n",
        "pandas_code": "(pd.to_datetime(df.loc[df['no'] == 81, 'original air date'].values[0]) - pd.to_datetime(df.loc[df['no'] == 82, 'original air date'].values[0])).days <= 2",
        "pandas_eval": "True"
    },
    {
        "id": 1963,
        "statement": "doris egan leonard dick write 2 episode this season",
        "label": 1,
        "table_caption": "house (season 4)",
        "table_text": "no#-#title#directed by#written by#us viewers (million)#rank (week)#original air date\n72#2#the right stuff#deran sarafian#doris egan & leonard dick#17.44#6#october 2 , 2007\n73#3#97 seconds#david platt#russel friend & garrett lerner#18.03#5#october 9 , 2007\n74#4#guardian angels#deran sarafian#david hoselton#18.10#5#october 23 , 2007\n75#5#mirror mirror#david platt#david foster#17.29#7#october 30 , 2007\n77#7#ugly#david straiton#sean whitesell#16.95#6#november 13 , 2007\n78#8#you don't want to know#lesli linka glatter#sara hess#16.88#6#november 20 , 2007\n79#9#games#deran sarafian#eli attie#16.96#7#november 27 , 2007\n80#10#it 's a wonderful lie#matt shakman#pamela davis#22.56#6#january 29 , 2008\n81#11#frozen#david straiton#liz friedman#29.04#3#february 3 , 2008\n82#12#don't ever change#deran sarafian#doris egan & leonard dick#23.15#3#february 5 , 2008\n83#13#no more mr nice guy#deran sarafian#david hoselton & david shore#14.51#n / a#april 28 , 2008\n",
        "pandas_code": "len(df[df['written by'].str.contains('doris egan & leonard dick')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1964,
        "statement": "the last episode of the season have the least number of viewer",
        "label": 1,
        "table_caption": "house (season 4)",
        "table_text": "no#-#title#directed by#written by#us viewers (million)#rank (week)#original air date\n72#2#the right stuff#deran sarafian#doris egan & leonard dick#17.44#6#october 2 , 2007\n73#3#97 seconds#david platt#russel friend & garrett lerner#18.03#5#october 9 , 2007\n74#4#guardian angels#deran sarafian#david hoselton#18.10#5#october 23 , 2007\n75#5#mirror mirror#david platt#david foster#17.29#7#october 30 , 2007\n77#7#ugly#david straiton#sean whitesell#16.95#6#november 13 , 2007\n78#8#you don't want to know#lesli linka glatter#sara hess#16.88#6#november 20 , 2007\n79#9#games#deran sarafian#eli attie#16.96#7#november 27 , 2007\n80#10#it 's a wonderful lie#matt shakman#pamela davis#22.56#6#january 29 , 2008\n81#11#frozen#david straiton#liz friedman#29.04#3#february 3 , 2008\n82#12#don't ever change#deran sarafian#doris egan & leonard dick#23.15#3#february 5 , 2008\n83#13#no more mr nice guy#deran sarafian#david hoselton & david shore#14.51#n / a#april 28 , 2008\n",
        "pandas_code": "df['us viewers (million)'].iloc[-1] == df['us viewers (million)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1965,
        "statement": "ken hick be 1 of the 14 player whose nationality be canada",
        "label": 1,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "df[df['player'] == 'ken hicks']['nationality'].iloc[0] == 'canada'",
        "pandas_eval": "True"
    },
    {
        "id": 1966,
        "statement": "canada have a greater number of player than the usa",
        "label": 1,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "(df['nationality'].value_counts().get('canada', 0) > df['nationality'].value_counts().get('usa', 0))",
        "pandas_eval": "True"
    },
    {
        "id": 1967,
        "statement": "the california golden seal have 2 draft year with 3 round of selection each",
        "label": 1,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "df.groupby('draft')['round'].nunique().eq(3).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 1968,
        "statement": "the draft year 1969 have the highest number of round selection",
        "label": 1,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "df[df['draft'] == 1969]['round'].nunique() == df.groupby('draft')['round'].nunique().max()",
        "pandas_eval": "True"
    },
    {
        "id": 1969,
        "statement": "kevin smith be from canada while gary wood be from the usa",
        "label": 1,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "(df[df['player'] == 'kevin smith']['nationality'].iloc[0] == 'canada') & (df[df['player'] == 'gary wood']['nationality'].iloc[0] == 'usa')",
        "pandas_eval": "True"
    },
    {
        "id": 1970,
        "statement": "ken hick be 1 of the 14 player whose nationality be canada and all player have the nationality canada",
        "label": 0,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "all(df['nationality'] == 'canada') and df[df['player'] == 'ken hicks'].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 1971,
        "statement": "canada have a smaller number of player than the usa",
        "label": 0,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "(df[df['nationality'] == 'canada'].shape[0] < df[df['nationality'] == 'usa'].shape[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1972,
        "statement": "the california golden seal have 3 draft year with only 2 round of selection each",
        "label": 0,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "df.groupby('draft')['round'].nunique().eq(2).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 1973,
        "statement": "the draft year 1969 have the highest number of round selection by 6 more than the next highest year",
        "label": 0,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "(df[df['draft'] == 1969]['round'].nunique() - df[df['draft'] != 1969].groupby('draft')['round'].nunique().max()) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 1974,
        "statement": "kevin smith be from canada while gary wood be from the usa and both be pick in the same numbered round",
        "label": 0,
        "table_caption": "list of california golden seals draft picks",
        "table_text": "draft#round#pick#player#nationality\n1967#1#3#ken hicks#canada\n1967#2#12#gary wood#usa\n1967#3#18#kevin smith#canada\n1968#2#13#doug smith#canada\n1968#3#20#jim trewin#canada\n1969#1#7#tony featherstone#canada\n1969#2#18#ron stackhouse#canada\n1969#3#29#don o'donoghue#canada\n1969#4#41#pierre farmer#canada\n1969#5#53#warren harrison#canada\n1969#6#65#neil nicholson#canada\n1969#7#76#pete vipond#canada\n1970#1#10#chris oddleifson#canada\n1970#2#19#pete laframboise#canada\n1970#3#33#randy rota#canada\n",
        "pandas_code": "(df[df['player'] == 'kevin smith']['nationality'].iloc[0] == 'canada') & (df[df['player'] == 'gary wood']['nationality'].iloc[0] == 'usa') & (df[df['player'] == 'kevin smith']['round'].iloc[0] == df[df['player'] == 'gary wood']['round'].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 1975,
        "statement": "the paulistano team score the most point in brazilian football 1926",
        "label": 1,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "df[df['team'] == 'paulistano']['points'].max() == df['points'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1976,
        "statement": "3 team have a difference of less than 0",
        "label": 1,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "(df['difference'].apply(lambda x: int(x.replace(' ', '')) < 0).sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1977,
        "statement": "atl\u00e9tico santista have 3 more point than paulista in 1926",
        "label": 1,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "(df[df['team'] == 'atl\u00e9tico santista']['points'].values[0] - df[df['team'] == 'paulista']['points'].values[0]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 1978,
        "statement": "there be 4 team that lost less than 5 game",
        "label": 1,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "(df['lost'] < 5).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 1979,
        "statement": "team britannia lost the most game for the 1926 brazilian football season",
        "label": 1,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "df.loc[df['team'] == 'britannia', 'lost'].values[0] == df['lost'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 1980,
        "statement": "the paulistano team score zero point in 1926",
        "label": 0,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "df[df['team'] == 'paulistano']['points'].eq(0).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1981,
        "statement": "no team have a negative difference",
        "label": 0,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "all(df['difference'].str.replace(' ', '').astype(int) >= 0)",
        "pandas_eval": "False"
    },
    {
        "id": 1982,
        "statement": "paulista have the most point in 1926",
        "label": 0,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "df.loc[df['team'] == 'paulista', 'points'].values[0] == df['points'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1983,
        "statement": "every team lost less than 5 game",
        "label": 0,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "(df['lost'] < 5).all()",
        "pandas_eval": "False"
    },
    {
        "id": 1984,
        "statement": "team britannia be undefeated in the 1926 brazilian football season",
        "label": 0,
        "table_caption": "1926 in brazilian football",
        "table_text": "position#team#points#played#drawn#lost#against#difference\n1#paulistano#24#14#2#1#14#41\n2#germnia#18#14#2#4#28#10\n3#independ\u00eancia#17#14#3#4#30#7\n4#ant\u00e1rtica#17#14#5#3#19#6\n5#aa palmeiras#15#14#3#5#24#4\n6#atl\u00e9tico santista#11#14#1#8#32#- 2\n7#paulista#8#14#4#8#46#- 22\n8#britannia#2#14#2#12#64#- 44\n",
        "pandas_code": "df[df['team'] == 'britannia']['lost'].iloc[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 1985,
        "statement": "3 of the runner have the nationality of united state",
        "label": 1,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "(df['nationality'].value_counts()['united states'] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 1986,
        "statement": "leslie djhone have a lower reaction time than any other reaction time",
        "label": 1,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df[df['athlete'] == 'leslie djhone']['reaction time'].values[0] < df[df['athlete'] != 'leslie djhone']['reaction time'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 1987,
        "statement": "the highest reaction time belong to a runner who have unite state as a nationality",
        "label": 1,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df[df['reaction time'] == df['reaction time'].max()]['nationality'].values[0] == 'united states'",
        "pandas_eval": "True"
    },
    {
        "id": 1988,
        "statement": "martyn rooney have a lower time than 2 other player in the race",
        "label": 1,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "(df['time'] > df[df['athlete'] == 'martyn rooney']['time'].values[0]).sum() >= 2",
        "pandas_eval": "True"
    },
    {
        "id": 1989,
        "statement": "nobody from the united state have a start time lower than the racer from france",
        "label": 1,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df[(df['nationality'] == 'united states') & (df['reaction time'] < df[df['nationality'] == 'france']['reaction time'].values[0])].empty",
        "pandas_eval": "True"
    },
    {
        "id": 1990,
        "statement": "3 of the runner have the nationality of united state less than any other nationality",
        "label": 0,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df['nationality'].value_counts()['united states'] == 3 and df['nationality'].value_counts()['united states'] < df['nationality'].value_counts().drop('united states').min()",
        "pandas_eval": "False"
    },
    {
        "id": 1991,
        "statement": "leslie djhone have a lower time and reaction time than any other reaction time",
        "label": 0,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "(df[df['athlete'] == 'leslie djhone']['time'].values[0] < df['time'].min()) & (df[df['athlete'] == 'leslie djhone']['reaction time'].values[0] < df['reaction time'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 1992,
        "statement": "the highest reaction time belong to a runner who have a nationality that no other runner have",
        "label": 0,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df[df['reaction time'] == df['reaction time'].max()]['nationality'].isin(df['nationality'].value_counts()[df['nationality'].value_counts() == 1].index).any()",
        "pandas_eval": "False"
    },
    {
        "id": 1993,
        "statement": "martyn rooney have a lower time than only 1 other player in the race",
        "label": 0,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "(df[df['athlete'] == 'martyn rooney']['time'].values[0] > df['time'].sort_values().iloc[-2])",
        "pandas_eval": "False"
    },
    {
        "id": 1994,
        "statement": "nobody from the united state have a start time higher than the racer from france",
        "label": 0,
        "table_caption": "athletics at the 2008 summer olympics - men 's 400 metres",
        "table_text": "lane#athlete#nationality#time#reaction time\n4#lashawn merritt#united states#43.75#0.318\n7#jeremy wariner#united states#44.74#0.209\n9#david neville#united states#44.80#0.293\n5#chris brown#bahamas#44.84#0.231\n6#leslie djhone#france#45.11#0.164\n8#martyn rooney#great britain#45.12#0.208\n2#renny quow#trinidad and tobago#45.22#0.201\n3#johan wissman#sweden#45.39#0.218\n",
        "pandas_code": "df[df['nationality'] == 'united states']['reaction time'].max() <= df[df['nationality'] == 'france']['reaction time'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 1995,
        "statement": "the home team score range from 8.14 up to 16.16",
        "label": 1,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "all(df['home team score'].apply(lambda x: int(x.split('.')[0]) * 6 + int(x.split('.')[1].split()[0])).between(62, 112))",
        "pandas_eval": "True"
    },
    {
        "id": 1997,
        "statement": "the venue that hold the game with the greatest number of spectator be western oval",
        "label": 1,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "df.loc[df['crowd'].idxmax(), 'venue'] == 'western oval'",
        "pandas_eval": "True"
    },
    {
        "id": 1998,
        "statement": "the venue that hold the game with the lowest number of spectator be brunswick street oval",
        "label": 1,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "df.loc[df['crowd'].idxmin(), 'venue'] == 'brunswick street oval'",
        "pandas_eval": "True"
    },
    {
        "id": 1999,
        "statement": "the crowd number range from a low of 5000 up to a high of 30000 viewer",
        "label": 1,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "(df['crowd'].min() == 5000) & (df['crowd'].max() == 30000)",
        "pandas_eval": "True"
    },
    {
        "id": 2000,
        "statement": "the home team score range from below 8 to above 16",
        "label": 0,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "(df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 8).any() and (df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) > 16).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2001,
        "statement": "the away team score range from below 9 to above 17",
        "label": 0,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "(df['away team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 9).any() and (df['away team score'].str.extract(r'(\\d+)\\.')[0].astype(int) > 17).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2002,
        "statement": "the venue that hold the game with the second highest attendance be western oval",
        "label": 0,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "df.sort_values('crowd', ascending=False).iloc[1]['venue'] == 'western oval'",
        "pandas_eval": "False"
    },
    {
        "id": 2003,
        "statement": "the venue that hold the game with the second lowest attendance be brunswick street oval",
        "label": 0,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "df.sort_values('crowd').iloc[1]['venue'] == 'brunswick street oval'",
        "pandas_eval": "False"
    },
    {
        "id": 2004,
        "statement": "the attendance never exceed more than 20000",
        "label": 0,
        "table_caption": "1945 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ngeelong#13.14 (92)#st kilda#9.11 (65)#kardinia park#7500#1 september 1945\nfitzroy#14.22 (106)#melbourne#15.11 (101)#brunswick street oval#5000#1 september 1945\nsouth melbourne#16.16 (112)#hawthorn#11.10 (76)#junction oval#12000#1 september 1945\nnorth melbourne#14.17 (101)#essendon#13.17 (95)#arden street oval#12000#1 september 1945\nrichmond#8.19 (67)#collingwood#12.15 (87)#punt road oval#23000#1 september 1945\nfootscray#8.14 (62)#carlton#16.19 (115)#western oval#30000#1 september 1945\n",
        "pandas_code": "df['crowd'].max() <= 20000",
        "pandas_eval": "False"
    },
    {
        "id": 2005,
        "statement": "the university of kansa be 1 of 8 host from the midwest region",
        "label": 1,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "len(df[(df['region'] == 'midwest') & (df['host'] == 'university of kansas')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2006,
        "statement": "the william r johnson coliseum be not the venue locate in austin",
        "label": 1,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "df[df['venue'] == 'william r johnson coliseum']['city'].iloc[0] != 'austin'",
        "pandas_eval": "True"
    },
    {
        "id": 2007,
        "statement": "a total of 3 venue be locate in the state of california",
        "label": 1,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "(df['state'] == 'california').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2008,
        "statement": "2 out of 3 of the texas venue be locate in the midwest region",
        "label": 1,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "(df[(df['state'] == 'texas') & (df['region'] == 'midwest')].shape[0] / df[df['state'] == 'texas'].shape[0]) == (2/3)",
        "pandas_eval": "True"
    },
    {
        "id": 2009,
        "statement": "bowl green state university be locate in the same state as ohio state university",
        "label": 1,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "df[df['host'] == 'bowling green state university']['state'].values[0] == df[df['host'] == 'ohio state university']['state'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2010,
        "statement": "the university of kansa be 1 of 4 host from the midwest region",
        "label": 0,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "len(df[(df['region'] == 'midwest') & (df['host'] == 'university of kansas')]) == 1 and len(df[df['region'] == 'midwest']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2011,
        "statement": "the maple pavilion be the venue locate in austin",
        "label": 0,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "df[(df['venue'] == 'maples pavilion') & (df['city'] == 'austin')].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 2012,
        "statement": "3 venue be locate in the state of ohio",
        "label": 0,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "len(df[df['state'] == 'ohio']) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2013,
        "statement": "all 3 of the texas venue be locate in the midwest region",
        "label": 0,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "all(df[df['state'] == 'texas']['region'] == 'midwest')",
        "pandas_eval": "False"
    },
    {
        "id": 2014,
        "statement": "georgetown university be locate in the same state as ohio state university",
        "label": 0,
        "table_caption": "1993 ncaa women 's division i basketball tournament",
        "table_text": "region#host#venue#city#state\neast#university of miami#knight sports complex#coral gables#florida\neast#bowling green state university#anderson arena#bowling green#ohio\neast#georgetown university#mcdonough gymnasium#washington#district of columbia\neast#university of vermont#patrick gym#burlington#vermont\neast#western kentucky university#ea diddle arena#bowling green#kentucky\neast#pennsylvania state university#recreation building (rec hall)#university park#pennsylvania\neast#ohio state university#st john arena#columbus#ohio\neast#university of virginia#university hall (university of virginia)#charlottesville#virginia\nmideast#northwestern university#welsh - ryan arena#evanston#illinois\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of alabama#coleman coliseum#tuscaloosa#alabama\nmideast#university of connecticut#harry a gampel pavilion#storrs#connecticut\nmideast#old dominion university#old dominion university fieldhouse#norfolk#virginia\nmideast#university of north carolina#carmichael auditorium#chapel hill#north carolina\nmideast#university of tennessee#thompson - boling arena#knoxville#tennessee\nmideast#auburn university#memorial coliseum (beard - eaves - memorial coliseum)#auburn#alabama\nmidwest#university of kansas#allen field house#lawrence#kansas\nmidwest#clemson university#littlejohn coliseum#clemson#south carolina\nmidwest#missouri state university#hammons student center#springfield#missouri\nmidwest#louisiana tech university#thomas assembly center#ruston#louisiana\nmidwest#vanderbilt university#striplin gymnasium#nashville#tennessee\nmidwest#stephen f austin university#william r johnson coliseum#nacogdoches#texas\nmidwest#university of maryland#cole field house#college park#maryland\nmidwest#university of texas#frank erwin center#austin#texas\nwest#university of california , santa barbara#uc santa barbara events center#santa barbara#california\nwest#university of georgia#georgia coliseum (stegeman coliseum)#athens#georgia\nwest#university of washington#hec edmundson pavilion#seattle#washington\nwest#university of nebraska#bob devaney sports center#lincoln#nebraska\nwest#stanford university#maples pavilion#stanford#california\nwest#texas tech university#lubbock municipal coliseum#lubbock#texas\nwest#university of southern california#los angeles memorial sports arena#los angeles#california\nwest#university of colorado#cu events center (coors events center)#boulder#colorado\n",
        "pandas_code": "df[df['host'] == 'georgetown university']['state'].values[0] == df[df['host'] == 'ohio state university']['state'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2015,
        "statement": "comercial - sp have the lowest goal differential of any team in 1947 brazilian football",
        "label": 1,
        "table_caption": "1947 in brazilian football",
        "table_text": "position#team#points#played#won#drawn#lost#for#against#difference\n1#palmeiras#36#20#17#2#1#51#16#35\n2#corinthians#32#20#14#4#2#54#19#35\n3#portuguesa#27#20#11#5#4#43#28#15\n4#s\u00e3o paulo#25#20#8#9#3#48#27#21\n5#ypiranga - sp#21#20#9#3#8#36#26#10\n6#santos#19#20#6#7#7#33#27#6\n7#juventus#16#20#5#6#9#29#45#- 16\n8#portuguesa santista#15#20#6#3#11#27#42#- 15\n9#comercial - sp#11#20#5#1#14#25#59#- 34\n10#nacional - sp#10#20#3#4#13#25#47#- 22\n",
        "pandas_code": "int(df.loc[df['team'] == 'comercial - sp', 'difference'].iloc[0].replace(' ', '')) == int(df['difference'].str.replace(' ', '').astype(int).min())",
        "pandas_eval": "True"
    },
    {
        "id": 2016,
        "statement": "the average number of point for a team in brazilian football 1947 be 21.2",
        "label": 1,
        "table_caption": "1947 in brazilian football",
        "table_text": "position#team#points#played#won#drawn#lost#for#against#difference\n1#palmeiras#36#20#17#2#1#51#16#35\n2#corinthians#32#20#14#4#2#54#19#35\n3#portuguesa#27#20#11#5#4#43#28#15\n4#s\u00e3o paulo#25#20#8#9#3#48#27#21\n5#ypiranga - sp#21#20#9#3#8#36#26#10\n6#santos#19#20#6#7#7#33#27#6\n7#juventus#16#20#5#6#9#29#45#- 16\n8#portuguesa santista#15#20#6#3#11#27#42#- 15\n9#comercial - sp#11#20#5#1#14#25#59#- 34\n10#nacional - sp#10#20#3#4#13#25#47#- 22\n",
        "pandas_code": "df['points'].mean() == 21.2",
        "pandas_eval": "True"
    },
    {
        "id": 2017,
        "statement": "s\u00e3o paulo have more draw than any other team",
        "label": 1,
        "table_caption": "1947 in brazilian football",
        "table_text": "position#team#points#played#won#drawn#lost#for#against#difference\n1#palmeiras#36#20#17#2#1#51#16#35\n2#corinthians#32#20#14#4#2#54#19#35\n3#portuguesa#27#20#11#5#4#43#28#15\n4#s\u00e3o paulo#25#20#8#9#3#48#27#21\n5#ypiranga - sp#21#20#9#3#8#36#26#10\n6#santos#19#20#6#7#7#33#27#6\n7#juventus#16#20#5#6#9#29#45#- 16\n8#portuguesa santista#15#20#6#3#11#27#42#- 15\n9#comercial - sp#11#20#5#1#14#25#59#- 34\n10#nacional - sp#10#20#3#4#13#25#47#- 22\n",
        "pandas_code": "df[df['team'] == 's\u00e3o paulo']['drawn'].values[0] == df['drawn'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2018,
        "statement": "4 team have a negative point differential",
        "label": 1,
        "table_caption": "1947 in brazilian football",
        "table_text": "position#team#points#played#won#drawn#lost#for#against#difference\n1#palmeiras#36#20#17#2#1#51#16#35\n2#corinthians#32#20#14#4#2#54#19#35\n3#portuguesa#27#20#11#5#4#43#28#15\n4#s\u00e3o paulo#25#20#8#9#3#48#27#21\n5#ypiranga - sp#21#20#9#3#8#36#26#10\n6#santos#19#20#6#7#7#33#27#6\n7#juventus#16#20#5#6#9#29#45#- 16\n8#portuguesa santista#15#20#6#3#11#27#42#- 15\n9#comercial - sp#11#20#5#1#14#25#59#- 34\n10#nacional - sp#10#20#3#4#13#25#47#- 22\n",
        "pandas_code": "sum(df['difference'].str.contains('-')) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2019,
        "statement": "corinthian have 29 more goal for than nacional - sp",
        "label": 1,
        "table_caption": "1947 in brazilian football",
        "table_text": "position#team#points#played#won#drawn#lost#for#against#difference\n1#palmeiras#36#20#17#2#1#51#16#35\n2#corinthians#32#20#14#4#2#54#19#35\n3#portuguesa#27#20#11#5#4#43#28#15\n4#s\u00e3o paulo#25#20#8#9#3#48#27#21\n5#ypiranga - sp#21#20#9#3#8#36#26#10\n6#santos#19#20#6#7#7#33#27#6\n7#juventus#16#20#5#6#9#29#45#- 16\n8#portuguesa santista#15#20#6#3#11#27#42#- 15\n9#comercial - sp#11#20#5#1#14#25#59#- 34\n10#nacional - sp#10#20#3#4#13#25#47#- 22\n",
        "pandas_code": "(df.loc[df['team'] == 'corinthians', 'for'].values[0] - df.loc[df['team'] == 'nacional - sp', 'for'].values[0]) == 29",
        "pandas_eval": "True"
    },
    {
        "id": 2021,
        "statement": "out of the 5 rank qualifier 2 contestant score an average of 418 point",
        "label": 1,
        "table_caption": "2007 - 08 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall wc points (rank)\n1#gregor schlierenzauer#aut#217.0#231.0#442.1#1561 (2)\n2#martin koch#aut#216.5#229.5#435.2#569 (14)\n3#janne happonen#fin#203.5#220.5#418.8#755 (8)\n4#robert kranjec#slo#210.5#214.5#418.5#233 (25)\n5#janne ahonen#fin#200.0#208.5#397.2#1291 (3)\n",
        "pandas_code": "len(df[(df['points'] >= 418) & (df['points'] < 419)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2022,
        "statement": "in the 2007 - 08 fis ski jumping world cup gregor schlierenzauer achieve the highest point",
        "label": 1,
        "table_caption": "2007 - 08 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall wc points (rank)\n1#gregor schlierenzauer#aut#217.0#231.0#442.1#1561 (2)\n2#martin koch#aut#216.5#229.5#435.2#569 (14)\n3#janne happonen#fin#203.5#220.5#418.8#755 (8)\n4#robert kranjec#slo#210.5#214.5#418.5#233 (25)\n5#janne ahonen#fin#200.0#208.5#397.2#1291 (3)\n",
        "pandas_code": "df[df['name'] == 'gregor schlierenzauer']['points'].max() == df['points'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2023,
        "statement": "there be 2 contestant that have a nationality of australian",
        "label": 1,
        "table_caption": "2007 - 08 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall wc points (rank)\n1#gregor schlierenzauer#aut#217.0#231.0#442.1#1561 (2)\n2#martin koch#aut#216.5#229.5#435.2#569 (14)\n3#janne happonen#fin#203.5#220.5#418.8#755 (8)\n4#robert kranjec#slo#210.5#214.5#418.5#233 (25)\n5#janne ahonen#fin#200.0#208.5#397.2#1291 (3)\n",
        "pandas_code": "df[df['nationality'] == 'aut'].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2024,
        "statement": "there be 2 contestant that have a nationality of fin",
        "label": 1,
        "table_caption": "2007 - 08 fis ski jumping world cup",
        "table_text": "rank#name#nationality#1st (m)#2nd (m)#points#overall wc points (rank)\n1#gregor schlierenzauer#aut#217.0#231.0#442.1#1561 (2)\n2#martin koch#aut#216.5#229.5#435.2#569 (14)\n3#janne happonen#fin#203.5#220.5#418.8#755 (8)\n4#robert kranjec#slo#210.5#214.5#418.5#233 (25)\n5#janne ahonen#fin#200.0#208.5#397.2#1291 (3)\n",
        "pandas_code": "(df['nationality'] == 'fin').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2025,
        "statement": "the amsterdam admiral haven't win 2 time on the same day",
        "label": 1,
        "table_caption": "2007 amsterdam admirals season",
        "table_text": "week#date#kickoff#opponent#final score#team record#game site#attendance\n1#saturday , april 14#7:00 pm#frankfurt galaxy#l 14 - 30#0 - 1#commerzbank - arena#38125\n2#friday , april 20#8:00 pm#rhein fire#l 10 - 16#0 - 2#amsterdam arena#14611\n3#saturday , april 28#6:00 pm#berlin thunder#w 14 - 10#1 - 2#olympic stadium#11942\n4#sunday , may 6#3:00 pm#frankfurt galaxy#w 19 - 17#2 - 2#amsterdam arena#10788\n5#saturday , may 12#6:00 pm#hamburg sea devils#l 17 - 24#2 - 3#aol arena#15271\n6#friday , may 18#8:00 pm#hamburg sea devils#w 41 - 31#3 - 3#amsterdam arena#9384\n7#friday , may 25#8:00 pm#cologne centurions#l 7 - 30#3 - 4#amsterdam arena#11714\n8#sunday , june 3#4:00 pm#rhein fire#l 38 - 41#3 - 5#ltu arena#20355\n9#saturday , june 9#6:00 pm#cologne centurions#l 13 - 31#3 - 6#rheinenergiestadion#12878\n",
        "pandas_code": "len(df[df['final score'].str.startswith('w')].groupby('date').filter(lambda x: len(x) > 1)) == 0",
        "pandas_eval": "True"
    },
    {
        "id": 2026,
        "statement": "the amsterdam admiral haven't win a 4:00 pm kickoff",
        "label": 1,
        "table_caption": "2007 amsterdam admirals season",
        "table_text": "week#date#kickoff#opponent#final score#team record#game site#attendance\n1#saturday , april 14#7:00 pm#frankfurt galaxy#l 14 - 30#0 - 1#commerzbank - arena#38125\n2#friday , april 20#8:00 pm#rhein fire#l 10 - 16#0 - 2#amsterdam arena#14611\n3#saturday , april 28#6:00 pm#berlin thunder#w 14 - 10#1 - 2#olympic stadium#11942\n4#sunday , may 6#3:00 pm#frankfurt galaxy#w 19 - 17#2 - 2#amsterdam arena#10788\n5#saturday , may 12#6:00 pm#hamburg sea devils#l 17 - 24#2 - 3#aol arena#15271\n6#friday , may 18#8:00 pm#hamburg sea devils#w 41 - 31#3 - 3#amsterdam arena#9384\n7#friday , may 25#8:00 pm#cologne centurions#l 7 - 30#3 - 4#amsterdam arena#11714\n8#sunday , june 3#4:00 pm#rhein fire#l 38 - 41#3 - 5#ltu arena#20355\n9#saturday , june 9#6:00 pm#cologne centurions#l 13 - 31#3 - 6#rheinenergiestadion#12878\n",
        "pandas_code": "not any(df[df['kickoff'] == '4:00 pm']['final score'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 2027,
        "statement": "the amsterdam admiral have never win against rhein fire",
        "label": 1,
        "table_caption": "2007 amsterdam admirals season",
        "table_text": "week#date#kickoff#opponent#final score#team record#game site#attendance\n1#saturday , april 14#7:00 pm#frankfurt galaxy#l 14 - 30#0 - 1#commerzbank - arena#38125\n2#friday , april 20#8:00 pm#rhein fire#l 10 - 16#0 - 2#amsterdam arena#14611\n3#saturday , april 28#6:00 pm#berlin thunder#w 14 - 10#1 - 2#olympic stadium#11942\n4#sunday , may 6#3:00 pm#frankfurt galaxy#w 19 - 17#2 - 2#amsterdam arena#10788\n5#saturday , may 12#6:00 pm#hamburg sea devils#l 17 - 24#2 - 3#aol arena#15271\n6#friday , may 18#8:00 pm#hamburg sea devils#w 41 - 31#3 - 3#amsterdam arena#9384\n7#friday , may 25#8:00 pm#cologne centurions#l 7 - 30#3 - 4#amsterdam arena#11714\n8#sunday , june 3#4:00 pm#rhein fire#l 38 - 41#3 - 5#ltu arena#20355\n9#saturday , june 9#6:00 pm#cologne centurions#l 13 - 31#3 - 6#rheinenergiestadion#12878\n",
        "pandas_code": "not any(df[df['opponent'] == 'rhein fire']['final score'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 2028,
        "statement": "the amsterdam admiral average more attendance on day they lose",
        "label": 1,
        "table_caption": "2007 amsterdam admirals season",
        "table_text": "week#date#kickoff#opponent#final score#team record#game site#attendance\n1#saturday , april 14#7:00 pm#frankfurt galaxy#l 14 - 30#0 - 1#commerzbank - arena#38125\n2#friday , april 20#8:00 pm#rhein fire#l 10 - 16#0 - 2#amsterdam arena#14611\n3#saturday , april 28#6:00 pm#berlin thunder#w 14 - 10#1 - 2#olympic stadium#11942\n4#sunday , may 6#3:00 pm#frankfurt galaxy#w 19 - 17#2 - 2#amsterdam arena#10788\n5#saturday , may 12#6:00 pm#hamburg sea devils#l 17 - 24#2 - 3#aol arena#15271\n6#friday , may 18#8:00 pm#hamburg sea devils#w 41 - 31#3 - 3#amsterdam arena#9384\n7#friday , may 25#8:00 pm#cologne centurions#l 7 - 30#3 - 4#amsterdam arena#11714\n8#sunday , june 3#4:00 pm#rhein fire#l 38 - 41#3 - 5#ltu arena#20355\n9#saturday , june 9#6:00 pm#cologne centurions#l 13 - 31#3 - 6#rheinenergiestadion#12878\n",
        "pandas_code": "df.groupby(df['final score'].str.lower().str.startswith('l'))['attendance'].mean().get(True, 0) > df.groupby(df['final score'].str.lower().str.startswith('l'))['attendance'].mean().get(False, 0)",
        "pandas_eval": "True"
    },
    {
        "id": 2029,
        "statement": "the amsterdam admiral have never win at aol arena or ltu arena",
        "label": 1,
        "table_caption": "2007 amsterdam admirals season",
        "table_text": "week#date#kickoff#opponent#final score#team record#game site#attendance\n1#saturday , april 14#7:00 pm#frankfurt galaxy#l 14 - 30#0 - 1#commerzbank - arena#38125\n2#friday , april 20#8:00 pm#rhein fire#l 10 - 16#0 - 2#amsterdam arena#14611\n3#saturday , april 28#6:00 pm#berlin thunder#w 14 - 10#1 - 2#olympic stadium#11942\n4#sunday , may 6#3:00 pm#frankfurt galaxy#w 19 - 17#2 - 2#amsterdam arena#10788\n5#saturday , may 12#6:00 pm#hamburg sea devils#l 17 - 24#2 - 3#aol arena#15271\n6#friday , may 18#8:00 pm#hamburg sea devils#w 41 - 31#3 - 3#amsterdam arena#9384\n7#friday , may 25#8:00 pm#cologne centurions#l 7 - 30#3 - 4#amsterdam arena#11714\n8#sunday , june 3#4:00 pm#rhein fire#l 38 - 41#3 - 5#ltu arena#20355\n9#saturday , june 9#6:00 pm#cologne centurions#l 13 - 31#3 - 6#rheinenergiestadion#12878\n",
        "pandas_code": "not any(df.loc[df['game site'].isin(['aol arena', 'ltu arena']), 'final score'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 2030,
        "statement": "perth glory be the oldest club",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "team#location#stadium#founded#joined#head coach#captain\nadelaide united#adelaide , sa#coopers stadium#2003#2005#josep gombau#eugene galekovi\u00e4\u2021\nbrisbane roar#brisbane , qld#suncorp stadium#2004#2005#mike mulvey#matt smith\ncentral coast mariners#gosford , nsw#bluetongue stadium#2004#2005#graham arnold#john hutchinson\nmelbourne heart#melbourne , vic#aami park#2008#2010#john aloisi#harry kewell\nmelbourne victory#melbourne , vic#aami park & etihad stadium#2004#2005#kevin muscat#mark milligan\nnewcastle jets#newcastle , nsw#hunter stadium#2000#2005#gary van egmond#ruben zadkovich\nperth glory#perth , wa#nib stadium#1995#2005#alistair edwards#jacob burns\nsydney fc#sydney , nsw#allianz stadium#2004#2005#frank farina#alessandro del piero\nwellington phoenix#wellington , nzl#westpac stadium#2007#2007#ernie merrick#andrew durante\n",
        "pandas_code": "df[df['team'] == 'perth glory']['founded'].iloc[0] == df['founded'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2031,
        "statement": "melbourne heart be the newest club",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "team#location#stadium#founded#joined#head coach#captain\nadelaide united#adelaide , sa#coopers stadium#2003#2005#josep gombau#eugene galekovi\u00e4\u2021\nbrisbane roar#brisbane , qld#suncorp stadium#2004#2005#mike mulvey#matt smith\ncentral coast mariners#gosford , nsw#bluetongue stadium#2004#2005#graham arnold#john hutchinson\nmelbourne heart#melbourne , vic#aami park#2008#2010#john aloisi#harry kewell\nmelbourne victory#melbourne , vic#aami park & etihad stadium#2004#2005#kevin muscat#mark milligan\nnewcastle jets#newcastle , nsw#hunter stadium#2000#2005#gary van egmond#ruben zadkovich\nperth glory#perth , wa#nib stadium#1995#2005#alistair edwards#jacob burns\nsydney fc#sydney , nsw#allianz stadium#2004#2005#frank farina#alessandro del piero\nwellington phoenix#wellington , nzl#westpac stadium#2007#2007#ernie merrick#andrew durante\n",
        "pandas_code": "df[df['team'] == 'melbourne heart']['joined'].max() == df['joined'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2032,
        "statement": "melbourne heart and victory share a stadium",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "team#location#stadium#founded#joined#head coach#captain\nadelaide united#adelaide , sa#coopers stadium#2003#2005#josep gombau#eugene galekovi\u00e4\u2021\nbrisbane roar#brisbane , qld#suncorp stadium#2004#2005#mike mulvey#matt smith\ncentral coast mariners#gosford , nsw#bluetongue stadium#2004#2005#graham arnold#john hutchinson\nmelbourne heart#melbourne , vic#aami park#2008#2010#john aloisi#harry kewell\nmelbourne victory#melbourne , vic#aami park & etihad stadium#2004#2005#kevin muscat#mark milligan\nnewcastle jets#newcastle , nsw#hunter stadium#2000#2005#gary van egmond#ruben zadkovich\nperth glory#perth , wa#nib stadium#1995#2005#alistair edwards#jacob burns\nsydney fc#sydney , nsw#allianz stadium#2004#2005#frank farina#alessandro del piero\nwellington phoenix#wellington , nzl#westpac stadium#2007#2007#ernie merrick#andrew durante\n",
        "pandas_code": "any('aami park' in stadium for stadium in df[df['team'].isin(['melbourne heart', 'melbourne victory'])]['stadium'])",
        "pandas_eval": "True"
    },
    {
        "id": 2033,
        "statement": "4 team be found in 2004",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "team#location#stadium#founded#joined#head coach#captain\nadelaide united#adelaide , sa#coopers stadium#2003#2005#josep gombau#eugene galekovi\u00e4\u2021\nbrisbane roar#brisbane , qld#suncorp stadium#2004#2005#mike mulvey#matt smith\ncentral coast mariners#gosford , nsw#bluetongue stadium#2004#2005#graham arnold#john hutchinson\nmelbourne heart#melbourne , vic#aami park#2008#2010#john aloisi#harry kewell\nmelbourne victory#melbourne , vic#aami park & etihad stadium#2004#2005#kevin muscat#mark milligan\nnewcastle jets#newcastle , nsw#hunter stadium#2000#2005#gary van egmond#ruben zadkovich\nperth glory#perth , wa#nib stadium#1995#2005#alistair edwards#jacob burns\nsydney fc#sydney , nsw#allianz stadium#2004#2005#frank farina#alessandro del piero\nwellington phoenix#wellington , nzl#westpac stadium#2007#2007#ernie merrick#andrew durante\n",
        "pandas_code": "len(df[df['founded'] == 2004]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2034,
        "statement": "melbourne be the only city with 2 team participate in a league",
        "label": 1,
        "table_caption": "a - league",
        "table_text": "team#location#stadium#founded#joined#head coach#captain\nadelaide united#adelaide , sa#coopers stadium#2003#2005#josep gombau#eugene galekovi\u00e4\u2021\nbrisbane roar#brisbane , qld#suncorp stadium#2004#2005#mike mulvey#matt smith\ncentral coast mariners#gosford , nsw#bluetongue stadium#2004#2005#graham arnold#john hutchinson\nmelbourne heart#melbourne , vic#aami park#2008#2010#john aloisi#harry kewell\nmelbourne victory#melbourne , vic#aami park & etihad stadium#2004#2005#kevin muscat#mark milligan\nnewcastle jets#newcastle , nsw#hunter stadium#2000#2005#gary van egmond#ruben zadkovich\nperth glory#perth , wa#nib stadium#1995#2005#alistair edwards#jacob burns\nsydney fc#sydney , nsw#allianz stadium#2004#2005#frank farina#alessandro del piero\nwellington phoenix#wellington , nzl#westpac stadium#2007#2007#ernie merrick#andrew durante\n",
        "pandas_code": "(df['location'].str.contains('melbourne').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2035,
        "statement": "the highest - attend game be against washington at the mci center",
        "label": 1,
        "table_caption": "2002 - 03 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n56#march 2#boston#w 104 - 92 (ot)#antonio davis (19)#michael bradley (13)#alvin williams (6)#air canada centre 19800#18 - 38\n57#march 4#washington#w 89 - 86 (ot)#vince carter (24)#michael bradley , jerome williams (7)#antonio davis (5)#mci center 20173#19 - 38\n58#march 5#houston#l 95 - 97 (ot)#vince carter (21)#jerome williams (10)#antonio davis (6)#air canada centre 20171#19 - 39\n59#march 8#atlanta#w 107 - 98 (ot)#vince carter (43)#jerome williams (15)#antonio davis (8)#philips arena 19445#20 - 39\n60#march 9#memphis#l 106 - 119 (ot)#vince carter (26)#antonio davis (8)#alvin williams (9)#air canada centre 19138#20 - 40\n61#march 11#denver#l 87 - 95 (ot)#vince carter (21)#michael bradley (12)#alvin williams (6)#pepsi center 13409#20 - 41\n62#march 12#portland#l 103 - 125 (ot)#vince carter (21)#michael bradley (10)#rafer alston (6)#rose garden 19991#20 - 42\n63#march 14#sacramento#l 84 - 119 (ot)#vince carter , morris peterson (16)#mamadou n'diaye (10)#rafer alston (7)#arco arena 17317#20 - 43\n64#march 16#la clippers#l 110 - 111 (ot)#vince carter (28)#antonio davis , jerome williams (8)#alvin williams (5)#staples center 18268#20 - 44\n65#march 17#phoenix#l 91 - 95 (ot)#morris peterson (17)#antonio davis (15)#alvin williams (7)#america west arena 15326#20 - 45\n66#march 19#atlanta#w 87 - 86 (ot)#vince carter (27)#jerome williams (10)#alvin williams (6)#air canada centre 17885#21 - 45\n67#march 21#miami#l 98 - 107 (ot)#vince carter (30)#jerome williams (9)#alvin williams (7)#american airlines arena 14492#21 - 46\n68#march 23#philadelphia#l 95 - 112 (ot)#vince carter (22)#antonio davis (9)#vince carter (9)#air canada centre 19800#21 - 47\n69#march 24#new york#l 90 - 100 (ot)#antonio davis (23)#antonio davis (12)#alvin williams (8)#madison square garden 18824#21 - 48\n70#march 26#cleveland#w 89 - 83 (ot)#morris peterson (21)#jelani mccoy (8)#rafer alston (6)#air canada centre 16832#22 - 48\n71#march 28#new orleans#l 92 - 101 (ot)#vince carter (21)#michael bradley (11)#alvin williams (5)#air canada centre 18773#22 - 49\n",
        "pandas_code": "df[df['team'] == 'washington']['location attendance'].str.extract('(\\d+)')[0].astype(int).max() == 20173",
        "pandas_eval": "True"
    },
    {
        "id": 2036,
        "statement": "the average attendance of each game during march of the 2002 - 03 toronto raptor season be 18102.75",
        "label": 1,
        "table_caption": "2002 - 03 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n56#march 2#boston#w 104 - 92 (ot)#antonio davis (19)#michael bradley (13)#alvin williams (6)#air canada centre 19800#18 - 38\n57#march 4#washington#w 89 - 86 (ot)#vince carter (24)#michael bradley , jerome williams (7)#antonio davis (5)#mci center 20173#19 - 38\n58#march 5#houston#l 95 - 97 (ot)#vince carter (21)#jerome williams (10)#antonio davis (6)#air canada centre 20171#19 - 39\n59#march 8#atlanta#w 107 - 98 (ot)#vince carter (43)#jerome williams (15)#antonio davis (8)#philips arena 19445#20 - 39\n60#march 9#memphis#l 106 - 119 (ot)#vince carter (26)#antonio davis (8)#alvin williams (9)#air canada centre 19138#20 - 40\n61#march 11#denver#l 87 - 95 (ot)#vince carter (21)#michael bradley (12)#alvin williams (6)#pepsi center 13409#20 - 41\n62#march 12#portland#l 103 - 125 (ot)#vince carter (21)#michael bradley (10)#rafer alston (6)#rose garden 19991#20 - 42\n63#march 14#sacramento#l 84 - 119 (ot)#vince carter , morris peterson (16)#mamadou n'diaye (10)#rafer alston (7)#arco arena 17317#20 - 43\n64#march 16#la clippers#l 110 - 111 (ot)#vince carter (28)#antonio davis , jerome williams (8)#alvin williams (5)#staples center 18268#20 - 44\n65#march 17#phoenix#l 91 - 95 (ot)#morris peterson (17)#antonio davis (15)#alvin williams (7)#america west arena 15326#20 - 45\n66#march 19#atlanta#w 87 - 86 (ot)#vince carter (27)#jerome williams (10)#alvin williams (6)#air canada centre 17885#21 - 45\n67#march 21#miami#l 98 - 107 (ot)#vince carter (30)#jerome williams (9)#alvin williams (7)#american airlines arena 14492#21 - 46\n68#march 23#philadelphia#l 95 - 112 (ot)#vince carter (22)#antonio davis (9)#vince carter (9)#air canada centre 19800#21 - 47\n69#march 24#new york#l 90 - 100 (ot)#antonio davis (23)#antonio davis (12)#alvin williams (8)#madison square garden 18824#21 - 48\n70#march 26#cleveland#w 89 - 83 (ot)#morris peterson (21)#jelani mccoy (8)#rafer alston (6)#air canada centre 16832#22 - 48\n71#march 28#new orleans#l 92 - 101 (ot)#vince carter (21)#michael bradley (11)#alvin williams (5)#air canada centre 18773#22 - 49\n",
        "pandas_code": "abs(df['location attendance'].str.extract('(\\d+)')[0].astype(int).mean() - 18102.75) < 1e-4",
        "pandas_eval": "True"
    },
    {
        "id": 2037,
        "statement": "vince carter be the high point scorer more often than any other player during march of the 2002 - 03 toronto raptor season",
        "label": 1,
        "table_caption": "2002 - 03 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n56#march 2#boston#w 104 - 92 (ot)#antonio davis (19)#michael bradley (13)#alvin williams (6)#air canada centre 19800#18 - 38\n57#march 4#washington#w 89 - 86 (ot)#vince carter (24)#michael bradley , jerome williams (7)#antonio davis (5)#mci center 20173#19 - 38\n58#march 5#houston#l 95 - 97 (ot)#vince carter (21)#jerome williams (10)#antonio davis (6)#air canada centre 20171#19 - 39\n59#march 8#atlanta#w 107 - 98 (ot)#vince carter (43)#jerome williams (15)#antonio davis (8)#philips arena 19445#20 - 39\n60#march 9#memphis#l 106 - 119 (ot)#vince carter (26)#antonio davis (8)#alvin williams (9)#air canada centre 19138#20 - 40\n61#march 11#denver#l 87 - 95 (ot)#vince carter (21)#michael bradley (12)#alvin williams (6)#pepsi center 13409#20 - 41\n62#march 12#portland#l 103 - 125 (ot)#vince carter (21)#michael bradley (10)#rafer alston (6)#rose garden 19991#20 - 42\n63#march 14#sacramento#l 84 - 119 (ot)#vince carter , morris peterson (16)#mamadou n'diaye (10)#rafer alston (7)#arco arena 17317#20 - 43\n64#march 16#la clippers#l 110 - 111 (ot)#vince carter (28)#antonio davis , jerome williams (8)#alvin williams (5)#staples center 18268#20 - 44\n65#march 17#phoenix#l 91 - 95 (ot)#morris peterson (17)#antonio davis (15)#alvin williams (7)#america west arena 15326#20 - 45\n66#march 19#atlanta#w 87 - 86 (ot)#vince carter (27)#jerome williams (10)#alvin williams (6)#air canada centre 17885#21 - 45\n67#march 21#miami#l 98 - 107 (ot)#vince carter (30)#jerome williams (9)#alvin williams (7)#american airlines arena 14492#21 - 46\n68#march 23#philadelphia#l 95 - 112 (ot)#vince carter (22)#antonio davis (9)#vince carter (9)#air canada centre 19800#21 - 47\n69#march 24#new york#l 90 - 100 (ot)#antonio davis (23)#antonio davis (12)#alvin williams (8)#madison square garden 18824#21 - 48\n70#march 26#cleveland#w 89 - 83 (ot)#morris peterson (21)#jelani mccoy (8)#rafer alston (6)#air canada centre 16832#22 - 48\n71#march 28#new orleans#l 92 - 101 (ot)#vince carter (21)#michael bradley (11)#alvin williams (5)#air canada centre 18773#22 - 49\n",
        "pandas_code": "df[df['date'].str.contains('march')]['high points'].str.extract(r'^(.*?) \\(')[0].value_counts().idxmax() == 'vince carter'",
        "pandas_eval": "True"
    },
    {
        "id": 2038,
        "statement": "the toronto raptor win 5 game during march of the 2002 - 03 season",
        "label": 1,
        "table_caption": "2002 - 03 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n56#march 2#boston#w 104 - 92 (ot)#antonio davis (19)#michael bradley (13)#alvin williams (6)#air canada centre 19800#18 - 38\n57#march 4#washington#w 89 - 86 (ot)#vince carter (24)#michael bradley , jerome williams (7)#antonio davis (5)#mci center 20173#19 - 38\n58#march 5#houston#l 95 - 97 (ot)#vince carter (21)#jerome williams (10)#antonio davis (6)#air canada centre 20171#19 - 39\n59#march 8#atlanta#w 107 - 98 (ot)#vince carter (43)#jerome williams (15)#antonio davis (8)#philips arena 19445#20 - 39\n60#march 9#memphis#l 106 - 119 (ot)#vince carter (26)#antonio davis (8)#alvin williams (9)#air canada centre 19138#20 - 40\n61#march 11#denver#l 87 - 95 (ot)#vince carter (21)#michael bradley (12)#alvin williams (6)#pepsi center 13409#20 - 41\n62#march 12#portland#l 103 - 125 (ot)#vince carter (21)#michael bradley (10)#rafer alston (6)#rose garden 19991#20 - 42\n63#march 14#sacramento#l 84 - 119 (ot)#vince carter , morris peterson (16)#mamadou n'diaye (10)#rafer alston (7)#arco arena 17317#20 - 43\n64#march 16#la clippers#l 110 - 111 (ot)#vince carter (28)#antonio davis , jerome williams (8)#alvin williams (5)#staples center 18268#20 - 44\n65#march 17#phoenix#l 91 - 95 (ot)#morris peterson (17)#antonio davis (15)#alvin williams (7)#america west arena 15326#20 - 45\n66#march 19#atlanta#w 87 - 86 (ot)#vince carter (27)#jerome williams (10)#alvin williams (6)#air canada centre 17885#21 - 45\n67#march 21#miami#l 98 - 107 (ot)#vince carter (30)#jerome williams (9)#alvin williams (7)#american airlines arena 14492#21 - 46\n68#march 23#philadelphia#l 95 - 112 (ot)#vince carter (22)#antonio davis (9)#vince carter (9)#air canada centre 19800#21 - 47\n69#march 24#new york#l 90 - 100 (ot)#antonio davis (23)#antonio davis (12)#alvin williams (8)#madison square garden 18824#21 - 48\n70#march 26#cleveland#w 89 - 83 (ot)#morris peterson (21)#jelani mccoy (8)#rafer alston (6)#air canada centre 16832#22 - 48\n71#march 28#new orleans#l 92 - 101 (ot)#vince carter (21)#michael bradley (11)#alvin williams (5)#air canada centre 18773#22 - 49\n",
        "pandas_code": "len(df[(df['date'].str.contains('march')) & (df['score'].str.startswith('w'))]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2039,
        "statement": "the average number of high rebound be 10.44 during march of the 2002 - 03 toronto raptor season",
        "label": 1,
        "table_caption": "2002 - 03 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n56#march 2#boston#w 104 - 92 (ot)#antonio davis (19)#michael bradley (13)#alvin williams (6)#air canada centre 19800#18 - 38\n57#march 4#washington#w 89 - 86 (ot)#vince carter (24)#michael bradley , jerome williams (7)#antonio davis (5)#mci center 20173#19 - 38\n58#march 5#houston#l 95 - 97 (ot)#vince carter (21)#jerome williams (10)#antonio davis (6)#air canada centre 20171#19 - 39\n59#march 8#atlanta#w 107 - 98 (ot)#vince carter (43)#jerome williams (15)#antonio davis (8)#philips arena 19445#20 - 39\n60#march 9#memphis#l 106 - 119 (ot)#vince carter (26)#antonio davis (8)#alvin williams (9)#air canada centre 19138#20 - 40\n61#march 11#denver#l 87 - 95 (ot)#vince carter (21)#michael bradley (12)#alvin williams (6)#pepsi center 13409#20 - 41\n62#march 12#portland#l 103 - 125 (ot)#vince carter (21)#michael bradley (10)#rafer alston (6)#rose garden 19991#20 - 42\n63#march 14#sacramento#l 84 - 119 (ot)#vince carter , morris peterson (16)#mamadou n'diaye (10)#rafer alston (7)#arco arena 17317#20 - 43\n64#march 16#la clippers#l 110 - 111 (ot)#vince carter (28)#antonio davis , jerome williams (8)#alvin williams (5)#staples center 18268#20 - 44\n65#march 17#phoenix#l 91 - 95 (ot)#morris peterson (17)#antonio davis (15)#alvin williams (7)#america west arena 15326#20 - 45\n66#march 19#atlanta#w 87 - 86 (ot)#vince carter (27)#jerome williams (10)#alvin williams (6)#air canada centre 17885#21 - 45\n67#march 21#miami#l 98 - 107 (ot)#vince carter (30)#jerome williams (9)#alvin williams (7)#american airlines arena 14492#21 - 46\n68#march 23#philadelphia#l 95 - 112 (ot)#vince carter (22)#antonio davis (9)#vince carter (9)#air canada centre 19800#21 - 47\n69#march 24#new york#l 90 - 100 (ot)#antonio davis (23)#antonio davis (12)#alvin williams (8)#madison square garden 18824#21 - 48\n70#march 26#cleveland#w 89 - 83 (ot)#morris peterson (21)#jelani mccoy (8)#rafer alston (6)#air canada centre 16832#22 - 48\n71#march 28#new orleans#l 92 - 101 (ot)#vince carter (21)#michael bradley (11)#alvin williams (5)#air canada centre 18773#22 - 49\n",
        "pandas_code": "round(df[df['date'].str.startswith('march')]['high rebounds'].apply(lambda x: int(x.split('(')[1].split(')')[0])).mean(), 2) == 10.44",
        "pandas_eval": "True"
    },
    {
        "id": 2040,
        "statement": "among the player with + 9 to par , rich beem win the most recently",
        "label": 1,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['to par'] == '+ 9'].sort_values(by='year (s) won', key=lambda x: x.str.extract('(\\d+)', expand=False).astype(int), ascending=False).iloc[0]['player'] == 'rich beem'",
        "pandas_eval": "True"
    },
    {
        "id": 2041,
        "statement": "among the player with + 9 to par , bob tway win the longest ago",
        "label": 1,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[(df['to par'] == '+ 9') & (df['player'] == 'bob tway')]['year (s) won'].str.split(', ').explode().astype(int).min() == 1986",
        "pandas_eval": "True"
    },
    {
        "id": 2043,
        "statement": "among the player with + 12 to par , davis love iii be the only 1 from the united state",
        "label": 1,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "(df[(df['to par'] == '+ 12') & (df['country'] == 'united states')].shape[0] == 1) & (df[(df['to par'] == '+ 12') & (df['player'] == 'davis love iii')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2044,
        "statement": "vijay singh be the only player not from the united state",
        "label": 1,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['player'] == 'vijay singh']['country'].eq('fiji').all() and df[df['player'] != 'vijay singh']['country'].eq('united states').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2045,
        "statement": "among the player with + 9 to par , bob tway win the most recently",
        "label": 0,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['to par'] == '+ 9'].sort_values(by='year (s) won', ascending=False).iloc[0]['player'] == 'bob tway'",
        "pandas_eval": "False"
    },
    {
        "id": 2046,
        "statement": "among the player with + 9 to par , vijay singh win the longest ago",
        "label": 0,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['to par'] == '+ 9']['year (s) won'].apply(lambda x: min(map(int, x.split(' , ')))).min() > min(map(int, df[df['player'] == 'vijay singh']['year (s) won'].str.split(' , ').explode().astype(int)))",
        "pandas_eval": "False"
    },
    {
        "id": 2047,
        "statement": "vijay singh and bob tway be the only player who win in 2 separate year",
        "label": 0,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['player'].isin(['vijay singh', 'bob tway'])]['year (s) won'].apply(lambda x: len(x.split(','))).eq(2).all() and ~df[~df['player'].isin(['vijay singh', 'bob tway'])]['year (s) won'].apply(lambda x: len(x.split(','))).gt(1).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2048,
        "statement": "mong the player with + 12 to par , vijay singh be the only 1 from the united state",
        "label": 0,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[(df['to par'] == '+ 12') & (df['country'] == 'united states')].shape[0] == 1 and df[(df['to par'] == '+ 12') & (df['player'] == 'vijay singh')].shape[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 2049,
        "statement": "mark brook be the only player not from the united state",
        "label": 0,
        "table_caption": "2008 pga championship",
        "table_text": "player#country#year (s) won#total#to par\nrich beem#united states#2002#149#+ 9\njohn daly#united states#1991#149#+ 9\nbob tway#united states#1986#149#+ 9\nmark brooks#united states#1996#150#+ 10\ndavis love iii#united states#1997#152#+ 12\nvijay singh#fiji#1998 , 2004#152#+ 12\n",
        "pandas_code": "df[df['player'] == 'mark brooks']['country'].eq('united states').all() == False",
        "pandas_eval": "False"
    },
    {
        "id": 2050,
        "statement": "the maryland terrapin have the most point in their game against siena in loudonville , ny",
        "label": 1,
        "table_caption": "2005 - 06 maryland terrapins women 's basketball team",
        "table_text": "date#location#opponent#terps points#opp points#record\nnov 18 / 05#loudonville , ny#siena#107#66#1 - 0\nnov 20 / 05#college park , md#xavier#93#68#2 - 0\nnov 24 / 05#st thomas , us virgin islands#gonzaga#88#50#3 - 0\nnov 25 / 05#st thomas , us virgin islands#michigan state#75#61#4 - 0\nnov 26 / 05#st thomas , us virgin islands#tennessee#75#80#4 - 1\ndec 1 / 05#college park#appalachian state#118#59#5 - 1\ndec 4 / 05#college park#mount st mary 's#102#53#6 - 1\ndec 7 / 05#west long branch , nj#monmouth#88#55#7 - 1\ndec 11 / 05#college park#arizona#92#67#8 - 1\ndec 13 / 05#college park#coppin state#69#38#9 - 1\ndec 22 / 05#fairfax , virginia#george mason#70#33#10 - 1\ndec 29 / 05#college park#central connecticut state#94#57#11 - 1\ndec 29 / 05#college park#furman#81#86#11 - 2\ndec 30 / 05#college park#furman#92#58#12 - 2\njan 2 / 06#college park#manhattan#82#49#13 - 2\njan 5 / 06#chestnut hill , massachusetts#boston college#67#64#14 - 2\njan 8 / 06#college park#duke#68#86#14 - 3\njan 16 / 06#college park#florida state#75#57#15 - 3\njan 20 / 06#charlottesville , virginia#virginia#84#74#16 - 3\njan 23 / 06#blacksburg , virginia#virginia tech#68#62#17 - 3\njan 26 / 06#college park#georgia tech#79#71#18 - 3\n",
        "pandas_code": "df[(df['opponent'] == 'siena') & (df['location'] == 'loudonville , ny')]['terps points'].values[0] > df[(df['opponent'] == 'siena') & (df['location'] == 'loudonville , ny')]['opp points'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2051,
        "statement": "the maryland terrapin have the least point in their game against boston college in chestnut hill , massachusetts",
        "label": 1,
        "table_caption": "2005 - 06 maryland terrapins women 's basketball team",
        "table_text": "date#location#opponent#terps points#opp points#record\nnov 18 / 05#loudonville , ny#siena#107#66#1 - 0\nnov 20 / 05#college park , md#xavier#93#68#2 - 0\nnov 24 / 05#st thomas , us virgin islands#gonzaga#88#50#3 - 0\nnov 25 / 05#st thomas , us virgin islands#michigan state#75#61#4 - 0\nnov 26 / 05#st thomas , us virgin islands#tennessee#75#80#4 - 1\ndec 1 / 05#college park#appalachian state#118#59#5 - 1\ndec 4 / 05#college park#mount st mary 's#102#53#6 - 1\ndec 7 / 05#west long branch , nj#monmouth#88#55#7 - 1\ndec 11 / 05#college park#arizona#92#67#8 - 1\ndec 13 / 05#college park#coppin state#69#38#9 - 1\ndec 22 / 05#fairfax , virginia#george mason#70#33#10 - 1\ndec 29 / 05#college park#central connecticut state#94#57#11 - 1\ndec 29 / 05#college park#furman#81#86#11 - 2\ndec 30 / 05#college park#furman#92#58#12 - 2\njan 2 / 06#college park#manhattan#82#49#13 - 2\njan 5 / 06#chestnut hill , massachusetts#boston college#67#64#14 - 2\njan 8 / 06#college park#duke#68#86#14 - 3\njan 16 / 06#college park#florida state#75#57#15 - 3\njan 20 / 06#charlottesville , virginia#virginia#84#74#16 - 3\njan 23 / 06#blacksburg , virginia#virginia tech#68#62#17 - 3\njan 26 / 06#college park#georgia tech#79#71#18 - 3\n",
        "pandas_code": "df.loc[(df['opponent'] == 'boston college') & (df['location'] == 'chestnut hill , massachusetts'), 'terps points'].min() == df['terps points'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2052,
        "statement": "the marlyland terrapins played 12 game in college park",
        "label": 1,
        "table_caption": "2005 - 06 maryland terrapins women 's basketball team",
        "table_text": "date#location#opponent#terps points#opp points#record\nnov 18 / 05#loudonville , ny#siena#107#66#1 - 0\nnov 20 / 05#college park , md#xavier#93#68#2 - 0\nnov 24 / 05#st thomas , us virgin islands#gonzaga#88#50#3 - 0\nnov 25 / 05#st thomas , us virgin islands#michigan state#75#61#4 - 0\nnov 26 / 05#st thomas , us virgin islands#tennessee#75#80#4 - 1\ndec 1 / 05#college park#appalachian state#118#59#5 - 1\ndec 4 / 05#college park#mount st mary 's#102#53#6 - 1\ndec 7 / 05#west long branch , nj#monmouth#88#55#7 - 1\ndec 11 / 05#college park#arizona#92#67#8 - 1\ndec 13 / 05#college park#coppin state#69#38#9 - 1\ndec 22 / 05#fairfax , virginia#george mason#70#33#10 - 1\ndec 29 / 05#college park#central connecticut state#94#57#11 - 1\ndec 29 / 05#college park#furman#81#86#11 - 2\ndec 30 / 05#college park#furman#92#58#12 - 2\njan 2 / 06#college park#manhattan#82#49#13 - 2\njan 5 / 06#chestnut hill , massachusetts#boston college#67#64#14 - 2\njan 8 / 06#college park#duke#68#86#14 - 3\njan 16 / 06#college park#florida state#75#57#15 - 3\njan 20 / 06#charlottesville , virginia#virginia#84#74#16 - 3\njan 23 / 06#blacksburg , virginia#virginia tech#68#62#17 - 3\njan 26 / 06#college park#georgia tech#79#71#18 - 3\n",
        "pandas_code": "len(df[df['location'].str.contains('college park')]) == 12",
        "pandas_eval": "True"
    },
    {
        "id": 2053,
        "statement": "the maryland terrapins play only 1 game in blacksburg , virginia",
        "label": 1,
        "table_caption": "2005 - 06 maryland terrapins women 's basketball team",
        "table_text": "date#location#opponent#terps points#opp points#record\nnov 18 / 05#loudonville , ny#siena#107#66#1 - 0\nnov 20 / 05#college park , md#xavier#93#68#2 - 0\nnov 24 / 05#st thomas , us virgin islands#gonzaga#88#50#3 - 0\nnov 25 / 05#st thomas , us virgin islands#michigan state#75#61#4 - 0\nnov 26 / 05#st thomas , us virgin islands#tennessee#75#80#4 - 1\ndec 1 / 05#college park#appalachian state#118#59#5 - 1\ndec 4 / 05#college park#mount st mary 's#102#53#6 - 1\ndec 7 / 05#west long branch , nj#monmouth#88#55#7 - 1\ndec 11 / 05#college park#arizona#92#67#8 - 1\ndec 13 / 05#college park#coppin state#69#38#9 - 1\ndec 22 / 05#fairfax , virginia#george mason#70#33#10 - 1\ndec 29 / 05#college park#central connecticut state#94#57#11 - 1\ndec 29 / 05#college park#furman#81#86#11 - 2\ndec 30 / 05#college park#furman#92#58#12 - 2\njan 2 / 06#college park#manhattan#82#49#13 - 2\njan 5 / 06#chestnut hill , massachusetts#boston college#67#64#14 - 2\njan 8 / 06#college park#duke#68#86#14 - 3\njan 16 / 06#college park#florida state#75#57#15 - 3\njan 20 / 06#charlottesville , virginia#virginia#84#74#16 - 3\njan 23 / 06#blacksburg , virginia#virginia tech#68#62#17 - 3\njan 26 / 06#college park#georgia tech#79#71#18 - 3\n",
        "pandas_code": "len(df[df['location'] == 'blacksburg , virginia']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2054,
        "statement": "the maryland terrapins play only 1 game in fairfax , virginia",
        "label": 1,
        "table_caption": "2005 - 06 maryland terrapins women 's basketball team",
        "table_text": "date#location#opponent#terps points#opp points#record\nnov 18 / 05#loudonville , ny#siena#107#66#1 - 0\nnov 20 / 05#college park , md#xavier#93#68#2 - 0\nnov 24 / 05#st thomas , us virgin islands#gonzaga#88#50#3 - 0\nnov 25 / 05#st thomas , us virgin islands#michigan state#75#61#4 - 0\nnov 26 / 05#st thomas , us virgin islands#tennessee#75#80#4 - 1\ndec 1 / 05#college park#appalachian state#118#59#5 - 1\ndec 4 / 05#college park#mount st mary 's#102#53#6 - 1\ndec 7 / 05#west long branch , nj#monmouth#88#55#7 - 1\ndec 11 / 05#college park#arizona#92#67#8 - 1\ndec 13 / 05#college park#coppin state#69#38#9 - 1\ndec 22 / 05#fairfax , virginia#george mason#70#33#10 - 1\ndec 29 / 05#college park#central connecticut state#94#57#11 - 1\ndec 29 / 05#college park#furman#81#86#11 - 2\ndec 30 / 05#college park#furman#92#58#12 - 2\njan 2 / 06#college park#manhattan#82#49#13 - 2\njan 5 / 06#chestnut hill , massachusetts#boston college#67#64#14 - 2\njan 8 / 06#college park#duke#68#86#14 - 3\njan 16 / 06#college park#florida state#75#57#15 - 3\njan 20 / 06#charlottesville , virginia#virginia#84#74#16 - 3\njan 23 / 06#blacksburg , virginia#virginia tech#68#62#17 - 3\njan 26 / 06#college park#georgia tech#79#71#18 - 3\n",
        "pandas_code": "len(df[df['location'] == 'fairfax , virginia']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2055,
        "statement": "all of the goal that john carew get in 2007 be at the ullevaal stadion , oslo venue",
        "label": 1,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df[df['date'].str.contains('2007')]['venue'] == 'ullevaal stadion , oslo')",
        "pandas_eval": "True"
    },
    {
        "id": 2056,
        "statement": "all of the goal earn by john carew before march 24 2001 be during friendly competition",
        "label": 1,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df[pd.to_datetime(df['date'], format='%d %B %Y') < pd.to_datetime('24 march 2001', format='%d %B %Y')]['competition'] == 'friendly')",
        "pandas_eval": "True"
    },
    {
        "id": 2057,
        "statement": "john carew win all of the game after march 26 , 2008",
        "label": 1,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df[(pd.to_datetime(df['date'], format='%d %B %Y') > pd.to_datetime('26 march 2008', format='%d %B %Y'))]['result'] == 'win')",
        "pandas_eval": "True"
    },
    {
        "id": 2059,
        "statement": "only 1 goal be score by john carew at the celje venue",
        "label": 1,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "(df[df['venue'] == 'celje'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2060,
        "statement": "all of the goal that john carew get in 2007 be at the windsor park , belfast",
        "label": 0,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df.loc[df['date'].str.contains('2007'), 'venue'] == 'windsor park , belfast')",
        "pandas_eval": "False"
    },
    {
        "id": 2061,
        "statement": "all of the goal earn by john carew after march 24 2001 be during friendly competition",
        "label": 0,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df[df['date'] > '24 march 2001']['competition'] == 'friendly')",
        "pandas_eval": "False"
    },
    {
        "id": 2062,
        "statement": "john carew lose all of the game after march 26 , 2008",
        "label": 0,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "all(df[(pd.to_datetime(df['date'], format='%d %B %Y') > pd.to_datetime('26 march 2008', format='%d %B %Y'))]['result'] == 'loss')",
        "pandas_eval": "False"
    },
    {
        "id": 2063,
        "statement": "john carew have 2 goal on june 6 2007 and 2 goal on august 16 , 2007",
        "label": 0,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "(df[(df['date'] == '6 june 2007') & (df['goal'] == 2)].shape[0] == 1) & (df[(df['date'] == '16 august 2007') & (df['goal'] == 2)].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2064,
        "statement": "only 4 goal be score by john carew at the celje venue",
        "label": 0,
        "table_caption": "john carew",
        "table_text": "goal#date#venue#score#result#competition\n1#22 january 1999#umm al - fahm , israel#3 - 3#draw#friendly\n2#4 february 2000#la manga , spain#1 - 1#draw#friendly\n3#3 june 2000#ullevaal stadion , oslo#1 - 0#win#friendly\n4#28 february 2001#windsor park , belfast#4 - 0#win#friendly\n5#24 march 2001#ullevaal stadion , oslo#2 - 3#loss#world cup 2002 qualifier\n6#6 june 2001#ullevaal stadion , oslo#1 - 1#draw#world cup 2002 qualifier\n7#5 september 2001#ullevaal stadion , oslo#3 - 2#win#world cup 2002 qualifier\n8#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n9#6 october 2001#yerevan#4 - 1#win#world cup 2002 qualifier\n10#7 september 2002#ullevaal stadion , oslo#2 - 2#draw#euro 2004 qualifier\n11#4 september 2004#palermo#1 - 2#loss#world cup 2006 qualifier\n12#13 october 2004#ullevaal stadion , oslo#3 - 0#win#world cup 2006 qualifier\n13#3 september 2005#celje#3 - 2#win#world cup 2006 qualifier\n14#15 november 2006#belgrade#1 - 1#draw#friendly\n15#24 march 2007#ullevaal stadion , oslo#1 - 2#loss#euro 2008 qualifier\n16#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n17#6 june 2007#ullevaal stadion , oslo#4 - 0#win#euro 2008 qualifier\n18#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n19#22 august 2007#ullevaal stadion , oslo#2 - 1#win#friendly\n20#12 september 2007#ullevaal stadion , oslo#2 - 2#draw#euro 2008 qualifier\n21#26 march 2008#podgorica city stadium , podgorica#3 - 1#loss#friendly\n22#14 november 2009#stade de gen\u00e8ve , geneva#0 - 1#win#friendly\n23#8 october 2010#antonis papadopoulos stadium , larnaca#1 - 2#win#uefa euro 2012 qualifying\n24#11 october 2011#ullevaal stadion , oslo#3 - 1#win#uefa euro 2012 qualifying\n",
        "pandas_code": "(df[(df['venue'] == 'celje') & (df['goal'] == 4)].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2065,
        "statement": "6 of the episode be direct by bob berlinger",
        "label": 1,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "(df['directed by'] == 'bob berlinger').sum() == 6",
        "pandas_eval": "True"
    },
    {
        "id": 2066,
        "statement": "the episode with the largest amount of viewer be crazy ten minute sale",
        "label": 1,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "df.loc[df['us viewers (millions)'].replace('n / a', '0').astype(float).idxmax(), 'title'] == 'crazy ten minute sale'",
        "pandas_eval": "True"
    },
    {
        "id": 2067,
        "statement": "episode number 1 have 2.2 more million viewer than episode 20",
        "label": 1,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "float(df[df['no in series'] == 1]['us viewers (millions)'].iloc[0]) - float(df[df['no in series'] == 20]['us viewers (millions)'].iloc[0]) == 2.2",
        "pandas_eval": "True"
    },
    {
        "id": 2068,
        "statement": "there be 3 episode air on october 2007",
        "label": 1,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "len(df[df['original us air date'].str.contains(r'october.*2007', case=False, regex=True)]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2069,
        "statement": "4 of the episode be written by matt goldman",
        "label": 1,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "len(df[df['written by'].str.contains('matt goldman', case=False)]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2070,
        "statement": "bob berlinger direct less episode then andew tsao",
        "label": 0,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "(df['directed by'].value_counts()['bob berlinger'] < df['directed by'].value_counts()['andrew tsao'])",
        "pandas_eval": "False"
    },
    {
        "id": 2071,
        "statement": "wizard school part 2 have more viewer than crazy ten minute sale do",
        "label": 0,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "df.loc[df['title'] == 'wizard school (part 2)', 'us viewers (millions)'].values[0] > df.loc[df['title'] == 'crazy ten minute sale', 'us viewers (millions)'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2072,
        "statement": "the difference in viewer between episode 1 and episode 20 , be less than a million",
        "label": 0,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "abs(float(df[df['no in series'] == 1]['us viewers (millions)'].values[0].replace('n / a', '0')) - float(df[df['no in series'] == 20]['us viewers (millions)'].values[0].replace('n / a', '0'))) < 1",
        "pandas_eval": "False"
    },
    {
        "id": 2073,
        "statement": "there be more episode in october 2007 , than in november 2007",
        "label": 0,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "(df['original us air date'].str.contains('october 2007').sum() > df['original us air date'].str.contains('november 2007').sum())",
        "pandas_eval": "False"
    },
    {
        "id": 2074,
        "statement": "write 2 episode , matt goldman write less episode than peter murrietta",
        "label": 0,
        "table_caption": "wizards of waverly place (season 1)",
        "table_text": "no in series#title#directed by#written by#us viewers (millions)#original us air date#prod code\n1#crazy ten minute sale#fred savage#todd j greenwald#5.9#october 12 , 2007#102\n2#first kiss#joe regalbuto#vince cheung & ben montanio#4.8#october 19 , 2007#104\n3#i almost drowned in a chocolate fountain#joe regalbuto#gigi mccreery & perry rein#3.7#october 26 , 2007#105\n4#new employee#bob berlinger#peter murrieta#n / a#november 2 , 2007#107\n5#disenchanted evening#mark cendrowski#jack sanderson#4.6#november 9 , 2007#114\n6#you can't always get what you carpet#fred savage#peter murrieta#4.0#november 10 , 2007#101\n7#alex 's choice#bob berlinger#matt goldman#n / a#november 16 , 2007#109\n8#curb your dragon#bob berlinger#gigi mccreery & perry rein#n / a#november 30 , 2007#108\n9#movies#mark cendrowski#justin varava#n / a#december 14 , 2007#113\n10#pop me and we both go down#bob berlinger#vince cheung & ben montanio#n / a#january 6 , 2008#103\n11#potion commotion#bob berlinger#todd j greenwald#n / a#february 10 , 2008#110\n12#justin 's little sister#andrew tsao#eve weston#n / a#march 9 , 2008#117\n13#wizard school (part 1)#mark cendrowski#vince cheung & ben montanio#3.7#april 6 , 2008#111\n14#wizard school (part 2)#mark cendrowski#gigi mccreery & perry rein#3.7#april 6 , 2008#112\n15#the supernatural#mark cendrowski#matt goldman#n / a#may 18 , 2008#115\n16#alex in the middle#bob berlinger#matt goldman#n / a#june 15 , 2008#106\n17#report card#andrew tsao#gigi mccreery , perry rein & peter murrieta#n / a#june 29 , 2008#118\n18#credit check#fred savage#todd j greenwald#n / a#july 6 , 2008#121\n19#alex 's spring fling#victor gonzalez#matt goldman#n / a#july 20 , 2008#119\n20#quincea\u00e3\u00b1era#victor gonzalez#gigi mccreery & perry rein#3.7#august 10 , 2008#116\n",
        "pandas_code": "(df['written by'].value_counts()['matt goldman'] < df['written by'].value_counts()['peter murrieta'])",
        "pandas_eval": "False"
    },
    {
        "id": 2075,
        "statement": "the 2008 - 09 supersport series show 3 with 4 match and 2 with 3 match",
        "label": 1,
        "table_caption": "2008 - 09 supersport series",
        "table_text": "player#team#matches#overs#wickets#economy rate#average#strike rate#bbi#bbm\nmakhaya ntini#warriors#4#152.4#24#2.18#13.91#38.1#6 / 85#9 / 109\nlonwabo tsotsobe#warriors#4#127.5#16#2.26#18.12#47.9#4 / 3#5 / 98\njuan theron#warriors#4#133.4#19#2.71#19.10#42.2#7 / 46#7 / 56\nmorn\u00e3 morkel#titans#3#92.2#17#3.51#19.17#32.7#6 / 47#11 / 56\npaul harris#titans#3#126.0#14#2.74#22.28#54.0#7 / 94#12 / 180\n",
        "pandas_code": "(df['matches'].value_counts().get(4, 0) == 3) & (df['matches'].value_counts().get(3, 0) == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2076,
        "statement": "morn\u00e3 morkel have a higher average by 0.07 than juan theron",
        "label": 1,
        "table_caption": "2008 - 09 supersport series",
        "table_text": "player#team#matches#overs#wickets#economy rate#average#strike rate#bbi#bbm\nmakhaya ntini#warriors#4#152.4#24#2.18#13.91#38.1#6 / 85#9 / 109\nlonwabo tsotsobe#warriors#4#127.5#16#2.26#18.12#47.9#4 / 3#5 / 98\njuan theron#warriors#4#133.4#19#2.71#19.10#42.2#7 / 46#7 / 56\nmorn\u00e3 morkel#titans#3#92.2#17#3.51#19.17#32.7#6 / 47#11 / 56\npaul harris#titans#3#126.0#14#2.74#22.28#54.0#7 / 94#12 / 180\n",
        "pandas_code": "abs(df[df['player'] == 'morn\u00e3 morkel']['average'].values[0] - df[df['player'] == 'juan theron']['average'].values[0] - 0.07) < 1e-9",
        "pandas_eval": "True"
    },
    {
        "id": 2077,
        "statement": "makhaya ntini of the warrior have the highest wicket of 24",
        "label": 1,
        "table_caption": "2008 - 09 supersport series",
        "table_text": "player#team#matches#overs#wickets#economy rate#average#strike rate#bbi#bbm\nmakhaya ntini#warriors#4#152.4#24#2.18#13.91#38.1#6 / 85#9 / 109\nlonwabo tsotsobe#warriors#4#127.5#16#2.26#18.12#47.9#4 / 3#5 / 98\njuan theron#warriors#4#133.4#19#2.71#19.10#42.2#7 / 46#7 / 56\nmorn\u00e3 morkel#titans#3#92.2#17#3.51#19.17#32.7#6 / 47#11 / 56\npaul harris#titans#3#126.0#14#2.74#22.28#54.0#7 / 94#12 / 180\n",
        "pandas_code": "df.loc[df['player'] == 'makhaya ntini', 'wickets'].values[0] == df['wickets'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2078,
        "statement": "makhaya ntini 's srike rate be 38.1 , not higher than that of lonwabo tsotsobe",
        "label": 1,
        "table_caption": "2008 - 09 supersport series",
        "table_text": "player#team#matches#overs#wickets#economy rate#average#strike rate#bbi#bbm\nmakhaya ntini#warriors#4#152.4#24#2.18#13.91#38.1#6 / 85#9 / 109\nlonwabo tsotsobe#warriors#4#127.5#16#2.26#18.12#47.9#4 / 3#5 / 98\njuan theron#warriors#4#133.4#19#2.71#19.10#42.2#7 / 46#7 / 56\nmorn\u00e3 morkel#titans#3#92.2#17#3.51#19.17#32.7#6 / 47#11 / 56\npaul harris#titans#3#126.0#14#2.74#22.28#54.0#7 / 94#12 / 180\n",
        "pandas_code": "df[df['player'] == 'makhaya ntini']['strike rate'].values[0] <= df[df['player'] == 'lonwabo tsotsobe']['strike rate'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2079,
        "statement": "makhaya ntini , lonwabo tsotsobe and juan theron be on the warrior team while morn\u00e3 morkel and paul harris be titan",
        "label": 1,
        "table_caption": "2008 - 09 supersport series",
        "table_text": "player#team#matches#overs#wickets#economy rate#average#strike rate#bbi#bbm\nmakhaya ntini#warriors#4#152.4#24#2.18#13.91#38.1#6 / 85#9 / 109\nlonwabo tsotsobe#warriors#4#127.5#16#2.26#18.12#47.9#4 / 3#5 / 98\njuan theron#warriors#4#133.4#19#2.71#19.10#42.2#7 / 46#7 / 56\nmorn\u00e3 morkel#titans#3#92.2#17#3.51#19.17#32.7#6 / 47#11 / 56\npaul harris#titans#3#126.0#14#2.74#22.28#54.0#7 / 94#12 / 180\n",
        "pandas_code": "all(df[df['player'].isin(['makhaya ntini', 'lonwabo tsotsobe', 'juan theron'])]['team'] == 'warriors') and all(df[df['player'].isin(['morn\u00e3 morkel', 'paul harris'])]['team'] == 'titans')",
        "pandas_eval": "True"
    },
    {
        "id": 2082,
        "statement": "bruno junqueira have the fastest time after qual 1",
        "label": 1,
        "table_caption": "2004 champ car grand prix of portland",
        "table_text": "name#team#qual 1#qual 2#best\ns\u00e9bastien bourdais#newman / haas racing#59.718#59.229#59.229\nbruno junqueira#newman / haas racing#59.266#1:00.067#59.266\npaul tracy#forsythe racing#59.791#59.314#59.314\njustin wilson#mi - jack conquest racing#1:00.344#59.414#59.414\na j allmendinger#rusport#59.803#59.654#59.654\nmario dom\u00ednguez#herdez competition#1:00.097#59.757#59.757\nalex tagliani#rocketsports racing#59.900#1:00.342#59.900\nmichel jourdain , jr#rusport#1:00.205#59.900#59.900\njimmy vasser#pkv racing#1:00.439#59.922#59.922\nryan hunter - reay#herdez competition#1:00.088#59.926#59.926\nroberto gonz\u00e1lez#pkv racing#1:00.081#59.987#59.987\nrodolfo lav\u00edn#forsythe racing#1:00.530#1:00.099#1:00.099\npatrick carpentier#forsythe racing#1:00.182#-#1:00.182\noriol servi\u00e0#dale coyne racing#1:04.785#1:00.206#1:00.206\nmario haberfeld#walker racing#1:00.971#1:00.678#1:00.678\ngast\u00f3n mazzacane#dale coyne racing#1:00.833#1:01.410#1:00.833\nalex sperafico#mi - jack conquest racing#1:01.333#1:01.442#1:01.333\nnelson philippe#rocketsports racing#1:01.531#1:02.060#1:01.531\n",
        "pandas_code": "df.loc[df['name'] == 'bruno junqueira', 'qual 1'].iloc[0] == min(df['qual 1'], key=lambda x: float(x.replace(':', '')) if ':' in x else float(x))",
        "pandas_eval": "True"
    },
    {
        "id": 2084,
        "statement": "there be 13 time over 1 minute for qual 1 and only 8 for qual 2",
        "label": 1,
        "table_caption": "2004 champ car grand prix of portland",
        "table_text": "name#team#qual 1#qual 2#best\ns\u00e9bastien bourdais#newman / haas racing#59.718#59.229#59.229\nbruno junqueira#newman / haas racing#59.266#1:00.067#59.266\npaul tracy#forsythe racing#59.791#59.314#59.314\njustin wilson#mi - jack conquest racing#1:00.344#59.414#59.414\na j allmendinger#rusport#59.803#59.654#59.654\nmario dom\u00ednguez#herdez competition#1:00.097#59.757#59.757\nalex tagliani#rocketsports racing#59.900#1:00.342#59.900\nmichel jourdain , jr#rusport#1:00.205#59.900#59.900\njimmy vasser#pkv racing#1:00.439#59.922#59.922\nryan hunter - reay#herdez competition#1:00.088#59.926#59.926\nroberto gonz\u00e1lez#pkv racing#1:00.081#59.987#59.987\nrodolfo lav\u00edn#forsythe racing#1:00.530#1:00.099#1:00.099\npatrick carpentier#forsythe racing#1:00.182#-#1:00.182\noriol servi\u00e0#dale coyne racing#1:04.785#1:00.206#1:00.206\nmario haberfeld#walker racing#1:00.971#1:00.678#1:00.678\ngast\u00f3n mazzacane#dale coyne racing#1:00.833#1:01.410#1:00.833\nalex sperafico#mi - jack conquest racing#1:01.333#1:01.442#1:01.333\nnelson philippe#rocketsports racing#1:01.531#1:02.060#1:01.531\n",
        "pandas_code": "(df['qual 1'].apply(lambda x: float(x.split(':')[0]) * 60 + float(x.split(':')[1]) if ':' in x else float(x)) > 60).sum() == 13 and (df['qual 2'].apply(lambda x: float(x.split(':')[0]) * 60 + float(x.split(':')[1]) if ':' in x and x != '-' else 0) > 60).sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2085,
        "statement": "forsythe racing have the top 2 time , but do not otherwise appear in the top 18",
        "label": 0,
        "table_caption": "2004 champ car grand prix of portland",
        "table_text": "name#team#qual 1#qual 2#best\ns\u00e9bastien bourdais#newman / haas racing#59.718#59.229#59.229\nbruno junqueira#newman / haas racing#59.266#1:00.067#59.266\npaul tracy#forsythe racing#59.791#59.314#59.314\njustin wilson#mi - jack conquest racing#1:00.344#59.414#59.414\na j allmendinger#rusport#59.803#59.654#59.654\nmario dom\u00ednguez#herdez competition#1:00.097#59.757#59.757\nalex tagliani#rocketsports racing#59.900#1:00.342#59.900\nmichel jourdain , jr#rusport#1:00.205#59.900#59.900\njimmy vasser#pkv racing#1:00.439#59.922#59.922\nryan hunter - reay#herdez competition#1:00.088#59.926#59.926\nroberto gonz\u00e1lez#pkv racing#1:00.081#59.987#59.987\nrodolfo lav\u00edn#forsythe racing#1:00.530#1:00.099#1:00.099\npatrick carpentier#forsythe racing#1:00.182#-#1:00.182\noriol servi\u00e0#dale coyne racing#1:04.785#1:00.206#1:00.206\nmario haberfeld#walker racing#1:00.971#1:00.678#1:00.678\ngast\u00f3n mazzacane#dale coyne racing#1:00.833#1:01.410#1:00.833\nalex sperafico#mi - jack conquest racing#1:01.333#1:01.442#1:01.333\nnelson philippe#rocketsports racing#1:01.531#1:02.060#1:01.531\n",
        "pandas_code": "((df[df['team'] == 'forsythe racing'].sort_values(by='best').head(2)['best'].isin(df.sort_values(by='best').head(18)['best'])) & (~df[df['team'] == 'forsythe racing'].sort_values(by='best').head(2).index.isin(df.sort_values(by='best').head(18).index))).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2087,
        "statement": "justin wilson have the fastest time after qual 1",
        "label": 0,
        "table_caption": "2004 champ car grand prix of portland",
        "table_text": "name#team#qual 1#qual 2#best\ns\u00e9bastien bourdais#newman / haas racing#59.718#59.229#59.229\nbruno junqueira#newman / haas racing#59.266#1:00.067#59.266\npaul tracy#forsythe racing#59.791#59.314#59.314\njustin wilson#mi - jack conquest racing#1:00.344#59.414#59.414\na j allmendinger#rusport#59.803#59.654#59.654\nmario dom\u00ednguez#herdez competition#1:00.097#59.757#59.757\nalex tagliani#rocketsports racing#59.900#1:00.342#59.900\nmichel jourdain , jr#rusport#1:00.205#59.900#59.900\njimmy vasser#pkv racing#1:00.439#59.922#59.922\nryan hunter - reay#herdez competition#1:00.088#59.926#59.926\nroberto gonz\u00e1lez#pkv racing#1:00.081#59.987#59.987\nrodolfo lav\u00edn#forsythe racing#1:00.530#1:00.099#1:00.099\npatrick carpentier#forsythe racing#1:00.182#-#1:00.182\noriol servi\u00e0#dale coyne racing#1:04.785#1:00.206#1:00.206\nmario haberfeld#walker racing#1:00.971#1:00.678#1:00.678\ngast\u00f3n mazzacane#dale coyne racing#1:00.833#1:01.410#1:00.833\nalex sperafico#mi - jack conquest racing#1:01.333#1:01.442#1:01.333\nnelson philippe#rocketsports racing#1:01.531#1:02.060#1:01.531\n",
        "pandas_code": "df.loc[df['name'] == 'justin wilson', 'qual 1'].iloc[0] == df['qual 1'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2089,
        "statement": "there be 12 time over 1 minute for qual 1 and only 7 for qual 2",
        "label": 0,
        "table_caption": "2004 champ car grand prix of portland",
        "table_text": "name#team#qual 1#qual 2#best\ns\u00e9bastien bourdais#newman / haas racing#59.718#59.229#59.229\nbruno junqueira#newman / haas racing#59.266#1:00.067#59.266\npaul tracy#forsythe racing#59.791#59.314#59.314\njustin wilson#mi - jack conquest racing#1:00.344#59.414#59.414\na j allmendinger#rusport#59.803#59.654#59.654\nmario dom\u00ednguez#herdez competition#1:00.097#59.757#59.757\nalex tagliani#rocketsports racing#59.900#1:00.342#59.900\nmichel jourdain , jr#rusport#1:00.205#59.900#59.900\njimmy vasser#pkv racing#1:00.439#59.922#59.922\nryan hunter - reay#herdez competition#1:00.088#59.926#59.926\nroberto gonz\u00e1lez#pkv racing#1:00.081#59.987#59.987\nrodolfo lav\u00edn#forsythe racing#1:00.530#1:00.099#1:00.099\npatrick carpentier#forsythe racing#1:00.182#-#1:00.182\noriol servi\u00e0#dale coyne racing#1:04.785#1:00.206#1:00.206\nmario haberfeld#walker racing#1:00.971#1:00.678#1:00.678\ngast\u00f3n mazzacane#dale coyne racing#1:00.833#1:01.410#1:00.833\nalex sperafico#mi - jack conquest racing#1:01.333#1:01.442#1:01.333\nnelson philippe#rocketsports racing#1:01.531#1:02.060#1:01.531\n",
        "pandas_code": "(df['qual 1'].apply(lambda x: int(x.split(':')[0]) if ':' in x else 0) > 1).sum() == 12 and (df['qual 2'].apply(lambda x: int(x.split(':')[0]) if ':' in x else 0) > 1).sum() == 7",
        "pandas_eval": "False"
    },
    {
        "id": 2092,
        "statement": "dmickey have the most high assist in all of the game",
        "label": 1,
        "table_caption": "1990 - 91 seattle supersonics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n72#april 3#sacramento kings#w 106 - 91#e johnson , s kemp (20)#s kemp (14)#n mcmillan , s threatt (6)#seattle center coliseum 11987#35 - 37\n73#april 5#los angeles clippers#l 90 - 109#e johnson (19)#s kemp (12)#n mcmillan (4)#los angeles memorial sports arena 12932#35 - 38\n74#april 7#w 124 - 117#denver nuggets#e johnson (31)#s kemp (10)#d mckey (4)#mcnichols sports arena 13779#36 - 38\n75#april 8#denver nuggets#w 118 - 112#b benjamin , e johnson , r pierce (19)#b benjamin , s kemp (10)#n mcmillan , g payton (8)#seattle center coliseum 12811#37 - 38\n76#april 10#los angeles clippers#w 140 - 108#b benjamin (22)#b benjamin (11)#s threatt (9)#tacoma dome 5714#38 - 38\n77#april 12#san antonio spurs#w 100 - 99#r pierce (21)#b benjamin , s kemp (8)#g payton (7)#seattle center coliseum 13340#39 - 38\n78#april 13#orlando magic#w 105 - 96#s kemp (21)#s kemp (7)#d barros (7)#seattle center coliseum 13073#40 - 38\n79#april 15#houston rockets#l 93 - 97#e johnson (22)#m cage (8)#s threatt (7)#seattle center coliseum 13760#40 - 39\n80#april 18#utah jazz#l 103 - 130#e johnson (17)#b benjamin (8)#s threatt (6)#salt palace 12616#40 - 40\n81#april 19#phoenix suns#w 104 - 93#r pierce (23)#b benjamin (10)#n mcmillan (7)#seattle center coliseum 12591#41 - 40\n82#april 21#los angeles lakers#l 100 - 103#e johnson (18)#b benjamin (12)#n mcmillan (8)#great western forum 17505#41 - 41\n",
        "pandas_code": "df['high assists'].apply(lambda x: 'd mckey' in x.lower()).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2094,
        "statement": "the seattle center coliseum host the most game",
        "label": 1,
        "table_caption": "1990 - 91 seattle supersonics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n72#april 3#sacramento kings#w 106 - 91#e johnson , s kemp (20)#s kemp (14)#n mcmillan , s threatt (6)#seattle center coliseum 11987#35 - 37\n73#april 5#los angeles clippers#l 90 - 109#e johnson (19)#s kemp (12)#n mcmillan (4)#los angeles memorial sports arena 12932#35 - 38\n74#april 7#w 124 - 117#denver nuggets#e johnson (31)#s kemp (10)#d mckey (4)#mcnichols sports arena 13779#36 - 38\n75#april 8#denver nuggets#w 118 - 112#b benjamin , e johnson , r pierce (19)#b benjamin , s kemp (10)#n mcmillan , g payton (8)#seattle center coliseum 12811#37 - 38\n76#april 10#los angeles clippers#w 140 - 108#b benjamin (22)#b benjamin (11)#s threatt (9)#tacoma dome 5714#38 - 38\n77#april 12#san antonio spurs#w 100 - 99#r pierce (21)#b benjamin , s kemp (8)#g payton (7)#seattle center coliseum 13340#39 - 38\n78#april 13#orlando magic#w 105 - 96#s kemp (21)#s kemp (7)#d barros (7)#seattle center coliseum 13073#40 - 38\n79#april 15#houston rockets#l 93 - 97#e johnson (22)#m cage (8)#s threatt (7)#seattle center coliseum 13760#40 - 39\n80#april 18#utah jazz#l 103 - 130#e johnson (17)#b benjamin (8)#s threatt (6)#salt palace 12616#40 - 40\n81#april 19#phoenix suns#w 104 - 93#r pierce (23)#b benjamin (10)#n mcmillan (7)#seattle center coliseum 12591#41 - 40\n82#april 21#los angeles lakers#l 100 - 103#e johnson (18)#b benjamin (12)#n mcmillan (8)#great western forum 17505#41 - 41\n",
        "pandas_code": "df['location attendance'].str.contains('seattle center coliseum').sum() > (len(df) / 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2095,
        "statement": "s kemp have the fewest high rebound in all of the game play",
        "label": 0,
        "table_caption": "1990 - 91 seattle supersonics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n72#april 3#sacramento kings#w 106 - 91#e johnson , s kemp (20)#s kemp (14)#n mcmillan , s threatt (6)#seattle center coliseum 11987#35 - 37\n73#april 5#los angeles clippers#l 90 - 109#e johnson (19)#s kemp (12)#n mcmillan (4)#los angeles memorial sports arena 12932#35 - 38\n74#april 7#w 124 - 117#denver nuggets#e johnson (31)#s kemp (10)#d mckey (4)#mcnichols sports arena 13779#36 - 38\n75#april 8#denver nuggets#w 118 - 112#b benjamin , e johnson , r pierce (19)#b benjamin , s kemp (10)#n mcmillan , g payton (8)#seattle center coliseum 12811#37 - 38\n76#april 10#los angeles clippers#w 140 - 108#b benjamin (22)#b benjamin (11)#s threatt (9)#tacoma dome 5714#38 - 38\n77#april 12#san antonio spurs#w 100 - 99#r pierce (21)#b benjamin , s kemp (8)#g payton (7)#seattle center coliseum 13340#39 - 38\n78#april 13#orlando magic#w 105 - 96#s kemp (21)#s kemp (7)#d barros (7)#seattle center coliseum 13073#40 - 38\n79#april 15#houston rockets#l 93 - 97#e johnson (22)#m cage (8)#s threatt (7)#seattle center coliseum 13760#40 - 39\n80#april 18#utah jazz#l 103 - 130#e johnson (17)#b benjamin (8)#s threatt (6)#salt palace 12616#40 - 40\n81#april 19#phoenix suns#w 104 - 93#r pierce (23)#b benjamin (10)#n mcmillan (7)#seattle center coliseum 12591#41 - 40\n82#april 21#los angeles lakers#l 100 - 103#e johnson (18)#b benjamin (12)#n mcmillan (8)#great western forum 17505#41 - 41\n",
        "pandas_code": "df['high rebounds'].apply(lambda x: 's kemp' in x).sum() == df['high rebounds'].apply(lambda x: any(player in x for player in ['e johnson', 'b benjamin', 'r pierce', 'm cage', 'd mckey', 'g payton', 's threatt', 'd barros', 'n mcmillan'])).min()",
        "pandas_eval": "False"
    },
    {
        "id": 2096,
        "statement": "the seattle center coliseum host the fewest amount of game",
        "label": 0,
        "table_caption": "1990 - 91 seattle supersonics season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n72#april 3#sacramento kings#w 106 - 91#e johnson , s kemp (20)#s kemp (14)#n mcmillan , s threatt (6)#seattle center coliseum 11987#35 - 37\n73#april 5#los angeles clippers#l 90 - 109#e johnson (19)#s kemp (12)#n mcmillan (4)#los angeles memorial sports arena 12932#35 - 38\n74#april 7#w 124 - 117#denver nuggets#e johnson (31)#s kemp (10)#d mckey (4)#mcnichols sports arena 13779#36 - 38\n75#april 8#denver nuggets#w 118 - 112#b benjamin , e johnson , r pierce (19)#b benjamin , s kemp (10)#n mcmillan , g payton (8)#seattle center coliseum 12811#37 - 38\n76#april 10#los angeles clippers#w 140 - 108#b benjamin (22)#b benjamin (11)#s threatt (9)#tacoma dome 5714#38 - 38\n77#april 12#san antonio spurs#w 100 - 99#r pierce (21)#b benjamin , s kemp (8)#g payton (7)#seattle center coliseum 13340#39 - 38\n78#april 13#orlando magic#w 105 - 96#s kemp (21)#s kemp (7)#d barros (7)#seattle center coliseum 13073#40 - 38\n79#april 15#houston rockets#l 93 - 97#e johnson (22)#m cage (8)#s threatt (7)#seattle center coliseum 13760#40 - 39\n80#april 18#utah jazz#l 103 - 130#e johnson (17)#b benjamin (8)#s threatt (6)#salt palace 12616#40 - 40\n81#april 19#phoenix suns#w 104 - 93#r pierce (23)#b benjamin (10)#n mcmillan (7)#seattle center coliseum 12591#41 - 40\n82#april 21#los angeles lakers#l 100 - 103#e johnson (18)#b benjamin (12)#n mcmillan (8)#great western forum 17505#41 - 41\n",
        "pandas_code": "df['location attendance'].str.contains('seattle center coliseum').sum() == df['location attendance'].value_counts().min()",
        "pandas_eval": "False"
    },
    {
        "id": 2097,
        "statement": "the miss earth contest have 42 delegate in 2001",
        "label": 1,
        "table_caption": "list of miss earth titleholders",
        "table_text": "year#finals venue#location#pageant date#number of delegates\n2001#university of the philippines theater#quezon city , philippines#oct 28#42\n2002#folk arts theater#pasay city , philippines#oct 29#53\n2003#university of the philippines theater#quezon city , philippines#nov 9#57\n2004#university of the philippines theater#quezon city , philippines#oct 24#61\n2005#university of the philippines theater#quezon city , philippines#oct 23#80\n2006#museum of the filipino people#manila , philippines#nov 26#82\n2007#university of the philippines theater#quezon city , philippines#nov 11#88\n2008#clark expo amphitheater#angeles city , philippines#nov 9#85\n2009#boracay convention center#boracay , philippines#nov 22#80\n2010#vinpearl amphitheater#nha trang , vietnam#dec 4#84\n2011#university of the philippines theater#quezon city , philippines#dec 3#84\n2012#versailles palace#muntinlupa city , philippines#nov 24#80\n2013#sm mall of asia arena#pasay city , philippines#dec 7#tbd\n",
        "pandas_code": "df[(df['year'] == 2001) & (df['number of delegates'] == '42')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2098,
        "statement": "the miss earth contest have 80 delegate in 2005",
        "label": 1,
        "table_caption": "list of miss earth titleholders",
        "table_text": "year#finals venue#location#pageant date#number of delegates\n2001#university of the philippines theater#quezon city , philippines#oct 28#42\n2002#folk arts theater#pasay city , philippines#oct 29#53\n2003#university of the philippines theater#quezon city , philippines#nov 9#57\n2004#university of the philippines theater#quezon city , philippines#oct 24#61\n2005#university of the philippines theater#quezon city , philippines#oct 23#80\n2006#museum of the filipino people#manila , philippines#nov 26#82\n2007#university of the philippines theater#quezon city , philippines#nov 11#88\n2008#clark expo amphitheater#angeles city , philippines#nov 9#85\n2009#boracay convention center#boracay , philippines#nov 22#80\n2010#vinpearl amphitheater#nha trang , vietnam#dec 4#84\n2011#university of the philippines theater#quezon city , philippines#dec 3#84\n2012#versailles palace#muntinlupa city , philippines#nov 24#80\n2013#sm mall of asia arena#pasay city , philippines#dec 7#tbd\n",
        "pandas_code": "df.loc[df['year'] == 2005, 'number of delegates'].values[0] == '80'",
        "pandas_eval": "True"
    },
    {
        "id": 2099,
        "statement": "the miss earth contest have 88 delegate in 2007",
        "label": 1,
        "table_caption": "list of miss earth titleholders",
        "table_text": "year#finals venue#location#pageant date#number of delegates\n2001#university of the philippines theater#quezon city , philippines#oct 28#42\n2002#folk arts theater#pasay city , philippines#oct 29#53\n2003#university of the philippines theater#quezon city , philippines#nov 9#57\n2004#university of the philippines theater#quezon city , philippines#oct 24#61\n2005#university of the philippines theater#quezon city , philippines#oct 23#80\n2006#museum of the filipino people#manila , philippines#nov 26#82\n2007#university of the philippines theater#quezon city , philippines#nov 11#88\n2008#clark expo amphitheater#angeles city , philippines#nov 9#85\n2009#boracay convention center#boracay , philippines#nov 22#80\n2010#vinpearl amphitheater#nha trang , vietnam#dec 4#84\n2011#university of the philippines theater#quezon city , philippines#dec 3#84\n2012#versailles palace#muntinlupa city , philippines#nov 24#80\n2013#sm mall of asia arena#pasay city , philippines#dec 7#tbd\n",
        "pandas_code": "(df[(df['year'] == 2007) & (df['number of delegates'] == '88')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 2100,
        "statement": "the miss earth contest have 80 delegate in 2012",
        "label": 1,
        "table_caption": "list of miss earth titleholders",
        "table_text": "year#finals venue#location#pageant date#number of delegates\n2001#university of the philippines theater#quezon city , philippines#oct 28#42\n2002#folk arts theater#pasay city , philippines#oct 29#53\n2003#university of the philippines theater#quezon city , philippines#nov 9#57\n2004#university of the philippines theater#quezon city , philippines#oct 24#61\n2005#university of the philippines theater#quezon city , philippines#oct 23#80\n2006#museum of the filipino people#manila , philippines#nov 26#82\n2007#university of the philippines theater#quezon city , philippines#nov 11#88\n2008#clark expo amphitheater#angeles city , philippines#nov 9#85\n2009#boracay convention center#boracay , philippines#nov 22#80\n2010#vinpearl amphitheater#nha trang , vietnam#dec 4#84\n2011#university of the philippines theater#quezon city , philippines#dec 3#84\n2012#versailles palace#muntinlupa city , philippines#nov 24#80\n2013#sm mall of asia arena#pasay city , philippines#dec 7#tbd\n",
        "pandas_code": "df.loc[df['year'] == 2012, 'number of delegates'].values[0] == '80'",
        "pandas_eval": "True"
    },
    {
        "id": 2101,
        "statement": "the miss earth contest have 84 delegate in 2011",
        "label": 1,
        "table_caption": "list of miss earth titleholders",
        "table_text": "year#finals venue#location#pageant date#number of delegates\n2001#university of the philippines theater#quezon city , philippines#oct 28#42\n2002#folk arts theater#pasay city , philippines#oct 29#53\n2003#university of the philippines theater#quezon city , philippines#nov 9#57\n2004#university of the philippines theater#quezon city , philippines#oct 24#61\n2005#university of the philippines theater#quezon city , philippines#oct 23#80\n2006#museum of the filipino people#manila , philippines#nov 26#82\n2007#university of the philippines theater#quezon city , philippines#nov 11#88\n2008#clark expo amphitheater#angeles city , philippines#nov 9#85\n2009#boracay convention center#boracay , philippines#nov 22#80\n2010#vinpearl amphitheater#nha trang , vietnam#dec 4#84\n2011#university of the philippines theater#quezon city , philippines#dec 3#84\n2012#versailles palace#muntinlupa city , philippines#nov 24#80\n2013#sm mall of asia arena#pasay city , philippines#dec 7#tbd\n",
        "pandas_code": "df.loc[df['year'] == 2011, 'number of delegates'].values[0] == '84'",
        "pandas_eval": "True"
    },
    {
        "id": 2102,
        "statement": "in the 2008 summer olympics row competition , the canadian athlete have the fastest time",
        "label": 1,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "df[df['country'] == 'canada']['time'].min() == df['time'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2103,
        "statement": "nathan twaddle george bridgewater miss tie for first place by 0:02.03 second",
        "label": 1,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "(df[df['athlete'] == 'nathan twaddle , george bridgewater']['time'].values[0] == '6:36.05') and (df[df['rank'] == 1]['time'].values[0] == '6:34.02')",
        "pandas_eval": "True"
    },
    {
        "id": 2104,
        "statement": "the czech republic athlete finish the row competition before the athlete from croatia",
        "label": 1,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "df[df['country'] == 'czech republic']['time'].values[0] < df[df['country'] == 'croatia']['time'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2105,
        "statement": "2 athlete from each country compete together in the 2008 summer olympics row competition",
        "label": 1,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "all(df['athlete'].apply(lambda x: len(x.split(',')) == 2))",
        "pandas_eval": "True"
    },
    {
        "id": 2106,
        "statement": "croatia be the only country to need more than 7 minute to cross the finish line",
        "label": 1,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "df[(df['country'] == 'croatia') & (df['time'] > '7:00.00')].shape[0] == 1 and df[df['time'] > '7:00.00'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2107,
        "statement": "in the 2012 summer olympics row competition , the canadian athlete have the fastest time",
        "label": 0,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 2109,
        "statement": "the czech republic athlete finish the row competition before the athlete from palestine",
        "label": 0,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "df[df['country'] == 'czech republic']['time'].iloc[0] < df[df['country'] == 'palestine']['time'].iloc[0] if 'palestine' in df['country'].values else False",
        "pandas_eval": "False"
    },
    {
        "id": 2110,
        "statement": "2 athlete from each country compete together in the 2015 winter olympics row competition",
        "label": 0,
        "table_caption": "rowing at the 2008 summer olympics - men 's coxless pair",
        "table_text": "rank#athlete#country#time#notes\n1#dave calder , scott frandsen#canada#6:34.02#fa\n2#nathan twaddle , george bridgewater#new zealand#6:36.05#fa\n3#shaun keeling , ramon di clemente#south africa#6:37.18#fa\n4#jakub makovi\u010dka , v\u00e1clav chalupa#czech republic#6:37.88#fb\n5#erwan peron , laurent cadot#france#6:44.29#fb\n6#sini\u0161a skelin , nik\u0161a skelin#croatia#7:14.50#fb\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 2112,
        "statement": "there be 3 school that previously go to an independent conference in the state conference",
        "label": 1,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "len(df[df['previous conference'] == 'independent']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2113,
        "statement": "martinsville school have the highest enrollment out of all the school",
        "label": 1,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "df.loc[df['enrollment'].idxmax(), 'school'] == 'martinsville'",
        "pandas_eval": "True"
    },
    {
        "id": 2114,
        "statement": "2 school in total join the state conference in 2006",
        "label": 1,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "len(df[df['year joined'] == 2006]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2115,
        "statement": "all the school in the conference be from indiana (in)",
        "label": 1,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "all(df['location'].str.contains(', in$'))",
        "pandas_eval": "True"
    },
    {
        "id": 2116,
        "statement": "mooresville have a higher enrollment than plainfield by 124",
        "label": 1,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "(df[df['school'] == 'mooresville']['enrollment'].values[0] - df[df['school'] == 'plainfield']['enrollment'].values[0]) == 124",
        "pandas_eval": "True"
    },
    {
        "id": 2117,
        "statement": "there be 4 school that previously go to an independent conference in the state conference",
        "label": 0,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "len(df[df['previous conference'] == 'independent']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2118,
        "statement": "decatur central have the highest enrollment out of all the school",
        "label": 0,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "df.loc[df['school'] == 'decatur central', 'enrollment'].values[0] == df['enrollment'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2119,
        "statement": "4 school in total join the state conference beginning in 2006",
        "label": 0,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "len(df[df['year joined'] == 2006]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2120,
        "statement": "all but 1 of the school in the conference be from indiana (in)",
        "label": 0,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "df['location'].str.contains('in').sum() == len(df) - 1",
        "pandas_eval": "False"
    },
    {
        "id": 2121,
        "statement": "mooresville have a higher enrollment than whiteland by 357",
        "label": 0,
        "table_caption": "mid - state conference (ihsaa)",
        "table_text": "school#location#mascot#enrollment#ihsaa class#county#year joined#previous conference\ndecatur central#indianapolis , in#hawks#1856#aaaaa#49 marion#2006#conference indiana\nfranklin#franklin , in#grizzly cubs#1592#aaaaa#41 johnson#1981#south central\ngreenwood#greenwood , in#woodmen#1389#aaaa#41 johnson#1942#independent\nmartinsville#martinsville , in#artesians#2012#aaaaa#55 morgan#2006#conference indiana\nmooresville#mooresville , in#pioneers#1428#aaaa#55 morgan#1942#independent\nplainfield#plainfield , in#quakers#1304#aaaa#32 hendricks#1942#independent\nwhiteland#whiteland , in#warriors#1785#aaaaa#41 johnson#1968#mid - hoosier\n",
        "pandas_code": "(df[df['school'] == 'mooresville']['enrollment'].values[0] - df[df['school'] == 'whiteland']['enrollment'].values[0]) == 357",
        "pandas_eval": "False"
    },
    {
        "id": 2122,
        "statement": "the official website of adilabad be http : / / adilabadnicin / and anantapur 's be http : / / anantapurnicin /",
        "label": 1,
        "table_caption": "list of districts of andhra pradesh",
        "table_text": "code#district#headquarters#population (2011)#area (km square)#density ( / km square)#official website\nad#adilabad#adilabad#2737738#16105#170#http : / /adilabadnicin /\nan#anantapur#anantapur#4083315#19130#213#http : / /anantapurnicin /\nch#chittoor#chittoor#4170468#15152#275#http : / /chittoornicin /\neg#east godavari#kakinada#5151549#10807#477#http : / /eastgodavarinicin /\ngu#guntur#guntur#4889230#11391#429#http : / /gunturnicin /\nhy#hyderabad#hyderabad#4010238#217#217#http : / /hyderabadnicin /\ncu#cuddapah#cuddapah#2884524#15359#188#http : / /kadapanicin /\nka#karimnagar#karimnagar#3811738#11823#322#http : / /karimnagarnicin /\nkh#khammam#khammam#2798214#16029#175#http : / /khammamnicin /\nkr#krishna#machilipatnam#4529009#8727#519#http : / /krishnanicin /\nku#kurnool#kurnool#4046601#17658#229#http : / /kurnoolnicin /\nma#mahbubnagar#mahabubnagar#4042191#18432#219#http : / /mahabubnagarnicin /\nme#medak#sangareddi#3031877#9699#313#http : / /medaknicin /\nna#nalgonda#nalgonda#3483648#14240#245#http : / /nalgondanicin /\nne#nellore#nellore#2966082#13076#227#http : / /nellorenicin /\nni#nizamabad#nizamabad#2552073#7956#321#http : / /nizamabadnicin /\npr#prakasam#ongole#3392764#17626#193#http : / /prakasamnicin /\nra#rangareddy#hyderabad#5296396#7493#707#http : / /rangareddynicin /\nsr#srikakulam#srikakulam#2699471#5837#462#http : / /srikakulamnicin /\nvs#vishakhapatnam#vishakhapatnam#4288113#11161#340#http : / /visakhapatnamnicin /\nvz#vizianagaram#vizianagaram#2342868#6539#384#http : / /vizianagaramnicin /\nwa#warangal#warangal#3522644#12846#252#http : / /warangalnicin /\n",
        "pandas_code": "(df[df['district'] == 'adilabad']['official website'].values[0] == 'http : / /adilabadnicin /') & (df[df['district'] == 'anantapur']['official website'].values[0] == 'http : / /anantapurnicin /')",
        "pandas_eval": "True"
    },
    {
        "id": 2123,
        "statement": "the biggest population in 2011 be in the district of rangareddy which be 5296396",
        "label": 1,
        "table_caption": "list of districts of andhra pradesh",
        "table_text": "code#district#headquarters#population (2011)#area (km square)#density ( / km square)#official website\nad#adilabad#adilabad#2737738#16105#170#http : / /adilabadnicin /\nan#anantapur#anantapur#4083315#19130#213#http : / /anantapurnicin /\nch#chittoor#chittoor#4170468#15152#275#http : / /chittoornicin /\neg#east godavari#kakinada#5151549#10807#477#http : / /eastgodavarinicin /\ngu#guntur#guntur#4889230#11391#429#http : / /gunturnicin /\nhy#hyderabad#hyderabad#4010238#217#217#http : / /hyderabadnicin /\ncu#cuddapah#cuddapah#2884524#15359#188#http : / /kadapanicin /\nka#karimnagar#karimnagar#3811738#11823#322#http : / /karimnagarnicin /\nkh#khammam#khammam#2798214#16029#175#http : / /khammamnicin /\nkr#krishna#machilipatnam#4529009#8727#519#http : / /krishnanicin /\nku#kurnool#kurnool#4046601#17658#229#http : / /kurnoolnicin /\nma#mahbubnagar#mahabubnagar#4042191#18432#219#http : / /mahabubnagarnicin /\nme#medak#sangareddi#3031877#9699#313#http : / /medaknicin /\nna#nalgonda#nalgonda#3483648#14240#245#http : / /nalgondanicin /\nne#nellore#nellore#2966082#13076#227#http : / /nellorenicin /\nni#nizamabad#nizamabad#2552073#7956#321#http : / /nizamabadnicin /\npr#prakasam#ongole#3392764#17626#193#http : / /prakasamnicin /\nra#rangareddy#hyderabad#5296396#7493#707#http : / /rangareddynicin /\nsr#srikakulam#srikakulam#2699471#5837#462#http : / /srikakulamnicin /\nvs#vishakhapatnam#vishakhapatnam#4288113#11161#340#http : / /visakhapatnamnicin /\nvz#vizianagaram#vizianagaram#2342868#6539#384#http : / /vizianagaramnicin /\nwa#warangal#warangal#3522644#12846#252#http : / /warangalnicin /\n",
        "pandas_code": "df[df['district'] == 'rangareddy']['population (2011)'].values[0] == 5296396",
        "pandas_eval": "True"
    },
    {
        "id": 2124,
        "statement": "the vishakhapatnam district have its headquarters in vishakhapatnam , while prakasam 's be in ongole",
        "label": 1,
        "table_caption": "list of districts of andhra pradesh",
        "table_text": "code#district#headquarters#population (2011)#area (km square)#density ( / km square)#official website\nad#adilabad#adilabad#2737738#16105#170#http : / /adilabadnicin /\nan#anantapur#anantapur#4083315#19130#213#http : / /anantapurnicin /\nch#chittoor#chittoor#4170468#15152#275#http : / /chittoornicin /\neg#east godavari#kakinada#5151549#10807#477#http : / /eastgodavarinicin /\ngu#guntur#guntur#4889230#11391#429#http : / /gunturnicin /\nhy#hyderabad#hyderabad#4010238#217#217#http : / /hyderabadnicin /\ncu#cuddapah#cuddapah#2884524#15359#188#http : / /kadapanicin /\nka#karimnagar#karimnagar#3811738#11823#322#http : / /karimnagarnicin /\nkh#khammam#khammam#2798214#16029#175#http : / /khammamnicin /\nkr#krishna#machilipatnam#4529009#8727#519#http : / /krishnanicin /\nku#kurnool#kurnool#4046601#17658#229#http : / /kurnoolnicin /\nma#mahbubnagar#mahabubnagar#4042191#18432#219#http : / /mahabubnagarnicin /\nme#medak#sangareddi#3031877#9699#313#http : / /medaknicin /\nna#nalgonda#nalgonda#3483648#14240#245#http : / /nalgondanicin /\nne#nellore#nellore#2966082#13076#227#http : / /nellorenicin /\nni#nizamabad#nizamabad#2552073#7956#321#http : / /nizamabadnicin /\npr#prakasam#ongole#3392764#17626#193#http : / /prakasamnicin /\nra#rangareddy#hyderabad#5296396#7493#707#http : / /rangareddynicin /\nsr#srikakulam#srikakulam#2699471#5837#462#http : / /srikakulamnicin /\nvs#vishakhapatnam#vishakhapatnam#4288113#11161#340#http : / /visakhapatnamnicin /\nvz#vizianagaram#vizianagaram#2342868#6539#384#http : / /vizianagaramnicin /\nwa#warangal#warangal#3522644#12846#252#http : / /warangalnicin /\n",
        "pandas_code": "(df[df['district'] == 'vishakhapatnam']['headquarters'].values[0] == 'vishakhapatnam') & (df[df['district'] == 'prakasam']['headquarters'].values[0] == 'ongole')",
        "pandas_eval": "True"
    },
    {
        "id": 2125,
        "statement": "the area (km square) of rangareddy be about 10000 smaller than prakasam",
        "label": 1,
        "table_caption": "list of districts of andhra pradesh",
        "table_text": "code#district#headquarters#population (2011)#area (km square)#density ( / km square)#official website\nad#adilabad#adilabad#2737738#16105#170#http : / /adilabadnicin /\nan#anantapur#anantapur#4083315#19130#213#http : / /anantapurnicin /\nch#chittoor#chittoor#4170468#15152#275#http : / /chittoornicin /\neg#east godavari#kakinada#5151549#10807#477#http : / /eastgodavarinicin /\ngu#guntur#guntur#4889230#11391#429#http : / /gunturnicin /\nhy#hyderabad#hyderabad#4010238#217#217#http : / /hyderabadnicin /\ncu#cuddapah#cuddapah#2884524#15359#188#http : / /kadapanicin /\nka#karimnagar#karimnagar#3811738#11823#322#http : / /karimnagarnicin /\nkh#khammam#khammam#2798214#16029#175#http : / /khammamnicin /\nkr#krishna#machilipatnam#4529009#8727#519#http : / /krishnanicin /\nku#kurnool#kurnool#4046601#17658#229#http : / /kurnoolnicin /\nma#mahbubnagar#mahabubnagar#4042191#18432#219#http : / /mahabubnagarnicin /\nme#medak#sangareddi#3031877#9699#313#http : / /medaknicin /\nna#nalgonda#nalgonda#3483648#14240#245#http : / /nalgondanicin /\nne#nellore#nellore#2966082#13076#227#http : / /nellorenicin /\nni#nizamabad#nizamabad#2552073#7956#321#http : / /nizamabadnicin /\npr#prakasam#ongole#3392764#17626#193#http : / /prakasamnicin /\nra#rangareddy#hyderabad#5296396#7493#707#http : / /rangareddynicin /\nsr#srikakulam#srikakulam#2699471#5837#462#http : / /srikakulamnicin /\nvs#vishakhapatnam#vishakhapatnam#4288113#11161#340#http : / /visakhapatnamnicin /\nvz#vizianagaram#vizianagaram#2342868#6539#384#http : / /vizianagaramnicin /\nwa#warangal#warangal#3522644#12846#252#http : / /warangalnicin /\n",
        "pandas_code": "abs(df[df['district'] == 'rangareddy']['area (km square)'].values[0] - df[df['district'] == 'prakasam']['area (km square)'].values[0] + 10000) < 1000",
        "pandas_eval": "True"
    },
    {
        "id": 2126,
        "statement": "the code for district warangal be wa and the code for vizianagaram be vz",
        "label": 1,
        "table_caption": "list of districts of andhra pradesh",
        "table_text": "code#district#headquarters#population (2011)#area (km square)#density ( / km square)#official website\nad#adilabad#adilabad#2737738#16105#170#http : / /adilabadnicin /\nan#anantapur#anantapur#4083315#19130#213#http : / /anantapurnicin /\nch#chittoor#chittoor#4170468#15152#275#http : / /chittoornicin /\neg#east godavari#kakinada#5151549#10807#477#http : / /eastgodavarinicin /\ngu#guntur#guntur#4889230#11391#429#http : / /gunturnicin /\nhy#hyderabad#hyderabad#4010238#217#217#http : / /hyderabadnicin /\ncu#cuddapah#cuddapah#2884524#15359#188#http : / /kadapanicin /\nka#karimnagar#karimnagar#3811738#11823#322#http : / /karimnagarnicin /\nkh#khammam#khammam#2798214#16029#175#http : / /khammamnicin /\nkr#krishna#machilipatnam#4529009#8727#519#http : / /krishnanicin /\nku#kurnool#kurnool#4046601#17658#229#http : / /kurnoolnicin /\nma#mahbubnagar#mahabubnagar#4042191#18432#219#http : / /mahabubnagarnicin /\nme#medak#sangareddi#3031877#9699#313#http : / /medaknicin /\nna#nalgonda#nalgonda#3483648#14240#245#http : / /nalgondanicin /\nne#nellore#nellore#2966082#13076#227#http : / /nellorenicin /\nni#nizamabad#nizamabad#2552073#7956#321#http : / /nizamabadnicin /\npr#prakasam#ongole#3392764#17626#193#http : / /prakasamnicin /\nra#rangareddy#hyderabad#5296396#7493#707#http : / /rangareddynicin /\nsr#srikakulam#srikakulam#2699471#5837#462#http : / /srikakulamnicin /\nvs#vishakhapatnam#vishakhapatnam#4288113#11161#340#http : / /visakhapatnamnicin /\nvz#vizianagaram#vizianagaram#2342868#6539#384#http : / /vizianagaramnicin /\nwa#warangal#warangal#3522644#12846#252#http : / /warangalnicin /\n",
        "pandas_code": "(df.loc[df['district'] == 'warangal', 'code'].values[0] == 'wa') & (df.loc[df['district'] == 'vizianagaram', 'code'].values[0] == 'vz')",
        "pandas_eval": "True"
    },
    {
        "id": 2127,
        "statement": "william f claxton direct the most episode",
        "label": 1,
        "table_caption": "list of little house on the prairie episodes",
        "table_text": "no in series#title#directed by#written by#air date#production code\n1#a harvest of friends#michael landon#blanche hanalis , john hawkins & william putman#september 11 , 1974#1002\n2#country girls#william f claxton#blanche hanalis & juanita bartlett#september 18 , 1974#1001\n3#100 mile walk#william f claxton#blanche hanalis & ward hawkins#september 25 , 1974#1003\n4#mr edwards' homecoming#michael landon#blanche hanalis & joel murcott#october 2 , 1974#1004\n5#the love of johnny johnson#william f claxton#blanche hanalis & gerry day#october 9 , 1974#1005\n6#if i should wake before i die#victor french#blanche hanalis & harold swanton#october 23 , 1974#1006\n7#town party , country party#alf kjellin#blanche hanalis & juanita bartlett#october 30 , 1974#1007\n8#ma 's holiday#leo penn#blanche hanalis & dale eunson#november 6 , 1974#1010\n9#school mom#william f claxton#blanche hanalis , ward hawkins & jean rouverol#november 13 , 1974#1011\n10#the raccoon#william f claxton#blanche hanalis & joseph bonaduce#november 20 , 1974#1013\n11#the voice of tinker jones#leo penn#tony kayden & michael russnow#december 4 , 1974#1012\n12#the award#william f claxton#michael landon#december 11 , 1974#1014\n13 / 14#the lord is my shepherd#michael landon#michael landon#december 18 , 1974#1008 / 1009\n15#christmas at plum creek#william f claxton#arthur heinemann#december 25 , 1974#1015\n16#family quarrel#william f claxton#ward hawkins#january 8 , 1975#1016\n20#child of pain#victor french#john meston#february 12 , 1975#1020\n21#money crop#leo penn#teleplay by : ward hawkins story by : john meston#february 19 , 1975#1021\n",
        "pandas_code": "df['directed by'].value_counts().idxmax() == 'william f claxton'",
        "pandas_eval": "True"
    },
    {
        "id": 2128,
        "statement": "the average number of episode direct by each director be 3.4",
        "label": 1,
        "table_caption": "list of little house on the prairie episodes",
        "table_text": "no in series#title#directed by#written by#air date#production code\n1#a harvest of friends#michael landon#blanche hanalis , john hawkins & william putman#september 11 , 1974#1002\n2#country girls#william f claxton#blanche hanalis & juanita bartlett#september 18 , 1974#1001\n3#100 mile walk#william f claxton#blanche hanalis & ward hawkins#september 25 , 1974#1003\n4#mr edwards' homecoming#michael landon#blanche hanalis & joel murcott#october 2 , 1974#1004\n5#the love of johnny johnson#william f claxton#blanche hanalis & gerry day#october 9 , 1974#1005\n6#if i should wake before i die#victor french#blanche hanalis & harold swanton#october 23 , 1974#1006\n7#town party , country party#alf kjellin#blanche hanalis & juanita bartlett#october 30 , 1974#1007\n8#ma 's holiday#leo penn#blanche hanalis & dale eunson#november 6 , 1974#1010\n9#school mom#william f claxton#blanche hanalis , ward hawkins & jean rouverol#november 13 , 1974#1011\n10#the raccoon#william f claxton#blanche hanalis & joseph bonaduce#november 20 , 1974#1013\n11#the voice of tinker jones#leo penn#tony kayden & michael russnow#december 4 , 1974#1012\n12#the award#william f claxton#michael landon#december 11 , 1974#1014\n13 / 14#the lord is my shepherd#michael landon#michael landon#december 18 , 1974#1008 / 1009\n15#christmas at plum creek#william f claxton#arthur heinemann#december 25 , 1974#1015\n16#family quarrel#william f claxton#ward hawkins#january 8 , 1975#1016\n20#child of pain#victor french#john meston#february 12 , 1975#1020\n21#money crop#leo penn#teleplay by : ward hawkins story by : john meston#february 19 , 1975#1021\n",
        "pandas_code": "df['directed by'].value_counts().mean() == 3.4",
        "pandas_eval": "True"
    },
    {
        "id": 2129,
        "statement": "michael landon write more episode than john meston",
        "label": 1,
        "table_caption": "list of little house on the prairie episodes",
        "table_text": "no in series#title#directed by#written by#air date#production code\n1#a harvest of friends#michael landon#blanche hanalis , john hawkins & william putman#september 11 , 1974#1002\n2#country girls#william f claxton#blanche hanalis & juanita bartlett#september 18 , 1974#1001\n3#100 mile walk#william f claxton#blanche hanalis & ward hawkins#september 25 , 1974#1003\n4#mr edwards' homecoming#michael landon#blanche hanalis & joel murcott#october 2 , 1974#1004\n5#the love of johnny johnson#william f claxton#blanche hanalis & gerry day#october 9 , 1974#1005\n6#if i should wake before i die#victor french#blanche hanalis & harold swanton#october 23 , 1974#1006\n7#town party , country party#alf kjellin#blanche hanalis & juanita bartlett#october 30 , 1974#1007\n8#ma 's holiday#leo penn#blanche hanalis & dale eunson#november 6 , 1974#1010\n9#school mom#william f claxton#blanche hanalis , ward hawkins & jean rouverol#november 13 , 1974#1011\n10#the raccoon#william f claxton#blanche hanalis & joseph bonaduce#november 20 , 1974#1013\n11#the voice of tinker jones#leo penn#tony kayden & michael russnow#december 4 , 1974#1012\n12#the award#william f claxton#michael landon#december 11 , 1974#1014\n13 / 14#the lord is my shepherd#michael landon#michael landon#december 18 , 1974#1008 / 1009\n15#christmas at plum creek#william f claxton#arthur heinemann#december 25 , 1974#1015\n16#family quarrel#william f claxton#ward hawkins#january 8 , 1975#1016\n20#child of pain#victor french#john meston#february 12 , 1975#1020\n21#money crop#leo penn#teleplay by : ward hawkins story by : john meston#february 19 , 1975#1021\n",
        "pandas_code": "df['directed by'].str.contains('michael landon', case=False).sum() > df['written by'].str.contains('john meston', case=False).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2130,
        "statement": "leo penn direct 2 more episode than alf kjellin",
        "label": 1,
        "table_caption": "list of little house on the prairie episodes",
        "table_text": "no in series#title#directed by#written by#air date#production code\n1#a harvest of friends#michael landon#blanche hanalis , john hawkins & william putman#september 11 , 1974#1002\n2#country girls#william f claxton#blanche hanalis & juanita bartlett#september 18 , 1974#1001\n3#100 mile walk#william f claxton#blanche hanalis & ward hawkins#september 25 , 1974#1003\n4#mr edwards' homecoming#michael landon#blanche hanalis & joel murcott#october 2 , 1974#1004\n5#the love of johnny johnson#william f claxton#blanche hanalis & gerry day#october 9 , 1974#1005\n6#if i should wake before i die#victor french#blanche hanalis & harold swanton#october 23 , 1974#1006\n7#town party , country party#alf kjellin#blanche hanalis & juanita bartlett#october 30 , 1974#1007\n8#ma 's holiday#leo penn#blanche hanalis & dale eunson#november 6 , 1974#1010\n9#school mom#william f claxton#blanche hanalis , ward hawkins & jean rouverol#november 13 , 1974#1011\n10#the raccoon#william f claxton#blanche hanalis & joseph bonaduce#november 20 , 1974#1013\n11#the voice of tinker jones#leo penn#tony kayden & michael russnow#december 4 , 1974#1012\n12#the award#william f claxton#michael landon#december 11 , 1974#1014\n13 / 14#the lord is my shepherd#michael landon#michael landon#december 18 , 1974#1008 / 1009\n15#christmas at plum creek#william f claxton#arthur heinemann#december 25 , 1974#1015\n16#family quarrel#william f claxton#ward hawkins#january 8 , 1975#1016\n20#child of pain#victor french#john meston#february 12 , 1975#1020\n21#money crop#leo penn#teleplay by : ward hawkins story by : john meston#february 19 , 1975#1021\n",
        "pandas_code": "(df['directed by'].value_counts()['leo penn'] - df['directed by'].value_counts()['alf kjellin']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2131,
        "statement": "1 episode be write and direct by the same individual",
        "label": 1,
        "table_caption": "list of little house on the prairie episodes",
        "table_text": "no in series#title#directed by#written by#air date#production code\n1#a harvest of friends#michael landon#blanche hanalis , john hawkins & william putman#september 11 , 1974#1002\n2#country girls#william f claxton#blanche hanalis & juanita bartlett#september 18 , 1974#1001\n3#100 mile walk#william f claxton#blanche hanalis & ward hawkins#september 25 , 1974#1003\n4#mr edwards' homecoming#michael landon#blanche hanalis & joel murcott#october 2 , 1974#1004\n5#the love of johnny johnson#william f claxton#blanche hanalis & gerry day#october 9 , 1974#1005\n6#if i should wake before i die#victor french#blanche hanalis & harold swanton#october 23 , 1974#1006\n7#town party , country party#alf kjellin#blanche hanalis & juanita bartlett#october 30 , 1974#1007\n8#ma 's holiday#leo penn#blanche hanalis & dale eunson#november 6 , 1974#1010\n9#school mom#william f claxton#blanche hanalis , ward hawkins & jean rouverol#november 13 , 1974#1011\n10#the raccoon#william f claxton#blanche hanalis & joseph bonaduce#november 20 , 1974#1013\n11#the voice of tinker jones#leo penn#tony kayden & michael russnow#december 4 , 1974#1012\n12#the award#william f claxton#michael landon#december 11 , 1974#1014\n13 / 14#the lord is my shepherd#michael landon#michael landon#december 18 , 1974#1008 / 1009\n15#christmas at plum creek#william f claxton#arthur heinemann#december 25 , 1974#1015\n16#family quarrel#william f claxton#ward hawkins#january 8 , 1975#1016\n20#child of pain#victor french#john meston#february 12 , 1975#1020\n21#money crop#leo penn#teleplay by : ward hawkins story by : john meston#february 19 , 1975#1021\n",
        "pandas_code": "any((df['directed by'] == df['written by'].str.split(' & ').str[0]) | (df['directed by'] == df['written by'].str.split(' & ').str[1]))",
        "pandas_eval": "True"
    },
    {
        "id": 2132,
        "statement": "the torneo rep\u00fablica have the least match play of any tournament",
        "label": 1,
        "table_caption": "paraguayan football derby",
        "table_text": "tournament#matches#wins olimpia#empates#wins cerro#goals olimpia#goals cerro\nparaguayan league#278#94#85#99#357#356\ncopa libertadores de am\u00e9rica#32#11#12#9#43#39\nqualifiers for international cups#5#3#0#2#8#6\ntorneo rep\u00fablica#2#1#1#0#2#1\nsub - totals oficial matches#317#109#98#110#410#402\nfriendly matches#83#34#18#31#149#144\n",
        "pandas_code": "df.loc[df['tournament'] == 'torneo rep\u00fablica', 'matches'].values[0] == df['matches'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2133,
        "statement": "copa libertadores de am\u00e9rica have less match play than paraguayan league",
        "label": 1,
        "table_caption": "paraguayan football derby",
        "table_text": "tournament#matches#wins olimpia#empates#wins cerro#goals olimpia#goals cerro\nparaguayan league#278#94#85#99#357#356\ncopa libertadores de am\u00e9rica#32#11#12#9#43#39\nqualifiers for international cups#5#3#0#2#8#6\ntorneo rep\u00fablica#2#1#1#0#2#1\nsub - totals oficial matches#317#109#98#110#410#402\nfriendly matches#83#34#18#31#149#144\n",
        "pandas_code": "df[df['tournament'] == 'copa libertadores de am\u00e9rica']['matches'].values[0] < df[df['tournament'] == 'paraguayan league']['matches'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2134,
        "statement": "qualifier for international cup have less match play than paraguayan league",
        "label": 1,
        "table_caption": "paraguayan football derby",
        "table_text": "tournament#matches#wins olimpia#empates#wins cerro#goals olimpia#goals cerro\nparaguayan league#278#94#85#99#357#356\ncopa libertadores de am\u00e9rica#32#11#12#9#43#39\nqualifiers for international cups#5#3#0#2#8#6\ntorneo rep\u00fablica#2#1#1#0#2#1\nsub - totals oficial matches#317#109#98#110#410#402\nfriendly matches#83#34#18#31#149#144\n",
        "pandas_code": "df[df['tournament'] == 'qualifiers for international cups']['matches'].values[0] < df[df['tournament'] == 'paraguayan league']['matches'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2135,
        "statement": "torneo rep\u00fablica have less match play than paraguayan league",
        "label": 1,
        "table_caption": "paraguayan football derby",
        "table_text": "tournament#matches#wins olimpia#empates#wins cerro#goals olimpia#goals cerro\nparaguayan league#278#94#85#99#357#356\ncopa libertadores de am\u00e9rica#32#11#12#9#43#39\nqualifiers for international cups#5#3#0#2#8#6\ntorneo rep\u00fablica#2#1#1#0#2#1\nsub - totals oficial matches#317#109#98#110#410#402\nfriendly matches#83#34#18#31#149#144\n",
        "pandas_code": "df[df['tournament'] == 'torneo rep\u00fablica']['matches'].values[0] < df[df['tournament'] == 'paraguayan league']['matches'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2136,
        "statement": "torneo rep\u00fablica have less match play than copa libertadores de am\u00e9rica",
        "label": 1,
        "table_caption": "paraguayan football derby",
        "table_text": "tournament#matches#wins olimpia#empates#wins cerro#goals olimpia#goals cerro\nparaguayan league#278#94#85#99#357#356\ncopa libertadores de am\u00e9rica#32#11#12#9#43#39\nqualifiers for international cups#5#3#0#2#8#6\ntorneo rep\u00fablica#2#1#1#0#2#1\nsub - totals oficial matches#317#109#98#110#410#402\nfriendly matches#83#34#18#31#149#144\n",
        "pandas_code": "df[df['tournament'] == 'torneo rep\u00fablica']['matches'].values[0] < df[df['tournament'] == 'copa libertadores de am\u00e9rica']['matches'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2137,
        "statement": "most of the presenter first name be michael in great railway jorneys",
        "label": 1,
        "table_caption": "great railway journeys",
        "table_text": "episode no#episode title#uk broadcast date#presenter#details of journey#countries visited\n1.1#coast to coast#1980 - 10 - 30#ludovic kennedy#new york to los angeles#usa\n1.2#the long straight#1980 - 11 - 06#michael frayn#sydney to perth#australia\n1.3#deccan#1980 - 11 - 13#brian b thompson#bombay to cochin#india\n1.4#confessions of a trainspotter#1980 - 11 - 27#michael palin#london to the kyle of lochalsh#england , scotland\n1.5#zambezi express#1980 - 12 - 04#michael wood#cape town to victoria falls#south africa , botswana , zimbabwe - rhodesia\n1.6#three miles high#1980 - 12 - 11#miles kington#lima to la paz#peru , bolivia\n",
        "pandas_code": "(df['presenter'].str.split().str[0].value_counts().idxmax() == 'michael')",
        "pandas_eval": "True"
    },
    {
        "id": 2138,
        "statement": "coast to coast broadcast in uk before the long straight",
        "label": 1,
        "table_caption": "great railway journeys",
        "table_text": "episode no#episode title#uk broadcast date#presenter#details of journey#countries visited\n1.1#coast to coast#1980 - 10 - 30#ludovic kennedy#new york to los angeles#usa\n1.2#the long straight#1980 - 11 - 06#michael frayn#sydney to perth#australia\n1.3#deccan#1980 - 11 - 13#brian b thompson#bombay to cochin#india\n1.4#confessions of a trainspotter#1980 - 11 - 27#michael palin#london to the kyle of lochalsh#england , scotland\n1.5#zambezi express#1980 - 12 - 04#michael wood#cape town to victoria falls#south africa , botswana , zimbabwe - rhodesia\n1.6#three miles high#1980 - 12 - 11#miles kington#lima to la paz#peru , bolivia\n",
        "pandas_code": "df.loc[df['episode title'] == 'coast to coast', 'uk broadcast date'].values[0] < df.loc[df['episode title'] == 'the long straight', 'uk broadcast date'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2139,
        "statement": "2 episode list 1 countries visit",
        "label": 1,
        "table_caption": "great railway journeys",
        "table_text": "episode no#episode title#uk broadcast date#presenter#details of journey#countries visited\n1.1#coast to coast#1980 - 10 - 30#ludovic kennedy#new york to los angeles#usa\n1.2#the long straight#1980 - 11 - 06#michael frayn#sydney to perth#australia\n1.3#deccan#1980 - 11 - 13#brian b thompson#bombay to cochin#india\n1.4#confessions of a trainspotter#1980 - 11 - 27#michael palin#london to the kyle of lochalsh#england , scotland\n1.5#zambezi express#1980 - 12 - 04#michael wood#cape town to victoria falls#south africa , botswana , zimbabwe - rhodesia\n1.6#three miles high#1980 - 12 - 11#miles kington#lima to la paz#peru , bolivia\n",
        "pandas_code": "len(df[df['countries visited'].str.contains('usa|australia', case=False, regex=True)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2140,
        "statement": "three episode have three word in the title",
        "label": 1,
        "table_caption": "great railway journeys",
        "table_text": "episode no#episode title#uk broadcast date#presenter#details of journey#countries visited\n1.1#coast to coast#1980 - 10 - 30#ludovic kennedy#new york to los angeles#usa\n1.2#the long straight#1980 - 11 - 06#michael frayn#sydney to perth#australia\n1.3#deccan#1980 - 11 - 13#brian b thompson#bombay to cochin#india\n1.4#confessions of a trainspotter#1980 - 11 - 27#michael palin#london to the kyle of lochalsh#england , scotland\n1.5#zambezi express#1980 - 12 - 04#michael wood#cape town to victoria falls#south africa , botswana , zimbabwe - rhodesia\n1.6#three miles high#1980 - 12 - 11#miles kington#lima to la paz#peru , bolivia\n",
        "pandas_code": "sum(df['episode title'].str.split().apply(len) == 3) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2141,
        "statement": "australia be visit before india",
        "label": 1,
        "table_caption": "great railway journeys",
        "table_text": "episode no#episode title#uk broadcast date#presenter#details of journey#countries visited\n1.1#coast to coast#1980 - 10 - 30#ludovic kennedy#new york to los angeles#usa\n1.2#the long straight#1980 - 11 - 06#michael frayn#sydney to perth#australia\n1.3#deccan#1980 - 11 - 13#brian b thompson#bombay to cochin#india\n1.4#confessions of a trainspotter#1980 - 11 - 27#michael palin#london to the kyle of lochalsh#england , scotland\n1.5#zambezi express#1980 - 12 - 04#michael wood#cape town to victoria falls#south africa , botswana , zimbabwe - rhodesia\n1.6#three miles high#1980 - 12 - 11#miles kington#lima to la paz#peru , bolivia\n",
        "pandas_code": "(df[df['countries visited'] == 'australia']['uk broadcast date'].iloc[0] < df[df['countries visited'] == 'india']['uk broadcast date'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2142,
        "statement": "when the runner - up be peter hanson the margin of victory be 5 strike at the scandinavian master by carlsberg",
        "label": 1,
        "table_caption": "luke donald",
        "table_text": "no#date#tournament#winning score#to par#margin of victory#runner (s) - up\n1#1 aug 2004#scandinavian masters by carlsberg#69 + 65 + 69 + 69 = 272#16#5 strokes#peter hanson\n2#5 sep 2004#omega european masters#67 + 67 + 65 + 66 = 265#19#5 strokes#miguel \u00e1ngel jim\u00e9nez\n3#30 may 2010#madrid masters#65 + 67 + 68 + 67 = 267#21#1 stroke#rhys davies\n4#27 feb 2011#wgc - accenture match play championship#3 and 2#3 and 2#3 and 2#martin kaymer\n5#29 may 2011#bmw pga championship#64 + 72 + 72 + 70 = 278#6#playoff#lee westwood\n6#10 jul 2011#barclays scottish open#67 + 67 + 63 = 197#19#4 strokes#fredrik andersson hed\n",
        "pandas_code": "((df['runner (s) - up'] == 'peter hanson') & (df['margin of victory'] == '5 strokes') & (df['tournament'] == 'scandinavian masters by carlsberg')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2143,
        "statement": "the date for the madrid master be 30 may 2010 with the to par be 21 and margin of victory 1 stroke",
        "label": 1,
        "table_caption": "luke donald",
        "table_text": "no#date#tournament#winning score#to par#margin of victory#runner (s) - up\n1#1 aug 2004#scandinavian masters by carlsberg#69 + 65 + 69 + 69 = 272#16#5 strokes#peter hanson\n2#5 sep 2004#omega european masters#67 + 67 + 65 + 66 = 265#19#5 strokes#miguel \u00e1ngel jim\u00e9nez\n3#30 may 2010#madrid masters#65 + 67 + 68 + 67 = 267#21#1 stroke#rhys davies\n4#27 feb 2011#wgc - accenture match play championship#3 and 2#3 and 2#3 and 2#martin kaymer\n5#29 may 2011#bmw pga championship#64 + 72 + 72 + 70 = 278#6#playoff#lee westwood\n6#10 jul 2011#barclays scottish open#67 + 67 + 63 = 197#19#4 strokes#fredrik andersson hed\n",
        "pandas_code": "((df['date'] == '30 may 2010') & (df['tournament'] == 'madrid masters') & (df['to par'] == '21') & (df['margin of victory'] == '1 stroke')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2144,
        "statement": "when the margin of victory be 4 stroke and the date 10 jul 2011 the tournament be barclays scottish open where frederik andersson hed be the runner - up",
        "label": 1,
        "table_caption": "luke donald",
        "table_text": "no#date#tournament#winning score#to par#margin of victory#runner (s) - up\n1#1 aug 2004#scandinavian masters by carlsberg#69 + 65 + 69 + 69 = 272#16#5 strokes#peter hanson\n2#5 sep 2004#omega european masters#67 + 67 + 65 + 66 = 265#19#5 strokes#miguel \u00e1ngel jim\u00e9nez\n3#30 may 2010#madrid masters#65 + 67 + 68 + 67 = 267#21#1 stroke#rhys davies\n4#27 feb 2011#wgc - accenture match play championship#3 and 2#3 and 2#3 and 2#martin kaymer\n5#29 may 2011#bmw pga championship#64 + 72 + 72 + 70 = 278#6#playoff#lee westwood\n6#10 jul 2011#barclays scottish open#67 + 67 + 63 = 197#19#4 strokes#fredrik andersson hed\n",
        "pandas_code": "(df[(df['margin of victory'] == '4 strokes') & (df['date'] == '10 jul 2011') & (df['tournament'] == 'barclays scottish open') & (df['runner (s) - up'] == 'fredrik andersson hed')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2145,
        "statement": "the blackberry storm 9530 have the software platform 4.2.0.179 with carrier telus mobility",
        "label": 1,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "df[(df['device'] == 'blackberry storm 9530') & (df['software platform'] == '4.2.0.179') & (df['carrier'] == 'telus mobility')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 2146,
        "statement": "only vodafone au offer the blackberry storm 9500 and its package version be 5.0.0.742",
        "label": 1,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "(df[(df['device'] == 'blackberry storm 9500') & (df['carrier'] == 'vodafone au') & (df['package version'] == '5.0.0.742')].shape[0] == 1) and (df[df['device'] == 'blackberry storm 9500'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2147,
        "statement": "the software platform 4.2.0.198 cam with the device offer by vodafone au",
        "label": 1,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "df[(df['device'] == 'blackberry storm 9500') & (df['carrier'] == 'vodafone au') & (df['software platform'] == '4.2.0.198')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2148,
        "statement": "a total of 5 carrier offer the blackberry storm 9530",
        "label": 1,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "len(df[df['device'] == 'blackberry storm 9530']['carrier'].unique()) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2149,
        "statement": "bell mobility 's package version and application be 5.0.0.419",
        "label": 1,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "df[(df['carrier'] == 'bell mobility') & (df['package version'] == '5.0.0.419') & (df['applications'] == '5.0.0.419')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2150,
        "statement": "the blackberry storm 9530 have the software platform 4.7.0.208 with carrier telus mobility",
        "label": 0,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "((df['device'] == 'blackberry storm 9530') & (df['carrier'] == 'telus mobility') & (df['software platform'] == '4.7.0.208')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2151,
        "statement": "only vodafone au offer the blackberry storm 9500 and its package version be 5.0.0.419",
        "label": 0,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "(df[(df['device'] == 'blackberry storm 9500') & (df['carrier'] == 'vodafone au') & (df['package version'] == '5.0.0.419')].shape[0] == 1) & (df[df['device'] == 'blackberry storm 9500'].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2152,
        "statement": "the software platform 4.2.0.198 cam with the device offer by bell mobility",
        "label": 0,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "df[(df['software platform'] == '4.2.0.198') & (df['carrier'] == 'bell mobility')].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 2153,
        "statement": "a total of 6 carrier offer the blackberry storm 9530",
        "label": 0,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "len(df[df['device'] == 'blackberry storm 9530']['carrier'].unique()) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2154,
        "statement": "vodafone au 's package version and application be 5.0.0.419",
        "label": 0,
        "table_caption": "blackberry storm",
        "table_text": "device#carrier#package version#applications#software platform\nblackberry storm 9530#mts mobility#5.0.0.808#5.0.0.419#4.2.0.179\nblackberry storm 9530#verizon wireless#5.0.0.328#5.0.0.328#4.2.0.128\nblackberry storm 9530#telus mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#bell mobility#5.0.0.419#5.0.0.419#4.2.0.179\nblackberry storm 9530#iusacell#4.7.0.208#4.7.0.151#4.0.0.186\nblackberry storm 9500#vodafone au#5.0.0.742#5.0.0.451#4.2.0.198\n",
        "pandas_code": "df[(df['carrier'] == 'vodafone au') & (df['package version'] == '5.0.0.419') & (df['applications'] == '5.0.0.419')].shape[0] > 0",
        "pandas_eval": "False"
    },
    {
        "id": 2155,
        "statement": "the third episode in the season have the second highest number of us viewer",
        "label": 1,
        "table_caption": "lost (season 6)",
        "table_text": "no in series#no in season#title#directed by#written by#featured character (s)#original air date#us viewers (million)\n104 / 105#1 / 2#la x#jack bender#damon lindelof & carlton cuse#various#february 2 , 2010#12.09\n106#3#what kate does#paul edwards#edward kitsis & adam horowitz#kate#february 9 , 2010#11.05\n107#4#the substitute#tucker gates#elizabeth sarnoff & melinda hsu taylor#locke#february 16 , 2010#9.82\n108#5#lighthouse#jack bender#carlton cuse & damon lindelof#jack#february 23 , 2010#9.95\n109#6#sundown#bobby roth#paul zbyszewski & graham roland#sayid#march 2 , 2010#9.29\n110#7#dr linus#mario van peebles#edward kitsis & adam horowitz#ben#march 9 , 2010#9.49\n111#8#recon#jack bender#elizabeth sarnoff & jim galasso#sawyer#march 16 , 2010#8.87\n112#9#ab aeterno#tucker gates#melinda hsu taylor & greggory nations#richard#march 23 , 2010#9.31\n113#10#the package#paul edwards#paul zbyszewski & graham roland#sun & jin#march 30 , 2010#10.13\n114#11#happily ever after#jack bender#carlton cuse & damon lindelof#desmond#april 6 , 2010#9.55\n115#12#everybody loves hugo#daniel attias#edward kitsis & adam horowitz#hurley#april 13 , 2010#9.48\n116#13#the last recruit#stephen semel#paul zbyszewski & graham roland#various#april 20 , 2010#9.53\n117#14#the candidate#jack bender#elizabeth sarnoff & jim galasso#jack & locke#may 4 , 2010#9.59\n118#15#across the sea#tucker gates#carlton cuse & damon lindelof#jacob & man in black#may 11 , 2010#10.32\n119#16#what they died for#paul edwards#edward kitsis , adam horowitz & elizabeth sarnoff#various#may 18 , 2010#10.47\n",
        "pandas_code": "df.sort_values('us viewers (million)', ascending=False).iloc[1]['no in season'] == '3'",
        "pandas_eval": "True"
    },
    {
        "id": 2157,
        "statement": "elizabeth sarnoff write most frequently with jim galasso",
        "label": 1,
        "table_caption": "lost (season 6)",
        "table_text": "no in series#no in season#title#directed by#written by#featured character (s)#original air date#us viewers (million)\n104 / 105#1 / 2#la x#jack bender#damon lindelof & carlton cuse#various#february 2 , 2010#12.09\n106#3#what kate does#paul edwards#edward kitsis & adam horowitz#kate#february 9 , 2010#11.05\n107#4#the substitute#tucker gates#elizabeth sarnoff & melinda hsu taylor#locke#february 16 , 2010#9.82\n108#5#lighthouse#jack bender#carlton cuse & damon lindelof#jack#february 23 , 2010#9.95\n109#6#sundown#bobby roth#paul zbyszewski & graham roland#sayid#march 2 , 2010#9.29\n110#7#dr linus#mario van peebles#edward kitsis & adam horowitz#ben#march 9 , 2010#9.49\n111#8#recon#jack bender#elizabeth sarnoff & jim galasso#sawyer#march 16 , 2010#8.87\n112#9#ab aeterno#tucker gates#melinda hsu taylor & greggory nations#richard#march 23 , 2010#9.31\n113#10#the package#paul edwards#paul zbyszewski & graham roland#sun & jin#march 30 , 2010#10.13\n114#11#happily ever after#jack bender#carlton cuse & damon lindelof#desmond#april 6 , 2010#9.55\n115#12#everybody loves hugo#daniel attias#edward kitsis & adam horowitz#hurley#april 13 , 2010#9.48\n116#13#the last recruit#stephen semel#paul zbyszewski & graham roland#various#april 20 , 2010#9.53\n117#14#the candidate#jack bender#elizabeth sarnoff & jim galasso#jack & locke#may 4 , 2010#9.59\n118#15#across the sea#tucker gates#carlton cuse & damon lindelof#jacob & man in black#may 11 , 2010#10.32\n119#16#what they died for#paul edwards#edward kitsis , adam horowitz & elizabeth sarnoff#various#may 18 , 2010#10.47\n",
        "pandas_code": "(df['written by'].str.contains('elizabeth sarnoff & jim galasso').sum() >= df['written by'].str.contains('elizabeth sarnoff').sum() - df['written by'].str.contains('elizabeth sarnoff & jim galasso').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 2158,
        "statement": "the episode title the candidate have more us viewer than the sixth episode of the season",
        "label": 1,
        "table_caption": "lost (season 6)",
        "table_text": "no in series#no in season#title#directed by#written by#featured character (s)#original air date#us viewers (million)\n104 / 105#1 / 2#la x#jack bender#damon lindelof & carlton cuse#various#february 2 , 2010#12.09\n106#3#what kate does#paul edwards#edward kitsis & adam horowitz#kate#february 9 , 2010#11.05\n107#4#the substitute#tucker gates#elizabeth sarnoff & melinda hsu taylor#locke#february 16 , 2010#9.82\n108#5#lighthouse#jack bender#carlton cuse & damon lindelof#jack#february 23 , 2010#9.95\n109#6#sundown#bobby roth#paul zbyszewski & graham roland#sayid#march 2 , 2010#9.29\n110#7#dr linus#mario van peebles#edward kitsis & adam horowitz#ben#march 9 , 2010#9.49\n111#8#recon#jack bender#elizabeth sarnoff & jim galasso#sawyer#march 16 , 2010#8.87\n112#9#ab aeterno#tucker gates#melinda hsu taylor & greggory nations#richard#march 23 , 2010#9.31\n113#10#the package#paul edwards#paul zbyszewski & graham roland#sun & jin#march 30 , 2010#10.13\n114#11#happily ever after#jack bender#carlton cuse & damon lindelof#desmond#april 6 , 2010#9.55\n115#12#everybody loves hugo#daniel attias#edward kitsis & adam horowitz#hurley#april 13 , 2010#9.48\n116#13#the last recruit#stephen semel#paul zbyszewski & graham roland#various#april 20 , 2010#9.53\n117#14#the candidate#jack bender#elizabeth sarnoff & jim galasso#jack & locke#may 4 , 2010#9.59\n118#15#across the sea#tucker gates#carlton cuse & damon lindelof#jacob & man in black#may 11 , 2010#10.32\n119#16#what they died for#paul edwards#edward kitsis , adam horowitz & elizabeth sarnoff#various#may 18 , 2010#10.47\n",
        "pandas_code": "df[df['title'] == 'the candidate']['us viewers (million)'].values[0] > df[df['no in season'] == '6']['us viewers (million)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2159,
        "statement": "the episode with a featured character of sayid be written by the same people who write the episode title the last recruit",
        "label": 1,
        "table_caption": "lost (season 6)",
        "table_text": "no in series#no in season#title#directed by#written by#featured character (s)#original air date#us viewers (million)\n104 / 105#1 / 2#la x#jack bender#damon lindelof & carlton cuse#various#february 2 , 2010#12.09\n106#3#what kate does#paul edwards#edward kitsis & adam horowitz#kate#february 9 , 2010#11.05\n107#4#the substitute#tucker gates#elizabeth sarnoff & melinda hsu taylor#locke#february 16 , 2010#9.82\n108#5#lighthouse#jack bender#carlton cuse & damon lindelof#jack#february 23 , 2010#9.95\n109#6#sundown#bobby roth#paul zbyszewski & graham roland#sayid#march 2 , 2010#9.29\n110#7#dr linus#mario van peebles#edward kitsis & adam horowitz#ben#march 9 , 2010#9.49\n111#8#recon#jack bender#elizabeth sarnoff & jim galasso#sawyer#march 16 , 2010#8.87\n112#9#ab aeterno#tucker gates#melinda hsu taylor & greggory nations#richard#march 23 , 2010#9.31\n113#10#the package#paul edwards#paul zbyszewski & graham roland#sun & jin#march 30 , 2010#10.13\n114#11#happily ever after#jack bender#carlton cuse & damon lindelof#desmond#april 6 , 2010#9.55\n115#12#everybody loves hugo#daniel attias#edward kitsis & adam horowitz#hurley#april 13 , 2010#9.48\n116#13#the last recruit#stephen semel#paul zbyszewski & graham roland#various#april 20 , 2010#9.53\n117#14#the candidate#jack bender#elizabeth sarnoff & jim galasso#jack & locke#may 4 , 2010#9.59\n118#15#across the sea#tucker gates#carlton cuse & damon lindelof#jacob & man in black#may 11 , 2010#10.32\n119#16#what they died for#paul edwards#edward kitsis , adam horowitz & elizabeth sarnoff#various#may 18 , 2010#10.47\n",
        "pandas_code": "df[df['featured character (s)'] == 'sayid']['written by'].values[0] == df[df['title'] == 'the last recruit']['written by'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2160,
        "statement": "the dow jones open invitational in new jersey offer the largest cash prize at 60000 while , westchester classic in new york offer 50 , 00",
        "label": 1,
        "table_caption": "1970 pga tour",
        "table_text": "date#tournament#location#winner#score#1st prize\njan 11#los angeles open#california#billy casper (44)#276 ( - 8)#20000\njan 18#phoenix open invitational#arizona#dale douglass (3)#271 ( - 13)#20000\njan 25#bing crosby national pro - am#california#bert yancey (6)#278 ( - 10)#25000\nfeb 1#andy williams - san diego open invitational#california#pete brown (2)#275 ( - 13)#30000\nfeb 8#bob hope desert classic#california#bruce devlin (5)#339 ( - 21)#25000\nfeb 15#tucson open invitational#arizona#lee trevino (4)#275 ( - 13)#20000\nfeb 22#san antonio open invitational#texas#ron cerrudo (2)#273 ( - 7)#20000\nmar 1#doral - eastern open invitational#florida#mike hill (1)#279 ( - 9)#30000\nmar 8#florida citrus invitational#florida#bob lunn (4)#271 ( - 17)#30000\nmar 15#monsanto open#florida#dick lotz (2)#275 ( - 9)#30000\nmar 23#greater jacksonville open#florida#don january (8)#279 ( - 9)#20000\nmar 29#national airlines open invitational#florida#lee trevino (5)#274 ( - 14)#40000\napr 5#greater greensboro open#north carolina#gary player (13)#271 ( - 13)#36000\napr 12#masters tournament#georgia#billy casper (45)#279 ( - 9)#25000\napr 20#greater new orleans open invitational#louisiana#miller barber (5)#278 ( - 10)#25000\napr 26#tournament of champions#california#frank beard (9)#273 ( - 15)#30000\napr 26#tallahassee open invitational#florida#harold henning (2)#277 ( - 11)#10000\nmay 3#byron nelson golf classic#texas#jack nicklaus (31)#274 ( - 6)#20000\nmay 10#houston champions international#texas#gibby gilbert (1)#282 ( - 2)#23000\nmay 17#colonial national invitation#texas#homero blancas (2)#273 ( - 7)#25000\nmay 24#atlanta classic#georgia#tommy aaron (2)#275 ( - 13)#25000\nmay 31#danny thomas memphis classic#tennessee#dave hill (8)#267 ( - 13)#30000\njun 7#kemper open#north carolina#dick lotz (3)#278 ( - 10)#30000\njun 14#western open#illinois#hugh royer , jr (1)#273 ( - 11)#26000\njun 21#us open#minnesota#tony jacklin (3)#281 ( - 7)#30000\njun 28#cleveland open#ohio#bruce devlin (6)#268 ( - 12)#30000\njul 5#canadian open#canada#kermit zarley (2)#279 ( - 9)#25000\njul 12#greater milwaukee open#wisconsin#deane beman (2)#276 ( - 12)#22000\njul 12#british open#scotland#jack nicklaus (32)#283 ( - 5)#12600\njul 19#ivb - philadelphia golf classic#pennsylvania#billy casper (46)#274 ( - 14)#30000\njul 26#national four - ball championship pga players#pennsylvania#jack nicklaus (33) arnold palmer (57)#259 ( - 25)#20000 each\naug 2#westchester classic#new york#bruce crampton (8)#273 ( - 15)#50000\naug 9#american golf classic#ohio#frank beard (10)#276 ( - 4)#30000\naug 16#pga championship#oklahoma#dave stockton (4)#279 ( - 1)#40000\naug 24#avco golf classic#massachusetts#billy casper (47)#277 ( - 11)#32000\naug 30#dow jones open invitational#new jersey#bobby nichols (8)#276 ( - 12)#60000\nsep 7#greater hartford open invitational#connecticut#bob murphy (3)#267 ( - 17)#20000\nsep 20#robinson open golf classic#illinois#george knudson (7)#268 ( - 16)#20000\nsep 27#green island open invitational#georgia#mason rudolph (5)#274 ( - 6)#12000\noct 4#azalea open invitational#north carolina#cesar sanudo (1)#269 ( - 15)#12000\noct 25#kaiser international open invitational#california#ken still (3)#280 ( - 8)#30000\nnov 1#sahara invitational#nevada#babe hiskey (1)#276 ( - 12)#20000\nnov 29#heritage golf classic#south carolina#bob goalby (10)#280 ( - 4)#20000\ndec 6#coral springs open invitational#florida#bill garrett (1)#272 ( - 12)#25000\ndec 13#bahama islands open#bahamas#doug sanders (19)#272 ( - 16)#26000\n",
        "pandas_code": "(df[df['tournament'] == 'dow jones open invitational']['1st prize'].values[0] == '60000') & (df[df['tournament'] == 'westchester classic']['1st prize'].values[0] == '50000')",
        "pandas_eval": "True"
    },
    {
        "id": 2161,
        "statement": "7 tournament be hold in florida , 5 be hold in march , 1 in april , and 1 in december",
        "label": 1,
        "table_caption": "1970 pga tour",
        "table_text": "date#tournament#location#winner#score#1st prize\njan 11#los angeles open#california#billy casper (44)#276 ( - 8)#20000\njan 18#phoenix open invitational#arizona#dale douglass (3)#271 ( - 13)#20000\njan 25#bing crosby national pro - am#california#bert yancey (6)#278 ( - 10)#25000\nfeb 1#andy williams - san diego open invitational#california#pete brown (2)#275 ( - 13)#30000\nfeb 8#bob hope desert classic#california#bruce devlin (5)#339 ( - 21)#25000\nfeb 15#tucson open invitational#arizona#lee trevino (4)#275 ( - 13)#20000\nfeb 22#san antonio open invitational#texas#ron cerrudo (2)#273 ( - 7)#20000\nmar 1#doral - eastern open invitational#florida#mike hill (1)#279 ( - 9)#30000\nmar 8#florida citrus invitational#florida#bob lunn (4)#271 ( - 17)#30000\nmar 15#monsanto open#florida#dick lotz (2)#275 ( - 9)#30000\nmar 23#greater jacksonville open#florida#don january (8)#279 ( - 9)#20000\nmar 29#national airlines open invitational#florida#lee trevino (5)#274 ( - 14)#40000\napr 5#greater greensboro open#north carolina#gary player (13)#271 ( - 13)#36000\napr 12#masters tournament#georgia#billy casper (45)#279 ( - 9)#25000\napr 20#greater new orleans open invitational#louisiana#miller barber (5)#278 ( - 10)#25000\napr 26#tournament of champions#california#frank beard (9)#273 ( - 15)#30000\napr 26#tallahassee open invitational#florida#harold henning (2)#277 ( - 11)#10000\nmay 3#byron nelson golf classic#texas#jack nicklaus (31)#274 ( - 6)#20000\nmay 10#houston champions international#texas#gibby gilbert (1)#282 ( - 2)#23000\nmay 17#colonial national invitation#texas#homero blancas (2)#273 ( - 7)#25000\nmay 24#atlanta classic#georgia#tommy aaron (2)#275 ( - 13)#25000\nmay 31#danny thomas memphis classic#tennessee#dave hill (8)#267 ( - 13)#30000\njun 7#kemper open#north carolina#dick lotz (3)#278 ( - 10)#30000\njun 14#western open#illinois#hugh royer , jr (1)#273 ( - 11)#26000\njun 21#us open#minnesota#tony jacklin (3)#281 ( - 7)#30000\njun 28#cleveland open#ohio#bruce devlin (6)#268 ( - 12)#30000\njul 5#canadian open#canada#kermit zarley (2)#279 ( - 9)#25000\njul 12#greater milwaukee open#wisconsin#deane beman (2)#276 ( - 12)#22000\njul 12#british open#scotland#jack nicklaus (32)#283 ( - 5)#12600\njul 19#ivb - philadelphia golf classic#pennsylvania#billy casper (46)#274 ( - 14)#30000\njul 26#national four - ball championship pga players#pennsylvania#jack nicklaus (33) arnold palmer (57)#259 ( - 25)#20000 each\naug 2#westchester classic#new york#bruce crampton (8)#273 ( - 15)#50000\naug 9#american golf classic#ohio#frank beard (10)#276 ( - 4)#30000\naug 16#pga championship#oklahoma#dave stockton (4)#279 ( - 1)#40000\naug 24#avco golf classic#massachusetts#billy casper (47)#277 ( - 11)#32000\naug 30#dow jones open invitational#new jersey#bobby nichols (8)#276 ( - 12)#60000\nsep 7#greater hartford open invitational#connecticut#bob murphy (3)#267 ( - 17)#20000\nsep 20#robinson open golf classic#illinois#george knudson (7)#268 ( - 16)#20000\nsep 27#green island open invitational#georgia#mason rudolph (5)#274 ( - 6)#12000\noct 4#azalea open invitational#north carolina#cesar sanudo (1)#269 ( - 15)#12000\noct 25#kaiser international open invitational#california#ken still (3)#280 ( - 8)#30000\nnov 1#sahara invitational#nevada#babe hiskey (1)#276 ( - 12)#20000\nnov 29#heritage golf classic#south carolina#bob goalby (10)#280 ( - 4)#20000\ndec 6#coral springs open invitational#florida#bill garrett (1)#272 ( - 12)#25000\ndec 13#bahama islands open#bahamas#doug sanders (19)#272 ( - 16)#26000\n",
        "pandas_code": "(df['location'].value_counts()['florida'] == 7) & (df[df['date'].str.startswith('mar')]['location'].value_counts()['florida'] == 5) & (df[df['date'].str.startswith('apr')]['location'].value_counts()['florida'] == 1) & (df[df['date'].str.startswith('dec')]['location'].value_counts()['florida'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2162,
        "statement": "bob goalby win the heritage golf classic at four under par",
        "label": 1,
        "table_caption": "1970 pga tour",
        "table_text": "date#tournament#location#winner#score#1st prize\njan 11#los angeles open#california#billy casper (44)#276 ( - 8)#20000\njan 18#phoenix open invitational#arizona#dale douglass (3)#271 ( - 13)#20000\njan 25#bing crosby national pro - am#california#bert yancey (6)#278 ( - 10)#25000\nfeb 1#andy williams - san diego open invitational#california#pete brown (2)#275 ( - 13)#30000\nfeb 8#bob hope desert classic#california#bruce devlin (5)#339 ( - 21)#25000\nfeb 15#tucson open invitational#arizona#lee trevino (4)#275 ( - 13)#20000\nfeb 22#san antonio open invitational#texas#ron cerrudo (2)#273 ( - 7)#20000\nmar 1#doral - eastern open invitational#florida#mike hill (1)#279 ( - 9)#30000\nmar 8#florida citrus invitational#florida#bob lunn (4)#271 ( - 17)#30000\nmar 15#monsanto open#florida#dick lotz (2)#275 ( - 9)#30000\nmar 23#greater jacksonville open#florida#don january (8)#279 ( - 9)#20000\nmar 29#national airlines open invitational#florida#lee trevino (5)#274 ( - 14)#40000\napr 5#greater greensboro open#north carolina#gary player (13)#271 ( - 13)#36000\napr 12#masters tournament#georgia#billy casper (45)#279 ( - 9)#25000\napr 20#greater new orleans open invitational#louisiana#miller barber (5)#278 ( - 10)#25000\napr 26#tournament of champions#california#frank beard (9)#273 ( - 15)#30000\napr 26#tallahassee open invitational#florida#harold henning (2)#277 ( - 11)#10000\nmay 3#byron nelson golf classic#texas#jack nicklaus (31)#274 ( - 6)#20000\nmay 10#houston champions international#texas#gibby gilbert (1)#282 ( - 2)#23000\nmay 17#colonial national invitation#texas#homero blancas (2)#273 ( - 7)#25000\nmay 24#atlanta classic#georgia#tommy aaron (2)#275 ( - 13)#25000\nmay 31#danny thomas memphis classic#tennessee#dave hill (8)#267 ( - 13)#30000\njun 7#kemper open#north carolina#dick lotz (3)#278 ( - 10)#30000\njun 14#western open#illinois#hugh royer , jr (1)#273 ( - 11)#26000\njun 21#us open#minnesota#tony jacklin (3)#281 ( - 7)#30000\njun 28#cleveland open#ohio#bruce devlin (6)#268 ( - 12)#30000\njul 5#canadian open#canada#kermit zarley (2)#279 ( - 9)#25000\njul 12#greater milwaukee open#wisconsin#deane beman (2)#276 ( - 12)#22000\njul 12#british open#scotland#jack nicklaus (32)#283 ( - 5)#12600\njul 19#ivb - philadelphia golf classic#pennsylvania#billy casper (46)#274 ( - 14)#30000\njul 26#national four - ball championship pga players#pennsylvania#jack nicklaus (33) arnold palmer (57)#259 ( - 25)#20000 each\naug 2#westchester classic#new york#bruce crampton (8)#273 ( - 15)#50000\naug 9#american golf classic#ohio#frank beard (10)#276 ( - 4)#30000\naug 16#pga championship#oklahoma#dave stockton (4)#279 ( - 1)#40000\naug 24#avco golf classic#massachusetts#billy casper (47)#277 ( - 11)#32000\naug 30#dow jones open invitational#new jersey#bobby nichols (8)#276 ( - 12)#60000\nsep 7#greater hartford open invitational#connecticut#bob murphy (3)#267 ( - 17)#20000\nsep 20#robinson open golf classic#illinois#george knudson (7)#268 ( - 16)#20000\nsep 27#green island open invitational#georgia#mason rudolph (5)#274 ( - 6)#12000\noct 4#azalea open invitational#north carolina#cesar sanudo (1)#269 ( - 15)#12000\noct 25#kaiser international open invitational#california#ken still (3)#280 ( - 8)#30000\nnov 1#sahara invitational#nevada#babe hiskey (1)#276 ( - 12)#20000\nnov 29#heritage golf classic#south carolina#bob goalby (10)#280 ( - 4)#20000\ndec 6#coral springs open invitational#florida#bill garrett (1)#272 ( - 12)#25000\ndec 13#bahama islands open#bahamas#doug sanders (19)#272 ( - 16)#26000\n",
        "pandas_code": "df[(df['winner'].str.contains('bob goalby')) & (df['tournament'] == 'heritage golf classic') & (df['score'].str.contains('- 4'))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2163,
        "statement": "the greater hartford open invitational be hold on sep 7 in connecticut",
        "label": 1,
        "table_caption": "1970 pga tour",
        "table_text": "date#tournament#location#winner#score#1st prize\njan 11#los angeles open#california#billy casper (44)#276 ( - 8)#20000\njan 18#phoenix open invitational#arizona#dale douglass (3)#271 ( - 13)#20000\njan 25#bing crosby national pro - am#california#bert yancey (6)#278 ( - 10)#25000\nfeb 1#andy williams - san diego open invitational#california#pete brown (2)#275 ( - 13)#30000\nfeb 8#bob hope desert classic#california#bruce devlin (5)#339 ( - 21)#25000\nfeb 15#tucson open invitational#arizona#lee trevino (4)#275 ( - 13)#20000\nfeb 22#san antonio open invitational#texas#ron cerrudo (2)#273 ( - 7)#20000\nmar 1#doral - eastern open invitational#florida#mike hill (1)#279 ( - 9)#30000\nmar 8#florida citrus invitational#florida#bob lunn (4)#271 ( - 17)#30000\nmar 15#monsanto open#florida#dick lotz (2)#275 ( - 9)#30000\nmar 23#greater jacksonville open#florida#don january (8)#279 ( - 9)#20000\nmar 29#national airlines open invitational#florida#lee trevino (5)#274 ( - 14)#40000\napr 5#greater greensboro open#north carolina#gary player (13)#271 ( - 13)#36000\napr 12#masters tournament#georgia#billy casper (45)#279 ( - 9)#25000\napr 20#greater new orleans open invitational#louisiana#miller barber (5)#278 ( - 10)#25000\napr 26#tournament of champions#california#frank beard (9)#273 ( - 15)#30000\napr 26#tallahassee open invitational#florida#harold henning (2)#277 ( - 11)#10000\nmay 3#byron nelson golf classic#texas#jack nicklaus (31)#274 ( - 6)#20000\nmay 10#houston champions international#texas#gibby gilbert (1)#282 ( - 2)#23000\nmay 17#colonial national invitation#texas#homero blancas (2)#273 ( - 7)#25000\nmay 24#atlanta classic#georgia#tommy aaron (2)#275 ( - 13)#25000\nmay 31#danny thomas memphis classic#tennessee#dave hill (8)#267 ( - 13)#30000\njun 7#kemper open#north carolina#dick lotz (3)#278 ( - 10)#30000\njun 14#western open#illinois#hugh royer , jr (1)#273 ( - 11)#26000\njun 21#us open#minnesota#tony jacklin (3)#281 ( - 7)#30000\njun 28#cleveland open#ohio#bruce devlin (6)#268 ( - 12)#30000\njul 5#canadian open#canada#kermit zarley (2)#279 ( - 9)#25000\njul 12#greater milwaukee open#wisconsin#deane beman (2)#276 ( - 12)#22000\njul 12#british open#scotland#jack nicklaus (32)#283 ( - 5)#12600\njul 19#ivb - philadelphia golf classic#pennsylvania#billy casper (46)#274 ( - 14)#30000\njul 26#national four - ball championship pga players#pennsylvania#jack nicklaus (33) arnold palmer (57)#259 ( - 25)#20000 each\naug 2#westchester classic#new york#bruce crampton (8)#273 ( - 15)#50000\naug 9#american golf classic#ohio#frank beard (10)#276 ( - 4)#30000\naug 16#pga championship#oklahoma#dave stockton (4)#279 ( - 1)#40000\naug 24#avco golf classic#massachusetts#billy casper (47)#277 ( - 11)#32000\naug 30#dow jones open invitational#new jersey#bobby nichols (8)#276 ( - 12)#60000\nsep 7#greater hartford open invitational#connecticut#bob murphy (3)#267 ( - 17)#20000\nsep 20#robinson open golf classic#illinois#george knudson (7)#268 ( - 16)#20000\nsep 27#green island open invitational#georgia#mason rudolph (5)#274 ( - 6)#12000\noct 4#azalea open invitational#north carolina#cesar sanudo (1)#269 ( - 15)#12000\noct 25#kaiser international open invitational#california#ken still (3)#280 ( - 8)#30000\nnov 1#sahara invitational#nevada#babe hiskey (1)#276 ( - 12)#20000\nnov 29#heritage golf classic#south carolina#bob goalby (10)#280 ( - 4)#20000\ndec 6#coral springs open invitational#florida#bill garrett (1)#272 ( - 12)#25000\ndec 13#bahama islands open#bahamas#doug sanders (19)#272 ( - 16)#26000\n",
        "pandas_code": "((df['tournament'] == 'greater hartford open invitational') & (df['date'] == 'sep 7') & (df['location'] == 'connecticut')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2164,
        "statement": "11 tournament offer 20000 as the first prize , only four offer less than that",
        "label": 1,
        "table_caption": "1970 pga tour",
        "table_text": "date#tournament#location#winner#score#1st prize\njan 11#los angeles open#california#billy casper (44)#276 ( - 8)#20000\njan 18#phoenix open invitational#arizona#dale douglass (3)#271 ( - 13)#20000\njan 25#bing crosby national pro - am#california#bert yancey (6)#278 ( - 10)#25000\nfeb 1#andy williams - san diego open invitational#california#pete brown (2)#275 ( - 13)#30000\nfeb 8#bob hope desert classic#california#bruce devlin (5)#339 ( - 21)#25000\nfeb 15#tucson open invitational#arizona#lee trevino (4)#275 ( - 13)#20000\nfeb 22#san antonio open invitational#texas#ron cerrudo (2)#273 ( - 7)#20000\nmar 1#doral - eastern open invitational#florida#mike hill (1)#279 ( - 9)#30000\nmar 8#florida citrus invitational#florida#bob lunn (4)#271 ( - 17)#30000\nmar 15#monsanto open#florida#dick lotz (2)#275 ( - 9)#30000\nmar 23#greater jacksonville open#florida#don january (8)#279 ( - 9)#20000\nmar 29#national airlines open invitational#florida#lee trevino (5)#274 ( - 14)#40000\napr 5#greater greensboro open#north carolina#gary player (13)#271 ( - 13)#36000\napr 12#masters tournament#georgia#billy casper (45)#279 ( - 9)#25000\napr 20#greater new orleans open invitational#louisiana#miller barber (5)#278 ( - 10)#25000\napr 26#tournament of champions#california#frank beard (9)#273 ( - 15)#30000\napr 26#tallahassee open invitational#florida#harold henning (2)#277 ( - 11)#10000\nmay 3#byron nelson golf classic#texas#jack nicklaus (31)#274 ( - 6)#20000\nmay 10#houston champions international#texas#gibby gilbert (1)#282 ( - 2)#23000\nmay 17#colonial national invitation#texas#homero blancas (2)#273 ( - 7)#25000\nmay 24#atlanta classic#georgia#tommy aaron (2)#275 ( - 13)#25000\nmay 31#danny thomas memphis classic#tennessee#dave hill (8)#267 ( - 13)#30000\njun 7#kemper open#north carolina#dick lotz (3)#278 ( - 10)#30000\njun 14#western open#illinois#hugh royer , jr (1)#273 ( - 11)#26000\njun 21#us open#minnesota#tony jacklin (3)#281 ( - 7)#30000\njun 28#cleveland open#ohio#bruce devlin (6)#268 ( - 12)#30000\njul 5#canadian open#canada#kermit zarley (2)#279 ( - 9)#25000\njul 12#greater milwaukee open#wisconsin#deane beman (2)#276 ( - 12)#22000\njul 12#british open#scotland#jack nicklaus (32)#283 ( - 5)#12600\njul 19#ivb - philadelphia golf classic#pennsylvania#billy casper (46)#274 ( - 14)#30000\njul 26#national four - ball championship pga players#pennsylvania#jack nicklaus (33) arnold palmer (57)#259 ( - 25)#20000 each\naug 2#westchester classic#new york#bruce crampton (8)#273 ( - 15)#50000\naug 9#american golf classic#ohio#frank beard (10)#276 ( - 4)#30000\naug 16#pga championship#oklahoma#dave stockton (4)#279 ( - 1)#40000\naug 24#avco golf classic#massachusetts#billy casper (47)#277 ( - 11)#32000\naug 30#dow jones open invitational#new jersey#bobby nichols (8)#276 ( - 12)#60000\nsep 7#greater hartford open invitational#connecticut#bob murphy (3)#267 ( - 17)#20000\nsep 20#robinson open golf classic#illinois#george knudson (7)#268 ( - 16)#20000\nsep 27#green island open invitational#georgia#mason rudolph (5)#274 ( - 6)#12000\noct 4#azalea open invitational#north carolina#cesar sanudo (1)#269 ( - 15)#12000\noct 25#kaiser international open invitational#california#ken still (3)#280 ( - 8)#30000\nnov 1#sahara invitational#nevada#babe hiskey (1)#276 ( - 12)#20000\nnov 29#heritage golf classic#south carolina#bob goalby (10)#280 ( - 4)#20000\ndec 6#coral springs open invitational#florida#bill garrett (1)#272 ( - 12)#25000\ndec 13#bahama islands open#bahamas#doug sanders (19)#272 ( - 16)#26000\n",
        "pandas_code": "(df['1st prize'].replace(' each', '', regex=True).astype(int) == 20000).sum() == 11 and (df['1st prize'].replace(' each', '', regex=True).astype(int) < 20000).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2165,
        "statement": "the 1988 season have more people in attendance than the 1992 - 1993 season",
        "label": 1,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "int(df[df['season'] == '1988']['cup final attendance'].iloc[0]) > int(df[df['season'] == '1992 - 93']['cup final attendance'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2166,
        "statement": "from 1990 to 1997 , the team with the most win be the parramatta eagle",
        "label": 1,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "df[(df['season'].str[:4].astype(int) >= 1990) & (df['season'].str[:4].astype(int) <= 1997)]['winningteam'].value_counts().idxmax().startswith('parramatta eagles')",
        "pandas_eval": "True"
    },
    {
        "id": 2167,
        "statement": "the game play in the 1990 - 91 season have more people in attendance than the game where collingwood warrior be the winning team",
        "label": 1,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "df[df['season'] == '1990 - 91']['cup final attendance'].astype(int).max() > df[df['winningteam'] == 'collingwood warriors (1)']['cup final attendance'].astype(int).max()",
        "pandas_eval": "True"
    },
    {
        "id": 2168,
        "statement": "adelaide city be the losing team before they be the winning team",
        "label": 1,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "(df[df['losingteam'] == 'adelaide city']['season'].min() < df[df['winningteam'].str.contains('adelaide city')]['season'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 2170,
        "statement": "the 1998 season have the fewest people in attendance ever",
        "label": 0,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "df[df['season'] == '1998']['cup final attendance'].str.split().explode().astype(int).min() == df['cup final attendance'].str.split().explode().astype(int).min()",
        "pandas_eval": "False"
    },
    {
        "id": 2171,
        "statement": "the parramatta eagle have never win a game",
        "label": 0,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "not any(df['winningteam'].str.contains('parramatta eagle'))",
        "pandas_eval": "False"
    },
    {
        "id": 2172,
        "statement": "the most people attend at when collingwood warrior be the winning team",
        "label": 0,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "df[df['winningteam'].str.contains('collingwood warriors')]['cup final attendance'].max() == df['cup final attendance'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2173,
        "statement": "adelaide city have never lose a game",
        "label": 0,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "not any(df['losingteam'].str.contains('adelaide city', case=False, na=False))",
        "pandas_eval": "False"
    },
    {
        "id": 2174,
        "statement": "no team have ever win the cup final 2 time",
        "label": 0,
        "table_caption": "list of nsl champions",
        "table_text": "season#cup finaldate#winningteam#score#losingteam#cup final attendance\n1977#9 october 1977#brisbane city (1)#1 - 1 (5 - 3 penalties)#marconi stallions#9000\n1978#8 october 1978#brisbane city (2)#2 - 1#adelaide city#6964\n1979#30 september 1979#adelaide city (1)#3 - 1#st george#9554\n1980#5 october 1980 15 october 1980#marconi stallions (1)#0 - 0 (aet) 3 - 0 (replay)#heidelberg united#7000 5000\n1981#20 september 1981#brisbane lions (1)#3 - 1#west adelaide#8132\n1982#12 september 1982#apia leichhardt (2)#2 - 1#heidelberg united#7000\n1983#6 november 1983 13 november 1983#sydney olympic (1)#1 - 0 1 - 0#heidelberg united#9420 6000\n1984#13 september 1984#newcastle rosebud united (1)#1 - 0#melbourne knights#5000\n1985#11 august 1985#sydney olympic (2)#2 - 1#preston lions#11187\n1986#17 september 1986#sydney city (1)#3 - 2 (aet)#west adelaide#5200\n1987#14 october 1987 18 october 1987#sydney united (1)#1 - 0 1 - 0#south melbourne#5845 6000\n1988#14 august 1988#apia leichhardt (2)#0 - 0 (5 - 3 penalties)#brunswick juventus#5200\n1989#20 august 1989#adelaide city (2)#2 - 0#sydney olympic#10000\n1989 - 90#25 april 1990#south melbourne (1)#4 - 1#sydney olympic#7000\n1990 - 91#7 april 1991#parramatta eagles (1)#1 - 0#preston lions#8749\n1991 - 92#7 april 1992#adelaide city (3)#2 - 1#marconi stallions#6500\n1992 - 93#18 april 1993#heidelberg united (1)#2 - 0#parramatta eagles#4596\n1993 - 94#17 october 1993#parramatta eagles (2)#2 - 0#sydney united#5156\n1994 - 95#16 october 1994#melbourne knights (1)#6 - 0#heidelberg united#6000\n1995 - 96#27 january 1996#south melbourne (2)#3 - 1#newcastle breakers#7000\n1996 - 97#6 october 1996#collingwood warriors (1)#1 - 0#marconi stallions#5327\n",
        "pandas_code": "df['winningteam'].apply(lambda x: x.split('(')[1].strip(')')).astype(int).max() < 2",
        "pandas_eval": "False"
    },
    {
        "id": 2175,
        "statement": "3 of the game play on may 31 , 2008 in the oafl be play at humber college north",
        "label": 1,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[(df['date'] == '2008 - 05 - 31') & (df['ground'] == 'humber college north')].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2176,
        "statement": "ottawa swan beat the high park demon by 79 point",
        "label": 1,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[(df['home'] == 'ottawa swans') & (df['away'] == 'high park demons') & (df['score'].apply(lambda x: int(x.split(' - ')[1]) - int(x.split(' - ')[0]) == 79))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2177,
        "statement": "the toronto downtown dingo v broadview hawks game last 10 minute",
        "label": 1,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[(df['home'] == 'toronto downtown dingos') & (df['away'] == 'broadview hawks')]['time'].iloc[0].endswith('00')",
        "pandas_eval": "True"
    },
    {
        "id": 2178,
        "statement": "the etobicoke kangaroo score the most point out of any team in the oafl on may 31 , 2008",
        "label": 1,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[df['date'] == '2008 - 05 - 31'].apply(lambda x: max(map(int, x['score'].split(' - '))), axis=1).idxmax() == df[(df['date'] == '2008 - 05 - 31') & ((df['home'] == 'etobicoke kangaroos') | (df['away'] == 'etobicoke kangaroos'))].apply(lambda x: max(map(int, x['score'].split(' - '))), axis=1).idxmax()",
        "pandas_eval": "True"
    },
    {
        "id": 2179,
        "statement": "3 separate game last 14 minute on may 31 , 2008",
        "label": 1,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "len(df[(df['date'] == '2008 - 05 - 31') & (df['time'].str.endswith(':00')) & (df['time'].str[:2].astype(int) >= 14)]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2180,
        "statement": "3 of the game play on may 31 , 2008 in the oafl be play at magaret green park",
        "label": 0,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[(df['date'] == '2008 - 05 - 31') & (df['ground'] == 'magaret green park')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2181,
        "statement": "the high park demon beat the ottawa swan by 79 point",
        "label": 0,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "((df['home'] == 'ottawa swans') & (df['away'] == 'high park demons') & (df['score'].apply(lambda x: abs(int(x.split(' - ')[0]) - int(x.split(' - ')[1])) == 79))).any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 2182,
        "statement": "the toronto downtown dingo v broadview hawks game last 11 minute",
        "label": 0,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "df[(df['home'] == 'toronto downtown dingos') & (df['away'] == 'broadview hawks')]['time'].iloc[0] == '11:00'",
        "pandas_eval": "False"
    },
    {
        "id": 2183,
        "statement": "the etobicoke kangaroo score the least point out of any team in the oafl on may 31 , 2008",
        "label": 0,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "(df[df['date'] == '2008 - 05 - 31'].apply(lambda row: int(row['score'].split(' - ')[1]) if 'etobicoke kangaroos' in row['away'] else int(row['score'].split(' - ')[0]) if 'etobicoke kangaroos' in row['home'] else float('inf'), axis=1).min() == df[df['date'] == '2008 - 05 - 31']['score'].apply(lambda x: min(map(int, x.split(' - ')))).min())",
        "pandas_eval": "False"
    },
    {
        "id": 2184,
        "statement": "4 separate game last 14 minute on may 31 , 2008",
        "label": 0,
        "table_caption": "2008 oafl season",
        "table_text": "date#time#home#away#score#ground\n2008 - 05 - 31#10:00#toronto downtown dingos#broadview hawks#34 - 86#humber college north\n2008 - 05 - 31#11:00#hamilton wildcats#etobicoke kangaroos#52 - 110#humber college north\n2008 - 05 - 31#14:00#ottawa swans#high park demons#20 - 99#rideau carleton raceway\n2008 - 05 - 31#14:00#guelph gargoyles#central blues#65 - 19#magaret green park\n2008 - 05 - 31#14:00#toronto eagles#toronto rebels#106 - 35#humber college north\n",
        "pandas_code": "len(df[(df['date'] == '2008 - 05 - 31') & (df['time'].str.endswith('14:00'))]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2185,
        "statement": "methane have a higher upper index mj / nm 3 than hydrogen",
        "label": 1,
        "table_caption": "wobbe index",
        "table_text": "fuel gas#upper index kcal / nm 3#lower index kcal / nm 3#upper index mj / nm 3#lower index mj / nm 3\nhydrogen#11528#9715#48.23#40.65\nmethane#12735#11452#53.28#47.91\nethane#16298#14931#68.19#62.47\nethylene#15253#14344#63.82#60.01\nnatural gas#12837#11597#53.71#48.52\npropane#19376#17817#81.07#74.54\npropylene#18413#17180#77.04#71.88\nn - butane#22066#20336#92.32#85.08\niso - butane#21980#20247#91.96#84.71\nbutylene - 1#21142#19728#88.46#82.54\nlpg#20755#19106#86.84#79.94\nacetylene#14655#14141#61.32#59.16\n",
        "pandas_code": "df.loc[df['fuel gas'] == 'methane', 'upper index mj / nm 3'].values[0] > df.loc[df['fuel gas'] == 'hydrogen', 'upper index mj / nm 3'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2186,
        "statement": "ethane have a higher upper index mj / nm 3 than methane",
        "label": 1,
        "table_caption": "wobbe index",
        "table_text": "fuel gas#upper index kcal / nm 3#lower index kcal / nm 3#upper index mj / nm 3#lower index mj / nm 3\nhydrogen#11528#9715#48.23#40.65\nmethane#12735#11452#53.28#47.91\nethane#16298#14931#68.19#62.47\nethylene#15253#14344#63.82#60.01\nnatural gas#12837#11597#53.71#48.52\npropane#19376#17817#81.07#74.54\npropylene#18413#17180#77.04#71.88\nn - butane#22066#20336#92.32#85.08\niso - butane#21980#20247#91.96#84.71\nbutylene - 1#21142#19728#88.46#82.54\nlpg#20755#19106#86.84#79.94\nacetylene#14655#14141#61.32#59.16\n",
        "pandas_code": "df.loc[df['fuel gas'] == 'ethane', 'upper index mj / nm 3'].values[0] > df.loc[df['fuel gas'] == 'methane', 'upper index mj / nm 3'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2187,
        "statement": "acetylene have a lower upper index mj / nm 3 than n - butane",
        "label": 1,
        "table_caption": "wobbe index",
        "table_text": "fuel gas#upper index kcal / nm 3#lower index kcal / nm 3#upper index mj / nm 3#lower index mj / nm 3\nhydrogen#11528#9715#48.23#40.65\nmethane#12735#11452#53.28#47.91\nethane#16298#14931#68.19#62.47\nethylene#15253#14344#63.82#60.01\nnatural gas#12837#11597#53.71#48.52\npropane#19376#17817#81.07#74.54\npropylene#18413#17180#77.04#71.88\nn - butane#22066#20336#92.32#85.08\niso - butane#21980#20247#91.96#84.71\nbutylene - 1#21142#19728#88.46#82.54\nlpg#20755#19106#86.84#79.94\nacetylene#14655#14141#61.32#59.16\n",
        "pandas_code": "df[df['fuel gas'] == 'acetylene']['upper index mj / nm 3'].values[0] < df[df['fuel gas'] == 'n - butane']['upper index mj / nm 3'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2188,
        "statement": "the upper index kcal / nm 3 for natural gas and ethylene be 12837 and 15253 , respectively",
        "label": 1,
        "table_caption": "wobbe index",
        "table_text": "fuel gas#upper index kcal / nm 3#lower index kcal / nm 3#upper index mj / nm 3#lower index mj / nm 3\nhydrogen#11528#9715#48.23#40.65\nmethane#12735#11452#53.28#47.91\nethane#16298#14931#68.19#62.47\nethylene#15253#14344#63.82#60.01\nnatural gas#12837#11597#53.71#48.52\npropane#19376#17817#81.07#74.54\npropylene#18413#17180#77.04#71.88\nn - butane#22066#20336#92.32#85.08\niso - butane#21980#20247#91.96#84.71\nbutylene - 1#21142#19728#88.46#82.54\nlpg#20755#19106#86.84#79.94\nacetylene#14655#14141#61.32#59.16\n",
        "pandas_code": "(df[df['fuel gas'] == 'natural gas']['upper index kcal / nm 3'].values[0] == 12837) & (df[df['fuel gas'] == 'ethylene']['upper index kcal / nm 3'].values[0] == 15253)",
        "pandas_eval": "True"
    },
    {
        "id": 2189,
        "statement": "ethane have a higher upper index kcal / nm 3 than ethylene",
        "label": 1,
        "table_caption": "wobbe index",
        "table_text": "fuel gas#upper index kcal / nm 3#lower index kcal / nm 3#upper index mj / nm 3#lower index mj / nm 3\nhydrogen#11528#9715#48.23#40.65\nmethane#12735#11452#53.28#47.91\nethane#16298#14931#68.19#62.47\nethylene#15253#14344#63.82#60.01\nnatural gas#12837#11597#53.71#48.52\npropane#19376#17817#81.07#74.54\npropylene#18413#17180#77.04#71.88\nn - butane#22066#20336#92.32#85.08\niso - butane#21980#20247#91.96#84.71\nbutylene - 1#21142#19728#88.46#82.54\nlpg#20755#19106#86.84#79.94\nacetylene#14655#14141#61.32#59.16\n",
        "pandas_code": "df[df['fuel gas'] == 'ethane']['upper index kcal / nm 3'].values[0] > df[df['fuel gas'] == 'ethylene']['upper index kcal / nm 3'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2190,
        "statement": "total goal score for and against be 11 in 4 match",
        "label": 1,
        "table_caption": "gary mcallister",
        "table_text": "date#venue#score#result#competition\n17 october 1990#hampden park , glasgow , scotland#2 - 1#win#uefa euro 1992 qualifying\n20 may 1992#varsity stadium , toronto , canada#1 - 3#win#friendly\n18 june 1992#idrottsparken , norrk\u00f6ping , sweden#0 - 3#win#uefa euro 1992\n8 june 1997#dynama stadium , minsk , belarus#0 - 1#win#1998 world cup qualification\n",
        "pandas_code": "(df['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).sum() == 11)",
        "pandas_eval": "True"
    },
    {
        "id": 2191,
        "statement": "gary mcallister win 4 out of 4 match , start in 1990 and finish in 1997",
        "label": 1,
        "table_caption": "gary mcallister",
        "table_text": "date#venue#score#result#competition\n17 october 1990#hampden park , glasgow , scotland#2 - 1#win#uefa euro 1992 qualifying\n20 may 1992#varsity stadium , toronto , canada#1 - 3#win#friendly\n18 june 1992#idrottsparken , norrk\u00f6ping , sweden#0 - 3#win#uefa euro 1992\n8 june 1997#dynama stadium , minsk , belarus#0 - 1#win#1998 world cup qualification\n",
        "pandas_code": "(df['result'].eq('win').all()) & (df['date'].str.contains('1990').any()) & (df['date'].str.contains('1997').any())",
        "pandas_eval": "True"
    },
    {
        "id": 2192,
        "statement": "2 out of 4 win win without a goal score against",
        "label": 1,
        "table_caption": "gary mcallister",
        "table_text": "date#venue#score#result#competition\n17 october 1990#hampden park , glasgow , scotland#2 - 1#win#uefa euro 1992 qualifying\n20 may 1992#varsity stadium , toronto , canada#1 - 3#win#friendly\n18 june 1992#idrottsparken , norrk\u00f6ping , sweden#0 - 3#win#uefa euro 1992\n8 june 1997#dynama stadium , minsk , belarus#0 - 1#win#1998 world cup qualification\n",
        "pandas_code": "(df['result'] == 'win').sum() == 4 and (df['score'].str.startswith('0 -')).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2193,
        "statement": "all 4 venue be in different country , start in 1990 and finish in 1997",
        "label": 1,
        "table_caption": "gary mcallister",
        "table_text": "date#venue#score#result#competition\n17 october 1990#hampden park , glasgow , scotland#2 - 1#win#uefa euro 1992 qualifying\n20 may 1992#varsity stadium , toronto , canada#1 - 3#win#friendly\n18 june 1992#idrottsparken , norrk\u00f6ping , sweden#0 - 3#win#uefa euro 1992\n8 june 1997#dynama stadium , minsk , belarus#0 - 1#win#1998 world cup qualification\n",
        "pandas_code": "len(df['venue'].apply(lambda x: x.split(',')[-1].strip()).unique()) == 4 and df['date'].apply(lambda x: int(x.split()[-1])).between(1990, 1997).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2194,
        "statement": "only 1 out of 4 game be a friendly , on may 20 1992",
        "label": 1,
        "table_caption": "gary mcallister",
        "table_text": "date#venue#score#result#competition\n17 october 1990#hampden park , glasgow , scotland#2 - 1#win#uefa euro 1992 qualifying\n20 may 1992#varsity stadium , toronto , canada#1 - 3#win#friendly\n18 june 1992#idrottsparken , norrk\u00f6ping , sweden#0 - 3#win#uefa euro 1992\n8 june 1997#dynama stadium , minsk , belarus#0 - 1#win#1998 world cup qualification\n",
        "pandas_code": "(df['competition'].eq('friendly').sum() == 1) & (df[df['competition'] == 'friendly']['date'].eq('20 may 1992').all())",
        "pandas_eval": "True"
    },
    {
        "id": 2195,
        "statement": "3 condition show decreased platelet count",
        "label": 1,
        "table_caption": "bleeding time",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\n",
        "pandas_code": "len(df[df['platelet count'] == 'decreased']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2196,
        "statement": "7 condition have a prolonged prothrombin time",
        "label": 1,
        "table_caption": "bleeding time",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\n",
        "pandas_code": "len(df[df['prothrombin time'] == 'prolonged']) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 2197,
        "statement": "9 condition have a prolonged bleeding time",
        "label": 1,
        "table_caption": "bleeding time",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\n",
        "pandas_code": "len(df[df['bleeding time'] == 'prolonged']) == 9",
        "pandas_eval": "True"
    },
    {
        "id": 2198,
        "statement": "4 of the condition mention the word , deficiency in their name",
        "label": 1,
        "table_caption": "bleeding time",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\n",
        "pandas_code": "sum(df['condition'].str.contains('deficiency', case=False)) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2199,
        "statement": "3 of the condition mention the word , factor in their name",
        "label": 1,
        "table_caption": "bleeding time",
        "table_text": "condition#prothrombin time#partial thromboplastin time#bleeding time#platelet count\nvitamin k deficiency or warfarin#prolonged#normal or mildly prolonged#unaffected#unaffected\ndisseminated intravascular coagulation#prolonged#prolonged#prolonged#decreased\nvon willebrand disease#unaffected#prolonged or unaffected#prolonged#unaffected\nhemophilia#unaffected#prolonged#unaffected#unaffected\naspirin#unaffected#unaffected#prolonged#unaffected\nthrombocytopenia#unaffected#unaffected#prolonged#decreased\nliver failure , early#prolonged#unaffected#unaffected#unaffected\nliver failure , end - stage#prolonged#prolonged#prolonged#decreased\nuremia#unaffected#unaffected#prolonged#unaffected\ncongenital afibrinogenemia#prolonged#prolonged#prolonged#unaffected\nfactor v deficiency#prolonged#prolonged#unaffected#unaffected\nfactor x deficiency as seen in amyloid purpura#prolonged#prolonged#unaffected#unaffected\nglanzmann 's thrombasthenia#unaffected#unaffected#prolonged#unaffected\nbernard - soulier syndrome#unaffected#unaffected#prolonged#decreased or unaffected\nfactor xii deficiency#unaffected#prolonged#unaffected#unaffected\n",
        "pandas_code": "df['condition'].str.contains('factor', case=False).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2200,
        "statement": "the 1949 victorian football league season be the 53rd season of the elite australian rule football competition",
        "label": 1,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "df['date'].str.contains('1949').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2201,
        "statement": "during round 12 of the competition , arden street oval be the venue with the highest crowd",
        "label": 1,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "(df[df['venue'] == 'arden street oval']['crowd'].max() == df['crowd'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 2202,
        "statement": "all event in round 12 take place on 9 july 1949",
        "label": 1,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "all(df['date'] == '9 july 1949')",
        "pandas_eval": "True"
    },
    {
        "id": 2204,
        "statement": "home team melbourne against away team geelong be hold in mcg with a total of 20000 crowd",
        "label": 1,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "((df['home team'] == 'melbourne') & (df['away team'] == 'geelong') & (df['venue'] == 'mcg') & (df['crowd'] == 20000)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2205,
        "statement": "the 1949 victorian football league season be not the 53rd season of the elite australian rule football competition",
        "label": 0,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 2206,
        "statement": "during round 12 of the competition , arden street oval be the venue with the lowest crowd",
        "label": 0,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "(df[df['venue'] == 'arden street oval']['crowd'].min() == df['crowd'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 2207,
        "statement": "only 1 event in round 12 take place on 9 july 1949",
        "label": 0,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "len(df[(df['date'] == '9 july 1949')]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2208,
        "statement": "home team footscray score almost double the score of the away team st kilda",
        "label": 0,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "((df[df['home team'] == 'footscray']['home team score'].str.extract(r'(\\\\d+)').astype(float).iloc[0] * 2) > df[df['away team'] == 'st kilda']['away team score'].str.extract(r'(\\\\d+)').astype(float).iloc[0]).item()",
        "pandas_eval": "False"
    },
    {
        "id": 2209,
        "statement": "home team melbourne against away team geelong be hold in mcg with a total of 14000 crowd",
        "label": 0,
        "table_caption": "1949 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nfootscray#12.11 (83)#richmond#7.18 (60)#western oval#14000#9 july 1949\nfitzroy#14.10 (94)#st kilda#6.13 (49)#brunswick street oval#7000#9 july 1949\nmelbourne#14.17 (101)#geelong#14.14 (98)#mcg#20000#9 july 1949\nhawthorn#7.0 (42)#essendon#16.16 (112)#glenferrie oval#7000#9 july 1949\nnorth melbourne#8.6 (54)#collingwood#4.13 (37)#arden street oval#21000#9 july 1949\nsouth melbourne#6.12 (48)#carlton#11.15 (81)#lake oval#15000#9 july 1949\n",
        "pandas_code": "((df['home team'] == 'melbourne') & (df['away team'] == 'geelong') & (df['venue'] == 'mcg') & (df['crowd'] == 14000)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2211,
        "statement": "2 woman from australia , wendy turnbull and kerry melville reid , have 0 - 1 record for the us open single final",
        "label": 1,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "all(df[(df['player'].isin(['wendy turnbull', 'kerry melville reid'])) & (df['nationality'] == 'australia')]['win - loss'] == '0 - 1')",
        "pandas_eval": "True"
    },
    {
        "id": 2212,
        "statement": "virginia wade be the only player from the united kingdom on this table and play the earliest , in 1968",
        "label": 1,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "(df[df['nationality'] == 'united kingdom']['player'].unique() == ['virginia wade']) & (df[df['player'] == 'virginia wade']['year (s)'].str.contains('1968')).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2213,
        "statement": "monica seles be the only player on this list to represent 2 county , yugoslavia and the united state",
        "label": 1,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "df[(df['player'] == 'monica seles') & (df['nationality'] == 'yugoslavia united states')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2214,
        "statement": "there be 8 player with 0 - 1 record in the us open single final for woman",
        "label": 1,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "len(df[df['win - loss'] == '0 - 1']) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2215,
        "statement": "2 woman from australia , wendy turnbull and kerry melville reid , have 1 - 0 record for the us open single final",
        "label": 0,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "all(df[(df['player'].isin(['wendy turnbull', 'kerry melville reid'])) & (df['nationality'] == 'australia')]['win - loss'] == '1 - 0')",
        "pandas_eval": "False"
    },
    {
        "id": 2216,
        "statement": "virginia wade be the only player from the united kingdom on this table and play the earliest , in 1965",
        "label": 0,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "df[(df['nationality'] == 'united kingdom') & (df['year (s)'].str.contains('1965'))].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2217,
        "statement": "monica seles represent a country call yugoslavia united state",
        "label": 0,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "df[df['player'] == 'monica seles']['nationality'].iloc[0] != 'yugoslavia united states'",
        "pandas_eval": "False"
    },
    {
        "id": 2218,
        "statement": "there be 11 player with 0 - 1 record in the us open single final for woman",
        "label": 0,
        "table_caption": "list of us open singles finalists during the open era",
        "table_text": "player#nationality#finals#win - loss#year (s)\nbillie jean king#united states#4#3 - 1#1969 , 1971 , 1972 , 1974\nkim clijsters#belgium#4#3 - 1#2003 , 2005 , 2009 , 2010\nmonica seles#yugoslavia united states#4#2 - 2#1991 , 1992 , 1995 , 1996\nvenus williams +#united states#4#2 - 2#1997 , 2000 , 2001 , 2002\nmargaret court#australia#3#3 - 0#1969 , 1970 , 1973\njustine henin#belgium#3#2 - 1#2003 , 2006 , 2007\nhana mandl\u00edkov\u00e1#czechoslovakia#3#1 - 2#1980 , 1982 , 1985\nmartina hingis#switzerland#3#1 - 2#1997 , 1998 , 1999\ntracy austin#united states#2#2 - 0#1979 , 1981\ngabriela sabatini#argentina#2#1 - 1#1988 , 1990\nlindsay davenport#united states#2#1 - 1#1998 , 2000\nsvetlana kuznetsova#russia#2#1 - 1#2004 , 2007\nrosemary casals#united states#2#0 - 2#1970 , 1971\nhelena sukov\u00e1#czechoslovakia#2#0 - 2#1986 , 1993\nvictoria azarenka +#belarus#2#0 - 2#2012 , 2013\nvirginia wade#united kingdom#1#1 - 0#1968\nmaria sharapova#russia#1#1 - 0#2006\nsamantha stosur +#australia#1#1 - 0#2011\nnancy richey#united states#1#0 - 1#1969\nkerry melville reid#australia#1#0 - 1#1972\nwendy turnbull#australia#1#0 - 1#1977\npam shriver#united states#1#0 - 1#1978\nelena dementieva#russia#1#0 - 1#2004\nmary pierce#france#1#0 - 1#2005\njelena jankovi\u0107 +#serbia#1#0 - 1#2008\ncaroline wozniacki +#denmark#1#0 - 1#2009\n",
        "pandas_code": "(df['win - loss'] == '0 - 1').sum() == 11",
        "pandas_eval": "False"
    },
    {
        "id": 2219,
        "statement": "hajduk split a , b have their first season in top division the earliest , in 1923",
        "label": 1,
        "table_caption": "prva hnl",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in prva hnl#first season of current spell in top division#top division titles#last top division title\ndinamo zagreb a , b#001 1st#1946 - 47#68#23#1946 - 47#19 c#2012 - 13\nhajduk split a , b#004 4th#1923#86#23#1923#15 d#2004 - 05\nhrvatski dragovoljac#zzz 1st in 2.hnl#1995 - 96#9#9#2013 - 14#0#n / a\nistra 1961#006 6th#2004 - 05#8#8#2009 - 10#0#n / a\nlokomotiva b#002 2nd#1946 - 47#15#5#2009 - 10#0#n / a\nosijek a , b#007 7th#1953 - 54#38#23#1981 - 82#0#n / a\nrijeka a , b#003 3rd#1946 - 47#52#23#1974 - 75#0#n / a\nslaven belupo b#008 8th#1997 - 98#17#17#1997 - 98#0#n / a\nrnk split b#005 5th#1957 - 58#6#4#2010 - 11#0#n / a\n",
        "pandas_code": "df[df['club'] == 'hajduk split a , b']['first season in top division'].iloc[0] == '1923'",
        "pandas_eval": "True"
    },
    {
        "id": 2220,
        "statement": "istra 1961 have their first season in top division the latest , in 2004",
        "label": 1,
        "table_caption": "prva hnl",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in prva hnl#first season of current spell in top division#top division titles#last top division title\ndinamo zagreb a , b#001 1st#1946 - 47#68#23#1946 - 47#19 c#2012 - 13\nhajduk split a , b#004 4th#1923#86#23#1923#15 d#2004 - 05\nhrvatski dragovoljac#zzz 1st in 2.hnl#1995 - 96#9#9#2013 - 14#0#n / a\nistra 1961#006 6th#2004 - 05#8#8#2009 - 10#0#n / a\nlokomotiva b#002 2nd#1946 - 47#15#5#2009 - 10#0#n / a\nosijek a , b#007 7th#1953 - 54#38#23#1981 - 82#0#n / a\nrijeka a , b#003 3rd#1946 - 47#52#23#1974 - 75#0#n / a\nslaven belupo b#008 8th#1997 - 98#17#17#1997 - 98#0#n / a\nrnk split b#005 5th#1957 - 58#6#4#2010 - 11#0#n / a\n",
        "pandas_code": "df[df['club'] == 'istra 1961']['first season in top division'].iloc[0] == '2004 - 05'",
        "pandas_eval": "True"
    },
    {
        "id": 2221,
        "statement": "osijek a , b and rijeka a , b have at least 35 season in the top division",
        "label": 1,
        "table_caption": "prva hnl",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in prva hnl#first season of current spell in top division#top division titles#last top division title\ndinamo zagreb a , b#001 1st#1946 - 47#68#23#1946 - 47#19 c#2012 - 13\nhajduk split a , b#004 4th#1923#86#23#1923#15 d#2004 - 05\nhrvatski dragovoljac#zzz 1st in 2.hnl#1995 - 96#9#9#2013 - 14#0#n / a\nistra 1961#006 6th#2004 - 05#8#8#2009 - 10#0#n / a\nlokomotiva b#002 2nd#1946 - 47#15#5#2009 - 10#0#n / a\nosijek a , b#007 7th#1953 - 54#38#23#1981 - 82#0#n / a\nrijeka a , b#003 3rd#1946 - 47#52#23#1974 - 75#0#n / a\nslaven belupo b#008 8th#1997 - 98#17#17#1997 - 98#0#n / a\nrnk split b#005 5th#1957 - 58#6#4#2010 - 11#0#n / a\n",
        "pandas_code": "(df[df['club'].isin(['osijek a , b', 'rijeka a , b'])]['number of seasons in top division'] >= 35).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2222,
        "statement": "4 of the club have 23 season in prva hnl",
        "label": 1,
        "table_caption": "prva hnl",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in prva hnl#first season of current spell in top division#top division titles#last top division title\ndinamo zagreb a , b#001 1st#1946 - 47#68#23#1946 - 47#19 c#2012 - 13\nhajduk split a , b#004 4th#1923#86#23#1923#15 d#2004 - 05\nhrvatski dragovoljac#zzz 1st in 2.hnl#1995 - 96#9#9#2013 - 14#0#n / a\nistra 1961#006 6th#2004 - 05#8#8#2009 - 10#0#n / a\nlokomotiva b#002 2nd#1946 - 47#15#5#2009 - 10#0#n / a\nosijek a , b#007 7th#1953 - 54#38#23#1981 - 82#0#n / a\nrijeka a , b#003 3rd#1946 - 47#52#23#1974 - 75#0#n / a\nslaven belupo b#008 8th#1997 - 98#17#17#1997 - 98#0#n / a\nrnk split b#005 5th#1957 - 58#6#4#2010 - 11#0#n / a\n",
        "pandas_code": "(df['number of seasons in prva hnl'] == 23).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2223,
        "statement": "all of the team except 2 of them have no top division title",
        "label": 1,
        "table_caption": "prva hnl",
        "table_text": "club#position in 2012 - 13#first season in top division#number of seasons in top division#number of seasons in prva hnl#first season of current spell in top division#top division titles#last top division title\ndinamo zagreb a , b#001 1st#1946 - 47#68#23#1946 - 47#19 c#2012 - 13\nhajduk split a , b#004 4th#1923#86#23#1923#15 d#2004 - 05\nhrvatski dragovoljac#zzz 1st in 2.hnl#1995 - 96#9#9#2013 - 14#0#n / a\nistra 1961#006 6th#2004 - 05#8#8#2009 - 10#0#n / a\nlokomotiva b#002 2nd#1946 - 47#15#5#2009 - 10#0#n / a\nosijek a , b#007 7th#1953 - 54#38#23#1981 - 82#0#n / a\nrijeka a , b#003 3rd#1946 - 47#52#23#1974 - 75#0#n / a\nslaven belupo b#008 8th#1997 - 98#17#17#1997 - 98#0#n / a\nrnk split b#005 5th#1957 - 58#6#4#2010 - 11#0#n / a\n",
        "pandas_code": "len(df[df['top division titles'] != '0']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2224,
        "statement": "jeff green have the highest rebound in a game from game 75 through 81",
        "label": 1,
        "table_caption": "2008 - 09 oklahoma city thunder season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#portland#l 72 - 107 (ot)#kevin durant (13)#russell westbrook (8)#chucky atkins , russell westbrook (4)#ford center 19136#21 - 54\n76#april 5#indiana#l 99 - 117 (ot)#kevin durant (25)#shaun livingston , russell westbrook (7)#shaun livingston (5)#ford center 19136#21 - 55\n77#april 7#san antonio#l 89 - 99 (ot)#kevin durant (24)#thabo sefolosha (9)#kevin durant , russell westbrook (6)#ford center 19136#21 - 56\n78#april 8#denver#l 112 - 122 (ot)#kevin durant (31)#kevin durant (7)#russell westbrook (11)#pepsi center 16536#21 - 57\n79#april 10#charlotte#w 84 - 81 (ot)#kevin durant (20)#jeff green (11)#russell westbrook (11)#ford center 19136#22 - 57\n80#april 11#milwaukee#l 98 - 115 (ot)#kevin durant (19)#kyle weaver (9)#earl watson (7)#bradley center 15418#22 - 58\n81#april 13#portland#l 83 - 113 (ot)#earl watson (16)#nenad krsti\u0107 (7)#russell westbrook (7)#rose garden 20655#22 - 59\n",
        "pandas_code": "df.loc[df['game'].between(75, 81), 'high rebounds'].str.contains('jeff green').any()",
        "pandas_eval": "True"
    },
    {
        "id": 2225,
        "statement": "kevin durant have the highest point between game 75 through 81",
        "label": 1,
        "table_caption": "2008 - 09 oklahoma city thunder season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#portland#l 72 - 107 (ot)#kevin durant (13)#russell westbrook (8)#chucky atkins , russell westbrook (4)#ford center 19136#21 - 54\n76#april 5#indiana#l 99 - 117 (ot)#kevin durant (25)#shaun livingston , russell westbrook (7)#shaun livingston (5)#ford center 19136#21 - 55\n77#april 7#san antonio#l 89 - 99 (ot)#kevin durant (24)#thabo sefolosha (9)#kevin durant , russell westbrook (6)#ford center 19136#21 - 56\n78#april 8#denver#l 112 - 122 (ot)#kevin durant (31)#kevin durant (7)#russell westbrook (11)#pepsi center 16536#21 - 57\n79#april 10#charlotte#w 84 - 81 (ot)#kevin durant (20)#jeff green (11)#russell westbrook (11)#ford center 19136#22 - 57\n80#april 11#milwaukee#l 98 - 115 (ot)#kevin durant (19)#kyle weaver (9)#earl watson (7)#bradley center 15418#22 - 58\n81#april 13#portland#l 83 - 113 (ot)#earl watson (16)#nenad krsti\u0107 (7)#russell westbrook (7)#rose garden 20655#22 - 59\n",
        "pandas_code": "all(df.loc[75:81, 'high points'].str.contains('kevin durant'))",
        "pandas_eval": "True"
    },
    {
        "id": 2226,
        "statement": "the average location attendance for the ford center 19136",
        "label": 1,
        "table_caption": "2008 - 09 oklahoma city thunder season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#portland#l 72 - 107 (ot)#kevin durant (13)#russell westbrook (8)#chucky atkins , russell westbrook (4)#ford center 19136#21 - 54\n76#april 5#indiana#l 99 - 117 (ot)#kevin durant (25)#shaun livingston , russell westbrook (7)#shaun livingston (5)#ford center 19136#21 - 55\n77#april 7#san antonio#l 89 - 99 (ot)#kevin durant (24)#thabo sefolosha (9)#kevin durant , russell westbrook (6)#ford center 19136#21 - 56\n78#april 8#denver#l 112 - 122 (ot)#kevin durant (31)#kevin durant (7)#russell westbrook (11)#pepsi center 16536#21 - 57\n79#april 10#charlotte#w 84 - 81 (ot)#kevin durant (20)#jeff green (11)#russell westbrook (11)#ford center 19136#22 - 57\n80#april 11#milwaukee#l 98 - 115 (ot)#kevin durant (19)#kyle weaver (9)#earl watson (7)#bradley center 15418#22 - 58\n81#april 13#portland#l 83 - 113 (ot)#earl watson (16)#nenad krsti\u0107 (7)#russell westbrook (7)#rose garden 20655#22 - 59\n",
        "pandas_code": "df[df['location attendance'] == 'ford center 19136']['location attendance'].str.extract('(\\d+)')[0].astype(int).mean() == 19136",
        "pandas_eval": "True"
    },
    {
        "id": 2228,
        "statement": "rise garden have the highest location attendance",
        "label": 1,
        "table_caption": "2008 - 09 oklahoma city thunder season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#portland#l 72 - 107 (ot)#kevin durant (13)#russell westbrook (8)#chucky atkins , russell westbrook (4)#ford center 19136#21 - 54\n76#april 5#indiana#l 99 - 117 (ot)#kevin durant (25)#shaun livingston , russell westbrook (7)#shaun livingston (5)#ford center 19136#21 - 55\n77#april 7#san antonio#l 89 - 99 (ot)#kevin durant (24)#thabo sefolosha (9)#kevin durant , russell westbrook (6)#ford center 19136#21 - 56\n78#april 8#denver#l 112 - 122 (ot)#kevin durant (31)#kevin durant (7)#russell westbrook (11)#pepsi center 16536#21 - 57\n79#april 10#charlotte#w 84 - 81 (ot)#kevin durant (20)#jeff green (11)#russell westbrook (11)#ford center 19136#22 - 57\n80#april 11#milwaukee#l 98 - 115 (ot)#kevin durant (19)#kyle weaver (9)#earl watson (7)#bradley center 15418#22 - 58\n81#april 13#portland#l 83 - 113 (ot)#earl watson (16)#nenad krsti\u0107 (7)#russell westbrook (7)#rose garden 20655#22 - 59\n",
        "pandas_code": "(df['location attendance'].str.extract('(\\\\d+)').astype(int).max() == int(df[df['location attendance'].str.contains('rose garden')]['location attendance'].str.extract('(\\\\d+)').iloc[0])).item()",
        "pandas_eval": "True"
    },
    {
        "id": 2230,
        "statement": "john mcenroe , at then begin of 1986 , be rat higher than jimmy connors",
        "label": 1,
        "table_caption": "1986 grand prix (tennis)",
        "table_text": "week of#tournament#champion#runner - up#semifinalists#quarterfinalists\n15 september#volvo championships los angeles , united states#john mcenroe 6 - 2 , 6 - 3#stefan edberg#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n15 september#volvo championships los angeles , united states#stefan edberg anders j\u00e4rryd 3 - 6 , 7 - 5 , 7 - 6#peter fleming john mcenroe#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n22 september#transamerica open san francisco , united states#john mcenroe 7 - 6 , 6 - 3#jimmy connors#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#transamerica open san francisco , united states#peter fleming john mcenroe 6 - 4 , 7 - 6#mike de palmer gary donnelly#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#torneo godo marlboro barcelona , spain#kent carlsson 6 - 2 , 6 - 2 , 6 - 0#andreas maurer#horst skoff tom\u00e1\u0161 \u0161m\u00edd#milan \u0161rejber david de miguel - lapiedra guy forget\n",
        "pandas_code": "df[df['champion'].str.contains('john mcenroe') & df['runner - up'].str.contains('jimmy connors')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2231,
        "statement": "there be 4 american in the top 10 tennis player of 1986",
        "label": 1,
        "table_caption": "1986 grand prix (tennis)",
        "table_text": "week of#tournament#champion#runner - up#semifinalists#quarterfinalists\n15 september#volvo championships los angeles , united states#john mcenroe 6 - 2 , 6 - 3#stefan edberg#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n15 september#volvo championships los angeles , united states#stefan edberg anders j\u00e4rryd 3 - 6 , 7 - 5 , 7 - 6#peter fleming john mcenroe#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n22 september#transamerica open san francisco , united states#john mcenroe 7 - 6 , 6 - 3#jimmy connors#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#transamerica open san francisco , united states#peter fleming john mcenroe 6 - 4 , 7 - 6#mike de palmer gary donnelly#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#torneo godo marlboro barcelona , spain#kent carlsson 6 - 2 , 6 - 2 , 6 - 0#andreas maurer#horst skoff tom\u00e1\u0161 \u0161m\u00edd#milan \u0161rejber david de miguel - lapiedra guy forget\n",
        "pandas_code": "len(df[df['tournament'].str.contains('united states') & df['champion'].str.contains('john mcenroe|stefan edberg|jimmy connors|peter fleming')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2232,
        "statement": "jimmy connors , at then end of 1986 , be rat higher than john mcenroe",
        "label": 1,
        "table_caption": "1986 grand prix (tennis)",
        "table_text": "week of#tournament#champion#runner - up#semifinalists#quarterfinalists\n15 september#volvo championships los angeles , united states#john mcenroe 6 - 2 , 6 - 3#stefan edberg#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n15 september#volvo championships los angeles , united states#stefan edberg anders j\u00e4rryd 3 - 6 , 7 - 5 , 7 - 6#peter fleming john mcenroe#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n22 september#transamerica open san francisco , united states#john mcenroe 7 - 6 , 6 - 3#jimmy connors#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#transamerica open san francisco , united states#peter fleming john mcenroe 6 - 4 , 7 - 6#mike de palmer gary donnelly#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#torneo godo marlboro barcelona , spain#kent carlsson 6 - 2 , 6 - 2 , 6 - 0#andreas maurer#horst skoff tom\u00e1\u0161 \u0161m\u00edd#milan \u0161rejber david de miguel - lapiedra guy forget\n",
        "pandas_code": "df[df['runner - up'].str.contains('jimmy connors')].index[0] > df[df['champion'].str.contains('john mcenroe')].index[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2233,
        "statement": "ivan lendl be the best player at both the beginning and end of 1986",
        "label": 1,
        "table_caption": "1986 grand prix (tennis)",
        "table_text": "week of#tournament#champion#runner - up#semifinalists#quarterfinalists\n15 september#volvo championships los angeles , united states#john mcenroe 6 - 2 , 6 - 3#stefan edberg#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n15 september#volvo championships los angeles , united states#stefan edberg anders j\u00e4rryd 3 - 6 , 7 - 5 , 7 - 6#peter fleming john mcenroe#peter lundgren brad gilbert#derrick rostagno jaime yzaga david pate pat cash\n22 september#transamerica open san francisco , united states#john mcenroe 7 - 6 , 6 - 3#jimmy connors#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#transamerica open san francisco , united states#peter fleming john mcenroe 6 - 4 , 7 - 6#mike de palmer gary donnelly#stefan edberg anders j\u00e4rryd#jim grabb john sadri david pate peter lundgren\n22 september#torneo godo marlboro barcelona , spain#kent carlsson 6 - 2 , 6 - 2 , 6 - 0#andreas maurer#horst skoff tom\u00e1\u0161 \u0161m\u00edd#milan \u0161rejber david de miguel - lapiedra guy forget\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 2234,
        "statement": "only italy have a higher % won than brazil in the english football club in international competition",
        "label": 1,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "df[df['nation'].isin(['italy', 'brazil'])].sort_values('% won', ascending=False).iloc[0]['nation'] == 'italy'",
        "pandas_eval": "True"
    },
    {
        "id": 2235,
        "statement": "brazil have the most number of year won in the english football club in international competition",
        "label": 1,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "df.loc[df['nation'] == 'brazil', 'years won'].str.split(' , ').explode().nunique() > df.loc[df['nation'] != 'brazil', 'years won'].str.split(' , ').explode().groupby(df['nation']).nunique().max()",
        "pandas_eval": "True"
    },
    {
        "id": 2236,
        "statement": "england have a lower % won than spain in the english football club in international competition",
        "label": 1,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "df[df['nation'] == 'england']['% won'].values[0] < df[df['nation'] == 'spain']['% won'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2237,
        "statement": "there be 3 team with a 0 % won in the english football club in international competition",
        "label": 1,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "(df['% won'] == 0).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2238,
        "statement": "only 1 team be winner 1 time english football club in international competition",
        "label": 1,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "df[(df['nation'] == 'england') & (df['winners'] == 1)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2239,
        "statement": "italy win a greater percentage of game than brazil in international competition",
        "label": 0,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "df[df['nation'] == 'italy']['% won'].values[0] <= df[df['nation'] == 'brazil']['% won'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2240,
        "statement": "argentina win in more year than any other nation",
        "label": 0,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "(df[df['nation'] == 'argentina']['years won'].str.split(' , ').explode().nunique() > df[df['nation'] != 'argentina']['years won'].str.split(' , ').explode().groupby(df['nation']).nunique().max())",
        "pandas_eval": "False"
    },
    {
        "id": 2241,
        "statement": "england play in fewer final than spain in international competition",
        "label": 0,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "(df[df['nation'] == 'england']['finalists'].values[0] < df[df['nation'] == 'spain']['finalists'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2242,
        "statement": "5 team didn't win any game in international competition",
        "label": 0,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "len(df[df['winners'] == 0]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2243,
        "statement": "all the team win more than 1 time in international competition",
        "label": 0,
        "table_caption": "english football clubs in international competition",
        "table_text": "nation#finalists#winners#runners - up#% won#years won\nbrazil#6#4#2#80#2000 , 2005 , 2006 , 2012\nspain#3#2#1#67#2009 , 2011\nitaly#2#2#0#100#2007 , 2010\nengland#3#1#2#33#2008\nargentina#2#0#2#0#2007 , 2009\necuador#1#0#1#0#2008\ndr congo#1#0#1#0#2010\n",
        "pandas_code": "all(df['winners'] > 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2244,
        "statement": "4 of the game take place in the month of september",
        "label": 1,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "len(df[df['date'].str.contains('september')]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2245,
        "statement": "there be more game in november than there be in december",
        "label": 1,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "len(df[df['date'].str.contains('november')]) > len(df[df['date'].str.contains('december')])",
        "pandas_eval": "True"
    },
    {
        "id": 2246,
        "statement": "the game in week 16 do not take place in october",
        "label": 1,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "df[df['week'] == 16]['date'].str.contains('october').any() == False",
        "pandas_eval": "True"
    },
    {
        "id": 2247,
        "statement": "the game with the largest attendance be in week 17",
        "label": 1,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'week'] == 17",
        "pandas_eval": "True"
    },
    {
        "id": 2248,
        "statement": "the game in week 12 have 14435 more crowd attendance than the game in week 13",
        "label": 1,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "(df[df['week'] == 12]['attendance'].values[0] - df[df['week'] == 13]['attendance'].values[0]) == 14435",
        "pandas_eval": "True"
    },
    {
        "id": 2249,
        "statement": "5 of the game take place in the month of september",
        "label": 0,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "len(df[df['date'].str.contains('september')]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2250,
        "statement": "there be more game in december than there be game in november",
        "label": 0,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "len(df[df['date'].str.contains('december')]) > len(df[df['date'].str.contains('november')])",
        "pandas_eval": "False"
    },
    {
        "id": 2251,
        "statement": "game in week 16 be hold in october",
        "label": 0,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "df[df['week'] == 16]['date'].str.contains('october').any()",
        "pandas_eval": "False"
    },
    {
        "id": 2252,
        "statement": "the game with the largest attendance be in week 6",
        "label": 0,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'week'] == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2253,
        "statement": "the game in week 12 have 20000 less crowd attendance that the game in week 13",
        "label": 0,
        "table_caption": "1995 arizona cardinals season",
        "table_text": "week#date#opponent#result#attendance\n1#september 3 , 1995#washington redskins#l 27 - 7#52731\n2#september 10 , 1995#philadelphia eagles#l 31 - 19#45004\n3#september 17 , 1995#detroit lions#w 20 - 17#58727\n4#september 24 , 1995#dallas cowboys#l 34 - 20#64560\n5#october 1 , 1995#kansas city chiefs#l 24 - 3#50211\n6#october 8 , 1995#new york giants#l 27 - 21#68463\n7#october 15 , 1995#washington redskins#w 24 - 20#42370\n9#october 29 , 1995#seattle seahawks#w 20 - 14#39600\n10#november 5 , 1995#denver broncos#l 38 - 6#71488\n11#november 12 , 1995#minnesota vikings#l 30 - 24#51342\n12#november 19 , 1995#carolina panthers#l 27 - 7#49582\n13#november 26 , 1995#atlanta falcons#w 40 - 37#35147\n14#november 30 , 1995#new york giants#l 10 - 6#44246\n15#december 9 , 1995#san diego chargers#l 28 - 25#55258\n16#december 17 , 1995#philadelphia eagles#l 21 - 20#62076\n17#december 25 , 1995#dallas cowboys#l 37 - 13#72394\n",
        "pandas_code": "df.loc[df['week'] == 12, 'attendance'].values[0] == df.loc[df['week'] == 13, 'attendance'].values[0] - 20000",
        "pandas_eval": "False"
    },
    {
        "id": 2254,
        "statement": "the weightlifter with the best clean & jerk be jos\u00e9 oliver ru\u00edz",
        "label": 1,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['clean & jerk'].idxmax(), 'name'] == 'jos\u00e9 oliver ru\u00edz ( col )'",
        "pandas_eval": "True"
    },
    {
        "id": 2255,
        "statement": "the weightlifter with the worst clean & jerk be edward silva",
        "label": 1,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['clean & jerk'].idxmin(), 'name'] == 'edward silva ( uru )'",
        "pandas_eval": "True"
    },
    {
        "id": 2256,
        "statement": "the weightlifter with the best snatch be jadier valladares",
        "label": 1,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmax(), 'name'] == 'jadier valladares ( cub )'",
        "pandas_eval": "True"
    },
    {
        "id": 2257,
        "statement": "the weightlifter with the worst snatch be edward silva",
        "label": 1,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmin(), 'name'] == 'edward silva ( uru )'",
        "pandas_eval": "True"
    },
    {
        "id": 2259,
        "statement": "the weightlifter with the best snatch be jos\u00e9 oliver ru\u00edz",
        "label": 0,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmax(), 'name'] == 'jos\u00e9 oliver ru\u00edz ( col )'",
        "pandas_eval": "False"
    },
    {
        "id": 2260,
        "statement": "the weightlifter with the worst snatch be edward silva",
        "label": 0,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmin(), 'name'] != 'edward silva ( uru )'",
        "pandas_eval": "False"
    },
    {
        "id": 2261,
        "statement": "the weightlifter with the best snatch be juan quiterio",
        "label": 0,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmax(), 'name'] == 'juan quiterio ( dom )'",
        "pandas_eval": "False"
    },
    {
        "id": 2262,
        "statement": "the weightlifter with the worst snatch be rafael andrade",
        "label": 0,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['snatch'].idxmin(), 'name'] == 'rafael andrade ( bra )'",
        "pandas_eval": "False"
    },
    {
        "id": 2263,
        "statement": "the weightlifter with the lowest bodyweight be buck ramsay",
        "label": 0,
        "table_caption": "weightlifting at the 2007 pan american games",
        "table_text": "name#bodyweight#snatch#clean & jerk#total (kg)\njos\u00e9 oliver ru\u00edz ( col )#84.45#160.0#203.0#363.0\njadier valladares ( cub )#84.50#161.0#202.0#363.0\nherbys m\u00e1rquez ( ven )#84.75#155.0#195.0#350.0\nkendrick farris ( usa )#84.15#158.0#191.0#349.0\njuan quiterio ( dom )#84.35#145.0#185.0#330.0\nbuck ramsay ( can )#84.75#140.0#178.0#318.0\nrafael andrade ( bra )#83.75#140.0#175.0#315.0\nedward silva ( uru )#84.10#120.0#150.0#270.0\n",
        "pandas_code": "df.loc[df['bodyweight'].idxmin(), 'name'] == 'buck ramsay ( can )'",
        "pandas_eval": "False"
    },
    {
        "id": 2264,
        "statement": "ainhoa cantalpaiedra and vicente segu\u00ed porres win series 2 and 3 , respectively",
        "label": 1,
        "table_caption": "operaci\u00f3n triunfo (spain)",
        "table_text": "series#year#winner#runner - up#third place#fourth place#fifth place#sixth place#host\n1#2001 - 2002#rosa l\u00f3pez#david bisbal#david bustamante#chenoa#manu tenorio#ver\u00f3nica romero#carlos lozano\n2#2002 - 2003#ainhoa cantalapiedra#manuel carrasco#beth rodergas#miguel n\u00e1ndez#hugo salazar#joan tena#carlos lozano\n3#2003#vicente segu\u00ed porres#ram\u00f3n del castillo#miguel cadenas#davinia cuevas#mario mart\u00ednez#leticia p\u00e9rez#carlos lozano\n4#2005#sergio rivero#soraya arnelas#v\u00edctor est\u00e9vez polo#idaira fernandez#fran dieli#edurne garc\u00eda#jes\u00fas v\u00e1zquez\n5#2006 - 2007#lorena g\u00f3mez#daniel zueras#leo segarra s\u00e1nchez#saray ram\u00edrez#moritz weisskopf#jose galisteo#jes\u00fas v\u00e1zquez\n6#2008#virginia maestro#pablo l\u00f3pez#chipper cooke#manu castellano#sandra criado#mimi segura#jes\u00fas v\u00e1zquez\n7#2009#mario \u00e1lvarez#brenda mau#jon allende#\u00e1ngel capel#patricia navarro#sylvia parejo#jes\u00fas v\u00e1zquez\n",
        "pandas_code": "(df.loc[df['series'] == 2, 'winner'].values[0] == 'ainhoa cantalapiedra') & (df.loc[df['series'] == 3, 'winner'].values[0] == 'vicente segu\u00ed porres')",
        "pandas_eval": "True"
    },
    {
        "id": 2265,
        "statement": "virginia maestro and mario \u00c1lvarez win series 6 and 7 , respectively",
        "label": 1,
        "table_caption": "operaci\u00f3n triunfo (spain)",
        "table_text": "series#year#winner#runner - up#third place#fourth place#fifth place#sixth place#host\n1#2001 - 2002#rosa l\u00f3pez#david bisbal#david bustamante#chenoa#manu tenorio#ver\u00f3nica romero#carlos lozano\n2#2002 - 2003#ainhoa cantalapiedra#manuel carrasco#beth rodergas#miguel n\u00e1ndez#hugo salazar#joan tena#carlos lozano\n3#2003#vicente segu\u00ed porres#ram\u00f3n del castillo#miguel cadenas#davinia cuevas#mario mart\u00ednez#leticia p\u00e9rez#carlos lozano\n4#2005#sergio rivero#soraya arnelas#v\u00edctor est\u00e9vez polo#idaira fernandez#fran dieli#edurne garc\u00eda#jes\u00fas v\u00e1zquez\n5#2006 - 2007#lorena g\u00f3mez#daniel zueras#leo segarra s\u00e1nchez#saray ram\u00edrez#moritz weisskopf#jose galisteo#jes\u00fas v\u00e1zquez\n6#2008#virginia maestro#pablo l\u00f3pez#chipper cooke#manu castellano#sandra criado#mimi segura#jes\u00fas v\u00e1zquez\n7#2009#mario \u00e1lvarez#brenda mau#jon allende#\u00e1ngel capel#patricia navarro#sylvia parejo#jes\u00fas v\u00e1zquez\n",
        "pandas_code": "(df.loc[df['series'] == 6, 'winner'].values[0] == 'virginia maestro') & (df.loc[df['series'] == 7, 'winner'].values[0] == 'mario \u00e1lvarez')",
        "pandas_eval": "True"
    },
    {
        "id": 2266,
        "statement": "leo segarra s\u00e1nchez and chipper cooke be in third place during series 5 and 6 , respectively",
        "label": 1,
        "table_caption": "operaci\u00f3n triunfo (spain)",
        "table_text": "series#year#winner#runner - up#third place#fourth place#fifth place#sixth place#host\n1#2001 - 2002#rosa l\u00f3pez#david bisbal#david bustamante#chenoa#manu tenorio#ver\u00f3nica romero#carlos lozano\n2#2002 - 2003#ainhoa cantalapiedra#manuel carrasco#beth rodergas#miguel n\u00e1ndez#hugo salazar#joan tena#carlos lozano\n3#2003#vicente segu\u00ed porres#ram\u00f3n del castillo#miguel cadenas#davinia cuevas#mario mart\u00ednez#leticia p\u00e9rez#carlos lozano\n4#2005#sergio rivero#soraya arnelas#v\u00edctor est\u00e9vez polo#idaira fernandez#fran dieli#edurne garc\u00eda#jes\u00fas v\u00e1zquez\n5#2006 - 2007#lorena g\u00f3mez#daniel zueras#leo segarra s\u00e1nchez#saray ram\u00edrez#moritz weisskopf#jose galisteo#jes\u00fas v\u00e1zquez\n6#2008#virginia maestro#pablo l\u00f3pez#chipper cooke#manu castellano#sandra criado#mimi segura#jes\u00fas v\u00e1zquez\n7#2009#mario \u00e1lvarez#brenda mau#jon allende#\u00e1ngel capel#patricia navarro#sylvia parejo#jes\u00fas v\u00e1zquez\n",
        "pandas_code": "(df.loc[df['series'] == 5, 'third place'].values[0] == 'leo segarra s\u00e1nchez') & (df.loc[df['series'] == 6, 'third place'].values[0] == 'chipper cooke')",
        "pandas_eval": "True"
    },
    {
        "id": 2267,
        "statement": "fran dieli and moritz weisskopf be in fifth place during series 4 and 5 , respectively",
        "label": 1,
        "table_caption": "operaci\u00f3n triunfo (spain)",
        "table_text": "series#year#winner#runner - up#third place#fourth place#fifth place#sixth place#host\n1#2001 - 2002#rosa l\u00f3pez#david bisbal#david bustamante#chenoa#manu tenorio#ver\u00f3nica romero#carlos lozano\n2#2002 - 2003#ainhoa cantalapiedra#manuel carrasco#beth rodergas#miguel n\u00e1ndez#hugo salazar#joan tena#carlos lozano\n3#2003#vicente segu\u00ed porres#ram\u00f3n del castillo#miguel cadenas#davinia cuevas#mario mart\u00ednez#leticia p\u00e9rez#carlos lozano\n4#2005#sergio rivero#soraya arnelas#v\u00edctor est\u00e9vez polo#idaira fernandez#fran dieli#edurne garc\u00eda#jes\u00fas v\u00e1zquez\n5#2006 - 2007#lorena g\u00f3mez#daniel zueras#leo segarra s\u00e1nchez#saray ram\u00edrez#moritz weisskopf#jose galisteo#jes\u00fas v\u00e1zquez\n6#2008#virginia maestro#pablo l\u00f3pez#chipper cooke#manu castellano#sandra criado#mimi segura#jes\u00fas v\u00e1zquez\n7#2009#mario \u00e1lvarez#brenda mau#jon allende#\u00e1ngel capel#patricia navarro#sylvia parejo#jes\u00fas v\u00e1zquez\n",
        "pandas_code": "df.loc[df['series'].isin([4, 5]), 'fifth place'].tolist() == ['fran dieli', 'moritz weisskopf']",
        "pandas_eval": "True"
    },
    {
        "id": 2268,
        "statement": "carlos lozano be the host during the first 3 series and jes\u00fas v\u00e1zquez be the host in the next 4",
        "label": 1,
        "table_caption": "operaci\u00f3n triunfo (spain)",
        "table_text": "series#year#winner#runner - up#third place#fourth place#fifth place#sixth place#host\n1#2001 - 2002#rosa l\u00f3pez#david bisbal#david bustamante#chenoa#manu tenorio#ver\u00f3nica romero#carlos lozano\n2#2002 - 2003#ainhoa cantalapiedra#manuel carrasco#beth rodergas#miguel n\u00e1ndez#hugo salazar#joan tena#carlos lozano\n3#2003#vicente segu\u00ed porres#ram\u00f3n del castillo#miguel cadenas#davinia cuevas#mario mart\u00ednez#leticia p\u00e9rez#carlos lozano\n4#2005#sergio rivero#soraya arnelas#v\u00edctor est\u00e9vez polo#idaira fernandez#fran dieli#edurne garc\u00eda#jes\u00fas v\u00e1zquez\n5#2006 - 2007#lorena g\u00f3mez#daniel zueras#leo segarra s\u00e1nchez#saray ram\u00edrez#moritz weisskopf#jose galisteo#jes\u00fas v\u00e1zquez\n6#2008#virginia maestro#pablo l\u00f3pez#chipper cooke#manu castellano#sandra criado#mimi segura#jes\u00fas v\u00e1zquez\n7#2009#mario \u00e1lvarez#brenda mau#jon allende#\u00e1ngel capel#patricia navarro#sylvia parejo#jes\u00fas v\u00e1zquez\n",
        "pandas_code": "(df['host'].iloc[:3] == 'carlos lozano').all() and (df['host'].iloc[3:] == 'jes\u00fas v\u00e1zquez').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2269,
        "statement": "the 3 swimmer rank first , second and third , complete the 100 metre butterfly in under 53 second",
        "label": 1,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "all(df[df['rank'].isin([1, 2, 3])]['time'] < 53)",
        "pandas_eval": "True"
    },
    {
        "id": 2270,
        "statement": "the united state be narrowly beat by australia",
        "label": 1,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "df[(df['nationality'] == 'australia') & (df['time'] < df[df['nationality'] == 'united states']['time'].values[0])].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2271,
        "statement": "franck esposito and jere h\u00e5rd swam in adjacent lane",
        "label": 1,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "any(abs(df[df['name'].isin(['franck esposito', 'jere h\u00e5rd'])]['lane'].diff().dropna()) == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2272,
        "statement": "there be 8 country compete in the 2000 summer olympics men 's 100 metre butterfly",
        "label": 1,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "len(df['nationality'].unique()) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2273,
        "statement": "there be a difference of 1.02 second between the fastest and slowest swimmer",
        "label": 1,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "abs(df['time'].max() - df['time'].min() - 1.02) < 1e-9",
        "pandas_eval": "True"
    },
    {
        "id": 2274,
        "statement": "only the first swimmer complete the 100 metre butterfly in under 53 second",
        "label": 0,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "df[df['rank'] == 1]['time'].iloc[0] >= 53",
        "pandas_eval": "False"
    },
    {
        "id": 2275,
        "statement": "the united state be narrowly beat by sweden",
        "label": 0,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "(df[df['nationality'] == 'united states']['time'].values[0] > df[df['nationality'] == 'sweden']['time'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2276,
        "statement": "thomas rupprath and jere h\u00e5rd swan in adjacent lane",
        "label": 0,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "any(abs(df[df['name'].isin(['thomas rupprath', 'jere h\u00e5rd'])]['lane'].diff().dropna()) == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2277,
        "statement": "10 country compete in the 2000 summer olympics men 's 100 metre butterfly",
        "label": 0,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "len(df['nationality'].unique()) == 10",
        "pandas_eval": "False"
    },
    {
        "id": 2278,
        "statement": "there be a difference of 2.11 second between the fastest and slowest swimmer",
        "label": 0,
        "table_caption": "swimming at the 2000 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#michael klim#australia#52.63\n2#2#ian crocker#united states#52.82\n3#3#lars fr\u00f6lander#sweden#52.84\n4#5#mike mintenko#canada#53.00\n5#1#thomas rupprath#germany#53.18\n6#6#anatoly polyakov#russia#53.32\n7#7#franck esposito#france#53.38\n8#8#jere h\u00e5rd#finland#53.65\n",
        "pandas_code": "abs(df['time'].max() - df['time'].min()) == 2.11",
        "pandas_eval": "False"
    },
    {
        "id": 2279,
        "statement": "hard surface be play on more often than clay surface",
        "label": 1,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "(df['surface'].value_counts().get('hard', 0) > df['surface'].value_counts().get('clay', 0))",
        "pandas_eval": "True"
    },
    {
        "id": 2280,
        "statement": "5 out of 7 of the tournament be play in a european country",
        "label": 1,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "sum(df['tournament'].str.contains('germany|slovakia|italy|france|spain')) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2281,
        "statement": "the most common score for anastasija to score be 6 - 1",
        "label": 1,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "df['score'].str.contains('6 - 1').sum() > df['score'].str.contains('6 - 2').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2282,
        "statement": "no tournament happen between september and february",
        "label": 1,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "df['date'].apply(lambda x: pd.to_datetime(x).month).between(9, 2).any() == False",
        "pandas_eval": "True"
    },
    {
        "id": 2283,
        "statement": "the year that hold the most tournament be 2008",
        "label": 1,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "df['date'].str.extract(r'(\\d{4})')[0].value_counts().idxmax() == '2008'",
        "pandas_eval": "True"
    },
    {
        "id": 2284,
        "statement": "clay surface be play on more often then hard surface",
        "label": 0,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "(df['surface'].value_counts()['clay'] > df['surface'].value_counts()['hard'])",
        "pandas_eval": "False"
    },
    {
        "id": 2285,
        "statement": "all the tournament be play in a european country",
        "label": 0,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "all(df['tournament'].str.contains('germany|slovakia|italy|france|spain'))",
        "pandas_eval": "False"
    },
    {
        "id": 2286,
        "statement": "the least common score be 6 - 1",
        "label": 0,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "df['score'].value_counts().idxmin() == '6 - 1'",
        "pandas_eval": "False"
    },
    {
        "id": 2287,
        "statement": "all the tournament happen between september and february",
        "label": 0,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "all(df['date'].str.extract(r'(\\w+) \\d+ , \\d{4}')[0].isin(['september', 'october', 'november', 'december', 'january', 'february']))",
        "pandas_eval": "False"
    },
    {
        "id": 2288,
        "statement": "the least amount of tournament be hold in 2008",
        "label": 0,
        "table_caption": "anastasija sevastova",
        "table_text": "date#tournament#surface#opponent#score\naugust 6 , 2006#bad saulgau , germany#clay#josipa bek#6 - 1 , 6 - 0\naugust 20 , 2006#bratislava , slovakia#clay#klaudia malenovska#4 - 6 , 6 - 0 , 6 - 3\nmarch 22 , 2008#noida , india#hard#sunitha rao#6 - 2 , 6 - 1\njune 1 , 2008#galatina , italy#clay#estrella cabeza candela#6 - 4 , 6 - 4\njuly 27 , 2008#les contamines , france#hard#agustina lepore#6 - 4 , 3 - 6 , 6 - 3\nmarch 29 , 2009#la palma , spain#hard#krist\u00edna ku\u010dov\u00e1#4 - 6 , 6 - 1 , 6 - 1\nmay 3 , 2009#johannesburg , south africa#hard#eva hrdinov\u00e1#6 - 2 , 6 - 2\n",
        "pandas_code": "df['date'].str.contains('2008').sum() == min(df['date'].str[-4:].value_counts())",
        "pandas_eval": "False"
    },
    {
        "id": 2289,
        "statement": "january 2000 be the only month of the year to feature 2 playmate of the month",
        "label": 1,
        "table_caption": "list of playboy playmates of the month",
        "table_text": "#january#february#march#april#may#june#july#august#september#october#november#december\n2000#carol bernaola & darlene bernaola#suzanne stokes#nicole marie lenz#brande roderick#brooke berry#shannon stewart#neferteri shepherd#summer altice#kerissa fare#nichole van croft#buffy tyler#cara michelle\n2001#irina voronina#lauren michelle hill#miriam gonzalez#katie lohmann#crista nicole#heather spytek#kimberley stanfield#jennifer walcott#dalene kurtis#stephanie heinrich#lindsey vuolo#shanna moakler\n2002#nicole narain#anka romensky#tina jordan#heather carolin#christi shake#michele rogers#lauren anderson#christina santiago#shallan meiers#teri harrison#serria tawan#lani todd\n2003#rebecca ramos#charis boyle#pennelope jimenez#carmella decesare#laurie fetter#tailor james#marketa janska#colleen marie#luci victoria#audra lynn#divini rae#deisy teles & sarah teles\n2004#colleen shannon#aliya wolf#sandra hubby#krista kelly#nicole whitehead#hiromi oshima#stephanie glasson#pilar lastra#scarlett keegan#kimberly holland#cara zavaleta#tiffany fallon\n2005#destiny davis#amber campisi#jillian grace#courtney rachel culkin#jamie westenhiser#kara monaco#qiana chase#tamara witmer#vanessa hoelsher#amanda paige#raquel gibson#christine smith\n2006#athena lundberg#cassandra lynn#monica leigh#holley ann dorrough#alison waite#stephanie larimore#sara jean underwood#nicole voss#janine habeck#jordan monroe#sarah elizabeth#kia drayton\n2007#jayde nicole#heather rene smith#tyran richard#giuliana marino#shannon james#brittany binger#tiffany selby#tamara sky#patrice hollis#spencer scott#lindsay wagner#sasckya porto\n2008#sandra nilsson#michelle mclaughlin#ida ljungqvist#regina deutinger#aj alexander#juliette frett\u00e3#laura croft#kayla collins#valerie mason#kelly carrington#grace kim#jennifer and natalie jo campbell\n",
        "pandas_code": "len(df[df['Unnamed: 0'] == 2000]['january'].iloc[0].split(' & ')) == 2 and all(len(df[df['Unnamed: 0'] == 2000][month].iloc[0].split(' & ')) == 1 for month in df.columns[2:])",
        "pandas_eval": "True"
    },
    {
        "id": 2290,
        "statement": "december 2003 feature sister as playmate of the month",
        "label": 1,
        "table_caption": "list of playboy playmates of the month",
        "table_text": "#january#february#march#april#may#june#july#august#september#october#november#december\n2000#carol bernaola & darlene bernaola#suzanne stokes#nicole marie lenz#brande roderick#brooke berry#shannon stewart#neferteri shepherd#summer altice#kerissa fare#nichole van croft#buffy tyler#cara michelle\n2001#irina voronina#lauren michelle hill#miriam gonzalez#katie lohmann#crista nicole#heather spytek#kimberley stanfield#jennifer walcott#dalene kurtis#stephanie heinrich#lindsey vuolo#shanna moakler\n2002#nicole narain#anka romensky#tina jordan#heather carolin#christi shake#michele rogers#lauren anderson#christina santiago#shallan meiers#teri harrison#serria tawan#lani todd\n2003#rebecca ramos#charis boyle#pennelope jimenez#carmella decesare#laurie fetter#tailor james#marketa janska#colleen marie#luci victoria#audra lynn#divini rae#deisy teles & sarah teles\n2004#colleen shannon#aliya wolf#sandra hubby#krista kelly#nicole whitehead#hiromi oshima#stephanie glasson#pilar lastra#scarlett keegan#kimberly holland#cara zavaleta#tiffany fallon\n2005#destiny davis#amber campisi#jillian grace#courtney rachel culkin#jamie westenhiser#kara monaco#qiana chase#tamara witmer#vanessa hoelsher#amanda paige#raquel gibson#christine smith\n2006#athena lundberg#cassandra lynn#monica leigh#holley ann dorrough#alison waite#stephanie larimore#sara jean underwood#nicole voss#janine habeck#jordan monroe#sarah elizabeth#kia drayton\n2007#jayde nicole#heather rene smith#tyran richard#giuliana marino#shannon james#brittany binger#tiffany selby#tamara sky#patrice hollis#spencer scott#lindsay wagner#sasckya porto\n2008#sandra nilsson#michelle mclaughlin#ida ljungqvist#regina deutinger#aj alexander#juliette frett\u00e3#laura croft#kayla collins#valerie mason#kelly carrington#grace kim#jennifer and natalie jo campbell\n",
        "pandas_code": "df.loc[df['Unnamed: 0'] == 2003, 'december'].iloc[0] == 'deisy teles & sarah teles'",
        "pandas_eval": "True"
    },
    {
        "id": 2291,
        "statement": "december 2008 feature sister as playmate of the month for the first time since 2003",
        "label": 1,
        "table_caption": "list of playboy playmates of the month",
        "table_text": "#january#february#march#april#may#june#july#august#september#october#november#december\n2000#carol bernaola & darlene bernaola#suzanne stokes#nicole marie lenz#brande roderick#brooke berry#shannon stewart#neferteri shepherd#summer altice#kerissa fare#nichole van croft#buffy tyler#cara michelle\n2001#irina voronina#lauren michelle hill#miriam gonzalez#katie lohmann#crista nicole#heather spytek#kimberley stanfield#jennifer walcott#dalene kurtis#stephanie heinrich#lindsey vuolo#shanna moakler\n2002#nicole narain#anka romensky#tina jordan#heather carolin#christi shake#michele rogers#lauren anderson#christina santiago#shallan meiers#teri harrison#serria tawan#lani todd\n2003#rebecca ramos#charis boyle#pennelope jimenez#carmella decesare#laurie fetter#tailor james#marketa janska#colleen marie#luci victoria#audra lynn#divini rae#deisy teles & sarah teles\n2004#colleen shannon#aliya wolf#sandra hubby#krista kelly#nicole whitehead#hiromi oshima#stephanie glasson#pilar lastra#scarlett keegan#kimberly holland#cara zavaleta#tiffany fallon\n2005#destiny davis#amber campisi#jillian grace#courtney rachel culkin#jamie westenhiser#kara monaco#qiana chase#tamara witmer#vanessa hoelsher#amanda paige#raquel gibson#christine smith\n2006#athena lundberg#cassandra lynn#monica leigh#holley ann dorrough#alison waite#stephanie larimore#sara jean underwood#nicole voss#janine habeck#jordan monroe#sarah elizabeth#kia drayton\n2007#jayde nicole#heather rene smith#tyran richard#giuliana marino#shannon james#brittany binger#tiffany selby#tamara sky#patrice hollis#spencer scott#lindsay wagner#sasckya porto\n2008#sandra nilsson#michelle mclaughlin#ida ljungqvist#regina deutinger#aj alexander#juliette frett\u00e3#laura croft#kayla collins#valerie mason#kelly carrington#grace kim#jennifer and natalie jo campbell\n",
        "pandas_code": "df.loc[df['Unnamed: 0'] == 2008, 'december'].iloc[0] == 'jennifer and natalie jo campbell'",
        "pandas_eval": "True"
    },
    {
        "id": 2292,
        "statement": "1953 be the only year to feature only 1 playmate of the month",
        "label": 1,
        "table_caption": "list of playboy playmates of the month",
        "table_text": "#january#february#march#april#may#june#july#august#september#october#november#december\n2000#carol bernaola & darlene bernaola#suzanne stokes#nicole marie lenz#brande roderick#brooke berry#shannon stewart#neferteri shepherd#summer altice#kerissa fare#nichole van croft#buffy tyler#cara michelle\n2001#irina voronina#lauren michelle hill#miriam gonzalez#katie lohmann#crista nicole#heather spytek#kimberley stanfield#jennifer walcott#dalene kurtis#stephanie heinrich#lindsey vuolo#shanna moakler\n2002#nicole narain#anka romensky#tina jordan#heather carolin#christi shake#michele rogers#lauren anderson#christina santiago#shallan meiers#teri harrison#serria tawan#lani todd\n2003#rebecca ramos#charis boyle#pennelope jimenez#carmella decesare#laurie fetter#tailor james#marketa janska#colleen marie#luci victoria#audra lynn#divini rae#deisy teles & sarah teles\n2004#colleen shannon#aliya wolf#sandra hubby#krista kelly#nicole whitehead#hiromi oshima#stephanie glasson#pilar lastra#scarlett keegan#kimberly holland#cara zavaleta#tiffany fallon\n2005#destiny davis#amber campisi#jillian grace#courtney rachel culkin#jamie westenhiser#kara monaco#qiana chase#tamara witmer#vanessa hoelsher#amanda paige#raquel gibson#christine smith\n2006#athena lundberg#cassandra lynn#monica leigh#holley ann dorrough#alison waite#stephanie larimore#sara jean underwood#nicole voss#janine habeck#jordan monroe#sarah elizabeth#kia drayton\n2007#jayde nicole#heather rene smith#tyran richard#giuliana marino#shannon james#brittany binger#tiffany selby#tamara sky#patrice hollis#spencer scott#lindsay wagner#sasckya porto\n2008#sandra nilsson#michelle mclaughlin#ida ljungqvist#regina deutinger#aj alexander#juliette frett\u00e3#laura croft#kayla collins#valerie mason#kelly carrington#grace kim#jennifer and natalie jo campbell\n",
        "pandas_code": "not (df['Unnamed: 0'] == 1953).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2293,
        "statement": "march , 1955 be the first month that an issue be not publish",
        "label": 1,
        "table_caption": "list of playboy playmates of the month",
        "table_text": "#january#february#march#april#may#june#july#august#september#october#november#december\n2000#carol bernaola & darlene bernaola#suzanne stokes#nicole marie lenz#brande roderick#brooke berry#shannon stewart#neferteri shepherd#summer altice#kerissa fare#nichole van croft#buffy tyler#cara michelle\n2001#irina voronina#lauren michelle hill#miriam gonzalez#katie lohmann#crista nicole#heather spytek#kimberley stanfield#jennifer walcott#dalene kurtis#stephanie heinrich#lindsey vuolo#shanna moakler\n2002#nicole narain#anka romensky#tina jordan#heather carolin#christi shake#michele rogers#lauren anderson#christina santiago#shallan meiers#teri harrison#serria tawan#lani todd\n2003#rebecca ramos#charis boyle#pennelope jimenez#carmella decesare#laurie fetter#tailor james#marketa janska#colleen marie#luci victoria#audra lynn#divini rae#deisy teles & sarah teles\n2004#colleen shannon#aliya wolf#sandra hubby#krista kelly#nicole whitehead#hiromi oshima#stephanie glasson#pilar lastra#scarlett keegan#kimberly holland#cara zavaleta#tiffany fallon\n2005#destiny davis#amber campisi#jillian grace#courtney rachel culkin#jamie westenhiser#kara monaco#qiana chase#tamara witmer#vanessa hoelsher#amanda paige#raquel gibson#christine smith\n2006#athena lundberg#cassandra lynn#monica leigh#holley ann dorrough#alison waite#stephanie larimore#sara jean underwood#nicole voss#janine habeck#jordan monroe#sarah elizabeth#kia drayton\n2007#jayde nicole#heather rene smith#tyran richard#giuliana marino#shannon james#brittany binger#tiffany selby#tamara sky#patrice hollis#spencer scott#lindsay wagner#sasckya porto\n2008#sandra nilsson#michelle mclaughlin#ida ljungqvist#regina deutinger#aj alexander#juliette frett\u00e3#laura croft#kayla collins#valerie mason#kelly carrington#grace kim#jennifer and natalie jo campbell\n",
        "pandas_code": "df[df['Unnamed: 0'] == 1955]['march'].isna().all()",
        "pandas_eval": "True"
    },
    {
        "id": 2294,
        "statement": "in the 1987 - 88 fa cup , there be 18 tie play on 5 december 1987",
        "label": 1,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "len(df[df['date'] == '5 december 1987']) == 18",
        "pandas_eval": "True"
    },
    {
        "id": 2295,
        "statement": "in the 1987 - 88 fa cup , there be 3 tie that end in a 1 - 1 final score",
        "label": 1,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "(df['score'] == '1 - 1').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2296,
        "statement": "in the 1987 - 88 fa cup , there be 3 tie that end in a 2 - 0 final score",
        "label": 1,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "(df['score'] == '2 - 0').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2297,
        "statement": "in the 1987 - 88 fa cup , the kidderminster harrier and maidstone united play 4 tie against each other",
        "label": 1,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "len(df[((df['home team'] == 'kidderminster harriers') & (df['away team'] == 'maidstone united')) | ((df['home team'] == 'maidstone united') & (df['away team'] == 'kidderminster harriers'))]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2298,
        "statement": "in the 1987 - 88 fa cup , v rugby and the bristol rover play 2 tie against each other",
        "label": 1,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "df[((df['home team'] == 'rugby') & (df['away team'] == 'bristol rovers')) | ((df['home team'] == 'bristol rovers') & (df['away team'] == 'rugby'))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2299,
        "statement": "in the 1987 - 88 fa cup , there be 19 tie play on 5 december 1987",
        "label": 0,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "len(df[df['date'] == '5 december 1987']) == 19",
        "pandas_eval": "False"
    },
    {
        "id": 2300,
        "statement": "in the 1987 - 88 fa cup , there be 5 tie that end in a 1 - 1 final score",
        "label": 0,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "(df['score'] == '1 - 1').sum() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2301,
        "statement": "in the 1987 - 88 fa cup , there be 6 tie that end in a 2 - 0 final score",
        "label": 0,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "(df['score'] == '2 - 0').sum() == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2302,
        "statement": "in the 1987 - 88 fa cup , the kidderminster harrier and maidstone united play 6 tie against each other",
        "label": 0,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "((df['home team'] == 'kidderminster harriers') & (df['away team'] == 'maidstone united') | (df['home team'] == 'maidstone united') & (df['away team'] == 'kidderminster harriers')).sum() == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2303,
        "statement": "in the 1987 - 88 fa cup , v rugby and the bristol rover play 8 tie against each other",
        "label": 0,
        "table_caption": "1987 - 88 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#bristol city#0 - 1#torquay united#5 december 1987\n2#gillingham#2 - 1#walsall#5 december 1987\n3#grimsby town#0 - 0#halifax town#5 december 1987\nreplay#halifax town#2 - 0#grimsby town#8 december 1987\n4#northwich victoria#0 - 2#blackpool#6 december 1987\n5#macclesfield town#4 - 0#rotherham united#6 december 1987\n6#wrexham#1 - 2#bolton wanderers#5 december 1987\n7#maidstone united#1 - 1#kidderminster harriers#5 december 1987\nreplay#kidderminster harriers#2 - 2#maidstone united#7 december 1987\nreplay#kidderminster harriers#0 - 0#maidstone united#14 december 1987\nreplay#maidstone united#2 - 1#kidderminster harriers#16 december 1987\n8#northampton town#1 - 2#brighton & hove albion#5 december 1987\n9#scunthorpe united#2 - 1#sunderland#5 december 1987\n10#mansfield town#4 - 3#lincoln city#5 december 1987\n11#port vale#2 - 0#notts county#5 december 1987\n12#runcorn#0 - 1#stockport county#5 december 1987\n13#york city#1 - 1#hartlepool united#5 december 1987\nreplay#hartlepool united#3 - 1#york city#9 december 1987\n14#wigan athletic#1 - 3#wolverhampton wanderers#5 december 1987\n15#peterborough united#1 - 3#sutton united#5 december 1987\n16#colchester united#3 - 2#hereford united#5 december 1987\n17#leyton orient#2 - 0#swansea city#5 december 1987\n18#cambridge united#0 - 1#yeovil town#5 december 1987\n19#rugby#1 - 1#bristol rovers#5 december 1987\nreplay#bristol rovers#4 - 0#rugby#17 december 1987\n20#welling united#0 - 1#bath city#5 december 1987\n",
        "pandas_code": "((df['home team'] == 'rugby') & (df['away team'] == 'bristol rovers') & (df['tie no'] == '8')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2304,
        "statement": "there be 2 team that have a winning percentage in the 80s , while only 1 team have a winning percentage in the 70",
        "label": 1,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df['percentage!'].str.strip('%').astype(int).between(80, 89).sum() == 2) & (df['percentage!'].str.strip('%').astype(int).between(70, 79).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2305,
        "statement": "san sebastian college have a winning percentage 9 point higher than the university of santo tomas",
        "label": 1,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df[df['team'] == 'san sebastian college - recoletos']['percentage!'].str.strip('%').astype(float).iloc[0] - df[df['team'] == 'university of santo tomas']['percentage!'].str.strip('%').astype(float).iloc[0]) == 9",
        "pandas_eval": "True"
    },
    {
        "id": 2306,
        "statement": "the most set any team win be 18 , which 3 team achieve",
        "label": 1,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "df['sets won'].max() == 18 and (df['sets won'] == 18).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2307,
        "statement": "there be 3 different team that win 18 set during the season",
        "label": 1,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "len(df[df['sets won'] == 18]['team'].unique()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2308,
        "statement": "there be 3 team that finish with a win percentage under 50 percent",
        "label": 1,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df['percentage!'].str.replace('%', '').astype(float) < 50).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2309,
        "statement": "there be 3 team that have a winning percentage in the 80s , while only 1 team have a winning percentage in the 70",
        "label": 0,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df['percentage!'].str[:-1].astype(int).between(80, 89).sum() == 3) & (df['percentage!'].str[:-1].astype(int).between(70, 79).sum() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2310,
        "statement": "san sebastian college have a winning percentage 9 point lesser than the university of santo tomas",
        "label": 0,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df.loc[df['team'] == 'san sebastian college - recoletos', 'percentage!'].values[0].strip('%') == str(int(df.loc[df['team'] == 'university of santo tomas', 'percentage!'].values[0].strip('%')) - 9) + '%')",
        "pandas_eval": "False"
    },
    {
        "id": 2311,
        "statement": "the most set any team loss be 18 , which 3 team achieve",
        "label": 0,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "df[df['sets lost'] == 18].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2312,
        "statement": "there be 3 different team that win 12 set during the season",
        "label": 0,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "len(df[df['sets won'] == 12]['team'].unique()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2313,
        "statement": "there be 3 team that finish with a win percentage under 30 percent",
        "label": 0,
        "table_caption": "v - league 4th season 1st conference",
        "table_text": "rank#team#loss#sets won#sets lost#percentage!\n1#lyceum of the philippines university#1#18#7#86%\n2#san sebastian college - recoletos#1#18#9#82%\n3#university of santo tomas#3#16#10#73%\n4#ateneo de manila university#4#15#16#60%\n5#de la salle university#4#12#14#56%\n5#adamson university#4#18#19#48%\n7#far eastern university#5#10#19#34%\n8#colegio de san juan de letran#6#10#22#31%\n",
        "pandas_code": "(df['percentage!'].str.rstrip('%').astype(float) < 30).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2314,
        "statement": "tim southee be the highest ranked cricket player in new zealand",
        "label": 1,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['rank'] == 1]['player'].iloc[0] == 'tim southee'",
        "pandas_eval": "True"
    },
    {
        "id": 2315,
        "statement": "james franklin and nathan mccullum be the third and fourth highest rat cricket player in new zealand and both play in the hamilton venue",
        "label": 1,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[(df['player'].isin(['james franklin', 'nathan mccullum'])) & (df['rank'].isin([3, 4])) & (df['venue'] == 'hamilton')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2316,
        "statement": "mark gillespie be 1 of the 2 player who play cricket in dec 2010",
        "label": 1,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['date'].str.contains('december 2010')]['player'].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2317,
        "statement": "james franklin be rank in the middle of the top 5 cricket player in new zealand",
        "label": 1,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['player'] == 'james franklin']['rank'].iloc[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2318,
        "statement": "the best cricket record new zealand be play at the auckland venue",
        "label": 1,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['rank'] == 1]['venue'].iloc[0] == 'auckland'",
        "pandas_eval": "True"
    },
    {
        "id": 2319,
        "statement": "tim southee be 1 among the lowest ranked cricket player in new zealand",
        "label": 0,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['player'] == 'tim southee']['rank'].iloc[0] != 1",
        "pandas_eval": "False"
    },
    {
        "id": 2320,
        "statement": "james franklin and nathan mccullum be the third and fourth highest ranked cricket player in new zealand and both play in the durban venue",
        "label": 0,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "((df['player'] == 'james franklin') & (df['rank'] == 3) & (df['venue'] == 'durban')).any() & ((df['player'] == 'nathan mccullum') & (df['rank'] == 4) & (df['venue'] == 'durban')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2321,
        "statement": "the best cricket record new zealand be play at the auckland venue",
        "label": 0,
        "table_caption": "list of twenty20 cricket records for new zealand",
        "table_text": "rank#bowling#player#venue#date\n1#5 - 18#tim southee#auckland#26 december 2010\n2#4 - 7#mark gillespie#durban#12 september 2007\n3#4 - 15#james franklin#hamilton#12 february 2013\n4#4 - 16#nathan mccullum#hamilton#28 december 2010\n5#4 - 20#daniel vettori#johannesburg#16 september 2007\n",
        "pandas_code": "df[df['rank'] == 1]['venue'].values[0] != 'auckland'",
        "pandas_eval": "False"
    },
    {
        "id": 2322,
        "statement": "the longest set 1 on 2 july have a time of 26:24 and the shortest time in set 1 be 12:21",
        "label": 1,
        "table_caption": "2009 beach volleyball world championships",
        "table_text": "date#score#set 1#set 2#total\n2 july#0 - 2#12:21#13:21#25:42\n2 july#2 - 0#21:19#21:11#42:30\n2 july#2 - 1#18:21#23:21#56:55\n2 july#0 - 2#16:21#16:21#32:42\n2 july#2 - 1#16:21#21:16#52:44\n2 july#2 - 0#26:24#21:18#47:42\n2 july#0 - 2#20:22#15:21#45:43\n2 july#2 - 0#21:10#21:15#42:25\n2 july#0 - 2#18:21#10:21#28:42\n2 july#0 - 2#14:21#16:21#30:42\n2 july#0 - 2#15:21#15:21#30:42\n2 july#2 - 1#21:14#19:21#57:50\n2 july#2 - 1#18:21#24:22#59:58\n2 july#2 - 0#21:12#21:16#42:28\n2 july#2 - 0#21:17#21:15#42:32\n2 july#2 - 0#21:17#21:10#42:27\n",
        "pandas_code": "(df[df['date'] == '2 july']['set 1'].max() == '26:24') & (df[df['date'] == '2 july']['set 1'].min() == '12:21')",
        "pandas_eval": "True"
    },
    {
        "id": 2323,
        "statement": "the longest set 2 on 2 july have a time of 24:22 and the longest total time be 59:58",
        "label": 1,
        "table_caption": "2009 beach volleyball world championships",
        "table_text": "date#score#set 1#set 2#total\n2 july#0 - 2#12:21#13:21#25:42\n2 july#2 - 0#21:19#21:11#42:30\n2 july#2 - 1#18:21#23:21#56:55\n2 july#0 - 2#16:21#16:21#32:42\n2 july#2 - 1#16:21#21:16#52:44\n2 july#2 - 0#26:24#21:18#47:42\n2 july#0 - 2#20:22#15:21#45:43\n2 july#2 - 0#21:10#21:15#42:25\n2 july#0 - 2#18:21#10:21#28:42\n2 july#0 - 2#14:21#16:21#30:42\n2 july#0 - 2#15:21#15:21#30:42\n2 july#2 - 1#21:14#19:21#57:50\n2 july#2 - 1#18:21#24:22#59:58\n2 july#2 - 0#21:12#21:16#42:28\n2 july#2 - 0#21:17#21:15#42:32\n2 july#2 - 0#21:17#21:10#42:27\n",
        "pandas_code": "(df[df['date'] == '2 july']['set 2'].max() == '24:22') & (df[df['date'] == '2 july']['total'].max() == '59:58')",
        "pandas_eval": "True"
    },
    {
        "id": 2324,
        "statement": "2 match in set 1 be exactly the same time , 21:17",
        "label": 1,
        "table_caption": "2009 beach volleyball world championships",
        "table_text": "date#score#set 1#set 2#total\n2 july#0 - 2#12:21#13:21#25:42\n2 july#2 - 0#21:19#21:11#42:30\n2 july#2 - 1#18:21#23:21#56:55\n2 july#0 - 2#16:21#16:21#32:42\n2 july#2 - 1#16:21#21:16#52:44\n2 july#2 - 0#26:24#21:18#47:42\n2 july#0 - 2#20:22#15:21#45:43\n2 july#2 - 0#21:10#21:15#42:25\n2 july#0 - 2#18:21#10:21#28:42\n2 july#0 - 2#14:21#16:21#30:42\n2 july#0 - 2#15:21#15:21#30:42\n2 july#2 - 1#21:14#19:21#57:50\n2 july#2 - 1#18:21#24:22#59:58\n2 july#2 - 0#21:12#21:16#42:28\n2 july#2 - 0#21:17#21:15#42:32\n2 july#2 - 0#21:17#21:10#42:27\n",
        "pandas_code": "(df['set 1'].value_counts()['21:17'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2325,
        "statement": "on 2 july , there be 2 total that be exact the same time , 30:42",
        "label": 1,
        "table_caption": "2009 beach volleyball world championships",
        "table_text": "date#score#set 1#set 2#total\n2 july#0 - 2#12:21#13:21#25:42\n2 july#2 - 0#21:19#21:11#42:30\n2 july#2 - 1#18:21#23:21#56:55\n2 july#0 - 2#16:21#16:21#32:42\n2 july#2 - 1#16:21#21:16#52:44\n2 july#2 - 0#26:24#21:18#47:42\n2 july#0 - 2#20:22#15:21#45:43\n2 july#2 - 0#21:10#21:15#42:25\n2 july#0 - 2#18:21#10:21#28:42\n2 july#0 - 2#14:21#16:21#30:42\n2 july#0 - 2#15:21#15:21#30:42\n2 july#2 - 1#21:14#19:21#57:50\n2 july#2 - 1#18:21#24:22#59:58\n2 july#2 - 0#21:12#21:16#42:28\n2 july#2 - 0#21:17#21:15#42:32\n2 july#2 - 0#21:17#21:10#42:27\n",
        "pandas_code": "df[df['date'] == '2 july']['total'].value_counts().eq(2).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2326,
        "statement": "1 match in set 1 have the same time as the corresponding match in set 2: 16:21",
        "label": 1,
        "table_caption": "2009 beach volleyball world championships",
        "table_text": "date#score#set 1#set 2#total\n2 july#0 - 2#12:21#13:21#25:42\n2 july#2 - 0#21:19#21:11#42:30\n2 july#2 - 1#18:21#23:21#56:55\n2 july#0 - 2#16:21#16:21#32:42\n2 july#2 - 1#16:21#21:16#52:44\n2 july#2 - 0#26:24#21:18#47:42\n2 july#0 - 2#20:22#15:21#45:43\n2 july#2 - 0#21:10#21:15#42:25\n2 july#0 - 2#18:21#10:21#28:42\n2 july#0 - 2#14:21#16:21#30:42\n2 july#0 - 2#15:21#15:21#30:42\n2 july#2 - 1#21:14#19:21#57:50\n2 july#2 - 1#18:21#24:22#59:58\n2 july#2 - 0#21:12#21:16#42:28\n2 july#2 - 0#21:17#21:15#42:32\n2 july#2 - 0#21:17#21:10#42:27\n",
        "pandas_code": "((df['set 1'] == '16:21') & (df['set 2'] == '16:21')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2327,
        "statement": "2 game result in win",
        "label": 1,
        "table_caption": "gast\u00e3o elias",
        "table_text": "edition#round#date#against#surface#opponent#w / l#result\n2007 davis cup europe / africa group i#1r#9 - 11 february 2007#georgia#carpet#george khrikadze#win#6 - 3 , 7 - 6 (7 - 5)\n2007 davis cup europe / africa group i#gi po#21 - 23 september 2007#netherlands#hard#robin haase#loss#1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6\n2008 davis cup europe / africa group ii#1r#11 - 13 april 2008#tunisia#clay#walid jallali#loss#5 - 7 , 2 - 6\n2008 davis cup europe / africa group ii#sf#19 - 21 september 2008#ukraine#hard#sergiy stakhovsky#loss#4 - 6 , 6 - 7 (5 - 7) , 4 - 6\n2012 davis cup europe / africa group i#gi po#14 - 16 september 2012#slovakia#hard#martin kli\u017ean#loss#6 - 3 , 2 - 6 , 6 - 7 (4 - 7) , 2 - 6\n2013 davis cup europe / africa group ii#2r#5 - 7 april 2013#lithuania#clay#lukas mugevicius#win#6 - 0 , 6 - 1 , 6 - 2\n",
        "pandas_code": "len(df[df['w / l'] == 'win']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2328,
        "statement": "4 game result in loss",
        "label": 1,
        "table_caption": "gast\u00e3o elias",
        "table_text": "edition#round#date#against#surface#opponent#w / l#result\n2007 davis cup europe / africa group i#1r#9 - 11 february 2007#georgia#carpet#george khrikadze#win#6 - 3 , 7 - 6 (7 - 5)\n2007 davis cup europe / africa group i#gi po#21 - 23 september 2007#netherlands#hard#robin haase#loss#1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6\n2008 davis cup europe / africa group ii#1r#11 - 13 april 2008#tunisia#clay#walid jallali#loss#5 - 7 , 2 - 6\n2008 davis cup europe / africa group ii#sf#19 - 21 september 2008#ukraine#hard#sergiy stakhovsky#loss#4 - 6 , 6 - 7 (5 - 7) , 4 - 6\n2012 davis cup europe / africa group i#gi po#14 - 16 september 2012#slovakia#hard#martin kli\u017ean#loss#6 - 3 , 2 - 6 , 6 - 7 (4 - 7) , 2 - 6\n2013 davis cup europe / africa group ii#2r#5 - 7 april 2013#lithuania#clay#lukas mugevicius#win#6 - 0 , 6 - 1 , 6 - 2\n",
        "pandas_code": "any(df[df['w / l'] == 'loss']['result'].str.contains(r'\\b4\\b'))",
        "pandas_eval": "True"
    },
    {
        "id": 2329,
        "statement": "the surface be hard on september 2007 , september 2008 , and september 2012",
        "label": 1,
        "table_caption": "gast\u00e3o elias",
        "table_text": "edition#round#date#against#surface#opponent#w / l#result\n2007 davis cup europe / africa group i#1r#9 - 11 february 2007#georgia#carpet#george khrikadze#win#6 - 3 , 7 - 6 (7 - 5)\n2007 davis cup europe / africa group i#gi po#21 - 23 september 2007#netherlands#hard#robin haase#loss#1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6\n2008 davis cup europe / africa group ii#1r#11 - 13 april 2008#tunisia#clay#walid jallali#loss#5 - 7 , 2 - 6\n2008 davis cup europe / africa group ii#sf#19 - 21 september 2008#ukraine#hard#sergiy stakhovsky#loss#4 - 6 , 6 - 7 (5 - 7) , 4 - 6\n2012 davis cup europe / africa group i#gi po#14 - 16 september 2012#slovakia#hard#martin kli\u017ean#loss#6 - 3 , 2 - 6 , 6 - 7 (4 - 7) , 2 - 6\n2013 davis cup europe / africa group ii#2r#5 - 7 april 2013#lithuania#clay#lukas mugevicius#win#6 - 0 , 6 - 1 , 6 - 2\n",
        "pandas_code": "all(df[(df['date'].str.contains('september 2007|september 2008|september 2012'))]['surface'] == 'hard')",
        "pandas_eval": "True"
    },
    {
        "id": 2330,
        "statement": "the surface be clay on april 2008 and april 2013",
        "label": 1,
        "table_caption": "gast\u00e3o elias",
        "table_text": "edition#round#date#against#surface#opponent#w / l#result\n2007 davis cup europe / africa group i#1r#9 - 11 february 2007#georgia#carpet#george khrikadze#win#6 - 3 , 7 - 6 (7 - 5)\n2007 davis cup europe / africa group i#gi po#21 - 23 september 2007#netherlands#hard#robin haase#loss#1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6\n2008 davis cup europe / africa group ii#1r#11 - 13 april 2008#tunisia#clay#walid jallali#loss#5 - 7 , 2 - 6\n2008 davis cup europe / africa group ii#sf#19 - 21 september 2008#ukraine#hard#sergiy stakhovsky#loss#4 - 6 , 6 - 7 (5 - 7) , 4 - 6\n2012 davis cup europe / africa group i#gi po#14 - 16 september 2012#slovakia#hard#martin kli\u017ean#loss#6 - 3 , 2 - 6 , 6 - 7 (4 - 7) , 2 - 6\n2013 davis cup europe / africa group ii#2r#5 - 7 april 2013#lithuania#clay#lukas mugevicius#win#6 - 0 , 6 - 1 , 6 - 2\n",
        "pandas_code": "((df['date'].str.contains('april 2008')) & (df['surface'] == 'clay')).any() & ((df['date'].str.contains('april 2013')) & (df['surface'] == 'clay')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2331,
        "statement": "only georgia have a game with a carpet surface",
        "label": 1,
        "table_caption": "gast\u00e3o elias",
        "table_text": "edition#round#date#against#surface#opponent#w / l#result\n2007 davis cup europe / africa group i#1r#9 - 11 february 2007#georgia#carpet#george khrikadze#win#6 - 3 , 7 - 6 (7 - 5)\n2007 davis cup europe / africa group i#gi po#21 - 23 september 2007#netherlands#hard#robin haase#loss#1 - 6 , 1 - 6 , 6 - 2 , 7 - 5 , 2 - 6\n2008 davis cup europe / africa group ii#1r#11 - 13 april 2008#tunisia#clay#walid jallali#loss#5 - 7 , 2 - 6\n2008 davis cup europe / africa group ii#sf#19 - 21 september 2008#ukraine#hard#sergiy stakhovsky#loss#4 - 6 , 6 - 7 (5 - 7) , 4 - 6\n2012 davis cup europe / africa group i#gi po#14 - 16 september 2012#slovakia#hard#martin kli\u017ean#loss#6 - 3 , 2 - 6 , 6 - 7 (4 - 7) , 2 - 6\n2013 davis cup europe / africa group ii#2r#5 - 7 april 2013#lithuania#clay#lukas mugevicius#win#6 - 0 , 6 - 1 , 6 - 2\n",
        "pandas_code": "df[df['surface'] == 'carpet']['against'].unique().tolist() == ['georgia']",
        "pandas_eval": "True"
    },
    {
        "id": 2332,
        "statement": "mit have the highest enrollment of the institution on the list",
        "label": 1,
        "table_caption": "new england women 's and men 's athletic conference",
        "table_text": "institution#location#nickname#founded#type#enrollment#joined\nbabson college#wellesley , massachusetts#beavers#1919#private / non - sectarian#3200#1985\nclark university#worcester , massachusetts#cougars#1887#private / non - sectarian#2780#1995\nemerson college#boston , massachusetts#lions#1880#private / non - sectarian#4290#2013\nmassachusetts institute of technology#cambridge , massachusetts#engineers#1861#private / non - sectarian#10253#1985\nmount holyoke college#south hadley , massachusetts#lyons#1837#private / non - sectarian#2100#1987\nsmith college#northampton , massachusetts#pioneers#1871#private / non - sectarian#2600#1985\nspringfield college#springfield , massachusetts#pride#1885#private / non - sectarian#5062#1998\nunited states coast guard academy#new london , connecticut#bears#1876#federal / military#990#1998\nwellesley college#wellesley , massachusetts#blue#1870#private / non - sectarian#2300#1985\nwheaton college#norton , massachusetts#lyons#1834#private / non - sectarian#1550#1985\n",
        "pandas_code": "df.loc[df['enrollment'].idxmax(), 'institution'] == 'massachusetts institute of technology'",
        "pandas_eval": "True"
    },
    {
        "id": 2333,
        "statement": "2 college in the athletic conference be in wellesley , massachusetts",
        "label": 1,
        "table_caption": "new england women 's and men 's athletic conference",
        "table_text": "institution#location#nickname#founded#type#enrollment#joined\nbabson college#wellesley , massachusetts#beavers#1919#private / non - sectarian#3200#1985\nclark university#worcester , massachusetts#cougars#1887#private / non - sectarian#2780#1995\nemerson college#boston , massachusetts#lions#1880#private / non - sectarian#4290#2013\nmassachusetts institute of technology#cambridge , massachusetts#engineers#1861#private / non - sectarian#10253#1985\nmount holyoke college#south hadley , massachusetts#lyons#1837#private / non - sectarian#2100#1987\nsmith college#northampton , massachusetts#pioneers#1871#private / non - sectarian#2600#1985\nspringfield college#springfield , massachusetts#pride#1885#private / non - sectarian#5062#1998\nunited states coast guard academy#new london , connecticut#bears#1876#federal / military#990#1998\nwellesley college#wellesley , massachusetts#blue#1870#private / non - sectarian#2300#1985\nwheaton college#norton , massachusetts#lyons#1834#private / non - sectarian#1550#1985\n",
        "pandas_code": "len(df[df['location'] == 'wellesley , massachusetts']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2334,
        "statement": "wheaton college be found before any other institution in the conference",
        "label": 1,
        "table_caption": "new england women 's and men 's athletic conference",
        "table_text": "institution#location#nickname#founded#type#enrollment#joined\nbabson college#wellesley , massachusetts#beavers#1919#private / non - sectarian#3200#1985\nclark university#worcester , massachusetts#cougars#1887#private / non - sectarian#2780#1995\nemerson college#boston , massachusetts#lions#1880#private / non - sectarian#4290#2013\nmassachusetts institute of technology#cambridge , massachusetts#engineers#1861#private / non - sectarian#10253#1985\nmount holyoke college#south hadley , massachusetts#lyons#1837#private / non - sectarian#2100#1987\nsmith college#northampton , massachusetts#pioneers#1871#private / non - sectarian#2600#1985\nspringfield college#springfield , massachusetts#pride#1885#private / non - sectarian#5062#1998\nunited states coast guard academy#new london , connecticut#bears#1876#federal / military#990#1998\nwellesley college#wellesley , massachusetts#blue#1870#private / non - sectarian#2300#1985\nwheaton college#norton , massachusetts#lyons#1834#private / non - sectarian#1550#1985\n",
        "pandas_code": "df[df['institution'] == 'wheaton college']['founded'].iloc[0] == df['founded'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2335,
        "statement": "only 1 institution in the conference isn't private",
        "label": 1,
        "table_caption": "new england women 's and men 's athletic conference",
        "table_text": "institution#location#nickname#founded#type#enrollment#joined\nbabson college#wellesley , massachusetts#beavers#1919#private / non - sectarian#3200#1985\nclark university#worcester , massachusetts#cougars#1887#private / non - sectarian#2780#1995\nemerson college#boston , massachusetts#lions#1880#private / non - sectarian#4290#2013\nmassachusetts institute of technology#cambridge , massachusetts#engineers#1861#private / non - sectarian#10253#1985\nmount holyoke college#south hadley , massachusetts#lyons#1837#private / non - sectarian#2100#1987\nsmith college#northampton , massachusetts#pioneers#1871#private / non - sectarian#2600#1985\nspringfield college#springfield , massachusetts#pride#1885#private / non - sectarian#5062#1998\nunited states coast guard academy#new london , connecticut#bears#1876#federal / military#990#1998\nwellesley college#wellesley , massachusetts#blue#1870#private / non - sectarian#2300#1985\nwheaton college#norton , massachusetts#lyons#1834#private / non - sectarian#1550#1985\n",
        "pandas_code": "df[df['type'] != 'private / non - sectarian'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2337,
        "statement": "day the earth caught fire be the oldest title in the table",
        "label": 1,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "df[df['title'] == 'day the earth caught fire']['year'].min() == df['year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2338,
        "statement": "super hero be the newest title in the table",
        "label": 1,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "df.loc[df['title'] == 'super hero', 'year'].max() == df['year'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2339,
        "statement": "7 song from the table be perform by the misfit",
        "label": 1,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "((df['format'].str.contains('7')) & (df['artist'].str.contains('misfits'))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2340,
        "statement": "rock'em o - sock'em live! be only 1 of the 2 title that be live",
        "label": 1,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "len(df[df['title'] == \"rock'em o - sock'em live!\"]) == 1 and len(df[df['type'] == 'live album']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2341,
        "statement": "misfit / balzac be the oldest artist in the table",
        "label": 1,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "df[df['artist'].str.contains('misfits / balzac')]['year'].min() == df['year'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2342,
        "statement": "beyond the darkness be the oldest title in the table",
        "label": 0,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "df[df['title'] == 'beyond the darkness']['year'].min() == df['year'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2343,
        "statement": "super hero be the only title from 2013",
        "label": 0,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "(df[(df['year'] == 2013) & (df['title'] == 'super hero')].shape[0] == 1) & (df[df['year'] == 2013].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2344,
        "statement": "5 song from the table be perform by the misfit",
        "label": 0,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "len(df[df['artist'].str.contains('misfits', case=False, na=False)]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2345,
        "statement": "rock'em o - sock'em live! be the only live title",
        "label": 0,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "len(df[df['type'] == 'live album']) == 1 and df[df['type'] == 'live album']['title'].iloc[0] == \"rock'em o - sock'em live!\"",
        "pandas_eval": "False"
    },
    {
        "id": 2346,
        "statement": "misfit / balzac be the newest artist in the table",
        "label": 0,
        "table_caption": "misfits records",
        "table_text": "year#artist#title#format#type\n2002#misfits / balzac#day the earth caught fire#cd#single\n2003#misfits#project 1950#cd / dvd#studio album\n2003#balzac#beyond the darkness#cd / dvd#compilation album\n2005#misfits meet the nutley brass#fiend club lounge#cd#studio album\n2005#balzac#out of the grave and into the dark#cd / dvd#compilation album\n2006#osaka popstar#osaka popstar and the american legends of punk#cd / dvd#studio album\n2006#misfits#psycho in the wax museum#promotional 7#single\n2007#juicehead#the devil made me do it#cd#studio album\n2007#balzac#deep blue : chaos from darkism#cd / dvd#studio album\n2007#balzac#deep blue / alone#shaped picture disc#single\n2007#osaka popstar#shaolin monkeys#shaped picture disc#single\n2008#osaka popstar#rock'em o - sock'em live!#cd#live album\n2009#misfits#land of the dead#12#single\n2010#juicehead#rotting from the inside#7#single\n2010#balzac#the birth of hatred#cd / dvd#compilation album\n2011#juicehead#how to sail a sinking ship#cd#studio album\n2011#misfits#twilight of the dead#12#single\n2011#misfits#the devil 's rain#cd#studio album\n2012#klaus beyer covers osaka popstar#die shaolin affen ep#7#ep\n2012#juicehead x osaka popstar#waiting room#7#single\n2012#balzac#paradox#12#ep\n2013#misfits#dead alive!#cd , lp#live album\n2013#osaka popstar#super hero#12#single\n",
        "pandas_code": "df[df['artist'].str.contains('misfit / balzac', case=False, na=False)]['year'].max() == df['year'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2347,
        "statement": "the model number core i7 - 2920xm have the same socket (socket g2) as the model number core i7 - 2960xm",
        "label": 1,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "df[df['model number'].isin(['core i7 - 2920xm', 'core i7 - 2960xm'])]['socket'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2348,
        "statement": "2 model have a release date in october 2011 , model number core i7 - 2670qm and model number core i7 - 2675 gm",
        "label": 1,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "len(df[(df['release date'] == 'october 2011') & ((df['model number'] == 'core i7 - 2670qm') | (df['model number'] == 'core i7 - 2675qm'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2349,
        "statement": "the model number core i7 - 2820qm , part number ff8062700834709av8062700834912 , have a higher price than the model number corei7 - 2760qm , part number ff8062701065300av8062701065400",
        "label": 1,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "df[(df['model number'] == 'core i7 - 2820qm') & (df['part number (s)'] == 'ff8062700834709av8062700834912')]['release price ( usd )'].values[0] > df[(df['model number'] == 'corei7 - 2760qm') & (df['part number (s)'] == 'ff8062701065300av8062701065400')]['release price ( usd )'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2350,
        "statement": "7 model number be release in january 2011 and 2 be release in october 2011",
        "label": 1,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "(df[df['release date'] == 'january 2011']['model number'].nunique() == 7) & (df[df['release date'] == 'october 2011']['model number'].nunique() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2351,
        "statement": "3 model be release in september 2011 , 1 more than be release in october 2011",
        "label": 1,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "(df[df['release date'] == 'september 2011'].shape[0] == 3) & (df[df['release date'] == 'october 2011'].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2352,
        "statement": "the model number core i7 - 2920xm have the same socket (socket g2) as the model number corei7 - 2760qm",
        "label": 0,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "df[df['model number'] == 'core i7 - 2920xm']['socket'].values[0] == df[df['model number'] == 'corei7 - 2760qm']['socket'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2353,
        "statement": "2 model have a release date in october 2011 , corei7 - 2760qm and model number core i7 - 2675 gm",
        "label": 0,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "len(df[(df['release date'] == 'october 2011') & ((df['model number'] == 'corei7 - 2760qm') | (df['model number'] == 'core i7 - 2675 gm'))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2354,
        "statement": "the model number corei7 - 2720qm , part number ff8062700835817av8062700836011 , have a higher price than the model number corei7 - 2760qm , part number ff8062701065300av8062701065400",
        "label": 0,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "(df[(df['model number'] == 'corei7 - 2720qm') & (df['part number (s)'] == 'ff8062700835817av8062700836011')]['release price ( usd )'].values[0] > df[(df['model number'] == 'corei7 - 2760qm') & (df['part number (s)'] == 'ff8062701065300av8062701065400')]['release price ( usd )'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2355,
        "statement": "8 model number be release in january 2011 and 3 be release in october 2011",
        "label": 0,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "(df[df['release date'] == 'january 2011'].shape[0] == 8) & (df[df['release date'] == 'october 2011'].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 2356,
        "statement": "4 model be release in september 2011 , 1 more than be release in october 2011",
        "label": 0,
        "table_caption": "list of intel core i7 microprocessors",
        "table_text": "model number#sspec number#cores#frequency#turbo#l2 cache#l3 cache#gpu model#gpu frequency#socket#i / o bus#release date#part number (s)#release price ( usd )\nstandard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power#standard power\ncore i7 - 2630qm#sr02y (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socketg2#dmi 2.0#january 2011#ff8062700837005#378\ncore i7 - 2635qm#sr030 (d2)#4#2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#january 2011#av8062700837205#378\ncore i7 - 2670qm#sr02n (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1100 mhz#socket g2#dmi 2.0#october 2011#ff8062701065500#378\ncore i7 - 2675qm#sr02s (d2)#4#2.2 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1224#dmi 2.0#october 2011#av8062701065600#378\ncorei7 - 2720qm#sr014 (d2) sr00w (d2)#4#2.2 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700835817av8062700836011#378\ncorei7 - 2760qm#sr02w (d2) sr02r (d2)#4#2.4 ghz#8 / 8 / 10 / 11#4 256 kb#6 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065300av8062701065400#378\ncore i7 - 2820qm#sr012 (d2) sr00u (d2)#4#2.3 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#january 2011#ff8062700834709av8062700834912#568\ncore i7 - 2860qm#sr02x (d2) sr02q (d2)#4#2.5 ghz#8 / 8 / 10 / 11#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2bga - 1224#dmi 2.0#september 2011#ff8062701065100av8062701065200#568\ncore i7 - 2920xm#sr02e (d2)#4#2.5 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#january 2011#ff8062700834406#1096\ncore i7 - 2960xm#sr02f (d2)#4#2.7 ghz#7 / 7 / 9 / 10#4 256 kb#8 mb#hd graphics 3000#650 - 1300 mhz#socket g2#dmi 2.0#september 2011#ff8062700834603#1096\nstandard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded#standard power , embedded\ncore i7 - 2710qe#sr02t (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#socket g2#dmi 2.0#january 2011#ff8062700841002#378\ncore i7 - 2715qe#sr076 (d2)#4#2.1 ghz#6 / 6 / 8 / 9#4 256 kb#6 mb#hd graphics 3000#650 - 1200 mhz#bga - 1023#dmi 2.0#january 2011#av8062700843908#378\n",
        "pandas_code": "(df[df['release date'] == 'september 2011'].shape[0] == 4) & (df[df['release date'] == 'october 2011'].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2357,
        "statement": "the surface be clay for 5 tournament from 2004 through 2006",
        "label": 1,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['surface'] == 'clay') & (pd.to_datetime(df['date']).dt.year.between(2004, 2006))]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2358,
        "statement": "the surface be hard for 2 tournament , in 2006 and 2011",
        "label": 1,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['surface'] == 'hard') & (df['date'].str.contains('2006|2011'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2359,
        "statement": "there be at least 3 tournament that happen in 2006",
        "label": 1,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[df['date'].str.contains('2006')]['tournament'].unique()) >= 3",
        "pandas_eval": "True"
    },
    {
        "id": 2360,
        "statement": "there be only 1 tournament that happen in 2004 , at maribor , slovenia",
        "label": 1,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['date'].str.contains('2004')) & (df['tournament'] == 'maribor , slovenia')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2361,
        "statement": "there be only 1 tournament that happen in 2011 , at irapuato , mexico",
        "label": 1,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['date'].str.contains('2011')) & (df['tournament'] == 'irapuato , mexico')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2362,
        "statement": "the surface be clay for 6 tournament from 2004 through 2006",
        "label": 0,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "(df[(df['date'].str.contains('2004|2005|2006')) & (df['surface'] == 'clay')].shape[0] == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 2363,
        "statement": "the surface be hard for 1 tournament , in 2006",
        "label": 0,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "df[(df['surface'] == 'hard') & (df['date'].str.contains('2006'))].shape[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 2364,
        "statement": "there be only 1 tournament that happen in 2004 , at rabat , morocco",
        "label": 0,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['date'].str.contains('2004')) & (df['tournament'] == 'rabat , morocco')]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2365,
        "statement": "there be only 1 tournament that happen in 2010 , at irapuato , mexico",
        "label": 0,
        "table_caption": "andreja klepa\u010d",
        "table_text": "outcome#date#tournament#surface#opponent#score\nrunner - up#23 august 2004#maribor , slovenia#clay#ma\u0161a zec pe\u0161kiri\u010d#6 - 2 , 7 - 5\nwinner#25 april 2005#rabat , morocco#clay#dominika cibulkov\u00e1#6 - 1 , 3 - 6 , 6 - 4\nrunner - up#27 march 2006#abu dhabi , united arab emirates#hard#katerina avdiyenko#6 - 4 , 6 - 2\nwinner#11 july 2006#torun , poland#clay#joanna sakowicz#6 - 0 , 6 - 2\nrunner - up#18 july 2006#dnepropetrovsk , ukraine#clay#kristina antoniychuk#6 - 3 , 6 - 7 (5) , 6 - 4\nwinner#22 august 2006#maribor , slovenia#clay#dia evtimova#6 - 4 , 2 - 6 , 6 - 3\nrunner - up#8 march 2011#irapuato , mexico#hard#marina erakovic#7 - 5 , 6 - 4\n",
        "pandas_code": "len(df[(df['date'].str.contains('2010')) & (df['tournament'] == 'irapuato , mexico')]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2366,
        "statement": "wild eye have more copy than any other single by the vivian girl",
        "label": 1,
        "table_caption": "vivian girls",
        "table_text": "date#single#backed with#record label#format#other details\n2008#wild eyes#my baby wants me dead#plays with dolls / wild world#7 single#4000 copies\n2008#tell the world#i believe in nothing & damaged#woodsist#7 single#3000 copies\n2008#i can't stay#blind spot#in the red#7 single#2000 copies\n2008#surfin away & second date#girl don't tell me (wilson)#wild world#7 single#1000 copies\n2009#moped girls#death#for us#7 single#1500 copies\n2010#my love will follow me#he 's gone (the chantels cover)#wild world#7 single#2000 copies\n",
        "pandas_code": "(df[df['single'] == 'wild eyes']['other details'].str.extract('(\\\\d+)').astype(int).iloc[0] > df[df['single'] != 'wild eyes']['other details'].str.extract('(\\\\d+)').astype(int).max()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 2367,
        "statement": "wild world be the record label for the most vivian girl single",
        "label": 1,
        "table_caption": "vivian girls",
        "table_text": "date#single#backed with#record label#format#other details\n2008#wild eyes#my baby wants me dead#plays with dolls / wild world#7 single#4000 copies\n2008#tell the world#i believe in nothing & damaged#woodsist#7 single#3000 copies\n2008#i can't stay#blind spot#in the red#7 single#2000 copies\n2008#surfin away & second date#girl don't tell me (wilson)#wild world#7 single#1000 copies\n2009#moped girls#death#for us#7 single#1500 copies\n2010#my love will follow me#he 's gone (the chantels cover)#wild world#7 single#2000 copies\n",
        "pandas_code": "df['record label'].value_counts().idxmax() == 'wild world'",
        "pandas_eval": "True"
    },
    {
        "id": 2368,
        "statement": "the average number of copy be 2250",
        "label": 1,
        "table_caption": "vivian girls",
        "table_text": "date#single#backed with#record label#format#other details\n2008#wild eyes#my baby wants me dead#plays with dolls / wild world#7 single#4000 copies\n2008#tell the world#i believe in nothing & damaged#woodsist#7 single#3000 copies\n2008#i can't stay#blind spot#in the red#7 single#2000 copies\n2008#surfin away & second date#girl don't tell me (wilson)#wild world#7 single#1000 copies\n2009#moped girls#death#for us#7 single#1500 copies\n2010#my love will follow me#he 's gone (the chantels cover)#wild world#7 single#2000 copies\n",
        "pandas_code": "(df['other details'].str.extract('(\\\\d+)').astype(int).mean() == 2250).item()",
        "pandas_eval": "True"
    },
    {
        "id": 2369,
        "statement": "surfin away & second date have 1000 fewer copy than i can't stay",
        "label": 1,
        "table_caption": "vivian girls",
        "table_text": "date#single#backed with#record label#format#other details\n2008#wild eyes#my baby wants me dead#plays with dolls / wild world#7 single#4000 copies\n2008#tell the world#i believe in nothing & damaged#woodsist#7 single#3000 copies\n2008#i can't stay#blind spot#in the red#7 single#2000 copies\n2008#surfin away & second date#girl don't tell me (wilson)#wild world#7 single#1000 copies\n2009#moped girls#death#for us#7 single#1500 copies\n2010#my love will follow me#he 's gone (the chantels cover)#wild world#7 single#2000 copies\n",
        "pandas_code": "df.loc[df['single'] == 'i can\\'t stay', 'other details'].values[0].split()[0] == str(int(df.loc[df['single'] == 'surfin away & second date', 'other details'].values[0].split()[0]) + 1000)",
        "pandas_eval": "True"
    },
    {
        "id": 2370,
        "statement": "most of the single release by the vivian girl come out in 2008",
        "label": 1,
        "table_caption": "vivian girls",
        "table_text": "date#single#backed with#record label#format#other details\n2008#wild eyes#my baby wants me dead#plays with dolls / wild world#7 single#4000 copies\n2008#tell the world#i believe in nothing & damaged#woodsist#7 single#3000 copies\n2008#i can't stay#blind spot#in the red#7 single#2000 copies\n2008#surfin away & second date#girl don't tell me (wilson)#wild world#7 single#1000 copies\n2009#moped girls#death#for us#7 single#1500 copies\n2010#my love will follow me#he 's gone (the chantels cover)#wild world#7 single#2000 copies\n",
        "pandas_code": "df[df['date'] == 2008].shape[0] > df[df['date'] != 2008].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2371,
        "statement": "2 season have gp of 56",
        "label": 1,
        "table_caption": "dinamo riga",
        "table_text": "season#gp#w (ot / so)#l (ot / so)#pts#pts / gp#gf - ga#rank (league / conference)#top scorer\n2008 - 09#56#24 (3 / 2)#23 (1 / 3)#86#1.54#132 - 156#10th / -#marcel hossa (44)\n2009 - 10#56#23 (1 / 3)#22 (3 / 4)#84#1.50#174 - 175#13th / 8th#marcel hossa (55)\n2010 - 11#54#20 (2 / 5)#20 (5 / 2)#81#1.50#160 - 149#13th / 7th#lauris d\u0101rzi\u0146\u0161 (44)\n2011 - 12#54#20 (2 / 4)#21 (0 / 7)#79#1.46#129 - 136#15th / 7th#mi\u0137elis r\u0113dlihs (44)\n2012 - 13#52#11 (2 / 2)#31 (2 / 2)#51#0.98#109 - 151#24th / 14th#m\u0101rti\u0146\u0161 karsums (35)\n",
        "pandas_code": "len(df[df['gp'] == 56]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2372,
        "statement": "marcel hossa top before lauris d\u0101rzi\u0146\u0161 do",
        "label": 1,
        "table_caption": "dinamo riga",
        "table_text": "season#gp#w (ot / so)#l (ot / so)#pts#pts / gp#gf - ga#rank (league / conference)#top scorer\n2008 - 09#56#24 (3 / 2)#23 (1 / 3)#86#1.54#132 - 156#10th / -#marcel hossa (44)\n2009 - 10#56#23 (1 / 3)#22 (3 / 4)#84#1.50#174 - 175#13th / 8th#marcel hossa (55)\n2010 - 11#54#20 (2 / 5)#20 (5 / 2)#81#1.50#160 - 149#13th / 7th#lauris d\u0101rzi\u0146\u0161 (44)\n2011 - 12#54#20 (2 / 4)#21 (0 / 7)#79#1.46#129 - 136#15th / 7th#mi\u0137elis r\u0113dlihs (44)\n2012 - 13#52#11 (2 / 2)#31 (2 / 2)#51#0.98#109 - 151#24th / 14th#m\u0101rti\u0146\u0161 karsums (35)\n",
        "pandas_code": "df[df['top scorer'].str.contains('marcel hossa')]['season'].iloc[0] < df[df['top scorer'].str.contains('lauris d\u0101rzi\u0146\u0161')]['season'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2373,
        "statement": "86 be the highest point",
        "label": 1,
        "table_caption": "dinamo riga",
        "table_text": "season#gp#w (ot / so)#l (ot / so)#pts#pts / gp#gf - ga#rank (league / conference)#top scorer\n2008 - 09#56#24 (3 / 2)#23 (1 / 3)#86#1.54#132 - 156#10th / -#marcel hossa (44)\n2009 - 10#56#23 (1 / 3)#22 (3 / 4)#84#1.50#174 - 175#13th / 8th#marcel hossa (55)\n2010 - 11#54#20 (2 / 5)#20 (5 / 2)#81#1.50#160 - 149#13th / 7th#lauris d\u0101rzi\u0146\u0161 (44)\n2011 - 12#54#20 (2 / 4)#21 (0 / 7)#79#1.46#129 - 136#15th / 7th#mi\u0137elis r\u0113dlihs (44)\n2012 - 13#52#11 (2 / 2)#31 (2 / 2)#51#0.98#109 - 151#24th / 14th#m\u0101rti\u0146\u0161 karsums (35)\n",
        "pandas_code": "df['pts'].max() == 86",
        "pandas_eval": "True"
    },
    {
        "id": 2374,
        "statement": "the last season be in 2012 - 13 , which also have the lowest pt",
        "label": 1,
        "table_caption": "dinamo riga",
        "table_text": "season#gp#w (ot / so)#l (ot / so)#pts#pts / gp#gf - ga#rank (league / conference)#top scorer\n2008 - 09#56#24 (3 / 2)#23 (1 / 3)#86#1.54#132 - 156#10th / -#marcel hossa (44)\n2009 - 10#56#23 (1 / 3)#22 (3 / 4)#84#1.50#174 - 175#13th / 8th#marcel hossa (55)\n2010 - 11#54#20 (2 / 5)#20 (5 / 2)#81#1.50#160 - 149#13th / 7th#lauris d\u0101rzi\u0146\u0161 (44)\n2011 - 12#54#20 (2 / 4)#21 (0 / 7)#79#1.46#129 - 136#15th / 7th#mi\u0137elis r\u0113dlihs (44)\n2012 - 13#52#11 (2 / 2)#31 (2 / 2)#51#0.98#109 - 151#24th / 14th#m\u0101rti\u0146\u0161 karsums (35)\n",
        "pandas_code": "df.loc[df['season'] == '2012 - 13', 'pts'].iloc[0] == df['pts'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2375,
        "statement": "marcel hossa top scorer 2 time",
        "label": 1,
        "table_caption": "dinamo riga",
        "table_text": "season#gp#w (ot / so)#l (ot / so)#pts#pts / gp#gf - ga#rank (league / conference)#top scorer\n2008 - 09#56#24 (3 / 2)#23 (1 / 3)#86#1.54#132 - 156#10th / -#marcel hossa (44)\n2009 - 10#56#23 (1 / 3)#22 (3 / 4)#84#1.50#174 - 175#13th / 8th#marcel hossa (55)\n2010 - 11#54#20 (2 / 5)#20 (5 / 2)#81#1.50#160 - 149#13th / 7th#lauris d\u0101rzi\u0146\u0161 (44)\n2011 - 12#54#20 (2 / 4)#21 (0 / 7)#79#1.46#129 - 136#15th / 7th#mi\u0137elis r\u0113dlihs (44)\n2012 - 13#52#11 (2 / 2)#31 (2 / 2)#51#0.98#109 - 151#24th / 14th#m\u0101rti\u0146\u0161 karsums (35)\n",
        "pandas_code": "df['top scorer'].str.startswith('marcel hossa').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2376,
        "statement": "gerry cohen direct the most episode",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n236#1#twisted#gerry cohen#richard gurman#september 29 , 1996#11.01\n237#2#children of the corns#amanda bearse#matthew berry & eric abrams#october 6 , 1996#11.02\n238#3#kelly 's gotta habit#amanda bearse#laurie lee - goss & garry bowren#october 13 , 1996#11.03\n239#4#requiem for a chevyweight (part 1)#gerry cohen#steve faber & bob fisher#november 10 , 1996#11.04\n240#5#requiem for a chevyweight (part 2)#amanda bearse#russell marcus#november 17 , 1996#11.05\n241#6#a bundy thanksgiving#amanda bearse#vince cheung &bob montanio#november 24 , 1996#11.06\n242#7#the juggs have left the building#gerry cohen#vince cheung & ben montanio#december 1 , 1996#11.07\n243#8#god help ye merry bundymen#amanda bearse#steve faber & bob fisher#december 22 , 1996#11.08\n244#9#crimes against obesity#amanda bearse#russell marcus#december 29 , 1996#11.09\n245#10#the stepford peg#amanda bearse#valerie ahern & christian mclaughlin#january 5 , 1997#11.10\n246#11#bud on the side#sam w orender#valerie ahern & christian mclaughlin#january 12 , 1997#11.11\n247#12#grime and punishment#sam w orender#steve faber & bob fisher#january 19 , 1997#11.12\n249#14#breaking up is easy to do (part 1)#mark k samuels#eric abrams & matthew berry#february 23 , 1997#11.14\n250#15#breaking up is easy to do (part 2)#gerry cohen#russell marcus#february 23 , 1997#11.15\n251#16#breaking up is easy to do (part 3)#gerry cohen#russell marcus#march 2 , 1997#11.16\n252#17#live nude peg#amanda bearse#matthew berry & eric abrams#march 9 , 1997#11.17\n253#18#a babe in toyland#gerry cohen#valerie ahern & christian mclaughlin#march 16 , 1997#11.18\n254#19#birthday boy toy#gerry cohen#terry maloney & mindy morgenstern#march 30 , 1997#11.19\n255#20#lez be friends#gerry cohen#pamela eells#april 10 , 1997#11.22\n256#21#damn bundys#richard correll#ben montanio & vince cheung#april 28 , 1997#11.20\n257#22#the desperate half - hour (part 1)#gerry cohen#valerie ahern & christian mclaughlin#may 5 , 1997#11.23\n258#23#how to marry a moron (part 2)#gerry cohen#ben montanio & vince cheung#may 5 , 1997#11.24\n",
        "pandas_code": "df['directed by'].value_counts().idxmax() == 'gerry cohen'",
        "pandas_eval": "True"
    },
    {
        "id": 2377,
        "statement": "richard gurman only write 1 episode",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n236#1#twisted#gerry cohen#richard gurman#september 29 , 1996#11.01\n237#2#children of the corns#amanda bearse#matthew berry & eric abrams#october 6 , 1996#11.02\n238#3#kelly 's gotta habit#amanda bearse#laurie lee - goss & garry bowren#october 13 , 1996#11.03\n239#4#requiem for a chevyweight (part 1)#gerry cohen#steve faber & bob fisher#november 10 , 1996#11.04\n240#5#requiem for a chevyweight (part 2)#amanda bearse#russell marcus#november 17 , 1996#11.05\n241#6#a bundy thanksgiving#amanda bearse#vince cheung &bob montanio#november 24 , 1996#11.06\n242#7#the juggs have left the building#gerry cohen#vince cheung & ben montanio#december 1 , 1996#11.07\n243#8#god help ye merry bundymen#amanda bearse#steve faber & bob fisher#december 22 , 1996#11.08\n244#9#crimes against obesity#amanda bearse#russell marcus#december 29 , 1996#11.09\n245#10#the stepford peg#amanda bearse#valerie ahern & christian mclaughlin#january 5 , 1997#11.10\n246#11#bud on the side#sam w orender#valerie ahern & christian mclaughlin#january 12 , 1997#11.11\n247#12#grime and punishment#sam w orender#steve faber & bob fisher#january 19 , 1997#11.12\n249#14#breaking up is easy to do (part 1)#mark k samuels#eric abrams & matthew berry#february 23 , 1997#11.14\n250#15#breaking up is easy to do (part 2)#gerry cohen#russell marcus#february 23 , 1997#11.15\n251#16#breaking up is easy to do (part 3)#gerry cohen#russell marcus#march 2 , 1997#11.16\n252#17#live nude peg#amanda bearse#matthew berry & eric abrams#march 9 , 1997#11.17\n253#18#a babe in toyland#gerry cohen#valerie ahern & christian mclaughlin#march 16 , 1997#11.18\n254#19#birthday boy toy#gerry cohen#terry maloney & mindy morgenstern#march 30 , 1997#11.19\n255#20#lez be friends#gerry cohen#pamela eells#april 10 , 1997#11.22\n256#21#damn bundys#richard correll#ben montanio & vince cheung#april 28 , 1997#11.20\n257#22#the desperate half - hour (part 1)#gerry cohen#valerie ahern & christian mclaughlin#may 5 , 1997#11.23\n258#23#how to marry a moron (part 2)#gerry cohen#ben montanio & vince cheung#may 5 , 1997#11.24\n",
        "pandas_code": "df[df['written by'] == 'richard gurman'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2378,
        "statement": "ben montanio vince cheung write 2 episode together",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n236#1#twisted#gerry cohen#richard gurman#september 29 , 1996#11.01\n237#2#children of the corns#amanda bearse#matthew berry & eric abrams#october 6 , 1996#11.02\n238#3#kelly 's gotta habit#amanda bearse#laurie lee - goss & garry bowren#october 13 , 1996#11.03\n239#4#requiem for a chevyweight (part 1)#gerry cohen#steve faber & bob fisher#november 10 , 1996#11.04\n240#5#requiem for a chevyweight (part 2)#amanda bearse#russell marcus#november 17 , 1996#11.05\n241#6#a bundy thanksgiving#amanda bearse#vince cheung &bob montanio#november 24 , 1996#11.06\n242#7#the juggs have left the building#gerry cohen#vince cheung & ben montanio#december 1 , 1996#11.07\n243#8#god help ye merry bundymen#amanda bearse#steve faber & bob fisher#december 22 , 1996#11.08\n244#9#crimes against obesity#amanda bearse#russell marcus#december 29 , 1996#11.09\n245#10#the stepford peg#amanda bearse#valerie ahern & christian mclaughlin#january 5 , 1997#11.10\n246#11#bud on the side#sam w orender#valerie ahern & christian mclaughlin#january 12 , 1997#11.11\n247#12#grime and punishment#sam w orender#steve faber & bob fisher#january 19 , 1997#11.12\n249#14#breaking up is easy to do (part 1)#mark k samuels#eric abrams & matthew berry#february 23 , 1997#11.14\n250#15#breaking up is easy to do (part 2)#gerry cohen#russell marcus#february 23 , 1997#11.15\n251#16#breaking up is easy to do (part 3)#gerry cohen#russell marcus#march 2 , 1997#11.16\n252#17#live nude peg#amanda bearse#matthew berry & eric abrams#march 9 , 1997#11.17\n253#18#a babe in toyland#gerry cohen#valerie ahern & christian mclaughlin#march 16 , 1997#11.18\n254#19#birthday boy toy#gerry cohen#terry maloney & mindy morgenstern#march 30 , 1997#11.19\n255#20#lez be friends#gerry cohen#pamela eells#april 10 , 1997#11.22\n256#21#damn bundys#richard correll#ben montanio & vince cheung#april 28 , 1997#11.20\n257#22#the desperate half - hour (part 1)#gerry cohen#valerie ahern & christian mclaughlin#may 5 , 1997#11.23\n258#23#how to marry a moron (part 2)#gerry cohen#ben montanio & vince cheung#may 5 , 1997#11.24\n",
        "pandas_code": "len(df[df['written by'].str.contains('ben montanio & vince cheung')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2379,
        "statement": "richard correll only direct 1 episode",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n236#1#twisted#gerry cohen#richard gurman#september 29 , 1996#11.01\n237#2#children of the corns#amanda bearse#matthew berry & eric abrams#october 6 , 1996#11.02\n238#3#kelly 's gotta habit#amanda bearse#laurie lee - goss & garry bowren#october 13 , 1996#11.03\n239#4#requiem for a chevyweight (part 1)#gerry cohen#steve faber & bob fisher#november 10 , 1996#11.04\n240#5#requiem for a chevyweight (part 2)#amanda bearse#russell marcus#november 17 , 1996#11.05\n241#6#a bundy thanksgiving#amanda bearse#vince cheung &bob montanio#november 24 , 1996#11.06\n242#7#the juggs have left the building#gerry cohen#vince cheung & ben montanio#december 1 , 1996#11.07\n243#8#god help ye merry bundymen#amanda bearse#steve faber & bob fisher#december 22 , 1996#11.08\n244#9#crimes against obesity#amanda bearse#russell marcus#december 29 , 1996#11.09\n245#10#the stepford peg#amanda bearse#valerie ahern & christian mclaughlin#january 5 , 1997#11.10\n246#11#bud on the side#sam w orender#valerie ahern & christian mclaughlin#january 12 , 1997#11.11\n247#12#grime and punishment#sam w orender#steve faber & bob fisher#january 19 , 1997#11.12\n249#14#breaking up is easy to do (part 1)#mark k samuels#eric abrams & matthew berry#february 23 , 1997#11.14\n250#15#breaking up is easy to do (part 2)#gerry cohen#russell marcus#february 23 , 1997#11.15\n251#16#breaking up is easy to do (part 3)#gerry cohen#russell marcus#march 2 , 1997#11.16\n252#17#live nude peg#amanda bearse#matthew berry & eric abrams#march 9 , 1997#11.17\n253#18#a babe in toyland#gerry cohen#valerie ahern & christian mclaughlin#march 16 , 1997#11.18\n254#19#birthday boy toy#gerry cohen#terry maloney & mindy morgenstern#march 30 , 1997#11.19\n255#20#lez be friends#gerry cohen#pamela eells#april 10 , 1997#11.22\n256#21#damn bundys#richard correll#ben montanio & vince cheung#april 28 , 1997#11.20\n257#22#the desperate half - hour (part 1)#gerry cohen#valerie ahern & christian mclaughlin#may 5 , 1997#11.23\n258#23#how to marry a moron (part 2)#gerry cohen#ben montanio & vince cheung#may 5 , 1997#11.24\n",
        "pandas_code": "(df['directed by'] == 'richard correll').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2380,
        "statement": "amanda bearse direct the second most number of episode",
        "label": 1,
        "table_caption": "list of married... with children episodes",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n236#1#twisted#gerry cohen#richard gurman#september 29 , 1996#11.01\n237#2#children of the corns#amanda bearse#matthew berry & eric abrams#october 6 , 1996#11.02\n238#3#kelly 's gotta habit#amanda bearse#laurie lee - goss & garry bowren#october 13 , 1996#11.03\n239#4#requiem for a chevyweight (part 1)#gerry cohen#steve faber & bob fisher#november 10 , 1996#11.04\n240#5#requiem for a chevyweight (part 2)#amanda bearse#russell marcus#november 17 , 1996#11.05\n241#6#a bundy thanksgiving#amanda bearse#vince cheung &bob montanio#november 24 , 1996#11.06\n242#7#the juggs have left the building#gerry cohen#vince cheung & ben montanio#december 1 , 1996#11.07\n243#8#god help ye merry bundymen#amanda bearse#steve faber & bob fisher#december 22 , 1996#11.08\n244#9#crimes against obesity#amanda bearse#russell marcus#december 29 , 1996#11.09\n245#10#the stepford peg#amanda bearse#valerie ahern & christian mclaughlin#january 5 , 1997#11.10\n246#11#bud on the side#sam w orender#valerie ahern & christian mclaughlin#january 12 , 1997#11.11\n247#12#grime and punishment#sam w orender#steve faber & bob fisher#january 19 , 1997#11.12\n249#14#breaking up is easy to do (part 1)#mark k samuels#eric abrams & matthew berry#february 23 , 1997#11.14\n250#15#breaking up is easy to do (part 2)#gerry cohen#russell marcus#february 23 , 1997#11.15\n251#16#breaking up is easy to do (part 3)#gerry cohen#russell marcus#march 2 , 1997#11.16\n252#17#live nude peg#amanda bearse#matthew berry & eric abrams#march 9 , 1997#11.17\n253#18#a babe in toyland#gerry cohen#valerie ahern & christian mclaughlin#march 16 , 1997#11.18\n254#19#birthday boy toy#gerry cohen#terry maloney & mindy morgenstern#march 30 , 1997#11.19\n255#20#lez be friends#gerry cohen#pamela eells#april 10 , 1997#11.22\n256#21#damn bundys#richard correll#ben montanio & vince cheung#april 28 , 1997#11.20\n257#22#the desperate half - hour (part 1)#gerry cohen#valerie ahern & christian mclaughlin#may 5 , 1997#11.23\n258#23#how to marry a moron (part 2)#gerry cohen#ben montanio & vince cheung#may 5 , 1997#11.24\n",
        "pandas_code": "(df['directed by'].value_counts().iloc[1] == df[df['directed by'] == 'amanda bearse'].shape[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2381,
        "statement": "the hafslo parish be locate in hafslo , veltastrond , solvorn , and urnes",
        "label": 1,
        "table_caption": "luster , norway",
        "table_text": "parish (prestegjeld)#sub - parish (sogn)#church name#year built#location of the church\nhafslo parish#hafslo#hafslo kyrkje#1878#hafslo\nhafslo parish#hafslo#veitastrond kapell#1928#veitastrond\nhafslo parish#solvorn#solvorn kyrkje#1883#solvorn\nhafslo parish#solvorn#urnes stavkyrkje#1130#urnes\njostedal parish#fet og joranger#fet kyrkje#1894#fet\njostedal parish#fet og joranger#joranger kyrkje#1660#joranger\njostedal parish#gaupne#gaupne kyrkje#1908#gaupne\njostedal parish#gaupne#gaupne gamle kyrkje#1647#gaupne\njostedal parish#jostedal#jostedal kyrkje#1660#jostedal\nluster parish#dale#dale kyrkje#1250#luster\nluster parish#fortun#fortun kyrkje#1879#fortun\n",
        "pandas_code": "all(df[df['parish (prestegjeld)'] == 'hafslo parish']['location of the church'].isin(['hafslo', 'veitastrond', 'solvorn', 'urnes']))",
        "pandas_eval": "True"
    },
    {
        "id": 2382,
        "statement": "the jostedal parish be locate in fet , joranger , gaupne , and jostedal",
        "label": 1,
        "table_caption": "luster , norway",
        "table_text": "parish (prestegjeld)#sub - parish (sogn)#church name#year built#location of the church\nhafslo parish#hafslo#hafslo kyrkje#1878#hafslo\nhafslo parish#hafslo#veitastrond kapell#1928#veitastrond\nhafslo parish#solvorn#solvorn kyrkje#1883#solvorn\nhafslo parish#solvorn#urnes stavkyrkje#1130#urnes\njostedal parish#fet og joranger#fet kyrkje#1894#fet\njostedal parish#fet og joranger#joranger kyrkje#1660#joranger\njostedal parish#gaupne#gaupne kyrkje#1908#gaupne\njostedal parish#gaupne#gaupne gamle kyrkje#1647#gaupne\njostedal parish#jostedal#jostedal kyrkje#1660#jostedal\nluster parish#dale#dale kyrkje#1250#luster\nluster parish#fortun#fortun kyrkje#1879#fortun\n",
        "pandas_code": "df[df['parish (prestegjeld)'] == 'jostedal parish']['location of the church'].unique().tolist() == ['fet', 'joranger', 'gaupne', 'jostedal']",
        "pandas_eval": "True"
    },
    {
        "id": 2383,
        "statement": "the church in gaupne name gaupne kyrkje and gaupne gamle kyrkje be build in 1908 and 1647 respectively",
        "label": 1,
        "table_caption": "luster , norway",
        "table_text": "parish (prestegjeld)#sub - parish (sogn)#church name#year built#location of the church\nhafslo parish#hafslo#hafslo kyrkje#1878#hafslo\nhafslo parish#hafslo#veitastrond kapell#1928#veitastrond\nhafslo parish#solvorn#solvorn kyrkje#1883#solvorn\nhafslo parish#solvorn#urnes stavkyrkje#1130#urnes\njostedal parish#fet og joranger#fet kyrkje#1894#fet\njostedal parish#fet og joranger#joranger kyrkje#1660#joranger\njostedal parish#gaupne#gaupne kyrkje#1908#gaupne\njostedal parish#gaupne#gaupne gamle kyrkje#1647#gaupne\njostedal parish#jostedal#jostedal kyrkje#1660#jostedal\nluster parish#dale#dale kyrkje#1250#luster\nluster parish#fortun#fortun kyrkje#1879#fortun\n",
        "pandas_code": "df[(df['sub - parish (sogn)'] == 'gaupne') & (df['church name'].isin(['gaupne kyrkje', 'gaupne gamle kyrkje'])) & (df['year built'].isin([1908, 1647]))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2384,
        "statement": "the luster parish be locate in luster and fortun",
        "label": 1,
        "table_caption": "luster , norway",
        "table_text": "parish (prestegjeld)#sub - parish (sogn)#church name#year built#location of the church\nhafslo parish#hafslo#hafslo kyrkje#1878#hafslo\nhafslo parish#hafslo#veitastrond kapell#1928#veitastrond\nhafslo parish#solvorn#solvorn kyrkje#1883#solvorn\nhafslo parish#solvorn#urnes stavkyrkje#1130#urnes\njostedal parish#fet og joranger#fet kyrkje#1894#fet\njostedal parish#fet og joranger#joranger kyrkje#1660#joranger\njostedal parish#gaupne#gaupne kyrkje#1908#gaupne\njostedal parish#gaupne#gaupne gamle kyrkje#1647#gaupne\njostedal parish#jostedal#jostedal kyrkje#1660#jostedal\nluster parish#dale#dale kyrkje#1250#luster\nluster parish#fortun#fortun kyrkje#1879#fortun\n",
        "pandas_code": "all(df[(df['parish (prestegjeld)'] == 'luster parish') & (df['location of the church'].isin(['luster', 'fortun']))]['location of the church'].value_counts().index.isin(['luster', 'fortun']))",
        "pandas_eval": "True"
    },
    {
        "id": 2385,
        "statement": "there be 1 church build in the 12th century , which be name urnes stavkyrkje",
        "label": 1,
        "table_caption": "luster , norway",
        "table_text": "parish (prestegjeld)#sub - parish (sogn)#church name#year built#location of the church\nhafslo parish#hafslo#hafslo kyrkje#1878#hafslo\nhafslo parish#hafslo#veitastrond kapell#1928#veitastrond\nhafslo parish#solvorn#solvorn kyrkje#1883#solvorn\nhafslo parish#solvorn#urnes stavkyrkje#1130#urnes\njostedal parish#fet og joranger#fet kyrkje#1894#fet\njostedal parish#fet og joranger#joranger kyrkje#1660#joranger\njostedal parish#gaupne#gaupne kyrkje#1908#gaupne\njostedal parish#gaupne#gaupne gamle kyrkje#1647#gaupne\njostedal parish#jostedal#jostedal kyrkje#1660#jostedal\nluster parish#dale#dale kyrkje#1250#luster\nluster parish#fortun#fortun kyrkje#1879#fortun\n",
        "pandas_code": "df[(df['year built'] >= 1100) & (df['year built'] < 1200) & (df['church name'] == 'urnes stavkyrkje')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2386,
        "statement": "houkui tea and yellow mountain fur peak both originate in huang shan anhui",
        "label": 1,
        "table_caption": "china 's famous teas",
        "table_text": "#translated english name#chinese#pronunciation#place of origin#type#occurrences\n1#dragon well#\u897f\u6e56\u9f99\u4e95#xi hu long jing#hangzhou , zhejiang#green tea#10\n2#spring snail#\u6d1e\u5ead\u78a7\u87ba\u6625#dong ting bi luo chun#suzhou , jiangsu#green tea#10\n3#yellow mountain fur peak#\u9ec4\u5c71\u6bdb\u5cf0#hu\u00e1ng sh\u0101n m\u00e1o f\u0113ng#huang shan , anhui#green tea#10\n4#mount jun silver needle#\u541b\u5c71\u94f6\u9488#jun shan yin zhen#yueyang , hunan#yellow tea#10\n5#qi men red#\u7941\u95e8\u7ea2\u8336#qi men hong cha#qimen , anhui#black tea#10\n6#big red robe#\u6b66\u5937\u5927\u7d05\u888d#wu yi d\u00e0 h\u00f3ng p\u00e1o#wuyi mountains , fujian#oolong tea#10\n7#melon seed#\u516d\u5b89\u74dc\u7247#liu \u0101n gu\u0101 pi\u00e0n#lu'an , anhui#green tea#10\n8#iron goddess#\u5b89\u6eaa\u94c1\u89c2\u97f3#an xi ti\u011b gu\u0101n y\u012bn#anxi , fujian#oolong tea#10\n9#houkui tea#\u592a\u5e73\u7334\u9b41#tai ping hou kui#huang shan , anhui#green tea#10\n",
        "pandas_code": "all(df[df['translated english name'].isin(['houkui tea', 'yellow mountain fur peak'])]['place of origin'] == 'huang shan , anhui')",
        "pandas_eval": "True"
    },
    {
        "id": 2387,
        "statement": "big red robe and iron goddess be both type of oolong team",
        "label": 1,
        "table_caption": "china 's famous teas",
        "table_text": "#translated english name#chinese#pronunciation#place of origin#type#occurrences\n1#dragon well#\u897f\u6e56\u9f99\u4e95#xi hu long jing#hangzhou , zhejiang#green tea#10\n2#spring snail#\u6d1e\u5ead\u78a7\u87ba\u6625#dong ting bi luo chun#suzhou , jiangsu#green tea#10\n3#yellow mountain fur peak#\u9ec4\u5c71\u6bdb\u5cf0#hu\u00e1ng sh\u0101n m\u00e1o f\u0113ng#huang shan , anhui#green tea#10\n4#mount jun silver needle#\u541b\u5c71\u94f6\u9488#jun shan yin zhen#yueyang , hunan#yellow tea#10\n5#qi men red#\u7941\u95e8\u7ea2\u8336#qi men hong cha#qimen , anhui#black tea#10\n6#big red robe#\u6b66\u5937\u5927\u7d05\u888d#wu yi d\u00e0 h\u00f3ng p\u00e1o#wuyi mountains , fujian#oolong tea#10\n7#melon seed#\u516d\u5b89\u74dc\u7247#liu \u0101n gu\u0101 pi\u00e0n#lu'an , anhui#green tea#10\n8#iron goddess#\u5b89\u6eaa\u94c1\u89c2\u97f3#an xi ti\u011b gu\u0101n y\u012bn#anxi , fujian#oolong tea#10\n9#houkui tea#\u592a\u5e73\u7334\u9b41#tai ping hou kui#huang shan , anhui#green tea#10\n",
        "pandas_code": "all(df[df['translated english name'].isin(['big red robe', 'iron goddess'])]['type'] == 'oolong tea')",
        "pandas_eval": "True"
    },
    {
        "id": 2389,
        "statement": "5 of the tea be green tea",
        "label": 1,
        "table_caption": "china 's famous teas",
        "table_text": "#translated english name#chinese#pronunciation#place of origin#type#occurrences\n1#dragon well#\u897f\u6e56\u9f99\u4e95#xi hu long jing#hangzhou , zhejiang#green tea#10\n2#spring snail#\u6d1e\u5ead\u78a7\u87ba\u6625#dong ting bi luo chun#suzhou , jiangsu#green tea#10\n3#yellow mountain fur peak#\u9ec4\u5c71\u6bdb\u5cf0#hu\u00e1ng sh\u0101n m\u00e1o f\u0113ng#huang shan , anhui#green tea#10\n4#mount jun silver needle#\u541b\u5c71\u94f6\u9488#jun shan yin zhen#yueyang , hunan#yellow tea#10\n5#qi men red#\u7941\u95e8\u7ea2\u8336#qi men hong cha#qimen , anhui#black tea#10\n6#big red robe#\u6b66\u5937\u5927\u7d05\u888d#wu yi d\u00e0 h\u00f3ng p\u00e1o#wuyi mountains , fujian#oolong tea#10\n7#melon seed#\u516d\u5b89\u74dc\u7247#liu \u0101n gu\u0101 pi\u00e0n#lu'an , anhui#green tea#10\n8#iron goddess#\u5b89\u6eaa\u94c1\u89c2\u97f3#an xi ti\u011b gu\u0101n y\u012bn#anxi , fujian#oolong tea#10\n9#houkui tea#\u592a\u5e73\u7334\u9b41#tai ping hou kui#huang shan , anhui#green tea#10\n",
        "pandas_code": "df[df['type'] == 'green tea'].shape[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2390,
        "statement": "out of all of the tea , there be just 1 black tea call qi men red",
        "label": 1,
        "table_caption": "china 's famous teas",
        "table_text": "#translated english name#chinese#pronunciation#place of origin#type#occurrences\n1#dragon well#\u897f\u6e56\u9f99\u4e95#xi hu long jing#hangzhou , zhejiang#green tea#10\n2#spring snail#\u6d1e\u5ead\u78a7\u87ba\u6625#dong ting bi luo chun#suzhou , jiangsu#green tea#10\n3#yellow mountain fur peak#\u9ec4\u5c71\u6bdb\u5cf0#hu\u00e1ng sh\u0101n m\u00e1o f\u0113ng#huang shan , anhui#green tea#10\n4#mount jun silver needle#\u541b\u5c71\u94f6\u9488#jun shan yin zhen#yueyang , hunan#yellow tea#10\n5#qi men red#\u7941\u95e8\u7ea2\u8336#qi men hong cha#qimen , anhui#black tea#10\n6#big red robe#\u6b66\u5937\u5927\u7d05\u888d#wu yi d\u00e0 h\u00f3ng p\u00e1o#wuyi mountains , fujian#oolong tea#10\n7#melon seed#\u516d\u5b89\u74dc\u7247#liu \u0101n gu\u0101 pi\u00e0n#lu'an , anhui#green tea#10\n8#iron goddess#\u5b89\u6eaa\u94c1\u89c2\u97f3#an xi ti\u011b gu\u0101n y\u012bn#anxi , fujian#oolong tea#10\n9#houkui tea#\u592a\u5e73\u7334\u9b41#tai ping hou kui#huang shan , anhui#green tea#10\n",
        "pandas_code": "(df[df['type'] == 'black tea']['translated english name'].unique() == ['qi men red']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2391,
        "statement": "there be more win than loss",
        "label": 1,
        "table_caption": "1977 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 18 , 1977#seattle seahawks#w 29 - 14#1 - 0#kingdome#58991\n2#september 25 , 1977#new york jets#w 20 - 12#2 - 0#shea stadium#43439\n3#october 2 , 1977#buffalo bills#w 17 - 14#3 - 0#memorial stadium#49247\n4#october 9 , 1977#miami dolphins#w 45 - 28#4 - 0#memorial stadium#57829\n5#october 16 , 1977#kansas city chiefs#w 17 - 6#5 - 0#arrowhead stadium#63076\n6#october 23 , 1977#new england patriots#l 3 - 17#5 - 1#schaeffer stadium#60958\n7#october 30 , 1977#pittsburgh steelers#w 31 - 21#6 - 1#memorial stadium#60225\n8#november 7 , 1977#washington redskins#w 10 - 3#7 - 1#memorial stadium#57740\n9#november 13 , 1977#buffalo bills#w 31 - 13#8 - 1#rich stadium#39444\n10#november 20 , 1977#new york jets#w 33 - 12#9 - 1#memorial stadium#50957\n11#november 27 , 1977#denver broncos#l 13 - 27#9 - 2#mile high stadium#74939\n12#december 5 , 1977#miami dolphins#l 6 - 17#9 - 3#miami orange bowl#68977\n13#december 11 , 1977#detroit lions#l 10 - 13#9 - 4#memorial stadium#45124\n",
        "pandas_code": "df['result'].str.startswith('w').sum() > df['result'].str.startswith('l').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2392,
        "statement": "the attendance in week 10 be more than the attendance in week 9",
        "label": 1,
        "table_caption": "1977 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 18 , 1977#seattle seahawks#w 29 - 14#1 - 0#kingdome#58991\n2#september 25 , 1977#new york jets#w 20 - 12#2 - 0#shea stadium#43439\n3#october 2 , 1977#buffalo bills#w 17 - 14#3 - 0#memorial stadium#49247\n4#october 9 , 1977#miami dolphins#w 45 - 28#4 - 0#memorial stadium#57829\n5#october 16 , 1977#kansas city chiefs#w 17 - 6#5 - 0#arrowhead stadium#63076\n6#october 23 , 1977#new england patriots#l 3 - 17#5 - 1#schaeffer stadium#60958\n7#october 30 , 1977#pittsburgh steelers#w 31 - 21#6 - 1#memorial stadium#60225\n8#november 7 , 1977#washington redskins#w 10 - 3#7 - 1#memorial stadium#57740\n9#november 13 , 1977#buffalo bills#w 31 - 13#8 - 1#rich stadium#39444\n10#november 20 , 1977#new york jets#w 33 - 12#9 - 1#memorial stadium#50957\n11#november 27 , 1977#denver broncos#l 13 - 27#9 - 2#mile high stadium#74939\n12#december 5 , 1977#miami dolphins#l 6 - 17#9 - 3#miami orange bowl#68977\n13#december 11 , 1977#detroit lions#l 10 - 13#9 - 4#memorial stadium#45124\n",
        "pandas_code": "df[df['week'] == 10]['attendance'].values[0] > df[df['week'] == 9]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2393,
        "statement": "the highest attendance be in week 11",
        "label": 1,
        "table_caption": "1977 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 18 , 1977#seattle seahawks#w 29 - 14#1 - 0#kingdome#58991\n2#september 25 , 1977#new york jets#w 20 - 12#2 - 0#shea stadium#43439\n3#october 2 , 1977#buffalo bills#w 17 - 14#3 - 0#memorial stadium#49247\n4#october 9 , 1977#miami dolphins#w 45 - 28#4 - 0#memorial stadium#57829\n5#october 16 , 1977#kansas city chiefs#w 17 - 6#5 - 0#arrowhead stadium#63076\n6#october 23 , 1977#new england patriots#l 3 - 17#5 - 1#schaeffer stadium#60958\n7#october 30 , 1977#pittsburgh steelers#w 31 - 21#6 - 1#memorial stadium#60225\n8#november 7 , 1977#washington redskins#w 10 - 3#7 - 1#memorial stadium#57740\n9#november 13 , 1977#buffalo bills#w 31 - 13#8 - 1#rich stadium#39444\n10#november 20 , 1977#new york jets#w 33 - 12#9 - 1#memorial stadium#50957\n11#november 27 , 1977#denver broncos#l 13 - 27#9 - 2#mile high stadium#74939\n12#december 5 , 1977#miami dolphins#l 6 - 17#9 - 3#miami orange bowl#68977\n13#december 11 , 1977#detroit lions#l 10 - 13#9 - 4#memorial stadium#45124\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'week'] == 11",
        "pandas_eval": "True"
    },
    {
        "id": 2394,
        "statement": "the lowest attendance be in week 9",
        "label": 1,
        "table_caption": "1977 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 18 , 1977#seattle seahawks#w 29 - 14#1 - 0#kingdome#58991\n2#september 25 , 1977#new york jets#w 20 - 12#2 - 0#shea stadium#43439\n3#october 2 , 1977#buffalo bills#w 17 - 14#3 - 0#memorial stadium#49247\n4#october 9 , 1977#miami dolphins#w 45 - 28#4 - 0#memorial stadium#57829\n5#october 16 , 1977#kansas city chiefs#w 17 - 6#5 - 0#arrowhead stadium#63076\n6#october 23 , 1977#new england patriots#l 3 - 17#5 - 1#schaeffer stadium#60958\n7#october 30 , 1977#pittsburgh steelers#w 31 - 21#6 - 1#memorial stadium#60225\n8#november 7 , 1977#washington redskins#w 10 - 3#7 - 1#memorial stadium#57740\n9#november 13 , 1977#buffalo bills#w 31 - 13#8 - 1#rich stadium#39444\n10#november 20 , 1977#new york jets#w 33 - 12#9 - 1#memorial stadium#50957\n11#november 27 , 1977#denver broncos#l 13 - 27#9 - 2#mile high stadium#74939\n12#december 5 , 1977#miami dolphins#l 6 - 17#9 - 3#miami orange bowl#68977\n13#december 11 , 1977#detroit lions#l 10 - 13#9 - 4#memorial stadium#45124\n",
        "pandas_code": "df.loc[df['attendance'].idxmin(), 'week'] == 9",
        "pandas_eval": "True"
    },
    {
        "id": 2395,
        "statement": "they play the jet more than they play the seahawks",
        "label": 1,
        "table_caption": "1977 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 18 , 1977#seattle seahawks#w 29 - 14#1 - 0#kingdome#58991\n2#september 25 , 1977#new york jets#w 20 - 12#2 - 0#shea stadium#43439\n3#october 2 , 1977#buffalo bills#w 17 - 14#3 - 0#memorial stadium#49247\n4#october 9 , 1977#miami dolphins#w 45 - 28#4 - 0#memorial stadium#57829\n5#october 16 , 1977#kansas city chiefs#w 17 - 6#5 - 0#arrowhead stadium#63076\n6#october 23 , 1977#new england patriots#l 3 - 17#5 - 1#schaeffer stadium#60958\n7#october 30 , 1977#pittsburgh steelers#w 31 - 21#6 - 1#memorial stadium#60225\n8#november 7 , 1977#washington redskins#w 10 - 3#7 - 1#memorial stadium#57740\n9#november 13 , 1977#buffalo bills#w 31 - 13#8 - 1#rich stadium#39444\n10#november 20 , 1977#new york jets#w 33 - 12#9 - 1#memorial stadium#50957\n11#november 27 , 1977#denver broncos#l 13 - 27#9 - 2#mile high stadium#74939\n12#december 5 , 1977#miami dolphins#l 6 - 17#9 - 3#miami orange bowl#68977\n13#december 11 , 1977#detroit lions#l 10 - 13#9 - 4#memorial stadium#45124\n",
        "pandas_code": "df[df['opponent'].str.contains('jets', case=False)].shape[0] > df[df['opponent'].str.contains('seahawks', case=False)].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2396,
        "statement": "the year gerald riggs be select ninth overall in the draft , the atlanta falcon select 12 total player",
        "label": 1,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "df[df['name'] == 'gerald riggs']['overall'].iloc[0] == 9 and len(df) == 12",
        "pandas_eval": "True"
    },
    {
        "id": 2397,
        "statement": "the year run back gerald riggs be select in the draft by atlanta , the falcon select 2 other player at that position",
        "label": 1,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "(df[df['name'] == 'gerald riggs']['position'].values[0] == 'running back') and (df[df['position'] == 'running back'].shape[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 2398,
        "statement": "the atlanta falcon select wide receiver stacey bailey and jeff keller in the same draft year",
        "label": 1,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "len(df[(df['name'].isin(['stacey bailey', 'jeff keller'])) & (df['position'] == 'wide receiver')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2399,
        "statement": "defensive back von mansfield and rickey eberhart be select in the same draft year by the atlanta falcon",
        "label": 1,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "(df['name'].isin(['von mansfield', 'ricky eberhart'])).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2401,
        "statement": "the year gerald riggs be select ninth overall in the draft , the atlanta falcon select 11 total player",
        "label": 0,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "(df[df['name'] == 'gerald riggs']['overall'].values[0] == 9) and (len(df) == 11)",
        "pandas_eval": "False"
    },
    {
        "id": 2402,
        "statement": "the year run back gerald riggs be select in the draft by atlanta , the falcon select 3 other player at that position",
        "label": 0,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "len(df[(df['position'] == 'running back') & (df['overall'] < df[df['name'] == 'gerald riggs']['overall'].values[0])]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2403,
        "statement": "the atlanta falcon select punter stacey bailey and jeff keller in the same draft year",
        "label": 0,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "df[(df['name'].isin(['stacey bailey', 'jeff keller'])) & (df['position'] == 'punter')].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2404,
        "statement": "defensive back von mansfield and rickey eberhart be select in different draft year by the atlanta falcon",
        "label": 0,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "len(df[(df['name'].isin(['von mansfield', 'ricky eberhart'])) & (df['position'] == 'defensive back')]['overall'].unique()) != 2",
        "pandas_eval": "False"
    },
    {
        "id": 2405,
        "statement": "the year atlanta select defensive end doug rogers in the draft , the falcon select 3 other player who attend california college",
        "label": 0,
        "table_caption": "atlanta falcons draft history",
        "table_text": "round#pick#overall#name#position#college\n1#9#9#gerald riggs#running back#arizona state\n2#9#36#doug rogers#defensive end#stanford\n3#8#63#stacey bailey#wide receiver#san jose state\n4#12#95#reggie brown#running back#oregon\n5#11#122#von mansfield#defensive back#wisconsin\n6#10#149#mike kelley#quarterback#georgia tech\n7#9#176#david tolomu#running back#hawaii\n8#8#203#ricky eberhart#defensive back#morris brown\n9#12#235#mike horan#punter#long beach state\n10#11#262#curtis stowers#linebacker#mississippi state\n11#9#288#jeff keller#wide receiver#washington state\n12#9#315#dave levenick#linebacker#wisconsin\n",
        "pandas_code": "(df[df['name'] == 'doug rogers']['college'].values[0] == 'stanford') and (df[df['college'].str.contains('california')].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 2406,
        "statement": "punjab rank highest in both male and female obesity in india",
        "label": 1,
        "table_caption": "obesity in india",
        "table_text": "states#males (%)#males rank#females (%)#females rank\nindia#12.1#14#16#15\npunjab#30.3#1#37.5#1\nkerala#24.3#2#34#2\ngoa#20.8#3#27#3\ntamil nadu#19.8#4#24.4#4\nandhra pradesh#17.6#5#22.7#10\nsikkim#17.3#6#21#8\nmizoram#16.9#7#20.3#17\nhimachal pradesh#16#8#19.5#12\nmaharashtra#15.9#9#18.1#13\ngujarat#15.4#10#17.7#7\nharyana#14.4#11#17.6#6\nkarnataka#14#12#17.3#9\nmanipur#13.4#13#17.1#11\nuttarakhand#11.4#15#14.8#14\narunachal pradesh#10.6#16#12.5#19\nuttar pradesh#9.9#17#12#18\njammu and kashmir#8.7#18#11.1#5\nbihar#8.5#19#10.5#29\nnagaland#8.4#20#10.2#22\nrajasthan#8.4#20#9#20\nmeghalaya#8.2#22#8.9#26\norissa#6.9#23#8.6#25\nassam#6.7#24#7.8#21\nchattisgarh#6.5#25#7.6#27\nwest bengal#6.1#26#7.1#16\nmadhya pradesh#5.4#27#6.7#23\njharkhand#5.3#28#5.9#28\n",
        "pandas_code": "(df[df['states'] == 'punjab']['males rank'].values[0] == 1) & (df[df['states'] == 'punjab']['females rank'].values[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2407,
        "statement": "the average percentage of obese male in a state in india be 12.83%",
        "label": 1,
        "table_caption": "obesity in india",
        "table_text": "states#males (%)#males rank#females (%)#females rank\nindia#12.1#14#16#15\npunjab#30.3#1#37.5#1\nkerala#24.3#2#34#2\ngoa#20.8#3#27#3\ntamil nadu#19.8#4#24.4#4\nandhra pradesh#17.6#5#22.7#10\nsikkim#17.3#6#21#8\nmizoram#16.9#7#20.3#17\nhimachal pradesh#16#8#19.5#12\nmaharashtra#15.9#9#18.1#13\ngujarat#15.4#10#17.7#7\nharyana#14.4#11#17.6#6\nkarnataka#14#12#17.3#9\nmanipur#13.4#13#17.1#11\nuttarakhand#11.4#15#14.8#14\narunachal pradesh#10.6#16#12.5#19\nuttar pradesh#9.9#17#12#18\njammu and kashmir#8.7#18#11.1#5\nbihar#8.5#19#10.5#29\nnagaland#8.4#20#10.2#22\nrajasthan#8.4#20#9#20\nmeghalaya#8.2#22#8.9#26\norissa#6.9#23#8.6#25\nassam#6.7#24#7.8#21\nchattisgarh#6.5#25#7.6#27\nwest bengal#6.1#26#7.1#16\nmadhya pradesh#5.4#27#6.7#23\njharkhand#5.3#28#5.9#28\n",
        "pandas_code": "abs(df['males (%)'].mean() - 12.83) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 2408,
        "statement": "goa have a larger percentage of obese female than tamil nadu",
        "label": 1,
        "table_caption": "obesity in india",
        "table_text": "states#males (%)#males rank#females (%)#females rank\nindia#12.1#14#16#15\npunjab#30.3#1#37.5#1\nkerala#24.3#2#34#2\ngoa#20.8#3#27#3\ntamil nadu#19.8#4#24.4#4\nandhra pradesh#17.6#5#22.7#10\nsikkim#17.3#6#21#8\nmizoram#16.9#7#20.3#17\nhimachal pradesh#16#8#19.5#12\nmaharashtra#15.9#9#18.1#13\ngujarat#15.4#10#17.7#7\nharyana#14.4#11#17.6#6\nkarnataka#14#12#17.3#9\nmanipur#13.4#13#17.1#11\nuttarakhand#11.4#15#14.8#14\narunachal pradesh#10.6#16#12.5#19\nuttar pradesh#9.9#17#12#18\njammu and kashmir#8.7#18#11.1#5\nbihar#8.5#19#10.5#29\nnagaland#8.4#20#10.2#22\nrajasthan#8.4#20#9#20\nmeghalaya#8.2#22#8.9#26\norissa#6.9#23#8.6#25\nassam#6.7#24#7.8#21\nchattisgarh#6.5#25#7.6#27\nwest bengal#6.1#26#7.1#16\nmadhya pradesh#5.4#27#6.7#23\njharkhand#5.3#28#5.9#28\n",
        "pandas_code": "df[df['states'] == 'goa']['females (%)'].values[0] > df[df['states'] == 'tamil nadu']['females (%)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2409,
        "statement": "the average percentage of female who be obese in a state in india be 15.82%",
        "label": 1,
        "table_caption": "obesity in india",
        "table_text": "states#males (%)#males rank#females (%)#females rank\nindia#12.1#14#16#15\npunjab#30.3#1#37.5#1\nkerala#24.3#2#34#2\ngoa#20.8#3#27#3\ntamil nadu#19.8#4#24.4#4\nandhra pradesh#17.6#5#22.7#10\nsikkim#17.3#6#21#8\nmizoram#16.9#7#20.3#17\nhimachal pradesh#16#8#19.5#12\nmaharashtra#15.9#9#18.1#13\ngujarat#15.4#10#17.7#7\nharyana#14.4#11#17.6#6\nkarnataka#14#12#17.3#9\nmanipur#13.4#13#17.1#11\nuttarakhand#11.4#15#14.8#14\narunachal pradesh#10.6#16#12.5#19\nuttar pradesh#9.9#17#12#18\njammu and kashmir#8.7#18#11.1#5\nbihar#8.5#19#10.5#29\nnagaland#8.4#20#10.2#22\nrajasthan#8.4#20#9#20\nmeghalaya#8.2#22#8.9#26\norissa#6.9#23#8.6#25\nassam#6.7#24#7.8#21\nchattisgarh#6.5#25#7.6#27\nwest bengal#6.1#26#7.1#16\nmadhya pradesh#5.4#27#6.7#23\njharkhand#5.3#28#5.9#28\n",
        "pandas_code": "abs(df['females (%)'].mean() - 15.82) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 2411,
        "statement": "belarus win more bronze medal than silver medal at the 2011 icf canoe sprint world championship",
        "label": 1,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "df[df['nation'] == 'belarus']['bronze'].values[0] > df[df['nation'] == 'belarus']['silver'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2413,
        "statement": "5 nation won 3 bronze medal at the 2011 icf canoe sprint world championship",
        "label": 1,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "df[df['rank'] == '5']['bronze'].values[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2414,
        "statement": "canada win less gold medal than germany at the 2011 icf canoe sprint world championship",
        "label": 1,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "df[df['nation'] == 'canada']['gold'].values[0] < df[df['nation'] == 'germany']['gold'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2415,
        "statement": "more nation be rank 12th than be rank 20th",
        "label": 1,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "(df[df['rank'] == '12'].shape[0] > df[df['rank'] == '20'].shape[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2416,
        "statement": "belarus win more gold medal than any nation in the 2011 icf canoe sprint world championship",
        "label": 0,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "(df[df['nation'] == 'belarus']['gold'].values[0] > df[df['nation'] != 'total']['gold'].max())",
        "pandas_eval": "False"
    },
    {
        "id": 2417,
        "statement": "russia win more gold medal than germany at the 2011 icf canoe sprint world championship",
        "label": 0,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "df[df['nation'] == 'russia']['gold'].values[0] > df[df['nation'] == 'germany']['gold'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2418,
        "statement": "more than 5 nation win 3 bronze medal at the 2011 icf canoe sprint world championship",
        "label": 0,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "(df['bronze'] > 3).sum() > 5",
        "pandas_eval": "False"
    },
    {
        "id": 2419,
        "statement": "canada win more silver medal than germany at the 2011 icf canoe sprint world championship",
        "label": 0,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "(df[df['nation'] == 'canada']['silver'].values[0] > df[df['nation'] == 'germany']['silver'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2420,
        "statement": "only 1 nation be rank 12th",
        "label": 0,
        "table_caption": "2011 icf canoe sprint world championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#germany#6#2#3#11\n2#hungary#6#1#3#10\n3#canada#3#0#0#3\n4#russia#2#6#3#11\n5#poland#2#2#3#7\n6#belarus#1#3#4#8\n7#azerbaijan#1#3#0#4\n8#spain#1#1#2#4\n9#romania#1#1#1#3\n10#ukraine#1#0#3#4\n11#lithuania#1#1#0#2\n12#austria#1#0#0#1\n12#france#1#0#0#1\n12#new zealand#1#0#0#1\n12#slovakia#1#0#0#1\n16#great britain#0#3#0#3\n17#sweden#0#2#0#2\n18#australia#0#1#2#3\n19#bulgaria#0#1#1#2\n20#czech republic#0#1#0#1\n20#united states#0#1#0#1\n22#denmark#0#0#1#1\n22#italy#0#0#1#1\n22#norway#0#0#1#1\n22#uzbekistan#0#0#1#1\ntotal#total#29#29#29#87\n",
        "pandas_code": "len(df[df['rank'] == '12']) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2421,
        "statement": "the venue be home more time than it be away",
        "label": 1,
        "table_caption": "2007 - 08 1. ffc turbine potsdam season",
        "table_text": "match#date#opponent#venue#result#turbine goals#attendance#report\n1#19 august 2007#tsv crailsheim#away#1:0 (1:0)#none#522#report\n2#26 august 2007#wattenscheid 09#home#3:1 (1:1)#wich (21) , zietz (46) , schiewe (51)#1113#report\n3#7 october 2007#sg essen - sch\u00f6nebeck#away#1:1 (1:0)#peter (90. , pen)#3042#report\n4#14 october 2007#sc 07 bad neuenahr#home#2:0 (1:0)#mittag (29. , 66)#1507#report\n6#11 november 2007#1. ffc frankfurt#home#1:1 (0:0)#zietz (84)#1102#report\n7#18 november 2007#1. fc saarbr\u00fccken#home#4:0 (1:0)#wich (44. , 74) , sainio (84) , mittag (90)#715#report\n8#2 december 2007#fcr 2001 duisburg#away#0:1 (0:1)#mittag (3)#1062#report\n9#9 december 2007#bayern munich#home#1:2 (1:2)#i kerschowski (4)#811#report\n10#24 february 2008#hamburger sv#away#2:2 (1:1)#mittag (29) , wich (60)#720#report\n11#2 march 2008#sc freiburg#home#1:1 (1:0)#i kerschowski (38)#553#report\n12#24 march 2008#tsv crailsheim#home#2:1 (1:0)#peter (29) , zietz (85)#750#report\n13#30 march 2008#wattenscheid 09#away#0:5 (0:2)#zietz (16. , 56. , 66) , wich (40) , schmidt (75)#552#report\n14#6 april 2008#sg essen - sch\u00f6nebeck#home#3:0 (1:0)#zietz (25. , pen) , mittag (88) , wich (90)#913#report\n15#13 april 2008#sc 07 bad neuenahr#away#2:1 (1:1)#schmidt (30)#730#report\n16#4 may 2008#vfl wolfsburg#home#3:2 (2:1)#peter (6) , hagemann (32) , schmidt (67)#915#report\n17#12 may 2008#1. ffc frankfurt#away#4:0 (2:0)#none#3120#report\n18#18 may 2008#1. fc saarbr\u00fccken#away#0:2 (0:1)#wich (30. , 87)#1124#report\n19#25 may 2008#fcr 2001 duisburg#home#1:2 (0:1)#peter (74. , pen)#1182#report\n20#1 june 2008#bayern munich#away#7:2 (3:0)#mittag (47) , larsen kaurin (60)#520#report\n",
        "pandas_code": "(df['venue'].value_counts()['home'] > df['venue'].value_counts()['away'])",
        "pandas_eval": "True"
    },
    {
        "id": 2422,
        "statement": "the attendance be greatest on 12 may 2008",
        "label": 1,
        "table_caption": "2007 - 08 1. ffc turbine potsdam season",
        "table_text": "match#date#opponent#venue#result#turbine goals#attendance#report\n1#19 august 2007#tsv crailsheim#away#1:0 (1:0)#none#522#report\n2#26 august 2007#wattenscheid 09#home#3:1 (1:1)#wich (21) , zietz (46) , schiewe (51)#1113#report\n3#7 october 2007#sg essen - sch\u00f6nebeck#away#1:1 (1:0)#peter (90. , pen)#3042#report\n4#14 october 2007#sc 07 bad neuenahr#home#2:0 (1:0)#mittag (29. , 66)#1507#report\n6#11 november 2007#1. ffc frankfurt#home#1:1 (0:0)#zietz (84)#1102#report\n7#18 november 2007#1. fc saarbr\u00fccken#home#4:0 (1:0)#wich (44. , 74) , sainio (84) , mittag (90)#715#report\n8#2 december 2007#fcr 2001 duisburg#away#0:1 (0:1)#mittag (3)#1062#report\n9#9 december 2007#bayern munich#home#1:2 (1:2)#i kerschowski (4)#811#report\n10#24 february 2008#hamburger sv#away#2:2 (1:1)#mittag (29) , wich (60)#720#report\n11#2 march 2008#sc freiburg#home#1:1 (1:0)#i kerschowski (38)#553#report\n12#24 march 2008#tsv crailsheim#home#2:1 (1:0)#peter (29) , zietz (85)#750#report\n13#30 march 2008#wattenscheid 09#away#0:5 (0:2)#zietz (16. , 56. , 66) , wich (40) , schmidt (75)#552#report\n14#6 april 2008#sg essen - sch\u00f6nebeck#home#3:0 (1:0)#zietz (25. , pen) , mittag (88) , wich (90)#913#report\n15#13 april 2008#sc 07 bad neuenahr#away#2:1 (1:1)#schmidt (30)#730#report\n16#4 may 2008#vfl wolfsburg#home#3:2 (2:1)#peter (6) , hagemann (32) , schmidt (67)#915#report\n17#12 may 2008#1. ffc frankfurt#away#4:0 (2:0)#none#3120#report\n18#18 may 2008#1. fc saarbr\u00fccken#away#0:2 (0:1)#wich (30. , 87)#1124#report\n19#25 may 2008#fcr 2001 duisburg#home#1:2 (0:1)#peter (74. , pen)#1182#report\n20#1 june 2008#bayern munich#away#7:2 (3:0)#mittag (47) , larsen kaurin (60)#520#report\n",
        "pandas_code": "df.loc[df['date'] == '12 may 2008', 'attendance'].max() == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2423,
        "statement": "there be no turbine goal 2 time",
        "label": 1,
        "table_caption": "2007 - 08 1. ffc turbine potsdam season",
        "table_text": "match#date#opponent#venue#result#turbine goals#attendance#report\n1#19 august 2007#tsv crailsheim#away#1:0 (1:0)#none#522#report\n2#26 august 2007#wattenscheid 09#home#3:1 (1:1)#wich (21) , zietz (46) , schiewe (51)#1113#report\n3#7 october 2007#sg essen - sch\u00f6nebeck#away#1:1 (1:0)#peter (90. , pen)#3042#report\n4#14 october 2007#sc 07 bad neuenahr#home#2:0 (1:0)#mittag (29. , 66)#1507#report\n6#11 november 2007#1. ffc frankfurt#home#1:1 (0:0)#zietz (84)#1102#report\n7#18 november 2007#1. fc saarbr\u00fccken#home#4:0 (1:0)#wich (44. , 74) , sainio (84) , mittag (90)#715#report\n8#2 december 2007#fcr 2001 duisburg#away#0:1 (0:1)#mittag (3)#1062#report\n9#9 december 2007#bayern munich#home#1:2 (1:2)#i kerschowski (4)#811#report\n10#24 february 2008#hamburger sv#away#2:2 (1:1)#mittag (29) , wich (60)#720#report\n11#2 march 2008#sc freiburg#home#1:1 (1:0)#i kerschowski (38)#553#report\n12#24 march 2008#tsv crailsheim#home#2:1 (1:0)#peter (29) , zietz (85)#750#report\n13#30 march 2008#wattenscheid 09#away#0:5 (0:2)#zietz (16. , 56. , 66) , wich (40) , schmidt (75)#552#report\n14#6 april 2008#sg essen - sch\u00f6nebeck#home#3:0 (1:0)#zietz (25. , pen) , mittag (88) , wich (90)#913#report\n15#13 april 2008#sc 07 bad neuenahr#away#2:1 (1:1)#schmidt (30)#730#report\n16#4 may 2008#vfl wolfsburg#home#3:2 (2:1)#peter (6) , hagemann (32) , schmidt (67)#915#report\n17#12 may 2008#1. ffc frankfurt#away#4:0 (2:0)#none#3120#report\n18#18 may 2008#1. fc saarbr\u00fccken#away#0:2 (0:1)#wich (30. , 87)#1124#report\n19#25 may 2008#fcr 2001 duisburg#home#1:2 (0:1)#peter (74. , pen)#1182#report\n20#1 june 2008#bayern munich#away#7:2 (3:0)#mittag (47) , larsen kaurin (60)#520#report\n",
        "pandas_code": "df['turbine goals'].apply(lambda x: x == 'none').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2424,
        "statement": "the month of may appear more time than any other month",
        "label": 1,
        "table_caption": "2007 - 08 1. ffc turbine potsdam season",
        "table_text": "match#date#opponent#venue#result#turbine goals#attendance#report\n1#19 august 2007#tsv crailsheim#away#1:0 (1:0)#none#522#report\n2#26 august 2007#wattenscheid 09#home#3:1 (1:1)#wich (21) , zietz (46) , schiewe (51)#1113#report\n3#7 october 2007#sg essen - sch\u00f6nebeck#away#1:1 (1:0)#peter (90. , pen)#3042#report\n4#14 october 2007#sc 07 bad neuenahr#home#2:0 (1:0)#mittag (29. , 66)#1507#report\n6#11 november 2007#1. ffc frankfurt#home#1:1 (0:0)#zietz (84)#1102#report\n7#18 november 2007#1. fc saarbr\u00fccken#home#4:0 (1:0)#wich (44. , 74) , sainio (84) , mittag (90)#715#report\n8#2 december 2007#fcr 2001 duisburg#away#0:1 (0:1)#mittag (3)#1062#report\n9#9 december 2007#bayern munich#home#1:2 (1:2)#i kerschowski (4)#811#report\n10#24 february 2008#hamburger sv#away#2:2 (1:1)#mittag (29) , wich (60)#720#report\n11#2 march 2008#sc freiburg#home#1:1 (1:0)#i kerschowski (38)#553#report\n12#24 march 2008#tsv crailsheim#home#2:1 (1:0)#peter (29) , zietz (85)#750#report\n13#30 march 2008#wattenscheid 09#away#0:5 (0:2)#zietz (16. , 56. , 66) , wich (40) , schmidt (75)#552#report\n14#6 april 2008#sg essen - sch\u00f6nebeck#home#3:0 (1:0)#zietz (25. , pen) , mittag (88) , wich (90)#913#report\n15#13 april 2008#sc 07 bad neuenahr#away#2:1 (1:1)#schmidt (30)#730#report\n16#4 may 2008#vfl wolfsburg#home#3:2 (2:1)#peter (6) , hagemann (32) , schmidt (67)#915#report\n17#12 may 2008#1. ffc frankfurt#away#4:0 (2:0)#none#3120#report\n18#18 may 2008#1. fc saarbr\u00fccken#away#0:2 (0:1)#wich (30. , 87)#1124#report\n19#25 may 2008#fcr 2001 duisburg#home#1:2 (0:1)#peter (74. , pen)#1182#report\n20#1 june 2008#bayern munich#away#7:2 (3:0)#mittag (47) , larsen kaurin (60)#520#report\n",
        "pandas_code": "df['date'].str.extract(r'(\\w+) \\d{4}')[0].value_counts().idxmax() == 'may'",
        "pandas_eval": "True"
    },
    {
        "id": 2425,
        "statement": "on the date of 24 march 2008 there be more attendance than the date of 30 march 2008",
        "label": 1,
        "table_caption": "2007 - 08 1. ffc turbine potsdam season",
        "table_text": "match#date#opponent#venue#result#turbine goals#attendance#report\n1#19 august 2007#tsv crailsheim#away#1:0 (1:0)#none#522#report\n2#26 august 2007#wattenscheid 09#home#3:1 (1:1)#wich (21) , zietz (46) , schiewe (51)#1113#report\n3#7 october 2007#sg essen - sch\u00f6nebeck#away#1:1 (1:0)#peter (90. , pen)#3042#report\n4#14 october 2007#sc 07 bad neuenahr#home#2:0 (1:0)#mittag (29. , 66)#1507#report\n6#11 november 2007#1. ffc frankfurt#home#1:1 (0:0)#zietz (84)#1102#report\n7#18 november 2007#1. fc saarbr\u00fccken#home#4:0 (1:0)#wich (44. , 74) , sainio (84) , mittag (90)#715#report\n8#2 december 2007#fcr 2001 duisburg#away#0:1 (0:1)#mittag (3)#1062#report\n9#9 december 2007#bayern munich#home#1:2 (1:2)#i kerschowski (4)#811#report\n10#24 february 2008#hamburger sv#away#2:2 (1:1)#mittag (29) , wich (60)#720#report\n11#2 march 2008#sc freiburg#home#1:1 (1:0)#i kerschowski (38)#553#report\n12#24 march 2008#tsv crailsheim#home#2:1 (1:0)#peter (29) , zietz (85)#750#report\n13#30 march 2008#wattenscheid 09#away#0:5 (0:2)#zietz (16. , 56. , 66) , wich (40) , schmidt (75)#552#report\n14#6 april 2008#sg essen - sch\u00f6nebeck#home#3:0 (1:0)#zietz (25. , pen) , mittag (88) , wich (90)#913#report\n15#13 april 2008#sc 07 bad neuenahr#away#2:1 (1:1)#schmidt (30)#730#report\n16#4 may 2008#vfl wolfsburg#home#3:2 (2:1)#peter (6) , hagemann (32) , schmidt (67)#915#report\n17#12 may 2008#1. ffc frankfurt#away#4:0 (2:0)#none#3120#report\n18#18 may 2008#1. fc saarbr\u00fccken#away#0:2 (0:1)#wich (30. , 87)#1124#report\n19#25 may 2008#fcr 2001 duisburg#home#1:2 (0:1)#peter (74. , pen)#1182#report\n20#1 june 2008#bayern munich#away#7:2 (3:0)#mittag (47) , larsen kaurin (60)#520#report\n",
        "pandas_code": "df[df['date'] == '24 march 2008']['attendance'].values[0] > df[df['date'] == '30 march 2008']['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2426,
        "statement": "9 channel do not have analogue terrestrial channel",
        "label": 1,
        "table_caption": "television in the united kingdom",
        "table_text": "position#channel#analogue terrestrial channel#digital terrestrial channel#internet\n1#bbc one#1#1 50 (hd)#bbccouk\n2#itv#3#3 33 ( + 1) 51 (hd)#itv - itvcom\n3#bbc two#2#2#bbccouk\n4#channel 4#4 n / a (in wales)#4 8 (in wales) 13 ( + 1) 52 (hd , not wales)#channel4.com\n4#s4c#n / a#4 (in wales) 53 (hd , in wales) n / a (rest of uk)#s4ccouk\n5#channel 5#5#5 44 ( + 1)#n / a\n6#itv3#n / a#10#itvcom\n7#itv2#n / a#6 27 ( + 1)#itvcom\n8#e4#n / a#28 29 ( + 1)#e4.com\n9#sky sports 1#n / a#n / a#skysportscom\n10#sky1#n / a#n / a#skycom\n11#cbeebies#n / a#71#bbccouk\n12#itv4#n / a#24#itvcom\n13#bbc three#n / a#7#bbccouk\n",
        "pandas_code": "df[df['position'] == 9]['analogue terrestrial channel'].iloc[0] == 'n / a'",
        "pandas_eval": "True"
    },
    {
        "id": 2428,
        "statement": "4 out of 13 channel be available in hd",
        "label": 1,
        "table_caption": "television in the united kingdom",
        "table_text": "position#channel#analogue terrestrial channel#digital terrestrial channel#internet\n1#bbc one#1#1 50 (hd)#bbccouk\n2#itv#3#3 33 ( + 1) 51 (hd)#itv - itvcom\n3#bbc two#2#2#bbccouk\n4#channel 4#4 n / a (in wales)#4 8 (in wales) 13 ( + 1) 52 (hd , not wales)#channel4.com\n4#s4c#n / a#4 (in wales) 53 (hd , in wales) n / a (rest of uk)#s4ccouk\n5#channel 5#5#5 44 ( + 1)#n / a\n6#itv3#n / a#10#itvcom\n7#itv2#n / a#6 27 ( + 1)#itvcom\n8#e4#n / a#28 29 ( + 1)#e4.com\n9#sky sports 1#n / a#n / a#skysportscom\n10#sky1#n / a#n / a#skycom\n11#cbeebies#n / a#71#bbccouk\n12#itv4#n / a#24#itvcom\n13#bbc three#n / a#7#bbccouk\n",
        "pandas_code": "sum(df['digital terrestrial channel'].str.contains('hd', case=False, na=False)) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2429,
        "statement": "only 2 channel be available in wale in digital format",
        "label": 1,
        "table_caption": "television in the united kingdom",
        "table_text": "position#channel#analogue terrestrial channel#digital terrestrial channel#internet\n1#bbc one#1#1 50 (hd)#bbccouk\n2#itv#3#3 33 ( + 1) 51 (hd)#itv - itvcom\n3#bbc two#2#2#bbccouk\n4#channel 4#4 n / a (in wales)#4 8 (in wales) 13 ( + 1) 52 (hd , not wales)#channel4.com\n4#s4c#n / a#4 (in wales) 53 (hd , in wales) n / a (rest of uk)#s4ccouk\n5#channel 5#5#5 44 ( + 1)#n / a\n6#itv3#n / a#10#itvcom\n7#itv2#n / a#6 27 ( + 1)#itvcom\n8#e4#n / a#28 29 ( + 1)#e4.com\n9#sky sports 1#n / a#n / a#skysportscom\n10#sky1#n / a#n / a#skycom\n11#cbeebies#n / a#71#bbccouk\n12#itv4#n / a#24#itvcom\n13#bbc three#n / a#7#bbccouk\n",
        "pandas_code": "len(df[df['digital terrestrial channel'].str.contains('wales', case=False, na=False)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2431,
        "statement": "cheon be the least popular surname in south korea 2000",
        "label": 1,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "df.loc[df['2000 n south korea population'] == df['2000 n south korea population'].min(), 'hangul'].values[0] == '\ucc9c'",
        "pandas_eval": "True"
    },
    {
        "id": 2432,
        "statement": "there be 9813722 more people with the most popular surname in south korea than with the least popular 1",
        "label": 1,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "(df['2000 n south korea population'].max() - df['2000 n south korea population'].min()) == 9813722",
        "pandas_eval": "True"
    },
    {
        "id": 2433,
        "statement": "8 of 46 south korean surname be use by over a million people each in 2000",
        "label": 1,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "sum(df['2000 n south korea population'] > 1000000) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2434,
        "statement": "the 2 least popular surname combine be use by less of the population than the most popular surname",
        "label": 1,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "df.nsmallest(2, '2000 n south korea population')['2000 n south korea population'].sum() < df.nlargest(1, '2000 n south korea population')['2000 n south korea population'].sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2435,
        "statement": "gang be the only south korean surname with 5 hanja",
        "label": 1,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "len(df[df['hanja'].str.split(',').apply(len) == 5]) == 1 and df[df['hanja'].str.split(',').apply(len) == 5]['revised'].iloc[0] == 'gang'",
        "pandas_eval": "True"
    },
    {
        "id": 2436,
        "statement": "cheon be the most popular surnam in south korea 2000",
        "label": 0,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "df.loc[df['hangul'] == '\ucc9c', '2000 n south korea population'].values[0] == df['2000 n south korea population'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2437,
        "statement": "there be 9900000 more people with most popular surname in south korea than with the least popular 1",
        "label": 0,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "(df['2000 n south korea population'].max() - df['2000 n south korea population'].min()) >= 9900000",
        "pandas_eval": "False"
    },
    {
        "id": 2438,
        "statement": "8 of 46 south korean surname be use by less than a million people each in 2000",
        "label": 0,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "sum(df['2000 n south korea population'] < 1000000) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 2439,
        "statement": "the 2 least popular surname combine be use by equal the amount of the population than the most popular surname",
        "label": 0,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "df.nsmallest(2, '2000 n south korea population')['2000 n south korea population'].sum() == df.nlargest(1, '2000 n south korea population')['2000 n south korea population'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2440,
        "statement": "gang be 1 of 2 south korean surname with 5 hanja",
        "label": 0,
        "table_caption": "list of south korean surnames by prevalence",
        "table_text": "hangul#hanja#revised#mccune - reischauer#2000 n south korea population\n\uae40#\u91d1#gim#kim#9925949\n\uc774 (s) \ub9ac (n)#\u674e , \u4f0a , \u7570#i (s) ri (n)#yi (s) ri (n)#6796227\n\ubc15#\u6734#bak#pak#3895121\n\uc815#\u4e01 , \u7a0b , \u912d#jeong#ch\u014fng#2230611\n\ucd5c#\u5d14#choe#ch'oe#2169704\n\uc870#\u8d99 , \u66fa#jo#cho#1347730\n\uac15#\u59dc , \u525b , \u5eb7 , \u5f3a , \u5f4a#gang#kang#1169805\n\uc720 (s) \ub958 (n)#\u67f3 , \u5289 , \u516a , \u5ebe#yu (s) ryu (n)#yu (s) ryu (n)#1040984\n\uc724#\u5c39#yun#yun#948600\n\uc7a5#\u838a , \u7ae0 , \u5f35 , \u8523#jang#chang#943257\n\uc2e0#\u7533 , \u8f9b , \u613c#sin#sin#911556\n\uc784 (s) \ub9bc (n)#\u6797 , \u4efb#im (s) rim (n)#im (s) rim (n)#735493\n\ud55c#\u97d3 , \u6f22#han#han#715556\n\uc624#\u4f0d , \u5433#o#o#706908\n\uc11c#\u897f , \u5f90#seo#s\u014f#695249\n\uc804#\u5168 , \u7530 , \u9322#jeon#ch\u014fn#687867\n\uad8c#\u6b0a#gwon#kw\u014fn#652495\n\ud669#\u9ec3#hwang#hwang#644294\n\uc1a1#\u5b8b , \u677e#song#song#639082\n\uc548#\u5b89#an#an#637786\n\ud64d#\u6d2a#hong#hong#518635\n\uc591 (s) \ub7c9 (n)#\u6881 , \u694a , \u6a11 , \u8944#yang (s) ryang (n)#yang (s) ryang (n)#486645\n\uace0#\u9ad8#go#ko#435839\n\ubb38#\u9580 , \u6587#mun#mun#426927\n\uc190#\u5b6b#son#son#415182\n\ubc30#\u88f5#bae#pae#372064\n\ubc31#\u767d#baek#paek#351275\n\ud5c8#\u8a31#heo#h\u014f#300448\n\ub178 (s) \ub85c (n)#\u76e7 , \u9b6f , \u8def#no (s) ro (n)#no (s) ro (n)#290434\n\ub0a8#\u5357#nam#nam#257178\n\uc2ec#\u6c88#sim#sim#252255\n\uc8fc#\u5468 , \u6731#ju#chu#215010\n\ud558#\u6cb3 , \u590f#ha#ha#213758\n\uc131#\u6210 , \u661f#seong#s\u014fng#185363\n\ucc28#\u8eca#cha#ch'a#180589\n\uc6b0#\u4e8e , \u79b9#u#u#180141\n\ub098 (s) \ub77c (n)#\u7f85#na (s) ra (n)#na (s) ra (n)#172022\n\uc9c4#\u6649 , \u771e , \u9673 , \u79e6#jin#chin#170980\n\ubbfc#\u9594#min#min#159054\n\uc9c0#\u667a , \u6c60#ji#chi#147572\n\uc5c4#\u56b4#eom#\u014fm#132990\n\ubcc0#\u535e , \u908a#byeon#py\u014fn#131554\n\uc6d0#\u5143 , \u8881 , \u82d1#won#w\u014fn#120465\n\ubc29#\u65b9 , \u623f , \u90a6 , \u9f90#bang#pang#119703\n\ucc44#\u91c7 , \u83dc , \u8521#chae#ch'ae#119251\n\ucc9c#\u5929 , \u5343#cheon#ch'\u014fn#112227\n",
        "pandas_code": "df['hanja'].str.split(',').apply(lambda x: len([i.strip() for i in x])).eq(5).sum() == 2 and df[df['hangul'] == '\uac15']['hanja'].str.split(',').apply(lambda x: len([i.strip() for i in x])).eq(5).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2441,
        "statement": "the single j'voulais by the artist amine debut on the french single chart at number one in 2006",
        "label": 1,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "df[(df['artist'] == 'amine') & (df['number - one single (s)'] == \"j'voulais\") & (df['year'] == 2006) & (df['straight to 1 '] == 'yes')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2442,
        "statement": "bryan adams' single (everything i do) i do it for you remain at number one on the french single chart for 8 week in 1991",
        "label": 1,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "df[(df['artist'] == 'adams , bryan') & (df['number - one single (s)'] == '(everything i do) i do it for you') & (df['year'] == 1991) & (df['weeks at 1'] == 8)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2443,
        "statement": "both bryan adam and jean - philippe audin have single that be number one on the french single chart for 2 week in 1991",
        "label": 1,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "((df['artist'] == 'adams , bryan') & (df['year'] == 1991) & (df['weeks at 1'] == 8)).any() and ((df['artist'] == 'audin , jean - philippe') & (df['year'] == 1991) & (df['weeks at 1'] == 2)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2444,
        "statement": "there be 3 french artist or group that make it to number one on this chart",
        "label": 1,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "len(df[df['country'] == 'france']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2445,
        "statement": "only one artist from the united state make this list of number one song on the french single chart",
        "label": 1,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "df[df['country'] == 'united states'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2446,
        "statement": "the single j'voulais by the artist aventura debut on the french single chart at number one in 2003",
        "label": 0,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "((df['artist'] == 'aventura') & (df['number - one single (s)'] == \"j'voulais\") & (df['year'] == 2003) & (df['straight to 1 '] == 'yes')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2447,
        "statement": "bryan adams' single (everything i do) i do it for you remain at number 1 on the french single chart for 9 week in 1991",
        "label": 0,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "not df[(df['artist'] == 'adams , bryan') & (df['number - one single (s)'] == '(everything i do) i do it for you') & (df['year'] == 1991) & (df['weeks at 1'] == 9)].empty",
        "pandas_eval": "False"
    },
    {
        "id": 2448,
        "statement": "both ace of base and avalanche have single that be number one on the french single chart for 2 week in 1991",
        "label": 0,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "((df['artist'] == 'ace of base') & (df['year'] == 1991) & (df['weeks at 1'] == 2)).any() and ((df['artist'] == 'avalanche') & (df['year'] == 1991) & (df['weeks at 1'] == 2)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2449,
        "statement": "there be only one french artist that make it to number one on this chart",
        "label": 0,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "df[df['country'] == 'france']['artist'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2450,
        "statement": "2 artirsts from the united state make this list of number one song on the french single chart",
        "label": 0,
        "table_caption": "list of artists who reached number one on the french singles chart",
        "table_text": "artist#country#number - one single (s)#year#weeks at 1#straight to 1 \nace of base#sweden#happy nation#1993#4#no\nadams , bryan#canada#(everything i do) i do it for you#1991#8#no\nadams , bryan#canada#please forgive me#1993#2#no\naliz\u00e9e#france#l'aliz\u00e9#2000#2#no\namine#morocco#j'voulais#2006#4#yes\nantonn , kareen#france#si demain (turn around)#2004#10#no\naqua#denmark#barbie girl#1997#1#no\naudin , jean - philippe#france#song of ocarina#1991#2#no\navalanche#norway#johnny johnny come home#1989#8#no\naventura#united states#obsesi\u00f3n#2004#7#no\n",
        "pandas_code": "len(df[df['country'] == 'united states']) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2451,
        "statement": "in north carolina , more republican be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnorth carolina 2#lawrence h fountain#democratic#1952#re - elected#lawrence h fountain (d) unopposed\nnorth carolina 4#nick galifianakis#democratic#1966#re - elected#nick galifianakis (d) 52.4% jack hawke (r) 47.6%\nnorth carolina 5#wilmer mizell#republican#1968#re - elected#wilmer mizell (r) 58.1% james g white (d) 41.9%\nnorth carolina 8#earl b ruth#republican#1968#re - elected#earl b ruth (r) 56.1% h clifton blue (d) 43.9%\nnorth carolina 9#charles r jonas#republican#1952#re - elected#charles r jonas (r) 66.6% cy n bahakel (d) 33.4%\n",
        "pandas_code": "len(df[(df['party'] == 'republican') & (df['result'] == 're - elected')]) > len(df[(df['party'] == 'democratic') & (df['result'] == 're - elected')])",
        "pandas_eval": "True"
    },
    {
        "id": 2452,
        "statement": "when republican be re - elect , they win by more than 56% of the vote",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnorth carolina 2#lawrence h fountain#democratic#1952#re - elected#lawrence h fountain (d) unopposed\nnorth carolina 4#nick galifianakis#democratic#1966#re - elected#nick galifianakis (d) 52.4% jack hawke (r) 47.6%\nnorth carolina 5#wilmer mizell#republican#1968#re - elected#wilmer mizell (r) 58.1% james g white (d) 41.9%\nnorth carolina 8#earl b ruth#republican#1968#re - elected#earl b ruth (r) 56.1% h clifton blue (d) 43.9%\nnorth carolina 9#charles r jonas#republican#1952#re - elected#charles r jonas (r) 66.6% cy n bahakel (d) 33.4%\n",
        "pandas_code": "all(df[(df['party'] == 'republican') & (df['result'] == 're - elected')]['candidates'].str.extract(r'(\\d+\\.\\d+)%')[0].astype(float) > 56)",
        "pandas_eval": "True"
    },
    {
        "id": 2454,
        "statement": "republican be re - elect in district 5 and higher , as more republican voter be live in that area",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnorth carolina 2#lawrence h fountain#democratic#1952#re - elected#lawrence h fountain (d) unopposed\nnorth carolina 4#nick galifianakis#democratic#1966#re - elected#nick galifianakis (d) 52.4% jack hawke (r) 47.6%\nnorth carolina 5#wilmer mizell#republican#1968#re - elected#wilmer mizell (r) 58.1% james g white (d) 41.9%\nnorth carolina 8#earl b ruth#republican#1968#re - elected#earl b ruth (r) 56.1% h clifton blue (d) 43.9%\nnorth carolina 9#charles r jonas#republican#1952#re - elected#charles r jonas (r) 66.6% cy n bahakel (d) 33.4%\n",
        "pandas_code": "(df[(df['district'] == 'north carolina 5') & (df['party'] == 'republican') & (df['result'] == 're - elected')].shape[0] > 0) & (df[(df['district'] == 'north carolina 8') & (df['party'] == 'republican') & (df['result'] == 're - elected')].shape[0] > 0) & (df[(df['district'] == 'north carolina 9') & (df['party'] == 'republican') & (df['result'] == 're - elected')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 2455,
        "statement": "the democrat voter be live in district 2 and 4",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1970",
        "table_text": "district#incumbent#party#first elected#result#candidates\nnorth carolina 2#lawrence h fountain#democratic#1952#re - elected#lawrence h fountain (d) unopposed\nnorth carolina 4#nick galifianakis#democratic#1966#re - elected#nick galifianakis (d) 52.4% jack hawke (r) 47.6%\nnorth carolina 5#wilmer mizell#republican#1968#re - elected#wilmer mizell (r) 58.1% james g white (d) 41.9%\nnorth carolina 8#earl b ruth#republican#1968#re - elected#earl b ruth (r) 56.1% h clifton blue (d) 43.9%\nnorth carolina 9#charles r jonas#republican#1952#re - elected#charles r jonas (r) 66.6% cy n bahakel (d) 33.4%\n",
        "pandas_code": "all(df[df['district'].isin(['north carolina 2', 'north carolina 4'])]['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 2456,
        "statement": "2000 more crowd watch the game in corio oval than in glenferrie oval",
        "label": 1,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "(df[df['venue'] == 'corio oval']['crowd'].values[0] - df[df['venue'] == 'glenferrie oval']['crowd'].values[0]) == 2000",
        "pandas_eval": "True"
    },
    {
        "id": 2457,
        "statement": "the highest away team score be 20.13 (133) , the game with melbourne as the home team , against carlton",
        "label": 1,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "df[(df['home team'] == 'melbourne') & (df['away team'] == 'carlton') & (df['away team score'] == '20.13 (133)')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2458,
        "statement": "melbourne 's home team score be 14.12 (96) , greater than st kilda 's score of 13.9 (87)",
        "label": 1,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "df.loc[df['home team'] == 'melbourne', 'home team score'].values[0] == '14.12 (96)' and df.loc[df['home team'] == 'st kilda', 'home team score'].values[0] == '13.9 (87)'",
        "pandas_eval": "True"
    },
    {
        "id": 2459,
        "statement": "hawthorn v south melbourne be hold in glenferrie ovum while melbourne vs carlton be in mcg",
        "label": 1,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "((df['home team'] == 'hawthorn') & (df['away team'] == 'south melbourne') & (df['venue'] == 'glenferrie oval')).any() and ((df['home team'] == 'melbourne') & (df['away team'] == 'carlton') & (df['venue'] == 'mcg')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2460,
        "statement": "the biggest crowd be in windy hill with 18000 viewer",
        "label": 1,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "df.loc[df['venue'] == 'windy hill', 'crowd'].values[0] == 18000",
        "pandas_eval": "True"
    },
    {
        "id": 2461,
        "statement": "2000 more crowd watch the game in windy hill than in glenferrie oval",
        "label": 0,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "(df[df['venue'] == 'windy hill']['crowd'].values[0] - df[df['venue'] == 'glenferrie oval']['crowd'].values[0]) == 2000",
        "pandas_eval": "False"
    },
    {
        "id": 2463,
        "statement": "the biggest crowd be in junction oval",
        "label": 0,
        "table_caption": "1940 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#10.19 (79)#south melbourne#10.13 (73)#glenferrie oval#8000#10 august 1940\ngeelong#12.15 (87)#richmond#16.11 (107)#corio oval#10000#10 august 1940\nessendon#10.12 (72)#fitzroy#10.15 (75)#windy hill#18000#10 august 1940\ncollingwood#12.21 (93)#north melbourne#6.17 (53)#victoria park#6500#10 august 1940\nst kilda#13.9 (87)#footscray#15.22 (112)#junction oval#13000#10 august 1940\nmelbourne#14.12 (96)#carlton#20.13 (133)#mcg#16500#10 august 1940\n",
        "pandas_code": "df.loc[df['crowd'].idxmax(), 'venue'] == 'junction oval'",
        "pandas_eval": "False"
    },
    {
        "id": 2464,
        "statement": "wpeo , and wvel be the only channel to have zero night power",
        "label": 1,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "df[(df['call'].isin(['wpeo', 'wvel'])) & (df['night power'] == 0)].shape[0] == 2 and df[~df['call'].isin(['wpeo', 'wvel'])]['night power'].eq(0).sum() == 0",
        "pandas_eval": "True"
    },
    {
        "id": 2465,
        "statement": "news / talk , be the highest occur format with 2 channel from this format",
        "label": 1,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "(df['format'].value_counts().idxmax() == 'news / talk') & (df['format'].value_counts().max() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2468,
        "statement": "wirl , woam , and wmbd (am) be the only channel that have the same day power as night power",
        "label": 1,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "df[(df['call'].isin(['wirl', 'woam', 'wmbd (am)'])) & (df['day power ( w )'] == df['night power'])].shape[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2469,
        "statement": "wpeo , and wvel among 2 other channel do not have zero night power",
        "label": 0,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "len(df[(df['call'].isin(['wpeo', 'wvel'])) & (df['night power'] != 0)]) == 0 and len(df[(df['night power'] != 0) & (~df['call'].isin(['wpeo', 'wvel']))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2470,
        "statement": "news / talk , be the least occurring format while oldie be the highest",
        "label": 0,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "df['format'].value_counts().idxmax() == 'oldies' and df['format'].value_counts().idxmin() == 'news / talk'",
        "pandas_eval": "False"
    },
    {
        "id": 2471,
        "statement": "pekin be the only city on the list to have up to 3 channel",
        "label": 0,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "len(df[df['city'] == 'pekin']) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2472,
        "statement": "2 channel on the list have the same owner triad and wpw",
        "label": 0,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "df['owner'].value_counts().loc[['triad', 'wpw']].eq(2).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2473,
        "statement": "wirl , woam , and wmbd (am) be the only channel that have different day power as night power",
        "label": 0,
        "table_caption": "media in peoria , illinois",
        "table_text": "freq#call#city#owner#day power ( w )#night power#format#stereo\n1020#wpeo#peoria#pinebrook foundation#1000#0#christian talk#no\n1140#wvel#pekin#cumulus#5000#0#christian#no\n1290#wirl#peoria#triad#5000#5000#oldies#no\n1350#woam#peoria#nelson broadcasting#1000#1000#adult standards#no\n1470#wmbd (am)#peoria#triad#5000#5000#news / talk#no\n1560#wbys#canton#wpw#250#18#news / talk#no\n",
        "pandas_code": "df[(df['call'].isin(['wirl', 'woam', 'wmbd (am)'])) & (df['day power ( w )'] != df['night power'])].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2474,
        "statement": "noah m mason be elect first before anyone else",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 2#barratt o'hara#democratic#1948#lost re - election republican gain#richard b vail (r) 53.6% barratt o'hara (d) 46.4%\nillinois 3#neil j linehan#democratic#1948#lost re - election republican gain#fred e busbey (r) 57.2% neil j linehan (d) 42.8%\nillinois 6#thomas j o'brien#democratic#1942#re - elected#thomas j o'brien (d) 64.6% john m fay (r) 35.4%\nillinois 15#noah m mason#republican#1936#re - elected#noah m mason (r) 63.3% wayne f caskey (d) 36.7%\nillinois 20#sid simpson#republican#1942#re - elected#sid simpson (r) 59.3% howard manning (d) 40.7%\nillinois 25#melvin price#democratic#1944#re - elected#melvin price (d) 64.9% roger d jones (r) 35.1%\n",
        "pandas_code": "df[df['incumbent'] == 'noah m mason']['first elected'].iloc[0] == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2475,
        "statement": "only 2 of the list politician be republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 2#barratt o'hara#democratic#1948#lost re - election republican gain#richard b vail (r) 53.6% barratt o'hara (d) 46.4%\nillinois 3#neil j linehan#democratic#1948#lost re - election republican gain#fred e busbey (r) 57.2% neil j linehan (d) 42.8%\nillinois 6#thomas j o'brien#democratic#1942#re - elected#thomas j o'brien (d) 64.6% john m fay (r) 35.4%\nillinois 15#noah m mason#republican#1936#re - elected#noah m mason (r) 63.3% wayne f caskey (d) 36.7%\nillinois 20#sid simpson#republican#1942#re - elected#sid simpson (r) 59.3% howard manning (d) 40.7%\nillinois 25#melvin price#democratic#1944#re - elected#melvin price (d) 64.9% roger d jones (r) 35.1%\n",
        "pandas_code": "df[df['party'] == 'republican'].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2476,
        "statement": "2 of the list politician be first elect in 1942",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 2#barratt o'hara#democratic#1948#lost re - election republican gain#richard b vail (r) 53.6% barratt o'hara (d) 46.4%\nillinois 3#neil j linehan#democratic#1948#lost re - election republican gain#fred e busbey (r) 57.2% neil j linehan (d) 42.8%\nillinois 6#thomas j o'brien#democratic#1942#re - elected#thomas j o'brien (d) 64.6% john m fay (r) 35.4%\nillinois 15#noah m mason#republican#1936#re - elected#noah m mason (r) 63.3% wayne f caskey (d) 36.7%\nillinois 20#sid simpson#republican#1942#re - elected#sid simpson (r) 59.3% howard manning (d) 40.7%\nillinois 25#melvin price#democratic#1944#re - elected#melvin price (d) 64.9% roger d jones (r) 35.1%\n",
        "pandas_code": "len(df[df['first elected'] == 1942]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2477,
        "statement": "barratt o'hara and neil j linehan be first elect the latest out of the list",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 2#barratt o'hara#democratic#1948#lost re - election republican gain#richard b vail (r) 53.6% barratt o'hara (d) 46.4%\nillinois 3#neil j linehan#democratic#1948#lost re - election republican gain#fred e busbey (r) 57.2% neil j linehan (d) 42.8%\nillinois 6#thomas j o'brien#democratic#1942#re - elected#thomas j o'brien (d) 64.6% john m fay (r) 35.4%\nillinois 15#noah m mason#republican#1936#re - elected#noah m mason (r) 63.3% wayne f caskey (d) 36.7%\nillinois 20#sid simpson#republican#1942#re - elected#sid simpson (r) 59.3% howard manning (d) 40.7%\nillinois 25#melvin price#democratic#1944#re - elected#melvin price (d) 64.9% roger d jones (r) 35.1%\n",
        "pandas_code": "(df[df['incumbent'].isin([\"barratt o'hara\", 'neil j linehan'])]['first elected'].max() == df['first elected'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 2478,
        "statement": "2 of the list incumbent lose their re - election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nillinois 2#barratt o'hara#democratic#1948#lost re - election republican gain#richard b vail (r) 53.6% barratt o'hara (d) 46.4%\nillinois 3#neil j linehan#democratic#1948#lost re - election republican gain#fred e busbey (r) 57.2% neil j linehan (d) 42.8%\nillinois 6#thomas j o'brien#democratic#1942#re - elected#thomas j o'brien (d) 64.6% john m fay (r) 35.4%\nillinois 15#noah m mason#republican#1936#re - elected#noah m mason (r) 63.3% wayne f caskey (d) 36.7%\nillinois 20#sid simpson#republican#1942#re - elected#sid simpson (r) 59.3% howard manning (d) 40.7%\nillinois 25#melvin price#democratic#1944#re - elected#melvin price (d) 64.9% roger d jones (r) 35.1%\n",
        "pandas_code": "(df['result'].str.contains('lost re - election').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2479,
        "statement": "lee hsing be the director for both the silent wife and execution in autumn , neither of which be nominate",
        "label": 1,
        "table_caption": "list of taiwanese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1957 (30th)#amina#amina (\u963f\u7f8e\u5a1c)#yuan congmei#not nominated\n1964 (37th)#lovers' rock#q\u00edngr\u00e9n sh\u00ed (\u60c5\u4eba\u77f3)#pan lei#not nominated\n1966 (39th)#the silent wife#y\u01cen\u01da q\u00edngx\u012bn (\u555e\u5973\u60c5\u6df1)#lee hsing#not nominated\n1972 (45th)#execution in autumn#qi\u016bju\u00e9 (\u79cb\u6c7a)#lee hsing#not nominated\n1976 (49th)#the 800 heroes#b\u0101b\u01cei zhu\u00e0ngsh\u00ec (\u516b\u767e\u58ef\u58eb)#ting shan - si#not nominated\n1980 (53rd)#the legend of the six dynasty#li\u00f9ch\u00e1o gu\u00e0it\u00e1n (\u516d\u671d\u602a\u8ac7)#wong guk - gam#not nominated\n1981 (54th)#if i were for real#ji\u01cer\u00fa w\u01d2sh\u00ec zh\u0113nde (\u5047\u5982\u6211\u662f\u771f\u7684)#wang toon#not nominated\n1982 (55th)#the battle for the republic of china#x\u012bnh\u00e0i shu\u0101ngsh\u00ed (\u8f9b\u4ea5\u96d9\u5341)#ting shan - si#not nominated\n1983 (56th)#growing up#xi\u01ceo b\u00ec de g\u00f9sh\u00ec (\u5c0f\u7562\u7684\u6545\u4e8b)#chen kunhou#not nominated\n1984 (55th)#old mo 's second spring#l\u01ceo m\u00f2 de d\u00ec \u00e8r ge ch\u016bnti\u0101n (\u8001\u83ab\u7684\u7b2c\u4e8c\u500b\u6625\u5929)#lee you - ning#not nominated\n1985 (58th)#kuei - mei , a woman#w\u01d2 zh\u00e8y\u00e0ng gu\u00f2le y\u00ecsh\u0113ng (\u6211\u9019\u6a23\u904e\u4e86\u4e00\u751f)#chang yi#not nominated\n1986 (59th)#the heroic pioneers#t\u00e1ngsh\u0101n gu\u00f2 t\u00e1iw\u0101n (\u5510\u5c71\u904e\u53f0\u7063)#lee shing#not nominated\n1987 (60th)#osmanthus alley#gu\u00echu\u0101 xi\u00e0ng (\u6842\u82b1\u5df7)#chen kunhou#not nominated\n1988 (61st)#my mother 's teahouse#ch\u016bnqi\u016b ch\u00e1sh\u00ec (\u6625\u79cb\u8336\u5ba4)#chen kunhou#not nominated\n1989 (62nd)#a city of sadness#b\u0113iq\u00edng ch\u00e9ngsh\u00ec (\u60b2\u60c5\u57ce\u5e02)#hou hsiao - hsien#not nominated\n1990 (63rd)#song of the exile#k\u00e8t\u00fa qi\u016bh\u00e8n (\u5ba2\u9014\u79cb\u6068)#ann hui#not nominated\n1991 (64th)#a brighter summer day#g\u00fal\u01d0ngji\u0113 sh\u00e0oni\u00e1n sh\u0101r\u00e9n sh\u00ecji\u00e0n (\u726f\u5dba\u8857\u5c11\u5e74\u6bba\u4eba\u4e8b\u4ef6)#edward yang#not nominated\n1992 (65th)#peach blossom land#\u00e0nli\u00e0n t\u00e1ohu\u0101yu\u00e1n (\u6697\u6200\u6843\u82b1\u6e90)#stan lai#not nominated\n1993 (66th)#the wedding banquet#x\u01d0y\u00e0n (\u559c\u5bb4)#ang lee#nominee\n1994 (67th)#eat drink man woman#y\u01d0nsh\u00ed n\u00e1nn\u01da (\u98f2\u98df\u7537\u5973)#ang lee#nominee\n1995 (68th)#super citizen ko#ch\u0101oj\u00ed d\u00e0gu\u00f3m\u00edn (\u8d85\u7d1a\u5927\u570b\u6c11)#wan jen#not nominated\n1996 (69th)#tonight nobody goes home#j\u012bnti\u0101n b\u00f9hu\u00edji\u0101 (\u4eca\u5929\u4e0d\u56de\u5bb6)#sylvia chang#not nominated\n1997 (70th)#yours and mine#w\u01d2de sh\u00e9nj\u012bngb\u00ecng (\u6211\u7684\u795e\u7d93\u75c5)#wang siu - di#not nominated\n1998 (71st)#flowers of shanghai#h\u0101i sh\u00e0ng hu\u0101 (\u6d77\u4e0a\u82b1)#hou hsiao - hsien#not nominated\n1999 (72nd)#march of happiness#ti\u0101nm\u01ce ch\u00e1f\u00e1ng (\u5929\u99ac\u8336\u623f)#lin cheng - sheng#not nominated\n2000 (73rd)#crouching tiger , hidden dragon#w\u00f2h\u01d4 c\u00e1ngl\u00f3ng (\u81e5\u864e\u85cf\u9f8d)#ang lee#won academy award\n2001 (74th)#the cabbie#y\u00f9nzhu\u01censh\u01d2u zh\u012b li\u00e0n (\u904b\u8f49\u624b\u4e4b\u6200)#chen yiwen , huakun zhang#not nominated\n2002 (75th)#the best of times#m\u011bil\u00ec sh\u00edgu\u0101ng (\u7f8e\u9e97\u6642\u5149)#chang tso - chi#not nominated\n2003 (76th)#goodbye , dragon inn#b\u00fas\u00e0n (\u4e0d\u6563)#tsai ming - liang#not nominated\n2004 (77th)#20 30 40#20 30 40#sylvia chang#not nominated\n2005 (78th)#the wayward cloud#ti\u0101nbi\u0101n y\u00ecdu\u01d2y\u00fan (\u5929\u908a\u4e00\u6735\u96f2)#tsai ming - liang#not nominated\n2006 (79th)#blue cha cha#sh\u0113nh\u01cei (\u6df1\u6d77)#chen wen - tang#not nominated\n2007 (80th)#island etude#li\u00e0nx\u00edq\u01d4 (\u7df4\u7fd2\u66f2)#chen huai - en#not nominated\n2008 (81st)#cape no 7#h\u00e1iji\u01ceo q\u012bh\u00e0o (\u6d77\u89d2\u4e03\u865f)#wei te - sheng#not nominated\n2009 (82nd)#no puedo vivir sin ti#b\u00f9n\u00e9ng m\u00e9iy\u01d2u n\u01d0 (\u4e0d\u80fd\u6c92\u6709\u4f60)#leon dai#not nominated\n2010 (83rd)#monga#b\u00e1ng - kah (\u824b\u823a)#doze niu#not nominated\n2011 (84th)#warriors of the rainbow : seediq bale#s\u00e0i d\u00e9 k\u00e8\uff0eba l\u00e1i (\u8cfd\u5fb7\u514b\u2027\u5df4\u840a)#wei te - sheng#made january shortlist\n2012 (85th)#touch of the light#\u9006\u5149\u98db\u7fd4#chang rong - ji#not nominated\n",
        "pandas_code": "all(df[(df['director'] == 'lee hsing') & (df['film title used in nomination'].isin(['the silent wife', 'execution in autumn']))]['result'] == 'not nominated')",
        "pandas_eval": "True"
    },
    {
        "id": 2480,
        "statement": "ang lee be the director for both the wedding banquet and eat drink man woman , and both be nominate",
        "label": 1,
        "table_caption": "list of taiwanese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1957 (30th)#amina#amina (\u963f\u7f8e\u5a1c)#yuan congmei#not nominated\n1964 (37th)#lovers' rock#q\u00edngr\u00e9n sh\u00ed (\u60c5\u4eba\u77f3)#pan lei#not nominated\n1966 (39th)#the silent wife#y\u01cen\u01da q\u00edngx\u012bn (\u555e\u5973\u60c5\u6df1)#lee hsing#not nominated\n1972 (45th)#execution in autumn#qi\u016bju\u00e9 (\u79cb\u6c7a)#lee hsing#not nominated\n1976 (49th)#the 800 heroes#b\u0101b\u01cei zhu\u00e0ngsh\u00ec (\u516b\u767e\u58ef\u58eb)#ting shan - si#not nominated\n1980 (53rd)#the legend of the six dynasty#li\u00f9ch\u00e1o gu\u00e0it\u00e1n (\u516d\u671d\u602a\u8ac7)#wong guk - gam#not nominated\n1981 (54th)#if i were for real#ji\u01cer\u00fa w\u01d2sh\u00ec zh\u0113nde (\u5047\u5982\u6211\u662f\u771f\u7684)#wang toon#not nominated\n1982 (55th)#the battle for the republic of china#x\u012bnh\u00e0i shu\u0101ngsh\u00ed (\u8f9b\u4ea5\u96d9\u5341)#ting shan - si#not nominated\n1983 (56th)#growing up#xi\u01ceo b\u00ec de g\u00f9sh\u00ec (\u5c0f\u7562\u7684\u6545\u4e8b)#chen kunhou#not nominated\n1984 (55th)#old mo 's second spring#l\u01ceo m\u00f2 de d\u00ec \u00e8r ge ch\u016bnti\u0101n (\u8001\u83ab\u7684\u7b2c\u4e8c\u500b\u6625\u5929)#lee you - ning#not nominated\n1985 (58th)#kuei - mei , a woman#w\u01d2 zh\u00e8y\u00e0ng gu\u00f2le y\u00ecsh\u0113ng (\u6211\u9019\u6a23\u904e\u4e86\u4e00\u751f)#chang yi#not nominated\n1986 (59th)#the heroic pioneers#t\u00e1ngsh\u0101n gu\u00f2 t\u00e1iw\u0101n (\u5510\u5c71\u904e\u53f0\u7063)#lee shing#not nominated\n1987 (60th)#osmanthus alley#gu\u00echu\u0101 xi\u00e0ng (\u6842\u82b1\u5df7)#chen kunhou#not nominated\n1988 (61st)#my mother 's teahouse#ch\u016bnqi\u016b ch\u00e1sh\u00ec (\u6625\u79cb\u8336\u5ba4)#chen kunhou#not nominated\n1989 (62nd)#a city of sadness#b\u0113iq\u00edng ch\u00e9ngsh\u00ec (\u60b2\u60c5\u57ce\u5e02)#hou hsiao - hsien#not nominated\n1990 (63rd)#song of the exile#k\u00e8t\u00fa qi\u016bh\u00e8n (\u5ba2\u9014\u79cb\u6068)#ann hui#not nominated\n1991 (64th)#a brighter summer day#g\u00fal\u01d0ngji\u0113 sh\u00e0oni\u00e1n sh\u0101r\u00e9n sh\u00ecji\u00e0n (\u726f\u5dba\u8857\u5c11\u5e74\u6bba\u4eba\u4e8b\u4ef6)#edward yang#not nominated\n1992 (65th)#peach blossom land#\u00e0nli\u00e0n t\u00e1ohu\u0101yu\u00e1n (\u6697\u6200\u6843\u82b1\u6e90)#stan lai#not nominated\n1993 (66th)#the wedding banquet#x\u01d0y\u00e0n (\u559c\u5bb4)#ang lee#nominee\n1994 (67th)#eat drink man woman#y\u01d0nsh\u00ed n\u00e1nn\u01da (\u98f2\u98df\u7537\u5973)#ang lee#nominee\n1995 (68th)#super citizen ko#ch\u0101oj\u00ed d\u00e0gu\u00f3m\u00edn (\u8d85\u7d1a\u5927\u570b\u6c11)#wan jen#not nominated\n1996 (69th)#tonight nobody goes home#j\u012bnti\u0101n b\u00f9hu\u00edji\u0101 (\u4eca\u5929\u4e0d\u56de\u5bb6)#sylvia chang#not nominated\n1997 (70th)#yours and mine#w\u01d2de sh\u00e9nj\u012bngb\u00ecng (\u6211\u7684\u795e\u7d93\u75c5)#wang siu - di#not nominated\n1998 (71st)#flowers of shanghai#h\u0101i sh\u00e0ng hu\u0101 (\u6d77\u4e0a\u82b1)#hou hsiao - hsien#not nominated\n1999 (72nd)#march of happiness#ti\u0101nm\u01ce ch\u00e1f\u00e1ng (\u5929\u99ac\u8336\u623f)#lin cheng - sheng#not nominated\n2000 (73rd)#crouching tiger , hidden dragon#w\u00f2h\u01d4 c\u00e1ngl\u00f3ng (\u81e5\u864e\u85cf\u9f8d)#ang lee#won academy award\n2001 (74th)#the cabbie#y\u00f9nzhu\u01censh\u01d2u zh\u012b li\u00e0n (\u904b\u8f49\u624b\u4e4b\u6200)#chen yiwen , huakun zhang#not nominated\n2002 (75th)#the best of times#m\u011bil\u00ec sh\u00edgu\u0101ng (\u7f8e\u9e97\u6642\u5149)#chang tso - chi#not nominated\n2003 (76th)#goodbye , dragon inn#b\u00fas\u00e0n (\u4e0d\u6563)#tsai ming - liang#not nominated\n2004 (77th)#20 30 40#20 30 40#sylvia chang#not nominated\n2005 (78th)#the wayward cloud#ti\u0101nbi\u0101n y\u00ecdu\u01d2y\u00fan (\u5929\u908a\u4e00\u6735\u96f2)#tsai ming - liang#not nominated\n2006 (79th)#blue cha cha#sh\u0113nh\u01cei (\u6df1\u6d77)#chen wen - tang#not nominated\n2007 (80th)#island etude#li\u00e0nx\u00edq\u01d4 (\u7df4\u7fd2\u66f2)#chen huai - en#not nominated\n2008 (81st)#cape no 7#h\u00e1iji\u01ceo q\u012bh\u00e0o (\u6d77\u89d2\u4e03\u865f)#wei te - sheng#not nominated\n2009 (82nd)#no puedo vivir sin ti#b\u00f9n\u00e9ng m\u00e9iy\u01d2u n\u01d0 (\u4e0d\u80fd\u6c92\u6709\u4f60)#leon dai#not nominated\n2010 (83rd)#monga#b\u00e1ng - kah (\u824b\u823a)#doze niu#not nominated\n2011 (84th)#warriors of the rainbow : seediq bale#s\u00e0i d\u00e9 k\u00e8\uff0eba l\u00e1i (\u8cfd\u5fb7\u514b\u2027\u5df4\u840a)#wei te - sheng#made january shortlist\n2012 (85th)#touch of the light#\u9006\u5149\u98db\u7fd4#chang rong - ji#not nominated\n",
        "pandas_code": "all(df[(df['director'] == 'ang lee') & (df['film title used in nomination'].isin(['the wedding banquet', 'eat drink man woman']))]['result'] == 'nominee')",
        "pandas_eval": "True"
    },
    {
        "id": 2481,
        "statement": "the wayward cloud from 2005 (78th) and the best of time 2002 (75th) be not nominate",
        "label": 1,
        "table_caption": "list of taiwanese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1957 (30th)#amina#amina (\u963f\u7f8e\u5a1c)#yuan congmei#not nominated\n1964 (37th)#lovers' rock#q\u00edngr\u00e9n sh\u00ed (\u60c5\u4eba\u77f3)#pan lei#not nominated\n1966 (39th)#the silent wife#y\u01cen\u01da q\u00edngx\u012bn (\u555e\u5973\u60c5\u6df1)#lee hsing#not nominated\n1972 (45th)#execution in autumn#qi\u016bju\u00e9 (\u79cb\u6c7a)#lee hsing#not nominated\n1976 (49th)#the 800 heroes#b\u0101b\u01cei zhu\u00e0ngsh\u00ec (\u516b\u767e\u58ef\u58eb)#ting shan - si#not nominated\n1980 (53rd)#the legend of the six dynasty#li\u00f9ch\u00e1o gu\u00e0it\u00e1n (\u516d\u671d\u602a\u8ac7)#wong guk - gam#not nominated\n1981 (54th)#if i were for real#ji\u01cer\u00fa w\u01d2sh\u00ec zh\u0113nde (\u5047\u5982\u6211\u662f\u771f\u7684)#wang toon#not nominated\n1982 (55th)#the battle for the republic of china#x\u012bnh\u00e0i shu\u0101ngsh\u00ed (\u8f9b\u4ea5\u96d9\u5341)#ting shan - si#not nominated\n1983 (56th)#growing up#xi\u01ceo b\u00ec de g\u00f9sh\u00ec (\u5c0f\u7562\u7684\u6545\u4e8b)#chen kunhou#not nominated\n1984 (55th)#old mo 's second spring#l\u01ceo m\u00f2 de d\u00ec \u00e8r ge ch\u016bnti\u0101n (\u8001\u83ab\u7684\u7b2c\u4e8c\u500b\u6625\u5929)#lee you - ning#not nominated\n1985 (58th)#kuei - mei , a woman#w\u01d2 zh\u00e8y\u00e0ng gu\u00f2le y\u00ecsh\u0113ng (\u6211\u9019\u6a23\u904e\u4e86\u4e00\u751f)#chang yi#not nominated\n1986 (59th)#the heroic pioneers#t\u00e1ngsh\u0101n gu\u00f2 t\u00e1iw\u0101n (\u5510\u5c71\u904e\u53f0\u7063)#lee shing#not nominated\n1987 (60th)#osmanthus alley#gu\u00echu\u0101 xi\u00e0ng (\u6842\u82b1\u5df7)#chen kunhou#not nominated\n1988 (61st)#my mother 's teahouse#ch\u016bnqi\u016b ch\u00e1sh\u00ec (\u6625\u79cb\u8336\u5ba4)#chen kunhou#not nominated\n1989 (62nd)#a city of sadness#b\u0113iq\u00edng ch\u00e9ngsh\u00ec (\u60b2\u60c5\u57ce\u5e02)#hou hsiao - hsien#not nominated\n1990 (63rd)#song of the exile#k\u00e8t\u00fa qi\u016bh\u00e8n (\u5ba2\u9014\u79cb\u6068)#ann hui#not nominated\n1991 (64th)#a brighter summer day#g\u00fal\u01d0ngji\u0113 sh\u00e0oni\u00e1n sh\u0101r\u00e9n sh\u00ecji\u00e0n (\u726f\u5dba\u8857\u5c11\u5e74\u6bba\u4eba\u4e8b\u4ef6)#edward yang#not nominated\n1992 (65th)#peach blossom land#\u00e0nli\u00e0n t\u00e1ohu\u0101yu\u00e1n (\u6697\u6200\u6843\u82b1\u6e90)#stan lai#not nominated\n1993 (66th)#the wedding banquet#x\u01d0y\u00e0n (\u559c\u5bb4)#ang lee#nominee\n1994 (67th)#eat drink man woman#y\u01d0nsh\u00ed n\u00e1nn\u01da (\u98f2\u98df\u7537\u5973)#ang lee#nominee\n1995 (68th)#super citizen ko#ch\u0101oj\u00ed d\u00e0gu\u00f3m\u00edn (\u8d85\u7d1a\u5927\u570b\u6c11)#wan jen#not nominated\n1996 (69th)#tonight nobody goes home#j\u012bnti\u0101n b\u00f9hu\u00edji\u0101 (\u4eca\u5929\u4e0d\u56de\u5bb6)#sylvia chang#not nominated\n1997 (70th)#yours and mine#w\u01d2de sh\u00e9nj\u012bngb\u00ecng (\u6211\u7684\u795e\u7d93\u75c5)#wang siu - di#not nominated\n1998 (71st)#flowers of shanghai#h\u0101i sh\u00e0ng hu\u0101 (\u6d77\u4e0a\u82b1)#hou hsiao - hsien#not nominated\n1999 (72nd)#march of happiness#ti\u0101nm\u01ce ch\u00e1f\u00e1ng (\u5929\u99ac\u8336\u623f)#lin cheng - sheng#not nominated\n2000 (73rd)#crouching tiger , hidden dragon#w\u00f2h\u01d4 c\u00e1ngl\u00f3ng (\u81e5\u864e\u85cf\u9f8d)#ang lee#won academy award\n2001 (74th)#the cabbie#y\u00f9nzhu\u01censh\u01d2u zh\u012b li\u00e0n (\u904b\u8f49\u624b\u4e4b\u6200)#chen yiwen , huakun zhang#not nominated\n2002 (75th)#the best of times#m\u011bil\u00ec sh\u00edgu\u0101ng (\u7f8e\u9e97\u6642\u5149)#chang tso - chi#not nominated\n2003 (76th)#goodbye , dragon inn#b\u00fas\u00e0n (\u4e0d\u6563)#tsai ming - liang#not nominated\n2004 (77th)#20 30 40#20 30 40#sylvia chang#not nominated\n2005 (78th)#the wayward cloud#ti\u0101nbi\u0101n y\u00ecdu\u01d2y\u00fan (\u5929\u908a\u4e00\u6735\u96f2)#tsai ming - liang#not nominated\n2006 (79th)#blue cha cha#sh\u0113nh\u01cei (\u6df1\u6d77)#chen wen - tang#not nominated\n2007 (80th)#island etude#li\u00e0nx\u00edq\u01d4 (\u7df4\u7fd2\u66f2)#chen huai - en#not nominated\n2008 (81st)#cape no 7#h\u00e1iji\u01ceo q\u012bh\u00e0o (\u6d77\u89d2\u4e03\u865f)#wei te - sheng#not nominated\n2009 (82nd)#no puedo vivir sin ti#b\u00f9n\u00e9ng m\u00e9iy\u01d2u n\u01d0 (\u4e0d\u80fd\u6c92\u6709\u4f60)#leon dai#not nominated\n2010 (83rd)#monga#b\u00e1ng - kah (\u824b\u823a)#doze niu#not nominated\n2011 (84th)#warriors of the rainbow : seediq bale#s\u00e0i d\u00e9 k\u00e8\uff0eba l\u00e1i (\u8cfd\u5fb7\u514b\u2027\u5df4\u840a)#wei te - sheng#made january shortlist\n2012 (85th)#touch of the light#\u9006\u5149\u98db\u7fd4#chang rong - ji#not nominated\n",
        "pandas_code": "all(df[(df['year (ceremony)'].isin(['2005 (78th)', '2002 (75th)'])) & (df['film title used in nomination'].isin(['the wayward cloud', 'the best of times']))]['result'] == 'not nominated')",
        "pandas_eval": "True"
    },
    {
        "id": 2482,
        "statement": "doze niu be the director for monga and chang rong - ji be the director for touch of the light",
        "label": 1,
        "table_caption": "list of taiwanese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1957 (30th)#amina#amina (\u963f\u7f8e\u5a1c)#yuan congmei#not nominated\n1964 (37th)#lovers' rock#q\u00edngr\u00e9n sh\u00ed (\u60c5\u4eba\u77f3)#pan lei#not nominated\n1966 (39th)#the silent wife#y\u01cen\u01da q\u00edngx\u012bn (\u555e\u5973\u60c5\u6df1)#lee hsing#not nominated\n1972 (45th)#execution in autumn#qi\u016bju\u00e9 (\u79cb\u6c7a)#lee hsing#not nominated\n1976 (49th)#the 800 heroes#b\u0101b\u01cei zhu\u00e0ngsh\u00ec (\u516b\u767e\u58ef\u58eb)#ting shan - si#not nominated\n1980 (53rd)#the legend of the six dynasty#li\u00f9ch\u00e1o gu\u00e0it\u00e1n (\u516d\u671d\u602a\u8ac7)#wong guk - gam#not nominated\n1981 (54th)#if i were for real#ji\u01cer\u00fa w\u01d2sh\u00ec zh\u0113nde (\u5047\u5982\u6211\u662f\u771f\u7684)#wang toon#not nominated\n1982 (55th)#the battle for the republic of china#x\u012bnh\u00e0i shu\u0101ngsh\u00ed (\u8f9b\u4ea5\u96d9\u5341)#ting shan - si#not nominated\n1983 (56th)#growing up#xi\u01ceo b\u00ec de g\u00f9sh\u00ec (\u5c0f\u7562\u7684\u6545\u4e8b)#chen kunhou#not nominated\n1984 (55th)#old mo 's second spring#l\u01ceo m\u00f2 de d\u00ec \u00e8r ge ch\u016bnti\u0101n (\u8001\u83ab\u7684\u7b2c\u4e8c\u500b\u6625\u5929)#lee you - ning#not nominated\n1985 (58th)#kuei - mei , a woman#w\u01d2 zh\u00e8y\u00e0ng gu\u00f2le y\u00ecsh\u0113ng (\u6211\u9019\u6a23\u904e\u4e86\u4e00\u751f)#chang yi#not nominated\n1986 (59th)#the heroic pioneers#t\u00e1ngsh\u0101n gu\u00f2 t\u00e1iw\u0101n (\u5510\u5c71\u904e\u53f0\u7063)#lee shing#not nominated\n1987 (60th)#osmanthus alley#gu\u00echu\u0101 xi\u00e0ng (\u6842\u82b1\u5df7)#chen kunhou#not nominated\n1988 (61st)#my mother 's teahouse#ch\u016bnqi\u016b ch\u00e1sh\u00ec (\u6625\u79cb\u8336\u5ba4)#chen kunhou#not nominated\n1989 (62nd)#a city of sadness#b\u0113iq\u00edng ch\u00e9ngsh\u00ec (\u60b2\u60c5\u57ce\u5e02)#hou hsiao - hsien#not nominated\n1990 (63rd)#song of the exile#k\u00e8t\u00fa qi\u016bh\u00e8n (\u5ba2\u9014\u79cb\u6068)#ann hui#not nominated\n1991 (64th)#a brighter summer day#g\u00fal\u01d0ngji\u0113 sh\u00e0oni\u00e1n sh\u0101r\u00e9n sh\u00ecji\u00e0n (\u726f\u5dba\u8857\u5c11\u5e74\u6bba\u4eba\u4e8b\u4ef6)#edward yang#not nominated\n1992 (65th)#peach blossom land#\u00e0nli\u00e0n t\u00e1ohu\u0101yu\u00e1n (\u6697\u6200\u6843\u82b1\u6e90)#stan lai#not nominated\n1993 (66th)#the wedding banquet#x\u01d0y\u00e0n (\u559c\u5bb4)#ang lee#nominee\n1994 (67th)#eat drink man woman#y\u01d0nsh\u00ed n\u00e1nn\u01da (\u98f2\u98df\u7537\u5973)#ang lee#nominee\n1995 (68th)#super citizen ko#ch\u0101oj\u00ed d\u00e0gu\u00f3m\u00edn (\u8d85\u7d1a\u5927\u570b\u6c11)#wan jen#not nominated\n1996 (69th)#tonight nobody goes home#j\u012bnti\u0101n b\u00f9hu\u00edji\u0101 (\u4eca\u5929\u4e0d\u56de\u5bb6)#sylvia chang#not nominated\n1997 (70th)#yours and mine#w\u01d2de sh\u00e9nj\u012bngb\u00ecng (\u6211\u7684\u795e\u7d93\u75c5)#wang siu - di#not nominated\n1998 (71st)#flowers of shanghai#h\u0101i sh\u00e0ng hu\u0101 (\u6d77\u4e0a\u82b1)#hou hsiao - hsien#not nominated\n1999 (72nd)#march of happiness#ti\u0101nm\u01ce ch\u00e1f\u00e1ng (\u5929\u99ac\u8336\u623f)#lin cheng - sheng#not nominated\n2000 (73rd)#crouching tiger , hidden dragon#w\u00f2h\u01d4 c\u00e1ngl\u00f3ng (\u81e5\u864e\u85cf\u9f8d)#ang lee#won academy award\n2001 (74th)#the cabbie#y\u00f9nzhu\u01censh\u01d2u zh\u012b li\u00e0n (\u904b\u8f49\u624b\u4e4b\u6200)#chen yiwen , huakun zhang#not nominated\n2002 (75th)#the best of times#m\u011bil\u00ec sh\u00edgu\u0101ng (\u7f8e\u9e97\u6642\u5149)#chang tso - chi#not nominated\n2003 (76th)#goodbye , dragon inn#b\u00fas\u00e0n (\u4e0d\u6563)#tsai ming - liang#not nominated\n2004 (77th)#20 30 40#20 30 40#sylvia chang#not nominated\n2005 (78th)#the wayward cloud#ti\u0101nbi\u0101n y\u00ecdu\u01d2y\u00fan (\u5929\u908a\u4e00\u6735\u96f2)#tsai ming - liang#not nominated\n2006 (79th)#blue cha cha#sh\u0113nh\u01cei (\u6df1\u6d77)#chen wen - tang#not nominated\n2007 (80th)#island etude#li\u00e0nx\u00edq\u01d4 (\u7df4\u7fd2\u66f2)#chen huai - en#not nominated\n2008 (81st)#cape no 7#h\u00e1iji\u01ceo q\u012bh\u00e0o (\u6d77\u89d2\u4e03\u865f)#wei te - sheng#not nominated\n2009 (82nd)#no puedo vivir sin ti#b\u00f9n\u00e9ng m\u00e9iy\u01d2u n\u01d0 (\u4e0d\u80fd\u6c92\u6709\u4f60)#leon dai#not nominated\n2010 (83rd)#monga#b\u00e1ng - kah (\u824b\u823a)#doze niu#not nominated\n2011 (84th)#warriors of the rainbow : seediq bale#s\u00e0i d\u00e9 k\u00e8\uff0eba l\u00e1i (\u8cfd\u5fb7\u514b\u2027\u5df4\u840a)#wei te - sheng#made january shortlist\n2012 (85th)#touch of the light#\u9006\u5149\u98db\u7fd4#chang rong - ji#not nominated\n",
        "pandas_code": "(df[df['film title used in nomination'] == 'monga']['director'].iloc[0] == 'doze niu') & (df[df['film title used in nomination'] == 'touch of the light']['director'].iloc[0] == 'chang rong - ji')",
        "pandas_eval": "True"
    },
    {
        "id": 2483,
        "statement": "tsai ming - liang be the director for both the wayward cloud and goodbye , dragon inn , although neither be nominate",
        "label": 1,
        "table_caption": "list of taiwanese submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1957 (30th)#amina#amina (\u963f\u7f8e\u5a1c)#yuan congmei#not nominated\n1964 (37th)#lovers' rock#q\u00edngr\u00e9n sh\u00ed (\u60c5\u4eba\u77f3)#pan lei#not nominated\n1966 (39th)#the silent wife#y\u01cen\u01da q\u00edngx\u012bn (\u555e\u5973\u60c5\u6df1)#lee hsing#not nominated\n1972 (45th)#execution in autumn#qi\u016bju\u00e9 (\u79cb\u6c7a)#lee hsing#not nominated\n1976 (49th)#the 800 heroes#b\u0101b\u01cei zhu\u00e0ngsh\u00ec (\u516b\u767e\u58ef\u58eb)#ting shan - si#not nominated\n1980 (53rd)#the legend of the six dynasty#li\u00f9ch\u00e1o gu\u00e0it\u00e1n (\u516d\u671d\u602a\u8ac7)#wong guk - gam#not nominated\n1981 (54th)#if i were for real#ji\u01cer\u00fa w\u01d2sh\u00ec zh\u0113nde (\u5047\u5982\u6211\u662f\u771f\u7684)#wang toon#not nominated\n1982 (55th)#the battle for the republic of china#x\u012bnh\u00e0i shu\u0101ngsh\u00ed (\u8f9b\u4ea5\u96d9\u5341)#ting shan - si#not nominated\n1983 (56th)#growing up#xi\u01ceo b\u00ec de g\u00f9sh\u00ec (\u5c0f\u7562\u7684\u6545\u4e8b)#chen kunhou#not nominated\n1984 (55th)#old mo 's second spring#l\u01ceo m\u00f2 de d\u00ec \u00e8r ge ch\u016bnti\u0101n (\u8001\u83ab\u7684\u7b2c\u4e8c\u500b\u6625\u5929)#lee you - ning#not nominated\n1985 (58th)#kuei - mei , a woman#w\u01d2 zh\u00e8y\u00e0ng gu\u00f2le y\u00ecsh\u0113ng (\u6211\u9019\u6a23\u904e\u4e86\u4e00\u751f)#chang yi#not nominated\n1986 (59th)#the heroic pioneers#t\u00e1ngsh\u0101n gu\u00f2 t\u00e1iw\u0101n (\u5510\u5c71\u904e\u53f0\u7063)#lee shing#not nominated\n1987 (60th)#osmanthus alley#gu\u00echu\u0101 xi\u00e0ng (\u6842\u82b1\u5df7)#chen kunhou#not nominated\n1988 (61st)#my mother 's teahouse#ch\u016bnqi\u016b ch\u00e1sh\u00ec (\u6625\u79cb\u8336\u5ba4)#chen kunhou#not nominated\n1989 (62nd)#a city of sadness#b\u0113iq\u00edng ch\u00e9ngsh\u00ec (\u60b2\u60c5\u57ce\u5e02)#hou hsiao - hsien#not nominated\n1990 (63rd)#song of the exile#k\u00e8t\u00fa qi\u016bh\u00e8n (\u5ba2\u9014\u79cb\u6068)#ann hui#not nominated\n1991 (64th)#a brighter summer day#g\u00fal\u01d0ngji\u0113 sh\u00e0oni\u00e1n sh\u0101r\u00e9n sh\u00ecji\u00e0n (\u726f\u5dba\u8857\u5c11\u5e74\u6bba\u4eba\u4e8b\u4ef6)#edward yang#not nominated\n1992 (65th)#peach blossom land#\u00e0nli\u00e0n t\u00e1ohu\u0101yu\u00e1n (\u6697\u6200\u6843\u82b1\u6e90)#stan lai#not nominated\n1993 (66th)#the wedding banquet#x\u01d0y\u00e0n (\u559c\u5bb4)#ang lee#nominee\n1994 (67th)#eat drink man woman#y\u01d0nsh\u00ed n\u00e1nn\u01da (\u98f2\u98df\u7537\u5973)#ang lee#nominee\n1995 (68th)#super citizen ko#ch\u0101oj\u00ed d\u00e0gu\u00f3m\u00edn (\u8d85\u7d1a\u5927\u570b\u6c11)#wan jen#not nominated\n1996 (69th)#tonight nobody goes home#j\u012bnti\u0101n b\u00f9hu\u00edji\u0101 (\u4eca\u5929\u4e0d\u56de\u5bb6)#sylvia chang#not nominated\n1997 (70th)#yours and mine#w\u01d2de sh\u00e9nj\u012bngb\u00ecng (\u6211\u7684\u795e\u7d93\u75c5)#wang siu - di#not nominated\n1998 (71st)#flowers of shanghai#h\u0101i sh\u00e0ng hu\u0101 (\u6d77\u4e0a\u82b1)#hou hsiao - hsien#not nominated\n1999 (72nd)#march of happiness#ti\u0101nm\u01ce ch\u00e1f\u00e1ng (\u5929\u99ac\u8336\u623f)#lin cheng - sheng#not nominated\n2000 (73rd)#crouching tiger , hidden dragon#w\u00f2h\u01d4 c\u00e1ngl\u00f3ng (\u81e5\u864e\u85cf\u9f8d)#ang lee#won academy award\n2001 (74th)#the cabbie#y\u00f9nzhu\u01censh\u01d2u zh\u012b li\u00e0n (\u904b\u8f49\u624b\u4e4b\u6200)#chen yiwen , huakun zhang#not nominated\n2002 (75th)#the best of times#m\u011bil\u00ec sh\u00edgu\u0101ng (\u7f8e\u9e97\u6642\u5149)#chang tso - chi#not nominated\n2003 (76th)#goodbye , dragon inn#b\u00fas\u00e0n (\u4e0d\u6563)#tsai ming - liang#not nominated\n2004 (77th)#20 30 40#20 30 40#sylvia chang#not nominated\n2005 (78th)#the wayward cloud#ti\u0101nbi\u0101n y\u00ecdu\u01d2y\u00fan (\u5929\u908a\u4e00\u6735\u96f2)#tsai ming - liang#not nominated\n2006 (79th)#blue cha cha#sh\u0113nh\u01cei (\u6df1\u6d77)#chen wen - tang#not nominated\n2007 (80th)#island etude#li\u00e0nx\u00edq\u01d4 (\u7df4\u7fd2\u66f2)#chen huai - en#not nominated\n2008 (81st)#cape no 7#h\u00e1iji\u01ceo q\u012bh\u00e0o (\u6d77\u89d2\u4e03\u865f)#wei te - sheng#not nominated\n2009 (82nd)#no puedo vivir sin ti#b\u00f9n\u00e9ng m\u00e9iy\u01d2u n\u01d0 (\u4e0d\u80fd\u6c92\u6709\u4f60)#leon dai#not nominated\n2010 (83rd)#monga#b\u00e1ng - kah (\u824b\u823a)#doze niu#not nominated\n2011 (84th)#warriors of the rainbow : seediq bale#s\u00e0i d\u00e9 k\u00e8\uff0eba l\u00e1i (\u8cfd\u5fb7\u514b\u2027\u5df4\u840a)#wei te - sheng#made january shortlist\n2012 (85th)#touch of the light#\u9006\u5149\u98db\u7fd4#chang rong - ji#not nominated\n",
        "pandas_code": "(df[df['director'] == 'tsai ming - liang']['film title used in nomination'].isin(['the wayward cloud', 'goodbye , dragon inn']).all()) and (df[df['director'] == 'tsai ming - liang']['result'].isin(['not nominated']).all())",
        "pandas_eval": "True"
    },
    {
        "id": 2484,
        "statement": "season 2010 - 11 have the lowest away leg of 0 - 0",
        "label": 1,
        "table_caption": "fc dacia chi\u0219in\u0103u",
        "table_text": "season#round#opponents#home leg#away leg#aggregate\n2005 - 06#1#vaduz#0 - 2#1 - 0#1 - 2\n2008 - 09#1#borac cacak#1 - 1#1 - 3#2 - 4\n2009 - 10#2#m\u0161k \u017eilina#0 - 2#0 - 1#0 - 3\n2010 - 11#1#fk zeta#0 - 0#1 - 1#1 - 1 ( a )\n2010 - 11#2#kalmar ff#0 - 2#0 - 0#0 - 2\n2012 - 13#1#celje#1 - 0#1 - 0#2 - 0\n2012 - 13#2#elfsborg#1 - 0#0 - 2#1 - 2\n2013 - 14#1#teuta durr\u00ebs#2 - 0#1 - 3#3 - 3 ( a )\n",
        "pandas_code": "df.loc[df['season'] == '2010 - 11', 'away leg'].eq('0 - 0').any()",
        "pandas_eval": "True"
    },
    {
        "id": 2485,
        "statement": "season 2005 - 06 and 2012 - 13 have an aggregate of 1 - 2",
        "label": 1,
        "table_caption": "fc dacia chi\u0219in\u0103u",
        "table_text": "season#round#opponents#home leg#away leg#aggregate\n2005 - 06#1#vaduz#0 - 2#1 - 0#1 - 2\n2008 - 09#1#borac cacak#1 - 1#1 - 3#2 - 4\n2009 - 10#2#m\u0161k \u017eilina#0 - 2#0 - 1#0 - 3\n2010 - 11#1#fk zeta#0 - 0#1 - 1#1 - 1 ( a )\n2010 - 11#2#kalmar ff#0 - 2#0 - 0#0 - 2\n2012 - 13#1#celje#1 - 0#1 - 0#2 - 0\n2012 - 13#2#elfsborg#1 - 0#0 - 2#1 - 2\n2013 - 14#1#teuta durr\u00ebs#2 - 0#1 - 3#3 - 3 ( a )\n",
        "pandas_code": "any(df[df['season'].isin(['2005 - 06', '2012 - 13'])]['aggregate'] == '1 - 2')",
        "pandas_eval": "True"
    },
    {
        "id": 2486,
        "statement": "season 2005 - 06 and 2012 - 13 have an away leg of 1 - 0",
        "label": 1,
        "table_caption": "fc dacia chi\u0219in\u0103u",
        "table_text": "season#round#opponents#home leg#away leg#aggregate\n2005 - 06#1#vaduz#0 - 2#1 - 0#1 - 2\n2008 - 09#1#borac cacak#1 - 1#1 - 3#2 - 4\n2009 - 10#2#m\u0161k \u017eilina#0 - 2#0 - 1#0 - 3\n2010 - 11#1#fk zeta#0 - 0#1 - 1#1 - 1 ( a )\n2010 - 11#2#kalmar ff#0 - 2#0 - 0#0 - 2\n2012 - 13#1#celje#1 - 0#1 - 0#2 - 0\n2012 - 13#2#elfsborg#1 - 0#0 - 2#1 - 2\n2013 - 14#1#teuta durr\u00ebs#2 - 0#1 - 3#3 - 3 ( a )\n",
        "pandas_code": "all(df[df['season'].isin(['2005 - 06', '2012 - 13'])].groupby('season')['away leg'].apply(lambda x: (x == '1 - 0').any()))",
        "pandas_eval": "True"
    },
    {
        "id": 2487,
        "statement": "season 2010 - 11 have the lowest home leg of 0 - 0",
        "label": 1,
        "table_caption": "fc dacia chi\u0219in\u0103u",
        "table_text": "season#round#opponents#home leg#away leg#aggregate\n2005 - 06#1#vaduz#0 - 2#1 - 0#1 - 2\n2008 - 09#1#borac cacak#1 - 1#1 - 3#2 - 4\n2009 - 10#2#m\u0161k \u017eilina#0 - 2#0 - 1#0 - 3\n2010 - 11#1#fk zeta#0 - 0#1 - 1#1 - 1 ( a )\n2010 - 11#2#kalmar ff#0 - 2#0 - 0#0 - 2\n2012 - 13#1#celje#1 - 0#1 - 0#2 - 0\n2012 - 13#2#elfsborg#1 - 0#0 - 2#1 - 2\n2013 - 14#1#teuta durr\u00ebs#2 - 0#1 - 3#3 - 3 ( a )\n",
        "pandas_code": "df[(df['season'] == '2010 - 11') & (df['home leg'] == '0 - 0')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 2489,
        "statement": "milwaukee be the team 2 day in a row",
        "label": 1,
        "table_caption": "2008 - 09 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n33#january 2#milwaukee#l 75 - 103 (ot)#boris diaw (16)#emeka okafor (12)#boris diaw (6)#bradley center 15107#11 - 22\n34#january 3#milwaukee#w 102 - 92 (ot)#gerald wallace (24)#boris diaw , raymond felton , emeka okafor (6)#boris diaw (7)#time warner cable arena 14201#12 - 22\n35#january 6#boston#w 114 - 106 (ot)#raymond felton (25)#emeka okafor (17)#raymond felton (8)#time warner cable arena 17112#13 - 22\n36#january 7#cleveland#l 81 - 111 (ot)#raymond felton (15)#boris diaw (8)#boris diaw (6)#quicken loans arena 20562#13 - 23\n37#january 9#philadelphia#l 87 - 93 (ot)#emeka okafor (24)#emeka okafor (11)#raymond felton (8)#wachovia center 14235#13 - 24\n38#january 10#washington#w 92 - 89 (ot)#raja bell (19)#emeka okafor (6)#raymond felton (11)#verizon center 20173#14 - 24\n39#january 13#detroit#w 80 - 78 (ot)#raymond felton (23)#gerald wallace (10)#raymond felton (9)#the palace of auburn hills 22076#15 - 24\n40#january 17#portland#w 102 - 97 (ot)#gerald wallace (31)#gerald wallace (16)#raymond felton , boris diaw (7)#time warner cable arena 17482#16 - 24\n41#january 19#san antonio#l 84 - 86 (ot)#raja bell (25)#boris diaw (13)#raymond felton (9)#time warner cable arena 16160#16 - 25\n42#january 21#memphis#w 101 - 86 (ot)#raja bell (25)#emeka okafor (15)#gerald wallace (9)#time warner cable arena 11249#17 - 25\n43#january 23#phoenix#w 98 - 76 (ot)#gerald wallace (28)#boris diaw (11)#raja bell (8)#time warner cable arena 19104#18 - 25\n44#january 25#indiana#l 93 - 98 (ot)#raja bell , boris diaw (18)#emeka okafor (8)#raymond felton (12)#conseco fieldhouse 10936#18 - 26\n45#january 27#la lakers#w 117 - 110 (2ot)#boris diaw (23)#raymond felton , emeka okafor (11)#boris diaw , raymond felton (9)#staples center 18997#19 - 26\n46#january 28#portland#l 74 - 88 (ot)#emeka okafor (18)#emeka okafor , raymond felton (5)#raymond felton (8)#rose garden 20380#19 - 27\n",
        "pandas_code": "((df['team'].iloc[0] == 'milwaukee') & (df['team'].iloc[1] == 'milwaukee'))",
        "pandas_eval": "True"
    },
    {
        "id": 2490,
        "statement": "raymond felton have the high assist more often than any other player",
        "label": 1,
        "table_caption": "2008 - 09 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n33#january 2#milwaukee#l 75 - 103 (ot)#boris diaw (16)#emeka okafor (12)#boris diaw (6)#bradley center 15107#11 - 22\n34#january 3#milwaukee#w 102 - 92 (ot)#gerald wallace (24)#boris diaw , raymond felton , emeka okafor (6)#boris diaw (7)#time warner cable arena 14201#12 - 22\n35#january 6#boston#w 114 - 106 (ot)#raymond felton (25)#emeka okafor (17)#raymond felton (8)#time warner cable arena 17112#13 - 22\n36#january 7#cleveland#l 81 - 111 (ot)#raymond felton (15)#boris diaw (8)#boris diaw (6)#quicken loans arena 20562#13 - 23\n37#january 9#philadelphia#l 87 - 93 (ot)#emeka okafor (24)#emeka okafor (11)#raymond felton (8)#wachovia center 14235#13 - 24\n38#january 10#washington#w 92 - 89 (ot)#raja bell (19)#emeka okafor (6)#raymond felton (11)#verizon center 20173#14 - 24\n39#january 13#detroit#w 80 - 78 (ot)#raymond felton (23)#gerald wallace (10)#raymond felton (9)#the palace of auburn hills 22076#15 - 24\n40#january 17#portland#w 102 - 97 (ot)#gerald wallace (31)#gerald wallace (16)#raymond felton , boris diaw (7)#time warner cable arena 17482#16 - 24\n41#january 19#san antonio#l 84 - 86 (ot)#raja bell (25)#boris diaw (13)#raymond felton (9)#time warner cable arena 16160#16 - 25\n42#january 21#memphis#w 101 - 86 (ot)#raja bell (25)#emeka okafor (15)#gerald wallace (9)#time warner cable arena 11249#17 - 25\n43#january 23#phoenix#w 98 - 76 (ot)#gerald wallace (28)#boris diaw (11)#raja bell (8)#time warner cable arena 19104#18 - 25\n44#january 25#indiana#l 93 - 98 (ot)#raja bell , boris diaw (18)#emeka okafor (8)#raymond felton (12)#conseco fieldhouse 10936#18 - 26\n45#january 27#la lakers#w 117 - 110 (2ot)#boris diaw (23)#raymond felton , emeka okafor (11)#boris diaw , raymond felton (9)#staples center 18997#19 - 26\n46#january 28#portland#l 74 - 88 (ot)#emeka okafor (18)#emeka okafor , raymond felton (5)#raymond felton (8)#rose garden 20380#19 - 27\n",
        "pandas_code": "df['high assists'].str.contains('raymond felton').sum() > df['high assists'].apply(lambda x: any(player in x for player in df['high assists'].str.extract(r'([a-zA-Z ]+) \\(')[0].unique() if player != 'raymond felton')).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2491,
        "statement": "raymond felton be not the only player to have the high assist and high point in a game",
        "label": 1,
        "table_caption": "2008 - 09 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n33#january 2#milwaukee#l 75 - 103 (ot)#boris diaw (16)#emeka okafor (12)#boris diaw (6)#bradley center 15107#11 - 22\n34#january 3#milwaukee#w 102 - 92 (ot)#gerald wallace (24)#boris diaw , raymond felton , emeka okafor (6)#boris diaw (7)#time warner cable arena 14201#12 - 22\n35#january 6#boston#w 114 - 106 (ot)#raymond felton (25)#emeka okafor (17)#raymond felton (8)#time warner cable arena 17112#13 - 22\n36#january 7#cleveland#l 81 - 111 (ot)#raymond felton (15)#boris diaw (8)#boris diaw (6)#quicken loans arena 20562#13 - 23\n37#january 9#philadelphia#l 87 - 93 (ot)#emeka okafor (24)#emeka okafor (11)#raymond felton (8)#wachovia center 14235#13 - 24\n38#january 10#washington#w 92 - 89 (ot)#raja bell (19)#emeka okafor (6)#raymond felton (11)#verizon center 20173#14 - 24\n39#january 13#detroit#w 80 - 78 (ot)#raymond felton (23)#gerald wallace (10)#raymond felton (9)#the palace of auburn hills 22076#15 - 24\n40#january 17#portland#w 102 - 97 (ot)#gerald wallace (31)#gerald wallace (16)#raymond felton , boris diaw (7)#time warner cable arena 17482#16 - 24\n41#january 19#san antonio#l 84 - 86 (ot)#raja bell (25)#boris diaw (13)#raymond felton (9)#time warner cable arena 16160#16 - 25\n42#january 21#memphis#w 101 - 86 (ot)#raja bell (25)#emeka okafor (15)#gerald wallace (9)#time warner cable arena 11249#17 - 25\n43#january 23#phoenix#w 98 - 76 (ot)#gerald wallace (28)#boris diaw (11)#raja bell (8)#time warner cable arena 19104#18 - 25\n44#january 25#indiana#l 93 - 98 (ot)#raja bell , boris diaw (18)#emeka okafor (8)#raymond felton (12)#conseco fieldhouse 10936#18 - 26\n45#january 27#la lakers#w 117 - 110 (2ot)#boris diaw (23)#raymond felton , emeka okafor (11)#boris diaw , raymond felton (9)#staples center 18997#19 - 26\n46#january 28#portland#l 74 - 88 (ot)#emeka okafor (18)#emeka okafor , raymond felton (5)#raymond felton (8)#rose garden 20380#19 - 27\n",
        "pandas_code": "any(df['high points'].str.contains('raymond felton') & df['high assists'].str.contains('raymond felton'))",
        "pandas_eval": "True"
    },
    {
        "id": 2492,
        "statement": "time warner cable arena 's highest attendance be on january 23",
        "label": 1,
        "table_caption": "2008 - 09 charlotte bobcats season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n33#january 2#milwaukee#l 75 - 103 (ot)#boris diaw (16)#emeka okafor (12)#boris diaw (6)#bradley center 15107#11 - 22\n34#january 3#milwaukee#w 102 - 92 (ot)#gerald wallace (24)#boris diaw , raymond felton , emeka okafor (6)#boris diaw (7)#time warner cable arena 14201#12 - 22\n35#january 6#boston#w 114 - 106 (ot)#raymond felton (25)#emeka okafor (17)#raymond felton (8)#time warner cable arena 17112#13 - 22\n36#january 7#cleveland#l 81 - 111 (ot)#raymond felton (15)#boris diaw (8)#boris diaw (6)#quicken loans arena 20562#13 - 23\n37#january 9#philadelphia#l 87 - 93 (ot)#emeka okafor (24)#emeka okafor (11)#raymond felton (8)#wachovia center 14235#13 - 24\n38#january 10#washington#w 92 - 89 (ot)#raja bell (19)#emeka okafor (6)#raymond felton (11)#verizon center 20173#14 - 24\n39#january 13#detroit#w 80 - 78 (ot)#raymond felton (23)#gerald wallace (10)#raymond felton (9)#the palace of auburn hills 22076#15 - 24\n40#january 17#portland#w 102 - 97 (ot)#gerald wallace (31)#gerald wallace (16)#raymond felton , boris diaw (7)#time warner cable arena 17482#16 - 24\n41#january 19#san antonio#l 84 - 86 (ot)#raja bell (25)#boris diaw (13)#raymond felton (9)#time warner cable arena 16160#16 - 25\n42#january 21#memphis#w 101 - 86 (ot)#raja bell (25)#emeka okafor (15)#gerald wallace (9)#time warner cable arena 11249#17 - 25\n43#january 23#phoenix#w 98 - 76 (ot)#gerald wallace (28)#boris diaw (11)#raja bell (8)#time warner cable arena 19104#18 - 25\n44#january 25#indiana#l 93 - 98 (ot)#raja bell , boris diaw (18)#emeka okafor (8)#raymond felton (12)#conseco fieldhouse 10936#18 - 26\n45#january 27#la lakers#w 117 - 110 (2ot)#boris diaw (23)#raymond felton , emeka okafor (11)#boris diaw , raymond felton (9)#staples center 18997#19 - 26\n46#january 28#portland#l 74 - 88 (ot)#emeka okafor (18)#emeka okafor , raymond felton (5)#raymond felton (8)#rose garden 20380#19 - 27\n",
        "pandas_code": "(df[df['location attendance'].str.contains('time warner cable arena')]['location attendance'].str.extract('(\\\\d+)').astype(int).idxmax() == df[df['date'] == 'january 23'].index[0]).item()",
        "pandas_eval": "True"
    },
    {
        "id": 2494,
        "statement": "the game with the highest attendance occur on november 4 in los angeles where the 49ers lose to the ram in front of 54346 attendee",
        "label": 1,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "(df['attendance'].max() == 54346) & (df[df['attendance'] == 54346]['date'].iloc[0] == 'november 4') & (df[df['attendance'] == 54346]['home'].iloc[0] == 'los angeles rams') & (df[df['attendance'] == 54346]['visitor'].iloc[0] == 'san francisco 49ers')",
        "pandas_eval": "True"
    },
    {
        "id": 2495,
        "statement": "the game with the lowest attendance occur on november 25 in new york where the 49ers tie with the new york yank in front of 10184 attendee",
        "label": 1,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "(df['attendance'].min() == 10184) & (df[df['attendance'] == 10184]['date'].iloc[0] == 'november 25') & (df[df['attendance'] == 10184]['home'].iloc[0] == 'new york yanks') & (df[df['attendance'] == 10184]['visitor'].iloc[0] == 'san francisco 49ers') & (df[df['attendance'] == 10184]['score'].iloc[0] == '10 - 10')",
        "pandas_eval": "True"
    },
    {
        "id": 2496,
        "statement": "the 49ers score the most touchdown , 44 , at a home game against the ram on october 28",
        "label": 1,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "df[(df['date'] == 'october 28') & (df['home'] == 'san francisco 49ers') & (df['visitor'] == 'los angeles rams') & (df['score'].str.contains('44'))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2497,
        "statement": "the 49ers score the least amount of touchdown , 7 , in chicago against the bear on october 21",
        "label": 1,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "df[(df['date'] == 'october 21') & (df['home'] == 'chicago bears') & (df['visitor'] == 'san francisco 49ers') & (df['score'].str.split(' - ').str[1].astype(int) == 7)].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 2499,
        "statement": "the game with the highest attendance occur on november 4 in los angeles where the 49ers lose to the ram in front of 49538 attendee",
        "label": 0,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "(df['attendance'].max() == 49538) & (df[df['attendance'] == 49538]['date'].iloc[0] == 'november 4') & (df[df['attendance'] == 49538]['home'].iloc[0] == 'san francisco 49ers') & (df[df['attendance'] == 49538]['visitor'].iloc[0] == 'los angeles rams') & (df[df['attendance'] == 49538]['score'].iloc[0] == '44 - 17')",
        "pandas_eval": "False"
    },
    {
        "id": 2500,
        "statement": "the game with the lowest attendance occur on november 25 in new york where the 49ers tie with the yank in front of 15121 attendee",
        "label": 0,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "(df['attendance'].min() == 15121) & (df[df['attendance'] == 15121]['date'].iloc[0] == 'november 25') & (df[df['attendance'] == 15121]['home'].iloc[0] == 'new york yanks') & (df[df['attendance'] == 15121]['visitor'].iloc[0] == 'san francisco 49ers') & (df[df['attendance'] == 15121]['score'].iloc[0] == '10 - 10')",
        "pandas_eval": "False"
    },
    {
        "id": 2501,
        "statement": "the 49ers score the most point , 44 , at a home game against the cardinal on october 28",
        "label": 0,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "df[(df['home'] == 'san francisco 49ers') & (df['visitor'] == 'chicago cardinals') & (df['date'] == 'october 28') & (df['score'].str.split(' - ').apply(lambda x: int(x[0])) == 44)].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 2502,
        "statement": "the 49ers score their least amount of point , 7 , in chicago against the packer on october 21",
        "label": 0,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "(df[(df['home'] == 'chicago bears') & (df['visitor'] == 'san francisco 49ers') & (df['date'] == 'october 21') & (df['score'].str.split(' - ').apply(lambda x: int(x[1])) == 7)].empty)",
        "pandas_eval": "False"
    },
    {
        "id": 2503,
        "statement": "the 49ers play against most team only 1 time during the 1951 season , but they play the yank , packer and steelers 2 time each",
        "label": 0,
        "table_caption": "1951 san francisco 49ers season",
        "table_text": "date#visitor#score#home#record#attendance\nseptember 30#cleveland browns#24 - 10#san francisco 49ers#1 - 0 - 0#48263\noctober 6#san francisco 49ers#21 - 14#philadelphia eagles#1 - 1 - 0#23827\noctober 14#san francisco 49ers#28 - 24#pittsburgh steelers#2 - 1 - 0#27124\noctober 21#san francisco 49ers#13 - 7#chicago bears#2 - 2 - 0#42296\noctober 28#los angeles rams#44 - 17#san francisco 49ers#3 - 2 - 0#49538\nnovember 4#san francisco 49ers#23 - 16#los angeles rams#3 - 3 - 0#54346\nnovember 11#new york yanks#19 - 14#san francisco 49ers#4 - 3 - 0#25538\nnovember 18#chicago cardinals#27 - 21#san francisco 49ers#4 - 4 - 0#19658\nnovember 25#san francisco 49ers#10 - 10#new york yanks#4 - 4 - 1#10184\ndecember 2#san francisco 49ers#20 - 10#detroit lions#5 - 4 - 1#46467\ndecember 9#green bay packers#31 - 19#san francisco 49ers#6 - 4 - 1#15121\ndecember 16#detroit lions#21 - 17#san francisco 49ers#7 - 4 - 1#27276\n",
        "pandas_code": "df['visitor'].value_counts().add(df['home'].value_counts(), fill_value=0).eq(2).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2504,
        "statement": "only the alhambra and and ibiza / ibiza sc / ibiza st have be make consistently from 1998 to 2011",
        "label": 1,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "df[(df['model'].isin(['seat alhambra', 'seat ibiza / seat ibiza sc / seat ibiza st'])) & (df.iloc[:, 1:].ne('-').all(axis=1))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2505,
        "statement": "the mii be the car that have be manufacture the least",
        "label": 1,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "df[df['model'] == 'seat mii'].iloc[:, 1:].replace('-', 0).astype(int).sum().sum() == df.iloc[:, 1:].replace('-', 0).astype(int).sum(axis=1).min()",
        "pandas_eval": "True"
    },
    {
        "id": 2508,
        "statement": "1999 and 2000 be the only 2 year in which production total over 500000",
        "label": 1,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "(df.loc[df['model'] == 'total annual production annual', ['1999', '2000']].astype(int) > 500000).all(axis=1).all() and (df.loc[df['model'] == 'total annual production annual', df.columns.difference(['model', '1999', '2000'])].astype(int) <= 500000).all(axis=1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2509,
        "statement": "only the alhambra and the ibiza / ibiza sc / ibiza st have be make consistently from 1999 to 2011",
        "label": 0,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "all(df[(df['model'].str.contains('alhambra|ibiza / ibiza sc / ibiza st'))].iloc[:, 2:].replace('-', '0').astype(int).gt(0).all()) and not any(df[~df['model'].str.contains('alhambra|ibiza / ibiza sc / ibiza st')].iloc[:, 2:].replace('-', '0').astype(int).gt(0).all(axis=1))",
        "pandas_eval": "False"
    },
    {
        "id": 2510,
        "statement": "the mii , along with toledo , be car that have be manufacture the least",
        "label": 0,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "(df[df['model'].isin(['seat mii', 'seat toledo'])].iloc[:, 1:].replace('-', '0').astype(int).sum(axis=1) == df.iloc[:, 1:].replace('-', '0').astype(int).sum(axis=1).min()).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2511,
        "statement": "2006 be the final year for both the inca and the inca kombi",
        "label": 0,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "((df['model'] == 'seat inca') | (df['model'] == 'seat inca kombi')).any() and df.loc[(df['model'] == 'seat inca') | (df['model'] == 'seat inca kombi'), '2006'].notna().all() and df.loc[(df['model'] == 'seat inca') | (df['model'] == 'seat inca kombi'), '2007':].isna().all().all()",
        "pandas_eval": "False"
    },
    {
        "id": 2512,
        "statement": "the ibiza / ibiza sc / ibiza st be the 3rd most produce vehicle",
        "label": 0,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "(df[df['model'].str.contains('ibiza / ibiza sc / ibiza st')].iloc[:, 1:].sum().sum() == df.iloc[:, 1:].sum(axis=1).sort_values(ascending=False).iloc[2])",
        "pandas_eval": "False"
    },
    {
        "id": 2513,
        "statement": "1998 and 2001 be the only 2 year in which production total over 500000",
        "label": 0,
        "table_caption": "seat",
        "table_text": "model#1998#1999#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011\nseat marbella#2337#-#-#-#-#-#-#-#-#-#-#-#-#-\nseat arosa#38338#46410#28403#22980#19627#13814#9368#-#-#-#-#-#-#-\nseat mii#-#-#-#-#-#-#-#-#-#-#-#-#-#990\nseat ibiza / seat ibiza sc / seat ibiza st#180775#194245#199279#188427#197311#220497#183754#168645#183848#172206#192470#173715#189083#191183\nseat inca#17226#19221#16328#15207#11802#7982#-#-#-#-#-#-#-#-\nseat inca kombi#7708#8573#5534#5316#3879#2150#-#-#-#-#-#-#-#-\nseat c\u00f3rdoba / seat c\u00f3rdoba vario#108749#111894#97685#78770#58646#59348#46821#37568#31058#29747#20439#4861#-#-\nseat le\u00f3n#-#6080#93123#91939#93606#96536#90850#98130#126511#120630#96761#66368#79462#80736\nseat altea / seat altea xl / seat altea freetrack#-#-#-#-#-#-#67125#65174#58288#71377#54770#32791#43351#42329\nseat toledo#42325#105818#59480#47645#39503#36026#38962#20600#8613#4744#5484#571#-#-\nseat exeo / seat exeo st#-#-#-#-#-#-#-#-#-#-#369#22981#23108#19559\nseat alhambra#21300#27440#23924#26524#26308#23693#21580#14902#14352#14242#10282#6215#10023#18139\ntotal annual production annual#418758#519681#523756#476808#450682#460046#458460#405019#422670#412946#380575#307502#349000#353936\n",
        "pandas_code": "(df.loc[df['model'] == 'total annual production annual', ['1998', '2001']].astype(int).sum(axis=1) > 500000).all() and (df.loc[df['model'] == 'total annual production annual', df.columns.difference(['model', '1998', '2001'])].astype(int).sum(axis=1) <= 500000).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2514,
        "statement": "the lowest attendance figure for a game be 31204",
        "label": 1,
        "table_caption": "1984 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 2#cincinnati bengals#w 20 - 17#mile high stadium#1 - 0#74178\n2#september 9#chicago bears#l 0 - 27#soldier field#1 - 1#54335\n3#september 16#cleveland browns#w 24 - 14#cleveland stadium#2 - 1#61980\n4#september 23#kansas city chiefs#w 21 - 0#mile high stadium#3 - 1#74263\n5#september 30#los angeles raiders#w 16 - 13#mile high stadium#4 - 1#74833\n6#october 7#detroit lions#w 28 - 7#pontiac silverdome#5 - 1#55836\n7#october 15#green bay packers#w 17 - 14#mile high stadium#6 - 1#62546\n8#october 21#buffalo bills#w 37 - 7#rich stadium#7 - 1#31204\n9#october 28#los angeles raiders#w 22 - 19 (ot)#los angeles memorial coliseum#8 - 1#91020\n10#november 4#new england patriots#w 26 - 19#mile high stadium#9 - 1#74908\n11#november 11#san diego chargers#w 16 - 13#jack murphy stadium#10 - 1#53162\n12#november 18#minnesota vikings#w 42 - 21#mile high stadium#11 - 1#74716\n13#november 25#seattle seahawks#l 24 - 27#mile high stadium#11 - 2#74922\n14#december 2#kansas city chiefs#l 13 - 16#arrowhead stadium#11 - 3#38494\n15#december 9#san diego chargers#w 16 - 13#mile high stadium#12 - 3#74867\n",
        "pandas_code": "df['attendance'].min() == 31204",
        "pandas_eval": "True"
    },
    {
        "id": 2515,
        "statement": "the bronco be shut out 1 time during the season",
        "label": 1,
        "table_caption": "1984 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 2#cincinnati bengals#w 20 - 17#mile high stadium#1 - 0#74178\n2#september 9#chicago bears#l 0 - 27#soldier field#1 - 1#54335\n3#september 16#cleveland browns#w 24 - 14#cleveland stadium#2 - 1#61980\n4#september 23#kansas city chiefs#w 21 - 0#mile high stadium#3 - 1#74263\n5#september 30#los angeles raiders#w 16 - 13#mile high stadium#4 - 1#74833\n6#october 7#detroit lions#w 28 - 7#pontiac silverdome#5 - 1#55836\n7#october 15#green bay packers#w 17 - 14#mile high stadium#6 - 1#62546\n8#october 21#buffalo bills#w 37 - 7#rich stadium#7 - 1#31204\n9#october 28#los angeles raiders#w 22 - 19 (ot)#los angeles memorial coliseum#8 - 1#91020\n10#november 4#new england patriots#w 26 - 19#mile high stadium#9 - 1#74908\n11#november 11#san diego chargers#w 16 - 13#jack murphy stadium#10 - 1#53162\n12#november 18#minnesota vikings#w 42 - 21#mile high stadium#11 - 1#74716\n13#november 25#seattle seahawks#l 24 - 27#mile high stadium#11 - 2#74922\n14#december 2#kansas city chiefs#l 13 - 16#arrowhead stadium#11 - 3#38494\n15#december 9#san diego chargers#w 16 - 13#mile high stadium#12 - 3#74867\n",
        "pandas_code": "df['result'].str.startswith('l 0 -').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2516,
        "statement": "the bronco have 7 win and 1 loss at mile high stadium",
        "label": 1,
        "table_caption": "1984 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 2#cincinnati bengals#w 20 - 17#mile high stadium#1 - 0#74178\n2#september 9#chicago bears#l 0 - 27#soldier field#1 - 1#54335\n3#september 16#cleveland browns#w 24 - 14#cleveland stadium#2 - 1#61980\n4#september 23#kansas city chiefs#w 21 - 0#mile high stadium#3 - 1#74263\n5#september 30#los angeles raiders#w 16 - 13#mile high stadium#4 - 1#74833\n6#october 7#detroit lions#w 28 - 7#pontiac silverdome#5 - 1#55836\n7#october 15#green bay packers#w 17 - 14#mile high stadium#6 - 1#62546\n8#october 21#buffalo bills#w 37 - 7#rich stadium#7 - 1#31204\n9#october 28#los angeles raiders#w 22 - 19 (ot)#los angeles memorial coliseum#8 - 1#91020\n10#november 4#new england patriots#w 26 - 19#mile high stadium#9 - 1#74908\n11#november 11#san diego chargers#w 16 - 13#jack murphy stadium#10 - 1#53162\n12#november 18#minnesota vikings#w 42 - 21#mile high stadium#11 - 1#74716\n13#november 25#seattle seahawks#l 24 - 27#mile high stadium#11 - 2#74922\n14#december 2#kansas city chiefs#l 13 - 16#arrowhead stadium#11 - 3#38494\n15#december 9#san diego chargers#w 16 - 13#mile high stadium#12 - 3#74867\n",
        "pandas_code": "(df[(df['game site'] == 'mile high stadium') & (df['result'].str.startswith('w'))].shape[0] == 7) & (df[(df['game site'] == 'mile high stadium') & (df['result'].str.startswith('l'))].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2517,
        "statement": "denver win their only overtime game of the year , versus the raider",
        "label": 1,
        "table_caption": "1984 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 2#cincinnati bengals#w 20 - 17#mile high stadium#1 - 0#74178\n2#september 9#chicago bears#l 0 - 27#soldier field#1 - 1#54335\n3#september 16#cleveland browns#w 24 - 14#cleveland stadium#2 - 1#61980\n4#september 23#kansas city chiefs#w 21 - 0#mile high stadium#3 - 1#74263\n5#september 30#los angeles raiders#w 16 - 13#mile high stadium#4 - 1#74833\n6#october 7#detroit lions#w 28 - 7#pontiac silverdome#5 - 1#55836\n7#october 15#green bay packers#w 17 - 14#mile high stadium#6 - 1#62546\n8#october 21#buffalo bills#w 37 - 7#rich stadium#7 - 1#31204\n9#october 28#los angeles raiders#w 22 - 19 (ot)#los angeles memorial coliseum#8 - 1#91020\n10#november 4#new england patriots#w 26 - 19#mile high stadium#9 - 1#74908\n11#november 11#san diego chargers#w 16 - 13#jack murphy stadium#10 - 1#53162\n12#november 18#minnesota vikings#w 42 - 21#mile high stadium#11 - 1#74716\n13#november 25#seattle seahawks#l 24 - 27#mile high stadium#11 - 2#74922\n14#december 2#kansas city chiefs#l 13 - 16#arrowhead stadium#11 - 3#38494\n15#december 9#san diego chargers#w 16 - 13#mile high stadium#12 - 3#74867\n",
        "pandas_code": "df[df['result'].str.contains('ot') & df['opponent'].str.contains('raiders')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2518,
        "statement": "the most point score by the bronco in 1 game be 42",
        "label": 1,
        "table_caption": "1984 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 2#cincinnati bengals#w 20 - 17#mile high stadium#1 - 0#74178\n2#september 9#chicago bears#l 0 - 27#soldier field#1 - 1#54335\n3#september 16#cleveland browns#w 24 - 14#cleveland stadium#2 - 1#61980\n4#september 23#kansas city chiefs#w 21 - 0#mile high stadium#3 - 1#74263\n5#september 30#los angeles raiders#w 16 - 13#mile high stadium#4 - 1#74833\n6#october 7#detroit lions#w 28 - 7#pontiac silverdome#5 - 1#55836\n7#october 15#green bay packers#w 17 - 14#mile high stadium#6 - 1#62546\n8#october 21#buffalo bills#w 37 - 7#rich stadium#7 - 1#31204\n9#october 28#los angeles raiders#w 22 - 19 (ot)#los angeles memorial coliseum#8 - 1#91020\n10#november 4#new england patriots#w 26 - 19#mile high stadium#9 - 1#74908\n11#november 11#san diego chargers#w 16 - 13#jack murphy stadium#10 - 1#53162\n12#november 18#minnesota vikings#w 42 - 21#mile high stadium#11 - 1#74716\n13#november 25#seattle seahawks#l 24 - 27#mile high stadium#11 - 2#74922\n14#december 2#kansas city chiefs#l 13 - 16#arrowhead stadium#11 - 3#38494\n15#december 9#san diego chargers#w 16 - 13#mile high stadium#12 - 3#74867\n",
        "pandas_code": "df['result'].str.extract(r'(\\d+) - \\d+').astype(int).max().item() == 42",
        "pandas_eval": "True"
    },
    {
        "id": 2519,
        "statement": "kevin bond depart 2 week before alan buckley",
        "label": 1,
        "table_caption": "2008 - 09 football league two",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment#position in table\nbournemouth#kevin bond#contract terminated#1 september 2008#jimmy quinn#2 september 2008#23rd\ngrimsby town#alan buckley#contract terminated#15 september 2008#mike newell#6 october 2008#20th\nport vale#lee sinnott#mutual consent#22 september 2008#dean glover#6 october 2008#16th\nchester city#simon davies#contract terminated#11 november 2008#mark wright#14 november 2008#19th\nbarnet#paul fairclough#resigned#28 december 2008#ian hendon#21 april 2009#16th\n",
        "pandas_code": "(pd.to_datetime(df[df['outgoing manager'] == 'kevin bond']['date of vacancy'].iloc[0]) + pd.Timedelta(weeks=2)) <= pd.to_datetime(df[df['outgoing manager'] == 'alan buckley']['date of vacancy'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2520,
        "statement": "simon davies be the only outgoing manager in novemeber 2008",
        "label": 1,
        "table_caption": "2008 - 09 football league two",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment#position in table\nbournemouth#kevin bond#contract terminated#1 september 2008#jimmy quinn#2 september 2008#23rd\ngrimsby town#alan buckley#contract terminated#15 september 2008#mike newell#6 october 2008#20th\nport vale#lee sinnott#mutual consent#22 september 2008#dean glover#6 october 2008#16th\nchester city#simon davies#contract terminated#11 november 2008#mark wright#14 november 2008#19th\nbarnet#paul fairclough#resigned#28 december 2008#ian hendon#21 april 2009#16th\n",
        "pandas_code": "len(df[(df['outgoing manager'] == 'simon davies') & (df['date of vacancy'].str.contains('november 2008'))]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2521,
        "statement": "paul fairclough be the only outgoing manager to resign",
        "label": 1,
        "table_caption": "2008 - 09 football league two",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment#position in table\nbournemouth#kevin bond#contract terminated#1 september 2008#jimmy quinn#2 september 2008#23rd\ngrimsby town#alan buckley#contract terminated#15 september 2008#mike newell#6 october 2008#20th\nport vale#lee sinnott#mutual consent#22 september 2008#dean glover#6 october 2008#16th\nchester city#simon davies#contract terminated#11 november 2008#mark wright#14 november 2008#19th\nbarnet#paul fairclough#resigned#28 december 2008#ian hendon#21 april 2009#16th\n",
        "pandas_code": "len(df[df['outgoing manager'] == 'paul fairclough']) == 1 and len(df[df['manner of departure'] == 'resigned']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2522,
        "statement": "lee sinnott be the only outgoing manager to depart via mutual consent",
        "label": 1,
        "table_caption": "2008 - 09 football league two",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment#position in table\nbournemouth#kevin bond#contract terminated#1 september 2008#jimmy quinn#2 september 2008#23rd\ngrimsby town#alan buckley#contract terminated#15 september 2008#mike newell#6 october 2008#20th\nport vale#lee sinnott#mutual consent#22 september 2008#dean glover#6 october 2008#16th\nchester city#simon davies#contract terminated#11 november 2008#mark wright#14 november 2008#19th\nbarnet#paul fairclough#resigned#28 december 2008#ian hendon#21 april 2009#16th\n",
        "pandas_code": "(df['outgoing manager'] == 'lee sinnott').sum() == 1 and (df['manner of departure'] == 'mutual consent').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2523,
        "statement": "paul fairclough be the only manager to depart in december",
        "label": 1,
        "table_caption": "2008 - 09 football league two",
        "table_text": "team#outgoing manager#manner of departure#date of vacancy#replaced by#date of appointment#position in table\nbournemouth#kevin bond#contract terminated#1 september 2008#jimmy quinn#2 september 2008#23rd\ngrimsby town#alan buckley#contract terminated#15 september 2008#mike newell#6 october 2008#20th\nport vale#lee sinnott#mutual consent#22 september 2008#dean glover#6 october 2008#16th\nchester city#simon davies#contract terminated#11 november 2008#mark wright#14 november 2008#19th\nbarnet#paul fairclough#resigned#28 december 2008#ian hendon#21 april 2009#16th\n",
        "pandas_code": "len(df[df['date of vacancy'].str.contains('december') & (df['outgoing manager'] == 'paul fairclough')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2524,
        "statement": "the hughes hall college boat club have its best result in 2011 and 2012 , finish 27th overall",
        "label": 1,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "df[df['finish position'] == '27th']['year'].isin([2011, 2012]).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2525,
        "statement": "in 2011 , hughes hall successfully win all 4 heat , bump anglia ruskin , pembroke , homerton and robinson",
        "label": 1,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "all(df[df['year'] == 2011][['1st day', '2nd day', '3rd day', '4th day']].apply(lambda x: 'bumped anglia ruskin' in x['1st day'].lower() and 'bumped pembroke' in x['2nd day'].lower() and 'bumped homerton' in x['3rd day'].lower() and 'bumped robinson' in x['4th day'].lower(), axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 2526,
        "statement": "hughes hall bump other club 10 time , and be only bump 3 time , from 2008 to 2012",
        "label": 1,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "(df['1st day'].str.contains('bumped').sum() + df['2nd day'].str.contains('bumped').sum() + df['3rd day'].str.contains('bumped').sum() + df['4th day'].str.contains('bumped').sum() - (df['1st day'].str.contains('bumped by').sum() + df['2nd day'].str.contains('bumped by').sum() + df['3rd day'].str.contains('bumped by').sum() + df['4th day'].str.contains('bumped by').sum()) == 10) & (df['1st day'].str.contains('bumped by').sum() + df['2nd day'].str.contains('bumped by').sum() + df['3rd day'].str.contains('bumped by').sum() + df['4th day'].str.contains('bumped by').sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 2527,
        "statement": "hughes hall 's rank improve over 2008 - 2012 , rise from 33rd to 27th",
        "label": 1,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "df['finish position'].iloc[0] == '33rd' and df['finish position'].iloc[-1] == '27th'",
        "pandas_eval": "True"
    },
    {
        "id": 2528,
        "statement": "2011 be the only year that hughes hall be not row over in any of their race",
        "label": 1,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "df[(df['year'] == 2011) & (df['1st day'] != 'rowed - over') & (df['2nd day'] != 'rowed - over') & (df['3rd day'] != 'rowed - over') & (df['4th day'] != 'rowed - over')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2529,
        "statement": "the hughes hall college boat club have its best result in 2010 and 2012 , finish 27th overall",
        "label": 0,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "all(df.loc[df['year'].isin([2010, 2012]), 'finish position'] == '27th')",
        "pandas_eval": "False"
    },
    {
        "id": 2530,
        "statement": "in 2011 , hughes hall successfully win all 4 heat , bump corpus christi , pembroke , homerton and robinson",
        "label": 0,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "all(df[(df['year'] == 2011)][['1st day', '2nd day', '3rd day', '4th day']].apply(lambda x: 'bumped' in x).values)",
        "pandas_eval": "False"
    },
    {
        "id": 2531,
        "statement": "hughes hall bump other club 12 time , and be only bump 3 time , from 2008 to 2012",
        "label": 0,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "(df['1st day'].str.contains('bumped').sum() + df['2nd day'].str.contains('bumped').sum() + df['3rd day'].str.contains('bumped').sum() + df['4th day'].str.contains('bumped').sum() == 12) & (df['1st day'].str.contains('bumped by').sum() + df['2nd day'].str.contains('bumped by').sum() + df['3rd day'].str.contains('bumped by').sum() + df['4th day'].str.contains('bumped by').sum() == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 2532,
        "statement": "hughes hall 's rank improve over 2008 - 2012 , rise from 32nd to 27th",
        "label": 0,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "df['finish position'].apply(lambda x: int(x[:-2])).iloc[-1] > df['finish position'].apply(lambda x: int(x[:-2])).iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2533,
        "statement": "2008 be the only year that hughes hall be not row over in any of their race",
        "label": 0,
        "table_caption": "hughes hall college boat club",
        "table_text": "year#finish position#1st day#2nd day#3rd day#4th day\n2008#33rd#bumped corpus christi / newnham#rowed - over#rowed - over#bumped wolfson\n2009#31st#bumped st edmund 's#rowed - over#bumped darwin#rowed - over\n2010#31st#bumped by corpus christi#rowed - over#bumped caius#rowed - over\n2011#27th#bumped anglia ruskin#bumped pembroke#bumped homerton#bumped robinson\n2012#27th#bumped jesus#rowed - over#bumped by robinson#bumped by homerton\n",
        "pandas_code": "df[(df['year'] == 2008) & (df['1st day'] != 'rowed - over') & (df['2nd day'] != 'rowed - over') & (df['3rd day'] != 'rowed - over') & (df['4th day'] != 'rowed - over')].empty and df[(df['year'] != 2008) & ((df['1st day'] == 'rowed - over') | (df['2nd day'] == 'rowed - over') | (df['3rd day'] == 'rowed - over') | (df['4th day'] == 'rowed - over'))].shape[0] == df[df['year'] != 2008].shape[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2534,
        "statement": "the population of clair be larger than the population of saint - fran\u00e7ois - de - madawaska",
        "label": 1,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df[df['official name'] == 'clair']['population'].values[0] > df[df['official name'] == 'saint - fran\u00e7ois - de - madawaska']['population'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2535,
        "statement": "70% of the place in madawaska county , new brunswickare categorize as a village",
        "label": 1,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "(df['status'].value_counts(normalize=True)['village'] >= 0.7)",
        "pandas_eval": "True"
    },
    {
        "id": 2536,
        "statement": "the place with the smallest area have a population of 1343",
        "label": 1,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df.loc[df['area km 2'].idxmin(), 'population'] == 1343",
        "pandas_eval": "True"
    },
    {
        "id": 2537,
        "statement": "the village with a census ranking of 2531 have a larger area than the village with a census ranking of 2805",
        "label": 1,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df[df['census ranking'] == '2531 of 5008']['area km 2'].values[0] > df[df['census ranking'] == '2805 of 5008']['area km 2'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2538,
        "statement": "6 out of 10 place have a population less than 1000",
        "label": 1,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "(df['population'] < 1000).sum() == 6",
        "pandas_eval": "True"
    },
    {
        "id": 2539,
        "statement": "the population of clair be larger than the population of saint - andr\u00e9",
        "label": 0,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df[df['official name'] == 'clair']['population'].values[0] > df[df['official name'] == 'saint - andr\u00e9']['population'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2540,
        "statement": "40% of the place in madawaska county , new brunswick be categorize as a village",
        "label": 0,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "(df['status'].value_counts(normalize=True)['village'] == 0.4)",
        "pandas_eval": "False"
    },
    {
        "id": 2541,
        "statement": "the place with the smallest area have a population of 585",
        "label": 0,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df.loc[df['area km 2'].idxmin(), 'population'] == 585",
        "pandas_eval": "False"
    },
    {
        "id": 2542,
        "statement": "the village with a census rankin of 2531 have a larger area than the village with a census ranking of 2476",
        "label": 0,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "df[df['census ranking'] == '2531 of 5008']['area km 2'].values[0] <= df[df['census ranking'] == '2476 of 5008']['area km 2'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2543,
        "statement": "3 out of 10 place have a population less than 1250",
        "label": 0,
        "table_caption": "madawaska county , new brunswick",
        "table_text": "official name#status#area km 2#population#census ranking\nedmundston#city#106.92#16032#254 of 5008\nsaint - andr\u00e9#rural community#140.68#2479#1146 of 5008\nsaint - l\u00e9onard#town#5.20#1343#1723 of 5008\nsainte - anne - de - madawaska#village#9.21#1002#2068 of 5008\nclair#village#10.46#857#2276 of 5008\nrivi\u00e8re - verte#village#7.00#744#2476 of 5008\nlac - baker#village#37.12#719#2531 of 5008\nbaker brook#village#12.40#585#2805 of 5008\nsaint - fran\u00e7ois - de - madawaska#village#6.34#533#2915 of 5008\nsaint - hilaire#village#5.67#145#4187 of 5008\n",
        "pandas_code": "len(df[df['population'] < 1250]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2544,
        "statement": "all of the listed incumbent for massachusetts be deomcratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1994",
        "table_text": "district#incumbent#party#first elected#status#opponent\nmassachusetts1#john olver#democratic#1991#re - elected#john olver (d) unopposed\nmassachusetts4#barney frank#democratic#1980#re - elected#barney frank (d) unopposed\nmassachusetts5#marty meehan#democratic#1992#re - elected#marty meehan (d) 69.8% david e coleman (r) 30.1%\nmassachusetts7#ed markey#democratic#1976#re - elected#ed markey (d) 64.4% brad bailey (r) 35.5%\nmassachusetts8#joe kennedy#democratic#1986#re - elected#joe kennedy (d) unopposed\n",
        "pandas_code": "all(df['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 2545,
        "statement": "john olver , barney frank , and joe kennedy all run unopposed",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1994",
        "table_text": "district#incumbent#party#first elected#status#opponent\nmassachusetts1#john olver#democratic#1991#re - elected#john olver (d) unopposed\nmassachusetts4#barney frank#democratic#1980#re - elected#barney frank (d) unopposed\nmassachusetts5#marty meehan#democratic#1992#re - elected#marty meehan (d) 69.8% david e coleman (r) 30.1%\nmassachusetts7#ed markey#democratic#1976#re - elected#ed markey (d) 64.4% brad bailey (r) 35.5%\nmassachusetts8#joe kennedy#democratic#1986#re - elected#joe kennedy (d) unopposed\n",
        "pandas_code": "all(df[df['incumbent'].isin(['john olver', 'barney frank', 'joe kennedy'])]['opponent'].str.contains('unopposed'))",
        "pandas_eval": "True"
    },
    {
        "id": 2546,
        "statement": "john olver an marty meehan both ran in the 90s",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1994",
        "table_text": "district#incumbent#party#first elected#status#opponent\nmassachusetts1#john olver#democratic#1991#re - elected#john olver (d) unopposed\nmassachusetts4#barney frank#democratic#1980#re - elected#barney frank (d) unopposed\nmassachusetts5#marty meehan#democratic#1992#re - elected#marty meehan (d) 69.8% david e coleman (r) 30.1%\nmassachusetts7#ed markey#democratic#1976#re - elected#ed markey (d) 64.4% brad bailey (r) 35.5%\nmassachusetts8#joe kennedy#democratic#1986#re - elected#joe kennedy (d) unopposed\n",
        "pandas_code": "all(df[df['incumbent'].isin(['john olver', 'marty meehan'])]['first elected'] // 10 == 199)",
        "pandas_eval": "True"
    },
    {
        "id": 2547,
        "statement": "barney frank and ed markey both ran in the 80s",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1994",
        "table_text": "district#incumbent#party#first elected#status#opponent\nmassachusetts1#john olver#democratic#1991#re - elected#john olver (d) unopposed\nmassachusetts4#barney frank#democratic#1980#re - elected#barney frank (d) unopposed\nmassachusetts5#marty meehan#democratic#1992#re - elected#marty meehan (d) 69.8% david e coleman (r) 30.1%\nmassachusetts7#ed markey#democratic#1976#re - elected#ed markey (d) 64.4% brad bailey (r) 35.5%\nmassachusetts8#joe kennedy#democratic#1986#re - elected#joe kennedy (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'].isin(['barney frank', 'ed markey'])]['first elected'] < 1990).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2548,
        "statement": "all of the listed incumbent for massachusetts be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1994",
        "table_text": "district#incumbent#party#first elected#status#opponent\nmassachusetts1#john olver#democratic#1991#re - elected#john olver (d) unopposed\nmassachusetts4#barney frank#democratic#1980#re - elected#barney frank (d) unopposed\nmassachusetts5#marty meehan#democratic#1992#re - elected#marty meehan (d) 69.8% david e coleman (r) 30.1%\nmassachusetts7#ed markey#democratic#1976#re - elected#ed markey (d) 64.4% brad bailey (r) 35.5%\nmassachusetts8#joe kennedy#democratic#1986#re - elected#joe kennedy (d) unopposed\n",
        "pandas_code": "all(df[df['district'].str.startswith('massachusetts')]['status'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 2549,
        "statement": "2008 - 09 philadelphia 76ers lose both ot game to cleveland in the month of december",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia 76ers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n18#december 2#chicago#w 103 - 95 (ot)#andre miller (28)#elton brand (14)#andre iguodala (5)#united center 20485#8 - 10\n20#december 5#detroit#w 96 - 91 (ot)#andre miller (19)#andre iguodala (8)#andre iguodala (5)#the palace of auburn hills 22076#9 - 11\n21#december 6#new jersey#l 84 - 95 (ot)#andre iguodala (20)#andre iguodala (11)#andre miller (5)#wachovia center 13096#9 - 12\n22#december 10#cleveland#l 93 - 101 (ot)#andre iguodala (27)#elton brand (10)#andre miller (8)#wachovia center 15550#9 - 13\n23#december 12#cleveland#l 72 - 88 (ot)#willie green (19)#elton brand (11)#andre miller (7)#quicken loans arena 20562#9 - 14\n24#december 13#washington#w 104 - 89 (ot)#elton brand (27)#samuel dalembert (17)#andre miller (12)#wachovia center 15865#10 - 14\n25#december 17#milwaukee#w 93 - 88 (ot)#louis williams (25)#reggie evans (9)#andre iguodala (7)#wachovia center 11538#11 - 14\n26#december 19#washington#w 109 - 103 (ot)#louis williams (26)#andre iguodala (9)#andre miller (6)#verizon center 18323#12 - 14\n27#december 20#indiana#l 94 - 95 (ot)#andre iguodala (26)#samuel dalembert (14)#andre miller (12)#wachovia center 14599#12 - 15\n28#december 23#boston#l 91 - 110 (ot)#louis williams , marreese speights (16)#samuel dalembert (13)#louis williams , andre miller (8)#td banknorth garden 18624#12 - 16\n29#december 26#denver#l 101 - 105 (ot)#andre iguodala (24)#samuel dalembert (13)#andre miller (8)#pepsi center 19155#12 - 17\n30#december 29#utah#l 95 - 112 (ot)#andre iguodala , thaddeus young (17)#reggie evans (12)#andre miller (8)#energysolutions arena 19911#12 - 18\n",
        "pandas_code": "((df['team'] == 'cleveland') & (df['score'].str.startswith('l')) & (df['date'].str.contains('december'))).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2550,
        "statement": "2008 - 09 philadelphia 76ers win both ot game against washington in the month of december",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia 76ers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n18#december 2#chicago#w 103 - 95 (ot)#andre miller (28)#elton brand (14)#andre iguodala (5)#united center 20485#8 - 10\n20#december 5#detroit#w 96 - 91 (ot)#andre miller (19)#andre iguodala (8)#andre iguodala (5)#the palace of auburn hills 22076#9 - 11\n21#december 6#new jersey#l 84 - 95 (ot)#andre iguodala (20)#andre iguodala (11)#andre miller (5)#wachovia center 13096#9 - 12\n22#december 10#cleveland#l 93 - 101 (ot)#andre iguodala (27)#elton brand (10)#andre miller (8)#wachovia center 15550#9 - 13\n23#december 12#cleveland#l 72 - 88 (ot)#willie green (19)#elton brand (11)#andre miller (7)#quicken loans arena 20562#9 - 14\n24#december 13#washington#w 104 - 89 (ot)#elton brand (27)#samuel dalembert (17)#andre miller (12)#wachovia center 15865#10 - 14\n25#december 17#milwaukee#w 93 - 88 (ot)#louis williams (25)#reggie evans (9)#andre iguodala (7)#wachovia center 11538#11 - 14\n26#december 19#washington#w 109 - 103 (ot)#louis williams (26)#andre iguodala (9)#andre miller (6)#verizon center 18323#12 - 14\n27#december 20#indiana#l 94 - 95 (ot)#andre iguodala (26)#samuel dalembert (14)#andre miller (12)#wachovia center 14599#12 - 15\n28#december 23#boston#l 91 - 110 (ot)#louis williams , marreese speights (16)#samuel dalembert (13)#louis williams , andre miller (8)#td banknorth garden 18624#12 - 16\n29#december 26#denver#l 101 - 105 (ot)#andre iguodala (24)#samuel dalembert (13)#andre miller (8)#pepsi center 19155#12 - 17\n30#december 29#utah#l 95 - 112 (ot)#andre iguodala , thaddeus young (17)#reggie evans (12)#andre miller (8)#energysolutions arena 19911#12 - 18\n",
        "pandas_code": "((df['team'] == 'washington') & (df['score'].str.startswith('w')) & (df['date'].str.contains('december'))).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2551,
        "statement": "2008 - 09 philadelphia 76ers only play 2 team in ot 2 time in the month of december",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia 76ers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n18#december 2#chicago#w 103 - 95 (ot)#andre miller (28)#elton brand (14)#andre iguodala (5)#united center 20485#8 - 10\n20#december 5#detroit#w 96 - 91 (ot)#andre miller (19)#andre iguodala (8)#andre iguodala (5)#the palace of auburn hills 22076#9 - 11\n21#december 6#new jersey#l 84 - 95 (ot)#andre iguodala (20)#andre iguodala (11)#andre miller (5)#wachovia center 13096#9 - 12\n22#december 10#cleveland#l 93 - 101 (ot)#andre iguodala (27)#elton brand (10)#andre miller (8)#wachovia center 15550#9 - 13\n23#december 12#cleveland#l 72 - 88 (ot)#willie green (19)#elton brand (11)#andre miller (7)#quicken loans arena 20562#9 - 14\n24#december 13#washington#w 104 - 89 (ot)#elton brand (27)#samuel dalembert (17)#andre miller (12)#wachovia center 15865#10 - 14\n25#december 17#milwaukee#w 93 - 88 (ot)#louis williams (25)#reggie evans (9)#andre iguodala (7)#wachovia center 11538#11 - 14\n26#december 19#washington#w 109 - 103 (ot)#louis williams (26)#andre iguodala (9)#andre miller (6)#verizon center 18323#12 - 14\n27#december 20#indiana#l 94 - 95 (ot)#andre iguodala (26)#samuel dalembert (14)#andre miller (12)#wachovia center 14599#12 - 15\n28#december 23#boston#l 91 - 110 (ot)#louis williams , marreese speights (16)#samuel dalembert (13)#louis williams , andre miller (8)#td banknorth garden 18624#12 - 16\n29#december 26#denver#l 101 - 105 (ot)#andre iguodala (24)#samuel dalembert (13)#andre miller (8)#pepsi center 19155#12 - 17\n30#december 29#utah#l 95 - 112 (ot)#andre iguodala , thaddeus young (17)#reggie evans (12)#andre miller (8)#energysolutions arena 19911#12 - 18\n",
        "pandas_code": "len(df[(df['date'].str.contains('december')) & (df['score'].str.contains('ot'))].groupby('team').filter(lambda x: len(x) == 2)['team'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2552,
        "statement": "2008 - 09 philadelphia 76ers lose most of the ot game in the month of december",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia 76ers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n18#december 2#chicago#w 103 - 95 (ot)#andre miller (28)#elton brand (14)#andre iguodala (5)#united center 20485#8 - 10\n20#december 5#detroit#w 96 - 91 (ot)#andre miller (19)#andre iguodala (8)#andre iguodala (5)#the palace of auburn hills 22076#9 - 11\n21#december 6#new jersey#l 84 - 95 (ot)#andre iguodala (20)#andre iguodala (11)#andre miller (5)#wachovia center 13096#9 - 12\n22#december 10#cleveland#l 93 - 101 (ot)#andre iguodala (27)#elton brand (10)#andre miller (8)#wachovia center 15550#9 - 13\n23#december 12#cleveland#l 72 - 88 (ot)#willie green (19)#elton brand (11)#andre miller (7)#quicken loans arena 20562#9 - 14\n24#december 13#washington#w 104 - 89 (ot)#elton brand (27)#samuel dalembert (17)#andre miller (12)#wachovia center 15865#10 - 14\n25#december 17#milwaukee#w 93 - 88 (ot)#louis williams (25)#reggie evans (9)#andre iguodala (7)#wachovia center 11538#11 - 14\n26#december 19#washington#w 109 - 103 (ot)#louis williams (26)#andre iguodala (9)#andre miller (6)#verizon center 18323#12 - 14\n27#december 20#indiana#l 94 - 95 (ot)#andre iguodala (26)#samuel dalembert (14)#andre miller (12)#wachovia center 14599#12 - 15\n28#december 23#boston#l 91 - 110 (ot)#louis williams , marreese speights (16)#samuel dalembert (13)#louis williams , andre miller (8)#td banknorth garden 18624#12 - 16\n29#december 26#denver#l 101 - 105 (ot)#andre iguodala (24)#samuel dalembert (13)#andre miller (8)#pepsi center 19155#12 - 17\n30#december 29#utah#l 95 - 112 (ot)#andre iguodala , thaddeus young (17)#reggie evans (12)#andre miller (8)#energysolutions arena 19911#12 - 18\n",
        "pandas_code": "(df['score'].str.startswith('l') & df['date'].str.contains('december')).sum() > (df['score'].str.startswith('w') & df['date'].str.contains('december')).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2553,
        "statement": "2008 - 09 philadelphia 76ers win less than half of the ot game they play in december",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia 76ers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n18#december 2#chicago#w 103 - 95 (ot)#andre miller (28)#elton brand (14)#andre iguodala (5)#united center 20485#8 - 10\n20#december 5#detroit#w 96 - 91 (ot)#andre miller (19)#andre iguodala (8)#andre iguodala (5)#the palace of auburn hills 22076#9 - 11\n21#december 6#new jersey#l 84 - 95 (ot)#andre iguodala (20)#andre iguodala (11)#andre miller (5)#wachovia center 13096#9 - 12\n22#december 10#cleveland#l 93 - 101 (ot)#andre iguodala (27)#elton brand (10)#andre miller (8)#wachovia center 15550#9 - 13\n23#december 12#cleveland#l 72 - 88 (ot)#willie green (19)#elton brand (11)#andre miller (7)#quicken loans arena 20562#9 - 14\n24#december 13#washington#w 104 - 89 (ot)#elton brand (27)#samuel dalembert (17)#andre miller (12)#wachovia center 15865#10 - 14\n25#december 17#milwaukee#w 93 - 88 (ot)#louis williams (25)#reggie evans (9)#andre iguodala (7)#wachovia center 11538#11 - 14\n26#december 19#washington#w 109 - 103 (ot)#louis williams (26)#andre iguodala (9)#andre miller (6)#verizon center 18323#12 - 14\n27#december 20#indiana#l 94 - 95 (ot)#andre iguodala (26)#samuel dalembert (14)#andre miller (12)#wachovia center 14599#12 - 15\n28#december 23#boston#l 91 - 110 (ot)#louis williams , marreese speights (16)#samuel dalembert (13)#louis williams , andre miller (8)#td banknorth garden 18624#12 - 16\n29#december 26#denver#l 101 - 105 (ot)#andre iguodala (24)#samuel dalembert (13)#andre miller (8)#pepsi center 19155#12 - 17\n30#december 29#utah#l 95 - 112 (ot)#andre iguodala , thaddeus young (17)#reggie evans (12)#andre miller (8)#energysolutions arena 19911#12 - 18\n",
        "pandas_code": "(df['date'].str.contains('december') & df['score'].str.startswith('w')).sum() < (df['date'].str.contains('december') & df['score'].str.contains('ot')).sum() / 2",
        "pandas_eval": "True"
    },
    {
        "id": 2554,
        "statement": "jenny bicks be nominate 2 time for best writing by herself and one time in tandem",
        "label": 1,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "(df['nominee (s)'].str.contains('jenny bicks') & df['category'].str.contains('best writing')).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2555,
        "statement": "no sex and the city episode that be nominate for best write actually win from 1999 to 2004",
        "label": 1,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "all(df[(df['category'] == 'best writing - episodic comedy') & (df['year'].between(1999, 2004))]['result'] == 'nominated')",
        "pandas_eval": "True"
    },
    {
        "id": 2558,
        "statement": "2002 have 3 nomination , which be the most of any year",
        "label": 1,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "df[df['year'] == 2002].shape[0] == 3 and df['year'].value_counts().max() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2559,
        "statement": "jenny bicks be nominate 2 time in tandem for best writing",
        "label": 0,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "len(df[(df['nominee (s)'].str.contains('jenny bicks')) & (df['category'] == 'best writing - episodic comedy')]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2560,
        "statement": "a sex and the city episode nominate in 2004 won for best writing",
        "label": 0,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "df[(df['year'] == 2004) & (df['result'] == 'won') & (df['category'] == 'best writing - episodic comedy')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 2561,
        "statement": "cindy chupack be nominate only one time in only one year 2004",
        "label": 0,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "len(df[(df['nominee (s)'].str.contains('cindy chupack')) & (df['year'] == 2004)]) == 1 and len(df[(df['nominee (s)'].str.contains('cindy chupack')) & (df['year'] != 2004)]) == 0",
        "pandas_eval": "False"
    },
    {
        "id": 2562,
        "statement": "only one episode be nominate in 2003 , 2004 , and 1999",
        "label": 0,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "df[df['year'].isin([1999, 2003, 2004])].groupby('year')['episode'].nunique().eq(1).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2563,
        "statement": "2004 have 2 nomination which be the most for any year",
        "label": 0,
        "table_caption": "list of awards and nominations received by sex and the city",
        "table_text": "year#category#nominee (s)#episode#result\n1999#best writing - episodic comedy#jenny bicks#four women and a funeral#nominated\n1999#best writing - episodic comedy#cindy chupack#evolution#nominated\n2000#best writing - episodic comedy#cindy chupack#attack of the 5'10 woman#nominated\n2000#best writing - episodic comedy#michael patrick king#ex and the city#nominated\n2001#best writing - episodic comedy#cindy chupack#just say yes#nominated\n2001#best writing - episodic comedy#julie rottenberg and elisa zuritsky#my motherboard , my self#nominated\n2002#best writing - episodic comedy#cindy chupack#plus one is the loneliest number#nominated\n2002#best writing - episodic comedy#michael patrick king#i heart ny#nominated\n2002#best writing - episodic comedy#julie rottenberg and elisa zuritsky#change of a dress#nominated\n2003#best writing - episodic comedy#jenny bicks#a woman 's right to shoes#nominated\n2004#best writing - episodic comedy#jenny bicks and cindy chupack#splat!#nominated\n2004#best writing - episodic comedy#julie rottenberg and elisa zuritsky#the ick factor#nominated\n",
        "pandas_code": "df[df['year'] == 2004].shape[0] == 2 and df.groupby('year').size().max() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2564,
        "statement": "all player on the seattle supersonics all - time roster be from the united state",
        "label": 1,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "all(df['nationality'] == 'united states')",
        "pandas_eval": "True"
    },
    {
        "id": 2565,
        "statement": "power forward be the highest occur position on the seattle supersonics all - time roster with 4 player",
        "label": 1,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "(df['position'].str.contains('pf', case=False).sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 2566,
        "statement": "no 2 player on the seattle supersonics all - time roster be from the same college",
        "label": 1,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "df['from'].nunique() == len(df)",
        "pandas_eval": "True"
    },
    {
        "id": 2567,
        "statement": "the difference between the highest jersey number and the lowest be 43",
        "label": 1,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "(df['jersey number (s)'].max() - df['jersey number (s)'].min()) == 43",
        "pandas_eval": "True"
    },
    {
        "id": 2569,
        "statement": "half the player on the seattle supersonics all - time roster be from the united state",
        "label": 0,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "df['nationality'].value_counts(normalize=True)['united states'] == 0.5",
        "pandas_eval": "False"
    },
    {
        "id": 2570,
        "statement": "power forward occur in equal amount of other position on the seattle supersonics all - time roster",
        "label": 0,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "df['position'].str.contains('pf').sum() == (len(df) - df['position'].str.contains('pf').sum())",
        "pandas_eval": "False"
    },
    {
        "id": 2571,
        "statement": "mostl player on the seattle supersonics all - time roster come from the same college",
        "label": 0,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "df['from'].value_counts().max() > 1",
        "pandas_eval": "False"
    },
    {
        "id": 2572,
        "statement": "the difference between the highest jersey number and the lowest be only 12",
        "label": 0,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "(df['jersey number (s)'].max() - df['jersey number (s)'].min()) == 12",
        "pandas_eval": "False"
    },
    {
        "id": 2573,
        "statement": "the longest time between present day and the season they first start play for the supersonics be 1 decade",
        "label": 0,
        "table_caption": "seattle supersonics all - time roster",
        "table_text": "player#nationality#jersey number (s)#position#years#from\njim farmer#united states#21#pg / sg#1990#alabama\nnoel felix#united states#16#pf#2006#fresno state\nal fleming#united states#30#f#1978#arizona\nalphonso ford#united states#3#sg#1994#mississippi valley state\njake ford#united states#33#g#1970 - 1972#maryland eastern shore\nsherell ford#united states#1#sf#1995 - 1996#illinois (chicago)\njoseph forte#united states#40#sg#2002 - 2003#north carolina\ndanny fortson#united states#21#pf#2004 - 2007#cincinnati\ngreg foster#united states#44#pf / c#1999 - 2000#utep\njim fox#united states#31#pf / c#1972 - 1975#south carolina\n",
        "pandas_code": "((pd.to_datetime('today').year - df['years'].str.extract('(\\d{4})')[0].astype(int)).max() <= 10)",
        "pandas_eval": "False"
    },
    {
        "id": 2574,
        "statement": "tennessee change 2 time in 2 year",
        "label": 1,
        "table_caption": "88th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\ntennessee (2)#estes kefauver (d)#died august 10 , 1963#herbert s walters (d)#august 20 , 1963\ncalifornia (1)#clair engle (d)#died july 30 , 1964#pierre salinger (d)#august 4 , 1964\nsouth carolina (2)#strom thurmond (d)#changed political parties#strom thurmond (r)#september 16 , 1964\nnew mexico (1)#edwin l mechem (r)#resigned november 3 , 1964#joseph montoya (d)#november 4 , 1964\noklahoma (2)#j howard edmondson (d)#successor elected november 3 , 1964#fred r harris (d)#november 4 , 1964\ntennessee (2)#herbert s walters (d)#successor elected november 3 , 1964#ross bass (d)#november 4 , 1964\n",
        "pandas_code": "len(df[df['state (class)'].str.contains('tennessee')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2575,
        "statement": "most of the successor of these state be democratic",
        "label": 1,
        "table_caption": "88th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\ntennessee (2)#estes kefauver (d)#died august 10 , 1963#herbert s walters (d)#august 20 , 1963\ncalifornia (1)#clair engle (d)#died july 30 , 1964#pierre salinger (d)#august 4 , 1964\nsouth carolina (2)#strom thurmond (d)#changed political parties#strom thurmond (r)#september 16 , 1964\nnew mexico (1)#edwin l mechem (r)#resigned november 3 , 1964#joseph montoya (d)#november 4 , 1964\noklahoma (2)#j howard edmondson (d)#successor elected november 3 , 1964#fred r harris (d)#november 4 , 1964\ntennessee (2)#herbert s walters (d)#successor elected november 3 , 1964#ross bass (d)#november 4 , 1964\n",
        "pandas_code": "df['successor'].str.contains('(d)').sum() > len(df) / 2",
        "pandas_eval": "True"
    },
    {
        "id": 2576,
        "statement": "new mexico change from republican to democratic",
        "label": 1,
        "table_caption": "88th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\ntennessee (2)#estes kefauver (d)#died august 10 , 1963#herbert s walters (d)#august 20 , 1963\ncalifornia (1)#clair engle (d)#died july 30 , 1964#pierre salinger (d)#august 4 , 1964\nsouth carolina (2)#strom thurmond (d)#changed political parties#strom thurmond (r)#september 16 , 1964\nnew mexico (1)#edwin l mechem (r)#resigned november 3 , 1964#joseph montoya (d)#november 4 , 1964\noklahoma (2)#j howard edmondson (d)#successor elected november 3 , 1964#fred r harris (d)#november 4 , 1964\ntennessee (2)#herbert s walters (d)#successor elected november 3 , 1964#ross bass (d)#november 4 , 1964\n",
        "pandas_code": "df[(df['state (class)'] == 'new mexico (1)') & (df['reason for change'] == 'resigned november 3 , 1964') & (df['vacator'].str.contains('(r)')) & (df['successor'].str.contains('(d)'))].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2577,
        "statement": "3 state have their successor instal on november 4 , 1964",
        "label": 1,
        "table_caption": "88th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\ntennessee (2)#estes kefauver (d)#died august 10 , 1963#herbert s walters (d)#august 20 , 1963\ncalifornia (1)#clair engle (d)#died july 30 , 1964#pierre salinger (d)#august 4 , 1964\nsouth carolina (2)#strom thurmond (d)#changed political parties#strom thurmond (r)#september 16 , 1964\nnew mexico (1)#edwin l mechem (r)#resigned november 3 , 1964#joseph montoya (d)#november 4 , 1964\noklahoma (2)#j howard edmondson (d)#successor elected november 3 , 1964#fred r harris (d)#november 4 , 1964\ntennessee (2)#herbert s walters (d)#successor elected november 3 , 1964#ross bass (d)#november 4 , 1964\n",
        "pandas_code": "df[df['date of successors formal installation'] == 'november 4 , 1964']['state (class)'].nunique() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2578,
        "statement": "democratic successfully reelect in oklahoma and tennessee",
        "label": 1,
        "table_caption": "88th united states congress",
        "table_text": "state (class)#vacator#reason for change#successor#date of successors formal installation\ntennessee (2)#estes kefauver (d)#died august 10 , 1963#herbert s walters (d)#august 20 , 1963\ncalifornia (1)#clair engle (d)#died july 30 , 1964#pierre salinger (d)#august 4 , 1964\nsouth carolina (2)#strom thurmond (d)#changed political parties#strom thurmond (r)#september 16 , 1964\nnew mexico (1)#edwin l mechem (r)#resigned november 3 , 1964#joseph montoya (d)#november 4 , 1964\noklahoma (2)#j howard edmondson (d)#successor elected november 3 , 1964#fred r harris (d)#november 4 , 1964\ntennessee (2)#herbert s walters (d)#successor elected november 3 , 1964#ross bass (d)#november 4 , 1964\n",
        "pandas_code": "all(df[df['state (class)'].isin(['oklahoma (2)', 'tennessee (2)'])]['successor'].str.contains('(d)'))",
        "pandas_eval": "True"
    },
    {
        "id": 2579,
        "statement": "the derby race at epsom be the longest with 12 foot",
        "label": 1,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "df[(df['race'] == 'derby') & (df['course'] == 'epsom') & (df['dist (f)'] == 12)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2580,
        "statement": "south norfolk caterer maiden stake have the most runner with 18",
        "label": 1,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "df[df['race'] == 'south norfolk caterers maiden stakes']['runners'].max() == 18",
        "pandas_eval": "True"
    },
    {
        "id": 2581,
        "statement": "the course nad al aheba be use for both the maktoum bin rashid al maktoum challenge as well as the dubai world cup",
        "label": 1,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "len(df[(df['course'] == 'nad al sheba') & (df['race'].isin(['maktoum bin rashid al maktoum challenge', 'dubai world cup']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2583,
        "statement": "the derby race be the only race dubai millennium do not win",
        "label": 1,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "(df[df['race'] == 'derby']['placing'].iloc[0] != 1) and (df[df['race'] != 'derby']['placing'] == 1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2584,
        "statement": "the prix eugene adam race at maisons - laffitte be the longest at 10 foot",
        "label": 0,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "(df[(df['race'] == 'prix eugene adam') & (df['course'] == 'maisons - laffitte')]['dist (f)'].max() == df['dist (f)'].max())",
        "pandas_eval": "False"
    },
    {
        "id": 2585,
        "statement": "the queen elizabeth ii stake have the most runner , with 4",
        "label": 0,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "df[df['race'] == 'queen elizabeth ii stakes']['runners'].max() == df['runners'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2586,
        "statement": "the maktoum bin rashid al maktoum challenge be on the ascot course , and the dubai world cup be hold on the deauville course",
        "label": 0,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "(df[df['race'] == 'maktoum bin rashid al maktoum challenge']['course'].iloc[0] == 'ascot') & (df[df['race'] == 'dubai world cup']['course'].iloc[0] == 'deauville')",
        "pandas_eval": "False"
    },
    {
        "id": 2587,
        "statement": "saeed bin suroor and frankie dettori be the trainer and jockey for all the race",
        "label": 0,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "(df['trainer'] == 'saeed bin suroor').all() and (df['jockey'] == 'frankie dettori').all()",
        "pandas_eval": "False"
    },
    {
        "id": 2588,
        "statement": "dubai millennium lose the dubai world cup and the prix eugene adam",
        "label": 0,
        "table_caption": "dubai millennium",
        "table_text": "race#dist (f)#course#runners#placing#margin#jockey#trainer\nsouth norfolk caterers maiden stakes#8#yarmouth#18#1#5#frankie dettori#david loder\ndoncaster sponsorship club stakes#8#doncaster#4#1#9#frankie dettori#saeed bin suroor\npredominate stakes#10#goodwood#6#1#3.5#frankie dettori#saeed bin suroor\nderby#12#epsom#16#9#9.25#frankie dettori#saeed bin suroor\nprix eugene adam#10#maisons - laffitte#5#1#3#frankie dettori#saeed bin suroor\nprix jacques le marois#8#deauville#5#1#2.5#frankie dettori#saeed bin suroor\nqueen elizabeth ii stakes#8#ascot#4#1#6#frankie dettori#saeed bin suroor\nmaktoum bin rashid al maktoum challenge#10#nad al sheba#6#1#4.5#frankie dettori#saeed bin suroor\ndubai world cup#10#nad al sheba#13#1#6#frankie dettori#saeed bin suroor\nprince of wales 's stakes#10#ascot#6#1#8#jerry bailey#saeed bin suroor\n",
        "pandas_code": "((df['race'] == 'dubai world cup') & (df['placing'] != 1)).any() or ((df['race'] == 'prix eugene adam') & (df['placing'] != 1)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2590,
        "statement": "sonja sohn be nominate for slam 3 time and the wire 3 time",
        "label": 1,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "((df['film or series'] == 'slam') & (df['result'] == 'nominated')).sum() == 1 and ((df['film or series'] == 'the wire') & (df['result'] == 'nominated')).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2591,
        "statement": "3 of sonja sohn 's 6 nomination come before 2000 and 3 come after",
        "label": 1,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "(df['year'] < 2000).sum() == 3 and (df['year'] >= 2000).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2592,
        "statement": "2 out of 6 award be the image award , both time for outstanding support actress in a drama series",
        "label": 1,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "((df['award'] == 'image awards').sum() == 2) & ((df[df['award'] == 'image awards']['category'] == 'outstanding supporting actress in a drama series').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2593,
        "statement": "sonja sohn win best actress and the grand jury prize in 1998",
        "label": 1,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "((df['year'] == 1998) & (df['award'].isin(['gotham awards', 'sundance film festival'])) & (df['category'].isin(['best actress', 'grand jury prize'])) & (df['result'] == 'won')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2594,
        "statement": "sonja sohn have be nominate for , and win , 6 award",
        "label": 0,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "len(df[df['result'].isin(['won', 'nominated'])]['award'].unique()) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2595,
        "statement": "sonja sohn be nominate for 'slam' more time than 'the wire'",
        "label": 0,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "(df[(df['film or series'] == 'slam') & (df['result'] == 'nominated')].shape[0] > df[(df['film or series'] == 'the wire') & (df['result'] == 'nominated')].shape[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2596,
        "statement": "sonja sohn 's 6 nomination all come before the year 2000",
        "label": 0,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "all(df[df['result'] == 'nominated']['year'] < 2000)",
        "pandas_eval": "False"
    },
    {
        "id": 2597,
        "statement": "3 out of 6 award be the image award , all these be for best actress",
        "label": 0,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "len(df[df['award'] == 'image awards']) == 3 and all(df[df['award'] == 'image awards']['category'] == 'best actress')",
        "pandas_eval": "False"
    },
    {
        "id": 2598,
        "statement": "sonja sohn win best actress in a drama series best debut performance in 1998",
        "label": 0,
        "table_caption": "sonja sohn",
        "table_text": "year#award#category#film or series#result\n1998#gotham awards#best actress#slam#won\n1998#sundance film festival#grand jury prize#slam#won\n1998#independent spirit awards#best debut performance#slam#nominated\n2005#image awards#outstanding supporting actress in a drama series#the wire#nominated\n2008#asian excellence awards#outstanding television actress#the wire#won\n2009#image awards#outstanding supporting actress in a drama series#the wire#nominated\n",
        "pandas_code": "((df['year'] == 1998) & (df['category'].isin(['best actress in a drama series', 'best debut performance in a drama series'])) & (df['result'] == 'won')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2599,
        "statement": "the united state have the most champion between the year of 2002 and 2013",
        "label": 1,
        "table_caption": "canadian women 's open",
        "table_text": "year#dates#champion#country#score#to par#margin of victory#tournament location#purse ( us )#winners share\n2013#aug 22 - 25#lydia ko (a) (2)#new zealand#265#- 15#5 strokes#royal mayfair golf club , ( edmonton , ab )#2000000#300000\n2012#aug 23 - 26#lydia ko (a)#new zealand#275#- 13#3 strokes#vancouver golf club , ( coquitlam , bc )#2000000#300000\n2011#aug 25 - 28#brittany lincicome#united states#275#- 13#1 stroke#hillsdale golf & country club , ( mirabel , qc )#2250000#337500\n2010#aug 26 - 29#michelle wie#united states#276#- 12#3 strokes#st charles country club , ( winnipeg , mb )#2250000#337500\n2008#aug 14 - 17#katherine hull#australia#277#11#1 stroke#ottawa hunt and golf club ( ottawa , on )#2250000#337500\n2007#aaug 16 - 19#lorena ochoa#mexico#268#16#3 strokes#royal mayfair golf club ( edmonton , ab )#2250000#337500\n2006#aug 10 - 13#cristie kerr#united states#276#12#1 stroke#london hunt and country club ( london , on )#1700000#255000\n2005#jul 14 - 17#meena lee#south korea#279#9#1 stroke#glen arbour golf course ( halifax , ns )#1300000#195000\n2004#jul 8 - 11#meg mallon (3)#united states#270#18#4 strokes#legends on the niagara ( niagara falls , on )#1300000#195000\n2003#jul 10 - 13#beth daniel#united states#276#13#1 stroke#point grey golf & country club ( vancouver , bc )#1300000#195000\n2002#aug 15 - 18#meg mallon (2)#united states#284#4#3 strokes#summerlea golf and country club ( montreal , qc )#1200000#180000\n",
        "pandas_code": "df[(df['year'] >= 2002) & (df['year'] <= 2013)]['country'].value_counts().idxmax() == 'united states'",
        "pandas_eval": "True"
    },
    {
        "id": 2600,
        "statement": "lydia ko and meg mallon both win the canadian woman 's open 2 time",
        "label": 1,
        "table_caption": "canadian women 's open",
        "table_text": "year#dates#champion#country#score#to par#margin of victory#tournament location#purse ( us )#winners share\n2013#aug 22 - 25#lydia ko (a) (2)#new zealand#265#- 15#5 strokes#royal mayfair golf club , ( edmonton , ab )#2000000#300000\n2012#aug 23 - 26#lydia ko (a)#new zealand#275#- 13#3 strokes#vancouver golf club , ( coquitlam , bc )#2000000#300000\n2011#aug 25 - 28#brittany lincicome#united states#275#- 13#1 stroke#hillsdale golf & country club , ( mirabel , qc )#2250000#337500\n2010#aug 26 - 29#michelle wie#united states#276#- 12#3 strokes#st charles country club , ( winnipeg , mb )#2250000#337500\n2008#aug 14 - 17#katherine hull#australia#277#11#1 stroke#ottawa hunt and golf club ( ottawa , on )#2250000#337500\n2007#aaug 16 - 19#lorena ochoa#mexico#268#16#3 strokes#royal mayfair golf club ( edmonton , ab )#2250000#337500\n2006#aug 10 - 13#cristie kerr#united states#276#12#1 stroke#london hunt and country club ( london , on )#1700000#255000\n2005#jul 14 - 17#meena lee#south korea#279#9#1 stroke#glen arbour golf course ( halifax , ns )#1300000#195000\n2004#jul 8 - 11#meg mallon (3)#united states#270#18#4 strokes#legends on the niagara ( niagara falls , on )#1300000#195000\n2003#jul 10 - 13#beth daniel#united states#276#13#1 stroke#point grey golf & country club ( vancouver , bc )#1300000#195000\n2002#aug 15 - 18#meg mallon (2)#united states#284#4#3 strokes#summerlea golf and country club ( montreal , qc )#1200000#180000\n",
        "pandas_code": "df['champion'].str.contains('lydia ko').sum() == 2 and df['champion'].str.contains('meg mallon').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2601,
        "statement": "the following individual be all champion from the united state : brittany lincicome , michelle wie , cristie kerr , meg mallon , and beth daniel",
        "label": 1,
        "table_caption": "canadian women 's open",
        "table_text": "year#dates#champion#country#score#to par#margin of victory#tournament location#purse ( us )#winners share\n2013#aug 22 - 25#lydia ko (a) (2)#new zealand#265#- 15#5 strokes#royal mayfair golf club , ( edmonton , ab )#2000000#300000\n2012#aug 23 - 26#lydia ko (a)#new zealand#275#- 13#3 strokes#vancouver golf club , ( coquitlam , bc )#2000000#300000\n2011#aug 25 - 28#brittany lincicome#united states#275#- 13#1 stroke#hillsdale golf & country club , ( mirabel , qc )#2250000#337500\n2010#aug 26 - 29#michelle wie#united states#276#- 12#3 strokes#st charles country club , ( winnipeg , mb )#2250000#337500\n2008#aug 14 - 17#katherine hull#australia#277#11#1 stroke#ottawa hunt and golf club ( ottawa , on )#2250000#337500\n2007#aaug 16 - 19#lorena ochoa#mexico#268#16#3 strokes#royal mayfair golf club ( edmonton , ab )#2250000#337500\n2006#aug 10 - 13#cristie kerr#united states#276#12#1 stroke#london hunt and country club ( london , on )#1700000#255000\n2005#jul 14 - 17#meena lee#south korea#279#9#1 stroke#glen arbour golf course ( halifax , ns )#1300000#195000\n2004#jul 8 - 11#meg mallon (3)#united states#270#18#4 strokes#legends on the niagara ( niagara falls , on )#1300000#195000\n2003#jul 10 - 13#beth daniel#united states#276#13#1 stroke#point grey golf & country club ( vancouver , bc )#1300000#195000\n2002#aug 15 - 18#meg mallon (2)#united states#284#4#3 strokes#summerlea golf and country club ( montreal , qc )#1200000#180000\n",
        "pandas_code": "all(name in df[df['country'] == 'united states']['champion'].values for name in ['brittany lincicome', 'michelle wie', 'cristie kerr', 'beth daniel']) and any('meg mallon' in name for name in df[df['country'] == 'united states']['champion'].values)",
        "pandas_eval": "True"
    },
    {
        "id": 2602,
        "statement": "there be margin of victory of 1 stroke 5 time between the year of 2002 and 2013",
        "label": 1,
        "table_caption": "canadian women 's open",
        "table_text": "year#dates#champion#country#score#to par#margin of victory#tournament location#purse ( us )#winners share\n2013#aug 22 - 25#lydia ko (a) (2)#new zealand#265#- 15#5 strokes#royal mayfair golf club , ( edmonton , ab )#2000000#300000\n2012#aug 23 - 26#lydia ko (a)#new zealand#275#- 13#3 strokes#vancouver golf club , ( coquitlam , bc )#2000000#300000\n2011#aug 25 - 28#brittany lincicome#united states#275#- 13#1 stroke#hillsdale golf & country club , ( mirabel , qc )#2250000#337500\n2010#aug 26 - 29#michelle wie#united states#276#- 12#3 strokes#st charles country club , ( winnipeg , mb )#2250000#337500\n2008#aug 14 - 17#katherine hull#australia#277#11#1 stroke#ottawa hunt and golf club ( ottawa , on )#2250000#337500\n2007#aaug 16 - 19#lorena ochoa#mexico#268#16#3 strokes#royal mayfair golf club ( edmonton , ab )#2250000#337500\n2006#aug 10 - 13#cristie kerr#united states#276#12#1 stroke#london hunt and country club ( london , on )#1700000#255000\n2005#jul 14 - 17#meena lee#south korea#279#9#1 stroke#glen arbour golf course ( halifax , ns )#1300000#195000\n2004#jul 8 - 11#meg mallon (3)#united states#270#18#4 strokes#legends on the niagara ( niagara falls , on )#1300000#195000\n2003#jul 10 - 13#beth daniel#united states#276#13#1 stroke#point grey golf & country club ( vancouver , bc )#1300000#195000\n2002#aug 15 - 18#meg mallon (2)#united states#284#4#3 strokes#summerlea golf and country club ( montreal , qc )#1200000#180000\n",
        "pandas_code": "(df[(df['year'] >= 2002) & (df['year'] <= 2013)]['margin of victory'] == '1 stroke').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2603,
        "statement": "there be only 1 champion from the country of mexico between the year of 2002 and 2013",
        "label": 1,
        "table_caption": "canadian women 's open",
        "table_text": "year#dates#champion#country#score#to par#margin of victory#tournament location#purse ( us )#winners share\n2013#aug 22 - 25#lydia ko (a) (2)#new zealand#265#- 15#5 strokes#royal mayfair golf club , ( edmonton , ab )#2000000#300000\n2012#aug 23 - 26#lydia ko (a)#new zealand#275#- 13#3 strokes#vancouver golf club , ( coquitlam , bc )#2000000#300000\n2011#aug 25 - 28#brittany lincicome#united states#275#- 13#1 stroke#hillsdale golf & country club , ( mirabel , qc )#2250000#337500\n2010#aug 26 - 29#michelle wie#united states#276#- 12#3 strokes#st charles country club , ( winnipeg , mb )#2250000#337500\n2008#aug 14 - 17#katherine hull#australia#277#11#1 stroke#ottawa hunt and golf club ( ottawa , on )#2250000#337500\n2007#aaug 16 - 19#lorena ochoa#mexico#268#16#3 strokes#royal mayfair golf club ( edmonton , ab )#2250000#337500\n2006#aug 10 - 13#cristie kerr#united states#276#12#1 stroke#london hunt and country club ( london , on )#1700000#255000\n2005#jul 14 - 17#meena lee#south korea#279#9#1 stroke#glen arbour golf course ( halifax , ns )#1300000#195000\n2004#jul 8 - 11#meg mallon (3)#united states#270#18#4 strokes#legends on the niagara ( niagara falls , on )#1300000#195000\n2003#jul 10 - 13#beth daniel#united states#276#13#1 stroke#point grey golf & country club ( vancouver , bc )#1300000#195000\n2002#aug 15 - 18#meg mallon (2)#united states#284#4#3 strokes#summerlea golf and country club ( montreal , qc )#1200000#180000\n",
        "pandas_code": "(df[(df['year'] >= 2002) & (df['year'] <= 2013)]['country'] == 'mexico').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2604,
        "statement": "arezzo have less inhabitant than florence",
        "label": 1,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df[df['province'] == 'arezzo']['inhabitants'].values[0] < df[df['province'] == 'florence']['inhabitants'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2605,
        "statement": "pistoia be establish after pisa",
        "label": 1,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df[df['province'] == 'pistoia']['established'].values[0] > df[df['province'] == 'pisa']['established'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2606,
        "statement": "2 province have over 40000 inhabitant",
        "label": 1,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df[df['inhabitants'] > 400000].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2607,
        "statement": "simone bezzini be the president of siena and federica fratoni be the president of pistoia",
        "label": 1,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "(df[df['province'] == 'siena']['president'].values[0] == 'simone bezzini') & (df[df['province'] == 'pistoia']['president'].values[0] == 'federica fratoni')",
        "pandas_eval": "True"
    },
    {
        "id": 2608,
        "statement": "1 providence be establish before 1800",
        "label": 1,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df['established'].lt(1800).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2609,
        "statement": "florence have 4 time as many inhabitant as arezzo",
        "label": 0,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df[df['province'] == 'florence']['inhabitants'].values[0] == 4 * df[df['province'] == 'arezzo']['inhabitants'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2610,
        "statement": "pisa be establish 100 year befoe pistoria",
        "label": 0,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "(df[df['province'] == 'pisa']['established'].values[0] - df[df['province'] == 'pistoia']['established'].values[0]) == 100",
        "pandas_eval": "False"
    },
    {
        "id": 2611,
        "statement": "pisa be the only province to have over 40000 inhabitant",
        "label": 0,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "(df[df['inhabitants'] > 40000]['province'].tolist() == ['pisa'])",
        "pandas_eval": "False"
    },
    {
        "id": 2612,
        "statement": "the president of siena have 3 name",
        "label": 0,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "len(df[df['province'] == 'siena']['president'].values[0].split()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2613,
        "statement": "3 province be establish after 1900",
        "label": 0,
        "table_caption": "politics of tuscany",
        "table_text": "province#inhabitants#established#president#party#election\narezzo#348327#1826#roberto vasai#democratic party#2009\nflorence#991862#1848#andrea barducci#democratic party#2009\ngrosseto#227063#1766#leonardo marras#democratic party#2009\nlivorno#341453#1861#giorgio kutuf\u00e3\u00a0#democratic party#2009\nlucca#392182#1849#stefano baccelli#democratic party#2011\nmassa#203642#1859#osvaldo angeli#democratic party#2008\npisa#414154#1849#andrea pieroni#democratic party#2009\npistoia#292108#1927#federica fratoni#democratic party#2009\nprato#248174#1992#lambarto nazzareno gestri#democratic party#2009\nsiena#270333#1849#simone bezzini#democratic party#2009\n",
        "pandas_code": "df[df['established'] > 1900].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2614,
        "statement": "the ford senior player championship have among one of the greatest cash prize",
        "label": 1,
        "table_caption": "1996 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 21#puerto rico senior tournament of champions#puerto rico#800000#john bland (2)#207 ( - 9)#151000\nfeb 4#royal caribbean classic#florida#850000#bob murphy (9)#203 ( - 10)#127500\nfeb 11#greater naples intellinet challenge#florida#600000#al geiberger (10)#202 ( - 14)#90000\nfeb 18#gte suncoast classic#florida#750000#jack nicklaus (9)#211 ( - 2)#112500\nfeb 25#american express invitational#florida#900000#hale irwin (3)#197 ( - 19)#135000\nmar 3#fhp health care classic#california#800000#walter morgan (2)#199 ( - 11)#120000\nmar 17#toshiba senior classic#california#1000000#jim colbert (14)#201 ( - 12)#150000\nmar 31#sbc dominion seniors#texas#650000#tom weiskopf (3)#207 ( - 9)#97500\napr 7#the tradition#arizona#1000000#jack nicklaus (10)#272 ( - 16)#150000\napr 21#pga seniors' championship#florida#1100000#hale irwin (4)#280 ( - 8)#198000\napr 28#las vegas senior classic#nevada#1000000#jim colbert (15)#207 ( - 9)#150000\nmay 5#painewebber invitational#north carolina#800000#graham marsh (2)#206 ( - 10)#120000\nmay 12#nationwide championship#georgia#1200000#jim colbert (16)#206 ( - 10)#180000\nmay 19#cadillac nfl golf classic#new jersey#950000#bob murphy (10)#202 ( - 14)#142500\nmay 26#bellsouth senior classic at opryland#tennessee#1200000#isao aoki (5)#202 ( - 14)#180000\njun 2#bruno 's memorial classic#alabama#1050000#john bland (3)#208 ( - 8)#157500\njun 9#pittsburgh senior classic#pennsylvania#1100000#tom weiskopf (4)#205 ( - 11)#165000\njun 16#du maurier champions#canada#1100000#charles coody (5)#271 ( - 9)#165000\njun 23#bell atlantic classic#pennsylvania#900000#dale douglass (11)#206 ( - 4)#135000\njun 30#kroger senior classic#ohio#900000#isao aoki (6)#198 ( - 15)#135000\njul 7#us senior open#ohio#1200000#dave stockton (12)#277 ( - 11)#212500\njul 14#ford senior players championship#michigan#1500000#raymond floyd (13)#275 ( - 13)#225000\njul 21#burnet senior classic#minnesota#1250000#vicente fernandez (1)#205 ( - 11)#187500\njul 28#ameritech senior open#illinois#1100000#walter morgan (3)#205 ( - 11)#165000\naug 4#vfw senior championship#missouri#800000#dave eichelberger (2)#200 ( - 10)#135000\naug 11#first of america classic#michigan#850000#dave stockton (13)#206 ( - 10)#127500\naug 18#northville long island classic#new york#800000#john bland (4)#202 ( - 14)#120000\naug 25#bank of boston senior classic#massachusetts#800000#jim dent (10)#204 ( - 12)#120000\nsep 1#franklin quest championship#utah#800000#graham marsh (3)#202 ( - 14)#120000\nsep 8#boone valley classic#missouri#1200000#gibby gilbert (5)#203 ( - 10)#180000\nsep 15#bank one classic#kentucky#600000#mike hill (18)#207 ( - 9)#90000\nsep 22#brickyard crossing championship#indiana#750000#jimmy powell (4)#134 ( - 10)#112500\nsep 29#vantage championship#north carolina#1500000#jim colbert (17)#204 ( - 9)#225000\noct 6#ralphs senior classic#california#800000#gil morgan (1)#202 ( - 11)#120000\noct 13#the transamerica#california#700000#john bland (5)#204 ( - 12)#105000\noct 20#raley 's gold rush classic#california#800000#jim colbert (18)#202 ( - 14)#120000\noct 27#hyatt regency maui kaanapali classic#hawaii#650000#bob charles (23)#198 ( - 15)#97500\nnov 3#emerald coast classic#florida#1050000#lee trevino (27)#207 ( - 3)#157500\n",
        "pandas_code": "df[df['tournament'] == 'ford senior players championship']['1st prize'].values[0] > 200000",
        "pandas_eval": "True"
    },
    {
        "id": 2615,
        "statement": "5 of the location be california",
        "label": 1,
        "table_caption": "1996 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 21#puerto rico senior tournament of champions#puerto rico#800000#john bland (2)#207 ( - 9)#151000\nfeb 4#royal caribbean classic#florida#850000#bob murphy (9)#203 ( - 10)#127500\nfeb 11#greater naples intellinet challenge#florida#600000#al geiberger (10)#202 ( - 14)#90000\nfeb 18#gte suncoast classic#florida#750000#jack nicklaus (9)#211 ( - 2)#112500\nfeb 25#american express invitational#florida#900000#hale irwin (3)#197 ( - 19)#135000\nmar 3#fhp health care classic#california#800000#walter morgan (2)#199 ( - 11)#120000\nmar 17#toshiba senior classic#california#1000000#jim colbert (14)#201 ( - 12)#150000\nmar 31#sbc dominion seniors#texas#650000#tom weiskopf (3)#207 ( - 9)#97500\napr 7#the tradition#arizona#1000000#jack nicklaus (10)#272 ( - 16)#150000\napr 21#pga seniors' championship#florida#1100000#hale irwin (4)#280 ( - 8)#198000\napr 28#las vegas senior classic#nevada#1000000#jim colbert (15)#207 ( - 9)#150000\nmay 5#painewebber invitational#north carolina#800000#graham marsh (2)#206 ( - 10)#120000\nmay 12#nationwide championship#georgia#1200000#jim colbert (16)#206 ( - 10)#180000\nmay 19#cadillac nfl golf classic#new jersey#950000#bob murphy (10)#202 ( - 14)#142500\nmay 26#bellsouth senior classic at opryland#tennessee#1200000#isao aoki (5)#202 ( - 14)#180000\njun 2#bruno 's memorial classic#alabama#1050000#john bland (3)#208 ( - 8)#157500\njun 9#pittsburgh senior classic#pennsylvania#1100000#tom weiskopf (4)#205 ( - 11)#165000\njun 16#du maurier champions#canada#1100000#charles coody (5)#271 ( - 9)#165000\njun 23#bell atlantic classic#pennsylvania#900000#dale douglass (11)#206 ( - 4)#135000\njun 30#kroger senior classic#ohio#900000#isao aoki (6)#198 ( - 15)#135000\njul 7#us senior open#ohio#1200000#dave stockton (12)#277 ( - 11)#212500\njul 14#ford senior players championship#michigan#1500000#raymond floyd (13)#275 ( - 13)#225000\njul 21#burnet senior classic#minnesota#1250000#vicente fernandez (1)#205 ( - 11)#187500\njul 28#ameritech senior open#illinois#1100000#walter morgan (3)#205 ( - 11)#165000\naug 4#vfw senior championship#missouri#800000#dave eichelberger (2)#200 ( - 10)#135000\naug 11#first of america classic#michigan#850000#dave stockton (13)#206 ( - 10)#127500\naug 18#northville long island classic#new york#800000#john bland (4)#202 ( - 14)#120000\naug 25#bank of boston senior classic#massachusetts#800000#jim dent (10)#204 ( - 12)#120000\nsep 1#franklin quest championship#utah#800000#graham marsh (3)#202 ( - 14)#120000\nsep 8#boone valley classic#missouri#1200000#gibby gilbert (5)#203 ( - 10)#180000\nsep 15#bank one classic#kentucky#600000#mike hill (18)#207 ( - 9)#90000\nsep 22#brickyard crossing championship#indiana#750000#jimmy powell (4)#134 ( - 10)#112500\nsep 29#vantage championship#north carolina#1500000#jim colbert (17)#204 ( - 9)#225000\noct 6#ralphs senior classic#california#800000#gil morgan (1)#202 ( - 11)#120000\noct 13#the transamerica#california#700000#john bland (5)#204 ( - 12)#105000\noct 20#raley 's gold rush classic#california#800000#jim colbert (18)#202 ( - 14)#120000\noct 27#hyatt regency maui kaanapali classic#hawaii#650000#bob charles (23)#198 ( - 15)#97500\nnov 3#emerald coast classic#florida#1050000#lee trevino (27)#207 ( - 3)#157500\n",
        "pandas_code": "(df['location'].value_counts()['california'] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 2616,
        "statement": "dale douglas have only win a tournament in pennsylvania",
        "label": 1,
        "table_caption": "1996 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 21#puerto rico senior tournament of champions#puerto rico#800000#john bland (2)#207 ( - 9)#151000\nfeb 4#royal caribbean classic#florida#850000#bob murphy (9)#203 ( - 10)#127500\nfeb 11#greater naples intellinet challenge#florida#600000#al geiberger (10)#202 ( - 14)#90000\nfeb 18#gte suncoast classic#florida#750000#jack nicklaus (9)#211 ( - 2)#112500\nfeb 25#american express invitational#florida#900000#hale irwin (3)#197 ( - 19)#135000\nmar 3#fhp health care classic#california#800000#walter morgan (2)#199 ( - 11)#120000\nmar 17#toshiba senior classic#california#1000000#jim colbert (14)#201 ( - 12)#150000\nmar 31#sbc dominion seniors#texas#650000#tom weiskopf (3)#207 ( - 9)#97500\napr 7#the tradition#arizona#1000000#jack nicklaus (10)#272 ( - 16)#150000\napr 21#pga seniors' championship#florida#1100000#hale irwin (4)#280 ( - 8)#198000\napr 28#las vegas senior classic#nevada#1000000#jim colbert (15)#207 ( - 9)#150000\nmay 5#painewebber invitational#north carolina#800000#graham marsh (2)#206 ( - 10)#120000\nmay 12#nationwide championship#georgia#1200000#jim colbert (16)#206 ( - 10)#180000\nmay 19#cadillac nfl golf classic#new jersey#950000#bob murphy (10)#202 ( - 14)#142500\nmay 26#bellsouth senior classic at opryland#tennessee#1200000#isao aoki (5)#202 ( - 14)#180000\njun 2#bruno 's memorial classic#alabama#1050000#john bland (3)#208 ( - 8)#157500\njun 9#pittsburgh senior classic#pennsylvania#1100000#tom weiskopf (4)#205 ( - 11)#165000\njun 16#du maurier champions#canada#1100000#charles coody (5)#271 ( - 9)#165000\njun 23#bell atlantic classic#pennsylvania#900000#dale douglass (11)#206 ( - 4)#135000\njun 30#kroger senior classic#ohio#900000#isao aoki (6)#198 ( - 15)#135000\njul 7#us senior open#ohio#1200000#dave stockton (12)#277 ( - 11)#212500\njul 14#ford senior players championship#michigan#1500000#raymond floyd (13)#275 ( - 13)#225000\njul 21#burnet senior classic#minnesota#1250000#vicente fernandez (1)#205 ( - 11)#187500\njul 28#ameritech senior open#illinois#1100000#walter morgan (3)#205 ( - 11)#165000\naug 4#vfw senior championship#missouri#800000#dave eichelberger (2)#200 ( - 10)#135000\naug 11#first of america classic#michigan#850000#dave stockton (13)#206 ( - 10)#127500\naug 18#northville long island classic#new york#800000#john bland (4)#202 ( - 14)#120000\naug 25#bank of boston senior classic#massachusetts#800000#jim dent (10)#204 ( - 12)#120000\nsep 1#franklin quest championship#utah#800000#graham marsh (3)#202 ( - 14)#120000\nsep 8#boone valley classic#missouri#1200000#gibby gilbert (5)#203 ( - 10)#180000\nsep 15#bank one classic#kentucky#600000#mike hill (18)#207 ( - 9)#90000\nsep 22#brickyard crossing championship#indiana#750000#jimmy powell (4)#134 ( - 10)#112500\nsep 29#vantage championship#north carolina#1500000#jim colbert (17)#204 ( - 9)#225000\noct 6#ralphs senior classic#california#800000#gil morgan (1)#202 ( - 11)#120000\noct 13#the transamerica#california#700000#john bland (5)#204 ( - 12)#105000\noct 20#raley 's gold rush classic#california#800000#jim colbert (18)#202 ( - 14)#120000\noct 27#hyatt regency maui kaanapali classic#hawaii#650000#bob charles (23)#198 ( - 15)#97500\nnov 3#emerald coast classic#florida#1050000#lee trevino (27)#207 ( - 3)#157500\n",
        "pandas_code": "(df[df['winner'].str.contains('dale douglass')]['location'].unique() == ['pennsylvania']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2617,
        "statement": "207 be a win score 5 time",
        "label": 1,
        "table_caption": "1996 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 21#puerto rico senior tournament of champions#puerto rico#800000#john bland (2)#207 ( - 9)#151000\nfeb 4#royal caribbean classic#florida#850000#bob murphy (9)#203 ( - 10)#127500\nfeb 11#greater naples intellinet challenge#florida#600000#al geiberger (10)#202 ( - 14)#90000\nfeb 18#gte suncoast classic#florida#750000#jack nicklaus (9)#211 ( - 2)#112500\nfeb 25#american express invitational#florida#900000#hale irwin (3)#197 ( - 19)#135000\nmar 3#fhp health care classic#california#800000#walter morgan (2)#199 ( - 11)#120000\nmar 17#toshiba senior classic#california#1000000#jim colbert (14)#201 ( - 12)#150000\nmar 31#sbc dominion seniors#texas#650000#tom weiskopf (3)#207 ( - 9)#97500\napr 7#the tradition#arizona#1000000#jack nicklaus (10)#272 ( - 16)#150000\napr 21#pga seniors' championship#florida#1100000#hale irwin (4)#280 ( - 8)#198000\napr 28#las vegas senior classic#nevada#1000000#jim colbert (15)#207 ( - 9)#150000\nmay 5#painewebber invitational#north carolina#800000#graham marsh (2)#206 ( - 10)#120000\nmay 12#nationwide championship#georgia#1200000#jim colbert (16)#206 ( - 10)#180000\nmay 19#cadillac nfl golf classic#new jersey#950000#bob murphy (10)#202 ( - 14)#142500\nmay 26#bellsouth senior classic at opryland#tennessee#1200000#isao aoki (5)#202 ( - 14)#180000\njun 2#bruno 's memorial classic#alabama#1050000#john bland (3)#208 ( - 8)#157500\njun 9#pittsburgh senior classic#pennsylvania#1100000#tom weiskopf (4)#205 ( - 11)#165000\njun 16#du maurier champions#canada#1100000#charles coody (5)#271 ( - 9)#165000\njun 23#bell atlantic classic#pennsylvania#900000#dale douglass (11)#206 ( - 4)#135000\njun 30#kroger senior classic#ohio#900000#isao aoki (6)#198 ( - 15)#135000\njul 7#us senior open#ohio#1200000#dave stockton (12)#277 ( - 11)#212500\njul 14#ford senior players championship#michigan#1500000#raymond floyd (13)#275 ( - 13)#225000\njul 21#burnet senior classic#minnesota#1250000#vicente fernandez (1)#205 ( - 11)#187500\njul 28#ameritech senior open#illinois#1100000#walter morgan (3)#205 ( - 11)#165000\naug 4#vfw senior championship#missouri#800000#dave eichelberger (2)#200 ( - 10)#135000\naug 11#first of america classic#michigan#850000#dave stockton (13)#206 ( - 10)#127500\naug 18#northville long island classic#new york#800000#john bland (4)#202 ( - 14)#120000\naug 25#bank of boston senior classic#massachusetts#800000#jim dent (10)#204 ( - 12)#120000\nsep 1#franklin quest championship#utah#800000#graham marsh (3)#202 ( - 14)#120000\nsep 8#boone valley classic#missouri#1200000#gibby gilbert (5)#203 ( - 10)#180000\nsep 15#bank one classic#kentucky#600000#mike hill (18)#207 ( - 9)#90000\nsep 22#brickyard crossing championship#indiana#750000#jimmy powell (4)#134 ( - 10)#112500\nsep 29#vantage championship#north carolina#1500000#jim colbert (17)#204 ( - 9)#225000\noct 6#ralphs senior classic#california#800000#gil morgan (1)#202 ( - 11)#120000\noct 13#the transamerica#california#700000#john bland (5)#204 ( - 12)#105000\noct 20#raley 's gold rush classic#california#800000#jim colbert (18)#202 ( - 14)#120000\noct 27#hyatt regency maui kaanapali classic#hawaii#650000#bob charles (23)#198 ( - 15)#97500\nnov 3#emerald coast classic#florida#1050000#lee trevino (27)#207 ( - 3)#157500\n",
        "pandas_code": "df['score'].str.contains('207').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2618,
        "statement": "every purse be greater than 100000",
        "label": 1,
        "table_caption": "1996 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\njan 21#puerto rico senior tournament of champions#puerto rico#800000#john bland (2)#207 ( - 9)#151000\nfeb 4#royal caribbean classic#florida#850000#bob murphy (9)#203 ( - 10)#127500\nfeb 11#greater naples intellinet challenge#florida#600000#al geiberger (10)#202 ( - 14)#90000\nfeb 18#gte suncoast classic#florida#750000#jack nicklaus (9)#211 ( - 2)#112500\nfeb 25#american express invitational#florida#900000#hale irwin (3)#197 ( - 19)#135000\nmar 3#fhp health care classic#california#800000#walter morgan (2)#199 ( - 11)#120000\nmar 17#toshiba senior classic#california#1000000#jim colbert (14)#201 ( - 12)#150000\nmar 31#sbc dominion seniors#texas#650000#tom weiskopf (3)#207 ( - 9)#97500\napr 7#the tradition#arizona#1000000#jack nicklaus (10)#272 ( - 16)#150000\napr 21#pga seniors' championship#florida#1100000#hale irwin (4)#280 ( - 8)#198000\napr 28#las vegas senior classic#nevada#1000000#jim colbert (15)#207 ( - 9)#150000\nmay 5#painewebber invitational#north carolina#800000#graham marsh (2)#206 ( - 10)#120000\nmay 12#nationwide championship#georgia#1200000#jim colbert (16)#206 ( - 10)#180000\nmay 19#cadillac nfl golf classic#new jersey#950000#bob murphy (10)#202 ( - 14)#142500\nmay 26#bellsouth senior classic at opryland#tennessee#1200000#isao aoki (5)#202 ( - 14)#180000\njun 2#bruno 's memorial classic#alabama#1050000#john bland (3)#208 ( - 8)#157500\njun 9#pittsburgh senior classic#pennsylvania#1100000#tom weiskopf (4)#205 ( - 11)#165000\njun 16#du maurier champions#canada#1100000#charles coody (5)#271 ( - 9)#165000\njun 23#bell atlantic classic#pennsylvania#900000#dale douglass (11)#206 ( - 4)#135000\njun 30#kroger senior classic#ohio#900000#isao aoki (6)#198 ( - 15)#135000\njul 7#us senior open#ohio#1200000#dave stockton (12)#277 ( - 11)#212500\njul 14#ford senior players championship#michigan#1500000#raymond floyd (13)#275 ( - 13)#225000\njul 21#burnet senior classic#minnesota#1250000#vicente fernandez (1)#205 ( - 11)#187500\njul 28#ameritech senior open#illinois#1100000#walter morgan (3)#205 ( - 11)#165000\naug 4#vfw senior championship#missouri#800000#dave eichelberger (2)#200 ( - 10)#135000\naug 11#first of america classic#michigan#850000#dave stockton (13)#206 ( - 10)#127500\naug 18#northville long island classic#new york#800000#john bland (4)#202 ( - 14)#120000\naug 25#bank of boston senior classic#massachusetts#800000#jim dent (10)#204 ( - 12)#120000\nsep 1#franklin quest championship#utah#800000#graham marsh (3)#202 ( - 14)#120000\nsep 8#boone valley classic#missouri#1200000#gibby gilbert (5)#203 ( - 10)#180000\nsep 15#bank one classic#kentucky#600000#mike hill (18)#207 ( - 9)#90000\nsep 22#brickyard crossing championship#indiana#750000#jimmy powell (4)#134 ( - 10)#112500\nsep 29#vantage championship#north carolina#1500000#jim colbert (17)#204 ( - 9)#225000\noct 6#ralphs senior classic#california#800000#gil morgan (1)#202 ( - 11)#120000\noct 13#the transamerica#california#700000#john bland (5)#204 ( - 12)#105000\noct 20#raley 's gold rush classic#california#800000#jim colbert (18)#202 ( - 14)#120000\noct 27#hyatt regency maui kaanapali classic#hawaii#650000#bob charles (23)#198 ( - 15)#97500\nnov 3#emerald coast classic#florida#1050000#lee trevino (27)#207 ( - 3)#157500\n",
        "pandas_code": "all(df['purse'] > 100000)",
        "pandas_eval": "True"
    },
    {
        "id": 2619,
        "statement": "1 - 0 be both the score and result on 14 may 2002 and on 29 may 2009",
        "label": 1,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "all(df[(df['date'].isin(['14 may 2002', '29 may 2009']))][['score', 'result']].apply(lambda x: x == '1 - 0').all(axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 2620,
        "statement": "4 - 0 be the highest result and it be for 3 game",
        "label": 1,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "df['result'].value_counts().loc['4 - 0'] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2621,
        "statement": "the competition be 2006 fifa world cup qual on 8 september 2004 and on 13 october 2004",
        "label": 1,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "((df['competition'] == '2006 fifa world cup qual') & (df['date'].isin(['8 september 2004', '13 october 2004']))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2622,
        "statement": "millennium stadium , cardiff , wales be the venue in 9 of robert earnshaw 's game",
        "label": 1,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "(df['venue'] == 'millennium stadium , cardiff , wales').sum() == 9",
        "pandas_eval": "True"
    },
    {
        "id": 2624,
        "statement": "the only date that have a 1 - 0 score be on 29 may 2009",
        "label": 0,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "(df[df['score'] == '1 - 0']['date'] == '29 may 2009').all()",
        "pandas_eval": "False"
    },
    {
        "id": 2625,
        "statement": "there be more than 3 game that have the highest result of 4 - 0",
        "label": 0,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "len(df[df['result'] == '4 - 0']) > 3",
        "pandas_eval": "False"
    },
    {
        "id": 2626,
        "statement": "the competition of 2006 fifa world cup qual be only 1 time on the date of 13 october 2004",
        "label": 0,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "(df[df['competition'] == '2006 fifa world cup qual'].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2627,
        "statement": "less than half of the robert earnshaw 's game be at the venue of millennium stadium , cardiff , wale",
        "label": 0,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "(df['venue'].value_counts(normalize=True)['millennium stadium , cardiff , wales'] < 0.5)",
        "pandas_eval": "False"
    },
    {
        "id": 2628,
        "statement": "the game at the venue stadio olimpico , serravalle , san marino be later than the year 2008",
        "label": 0,
        "table_caption": "robert earnshaw",
        "table_text": "date#venue#score#result#competition\n14 may 2002#millennium stadium , cardiff , wales#1 - 0#1 - 0#friendly\n12 february 2003#millennium stadium , cardiff , wales#1 - 0#2 - 2#friendly\n11 october 2003#millennium stadium , cardiff , wales#2 - 3#2 - 3#uefa euro 2004 qual\n18 february 2004#millennium stadium , cardiff , wales#1 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#2 - 0#4 - 0#friendly\n18 february 2004#millennium stadium , cardiff , wales#3 - 0#4 - 0#friendly\n31 march 2004#stadium pusk\u00e1s ferenc , budapest , hungary#1 - 2#1 - 2#friendly\n8 september 2004#millennium stadium , cardiff , wales#2 - 2#2 - 2#2006 fifa world cup qual\n13 october 2004#millennium stadium , cardiff , wales#1 - 0#2 - 3#2006 fifa world cup qual\n27 may 2006#upc - arena , graz , austria#1 - 1#1 - 2#friendly\n27 may 2006#upc - arena , graz , austria#1 - 2#1 - 2#friendly\n11 october 2006#millennium stadium , cardiff , wales#2 - 0#3 - 1#uefa euro 2008 qual\n17 october 2007#stadio olimpico , serravalle , san marino#1 - 0#1 - 2#uefa euro 2008 qual\n29 may 2009#parc y scarlets , llanelli , wales#1 - 0#1 - 0#friendly\n25 may 2011#aviva stadium , dublin , ireland#1 - 0#1 - 3#2011 nations cup\n27 may 2011#aviva stadium , dublin , ireland#2 - 0#2 - 0#2011 nations cup\n",
        "pandas_code": "pd.to_datetime(df[df['venue'] == 'stadio olimpico , serravalle , san marino']['date']).dt.year.iloc[0] > 2008",
        "pandas_eval": "False"
    },
    {
        "id": 2629,
        "statement": "the longest homolog of transmembrane protein 222 be the gene with accession number np_001144071.1 , it be 233aa and be find in corn",
        "label": 1,
        "table_caption": "transmembrane protein 222",
        "table_text": "genus / species#common name#accession number#length#similarity#identity\nrattus norvegicus#rat#np_001107252.1#208aa#99%#96%\ncanis familiaris#dog#xp_852505.1#208aa#98%#96%\nmus musculus#mouse#np_079943.2#208aa#96%#95%\nsus scrofa#pig#xp_003127773.1#208aa#97%#94%\nequus caballus#horse#xp_001917747.1#207aa#94%#93%\ngallus gallus#chicken#xp_417729.1#182aa#90%#85%\ndanio rerio#zebrafish#np_001013334.1#174aa#83%#71%\nanopheles gambiae#mosquito#xp_320483.3#197aa#66%#53%\ndrosophila melanogaster#fruit fly#np_723362.1#196aa#74%#61%\ncaenorhabditis elegans#nematode#np_494762.2#168aa#72%#55%\nphytophthora infestans#late blight#xp_002902629.1#186aa#59%#48%\nzea mays#corn#np_001144071.1#233aa#61%#44%\noryza sativa#rice#np_001051577.1#204aa#61%#43%\narabidopsis thaliana#thall cress#np_190673.1#231aa#55%#36%\n",
        "pandas_code": "(df.loc[df['accession number'] == 'np_001144071.1', 'length'].iloc[0] == '233aa') & (df.loc[df['accession number'] == 'np_001144071.1', 'common name'].iloc[0] == 'corn')",
        "pandas_eval": "True"
    },
    {
        "id": 2630,
        "statement": "the organism with a homolog most similar to transmembrane protein 222 be the rat , with 99% similarity",
        "label": 1,
        "table_caption": "transmembrane protein 222",
        "table_text": "genus / species#common name#accession number#length#similarity#identity\nrattus norvegicus#rat#np_001107252.1#208aa#99%#96%\ncanis familiaris#dog#xp_852505.1#208aa#98%#96%\nmus musculus#mouse#np_079943.2#208aa#96%#95%\nsus scrofa#pig#xp_003127773.1#208aa#97%#94%\nequus caballus#horse#xp_001917747.1#207aa#94%#93%\ngallus gallus#chicken#xp_417729.1#182aa#90%#85%\ndanio rerio#zebrafish#np_001013334.1#174aa#83%#71%\nanopheles gambiae#mosquito#xp_320483.3#197aa#66%#53%\ndrosophila melanogaster#fruit fly#np_723362.1#196aa#74%#61%\ncaenorhabditis elegans#nematode#np_494762.2#168aa#72%#55%\nphytophthora infestans#late blight#xp_002902629.1#186aa#59%#48%\nzea mays#corn#np_001144071.1#233aa#61%#44%\noryza sativa#rice#np_001051577.1#204aa#61%#43%\narabidopsis thaliana#thall cress#np_190673.1#231aa#55%#36%\n",
        "pandas_code": "df.loc[df['similarity'] == '99%', 'common name'].values[0] == 'rat'",
        "pandas_eval": "True"
    },
    {
        "id": 2631,
        "statement": "the organism with a homolog least similar to transmembrane protein 222 be the thall cress , with 55% similarity",
        "label": 1,
        "table_caption": "transmembrane protein 222",
        "table_text": "genus / species#common name#accession number#length#similarity#identity\nrattus norvegicus#rat#np_001107252.1#208aa#99%#96%\ncanis familiaris#dog#xp_852505.1#208aa#98%#96%\nmus musculus#mouse#np_079943.2#208aa#96%#95%\nsus scrofa#pig#xp_003127773.1#208aa#97%#94%\nequus caballus#horse#xp_001917747.1#207aa#94%#93%\ngallus gallus#chicken#xp_417729.1#182aa#90%#85%\ndanio rerio#zebrafish#np_001013334.1#174aa#83%#71%\nanopheles gambiae#mosquito#xp_320483.3#197aa#66%#53%\ndrosophila melanogaster#fruit fly#np_723362.1#196aa#74%#61%\ncaenorhabditis elegans#nematode#np_494762.2#168aa#72%#55%\nphytophthora infestans#late blight#xp_002902629.1#186aa#59%#48%\nzea mays#corn#np_001144071.1#233aa#61%#44%\noryza sativa#rice#np_001051577.1#204aa#61%#43%\narabidopsis thaliana#thall cress#np_190673.1#231aa#55%#36%\n",
        "pandas_code": "df.loc[df['similarity'] == df['similarity'].min(), 'common name'].values[0] == 'thall cress'",
        "pandas_eval": "True"
    },
    {
        "id": 2632,
        "statement": "4 plant have homologs of transmembrane protein 222 , they be late blight , corn , rice , and thall cress",
        "label": 1,
        "table_caption": "transmembrane protein 222",
        "table_text": "genus / species#common name#accession number#length#similarity#identity\nrattus norvegicus#rat#np_001107252.1#208aa#99%#96%\ncanis familiaris#dog#xp_852505.1#208aa#98%#96%\nmus musculus#mouse#np_079943.2#208aa#96%#95%\nsus scrofa#pig#xp_003127773.1#208aa#97%#94%\nequus caballus#horse#xp_001917747.1#207aa#94%#93%\ngallus gallus#chicken#xp_417729.1#182aa#90%#85%\ndanio rerio#zebrafish#np_001013334.1#174aa#83%#71%\nanopheles gambiae#mosquito#xp_320483.3#197aa#66%#53%\ndrosophila melanogaster#fruit fly#np_723362.1#196aa#74%#61%\ncaenorhabditis elegans#nematode#np_494762.2#168aa#72%#55%\nphytophthora infestans#late blight#xp_002902629.1#186aa#59%#48%\nzea mays#corn#np_001144071.1#233aa#61%#44%\noryza sativa#rice#np_001051577.1#204aa#61%#43%\narabidopsis thaliana#thall cress#np_190673.1#231aa#55%#36%\n",
        "pandas_code": "len(df[df['common name'].isin(['late blight', 'corn', 'rice', 'thall cress'])]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2633,
        "statement": "the rat and mouse homologs of transmembrane protein 222 be nearly equal in length",
        "label": 1,
        "table_caption": "transmembrane protein 222",
        "table_text": "genus / species#common name#accession number#length#similarity#identity\nrattus norvegicus#rat#np_001107252.1#208aa#99%#96%\ncanis familiaris#dog#xp_852505.1#208aa#98%#96%\nmus musculus#mouse#np_079943.2#208aa#96%#95%\nsus scrofa#pig#xp_003127773.1#208aa#97%#94%\nequus caballus#horse#xp_001917747.1#207aa#94%#93%\ngallus gallus#chicken#xp_417729.1#182aa#90%#85%\ndanio rerio#zebrafish#np_001013334.1#174aa#83%#71%\nanopheles gambiae#mosquito#xp_320483.3#197aa#66%#53%\ndrosophila melanogaster#fruit fly#np_723362.1#196aa#74%#61%\ncaenorhabditis elegans#nematode#np_494762.2#168aa#72%#55%\nphytophthora infestans#late blight#xp_002902629.1#186aa#59%#48%\nzea mays#corn#np_001144071.1#233aa#61%#44%\noryza sativa#rice#np_001051577.1#204aa#61%#43%\narabidopsis thaliana#thall cress#np_190673.1#231aa#55%#36%\n",
        "pandas_code": "df[df['common name'].isin(['rat', 'mouse'])]['length'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2634,
        "statement": "the most people be first elect in 1942",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1944",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#paul h maloney#democratic#1942#re - elected#paul h maloney (d) unopposed\nlouisiana 3#james r domengeaux#democratic#1940#re - elected#james r domengeaux (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#charles e mckenzie#democratic#1942#re - elected#charles e mckenzie (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "df['first elected'].value_counts().idxmax() == 1942",
        "pandas_eval": "True"
    },
    {
        "id": 2635,
        "statement": "the fewest people be elect in 1936",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1944",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#paul h maloney#democratic#1942#re - elected#paul h maloney (d) unopposed\nlouisiana 3#james r domengeaux#democratic#1940#re - elected#james r domengeaux (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#charles e mckenzie#democratic#1942#re - elected#charles e mckenzie (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "df['first elected'].min() == 1936",
        "pandas_eval": "True"
    },
    {
        "id": 2636,
        "statement": "there be more people first elect in 1942 than 1940",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1944",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#paul h maloney#democratic#1942#re - elected#paul h maloney (d) unopposed\nlouisiana 3#james r domengeaux#democratic#1940#re - elected#james r domengeaux (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#charles e mckenzie#democratic#1942#re - elected#charles e mckenzie (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "(df['first elected'] == 1942).sum() > (df['first elected'] == 1940).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 2637,
        "statement": "all of the incumbent be from the democratic party",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1944",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#paul h maloney#democratic#1942#re - elected#paul h maloney (d) unopposed\nlouisiana 3#james r domengeaux#democratic#1940#re - elected#james r domengeaux (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#charles e mckenzie#democratic#1942#re - elected#charles e mckenzie (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "all(df['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 2638,
        "statement": "all of the incumbent be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1944",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#paul h maloney#democratic#1942#re - elected#paul h maloney (d) unopposed\nlouisiana 3#james r domengeaux#democratic#1940#re - elected#james r domengeaux (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#charles e mckenzie#democratic#1942#re - elected#charles e mckenzie (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "all(df['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 2639,
        "statement": "ben hogan be the first place player be - 5 to par while 2 player be tie for fifth , jimmy demaret and bill ogden at - 1 to par each",
        "label": 1,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "(df[df['player'] == 'ben hogan']['to par'].values[0] == '- 5') & (df[df['place'] == 't5']['player'].isin(['jimmy demaret', 'bill ogden']).all()) & (df[df['place'] == 't5']['to par'].eq('- 1').all())",
        "pandas_eval": "True"
    },
    {
        "id": 2640,
        "statement": "san snead be tie for seventh with a final score of 72 and e to par",
        "label": 1,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "df[(df['player'] == 'sam snead') & (df['place'] == 't7') & (df['score'] == 72) & (df['to par'] == 'e')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2641,
        "statement": "all player be from the united state with no score below 67 or above 72",
        "label": 1,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "all(df['country'] == 'united states') & (df['score'].between(67, 72).all())",
        "pandas_eval": "True"
    },
    {
        "id": 2642,
        "statement": "the 2 player tie for fifth place have score of 71 each",
        "label": 1,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "all(df[df['place'] == 't5']['score'] == 71)",
        "pandas_eval": "True"
    },
    {
        "id": 2643,
        "statement": "georgie fazio have a score of 70 on - 2 to par and be tie for second with frank souchak (a) who also have a score of 70 on - 2 to par",
        "label": 1,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "df[(df['player'] == 'george fazio') & (df['score'] == 70) & (df['to par'] == '- 2') & (df['place'] == 't2')].empty == False and df[(df['player'] == 'frank souchak (a)') & (df['score'] == 70) & (df['to par'] == '- 2') & (df['place'] == 't2')].empty == False",
        "pandas_eval": "True"
    },
    {
        "id": 2644,
        "statement": "ben hogan score be 2 time as much as the second place finisher",
        "label": 0,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "df[df['player'] == 'ben hogan']['score'].values[0] == 2 * df[df['place'] == 't2']['score'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2645,
        "statement": "san snead be tie for seventh with 2 other player",
        "label": 0,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "df[(df['place'] == 't7') & (df['to par'] == 'e')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2646,
        "statement": "all player be professional from the united state with a score between 67 and 72",
        "label": 0,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "all(df['country'] == 'united states') and all(df['score'].between(67, 72)) and all(~df['player'].str.contains('(a)'))",
        "pandas_eval": "False"
    },
    {
        "id": 2647,
        "statement": "the 2 player that tie for fifth have a score higher than george fazio",
        "label": 0,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "not all(df[df['place'] == 't5']['score'] > df[df['player'] == 'george fazio']['score'].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2648,
        "statement": "george facio have a score of 70 and tie for second with 2 other professional golfer",
        "label": 0,
        "table_caption": "1953 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#ben hogan#united states#67#- 5\nt2#walter burkemo#united states#70#- 2\nt2#george fazio#united states#70#- 2\nt2#frank souchak (a)#united states#70#- 2\nt5#jimmy demaret#united states#71#- 1\nt5#bill ogden#united states#71#- 1\nt7#lou barbaro#united states#72#e\nt7#jerry barber#united states#72#e\nt7#jay hebert#united states#72#e\nt7#sam snead#united states#72#e\n",
        "pandas_code": "(df['player'] == 'george fazio').any() and (df[df['player'] == 'george fazio']['score'].values[0] == 70) and (df[df['place'] == 't2']['player'].str.contains('(a)', regex=False).sum() == 0) and (df[df['place'] == 't2']['player'].nunique() == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 2649,
        "statement": "2 of the tournament pemra \u00f6zgen played be in izmir , turkey",
        "label": 1,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "(df['tournament'].value_counts()['izmir , turkey'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2651,
        "statement": "pemra \u00f6zgen win all of the tournament hold in istanbul , turkey",
        "label": 1,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "all(df[df['tournament'] == 'istanbul , turkey']['outcome'] == 'winner')",
        "pandas_eval": "True"
    },
    {
        "id": 2652,
        "statement": "pemra \u00f6zgen play against different opponent in the final for all of the tournament",
        "label": 1,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "df['opponent in the final'].nunique() == len(df)",
        "pandas_eval": "True"
    },
    {
        "id": 2653,
        "statement": "pemra \u00f6zgen play in a tournament in ashkelon , israel before he play in gaziantep , turkey",
        "label": 1,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "(df[df['tournament'] == 'ashkelon , israel']['date'].iloc[0] < df[df['tournament'] == 'gaziantep , turkey']['date'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2654,
        "statement": "2 of the tournament pemra ozgen play in be in gaziantep , turkey",
        "label": 0,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "df[df['tournament'].str.contains('gaziantep , turkey')].shape[0] != 2",
        "pandas_eval": "False"
    },
    {
        "id": 2655,
        "statement": "pemra ozgen play in a tournament in leimen , germany before he play in istanbul , turkey",
        "label": 0,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "(df[df['tournament'] == 'leimen , germany']['date'].iloc[0] > df[df['tournament'] == 'istanbul , turkey']['date'].iloc[0]) if 'leimen , germany' in df['tournament'].values and 'istanbul , turkey' in df['tournament'].values else False",
        "pandas_eval": "False"
    },
    {
        "id": 2656,
        "statement": "pemra ozgen lose all of the tournament hold in istanbul , turkey",
        "label": 0,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "all(df[df['tournament'].str.contains('istanbul , turkey')]['outcome'] == 'runner - up')",
        "pandas_eval": "False"
    },
    {
        "id": 2657,
        "statement": "pemra ozgen play against the same opponent in the final for all of the tournament",
        "label": 0,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "df['opponent in the final'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2658,
        "statement": "pemra ozgen play a tournament in gaziantep , turkey , but never play in the prior tournament in ashkelon , israel",
        "label": 0,
        "table_caption": "pemra \u00f6zgen",
        "table_text": "outcome#date#tournament#surface#opponent in the final#score\nwinner#11 july 2005#istanbul , turkey#hard#radana holu\u0161ov\u00e1#6 - 4 6 - 3\nrunner - up#21 nov 2005#ashkelon , israel#hard#sharon fichman#1 - 6 1 - 6\nrunner - up#26 may 2008#gaziantep , turkey#hard#cagla buyukakcay#5 - 7 4 - 6\nwinner#02 june 2008#izmir , turkey#hard#vivian segnini#6 - 2 7 - 6 (5)\nwinner#09 june 2008#istanbul , turkey#hard#ekaterine gorgodze#6 - 4 7 - 6 (1)\nwinner#13 july 2009#izmir , turkey#hard#sandra zaniewska#6 - 0 6 - 4\nwinner#02 aug 2010#gaziantep , turkey#hard#jade hopper#6 - 4 6 - 4\nwinner#09 aug 2010#istanbul , turkey#hard#magali de lattre#6 - 2 5 - 0 ret\nrunner - up#11 feb 2013#leimen , germany#hard (i)#julia kimmelmann#4 - 6 3 - 6\nwinner#15 july 2013#woking , great britain#hard#tara moore#3 - 6 7 - 5 7 - 6 (10)\n",
        "pandas_code": "(df['tournament'].str.contains('gaziantep , turkey', case=False).any()) & (~df['tournament'].str.contains('ashkelon , israel', case=False).any())",
        "pandas_eval": "False"
    },
    {
        "id": 2659,
        "statement": "oxford unite be the home team with the highest attendance",
        "label": 1,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['home team'] == 'oxford united']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2660,
        "statement": "the game with the highest attendance be on 8 december 2002",
        "label": 1,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['attendance'] == df['attendance'].max()]['date'].iloc[0] == '8 december 2002'",
        "pandas_eval": "True"
    },
    {
        "id": 2661,
        "statement": "darlington be the only home team that make a score of 4 - 1",
        "label": 1,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "(df['home team'] == 'darlington').sum() == 1 and df.loc[df['home team'] == 'darlington', 'score'].iloc[0] == '4 - 1'",
        "pandas_eval": "True"
    },
    {
        "id": 2662,
        "statement": "margate be the home team with the lowest attendance",
        "label": 1,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['home team'] == 'margate']['attendance'].min() == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2663,
        "statement": "the game with the lowest attendance be on 7 december 2002",
        "label": 1,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['attendance'] == df['attendance'].min()]['date'].iloc[0] == '7 december 2002'",
        "pandas_eval": "True"
    },
    {
        "id": 2664,
        "statement": "oxford unite be the home team with the lowest attendance",
        "label": 0,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['home team'] == 'oxford united']['attendance'].min() == df['attendance'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2665,
        "statement": "the game with the highest attendance be on 17 december 2002",
        "label": 0,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['attendance'] == df['attendance'].max()]['date'].iloc[0] == '17 december 2002'",
        "pandas_eval": "False"
    },
    {
        "id": 2666,
        "statement": "darlington be the only home team that make a score of 1 - 0",
        "label": 0,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[(df['home team'] == 'darlington') & (df['score'] == '1 - 0')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2667,
        "statement": "margate be the home team with the highest attendance",
        "label": 0,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['home team'] == 'margate']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2668,
        "statement": "the game with the lowest attendance be on 8 december 2002",
        "label": 0,
        "table_caption": "2002 - 03 fa cup",
        "table_text": "tie no#home team#score#away team#date#attendance\n1#blackpool#3 - 1#torquay united#7 december 2002#5014\n2#darlington#4 - 1#stevenage borough#7 december 2002#3351\n3#macclesfield town#2 - 0#vauxhall motors#7 december 2002#2972\n4#crewe alexandra#3 - 0#mansfield town#7 december 2002#4563\n5#shrewsbury town#3 - 1#barrow#7 december 2002#4210\n6#stockport county#0 - 3#plymouth argyle#7 december 2002#3571\n7#bristol rovers#1 - 1#rochdale#7 december 2002#4369\nreplay#rochdale#3 - 2#bristol rovers#17 december 2002#2206\n8#oldham athletic#1 - 2#cheltenham town#7 december 2002#4416\n9#southend united#1 - 1#bournemouth#7 december 2002#5721\nreplay#bournemouth#3 - 2#southend united#17 december 2002#5456\n10#exeter city#3 - 1#rushden & diamonds#7 december 2002#2277\n11#scunthorpe united#0 - 0#carlisle united#7 december 2002#3590\nreplay#carlisle united#0 - 1#scunthorpe united#23 december 2002#6809\n12#margate#0 - 3#cardiff city#7 december 2002#1362\n13#southport#0 - 3#farnborough town#7 december 2002#2534\n14#morecambe#3 - 2#chester city#7 december 2002#4293\n15#york city#1 - 2#brentford#7 december 2002#3517\n16#wigan athletic#3 - 0#luton town#7 december 2002#4544\n17#cambridge united#2 - 2#northampton town#7 december 2002#5076\nreplay#northampton town#0 - 1#cambridge united#17 december 2002#4591\n18#harrogate railway athletic#1 - 3#bristol city#8 december 2002#3500\n19#crawley town#1 - 2#dagenham & redbridge#7 december 2002#4516\n20#oxford united#1 - 0#swindon town#8 december 2002#11645\n",
        "pandas_code": "df[df['attendance'] == df['attendance'].min()]['date'].iloc[0] == '8 december 2002'",
        "pandas_eval": "False"
    },
    {
        "id": 2669,
        "statement": "there be 11889 more visitor from china in 2012 than in 2011",
        "label": 1,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "df.loc[df['country'] == 'china', '2012'].values[0] - df.loc[df['country'] == 'china', '2011'].values[0] == 11889",
        "pandas_eval": "True"
    },
    {
        "id": 2670,
        "statement": "in 2012 , more tourist from the united state visit than in the previous year",
        "label": 1,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "df.loc[df['country'] == 'united states', '2012'].values[0] > df.loc[df['country'] == 'united states', '2011'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2671,
        "statement": "in either year , there be more tourist from china than from france and thailand combine",
        "label": 1,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "any(df.loc[df['country'] == 'china', ['2011', '2012']].values.flatten() > df.loc[df['country'].isin(['france', 'thailand']), ['2011', '2012']].sum().values)",
        "pandas_eval": "True"
    },
    {
        "id": 2672,
        "statement": "cambodia be the only country with fewer tourist visit in 2012 than in 2011",
        "label": 1,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "(df[df['country'] == 'cambodia']['2012'].values[0] < df[df['country'] == 'cambodia']['2011'].values[0]) and (df[df['country'] != 'cambodia']['2012'] >= df[df['country'] != 'cambodia']['2011']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2673,
        "statement": "in 2012 , australia have only 82 more visitor to vietnam than in 2011",
        "label": 1,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "(df.loc[df['country'] == 'australia', '2012'].values[0] - df.loc[df['country'] == 'australia', '2011'].values[0]) == 82",
        "pandas_eval": "True"
    },
    {
        "id": 2674,
        "statement": "there be 69732 more visitor from china in 2012 than in 2011",
        "label": 0,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "(df[df['country'] == 'china']['2012'].values[0] - df[df['country'] == 'china']['2011'].values[0]) == 69732",
        "pandas_eval": "False"
    },
    {
        "id": 2675,
        "statement": "in 2013 , more tourist from the united state visit than in the previous year",
        "label": 0,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "df.loc[df['country'] == 'united states', '2013 (jansep) link'].astype(int).iloc[0] > df.loc[df['country'] == 'united states', '2012'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2676,
        "statement": "in either year , there be more tourist from malaysia than from france and thailand combine",
        "label": 0,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "any(df.loc[df['country'] == 'malaysia', ['2011', '2012']].values.flatten() > df.loc[df['country'].isin(['france', 'thailand']), ['2011', '2012']].sum().values)",
        "pandas_eval": "False"
    },
    {
        "id": 2677,
        "statement": "australia be the only country with fewer tourist visit in 2012 than in 2011",
        "label": 0,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "(df[df['country'] == 'australia']['2012'].values[0] < df[df['country'] == 'australia']['2011'].values[0]) and (df['2012'] >= df['2011']).sum() == len(df) - 1",
        "pandas_eval": "False"
    },
    {
        "id": 2678,
        "statement": "in 2013 , australia have only 82 more visitor to vietnam than in 2011",
        "label": 0,
        "table_caption": "tourism in vietnam",
        "table_text": "rank#country#2011#2012#2013 (jansep) link\n1#china#1416804#1428693#1358961\n2#south korea#536408#700917#562076\n3#japan#481519#576386#450004\n4#united states#439872#443826#328215\n5#cambodia#423440#331939#247725\n6#taiwan#361051#409385#297584\n7#australia#289762#289844#238647\n8#malaysia#233132#299041#237081\n9#france#211444#219721#157637\n10#thailand#181820#225866#195 , 031\n",
        "pandas_code": "(int(df[df['country'] == 'australia']['2013 (jansep) link'].str.replace(' , ', '').values[0]) - df[df['country'] == 'australia']['2011'].values[0]) == 82",
        "pandas_eval": "False"
    },
    {
        "id": 2679,
        "statement": "wrike do not have wikis or web publishing but tiki wiki cm groupware do have wikis or web publishing and both have calendaring software",
        "label": 1,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df[df['name'] == 'wrike'][['wikis', 'web publishing']].eq('no').all().all()) and (df[df['name'] == 'tiki wiki cms groupware'][['wikis', 'web publishing']].eq('yes').any().any()) and (df[df['name'].isin(['wrike', 'tiki wiki cms groupware'])]['calendaring software'].eq('yes').all())",
        "pandas_eval": "True"
    },
    {
        "id": 2680,
        "statement": "when ibm connection have a yes for wikis and a yes for web publishing , ibm lotus domino have a no for wikis and a no for web publishing",
        "label": 1,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "((df[df['name'] == 'ibm connections']['wikis'].values[0] == 'yes') & (df[df['name'] == 'ibm connections']['web publishing'].values[0] == 'yes') & (df[df['name'] == 'ibm lotus domino']['wikis'].values[0] == 'no') & (df[df['name'] == 'ibm lotus domino']['web publishing'].values[0] == 'no'))",
        "pandas_eval": "True"
    },
    {
        "id": 2681,
        "statement": "traction teampage have a yes for blog and a no for survey while mindview have a no for blog and a no for survey",
        "label": 1,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df.loc[df['name'] == 'traction teampage', 'blogs'].values[0] == 'yes' and df.loc[df['name'] == 'traction teampage', 'surveys'].values[0] == 'no') and (df.loc[df['name'] == 'mindview', 'blogs'].values[0] == 'no' and df.loc[df['name'] == 'mindview', 'surveys'].values[0] == 'no')",
        "pandas_eval": "True"
    },
    {
        "id": 2682,
        "statement": "wrike have a yes for discussion , a yes for charting , and a yes for bookmarking , tag , rating and comment and google apps have a yes for discussion , yes for charting , and yes for bookmarking , tag , rating and comment",
        "label": 1,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "((df[df['name'] == 'wrike']['discussion'].values[0] == 'yes') & (df[df['name'] == 'wrike']['charting'].values[0] == 'yes') & (df[df['name'] == 'wrike']['bookmarking , tagging , rating and comments'].values[0] == 'yes')) & ((df[df['name'] == 'google apps']['discussion'].values[0] == 'yes') & (df[df['name'] == 'google apps']['charting'].values[0] == 'yes') & (df[df['name'] == 'google apps']['bookmarking , tagging , rating and comments'].values[0] == 'yes'))",
        "pandas_eval": "True"
    },
    {
        "id": 2683,
        "statement": "kune have a yes for wikis , an in development for web publishing , and a yes for social software while mindview have a no for wikis , a yes for web publishing , and a no for social software",
        "label": 1,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df.loc[df['name'] == 'kune', ['wikis', 'web publishing', 'social software']].values == ['yes', 'in development', 'yes']).all() and (df.loc[df['name'] == 'mindview', ['wikis', 'web publishing', 'social software']].values == ['no', 'yes', 'no']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2684,
        "statement": "wrike do not have wikis or web publishing but tiki wiki cm groupware do have wikis or web publishing and both have calendar software in development",
        "label": 0,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df[df['name'] == 'wrike']['wikis'].values[0] == 'no' and df[df['name'] == 'wrike']['web publishing'].values[0] == 'no' and df[df['name'] == 'tiki wiki cms groupware']['wikis'].values[0] == 'yes' and df[df['name'] == 'tiki wiki cms groupware']['web publishing'].values[0] == 'yes' and df[df['name'] == 'wrike']['calendaring software'].values[0] == 'in development' and df[df['name'] == 'tiki wiki cms groupware']['calendaring software'].values[0] == 'in development')",
        "pandas_eval": "False"
    },
    {
        "id": 2685,
        "statement": "when ibm connection have a yes for wikis , ibm quikr have a no for wikis and a no for web publishing",
        "label": 0,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df.loc[df['name'] == 'ibm connections', 'wikis'].values[0] == 'yes') & (df.loc[df['name'] == 'ibm quickr', 'wikis'].values[0] == 'no') & (df.loc[df['name'] == 'ibm quickr', 'web publishing'].values[0] == 'no')",
        "pandas_eval": "False"
    },
    {
        "id": 2686,
        "statement": "traction teampage have a no for blog and a yes for survey while mindview have a no for blog and a no for survey",
        "label": 0,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df.loc[df['name'] == 'traction teampage', ['blogs', 'surveys']].values == ['no', 'yes']).all() and (df.loc[df['name'] == 'mindview', ['blogs', 'surveys']].values == ['no', 'no']).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2687,
        "statement": "google apps have a yes for discussion , a yes for charting , and a yes for bookmarking , tag , rating and comment and wrike have a yes for discussion , yes for chart and yes for bookmarking , tag , rating and comment",
        "label": 0,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "(df[df['name'] == 'google apps'][['discussion', 'charting', 'bookmarking , tagging , rating and comments']].eq('yes').all().all()) and (df[df['name'] == 'wrike'][['discussion', 'charting', 'bookmarking , tagging , rating and comments']].eq('yes').all().all()) == False",
        "pandas_eval": "False"
    },
    {
        "id": 2688,
        "statement": "microsoft sharepoint have a yes for wikis , an in development for web publishing , and a yes for social software while mindview have a no for wikis , a yes for web publishing and a no for social software",
        "label": 0,
        "table_caption": "list of collaborative software",
        "table_text": "name#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\ngoogle apps#yes , sites#yes , sites#yes#no#no#yes , simple#no#yes , simple#yes#yes#yes#no#no#yes#yes#yes#yes#yes\nibm connections#yes#yes#yes#yes , simple#yes#yes , simple#no#no#yes#yes#no#no#no#no#yes#yes#no#no\nibm lotus domino#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nibm quickr#yes#yes#yes#yes , simple#yes#yes , simple#yes#no#yes#yes#no#yes#no#no#no#no#no#no\nkune#yes#in development#yes#yes , simple#no#yes#yes , wave - based#no#yes#yes#yes (gadget)#no#no#no#in development#yes#no#real - time collaborative documents\nmicrosoft exchange server#no#no#yes#yes , simple#yes#no#no#no#no#no#no#no#no#no#no#no#no#no\nmicrosoft office#no#no#no#no#no#yes , simple#no#yes#no#no#no#no#no#yes#no#no#no#yes , desktop\nmicrosoft project server#no#no#yes#yes#no#no#no#no#no#no#no#yes#no#no#no#no#no#no\nmicrosoft sharepoint#yes#yes#yes#yes , simple#yes#yes#yes#yes , microsoft infopath#yes#yes#yes#yes#yes#yes#yes#yes#yes#yes , web\nmicrosoft team foundation server#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#no#yes#no\nmindview#no#yes#yes#yes#yes#yes#yes#yes#yes#no#no#yes#yes#yes#no#no#no#ms office interface\ntraction teampage#yes#yes#yes#yes#yes (basic)#yes#no#no#yes#yes#no#yes#yes#yes , metrics#yes#yes#yes#no\ntiki wiki cms groupware#yes#yes#yes#yes , simple#yes#yes#yes (newsletter)#yes#yes#yes#yes#yes#no#yes (basic)#yes#yes#yes#yes , web\nwrike#no#no#yes#yes#yes#yes#no#no#yes#no#no#yes#no#yes#yes#yes#no#yes , web\nname#wikis#web publishing#calendaring software#project management#workflow system#document management#list management#xml forms management and workflow#discussion#blogs#surveys#time tracking#business intelligence#charting#bookmarking , tagging , rating and comments#social software#enterprise search#office suite\n",
        "pandas_code": "((df['name'] == 'microsoft sharepoint') & (df['wikis'] == 'yes') & (df['web publishing'] == 'in development') & (df['social software'] == 'yes')).any() and ((df['name'] == 'mindview') & (df['wikis'] == 'no') & (df['web publishing'] == 'yes') & (df['social software'] == 'no')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2689,
        "statement": "williams have the highest point and assist in game 2",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "(df.loc[df['game'] == 2, 'high points'].str.contains('williams').any()) & (df.loc[df['game'] == 2, 'high assists'].str.contains('williams').any())",
        "pandas_eval": "True"
    },
    {
        "id": 2691,
        "statement": "okur have the most rebound in game 2",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df.loc[df['game'] == 2, 'high rebounds'].str.contains('okur').any()",
        "pandas_eval": "True"
    },
    {
        "id": 2692,
        "statement": "utah win the series in 6 game",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df[df['game'] == 6]['series'].iloc[0] == '4 - 2'",
        "pandas_eval": "True"
    },
    {
        "id": 2693,
        "statement": "houston lose the series in 6 game",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df[df['team'] == 'houston']['series'].iloc[-1] == '4 - 2'",
        "pandas_eval": "True"
    },
    {
        "id": 2694,
        "statement": "williams do not have the highest assist in game 2 but do have the highest point",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "(df.loc[df['game'] == 2, 'high assists'].str.contains('williams').any() == False) & (df.loc[df['game'] == 2, 'high points'].str.contains('williams').any())",
        "pandas_eval": "False"
    },
    {
        "id": 2695,
        "statement": "williams do not have the highest point in game 3 and do not have many assist",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "(df.loc[df['game'] == 3, 'high points'].str.contains('williams').any() == False) & (df.loc[df['game'] == 3, 'high assists'].str.contains('williams').any() == False)",
        "pandas_eval": "False"
    },
    {
        "id": 2696,
        "statement": "okur do poorly in game 2 and do not have the most rebound",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df.loc[df['game'] == 2, 'high rebounds'].str.contains('okur').any() and not df.loc[df['game'] == 2, 'high rebounds'].str.startswith('okur').any()",
        "pandas_eval": "False"
    },
    {
        "id": 2697,
        "statement": "utah lose the 6 game series with a record of 2 - 4",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df['series'].iloc[-1] != '4 - 2'",
        "pandas_eval": "False"
    },
    {
        "id": 2698,
        "statement": "houston win the 6 game series with a record of 4 - 2",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#series\n1#april 19#houston#93 - 82#kirilenko (21)#boozer (16)#williams (10)#toyota center 18213#1 - 0\n2#april 21#houston#90 - 84#williams (22)#okur (16)#williams (5)#toyota center 18158#2 - 0\n3#april 24#houston#92 - 94#williams (28)#boozer (13)#williams (12)#energysolutions arena 19911#2 - 1\n4#april 26#houston#86 - 82#williams (17)#okur (18)#williams (9)#energysolutions arena 19911#3 - 1\n5#april 29#houston#69 - 95#boozer (19)#boozer , okur (10)#williams (6)#toyota center 18269#3 - 2\n6#may 2#houston#113 - 91#williams (25)#okur (13)#williams (9)#energysolutions arena 19911#4 - 2\n",
        "pandas_code": "df['series'].iloc[-1] != '4 - 2'",
        "pandas_eval": "False"
    },
    {
        "id": 2699,
        "statement": "amy play 7 friendly game in the time period",
        "label": 1,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "len(df[(df['competition'] == 'friendly') & (pd.to_datetime(df['date'].str.replace(' ', ''), format='%Y-%m-%d').between('2008-01-01', '2012-12-31'))]) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 2700,
        "statement": "she play more game in vancouver than anywhere else",
        "label": 1,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "df['location'].value_counts().idxmax() == 'vancouver'",
        "pandas_eval": "True"
    },
    {
        "id": 2701,
        "statement": "there have never be more than 1 goal score against her in these match",
        "label": 1,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "all(df['result'].apply(lambda x: int(x.split(' - ')[1]) <= 1))",
        "pandas_eval": "True"
    },
    {
        "id": 2702,
        "statement": "amy be not on the field the whole match for the majority of the game",
        "label": 1,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "df['lineup'].str.contains('off|on').mean() > 0.5",
        "pandas_eval": "True"
    },
    {
        "id": 2703,
        "statement": "heather o 'reilly be the player with the most assist for amy 's goal",
        "label": 1,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "df['assist / pass'].value_counts().idxmax() == 'heather o \\'reilly'",
        "pandas_eval": "True"
    },
    {
        "id": 2704,
        "statement": "amy win all 7 friendly game she play",
        "label": 0,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "all(df[df['competition'] == 'friendly']['result'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])))",
        "pandas_eval": "False"
    },
    {
        "id": 2705,
        "statement": "she play the majority of her game in vancouver",
        "label": 0,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "df[df['location'] == 'vancouver'].shape[0] > df.shape[0] / 2",
        "pandas_eval": "False"
    },
    {
        "id": 2706,
        "statement": "amy 's team achieve a shutout in all of these match",
        "label": 0,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "all(df['result'].str.split(' - ').apply(lambda x: int(x[1]) == 0))",
        "pandas_eval": "False"
    },
    {
        "id": 2707,
        "statement": "amy be not on the field the whole match for any of the game",
        "label": 0,
        "table_caption": "amy rodriguez",
        "table_text": "date#location#lineup#assist / pass#score#result#competition\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#1 - 0#4 - 0#four nations tournament\n2008 - 01 - 16#guangzhou#start#heather o 'reilly#2 - 0#4 - 0#four nations tournament\n2008 - 03 - 10#alvor#on 63' ( off kai )#abby wambach#4 - 0#4 - 0#algarve cup - group stage\n2008 - 06 - 17#suwon#start#unassisted#1 - 0#1 - 0#peace queen cup - group stage\n2008 - 07 - 13#commerce , co#on 46' ( off kai )#abby wambach#1 - 0#1 - 0#friendly\n2008 - 08 - 12#shenyang#off 56' ( on kai )#rachel buehler#2 - 0#4 - 0#olympics - group stage\n2010 - 03 - 28#san diego , ca#off 46' ( on cheney )#heather o 'reilly#1 - 0#3 - 0#friendly\n2010 - 07 - 13#omaha , ne#start#abby wambach#1 - 0#1 - 1#friendly\n2010 - 10 - 28#cancun#off 57' ( on cheney )#abby wambach#3 - 0#5 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#heather o 'reilly#1 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#carli lloyd#6 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 10 - 30#cancun#start#alex morgan#9 - 0#9 - 0#concacaf world cup qualifying - group stage\n2010 - 11 - 27#bridgeview , il#start#megan rapinoe#1 - 0#1 - 0#fifa world cup qualifying playoff - second leg\n2011 - 01 - 25#chongqing#off 71' ( on morgan )#tobin heath#1 - 0#2 - 0#four nations tournament\n2011 - 03 - 02#santo antonio#off 63' ( on morgan )#lauren cheney#1 - 0#2 - 1#algarve cup group - stage\n2011 - 05 - 14#columbus , oh#off 64' ( on cheney )#abby wambach#2 - 0#2 - 0#friendly\n2011 - 05 - 18#cary , nc#off 61' ( on morgan )#heather o 'reilly#1 - 0#2 - 0#friendly\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#8 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#9 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#unassisted#10 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#lauren cheney#12 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 20#vancouver#on 46' ( off heath )#alex morgan#13 - 0#14 - 0#concacaf olympic qualifying - group stage\n2012 - 01 - 22#vancouver#start#abby wambach#4 - 0#13 - 0#concacaf olympic qualifying - group stage\n2012 - 04 - 03#chiba#on 71' ( off morgan )#unassisted#3 - 0#3 - 0#kirin challenge cup\n2012 - 06 - 30#sandy , ut#on 51' ( off morgan )#unassisted#2 - 1#2 - 1#friendly\n2012 - 12 - 12#houston , tx#on 75' ( off morgan )#heather o 'reilly#4 - 0#4 - 0#friendly\n",
        "pandas_code": "df['lineup'].apply(lambda x: 'start' not in x and 'off' not in x).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2708,
        "statement": "celta de vigo hold the number 1 position with a goal difference of + 39",
        "label": 1,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'celta de vigo']['position'].values[0] == 1) & (df[df['club'] == 'celta de vigo']['goal difference'].values[0] == '+ 39')",
        "pandas_eval": "True"
    },
    {
        "id": 2709,
        "statement": "ud salamanca play a total of 38 game result in 23 win , 5 draw and 10 loss",
        "label": 1,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "df[(df['club'] == 'ud salamanca') & (df['played'] == 38) & (df['wins'] == 23) & (df['draws'] == 5) & (df['losses'] == 10)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2710,
        "statement": "getafe deportivo 3 club hold the last position in the league with 39 goal for and 76 against , result in a goal difference of - 37",
        "label": 1,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'getafe deportivo 3']['position'].iloc[0] == 20) & (df[df['club'] == 'getafe deportivo 3']['goals for'].iloc[0] == 39) & (df[df['club'] == 'getafe deportivo 3']['goals against'].iloc[0] == 76) & (df[df['club'] == 'getafe deportivo 3']['goal difference'].iloc[0] == '- 37')",
        "pandas_eval": "True"
    },
    {
        "id": 2711,
        "statement": "real oviedo end the season with 9 game win , 13 draw and 16 loss",
        "label": 1,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'real oviedo'][['wins', 'draws', 'losses']].values == [9, 13, 16]).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2712,
        "statement": "linares cf score 42 goal and yield 57 , result in a goal difference of - 15",
        "label": 1,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "df[(df['club'] == 'linares cf') & (df['goals for'] == 42) & (df['goals against'] == 57) & (df['goal difference'] == '- 15')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2713,
        "statement": "celta de vigo hold the number 1 position with a goal difference of + 26",
        "label": 0,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "df[(df['club'] == 'celta de vigo') & (df['position'] == 1) & (df['goal difference'] == '+ 26')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 2714,
        "statement": "ud salamanca play a total of 39 game result in 23 win , 5 draw and 10 loss",
        "label": 0,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'ud salamanca']['played'].values[0] == 39)",
        "pandas_eval": "False"
    },
    {
        "id": 2715,
        "statement": "getafe deportivo 3 club hold the last position in the league with 39 goal for and 76 against , result in a goal difference of - 37",
        "label": 0,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'getafe deportivo 3']['position'].iloc[0] == 20) and (df[df['club'] == 'getafe deportivo 3']['goals for'].iloc[0] == 39) and (df[df['club'] == 'getafe deportivo 3']['goals against'].iloc[0] == 76) and (df[df['club'] == 'getafe deportivo 3']['goal difference'].iloc[0] != '- 37')",
        "pandas_eval": "False"
    },
    {
        "id": 2716,
        "statement": "real oviedo end the season with 6 game win , 14 draw and 16 loss",
        "label": 0,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "(df[df['club'] == 'real oviedo']['wins'].values[0] == 6) & (df[df['club'] == 'real oviedo']['draws'].values[0] == 14) & (df[df['club'] == 'real oviedo']['losses'].values[0] == 16)",
        "pandas_eval": "False"
    },
    {
        "id": 2717,
        "statement": "linares cf score 34 goal and yield 57 , result in a goal difference of - 15",
        "label": 0,
        "table_caption": "1981 - 82 segunda divisi\u00f3n",
        "table_text": "position#club#played#points#wins#draws#losses#goals for#goals against#goal difference\n1#celta de vigo#38#53 + 15#22#9#7#79#40#+ 39\n2#ud salamanca#38#51 + 13#23#5#10#70#33#+ 37\n3#cd m\u00e1laga#38#50 + 12#20#10#8#70#35#+ 35\n4#elche cf#38#50 + 12#19#12#7#55#31#+ 24\n5#real murcia#38#46 + 8#18#10#10#54#38#+ 16\n6#rcd mallorca#38#42 + 4#15#12#11#55#46#+ 9\n7#rayo vallecano#38#41 + 3#16#9#13#45#44#+ 1\n8#castilla cf#38#40 + 2#13#14#11#48#48#0\n9#burgos 2#38#40 + 2#15#10#13#46#41#+ 5\n10#atl\u00e9tico madrile\u00f1o#38#38#12#14#12#44#44#0\n11#ce sabadell fc#38#38#14#10#14#59#63#- 4\n12#deportivo de la coru\u00f1a#38#37 - 1#13#11#14#40#48#- 8\n13#c\u00f3rdoba cf#38#36 - 2#11#14#13#45#49#- 4\n14#recreativo de huelva#38#35 - 3#12#11#15#39#40#- 1\n15#linares cf#38#34 - 4#10#14#14#42#57#- 15\n16#real oviedo#38#31 - 7#9#13#16#34#53#- 19\n17#deportivo alav\u00e9s 2#38#29 - 9#11#7#20#38#52#- 14\n18#ad almer\u00eda 3#38#26 - 8 1#6#18#14#27#43#- 16\n19#levante ud 3#38#20 - 18#7#6#25#26#74#- 48\n20#getafe deportivo 3#38#19 - 19#5#9#24#39#76#- 37\n",
        "pandas_code": "((df['club'] == 'linares cf') & (df['goals for'] == 34) & (df['goals against'] == 57) & (df['goal difference'] == '- 15')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2718,
        "statement": "attachment be not the poison for the pali dosa , aversion be",
        "label": 1,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "not df[df['pali'] == 'dosa']['poison / klesha'].iloc[0] == 'attachment'",
        "pandas_eval": "True"
    },
    {
        "id": 2719,
        "statement": "the sanskrit for the tibetan phrag dog be not dvesha , it be irshya",
        "label": 1,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['tibetan'] == 'phrag dog', 'sanskrit'].iloc[0] != 'dvesha' and df.loc[df['tibetan'] == 'phrag dog', 'sanskrit'].iloc[0] == 'irshya'",
        "pandas_eval": "True"
    },
    {
        "id": 2720,
        "statement": "the alternate translation for aversion be not envy , it be anger hatred",
        "label": 1,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['poison / klesha'] == 'aversion', 'alternate translations'].iloc[0] == 'anger , hatred'",
        "pandas_eval": "True"
    },
    {
        "id": 2721,
        "statement": "ignorance have alternate translation include confusion bewilderment but not cone timeit",
        "label": 1,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df[df['poison / klesha'] == 'ignorance']['alternate translations'].str.contains('confusion|bewilderment').all() and not df[df['poison / klesha'] == 'ignorance']['alternate translations'].str.contains('cone timeit').any()",
        "pandas_eval": "True"
    },
    {
        "id": 2723,
        "statement": "attachment be the poison for the pali dosa",
        "label": 0,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['pali'] == 'dosa', 'poison / klesha'].values[0] == 'attachment'",
        "pandas_eval": "False"
    },
    {
        "id": 2724,
        "statement": "the sanskrit for the tibetan phrag dog be dvesha",
        "label": 0,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['tibetan'] == 'phrag dog', 'sanskrit'].values[0] == 'dvesha'",
        "pandas_eval": "False"
    },
    {
        "id": 2725,
        "statement": "the alternate translation for aversion be envy",
        "label": 0,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['poison / klesha'] == 'aversion', 'alternate translations'].values[0] == 'envy'",
        "pandas_eval": "False"
    },
    {
        "id": 2726,
        "statement": "ignorance have only 1 alternate translation : cone timeit",
        "label": 0,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "len(df.loc[df['poison / klesha'] == 'ignorance', 'alternate translations'].iloc[0].split(' , ')) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2727,
        "statement": "pride mean desire and passion , but not arrogance and cone timeit",
        "label": 0,
        "table_caption": "kleshas (buddhism)",
        "table_text": "poison / klesha#sanskrit#pali#tibetan#alternate translations\nignorance#moha avidya#moha avijja#gti mug ma rig pa#confusion , bewilderment , delusion\nattachment#r\ud6eega#lobha#'dod chags#desire , passion\naversion#dvesha#dosa#zhe sdang#anger , hatred\npride#m\ud6eena#m\ud6eena#nga rgyal#arrogance , conceit\njealousy#irshya#iss\ud6ee#phrag dog#envy\n",
        "pandas_code": "df.loc[df['poison / klesha'] == 'pride', 'alternate translations'].str.contains('desire , passion').any() and not df.loc[df['poison / klesha'] == 'pride', 'alternate translations'].str.contains('arrogance , conceit').any()",
        "pandas_eval": "False"
    },
    {
        "id": 2728,
        "statement": "of the 11 player , 6 be from the country unite state",
        "label": 1,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "(df['country'] == 'united states').sum() == 6",
        "pandas_eval": "True"
    },
    {
        "id": 2729,
        "statement": "mike weir be the only player from the country canada",
        "label": 1,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "(df[df['country'] == 'canada']['player'].nunique() == 1) & (df[df['country'] == 'canada']['player'].iloc[0] == 'mike weir')",
        "pandas_eval": "True"
    },
    {
        "id": 2730,
        "statement": "phil mickelson be 1 of 7 player with + 1 to par , all of which have place t5",
        "label": 1,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "df[(df['player'] == 'phil mickelson') & (df['to par'] == '+ 1') & (df['place'] == 't5')].shape[0] == 1 and df[(df['to par'] == '+ 1') & (df['place'] == 't5')].shape[0] == 7",
        "pandas_eval": "True"
    },
    {
        "id": 2731,
        "statement": "of the 11 player , 5 be from the country unite state",
        "label": 0,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "df[df['country'] == 'united states'].shape[0] == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2732,
        "statement": "mike weir and billy andrade be both from the country canada",
        "label": 0,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "(df[df['player'].isin(['mike weir', 'billy andrade'])]['country'] == 'canada').all()",
        "pandas_eval": "False"
    },
    {
        "id": 2733,
        "statement": "phil mickelson be 1 of 6 player with + 1 to par , all of which have place t5",
        "label": 0,
        "table_caption": "2003 pga championship",
        "table_text": "place#player#country#score#to par\n1#shaun micheel#united states#69 + 68 = 137#- 3\nt2#billy andrade#united states#67 + 72 = 139#- 1\nt2#mike weir#canada#68 + 71 = 139#- 1\n4#rod pampling#australia#66 + 74 = 140#e\nt5#chad campbell#united states#69 + 72 = 141#+ 1\nt5#jos\u00e9 c\u00f3ceres#argentina#73 + 68 = 141#+ 1\nt5#ernie els#south africa#71 + 70 = 141#+ 1\nt5#tim herron#united states#69 + 72 = 141#+ 1\nt5#phil mickelson#united states#66 + 75 = 141#+ 1\nt5#tom pernice , jr#united states#70 + 71 = 141#+ 1\nt5#adam scott#australia#72 + 69 = 141#+ 1\n",
        "pandas_code": "df[(df['player'] == 'phil mickelson') & (df['to par'] == '+ 1') & (df['place'] == 't5')].shape[0] == 1 and df[(df['to par'] == '+ 1') & (df['place'] == 't5')].shape[0] == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2734,
        "statement": "during march of the 2007 - 08 season , leo mainoldi be the lead scorer 3 time",
        "label": 1,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "(df['leading scorer'].str.contains('leo mainoldi').sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 2735,
        "statement": "during march of the 2007 - 08 season , leo mainoldi be the leading scorer 1 more time than rafael hettsheimer",
        "label": 1,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "(df['leading scorer'].str.contains('leo mainoldi').sum() - df['leading scorer'].str.contains('rafael hettsheimeir').sum()) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2736,
        "statement": "the total point score for and against be 960 during march",
        "label": 1,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "sum([int(s.split(' - ')[0]) + int(s.split(' - ')[1]) for s in df[df['date'].str.contains('march')]['score']]) == 960",
        "pandas_eval": "True"
    },
    {
        "id": 2737,
        "statement": "the highest scoring game be on march 2 with 179 point score , 4 more than the second highest scoring game",
        "label": 1,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "(df['score'].apply(lambda x: sum(map(int, x.split(' - ')))).max() == 179) and (df['score'].apply(lambda x: sum(map(int, x.split(' - ')))).sort_values(ascending=False).iloc[0] - df['score'].apply(lambda x: sum(map(int, x.split(' - ')))).sort_values(ascending=False).iloc[1] == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 2738,
        "statement": "brian jones be the lead scorer in 1 out of 6 game during the march 2007 - 08 season",
        "label": 1,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "df['leading scorer'].str.contains('brian jones').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2739,
        "statement": "during march of the 2007 - 08 season , leo mainoldi be never the lead scorer",
        "label": 0,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "not any(df['leading scorer'].str.contains('leo mainoldi'))",
        "pandas_eval": "False"
    },
    {
        "id": 2740,
        "statement": "during march of the 2007 - 08 season , rafael hettsheimer be the leading scorer 1 more time than leo mainoldi",
        "label": 0,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "(df['leading scorer'].str.contains('rafael hettsheimeir').sum() - df['leading scorer'].str.contains('leo mainoldi').sum()) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2741,
        "statement": "the total point score for and against be less than 960 during march",
        "label": 0,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "sum([int(s.split(' - ')[0]) + int(s.split(' - ')[1]) for s in df[df['date'].str.contains('march')]['score']]) < 960",
        "pandas_eval": "False"
    },
    {
        "id": 2742,
        "statement": "the lowest scoring game be on march 2 with 179 point score",
        "label": 0,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "not df.loc[df['date'] == 'march 2', 'score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).eq(179).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2743,
        "statement": "brian jones be the leading scorer during more game than any other player during the march 2007 - 08 season",
        "label": 0,
        "table_caption": "2007 - 08 ce lleida b\u00e0squet season",
        "table_text": "date#visitor#score#home#leading scorer#record\nmarch 2#plus pujol#78 - 101#alicante costa blanca#leo mainoldi (21)#13 - 9\nmarch 8#beirasar rosal\u00eda#80 - 78#plus pujol#brian jones (17)#14 - 9\nmarch 14#plus pujol#84 - 66#ciudad de la laguna#leo mainoldi (14)#14 - 10\nmarch 19#villa de los barrios#103 - 72#plus pujol#rafael hettsheimeir (31)#15 - 10\nmarch 26#plus pujol#78 - 65#melilla baloncesto#rafael hettsheimeir (17)#15 - 11\nmarch 30#fundaci\u00f3 basquetincacom#73 - 82#plus pujol#leo mainoldi (24)#15 - 12\n",
        "pandas_code": "df['leading scorer'].str.contains('brian jones').sum() > df['leading scorer'].str.extract(r'\\((\\d+)\\)')[0].astype(int).idxmax()",
        "pandas_eval": "False"
    },
    {
        "id": 2744,
        "statement": "average attendance for the month of april / 1998 for the tampa bay devil ray be 27669",
        "label": 1,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 2745,
        "statement": "3 pitcher get their fourth win in the month of april , sele , helling and hill",
        "label": 1,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "(~df['loss'].str.contains('sele', case=False, na=False)).sum() >= 4 and (~df['loss'].str.contains('helling', case=False, na=False)).sum() >= 4 and (~df['loss'].str.contains('hill', case=False, na=False)).sum() >= 4",
        "pandas_eval": "True"
    },
    {
        "id": 2746,
        "statement": "hern\u00e1ndez have 2 save in the month of april / 1998",
        "label": 1,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df['loss'].str.contains('hern\u00e1ndez').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2747,
        "statement": "the highest attendance in april / 1998 be on april 7 total attendance be 45768",
        "label": 1,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df[df['date'] == 'april 7']['attendance'].iloc[0] == 45768",
        "pandas_eval": "True"
    },
    {
        "id": 2749,
        "statement": "average attendance for the month of april / 1998 for the tampa bay devil ray be 31516",
        "label": 0,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df[df['date'].str.contains('april')]['attendance'].mean() == 31516",
        "pandas_eval": "False"
    },
    {
        "id": 2750,
        "statement": "5 pitcher get their fourth win in the month of april , sele , helling and hill",
        "label": 0,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df['loss'].str.contains('sele|helling|hill').sum() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2751,
        "statement": "hern\u00e1ndez have 3 save in the month of april / 1998",
        "label": 0,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df['loss'].str.contains('hern\u00e1ndez').sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2752,
        "statement": "the highest attendance in april / 1998 be on april 7 total attendance be 8964",
        "label": 0,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "df[df['date'] == 'april 7']['attendance'].iloc[0] == 8964",
        "pandas_eval": "False"
    },
    {
        "id": 2753,
        "statement": "the tampa bay devil ray play the twin 2 series in april / 2018 - first 1 away and the second at home",
        "label": 0,
        "table_caption": "1998 tampa bay devil rays season",
        "table_text": "date#opponent#score#loss#attendance#record\napril 1#tigers#11 - 8#moehler (0 - 1)#30109#1 - 1\napril 2#tigers#7 - 1#worrell (0 - 1)#28261#2 - 1\napril 3#white sox#10 - 4#gorecki (0 - 1)#31816#2 - 2\napril 4#white sox#8 - 2#eyre (0 - 1)#36599#3 - 2\napril 5#white sox#5 - 0#bere (0 - 1)#33733#4 - 2\napril 7#tigers#3 - 1#arrojo (1 - 1)#45768#4 - 3\napril 10#white sox#3 - 0#saunders (0 - 1)#13563#4 - 4\napril 11#white sox#5 - 1#bere (0 - 2)#17322#5 - 4\napril 12#white sox#4 - 1#navarro (0 - 2)#14004#6 - 4\napril 13#twins#13 - 12 (14)#trombley (0 - 2)#31969#7 - 4\napril 14#twins#8 - 2#springer (1 - 1)#34337#7 - 5\napril 16#angels#6 - 5#holtz (1 - 1)#22906#8 - 5\napril 17#angels#5 - 0#gorecki (1 - 2)#31516#8 - 6\napril 18#angels#8 - 1#dickson (0 - 3)#37878#9 - 6\napril 19#angels#6 - 0#watson (0 - 2)#34580#10 - 6\napril 21#rangers#4 - 0#springer (1 - 2)#26776#10 - 7\napril 22#rangers#7 - 2#saunders (1 - 2)#30770#10 - 8\napril 23#rangers#12 - 5#oliver (0 - 3)#33319#11 - 8\napril 24#angels#10 - 3#\u00e1lvarez (3 - 2)#27767#11 - 9\napril 25#angels#7 - 1#arrojo (2 - 2)#33395#11 - 10\napril 26#angels#2 - 1#springer (1 - 3)#26882#11 - 11\napril 27#athletics#7 - 6#hern\u00e1ndez (0 - 1)#25484#11 - 12\napril 28#athletics#4 - 3#johnson (1 - 1)#25138#11 - 13\napril 29#twins#2 - 0#\u00e1lvarez (3 - 3)#8964#11 - 14\napril 30#twins#2 - 0#hawkins (0 - 3)#8860#12 - 14\n",
        "pandas_code": "((df['opponent'] == 'twins').sum() == 2) & (df[df['opponent'] == 'twins'].iloc[0]['attendance'] < df[df['opponent'] == 'twins'].iloc[1]['attendance'])",
        "pandas_eval": "False"
    },
    {
        "id": 2754,
        "statement": "miller barber achieve the highest earnings in the 1982 senior pga tour",
        "label": 1,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df.loc[df['earnings'].idxmax(), 'player'] == 'miller barber'",
        "pandas_eval": "True"
    },
    {
        "id": 2755,
        "statement": "all the top 5 playerse in the 1982 senior pga tour be from the united state",
        "label": 1,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "all(df['country'] == 'united states')",
        "pandas_eval": "True"
    },
    {
        "id": 2756,
        "statement": "millber barber and bob goalby participate in the most event",
        "label": 1,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df.loc[df['player'].isin(['miller barber', 'bob goalby']), 'events'].max() == df['events'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2757,
        "statement": "in the 1982 senior pga tour , miller barber have the most win",
        "label": 1,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df[df['player'] == 'miller barber']['wins'].max() == df['wins'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2758,
        "statement": "even though bob goalby have the least amount of win , he be still rank 3rd in the 1982 senior pga tour",
        "label": 1,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "(df[df['player'] == 'bob goalby']['wins'].values[0] == df['wins'].min()) & (df[df['player'] == 'bob goalby']['rank'].values[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 2759,
        "statement": "miller barber achieve the lowest earnings in the 1982 senior pga tour",
        "label": 0,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df[df['player'] == 'miller barber']['earnings'].iloc[0] == df['earnings'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2760,
        "statement": "all the top 4 playerse in the 1982 senior pga tour be from the united state",
        "label": 0,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "not all(df.loc[df['rank'] <= 4, 'country'] == 'united states')",
        "pandas_eval": "False"
    },
    {
        "id": 2761,
        "statement": "millber barber and bob goalby participate in the least event",
        "label": 0,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df[df['player'].isin(['miller barber', 'bob goalby'])]['events'].min() == df['events'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2762,
        "statement": "in the 1982 senior pga tour , miller barber have the least win",
        "label": 0,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df[df['player'] == 'miller barber']['wins'].min() == df['wins'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2763,
        "statement": "even though bob goalby have the most amount of win , he be still rank 3rd in the 1982 senior pga tour",
        "label": 0,
        "table_caption": "1982 senior pga tour",
        "table_text": "rank#player#country#earnings#events#wins\n1#miller barber#united states#106890#10#3\n2#don january#united states#99508#8#2\n3#bob goalby#united states#94540#10#1\n4#arnold palmer#united states#73848#7#2\n5#billy casper#united states#71979#8#2\n",
        "pandas_code": "df.loc[df['player'] == 'bob goalby', 'wins'].max() == df['wins'].max() and df.loc[df['player'] == 'bob goalby', 'rank'].iloc[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2764,
        "statement": "the september 27 game be the only game the cancel by the strike",
        "label": 1,
        "table_caption": "1987 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 13 , 1987#denver broncos#l 17 - 40#mile high stadium#0 - 1#75999\n2#september 20 , 1987#kansas city chiefs#w 43 - 14#kingdome#1 - 1#61667\n3#september 27 , 1987#san diego chargers#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike\n4#october 4 , 1987#miami dolphins#w 24 - 20#kingdome#2 - 1#19448\n5#october 11 , 1987#cincinnati bengals#l 10 - 17#kingdome#2 - 2#31739\n6#october 18 , 1987#detroit lions#w 37 - 14#pontiac silverdome#3 - 2#8310\n7#october 25 , 1987#los angeles raiders#w 35 - 13#los angeles memorial coliseum#4 - 2#52735\n8#november 1 , 1987#minnesota vikings#w 28 - 17#kingdome#5 - 2#61134\n9#november 9 , 1987#new york jets#l 14 - 30#the meadowlands#5 - 3#60452\n10#november 15 , 1987#green bay packers#w 24 - 13#kingdome#6 - 3#60963\n11#november 22 , 1987#san diego chargers#w 34 - 3#kingdome#7 - 3#62144\n12#november 30 , 1987#los angeles raiders#l 14 - 37#kingdome#7 - 4#62802\n13#december 6 , 1987#pittsburgh steelers#l 9 - 13#three rivers stadium#7 - 5#48881\n14#december 13 , 1987#denver broncos#w 28 - 21#kingdome#8 - 5#61759\n15#december 20 , 1987#chicago bears#w 34 - 21#soldier field#9 - 5#62518\n",
        "pandas_code": "(df[df['date'] == 'september 27 , 1987']['result'].iloc[0] == \"cancelled due to player 's strike\") and (df['result'].value_counts()[\"cancelled due to player 's strike\"] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2765,
        "statement": "the mile high stadium game have higher attendance than any of the game at the kingdome",
        "label": 1,
        "table_caption": "1987 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 13 , 1987#denver broncos#l 17 - 40#mile high stadium#0 - 1#75999\n2#september 20 , 1987#kansas city chiefs#w 43 - 14#kingdome#1 - 1#61667\n3#september 27 , 1987#san diego chargers#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike\n4#october 4 , 1987#miami dolphins#w 24 - 20#kingdome#2 - 1#19448\n5#october 11 , 1987#cincinnati bengals#l 10 - 17#kingdome#2 - 2#31739\n6#october 18 , 1987#detroit lions#w 37 - 14#pontiac silverdome#3 - 2#8310\n7#october 25 , 1987#los angeles raiders#w 35 - 13#los angeles memorial coliseum#4 - 2#52735\n8#november 1 , 1987#minnesota vikings#w 28 - 17#kingdome#5 - 2#61134\n9#november 9 , 1987#new york jets#l 14 - 30#the meadowlands#5 - 3#60452\n10#november 15 , 1987#green bay packers#w 24 - 13#kingdome#6 - 3#60963\n11#november 22 , 1987#san diego chargers#w 34 - 3#kingdome#7 - 3#62144\n12#november 30 , 1987#los angeles raiders#l 14 - 37#kingdome#7 - 4#62802\n13#december 6 , 1987#pittsburgh steelers#l 9 - 13#three rivers stadium#7 - 5#48881\n14#december 13 , 1987#denver broncos#w 28 - 21#kingdome#8 - 5#61759\n15#december 20 , 1987#chicago bears#w 34 - 21#soldier field#9 - 5#62518\n",
        "pandas_code": "df[df['game site'] == 'mile high stadium']['attendance'].astype(int).max() > df[df['game site'] == 'kingdome']['attendance'].astype(int).max()",
        "pandas_eval": "True"
    },
    {
        "id": 2766,
        "statement": "the raider do not win both of the game where they be opponent",
        "label": 1,
        "table_caption": "1987 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 13 , 1987#denver broncos#l 17 - 40#mile high stadium#0 - 1#75999\n2#september 20 , 1987#kansas city chiefs#w 43 - 14#kingdome#1 - 1#61667\n3#september 27 , 1987#san diego chargers#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike\n4#october 4 , 1987#miami dolphins#w 24 - 20#kingdome#2 - 1#19448\n5#october 11 , 1987#cincinnati bengals#l 10 - 17#kingdome#2 - 2#31739\n6#october 18 , 1987#detroit lions#w 37 - 14#pontiac silverdome#3 - 2#8310\n7#october 25 , 1987#los angeles raiders#w 35 - 13#los angeles memorial coliseum#4 - 2#52735\n8#november 1 , 1987#minnesota vikings#w 28 - 17#kingdome#5 - 2#61134\n9#november 9 , 1987#new york jets#l 14 - 30#the meadowlands#5 - 3#60452\n10#november 15 , 1987#green bay packers#w 24 - 13#kingdome#6 - 3#60963\n11#november 22 , 1987#san diego chargers#w 34 - 3#kingdome#7 - 3#62144\n12#november 30 , 1987#los angeles raiders#l 14 - 37#kingdome#7 - 4#62802\n13#december 6 , 1987#pittsburgh steelers#l 9 - 13#three rivers stadium#7 - 5#48881\n14#december 13 , 1987#denver broncos#w 28 - 21#kingdome#8 - 5#61759\n15#december 20 , 1987#chicago bears#w 34 - 21#soldier field#9 - 5#62518\n",
        "pandas_code": "not df[(df['opponent'].str.contains('raiders', case=False)) & (df['result'].str.startswith('w'))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2768,
        "statement": "the kingdome host the game with the lowest result for the opponent side",
        "label": 1,
        "table_caption": "1987 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 13 , 1987#denver broncos#l 17 - 40#mile high stadium#0 - 1#75999\n2#september 20 , 1987#kansas city chiefs#w 43 - 14#kingdome#1 - 1#61667\n3#september 27 , 1987#san diego chargers#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike#cancelled due to player 's strike\n4#october 4 , 1987#miami dolphins#w 24 - 20#kingdome#2 - 1#19448\n5#october 11 , 1987#cincinnati bengals#l 10 - 17#kingdome#2 - 2#31739\n6#october 18 , 1987#detroit lions#w 37 - 14#pontiac silverdome#3 - 2#8310\n7#october 25 , 1987#los angeles raiders#w 35 - 13#los angeles memorial coliseum#4 - 2#52735\n8#november 1 , 1987#minnesota vikings#w 28 - 17#kingdome#5 - 2#61134\n9#november 9 , 1987#new york jets#l 14 - 30#the meadowlands#5 - 3#60452\n10#november 15 , 1987#green bay packers#w 24 - 13#kingdome#6 - 3#60963\n11#november 22 , 1987#san diego chargers#w 34 - 3#kingdome#7 - 3#62144\n12#november 30 , 1987#los angeles raiders#l 14 - 37#kingdome#7 - 4#62802\n13#december 6 , 1987#pittsburgh steelers#l 9 - 13#three rivers stadium#7 - 5#48881\n14#december 13 , 1987#denver broncos#w 28 - 21#kingdome#8 - 5#61759\n15#december 20 , 1987#chicago bears#w 34 - 21#soldier field#9 - 5#62518\n",
        "pandas_code": "df[df['game site'] == 'kingdome']['result'].apply(lambda x: int(x.split(' - ')[1]) if ' - ' in x else float('inf')).min() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2769,
        "statement": "glenn howard have more point win than any other skip",
        "label": 1,
        "table_caption": "2009 canadian olympic curling trials",
        "table_text": "province#skip#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends#shot pct\nalberta#kevin martin#6#1#52#38#32#27#5#7#87\nontario#glenn howard#6#1#45#37#33#26#5#7#88\nmanitoba#jeff stoughton#5#2#46#40#29#26#8#6#86\nalberta#kevin koe#4#3#53#36#31#26#2#6#88\nalberta#randy ferbey#3#4#47#50#26#30#4#3#86\nontario#wayne middaugh#2#5#43#49#30#32#2#8#85\nsaskatchewan#pat simmons#2#5#39#46#28#29#1#6#84\n",
        "pandas_code": "df.loc[df['skip'] == 'glenn howard', 'w'].values[0] == df['w'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2770,
        "statement": "the average shot pct be 86.29",
        "label": 1,
        "table_caption": "2009 canadian olympic curling trials",
        "table_text": "province#skip#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends#shot pct\nalberta#kevin martin#6#1#52#38#32#27#5#7#87\nontario#glenn howard#6#1#45#37#33#26#5#7#88\nmanitoba#jeff stoughton#5#2#46#40#29#26#8#6#86\nalberta#kevin koe#4#3#53#36#31#26#2#6#88\nalberta#randy ferbey#3#4#47#50#26#30#4#3#86\nontario#wayne middaugh#2#5#43#49#30#32#2#8#85\nsaskatchewan#pat simmons#2#5#39#46#28#29#1#6#84\n",
        "pandas_code": "abs(df['shot pct'].mean() - 86.29) < 0.01",
        "pandas_eval": "True"
    },
    {
        "id": 2771,
        "statement": "kevin martin have more win than pat simmons",
        "label": 1,
        "table_caption": "2009 canadian olympic curling trials",
        "table_text": "province#skip#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends#shot pct\nalberta#kevin martin#6#1#52#38#32#27#5#7#87\nontario#glenn howard#6#1#45#37#33#26#5#7#88\nmanitoba#jeff stoughton#5#2#46#40#29#26#8#6#86\nalberta#kevin koe#4#3#53#36#31#26#2#6#88\nalberta#randy ferbey#3#4#47#50#26#30#4#3#86\nontario#wayne middaugh#2#5#43#49#30#32#2#8#85\nsaskatchewan#pat simmons#2#5#39#46#28#29#1#6#84\n",
        "pandas_code": "(df[df['skip'] == 'kevin martin']['w'].values[0] > df[df['skip'] == 'pat simmons']['w'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2772,
        "statement": "2 skip have a shot pct of 88",
        "label": 1,
        "table_caption": "2009 canadian olympic curling trials",
        "table_text": "province#skip#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends#shot pct\nalberta#kevin martin#6#1#52#38#32#27#5#7#87\nontario#glenn howard#6#1#45#37#33#26#5#7#88\nmanitoba#jeff stoughton#5#2#46#40#29#26#8#6#86\nalberta#kevin koe#4#3#53#36#31#26#2#6#88\nalberta#randy ferbey#3#4#47#50#26#30#4#3#86\nontario#wayne middaugh#2#5#43#49#30#32#2#8#85\nsaskatchewan#pat simmons#2#5#39#46#28#29#1#6#84\n",
        "pandas_code": "(df['shot pct'] == 88).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2773,
        "statement": "kevin koe win 5 more end than randy ferbey",
        "label": 1,
        "table_caption": "2009 canadian olympic curling trials",
        "table_text": "province#skip#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends#shot pct\nalberta#kevin martin#6#1#52#38#32#27#5#7#87\nontario#glenn howard#6#1#45#37#33#26#5#7#88\nmanitoba#jeff stoughton#5#2#46#40#29#26#8#6#86\nalberta#kevin koe#4#3#53#36#31#26#2#6#88\nalberta#randy ferbey#3#4#47#50#26#30#4#3#86\nontario#wayne middaugh#2#5#43#49#30#32#2#8#85\nsaskatchewan#pat simmons#2#5#39#46#28#29#1#6#84\n",
        "pandas_code": "(df[df['skip'] == 'kevin koe']['ends won'].values[0] - df[df['skip'] == 'randy ferbey']['ends won'].values[0]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2774,
        "statement": "the 34th game be the only game where antoine walker have the high in point",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n32#january 1#minnesota#w 97 - 70 (ot)#dwyane wade (19)#alonzo mourning (10)#dwyane wade (9)#american airlines arena#19 - 13\n33#january 4#new orleans / oklahoma city#l 92 - 107 (ot)#shaquille o'neal , dwyane wade (19)#dwyane wade (10)#dwyane wade (10)#ford center#19 - 14\n34#january 6#phoenix#l 93 - 111 (ot)#antoine walker (22)#shaquille o'neal (13)#gerald fitch (6)#america west arena#19 - 15\n35#january 8#portland#w 118 - 89 (ot)#dwyane wade (31)#james posey (9)#jason williams (8)#rose garden#20 - 15\n36#january 11#golden state#w 110 - 96 (ot)#dwyane wade (32)#udonis haslem , shaquille o'neal (10)#dwyane wade (11)#the arena in oakland#21 - 15\n37#january 13#seattle#w 117 - 104 (ot)#james posey , jason williams (19)#dwyane wade (10)#dwyane wade (14)#keyarena#22 - 15\n38#january 14#utah#w 100 - 94 (ot)#dwyane wade (31)#shaquille o'neal (14)#dwyane wade (8)#delta center#23 - 15\n39#january 16#la lakers#l 92 - 100 (ot)#dwyane wade (34)#udonis haslem (12)#dwyane wade (7)#staples center#23 - 16\n40#january 20#san antonio#l 94 - 101 (ot)#dwyane wade (36)#james posey , antoine walker (7)#udonis haslem , jason williams (3)#american airlines arena#23 - 17\n41#january 22#sacramento#w 119 - 99 (ot)#shaquille o'neal (27)#dwyane wade , udonis haslem , shaquille o'neal (6)#jason williams (11)#american airlines arena#24 - 17\n42#january 24#memphis#w 94 - 82 (ot)#dwyane wade (25)#shaquille o'neal (15)#jason williams (6)#american airlines arena#25 - 17\n43#january 26#phoenix#l 98 - 107 (ot)#dwyane wade (25)#shaquille o'neal (12)#gary payton (8)#american airlines arena#25 - 18\n44#january 27#charlotte#w 91 - 85 (ot)#shaquille o'neal (23)#gerald fitch (8)#gary payton (8)#charlotte arena#26 - 18\n45#january 29#houston#w 101 - 95 (ot)#dwyane wade (32)#shaquille o'neal (14)#dwyane wade (9)#toyota center#27 - 18\n",
        "pandas_code": "df[df['game'] == 34]['high points'].str.contains('antoine walker').all() and (df['high points'].str.contains('antoine walker').sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2775,
        "statement": "the sacramento game be the only game where 3 player tie in high rebound",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n32#january 1#minnesota#w 97 - 70 (ot)#dwyane wade (19)#alonzo mourning (10)#dwyane wade (9)#american airlines arena#19 - 13\n33#january 4#new orleans / oklahoma city#l 92 - 107 (ot)#shaquille o'neal , dwyane wade (19)#dwyane wade (10)#dwyane wade (10)#ford center#19 - 14\n34#january 6#phoenix#l 93 - 111 (ot)#antoine walker (22)#shaquille o'neal (13)#gerald fitch (6)#america west arena#19 - 15\n35#january 8#portland#w 118 - 89 (ot)#dwyane wade (31)#james posey (9)#jason williams (8)#rose garden#20 - 15\n36#january 11#golden state#w 110 - 96 (ot)#dwyane wade (32)#udonis haslem , shaquille o'neal (10)#dwyane wade (11)#the arena in oakland#21 - 15\n37#january 13#seattle#w 117 - 104 (ot)#james posey , jason williams (19)#dwyane wade (10)#dwyane wade (14)#keyarena#22 - 15\n38#january 14#utah#w 100 - 94 (ot)#dwyane wade (31)#shaquille o'neal (14)#dwyane wade (8)#delta center#23 - 15\n39#january 16#la lakers#l 92 - 100 (ot)#dwyane wade (34)#udonis haslem (12)#dwyane wade (7)#staples center#23 - 16\n40#january 20#san antonio#l 94 - 101 (ot)#dwyane wade (36)#james posey , antoine walker (7)#udonis haslem , jason williams (3)#american airlines arena#23 - 17\n41#january 22#sacramento#w 119 - 99 (ot)#shaquille o'neal (27)#dwyane wade , udonis haslem , shaquille o'neal (6)#jason williams (11)#american airlines arena#24 - 17\n42#january 24#memphis#w 94 - 82 (ot)#dwyane wade (25)#shaquille o'neal (15)#jason williams (6)#american airlines arena#25 - 17\n43#january 26#phoenix#l 98 - 107 (ot)#dwyane wade (25)#shaquille o'neal (12)#gary payton (8)#american airlines arena#25 - 18\n44#january 27#charlotte#w 91 - 85 (ot)#shaquille o'neal (23)#gerald fitch (8)#gary payton (8)#charlotte arena#26 - 18\n45#january 29#houston#w 101 - 95 (ot)#dwyane wade (32)#shaquille o'neal (14)#dwyane wade (9)#toyota center#27 - 18\n",
        "pandas_code": "df[df['team'] == 'sacramento']['high rebounds'].str.contains(',').sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2776,
        "statement": "phoenix be the only team with 2 game on the chart",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n32#january 1#minnesota#w 97 - 70 (ot)#dwyane wade (19)#alonzo mourning (10)#dwyane wade (9)#american airlines arena#19 - 13\n33#january 4#new orleans / oklahoma city#l 92 - 107 (ot)#shaquille o'neal , dwyane wade (19)#dwyane wade (10)#dwyane wade (10)#ford center#19 - 14\n34#january 6#phoenix#l 93 - 111 (ot)#antoine walker (22)#shaquille o'neal (13)#gerald fitch (6)#america west arena#19 - 15\n35#january 8#portland#w 118 - 89 (ot)#dwyane wade (31)#james posey (9)#jason williams (8)#rose garden#20 - 15\n36#january 11#golden state#w 110 - 96 (ot)#dwyane wade (32)#udonis haslem , shaquille o'neal (10)#dwyane wade (11)#the arena in oakland#21 - 15\n37#january 13#seattle#w 117 - 104 (ot)#james posey , jason williams (19)#dwyane wade (10)#dwyane wade (14)#keyarena#22 - 15\n38#january 14#utah#w 100 - 94 (ot)#dwyane wade (31)#shaquille o'neal (14)#dwyane wade (8)#delta center#23 - 15\n39#january 16#la lakers#l 92 - 100 (ot)#dwyane wade (34)#udonis haslem (12)#dwyane wade (7)#staples center#23 - 16\n40#january 20#san antonio#l 94 - 101 (ot)#dwyane wade (36)#james posey , antoine walker (7)#udonis haslem , jason williams (3)#american airlines arena#23 - 17\n41#january 22#sacramento#w 119 - 99 (ot)#shaquille o'neal (27)#dwyane wade , udonis haslem , shaquille o'neal (6)#jason williams (11)#american airlines arena#24 - 17\n42#january 24#memphis#w 94 - 82 (ot)#dwyane wade (25)#shaquille o'neal (15)#jason williams (6)#american airlines arena#25 - 17\n43#january 26#phoenix#l 98 - 107 (ot)#dwyane wade (25)#shaquille o'neal (12)#gary payton (8)#american airlines arena#25 - 18\n44#january 27#charlotte#w 91 - 85 (ot)#shaquille o'neal (23)#gerald fitch (8)#gary payton (8)#charlotte arena#26 - 18\n45#january 29#houston#w 101 - 95 (ot)#dwyane wade (32)#shaquille o'neal (14)#dwyane wade (9)#toyota center#27 - 18\n",
        "pandas_code": "len(df[df['team'] == 'phoenix']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2777,
        "statement": "5 game take place at the american airline arena",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n32#january 1#minnesota#w 97 - 70 (ot)#dwyane wade (19)#alonzo mourning (10)#dwyane wade (9)#american airlines arena#19 - 13\n33#january 4#new orleans / oklahoma city#l 92 - 107 (ot)#shaquille o'neal , dwyane wade (19)#dwyane wade (10)#dwyane wade (10)#ford center#19 - 14\n34#january 6#phoenix#l 93 - 111 (ot)#antoine walker (22)#shaquille o'neal (13)#gerald fitch (6)#america west arena#19 - 15\n35#january 8#portland#w 118 - 89 (ot)#dwyane wade (31)#james posey (9)#jason williams (8)#rose garden#20 - 15\n36#january 11#golden state#w 110 - 96 (ot)#dwyane wade (32)#udonis haslem , shaquille o'neal (10)#dwyane wade (11)#the arena in oakland#21 - 15\n37#january 13#seattle#w 117 - 104 (ot)#james posey , jason williams (19)#dwyane wade (10)#dwyane wade (14)#keyarena#22 - 15\n38#january 14#utah#w 100 - 94 (ot)#dwyane wade (31)#shaquille o'neal (14)#dwyane wade (8)#delta center#23 - 15\n39#january 16#la lakers#l 92 - 100 (ot)#dwyane wade (34)#udonis haslem (12)#dwyane wade (7)#staples center#23 - 16\n40#january 20#san antonio#l 94 - 101 (ot)#dwyane wade (36)#james posey , antoine walker (7)#udonis haslem , jason williams (3)#american airlines arena#23 - 17\n41#january 22#sacramento#w 119 - 99 (ot)#shaquille o'neal (27)#dwyane wade , udonis haslem , shaquille o'neal (6)#jason williams (11)#american airlines arena#24 - 17\n42#january 24#memphis#w 94 - 82 (ot)#dwyane wade (25)#shaquille o'neal (15)#jason williams (6)#american airlines arena#25 - 17\n43#january 26#phoenix#l 98 - 107 (ot)#dwyane wade (25)#shaquille o'neal (12)#gary payton (8)#american airlines arena#25 - 18\n44#january 27#charlotte#w 91 - 85 (ot)#shaquille o'neal (23)#gerald fitch (8)#gary payton (8)#charlotte arena#26 - 18\n45#january 29#houston#w 101 - 95 (ot)#dwyane wade (32)#shaquille o'neal (14)#dwyane wade (9)#toyota center#27 - 18\n",
        "pandas_code": "len(df[df['location attendance'] == 'american airlines arena']) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 2778,
        "statement": "gary payton have the high in assist at the charloette arena and the america airline arena",
        "label": 1,
        "table_caption": "2005 - 06 miami heat season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n32#january 1#minnesota#w 97 - 70 (ot)#dwyane wade (19)#alonzo mourning (10)#dwyane wade (9)#american airlines arena#19 - 13\n33#january 4#new orleans / oklahoma city#l 92 - 107 (ot)#shaquille o'neal , dwyane wade (19)#dwyane wade (10)#dwyane wade (10)#ford center#19 - 14\n34#january 6#phoenix#l 93 - 111 (ot)#antoine walker (22)#shaquille o'neal (13)#gerald fitch (6)#america west arena#19 - 15\n35#january 8#portland#w 118 - 89 (ot)#dwyane wade (31)#james posey (9)#jason williams (8)#rose garden#20 - 15\n36#january 11#golden state#w 110 - 96 (ot)#dwyane wade (32)#udonis haslem , shaquille o'neal (10)#dwyane wade (11)#the arena in oakland#21 - 15\n37#january 13#seattle#w 117 - 104 (ot)#james posey , jason williams (19)#dwyane wade (10)#dwyane wade (14)#keyarena#22 - 15\n38#january 14#utah#w 100 - 94 (ot)#dwyane wade (31)#shaquille o'neal (14)#dwyane wade (8)#delta center#23 - 15\n39#january 16#la lakers#l 92 - 100 (ot)#dwyane wade (34)#udonis haslem (12)#dwyane wade (7)#staples center#23 - 16\n40#january 20#san antonio#l 94 - 101 (ot)#dwyane wade (36)#james posey , antoine walker (7)#udonis haslem , jason williams (3)#american airlines arena#23 - 17\n41#january 22#sacramento#w 119 - 99 (ot)#shaquille o'neal (27)#dwyane wade , udonis haslem , shaquille o'neal (6)#jason williams (11)#american airlines arena#24 - 17\n42#january 24#memphis#w 94 - 82 (ot)#dwyane wade (25)#shaquille o'neal (15)#jason williams (6)#american airlines arena#25 - 17\n43#january 26#phoenix#l 98 - 107 (ot)#dwyane wade (25)#shaquille o'neal (12)#gary payton (8)#american airlines arena#25 - 18\n44#january 27#charlotte#w 91 - 85 (ot)#shaquille o'neal (23)#gerald fitch (8)#gary payton (8)#charlotte arena#26 - 18\n45#january 29#houston#w 101 - 95 (ot)#dwyane wade (32)#shaquille o'neal (14)#dwyane wade (9)#toyota center#27 - 18\n",
        "pandas_code": "df[(df['high assists'].str.contains('gary payton')) & (df['location attendance'].isin(['charlotte arena', 'american airlines arena']))].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 2779,
        "statement": "in the 2011 british columbia tournament of heart , kristy lewis (richmond curl club) record the highest end won",
        "label": 1,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "df.loc[df['skip (club)'] == 'kristy lewis (richmond curling club)', 'ends won'].max() == df['ends won'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2780,
        "statement": "kelley law (royal city curl club) record 6 pf point more than kelly scott (kelowna curl club)",
        "label": 1,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "(df[df['skip (club)'] == 'kelley law (royal city curling club)']['pf'].values[0] - df[df['skip (club)'] == 'kelly scott (kelowna curling club)']['pf'].values[0]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 2781,
        "statement": "roselyn craig (duncan curl club) be 1 of the 3 skip (club) have 11 blank end each",
        "label": 1,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "df[df['skip (club)'].str.contains('roselyn craig') & (df['blank ends'] == 11)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2782,
        "statement": "adina tasaka (royal city curl club) have a lower stolen end than kristy lewis (richmond curl club)",
        "label": 1,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "df.loc[df['skip (club)'] == 'adina tasaka (royal city curling club)', 'stolen ends'].values[0] < df.loc[df['skip (club)'] == 'kristy lewis (richmond curling club)', 'stolen ends'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2783,
        "statement": "nicole backe (nanaimo curl club) have equal number of end won and end lost at 38 while jen fewster (prince george curl club) have 33 and 35 respectively",
        "label": 1,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "(df[df['skip (club)'] == 'nicole backe (nanaimo curling club)']['ends won'].values[0] == 38 and df[df['skip (club)'] == 'nicole backe (nanaimo curling club)']['ends lost'].values[0] == 38) and (df[df['skip (club)'] == 'jen fewster (prince george curling club)']['ends won'].values[0] == 33 and df[df['skip (club)'] == 'jen fewster (prince george curling club)']['ends lost'].values[0] == 35)",
        "pandas_eval": "True"
    },
    {
        "id": 2784,
        "statement": "in the 2011 british columbia tournament of heart , nicole backe (nanaimo curl club) record the highest end won",
        "label": 0,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "df.loc[df['skip (club)'] == 'nicole backe (nanaimo curling club)', 'ends won'].values[0] == df['ends won'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2785,
        "statement": "kelley law (royal city curl club) record 2 pf point more than kelly scott (kelowna curl club)",
        "label": 0,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "(df[df['skip (club)'] == 'kelley law (royal city curling club)']['pf'].values[0] - df[df['skip (club)'] == 'kelly scott (kelowna curling club)']['pf'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2786,
        "statement": "roselyn craig (duncan curl club) be the only 3 skip (club) to have11 blank end",
        "label": 0,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "((df['skip (club)'] == 'roselyn craig (duncan curling club)') & (df['blank ends'] == 11)).any() & (df['blank ends'].eq(11).sum() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2787,
        "statement": "adina tasaka (royal city curl club) have a higher stolen end than kristy lewis (richmond curl club)",
        "label": 0,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "df[df['skip (club)'] == 'adina tasaka (royal city curling club)']['stolen ends'].values[0] > df[df['skip (club)'] == 'kristy lewis (richmond curling club)']['stolen ends'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2788,
        "statement": "nicole backe (nanaimo curl club) have more end won than lose while jen fewster (prince george curl club) have 35 and 33 respectively",
        "label": 0,
        "table_caption": "2011 british columbia scotties tournament of hearts",
        "table_text": "skip (club)#w#l#pf#pa#ends won#ends lost#blank ends#stolen ends\nkelley law (royal city curling club)#8#1#66#36#39#26#13#14\nkelly scott (kelowna curling club)#7#2#60#48#34#36#18#7\nroselyn craig (duncan curling club)#6#3#52#46#37#33#11#8\nkristy lewis (richmond curling club)#5#4#67#52#42#35#7#11\nadina tasaka (royal city curling club)#5#4#49#53#36#39#11#7\nkarla thompson (kamloops curling club)#4#5#52#55#34#34#9#7\npatti knezevic (prince george curling club)#3#6#46#57#32#41#11#2\njen fewster (prince george curling club)#3#6#46#51#33#35#13#11\nnicole backe (nanaimo curling club)#3#6#62#56#38#38#12#11\n",
        "pandas_code": "(df[df['skip (club)'] == 'nicole backe (nanaimo curling club)']['ends won'].values[0] > df[df['skip (club)'] == 'nicole backe (nanaimo curling club)']['ends lost'].values[0]) & (df[df['skip (club)'] == 'jen fewster (prince george curling club)']['ends won'].values[0] == 35) & (df[df['skip (club)'] == 'jen fewster (prince george curling club)']['ends lost'].values[0] == 33)",
        "pandas_eval": "False"
    },
    {
        "id": 2789,
        "statement": "gilles villeneuve win 2 time in the 1981 formula one season",
        "label": 1,
        "table_caption": "1981 formula one season",
        "table_text": "rnd#race#date#location#pole position#fastest lap#race winner#constructor#report\n1#united states grand prix west#15 march#long beach#riccardo patrese#alan jones#alan jones#williams - ford#report\n2#brazilian grand prix#29 march#jacarepagu\u00e1#nelson piquet#marc surer#carlos reutemann#williams - ford#report\n3#argentine grand prix#12 april#buenos aires#nelson piquet#nelson piquet#nelson piquet#brabham - ford#report\n4#san marino grand prix#3 may#imola#gilles villeneuve#gilles villeneuve#nelson piquet#brabham - ford#report\n5#belgian grand prix#17 may#zolder#carlos reutemann#carlos reutemann#carlos reutemann#williams - ford#report\n6#monaco grand prix#31 may#monaco#nelson piquet#alan jones#gilles villeneuve#ferrari#report\n7#spanish grand prix#21 june#jarama#jacques laffite#alan jones#gilles villeneuve#ferrari#report\n8#french grand prix#5 july#dijon - prenois#ren\u00e9 arnoux#alain prost#alain prost#renault#report\n9#british grand prix#18 july#silverstone#ren\u00e9 arnoux#ren\u00e9 arnoux#john watson#mclaren - ford#report\n10#german grand prix#2 august#hockenheimring#alain prost#alan jones#nelson piquet#brabham - ford#report\n11#austrian grand prix#16 august#\u00f6sterreichring#ren\u00e9 arnoux#jacques laffite#jacques laffite#ligier - matra#report\n12#dutch grand prix#30 august#zandvoort#alain prost#alan jones#alain prost#renault#report\n13#italian grand prix#13 september#monza#ren\u00e9 arnoux#carlos reutemann#alain prost#renault#report\n14#canadian grand prix#27 september#\u00eele notre - dame#nelson piquet#john watson#jacques laffite#ligier - matra#report\n",
        "pandas_code": "(df['race winner'] == 'gilles villeneuve').sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2790,
        "statement": "alain prost and nelson piquet win the most in the 1981 formula one season",
        "label": 1,
        "table_caption": "1981 formula one season",
        "table_text": "rnd#race#date#location#pole position#fastest lap#race winner#constructor#report\n1#united states grand prix west#15 march#long beach#riccardo patrese#alan jones#alan jones#williams - ford#report\n2#brazilian grand prix#29 march#jacarepagu\u00e1#nelson piquet#marc surer#carlos reutemann#williams - ford#report\n3#argentine grand prix#12 april#buenos aires#nelson piquet#nelson piquet#nelson piquet#brabham - ford#report\n4#san marino grand prix#3 may#imola#gilles villeneuve#gilles villeneuve#nelson piquet#brabham - ford#report\n5#belgian grand prix#17 may#zolder#carlos reutemann#carlos reutemann#carlos reutemann#williams - ford#report\n6#monaco grand prix#31 may#monaco#nelson piquet#alan jones#gilles villeneuve#ferrari#report\n7#spanish grand prix#21 june#jarama#jacques laffite#alan jones#gilles villeneuve#ferrari#report\n8#french grand prix#5 july#dijon - prenois#ren\u00e9 arnoux#alain prost#alain prost#renault#report\n9#british grand prix#18 july#silverstone#ren\u00e9 arnoux#ren\u00e9 arnoux#john watson#mclaren - ford#report\n10#german grand prix#2 august#hockenheimring#alain prost#alan jones#nelson piquet#brabham - ford#report\n11#austrian grand prix#16 august#\u00f6sterreichring#ren\u00e9 arnoux#jacques laffite#jacques laffite#ligier - matra#report\n12#dutch grand prix#30 august#zandvoort#alain prost#alan jones#alain prost#renault#report\n13#italian grand prix#13 september#monza#ren\u00e9 arnoux#carlos reutemann#alain prost#renault#report\n14#canadian grand prix#27 september#\u00eele notre - dame#nelson piquet#john watson#jacques laffite#ligier - matra#report\n",
        "pandas_code": "(df['race winner'].value_counts().idxmax() == 'alain prost' or df['race winner'].value_counts().idxmax() == 'nelson piquet')",
        "pandas_eval": "True"
    },
    {
        "id": 2791,
        "statement": "alan jones have the most fastest lap in the 1981 formula one season",
        "label": 1,
        "table_caption": "1981 formula one season",
        "table_text": "rnd#race#date#location#pole position#fastest lap#race winner#constructor#report\n1#united states grand prix west#15 march#long beach#riccardo patrese#alan jones#alan jones#williams - ford#report\n2#brazilian grand prix#29 march#jacarepagu\u00e1#nelson piquet#marc surer#carlos reutemann#williams - ford#report\n3#argentine grand prix#12 april#buenos aires#nelson piquet#nelson piquet#nelson piquet#brabham - ford#report\n4#san marino grand prix#3 may#imola#gilles villeneuve#gilles villeneuve#nelson piquet#brabham - ford#report\n5#belgian grand prix#17 may#zolder#carlos reutemann#carlos reutemann#carlos reutemann#williams - ford#report\n6#monaco grand prix#31 may#monaco#nelson piquet#alan jones#gilles villeneuve#ferrari#report\n7#spanish grand prix#21 june#jarama#jacques laffite#alan jones#gilles villeneuve#ferrari#report\n8#french grand prix#5 july#dijon - prenois#ren\u00e9 arnoux#alain prost#alain prost#renault#report\n9#british grand prix#18 july#silverstone#ren\u00e9 arnoux#ren\u00e9 arnoux#john watson#mclaren - ford#report\n10#german grand prix#2 august#hockenheimring#alain prost#alan jones#nelson piquet#brabham - ford#report\n11#austrian grand prix#16 august#\u00f6sterreichring#ren\u00e9 arnoux#jacques laffite#jacques laffite#ligier - matra#report\n12#dutch grand prix#30 august#zandvoort#alain prost#alan jones#alain prost#renault#report\n13#italian grand prix#13 september#monza#ren\u00e9 arnoux#carlos reutemann#alain prost#renault#report\n14#canadian grand prix#27 september#\u00eele notre - dame#nelson piquet#john watson#jacques laffite#ligier - matra#report\n",
        "pandas_code": "df['fastest lap'].value_counts().idxmax() == 'alan jones'",
        "pandas_eval": "True"
    },
    {
        "id": 2792,
        "statement": "renault have the most car constructor in the 1981 formula one season",
        "label": 1,
        "table_caption": "1981 formula one season",
        "table_text": "rnd#race#date#location#pole position#fastest lap#race winner#constructor#report\n1#united states grand prix west#15 march#long beach#riccardo patrese#alan jones#alan jones#williams - ford#report\n2#brazilian grand prix#29 march#jacarepagu\u00e1#nelson piquet#marc surer#carlos reutemann#williams - ford#report\n3#argentine grand prix#12 april#buenos aires#nelson piquet#nelson piquet#nelson piquet#brabham - ford#report\n4#san marino grand prix#3 may#imola#gilles villeneuve#gilles villeneuve#nelson piquet#brabham - ford#report\n5#belgian grand prix#17 may#zolder#carlos reutemann#carlos reutemann#carlos reutemann#williams - ford#report\n6#monaco grand prix#31 may#monaco#nelson piquet#alan jones#gilles villeneuve#ferrari#report\n7#spanish grand prix#21 june#jarama#jacques laffite#alan jones#gilles villeneuve#ferrari#report\n8#french grand prix#5 july#dijon - prenois#ren\u00e9 arnoux#alain prost#alain prost#renault#report\n9#british grand prix#18 july#silverstone#ren\u00e9 arnoux#ren\u00e9 arnoux#john watson#mclaren - ford#report\n10#german grand prix#2 august#hockenheimring#alain prost#alan jones#nelson piquet#brabham - ford#report\n11#austrian grand prix#16 august#\u00f6sterreichring#ren\u00e9 arnoux#jacques laffite#jacques laffite#ligier - matra#report\n12#dutch grand prix#30 august#zandvoort#alain prost#alan jones#alain prost#renault#report\n13#italian grand prix#13 september#monza#ren\u00e9 arnoux#carlos reutemann#alain prost#renault#report\n14#canadian grand prix#27 september#\u00eele notre - dame#nelson piquet#john watson#jacques laffite#ligier - matra#report\n",
        "pandas_code": "'renault' in df['constructor'].value_counts().index[df['constructor'].value_counts() == df['constructor'].value_counts().max()]",
        "pandas_eval": "True"
    },
    {
        "id": 2793,
        "statement": "alain prost and nelson piquet both have 3 race winner in the 1981 formula one season",
        "label": 1,
        "table_caption": "1981 formula one season",
        "table_text": "rnd#race#date#location#pole position#fastest lap#race winner#constructor#report\n1#united states grand prix west#15 march#long beach#riccardo patrese#alan jones#alan jones#williams - ford#report\n2#brazilian grand prix#29 march#jacarepagu\u00e1#nelson piquet#marc surer#carlos reutemann#williams - ford#report\n3#argentine grand prix#12 april#buenos aires#nelson piquet#nelson piquet#nelson piquet#brabham - ford#report\n4#san marino grand prix#3 may#imola#gilles villeneuve#gilles villeneuve#nelson piquet#brabham - ford#report\n5#belgian grand prix#17 may#zolder#carlos reutemann#carlos reutemann#carlos reutemann#williams - ford#report\n6#monaco grand prix#31 may#monaco#nelson piquet#alan jones#gilles villeneuve#ferrari#report\n7#spanish grand prix#21 june#jarama#jacques laffite#alan jones#gilles villeneuve#ferrari#report\n8#french grand prix#5 july#dijon - prenois#ren\u00e9 arnoux#alain prost#alain prost#renault#report\n9#british grand prix#18 july#silverstone#ren\u00e9 arnoux#ren\u00e9 arnoux#john watson#mclaren - ford#report\n10#german grand prix#2 august#hockenheimring#alain prost#alan jones#nelson piquet#brabham - ford#report\n11#austrian grand prix#16 august#\u00f6sterreichring#ren\u00e9 arnoux#jacques laffite#jacques laffite#ligier - matra#report\n12#dutch grand prix#30 august#zandvoort#alain prost#alan jones#alain prost#renault#report\n13#italian grand prix#13 september#monza#ren\u00e9 arnoux#carlos reutemann#alain prost#renault#report\n14#canadian grand prix#27 september#\u00eele notre - dame#nelson piquet#john watson#jacques laffite#ligier - matra#report\n",
        "pandas_code": "(df['race winner'].value_counts()[['alain prost', 'nelson piquet']] == 3).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2794,
        "statement": "only 1 of the representative be a republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#george elliott hagan#democratic#1960#lost renomination democratic hold#ronald 'bo' ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 5#fletcher thompson#republican#1966#retired to run for us senate democratic gain#andrew young (d) 52.8% rodney m cook (r) 47.2%\ngeorgia 6#john james flynt , jr#democratic#1954#re - elected#john james flynt , jr (d) unopposed\ngeorgia 7#john w davis#democratic#1960#re - elected#john w davis (d) 58.3% charlie sherrill (r) 41.7%\ngeorgia 9#phillip m landrum#democratic#1952#re - elected#phillip m landrum (d) unopposed\n",
        "pandas_code": "df[df['party'] == 'republican'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2795,
        "statement": "all but 1 of the democratic representative be re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#george elliott hagan#democratic#1960#lost renomination democratic hold#ronald 'bo' ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 5#fletcher thompson#republican#1966#retired to run for us senate democratic gain#andrew young (d) 52.8% rodney m cook (r) 47.2%\ngeorgia 6#john james flynt , jr#democratic#1954#re - elected#john james flynt , jr (d) unopposed\ngeorgia 7#john w davis#democratic#1960#re - elected#john w davis (d) 58.3% charlie sherrill (r) 41.7%\ngeorgia 9#phillip m landrum#democratic#1952#re - elected#phillip m landrum (d) unopposed\n",
        "pandas_code": "df[(df['party'] == 'democratic') & (df['result'] != 're - elected')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2796,
        "statement": "all but 2 of the representative be unopposed",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#george elliott hagan#democratic#1960#lost renomination democratic hold#ronald 'bo' ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 5#fletcher thompson#republican#1966#retired to run for us senate democratic gain#andrew young (d) 52.8% rodney m cook (r) 47.2%\ngeorgia 6#john james flynt , jr#democratic#1954#re - elected#john james flynt , jr (d) unopposed\ngeorgia 7#john w davis#democratic#1960#re - elected#john w davis (d) 58.3% charlie sherrill (r) 41.7%\ngeorgia 9#phillip m landrum#democratic#1952#re - elected#phillip m landrum (d) unopposed\n",
        "pandas_code": "df['candidates'].str.contains('unopposed').sum() == (len(df) - 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2797,
        "statement": "all of the representative run in different district",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#george elliott hagan#democratic#1960#lost renomination democratic hold#ronald 'bo' ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 5#fletcher thompson#republican#1966#retired to run for us senate democratic gain#andrew young (d) 52.8% rodney m cook (r) 47.2%\ngeorgia 6#john james flynt , jr#democratic#1954#re - elected#john james flynt , jr (d) unopposed\ngeorgia 7#john w davis#democratic#1960#re - elected#john w davis (d) 58.3% charlie sherrill (r) 41.7%\ngeorgia 9#phillip m landrum#democratic#1952#re - elected#phillip m landrum (d) unopposed\n",
        "pandas_code": "df['district'].nunique() == len(df)",
        "pandas_eval": "True"
    },
    {
        "id": 2799,
        "statement": "the south african grand prix and mexican grand prix be both in march",
        "label": 1,
        "table_caption": "1992 formula one season",
        "table_text": "round#grand prix#date#location#pole position#fastest lap#winning driver#winning constructor#report\n1#south african grand prix#1 march#kyalami#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n2#mexican grand prix#22 march#hermanos rodr\u00e3\u00adguez#nigel mansell#gerhard berger#nigel mansell#williams - renault#report\n3#brazilian grand prix#5 april#interlagos#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n4#spanish grand prix#3 may#catalunya#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n5#san marino grand prix#17 may#imola#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n6#monaco grand prix#31 may#monaco#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n7#canadian grand prix#14 june#circuit gilles villeneuve#ayrton senna#gerhard berger#gerhard berger#mclaren - honda#report\n8#french grand prix#5 july#magny - cours#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n9#british grand prix#12 july#silverstone#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n10#german grand prix#26 july#hockenheimring#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n11#hungarian grand prix#16 august#hungaroring#riccardo patrese#nigel mansell#ayrton senna#mclaren - honda#report\n12#belgian grand prix#30 august#spa - francorchamps#nigel mansell#michael schumacher#michael schumacher#benetton - ford#report\n13#italian grand prix#13 september#monza#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n14#portuguese grand prix#27 september#estoril#nigel mansell#ayrton senna#nigel mansell#williams - renault#report\n15#japanese grand prix#25 october#suzuka#nigel mansell#nigel mansell#riccardo patrese#williams - renault#report\n",
        "pandas_code": "all(df[df['grand prix'].isin(['south african grand prix', 'mexican grand prix'])]['date'].str.contains('march'))",
        "pandas_eval": "True"
    },
    {
        "id": 2800,
        "statement": "nigel mansell have the fastest lap at least 6 time",
        "label": 1,
        "table_caption": "1992 formula one season",
        "table_text": "round#grand prix#date#location#pole position#fastest lap#winning driver#winning constructor#report\n1#south african grand prix#1 march#kyalami#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n2#mexican grand prix#22 march#hermanos rodr\u00e3\u00adguez#nigel mansell#gerhard berger#nigel mansell#williams - renault#report\n3#brazilian grand prix#5 april#interlagos#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n4#spanish grand prix#3 may#catalunya#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n5#san marino grand prix#17 may#imola#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n6#monaco grand prix#31 may#monaco#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n7#canadian grand prix#14 june#circuit gilles villeneuve#ayrton senna#gerhard berger#gerhard berger#mclaren - honda#report\n8#french grand prix#5 july#magny - cours#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n9#british grand prix#12 july#silverstone#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n10#german grand prix#26 july#hockenheimring#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n11#hungarian grand prix#16 august#hungaroring#riccardo patrese#nigel mansell#ayrton senna#mclaren - honda#report\n12#belgian grand prix#30 august#spa - francorchamps#nigel mansell#michael schumacher#michael schumacher#benetton - ford#report\n13#italian grand prix#13 september#monza#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n14#portuguese grand prix#27 september#estoril#nigel mansell#ayrton senna#nigel mansell#williams - renault#report\n15#japanese grand prix#25 october#suzuka#nigel mansell#nigel mansell#riccardo patrese#williams - renault#report\n",
        "pandas_code": "(df['fastest lap'].value_counts()['nigel mansell'] >= 6)",
        "pandas_eval": "True"
    },
    {
        "id": 2801,
        "statement": "nigel mansell be the fastest driver at least 7 time",
        "label": 1,
        "table_caption": "1992 formula one season",
        "table_text": "round#grand prix#date#location#pole position#fastest lap#winning driver#winning constructor#report\n1#south african grand prix#1 march#kyalami#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n2#mexican grand prix#22 march#hermanos rodr\u00e3\u00adguez#nigel mansell#gerhard berger#nigel mansell#williams - renault#report\n3#brazilian grand prix#5 april#interlagos#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n4#spanish grand prix#3 may#catalunya#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n5#san marino grand prix#17 may#imola#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n6#monaco grand prix#31 may#monaco#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n7#canadian grand prix#14 june#circuit gilles villeneuve#ayrton senna#gerhard berger#gerhard berger#mclaren - honda#report\n8#french grand prix#5 july#magny - cours#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n9#british grand prix#12 july#silverstone#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n10#german grand prix#26 july#hockenheimring#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n11#hungarian grand prix#16 august#hungaroring#riccardo patrese#nigel mansell#ayrton senna#mclaren - honda#report\n12#belgian grand prix#30 august#spa - francorchamps#nigel mansell#michael schumacher#michael schumacher#benetton - ford#report\n13#italian grand prix#13 september#monza#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n14#portuguese grand prix#27 september#estoril#nigel mansell#ayrton senna#nigel mansell#williams - renault#report\n15#japanese grand prix#25 october#suzuka#nigel mansell#nigel mansell#riccardo patrese#williams - renault#report\n",
        "pandas_code": "(df['fastest lap'] == 'nigel mansell').sum() >= 7",
        "pandas_eval": "True"
    },
    {
        "id": 2802,
        "statement": "ayrton senna be the win driver during exactly 3 race",
        "label": 1,
        "table_caption": "1992 formula one season",
        "table_text": "round#grand prix#date#location#pole position#fastest lap#winning driver#winning constructor#report\n1#south african grand prix#1 march#kyalami#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n2#mexican grand prix#22 march#hermanos rodr\u00e3\u00adguez#nigel mansell#gerhard berger#nigel mansell#williams - renault#report\n3#brazilian grand prix#5 april#interlagos#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n4#spanish grand prix#3 may#catalunya#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n5#san marino grand prix#17 may#imola#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n6#monaco grand prix#31 may#monaco#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n7#canadian grand prix#14 june#circuit gilles villeneuve#ayrton senna#gerhard berger#gerhard berger#mclaren - honda#report\n8#french grand prix#5 july#magny - cours#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n9#british grand prix#12 july#silverstone#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n10#german grand prix#26 july#hockenheimring#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n11#hungarian grand prix#16 august#hungaroring#riccardo patrese#nigel mansell#ayrton senna#mclaren - honda#report\n12#belgian grand prix#30 august#spa - francorchamps#nigel mansell#michael schumacher#michael schumacher#benetton - ford#report\n13#italian grand prix#13 september#monza#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n14#portuguese grand prix#27 september#estoril#nigel mansell#ayrton senna#nigel mansell#williams - renault#report\n15#japanese grand prix#25 october#suzuka#nigel mansell#nigel mansell#riccardo patrese#williams - renault#report\n",
        "pandas_code": "(df['winning driver'] == 'ayrton senna').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2803,
        "statement": "the french grand prix , british grand prix , and german grand prix be all in july",
        "label": 1,
        "table_caption": "1992 formula one season",
        "table_text": "round#grand prix#date#location#pole position#fastest lap#winning driver#winning constructor#report\n1#south african grand prix#1 march#kyalami#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n2#mexican grand prix#22 march#hermanos rodr\u00e3\u00adguez#nigel mansell#gerhard berger#nigel mansell#williams - renault#report\n3#brazilian grand prix#5 april#interlagos#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n4#spanish grand prix#3 may#catalunya#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n5#san marino grand prix#17 may#imola#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n6#monaco grand prix#31 may#monaco#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n7#canadian grand prix#14 june#circuit gilles villeneuve#ayrton senna#gerhard berger#gerhard berger#mclaren - honda#report\n8#french grand prix#5 july#magny - cours#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n9#british grand prix#12 july#silverstone#nigel mansell#nigel mansell#nigel mansell#williams - renault#report\n10#german grand prix#26 july#hockenheimring#nigel mansell#riccardo patrese#nigel mansell#williams - renault#report\n11#hungarian grand prix#16 august#hungaroring#riccardo patrese#nigel mansell#ayrton senna#mclaren - honda#report\n12#belgian grand prix#30 august#spa - francorchamps#nigel mansell#michael schumacher#michael schumacher#benetton - ford#report\n13#italian grand prix#13 september#monza#nigel mansell#nigel mansell#ayrton senna#mclaren - honda#report\n14#portuguese grand prix#27 september#estoril#nigel mansell#ayrton senna#nigel mansell#williams - renault#report\n15#japanese grand prix#25 october#suzuka#nigel mansell#nigel mansell#riccardo patrese#williams - renault#report\n",
        "pandas_code": "all(df[df['grand prix'].isin(['french grand prix', 'british grand prix', 'german grand prix'])]['date'].str.contains('july'))",
        "pandas_eval": "True"
    },
    {
        "id": 2804,
        "statement": "the model 1500 produce between 1963 and 1968 have the smallest displacement engine at 1488cc",
        "label": 1,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "df[(df['model'] == '1500') & (df['years'] == '1963 - 68') & (df['displacement'] == '1488cc')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 2805,
        "statement": "the model 1800 iniezione , and 2000 iniezione be the only 2 model to utilize a fuel injection engine",
        "label": 1,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "df[df['fuel system'] == 'fuel injection']['model'].isin(['1800 iniezione', '2000 iniezione']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2806,
        "statement": "6 model feature a single carburetor engine while only 2 feature a double carburetor engine",
        "label": 1,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "(df['fuel system'].value_counts()['single carburetor'] == 6) & (df['fuel system'].value_counts()['double carburetor'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2807,
        "statement": "despite have different displacement and fuel system all model utilize a lancia h4 ohv engine",
        "label": 1,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "all(df['engine'] == 'lancia h4 ohv')",
        "pandas_eval": "True"
    },
    {
        "id": 2808,
        "statement": "the model 2000 and 2000 iniezione , share the highest displacement with 1991cc",
        "label": 1,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "df[df['model'].isin(['2000', '2000 iniezione'])]['displacement'].eq('1991cc').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2809,
        "statement": "every model 1500 have a displacement engine of 1488cc",
        "label": 0,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "all(df[df['model'].str.contains('1500')]['displacement'] == '1488cc')",
        "pandas_eval": "False"
    },
    {
        "id": 2810,
        "statement": "only the 2000 iniezione fuel injection engine come with a displacement of 1991cc",
        "label": 0,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "all(df[(df['model'] == '2000 iniezione') & (df['fuel system'] == 'fuel injection')]['displacement'] == '1991cc') and not any(df[(df['model'] != '2000 iniezione') & (df['displacement'] == '1991cc')])",
        "pandas_eval": "False"
    },
    {
        "id": 2811,
        "statement": "no single carburetor engine come with a displacement higher than the 1800 sport with a double carburetor fuel system",
        "label": 0,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "not any((df['fuel system'] == 'single carburetor') & (df['displacement'].str.extract('(\\d+)')[0].astype(int) > int(df[df['model'] == '1800 sport']['displacement'].str.extract('(\\d+)')[0].iloc[0])))",
        "pandas_eval": "False"
    },
    {
        "id": 2812,
        "statement": "despite have the same lancia h4 ohv engine and the same 1800cc displacement , only the 1800 sport come with a single carburetor",
        "label": 0,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "((df['engine'] == 'lancia h4 ohv') & (df['displacement'] == '1800cc') & (df['model'] == '1800 sport') & (df['fuel system'] == 'single carburetor')).any() & ~((df['engine'] == 'lancia h4 ohv') & (df['displacement'] == '1800cc') & (df['model'] != '1800 sport') & (df['fuel system'] == 'single carburetor')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 2813,
        "statement": "the model that share the highest displacement of 1991cc both have a double carburetor fuel system",
        "label": 0,
        "table_caption": "lancia flavia",
        "table_text": "model#years#engine#displacement#power#fuel system\nberlina#1960 - 62#lancia h4 ohv#1500cc#n /a#single carburetor\ncoup\u00e3 , cab , sport#1962#lancia h4 ohv#1500cc#n /a#double carburetor\n1500#1963 - 68#lancia h4 ohv#1488cc#n /a#single carburetor\n1800#1963 - 68#lancia h4 ohv#1800cc#n /a#single carburetor\n1800 sport#1963 - 67#lancia h4 ohv#1800cc#n /a#double carburetor\n1800 iniezione#1965 - 68#lancia h4 ohv#1800cc#n /a#fuel injection\n1500#1969 - 70#lancia h4 ohv#1490cc#n /a#single carburetor\n1800#1969 - 70#lancia h4 ohv#1816cc#n /a#single carburetor\n2000#1969 - 74#lancia h4 ohv#1991cc#n /a#single carburetor\n2000 iniezione#1969 - 74#lancia h4 ohv#1991cc#n /a#fuel injection\n",
        "pandas_code": "len(df[(df['displacement'] == '1991cc') & (df['fuel system'] == 'double carburetor')]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2814,
        "statement": "more player be from the united state than from australia /",
        "label": 1,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "(df['country'].value_counts()['united states'] > df['country'].value_counts()['australia'])",
        "pandas_eval": "True"
    },
    {
        "id": 2815,
        "statement": "tiger wood have on the us open more time than jim furyk",
        "label": 1,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "df[df['player'] == 'tiger woods']['total'].values[0] < df[df['player'] == 'jim furyk']['total'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2816,
        "statement": "retief goosen and ernie el have the same total score",
        "label": 1,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "df[df['player'].isin(['retief goosen', 'ernie els'])]['total'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2817,
        "statement": "geoff ogilvy 's total be 3 point higher than retief goosen",
        "label": 1,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "df.loc[df['player'] == 'geoff ogilvy', 'total'].values[0] == df.loc[df['player'] == 'retief goosen', 'total'].values[0] + 3",
        "pandas_eval": "True"
    },
    {
        "id": 2818,
        "statement": "the player jim furyk have the highest total score",
        "label": 1,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "df[df['player'] == 'jim furyk']['total'].max() == df['total'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2819,
        "statement": "there be 1 more player from the united state than there be from australia",
        "label": 0,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "len(df[df['country'] == 'united states']) != len(df[df['country'] == 'australia']) + 1",
        "pandas_eval": "False"
    },
    {
        "id": 2820,
        "statement": "tiger wood have win the us open more time than retief goosen",
        "label": 0,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "len(df[df['player'] == 'tiger woods']['year (s) won'].str.split(',').explode()) > len(df[df['player'] == 'retief goosen']['year (s) won'].str.split(',').explode())",
        "pandas_eval": "False"
    },
    {
        "id": 2821,
        "statement": "retief goosen 's and ernie el 's total score be lower than jim furyk 's score",
        "label": 0,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "(df[df['player'].isin(['retief goosen', 'ernie els'])]['total'].max() >= df[df['player'] == 'jim furyk']['total'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 2822,
        "statement": "geoff ogilvy have the highest total score",
        "label": 0,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "df.loc[df['player'] == 'geoff ogilvy', 'total'].values[0] == df['total'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2823,
        "statement": "jim furyk be the only player from the united state",
        "label": 0,
        "table_caption": "2008 u.s. open (golf)",
        "table_text": "player#country#year (s) won#total#to par#finish\ntiger woods#united states#2000 , 2002#283#1#1\ngeoff ogilvy#australia#2006#292#+ 4#t9\nretief goosen#south africa#2001 , 2004#289#+ 5#t14\nernie els#south africa#1994 , 1997#289#+ 5#t14\njim furyk#united states#2003#293#+ 9#t36\n",
        "pandas_code": "len(df[(df['country'] == 'united states') & (df['player'] != 'jim furyk')]) == 0",
        "pandas_eval": "False"
    },
    {
        "id": 2824,
        "statement": "in michelle larcher de brito , 2012 fed cup europe / africa group i have the highest w - l result",
        "label": 1,
        "table_caption": "michelle larcher de brito",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2010 fed cup europe / africa group i#rr#3 february 2010#neuza silva#croatia#hard#jelena kostani\u0107 to\u0161i\u0107 silvia njiri\u0107#win#7 - 5 , 6 - 4\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria jo\u00e3o koehler#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria joao koehler#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#israel#hard#shahar pe'er julia glushko#win#6 - 2 , 4 - 6 , 6 - 4\n2013 fed cup europe / africa group i#rr#7 - 9 february 2013#joana vale costa#great britain#hard#laura robson heather watson#loss#2 - 6 , 1 - 6\n",
        "pandas_code": "df[(df['edition'] == '2012 fed cup europe / africa group i') & (df['w - l'] == 'win')].shape[0] == df[(df['edition'] == '2012 fed cup europe / africa group i')]['w - l'].value_counts().max()",
        "pandas_eval": "True"
    },
    {
        "id": 2825,
        "statement": "morocco morocco have the 4 - 6 may 20112011 fed cup europe / africa group ii",
        "label": 1,
        "table_caption": "michelle larcher de brito",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2010 fed cup europe / africa group i#rr#3 february 2010#neuza silva#croatia#hard#jelena kostani\u0107 to\u0161i\u0107 silvia njiri\u0107#win#7 - 5 , 6 - 4\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria jo\u00e3o koehler#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria joao koehler#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#israel#hard#shahar pe'er julia glushko#win#6 - 2 , 4 - 6 , 6 - 4\n2013 fed cup europe / africa group i#rr#7 - 9 february 2013#joana vale costa#great britain#hard#laura robson heather watson#loss#2 - 6 , 1 - 6\n",
        "pandas_code": "df[(df['against'] == 'morocco') & (df['date'] == '4 - 6 may 2011') & (df['edition'] == '2011 fed cup europe / africa group ii')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 2826,
        "statement": "laura robson heather watson have the only loss in w - l result",
        "label": 1,
        "table_caption": "michelle larcher de brito",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2010 fed cup europe / africa group i#rr#3 february 2010#neuza silva#croatia#hard#jelena kostani\u0107 to\u0161i\u0107 silvia njiri\u0107#win#7 - 5 , 6 - 4\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria jo\u00e3o koehler#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria joao koehler#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#israel#hard#shahar pe'er julia glushko#win#6 - 2 , 4 - 6 , 6 - 4\n2013 fed cup europe / africa group i#rr#7 - 9 february 2013#joana vale costa#great britain#hard#laura robson heather watson#loss#2 - 6 , 1 - 6\n",
        "pandas_code": "(df[(df['opponents'] == 'laura robson heather watson') & (df['w - l'] == 'loss')].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2827,
        "statement": "laura robson heather watson be the highest opponent than fatima el allami nadia lalami",
        "label": 1,
        "table_caption": "michelle larcher de brito",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2010 fed cup europe / africa group i#rr#3 february 2010#neuza silva#croatia#hard#jelena kostani\u0107 to\u0161i\u0107 silvia njiri\u0107#win#7 - 5 , 6 - 4\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria jo\u00e3o koehler#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria joao koehler#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#israel#hard#shahar pe'er julia glushko#win#6 - 2 , 4 - 6 , 6 - 4\n2013 fed cup europe / africa group i#rr#7 - 9 february 2013#joana vale costa#great britain#hard#laura robson heather watson#loss#2 - 6 , 1 - 6\n",
        "pandas_code": "df[df['opponents'] == 'laura robson heather watson'].shape[0] > df[df['opponents'] == 'fatima el allami nadia lalami'].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2828,
        "statement": "there be 4 hard surface and 2 clay surface",
        "label": 1,
        "table_caption": "michelle larcher de brito",
        "table_text": "edition#round#date#partnering#against#surface#opponents#w - l#result\n2010 fed cup europe / africa group i#rr#3 february 2010#neuza silva#croatia#hard#jelena kostani\u0107 to\u0161i\u0107 silvia njiri\u0107#win#7 - 5 , 6 - 4\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria jo\u00e3o koehler#morocco#clay#fatima el allami nadia lalami#win#6 - 3 , 6 - 2\n2011 fed cup europe / africa group ii#rr#4 - 6 may 2011#maria joao koehler#finland#clay#emma laine piia suomalainen#win#6 - 3 , 6 - 2\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#great britain#hard#laura robson heather watson#loss#5 - 7 , 0 - 6\n2012 fed cup europe / africa group i#rr#1 - 3 february 2012#maria joao koehler#israel#hard#shahar pe'er julia glushko#win#6 - 2 , 4 - 6 , 6 - 4\n2013 fed cup europe / africa group i#rr#7 - 9 february 2013#joana vale costa#great britain#hard#laura robson heather watson#loss#2 - 6 , 1 - 6\n",
        "pandas_code": "(df['surface'].value_counts() == pd.Series({'hard': 4, 'clay': 2})).all()",
        "pandas_eval": "True"
    },
    {
        "id": 2829,
        "statement": "the most goal score in 1 season be 358 in 1973 - 1974",
        "label": 1,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df.loc[df['goals for'] == 358, 'season'].values[0] == '1973 - 74'",
        "pandas_eval": "True"
    },
    {
        "id": 2830,
        "statement": "the hawk didn't finish any higher than 2nd place in the standing",
        "label": 1,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "all(df['standing'].str.extract(r'(\\d+)')[0].astype(int) >= 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2831,
        "statement": "the most game tie in 1 season be 12 , during the 1968 - 69 season",
        "label": 1,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df.loc[df['tied'].idxmax(), 'season'] == '1968 - 69'",
        "pandas_eval": "True"
    },
    {
        "id": 2832,
        "statement": "the most goal score against the hawk in 1 season be 366",
        "label": 1,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df['goals against'].max() == 366",
        "pandas_eval": "True"
    },
    {
        "id": 2833,
        "statement": "the most game lost in 1 season be 40",
        "label": 1,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df['lost'].max() == 40",
        "pandas_eval": "True"
    },
    {
        "id": 2834,
        "statement": "the lowest number of goal be score in the 1973 - 1974 season",
        "label": 0,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df.loc[df['season'] == '1973 - 74', 'goals for'].iloc[0] == df['goals for'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2835,
        "statement": "the hawk manage to finish 1st place in the standing",
        "label": 0,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "any(df['standing'].str.contains('1st'))",
        "pandas_eval": "False"
    },
    {
        "id": 2836,
        "statement": "only 3 game be tie during the 1968 - 69 season , the least of any season",
        "label": 0,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df[df['season'] == '1968 - 69']['tied'].iloc[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2837,
        "statement": "during the 1975 - 76 season , the least amount of goal be score against the hawk with 155",
        "label": 0,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df.loc[df['season'] == '1975 - 76', 'goals against'].min() == 155",
        "pandas_eval": "False"
    },
    {
        "id": 2838,
        "statement": "the fewest number of game lost in a season be 33 during the 1975 - 76 season",
        "label": 0,
        "table_caption": "st. catharines black hawks",
        "table_text": "season#games#lost#tied#points#pct %#goals for#goals against#standing\n1962 - 63#50#24#11#41#0.410#172#224#5th oha\n1963 - 64#56#20#7#65#0.580#244#215#3rd oha\n1964 - 65#56#28#9#41#0.420#236#253#7th oha\n1965 - 66#48#26#7#37#0.385#182#231#8th oha\n1966 - 67#48#20#9#47#0.490#175#155#5th oha\n1967 - 68#54#30#3#45#0.417#200#211#6th oha\n1968 - 69#54#11#12#74#0.685#296#206#2nd oha\n1969 - 70#54#18#6#66#0.611#268#210#3rd oha\n1970 - 71#62#17#5#85#0.685#343#236#2nd oha\n1971 - 72#63#31#7#57#0.452#258#311#7th oha\n1972 - 73#63#28#11#59#0.468#280#318#5th oha\n1973 - 74#70#23#6#88#0.629#358#278#2nd oha\n1974 - 75#70#33#7#67#0.479#284#300#6th oha\n1975 - 76#66#40#10#42#0.318#283#366#5th emms\n",
        "pandas_code": "df.loc[df['season'] == '1975 - 76', 'lost'].values[0] == 33",
        "pandas_eval": "False"
    },
    {
        "id": 2839,
        "statement": "john e rankin be the only representative to lose his renomination",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmississippi 1#thomas abernethy redistricted from 4th#democratic#1942#re - elected#thomas abernethy (d) unopposed\nmississippi 1#john e rankin#democratic#1920#lost renomination democratic loss#thomas abernethy (d) unopposed\nmississippi 2#jamie l whitten#democratic#1941#re - elected#jamie l whitten (d) unopposed\nmississippi 3#frank e smith#democratic#1950#re - elected#frank e smith (d) 87.2% paul clark (r) 12.8%\nmississippi 4#john bell williams redistricted from 7th#democratic#1946#re - elected#john bell williams (d) unopposed\n",
        "pandas_code": "df[(df['incumbent'] == 'john e rankin') & (df['result'] == 'lost renomination democratic loss')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2840,
        "statement": "jamie l whitten be re - elect after john e rankin lose his renomination",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmississippi 1#thomas abernethy redistricted from 4th#democratic#1942#re - elected#thomas abernethy (d) unopposed\nmississippi 1#john e rankin#democratic#1920#lost renomination democratic loss#thomas abernethy (d) unopposed\nmississippi 2#jamie l whitten#democratic#1941#re - elected#jamie l whitten (d) unopposed\nmississippi 3#frank e smith#democratic#1950#re - elected#frank e smith (d) 87.2% paul clark (r) 12.8%\nmississippi 4#john bell williams redistricted from 7th#democratic#1946#re - elected#john bell williams (d) unopposed\n",
        "pandas_code": "df[(df['incumbent'] == 'jamie l whitten') & (df['result'] == 're - elected') & (df['district'] == 'mississippi 2')].empty == False and df[(df['incumbent'] == 'john e rankin') & (df['result'] == 'lost renomination democratic loss') & (df['district'] == 'mississippi 1')].empty == False",
        "pandas_eval": "True"
    },
    {
        "id": 2842,
        "statement": "john bell williams be re - elect before frank e smith",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmississippi 1#thomas abernethy redistricted from 4th#democratic#1942#re - elected#thomas abernethy (d) unopposed\nmississippi 1#john e rankin#democratic#1920#lost renomination democratic loss#thomas abernethy (d) unopposed\nmississippi 2#jamie l whitten#democratic#1941#re - elected#jamie l whitten (d) unopposed\nmississippi 3#frank e smith#democratic#1950#re - elected#frank e smith (d) 87.2% paul clark (r) 12.8%\nmississippi 4#john bell williams redistricted from 7th#democratic#1946#re - elected#john bell williams (d) unopposed\n",
        "pandas_code": "df[df['incumbent'] == 'john bell williams redistricted from 7th']['first elected'].values[0] < df[df['incumbent'] == 'frank e smith']['first elected'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2843,
        "statement": "frank e smith be re - elect after jamie l whitten",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1952",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmississippi 1#thomas abernethy redistricted from 4th#democratic#1942#re - elected#thomas abernethy (d) unopposed\nmississippi 1#john e rankin#democratic#1920#lost renomination democratic loss#thomas abernethy (d) unopposed\nmississippi 2#jamie l whitten#democratic#1941#re - elected#jamie l whitten (d) unopposed\nmississippi 3#frank e smith#democratic#1950#re - elected#frank e smith (d) 87.2% paul clark (r) 12.8%\nmississippi 4#john bell williams redistricted from 7th#democratic#1946#re - elected#john bell williams (d) unopposed\n",
        "pandas_code": "df.loc[df['incumbent'] == 'frank e smith', 'first elected'].values[0] > df.loc[df['incumbent'] == 'jamie l whitten', 'first elected'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2844,
        "statement": "the only player not from the united state be steve elkington and vijay singh",
        "label": 1,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "(df[df['country'] != 'united states']['player'].sort_values().tolist() == ['steve elkington', 'vijay singh'])",
        "pandas_eval": "True"
    },
    {
        "id": 2845,
        "statement": "the most common country for a player be the united state",
        "label": 1,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "df['country'].value_counts().idxmax() == 'united states'",
        "pandas_eval": "True"
    },
    {
        "id": 2846,
        "statement": "the difference between steve elkington 's total and davis love iii be - 1",
        "label": 1,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "(df[df['player'] == 'steve elkington']['total'].values[0] - df[df['player'] == 'davis love iii']['total'].values[0]) == -1",
        "pandas_eval": "True"
    },
    {
        "id": 2847,
        "statement": "tiger wood and vijary singh be the only 2 player to have more than 1 year they have win",
        "label": 1,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "len(df[df['player'].isin(['tiger woods', 'vijay singh']) & df['year (s) won'].str.contains(',')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2848,
        "statement": "the difference between john daly and hal sutton 's score be 8",
        "label": 1,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "abs(df[df['player'] == 'john daly']['total'].values[0] - df[df['player'] == 'hal sutton']['total'].values[0]) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2849,
        "statement": "hal sutton and david tom be the only non american player",
        "label": 0,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "not df[(df['player'].isin(['hal sutton', 'david toms'])) & (df['country'] != 'united states')].empty and df[~df['player'].isin(['hal sutton', 'david toms']) & (df['country'] != 'united states')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 2850,
        "statement": "most player come from australia",
        "label": 0,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "df['country'].value_counts().idxmax() == 'australia'",
        "pandas_eval": "False"
    },
    {
        "id": 2851,
        "statement": "steve elkington and david love 's iii be + 12",
        "label": 0,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "(df[df['player'].isin(['steve elkington', 'davis love iii'])]['to par'] == '+ 12').all()",
        "pandas_eval": "False"
    },
    {
        "id": 2852,
        "statement": "2 player who do not have more than 1 year won be tiger wood and vijary",
        "label": 0,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "len(df[(df['player'].isin(['tiger woods', 'vijay singh'])) & (~df['year (s) won'].str.contains(','))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2853,
        "statement": "there be a difference of 10 between john daly and hall sutton score",
        "label": 0,
        "table_caption": "2005 pga championship",
        "table_text": "player#country#year (s) won#total#to par#finish\nsteve elkington#australia#1995#277#- 3#t2\ndavis love iii#united states#1997#278#- 2#t4\ntiger woods#united states#1999 , 2000#278#- 2#t4\nvijay singh#fiji#1998 , 2004#280#e#t10\ndavid toms#united states#2001#280#e#t10\njohn daly#united states#1991#292#+ 12#t74\nhal sutton#united states#1983#300#+ 20#79\n",
        "pandas_code": "abs(df[df['player'] == 'john daly']['total'].values[0] - df[df['player'] == 'hal sutton']['total'].values[0]) == 10",
        "pandas_eval": "False"
    },
    {
        "id": 2854,
        "statement": "3 club all lost 14 game",
        "label": 1,
        "table_caption": "2008 - 09 rugby - bundesliga",
        "table_text": "#club#played#won#drawn#lost#points for#points against#difference#points\n1#dsv 78 / 08 ricklingen#18#18#0#0#1138#135#1003#87\n2#tsv victoria linden#18#15#0#3#720#246#474#72\n3#usv potsdam#18#14#0#4#804#271#533#69\n4#fc st pauli rugby#18#11#0#7#632#311#321#56\n5#sg sv odin / vfr d\u00e3hren#18#11#0#7#509#328#181#52\n6#ru hohen neuendorf#18#9#0#9#452#401#51#45\n7#sc germania list#18#4#0#14#250#813#- 563#20\n8#hamburger rc#18#4#0#14#235#891#- 656#19\n9#berliner sv 92 rugby#18#3#1#14#201#857#- 656#15\n",
        "pandas_code": "all(df[df['club'].isin(['sc germania list', 'hamburger rc', 'berliner sv 92 rugby'])]['lost'] == 14)",
        "pandas_eval": "True"
    },
    {
        "id": 2855,
        "statement": "dsv 78 / 08 ricklingen win the most game",
        "label": 1,
        "table_caption": "2008 - 09 rugby - bundesliga",
        "table_text": "#club#played#won#drawn#lost#points for#points against#difference#points\n1#dsv 78 / 08 ricklingen#18#18#0#0#1138#135#1003#87\n2#tsv victoria linden#18#15#0#3#720#246#474#72\n3#usv potsdam#18#14#0#4#804#271#533#69\n4#fc st pauli rugby#18#11#0#7#632#311#321#56\n5#sg sv odin / vfr d\u00e3hren#18#11#0#7#509#328#181#52\n6#ru hohen neuendorf#18#9#0#9#452#401#51#45\n7#sc germania list#18#4#0#14#250#813#- 563#20\n8#hamburger rc#18#4#0#14#235#891#- 656#19\n9#berliner sv 92 rugby#18#3#1#14#201#857#- 656#15\n",
        "pandas_code": "df.loc[df['won'].idxmax(), 'club'] == 'dsv 78 / 08 ricklingen'",
        "pandas_eval": "True"
    },
    {
        "id": 2856,
        "statement": "dsv 78 / 08 ricklingen have the most point",
        "label": 1,
        "table_caption": "2008 - 09 rugby - bundesliga",
        "table_text": "#club#played#won#drawn#lost#points for#points against#difference#points\n1#dsv 78 / 08 ricklingen#18#18#0#0#1138#135#1003#87\n2#tsv victoria linden#18#15#0#3#720#246#474#72\n3#usv potsdam#18#14#0#4#804#271#533#69\n4#fc st pauli rugby#18#11#0#7#632#311#321#56\n5#sg sv odin / vfr d\u00e3hren#18#11#0#7#509#328#181#52\n6#ru hohen neuendorf#18#9#0#9#452#401#51#45\n7#sc germania list#18#4#0#14#250#813#- 563#20\n8#hamburger rc#18#4#0#14#235#891#- 656#19\n9#berliner sv 92 rugby#18#3#1#14#201#857#- 656#15\n",
        "pandas_code": "df.loc[df['points'].idxmax(), 'club'] == 'dsv 78 / 08 ricklingen'",
        "pandas_eval": "True"
    },
    {
        "id": 2857,
        "statement": "2 club win 11 game",
        "label": 1,
        "table_caption": "2008 - 09 rugby - bundesliga",
        "table_text": "#club#played#won#drawn#lost#points for#points against#difference#points\n1#dsv 78 / 08 ricklingen#18#18#0#0#1138#135#1003#87\n2#tsv victoria linden#18#15#0#3#720#246#474#72\n3#usv potsdam#18#14#0#4#804#271#533#69\n4#fc st pauli rugby#18#11#0#7#632#311#321#56\n5#sg sv odin / vfr d\u00e3hren#18#11#0#7#509#328#181#52\n6#ru hohen neuendorf#18#9#0#9#452#401#51#45\n7#sc germania list#18#4#0#14#250#813#- 563#20\n8#hamburger rc#18#4#0#14#235#891#- 656#19\n9#berliner sv 92 rugby#18#3#1#14#201#857#- 656#15\n",
        "pandas_code": "df.loc[df['club'] == 'fc st pauli rugby', 'won'].values[0] == 11",
        "pandas_eval": "True"
    },
    {
        "id": 2858,
        "statement": "berliner sv 92 rugby have the most draws",
        "label": 1,
        "table_caption": "2008 - 09 rugby - bundesliga",
        "table_text": "#club#played#won#drawn#lost#points for#points against#difference#points\n1#dsv 78 / 08 ricklingen#18#18#0#0#1138#135#1003#87\n2#tsv victoria linden#18#15#0#3#720#246#474#72\n3#usv potsdam#18#14#0#4#804#271#533#69\n4#fc st pauli rugby#18#11#0#7#632#311#321#56\n5#sg sv odin / vfr d\u00e3hren#18#11#0#7#509#328#181#52\n6#ru hohen neuendorf#18#9#0#9#452#401#51#45\n7#sc germania list#18#4#0#14#250#813#- 563#20\n8#hamburger rc#18#4#0#14#235#891#- 656#19\n9#berliner sv 92 rugby#18#3#1#14#201#857#- 656#15\n",
        "pandas_code": "df.loc[df['club'] == 'berliner sv 92 rugby', 'drawn'].values[0] == df['drawn'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 2859,
        "statement": "michael schumacher have more podium than ayrton senna",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#podiums#percentage\n1#michael schumacher#1991 - 2006 , 2010 - 2012#308#155#50.32%\n2#alain prost#1980 - 1991 , 1993#202#106#52.47%\n3#fernando alonso#2001 , 2003 - 2013#215#94#43.72%\n4#ayrton senna#1984 - 1994#162#80#49.38%\n5#kimi r\u00e4ikk\u00f6nen#2001 - 2009 , 2012 - 2013#194#77#39.69%\n6#rubens barrichello#1993 - 2011#326#68#20.85%\n7#david coulthard#1994 - 2008#247#62#25.10%\n8#nelson piquet#1978 - 1991#207#60#28.98%\n8#sebastian vettel#2007 - 2013#118#60#50.85%\n",
        "pandas_code": "df[df['driver'] == 'michael schumacher']['podiums'].values[0] > df[df['driver'] == 'ayrton senna']['podiums'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2861,
        "statement": "nelson piquet have the earliest season",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#podiums#percentage\n1#michael schumacher#1991 - 2006 , 2010 - 2012#308#155#50.32%\n2#alain prost#1980 - 1991 , 1993#202#106#52.47%\n3#fernando alonso#2001 , 2003 - 2013#215#94#43.72%\n4#ayrton senna#1984 - 1994#162#80#49.38%\n5#kimi r\u00e4ikk\u00f6nen#2001 - 2009 , 2012 - 2013#194#77#39.69%\n6#rubens barrichello#1993 - 2011#326#68#20.85%\n7#david coulthard#1994 - 2008#247#62#25.10%\n8#nelson piquet#1978 - 1991#207#60#28.98%\n8#sebastian vettel#2007 - 2013#118#60#50.85%\n",
        "pandas_code": "df.loc[df['driver'] == 'nelson piquet', 'seasons'].str.split(' - ').str[0].str.split(',').str[0].astype(int).min() == df['seasons'].str.split(' - ').str[0].str.split(',').str[0].astype(int).min()",
        "pandas_eval": "True"
    },
    {
        "id": 2862,
        "statement": "rubens barrichello race later than alain prost",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#podiums#percentage\n1#michael schumacher#1991 - 2006 , 2010 - 2012#308#155#50.32%\n2#alain prost#1980 - 1991 , 1993#202#106#52.47%\n3#fernando alonso#2001 , 2003 - 2013#215#94#43.72%\n4#ayrton senna#1984 - 1994#162#80#49.38%\n5#kimi r\u00e4ikk\u00f6nen#2001 - 2009 , 2012 - 2013#194#77#39.69%\n6#rubens barrichello#1993 - 2011#326#68#20.85%\n7#david coulthard#1994 - 2008#247#62#25.10%\n8#nelson piquet#1978 - 1991#207#60#28.98%\n8#sebastian vettel#2007 - 2013#118#60#50.85%\n",
        "pandas_code": "df[df['driver'] == 'rubens barrichello']['seasons'].values[0].split(' - ')[-1] > df[df['driver'] == 'alain prost']['seasons'].values[0].split(' - ')[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 2863,
        "statement": "fernando alonso have more entries than kimi r\u00e4ikk\u00f6nen",
        "label": 1,
        "table_caption": "list of formula one driver records",
        "table_text": "#driver#seasons#entries#podiums#percentage\n1#michael schumacher#1991 - 2006 , 2010 - 2012#308#155#50.32%\n2#alain prost#1980 - 1991 , 1993#202#106#52.47%\n3#fernando alonso#2001 , 2003 - 2013#215#94#43.72%\n4#ayrton senna#1984 - 1994#162#80#49.38%\n5#kimi r\u00e4ikk\u00f6nen#2001 - 2009 , 2012 - 2013#194#77#39.69%\n6#rubens barrichello#1993 - 2011#326#68#20.85%\n7#david coulthard#1994 - 2008#247#62#25.10%\n8#nelson piquet#1978 - 1991#207#60#28.98%\n8#sebastian vettel#2007 - 2013#118#60#50.85%\n",
        "pandas_code": "df[df['driver'] == 'fernando alonso']['entries'].values[0] > df[df['driver'] == 'kimi r\u00e4ikk\u00f6nen']['entries'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2864,
        "statement": "during the 1963 season the philadelphia eagle only have 2 win",
        "label": 1,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2865,
        "statement": "the philadelphia eagle lose all 4 of the game they play during november 1963",
        "label": 1,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "all(df[(df['date'].str.contains('november')) & (df['date'].str.contains('1963'))]['result'].str.startswith('l'))",
        "pandas_eval": "True"
    },
    {
        "id": 2866,
        "statement": "the philadelphia eagle tie 2 time in 1963",
        "label": 1,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "(df['result'].str.startswith('t').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2867,
        "statement": "the dallas cowboy be 1 of the 2 team that lose to the philadelphia eagle in 1963",
        "label": 1,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "df[df['opponent'] == 'dallas cowboys']['result'].str.startswith('l').any()",
        "pandas_eval": "True"
    },
    {
        "id": 2868,
        "statement": "the new york giant beat the philadelphia eagle by more than 20 point both time they play against each other during the 1963 season",
        "label": 1,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "all(df[(df['opponent'] == 'new york giants') & (df['result'].str.startswith('L'))]['result'].str.extract(r'(\\\\d+) - (\\\\d+)').astype(int).apply(lambda x: x[0] - x[1] > 20, axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 2869,
        "statement": "during the 1963 season the philadelphia eagle only have 5 win",
        "label": 0,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 5)",
        "pandas_eval": "False"
    },
    {
        "id": 2870,
        "statement": "the philadelphia eagle win all 4 of the game they play during november 1963",
        "label": 0,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "df[(df['date'].str.contains('november 1963')) & (df['result'].str.startswith('w'))].shape[0] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2871,
        "statement": "the philadelphia eagle tie thrice in 1963",
        "label": 0,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "(df['result'].str.startswith('t').sum() == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 2872,
        "statement": "the cleveland brownswas 1 of the 2 team that lose to the philadelphia eagle in 1963",
        "label": 0,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "len(df[df['result'].str.startswith('l')]['opponent'].unique()) == 2 and 'cleveland browns' in df[df['result'].str.startswith('l')]['opponent'].unique()",
        "pandas_eval": "False"
    },
    {
        "id": 2873,
        "statement": "the washington redskins beat the philadelphia eagle by more than 20 point both time they play against each other during the 1963 season the washington redskins",
        "label": 0,
        "table_caption": "1963 philadelphia eagles season",
        "table_text": "week#date#opponent#result#attendance\n1#september 15 , 1963#pittsburgh steelers#t 21 - 21#58205\n2#september 22 , 1963#st louis cardinals#l 28 - 24#60671\n3#september 29 , 1963#new york giants#l 37 - 14#60671\n4#october 6 , 1963#dallas cowboys#w 24 - 21#60671\n5#october 13 , 1963#washington redskins#w 37 - 24#49219\n6#october 20 , 1963#cleveland browns#l 37 - 7#75174\n7#october 27 , 1963#chicago bears#l 16 - 7#48514\n8#november 3 , 1963#cleveland browns#l 23 - 17#60671\n9#november 10 , 1963#new york giants#l 42 - 14#62936\n10#november 17 , 1963#dallas cowboys#l 27 - 20#23694\n11#november 24 , 1963#washington redskins#l 13 - 10#60671\n12#december 1 , 1963#pittsburgh steelers#t 20 - 20#16721\n13#december 8 , 1963#st louis cardinals#l 38 - 14#15979\n14#december 15 , 1963#minnesota vikings#l 34 - 13#57403\n",
        "pandas_code": "all(df[df['opponent'] == 'washington redskins']['result'].str.extract(r'(\\w) (\\d+) - (\\d+)').apply(lambda x: x[0] == 'w' and int(x[1]) - int(x[2]) > 20, axis=1))",
        "pandas_eval": "False"
    },
    {
        "id": 2874,
        "statement": "russell lee be the only player list on the utah jazz all - time roster that play position guard - forward",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\nstu lantz#22#united states#guard#1974 - 75#nebraska\nrusty larue#5#united states#guard#2001 - 02#wake forest\neric leckner#45#united states#forward - center#1988 - 90#wyoming\nron lee#18#united states#guard#1979 - 80#oregon\nrussell lee#11#united states#guard - forward#1974 - 75#marshall\ntim legler#18#united states#guard#1992#la salle\njim les#25#united states#guard#1988 - 89#bradley\nquincy lewis#20#united states#forward#1999 - 2002#minnesota\nrandy livingston#2#united states#guard#2004 - 05#lsu\nra\u00fal l\u00f3pez#24#spain#guard#2003 - 2005#real madrid ( spain )\n",
        "pandas_code": "len(df[(df['player'] == 'russell lee') & (df['position'] == 'guard - forward')]) == 1 and len(df[df['position'] == 'guard - forward']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2875,
        "statement": "tim legler be the only player on the utah jazz all - time roster that play for just 1 year",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\nstu lantz#22#united states#guard#1974 - 75#nebraska\nrusty larue#5#united states#guard#2001 - 02#wake forest\neric leckner#45#united states#forward - center#1988 - 90#wyoming\nron lee#18#united states#guard#1979 - 80#oregon\nrussell lee#11#united states#guard - forward#1974 - 75#marshall\ntim legler#18#united states#guard#1992#la salle\njim les#25#united states#guard#1988 - 89#bradley\nquincy lewis#20#united states#forward#1999 - 2002#minnesota\nrandy livingston#2#united states#guard#2004 - 05#lsu\nra\u00fal l\u00f3pez#24#spain#guard#2003 - 2005#real madrid ( spain )\n",
        "pandas_code": "(df[df['player'] == 'tim legler']['years for jazz'].str.split(' - ').apply(lambda x: len(x) == 1).all()) and (df['years for jazz'].str.split(' - ').apply(lambda x: len(x) == 1).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2876,
        "statement": "randy livingston have the lowest number on the utah jazz all - time roster",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\nstu lantz#22#united states#guard#1974 - 75#nebraska\nrusty larue#5#united states#guard#2001 - 02#wake forest\neric leckner#45#united states#forward - center#1988 - 90#wyoming\nron lee#18#united states#guard#1979 - 80#oregon\nrussell lee#11#united states#guard - forward#1974 - 75#marshall\ntim legler#18#united states#guard#1992#la salle\njim les#25#united states#guard#1988 - 89#bradley\nquincy lewis#20#united states#forward#1999 - 2002#minnesota\nrandy livingston#2#united states#guard#2004 - 05#lsu\nra\u00fal l\u00f3pez#24#spain#guard#2003 - 2005#real madrid ( spain )\n",
        "pandas_code": "df.loc[df['no'] == df['no'].min(), 'player'].values[0] == 'randy livingston'",
        "pandas_eval": "True"
    },
    {
        "id": 2877,
        "statement": "ra\u00fal l\u00f3pez be the only player not from the united state on the utah jazz all - time roster",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\nstu lantz#22#united states#guard#1974 - 75#nebraska\nrusty larue#5#united states#guard#2001 - 02#wake forest\neric leckner#45#united states#forward - center#1988 - 90#wyoming\nron lee#18#united states#guard#1979 - 80#oregon\nrussell lee#11#united states#guard - forward#1974 - 75#marshall\ntim legler#18#united states#guard#1992#la salle\njim les#25#united states#guard#1988 - 89#bradley\nquincy lewis#20#united states#forward#1999 - 2002#minnesota\nrandy livingston#2#united states#guard#2004 - 05#lsu\nra\u00fal l\u00f3pez#24#spain#guard#2003 - 2005#real madrid ( spain )\n",
        "pandas_code": "len(df[df['nationality'] != 'united states']) == 1 and df[df['nationality'] != 'united states']['player'].iloc[0] == 'ra\u00fal l\u00f3pez'",
        "pandas_eval": "True"
    },
    {
        "id": 2878,
        "statement": "the utah jazz all - time roster have 2 player who play for them in the year 1974 - 75",
        "label": 1,
        "table_caption": "utah jazz all - time roster",
        "table_text": "player#no#nationality#position#years for jazz#school / club team\nstu lantz#22#united states#guard#1974 - 75#nebraska\nrusty larue#5#united states#guard#2001 - 02#wake forest\neric leckner#45#united states#forward - center#1988 - 90#wyoming\nron lee#18#united states#guard#1979 - 80#oregon\nrussell lee#11#united states#guard - forward#1974 - 75#marshall\ntim legler#18#united states#guard#1992#la salle\njim les#25#united states#guard#1988 - 89#bradley\nquincy lewis#20#united states#forward#1999 - 2002#minnesota\nrandy livingston#2#united states#guard#2004 - 05#lsu\nra\u00fal l\u00f3pez#24#spain#guard#2003 - 2005#real madrid ( spain )\n",
        "pandas_code": "len(df[df['years for jazz'].str.contains('1974 - 75')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2879,
        "statement": "the steelers win every game that they play at 9:00 pm",
        "label": 1,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "all(df[df['time ( et )'] == '9:00 pm']['result'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 2880,
        "statement": "the steelers score less point on dec 15 than they do on dec 8",
        "label": 1,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "int(df[df['date'].str.contains('dec 15')]['result'].str.split(' - ').str[1].values[0]) < int(df[df['date'].str.contains('dec 8')]['result'].str.split(' - ').str[0].str.split().str[-1].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 2881,
        "statement": "the majority of the game the steelers play be at three river stadium",
        "label": 1,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "df['location'].value_counts().idxmax() == 'three rivers stadium'",
        "pandas_eval": "True"
    },
    {
        "id": 2882,
        "statement": "the steelers win more game than they lose in 1996",
        "label": 1,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() > df['result'].str.startswith('l').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 2883,
        "statement": "the be only 1 game which wasn't play at 1:00 pm or 9:00 pm",
        "label": 1,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "(~df['time ( et )'].isin(['1:00 pm', '9:00 pm']) & (df['time ( et )'] != '-')).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2884,
        "statement": "the steelers win every game that they play at 1:00 pm",
        "label": 0,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "all(df[df['time ( et )'] == '1:00 pm']['result'].str.startswith('w'))",
        "pandas_eval": "False"
    },
    {
        "id": 2886,
        "statement": "the majority of the game the steelers play be at 2 rivers stadium",
        "label": 0,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "(df['location'].value_counts(normalize=True)['three rivers stadium'] > 0.5)",
        "pandas_eval": "False"
    },
    {
        "id": 2887,
        "statement": "the steelers win less game than they lose in 1996",
        "label": 0,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() < df['result'].str.startswith('l').sum())",
        "pandas_eval": "False"
    },
    {
        "id": 2888,
        "statement": "the be only 2 game which wasn't play at 1:00 pm or 9:00 pm",
        "label": 0,
        "table_caption": "1996 pittsburgh steelers season",
        "table_text": "week#date#opponent#location#time ( et )#result#record\n1#sun sep 1#jacksonville jaguars#alltel stadium#1:00 pm#l 24 - 9#0 - 1\n2#sun sep 8#baltimore ravens#three rivers stadium#1:00 pm#w 31 - 17#1 - 1\n3#mon sep 16#buffalo bills#three rivers stadium#9:00 pm#w 24 - 6#2 - 1\n4#sun sep 22#-#-#-#-#\n5#sun sep 29#houston oilers#three rivers stadium#1:00 pm#w 30 - 16#3 - 1\n6#mon oct 7#kansas city chiefs#arrowhead stadium#9:00 pm#w 17 - 7#4 - 1\n7#sun oct 13#cincinnati bengals#three rivers stadium#1:00 pm#w 20 - 10#5 - 1\n8#sun oct 20#houston oilers#astrodome#4:00 pm#l 23 - 13#5 - 2\n9#sun oct 27#atlanta falcons#georgia dome#1:00 pm#w 20 - 17#6 - 2\n10#sun nov 3#st louis rams#three rivers stadium#1:00 pm#w 42 - 6#7 - 2\n11#sun nov 10#cincinnati bengals#cinergy field#1:00 pm#l 34 - 24#7 - 3\n12#sun nov 17#jacksonville jaguars#three rivers stadium#1:00 pm#w 28 - 3#8 - 3\n13#mon nov 25#miami dolphins#pro player stadium#9:00 pm#w 24 - 17#9 - 3\n14#sun dec 1#baltimore ravens#memorial stadium#1:00 pm#l 31 - 17#9 - 4\n15#sun dec 8#san diego chargers#three rivers stadium#1:00 pm#w 16 - 3#10 - 4\n16#sun dec 15#san francisco 49ers#three rivers stadium#1:00 pm#l 25 - 15#10 - 5\n17#sun dec 22#carolina panthers#ericsson stadium#1:00 pm#l 18 - 14#10 - 6\n",
        "pandas_code": "len(df[(df['time ( et )'] != '1:00 pm') & (df['time ( et )'] != '9:00 pm') & (df['time ( et )'] != '-')]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2889,
        "statement": "the tin drum be the only film list that win an academy award",
        "label": 1,
        "table_caption": "list of german submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1956 (29th)#the captain of k\u00f6penick#der hauptmann von k\u00f6penick#helmut k\u00e4utner#nominee\n1957 (30th)#the devil came at night#nachts , wenn der teufel kam#robert siodmak#nominee\n1958 (31st)#arms and the man#helden#franz peter wirth#nominee\n1959 (32nd)#the bridge#die br\u00fccke#bernhard wicki#nominee\n1960 (33rd)#faust#faust#peter gorski#not nominated\n1966 (39th)#young t\u00f6rless#der junge t\u00f6rless#volker schl\u00f6ndorff#not nominated\n1967 (40th)#tattoo#t\u00e4towierung#johannes schaaf#not nominated\n1968 (41st)#artists under the big top : perplexed#die artisten in der zirkuskuppel : ratlos#alexander kluge#not nominated\n1969 (42nd)#hunting scenes from bavaria#jagdszenen aus niederbayern#peter fleischmann#not nominated\n1970 (43rd)#ok#ok#michael verhoeven#not nominated\n1971 (44th)#the castle#das schlo\u00df#rudolf noelte#not nominated\n1972 (45th)#trotta#trotta#johannes schaaf#not nominated\n1973 (46th)#the pedestrian#der fu\u00dfg\u00e4nger#maximilian schell#nominee\n1974 (47th)#one or the other of us#einer von uns beiden#wolfgang petersen#not nominated\n1975 (48th)#the enigma of kaspar hauser#jeder f\u00fcr sich und gott gegen alle#werner herzog#not nominated\n1976 (49th)#the clown#ansichten eines clowns#vojt\u011bch jasn\u00fd#not nominated\n1977 (50th)#the american friend#der amerikanische freund#wim wenders#not nominated\n1978 (51st)#the glass cell#die gl\u00e4serne zelle#hans w gei\u00dfend\u00f6rfer#nominee\n1979 (52nd)#the tin drum#die blechtrommel#volker schl\u00f6ndorff#won academy award\n1980 (53rd)#fabian#fabian#wolf gremm#not nominated\n1982 (55th)#fitzcarraldo#fitzcarraldo#werner herzog#not nominated\n1983 (56th)#a woman in flames#die flambierte frau#robert van ackeren#not nominated\n1984 (57th)#man under suspicion#morgen in alabama#norbert k\u00fcckelmann#not nominated\n1985 (58th)#angry harvest#bittere ernte#agnieszka holland#nominee\n1986 (59th)#men\u2026#m\u00e4nner\u2026#doris d\u00f6rrie#not nominated\n1987 (60th)#wings of desire#der himmel \u00fcber berlin#wim wenders#not nominated\n1988 (61st)#yasemin#yasemin#hark bohm#not nominated\n",
        "pandas_code": "df[df['result'] == 'won academy award']['film title used in nomination'].eq('the tin drum').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2890,
        "statement": "there be over 5 film list that be nominate for an academy award",
        "label": 1,
        "table_caption": "list of german submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1956 (29th)#the captain of k\u00f6penick#der hauptmann von k\u00f6penick#helmut k\u00e4utner#nominee\n1957 (30th)#the devil came at night#nachts , wenn der teufel kam#robert siodmak#nominee\n1958 (31st)#arms and the man#helden#franz peter wirth#nominee\n1959 (32nd)#the bridge#die br\u00fccke#bernhard wicki#nominee\n1960 (33rd)#faust#faust#peter gorski#not nominated\n1966 (39th)#young t\u00f6rless#der junge t\u00f6rless#volker schl\u00f6ndorff#not nominated\n1967 (40th)#tattoo#t\u00e4towierung#johannes schaaf#not nominated\n1968 (41st)#artists under the big top : perplexed#die artisten in der zirkuskuppel : ratlos#alexander kluge#not nominated\n1969 (42nd)#hunting scenes from bavaria#jagdszenen aus niederbayern#peter fleischmann#not nominated\n1970 (43rd)#ok#ok#michael verhoeven#not nominated\n1971 (44th)#the castle#das schlo\u00df#rudolf noelte#not nominated\n1972 (45th)#trotta#trotta#johannes schaaf#not nominated\n1973 (46th)#the pedestrian#der fu\u00dfg\u00e4nger#maximilian schell#nominee\n1974 (47th)#one or the other of us#einer von uns beiden#wolfgang petersen#not nominated\n1975 (48th)#the enigma of kaspar hauser#jeder f\u00fcr sich und gott gegen alle#werner herzog#not nominated\n1976 (49th)#the clown#ansichten eines clowns#vojt\u011bch jasn\u00fd#not nominated\n1977 (50th)#the american friend#der amerikanische freund#wim wenders#not nominated\n1978 (51st)#the glass cell#die gl\u00e4serne zelle#hans w gei\u00dfend\u00f6rfer#nominee\n1979 (52nd)#the tin drum#die blechtrommel#volker schl\u00f6ndorff#won academy award\n1980 (53rd)#fabian#fabian#wolf gremm#not nominated\n1982 (55th)#fitzcarraldo#fitzcarraldo#werner herzog#not nominated\n1983 (56th)#a woman in flames#die flambierte frau#robert van ackeren#not nominated\n1984 (57th)#man under suspicion#morgen in alabama#norbert k\u00fcckelmann#not nominated\n1985 (58th)#angry harvest#bittere ernte#agnieszka holland#nominee\n1986 (59th)#men\u2026#m\u00e4nner\u2026#doris d\u00f6rrie#not nominated\n1987 (60th)#wings of desire#der himmel \u00fcber berlin#wim wenders#not nominated\n1988 (61st)#yasemin#yasemin#hark bohm#not nominated\n",
        "pandas_code": "df[df['result'] == 'nominee'].shape[0] > 5",
        "pandas_eval": "True"
    },
    {
        "id": 2891,
        "statement": "there be over 15 film list that be not nominate for an academy award",
        "label": 1,
        "table_caption": "list of german submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1956 (29th)#the captain of k\u00f6penick#der hauptmann von k\u00f6penick#helmut k\u00e4utner#nominee\n1957 (30th)#the devil came at night#nachts , wenn der teufel kam#robert siodmak#nominee\n1958 (31st)#arms and the man#helden#franz peter wirth#nominee\n1959 (32nd)#the bridge#die br\u00fccke#bernhard wicki#nominee\n1960 (33rd)#faust#faust#peter gorski#not nominated\n1966 (39th)#young t\u00f6rless#der junge t\u00f6rless#volker schl\u00f6ndorff#not nominated\n1967 (40th)#tattoo#t\u00e4towierung#johannes schaaf#not nominated\n1968 (41st)#artists under the big top : perplexed#die artisten in der zirkuskuppel : ratlos#alexander kluge#not nominated\n1969 (42nd)#hunting scenes from bavaria#jagdszenen aus niederbayern#peter fleischmann#not nominated\n1970 (43rd)#ok#ok#michael verhoeven#not nominated\n1971 (44th)#the castle#das schlo\u00df#rudolf noelte#not nominated\n1972 (45th)#trotta#trotta#johannes schaaf#not nominated\n1973 (46th)#the pedestrian#der fu\u00dfg\u00e4nger#maximilian schell#nominee\n1974 (47th)#one or the other of us#einer von uns beiden#wolfgang petersen#not nominated\n1975 (48th)#the enigma of kaspar hauser#jeder f\u00fcr sich und gott gegen alle#werner herzog#not nominated\n1976 (49th)#the clown#ansichten eines clowns#vojt\u011bch jasn\u00fd#not nominated\n1977 (50th)#the american friend#der amerikanische freund#wim wenders#not nominated\n1978 (51st)#the glass cell#die gl\u00e4serne zelle#hans w gei\u00dfend\u00f6rfer#nominee\n1979 (52nd)#the tin drum#die blechtrommel#volker schl\u00f6ndorff#won academy award\n1980 (53rd)#fabian#fabian#wolf gremm#not nominated\n1982 (55th)#fitzcarraldo#fitzcarraldo#werner herzog#not nominated\n1983 (56th)#a woman in flames#die flambierte frau#robert van ackeren#not nominated\n1984 (57th)#man under suspicion#morgen in alabama#norbert k\u00fcckelmann#not nominated\n1985 (58th)#angry harvest#bittere ernte#agnieszka holland#nominee\n1986 (59th)#men\u2026#m\u00e4nner\u2026#doris d\u00f6rrie#not nominated\n1987 (60th)#wings of desire#der himmel \u00fcber berlin#wim wenders#not nominated\n1988 (61st)#yasemin#yasemin#hark bohm#not nominated\n",
        "pandas_code": "len(df[df['result'] == 'not nominated']) > 15",
        "pandas_eval": "True"
    },
    {
        "id": 2892,
        "statement": "angry harvest be not a nominee in 1980",
        "label": 1,
        "table_caption": "list of german submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1956 (29th)#the captain of k\u00f6penick#der hauptmann von k\u00f6penick#helmut k\u00e4utner#nominee\n1957 (30th)#the devil came at night#nachts , wenn der teufel kam#robert siodmak#nominee\n1958 (31st)#arms and the man#helden#franz peter wirth#nominee\n1959 (32nd)#the bridge#die br\u00fccke#bernhard wicki#nominee\n1960 (33rd)#faust#faust#peter gorski#not nominated\n1966 (39th)#young t\u00f6rless#der junge t\u00f6rless#volker schl\u00f6ndorff#not nominated\n1967 (40th)#tattoo#t\u00e4towierung#johannes schaaf#not nominated\n1968 (41st)#artists under the big top : perplexed#die artisten in der zirkuskuppel : ratlos#alexander kluge#not nominated\n1969 (42nd)#hunting scenes from bavaria#jagdszenen aus niederbayern#peter fleischmann#not nominated\n1970 (43rd)#ok#ok#michael verhoeven#not nominated\n1971 (44th)#the castle#das schlo\u00df#rudolf noelte#not nominated\n1972 (45th)#trotta#trotta#johannes schaaf#not nominated\n1973 (46th)#the pedestrian#der fu\u00dfg\u00e4nger#maximilian schell#nominee\n1974 (47th)#one or the other of us#einer von uns beiden#wolfgang petersen#not nominated\n1975 (48th)#the enigma of kaspar hauser#jeder f\u00fcr sich und gott gegen alle#werner herzog#not nominated\n1976 (49th)#the clown#ansichten eines clowns#vojt\u011bch jasn\u00fd#not nominated\n1977 (50th)#the american friend#der amerikanische freund#wim wenders#not nominated\n1978 (51st)#the glass cell#die gl\u00e4serne zelle#hans w gei\u00dfend\u00f6rfer#nominee\n1979 (52nd)#the tin drum#die blechtrommel#volker schl\u00f6ndorff#won academy award\n1980 (53rd)#fabian#fabian#wolf gremm#not nominated\n1982 (55th)#fitzcarraldo#fitzcarraldo#werner herzog#not nominated\n1983 (56th)#a woman in flames#die flambierte frau#robert van ackeren#not nominated\n1984 (57th)#man under suspicion#morgen in alabama#norbert k\u00fcckelmann#not nominated\n1985 (58th)#angry harvest#bittere ernte#agnieszka holland#nominee\n1986 (59th)#men\u2026#m\u00e4nner\u2026#doris d\u00f6rrie#not nominated\n1987 (60th)#wings of desire#der himmel \u00fcber berlin#wim wenders#not nominated\n1988 (61st)#yasemin#yasemin#hark bohm#not nominated\n",
        "pandas_code": "df.loc[df['year (ceremony)'].str.contains('1980') & (df['film title used in nomination'] == 'angry harvest'), 'result'].empty or df.loc[df['year (ceremony)'].str.contains('1980') & (df['film title used in nomination'] == 'angry harvest'), 'result'].values[0] != 'nominee'",
        "pandas_eval": "True"
    },
    {
        "id": 2893,
        "statement": "faust , ok , trotta , fabian , fitzcarraldo , and yasemin be film list that do not need to have their name translate into english",
        "label": 1,
        "table_caption": "list of german submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#film title used in nomination#original title#director#result\n1956 (29th)#the captain of k\u00f6penick#der hauptmann von k\u00f6penick#helmut k\u00e4utner#nominee\n1957 (30th)#the devil came at night#nachts , wenn der teufel kam#robert siodmak#nominee\n1958 (31st)#arms and the man#helden#franz peter wirth#nominee\n1959 (32nd)#the bridge#die br\u00fccke#bernhard wicki#nominee\n1960 (33rd)#faust#faust#peter gorski#not nominated\n1966 (39th)#young t\u00f6rless#der junge t\u00f6rless#volker schl\u00f6ndorff#not nominated\n1967 (40th)#tattoo#t\u00e4towierung#johannes schaaf#not nominated\n1968 (41st)#artists under the big top : perplexed#die artisten in der zirkuskuppel : ratlos#alexander kluge#not nominated\n1969 (42nd)#hunting scenes from bavaria#jagdszenen aus niederbayern#peter fleischmann#not nominated\n1970 (43rd)#ok#ok#michael verhoeven#not nominated\n1971 (44th)#the castle#das schlo\u00df#rudolf noelte#not nominated\n1972 (45th)#trotta#trotta#johannes schaaf#not nominated\n1973 (46th)#the pedestrian#der fu\u00dfg\u00e4nger#maximilian schell#nominee\n1974 (47th)#one or the other of us#einer von uns beiden#wolfgang petersen#not nominated\n1975 (48th)#the enigma of kaspar hauser#jeder f\u00fcr sich und gott gegen alle#werner herzog#not nominated\n1976 (49th)#the clown#ansichten eines clowns#vojt\u011bch jasn\u00fd#not nominated\n1977 (50th)#the american friend#der amerikanische freund#wim wenders#not nominated\n1978 (51st)#the glass cell#die gl\u00e4serne zelle#hans w gei\u00dfend\u00f6rfer#nominee\n1979 (52nd)#the tin drum#die blechtrommel#volker schl\u00f6ndorff#won academy award\n1980 (53rd)#fabian#fabian#wolf gremm#not nominated\n1982 (55th)#fitzcarraldo#fitzcarraldo#werner herzog#not nominated\n1983 (56th)#a woman in flames#die flambierte frau#robert van ackeren#not nominated\n1984 (57th)#man under suspicion#morgen in alabama#norbert k\u00fcckelmann#not nominated\n1985 (58th)#angry harvest#bittere ernte#agnieszka holland#nominee\n1986 (59th)#men\u2026#m\u00e4nner\u2026#doris d\u00f6rrie#not nominated\n1987 (60th)#wings of desire#der himmel \u00fcber berlin#wim wenders#not nominated\n1988 (61st)#yasemin#yasemin#hark bohm#not nominated\n",
        "pandas_code": "df[df['film title used in nomination'].isin(['faust', 'ok', 'trotta', 'fabian', 'fitzcarraldo', 'yasemin'])].equals(df[df['original title'].isin(['faust', 'ok', 'trotta', 'fabian', 'fitzcarraldo', 'yasemin'])])",
        "pandas_eval": "True"
    },
    {
        "id": 2894,
        "statement": "the 2 competitor name barry be laps 14 and 13",
        "label": 1,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "df[(df['driver'].str.contains('barry')) & (df['laps'].isin([14, 13]))].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2895,
        "statement": "class af2 make up exactly 50% of all contestant",
        "label": 1,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "len(df[df['class'] == 'af2']) / len(df) == 0.5",
        "pandas_eval": "True"
    },
    {
        "id": 2896,
        "statement": "both bob and allan do not finish in any place , and don't share the same class",
        "label": 1,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "(df[df['driver'].isin(['bob salter', 'allan felton'])]['position'].isin(['dnf']).all()) and (df[df['driver'].isin(['bob salter', 'allan felton'])]['class'].nunique() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2897,
        "statement": "the player correspond to the highest laps minus bob salters' lap be ric price",
        "label": 1,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "df.loc[df['laps'] == (df['laps'].max() - df.loc[df['driver'] == 'bob salter', 'laps'].values[0]), 'driver'].values[0] == 'ric price'",
        "pandas_eval": "True"
    },
    {
        "id": 2899,
        "statement": "the 2 competitor name barry be laps 17 and 13",
        "label": 0,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "(df[df['driver'].str.contains('barry')]['laps'].isin([17, 13]).all())",
        "pandas_eval": "False"
    },
    {
        "id": 2900,
        "statement": "class af2 make up exactly 60% of all contestant",
        "label": 0,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "(df['class'].value_counts(normalize=True)['af2'] == 0.6)",
        "pandas_eval": "False"
    },
    {
        "id": 2901,
        "statement": "both bob and allan do not finish in any place , and share the same class",
        "label": 0,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "((df['driver'].isin(['bob salter', 'allan felton'])) & (df['position'] == 'dnf') & (df['class'] == 'a1\u00bdlf')).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2902,
        "statement": "the player correspond to the lowest laps minus bob salters' lap be ric price",
        "label": 0,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "df[df['driver'] == 'ric price']['laps'].iloc[0] == df['laps'].min() - df[df['driver'] == 'bob salter']['laps'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2903,
        "statement": "the number of lap that the driver who place just below andy macgregor complete be 19",
        "label": 0,
        "table_caption": "1965 australian one and a half litre championship",
        "table_text": "position#driver#entrant#class#laps\n1#bib stillwell#bs stillwell#a1\u00bdlf#20\n2#leo geoghegan#total team#a1\u00bdlf#20\n3#john ampt#j ampt#a1\u00bdlf#19\n4#glyn scott#glyn scott motors#a1\u00bdlf#19\n5#noel potts#n potts#a1\u00bdlf#18\n6#ric price#r price#af2#17\n7#lionel ayers#motor racing components#af2#16\n8#kevin bartlett#j mcguire#af2#16\n9#andy macgregor#total armadale#af2#16\ndnf#barry collerson#b collerson#af2#14\ndnf#barry lake#b lake#a1\u00bdlf#13\ndnf#max stewart#m stewart#af2#10\ndnf#mike champion#m champion#a1\u00bdlf#9\ndnf#phil west#kurt keller motors#af2#8\ndnf#bob salter#salter motors#a1\u00bdlf#3\ndnf#allan felton#kurt keller motors#af2#1\n",
        "pandas_code": "df.loc[df['driver'] == 'andy macgregor', 'laps'].values[0] - 1 == 19",
        "pandas_eval": "False"
    },
    {
        "id": 2904,
        "statement": "ren\u00e9 bonni\u00e8re appear to direct in episode 26 , 29 , and 31",
        "label": 1,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "all(df[df['episode'].isin(['26 (4)', '29 (7)', '31 (9)'])]['directed by'] == 'ren\u00e9 bonni\u00e8re')",
        "pandas_eval": "True"
    },
    {
        "id": 2905,
        "statement": "the original episode that feature jon cassar as a director be episode 23 which be prior to episode 36",
        "label": 1,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "df[(df['directed by'] == 'jon cassar') & (df['episode'] == '23 (1)') & (df['episode'].str.extract(r'(\\d+)')[0].astype(int) < 36)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2908,
        "statement": "elliot stern only write 1 episode episode 31 , whereas michael loceff write many more",
        "label": 1,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "(df[df['written by'] == 'elliot stern'].shape[0] == 1) & (df[df['written by'] == 'michael loceff'].shape[0] > 1)",
        "pandas_eval": "True"
    },
    {
        "id": 2909,
        "statement": "ren\u00e9 bonni\u00e8re never appear as a director in any episode",
        "label": 0,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "not any(df['directed by'] == 'ren\u00e9 bonni\u00e8re')",
        "pandas_eval": "False"
    },
    {
        "id": 2910,
        "statement": "john cassar do not appear as a director until episode 36",
        "label": 0,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "all(df[df['episode'].astype(str).str.extract(r'(\\d+)')[0].astype(int) < 36]['directed by'].str.lower() != 'jon cassar')",
        "pandas_eval": "False"
    },
    {
        "id": 2911,
        "statement": "elliot stern and michael loceff write the same number of episode",
        "label": 0,
        "table_caption": "list of la femme nikita episodes",
        "table_text": "episode#title#directed by#written by#original airdate\n23 (1)#hard landing#jon cassar#michael loceff#january 4 , 1998\n24 (2)#spec ops#tj scott#robert cochran#january 11 , 1998\n25 (3)#third person#jon cassar#michael loceff#january 18 , 1998\n26 (4)#approaching zero#ren\u00e9 bonni\u00e8re#michael loceff#february 1 , 1998\n27 (5)#new regime#jon cassar#robert cochran#march 1 , 1998\n28 (6)#mandatory refusal#ken girotti#david ehrman#march 8 , 1998\n29 (7)#half - life#ren\u00e9 bonni\u00e8re#maurice hurley#march 22 , 1998\n30 (8)#darkness visible#ken girotti#david ehrman#march 29 , 1998\n31 (9)#open heart#ren\u00e9 bonni\u00e8re#elliot stern#april 5 , 1998\n32 (10)#first mission#guy magar#peter mohan & jim henshaw#april 12 , 1998\n33 (11)#psychic pilgrim#ren\u00e9 bonni\u00e8re#michael loceff#april 19 , 1998\n34 (12)#soul sacrifice#david warry - smith#michael loceff#june 14 , 1998\n35 (13)#not was#ren\u00e9 bonni\u00e8re#michael loceff#june 21 , 1998\n36 (14)#double date#jon cassar#robert cochran#june 28 , 1998\n37 (15)#fuzzy logic#ken girotti#michael loceff#july 5 , 1998\n38 (16)#old habits#terry ingram#maurice hurley#july 12 , 1998\n39 (17)#inside out#ken girotti#maurice hurley#july 26 , 1998\n40 (18)#off profile#john fawcett#david ehrman#august 2 , 1998\n41 (19)#last night#clark johnson#robert cochran#august 9 , 1998\n42 (20)#in between#joseph scanlan#michael loceff#august 16 , 1998\n43 (21)#adrian 's garden#brad turner#michael loceff#august 23 , 1998\n44 (22)#end game#joseph scanlan#robert cochran & david ehrman#august 30 , 1998\n",
        "pandas_code": "df['written by'].value_counts()[['elliot stern', 'michael loceff']].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 2912,
        "statement": "some of the feature include wheeler rock , rabben , which be a peak , and proclamation island",
        "label": 1,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "any(df['name'].isin(['wheeler rocks', 'rabben', 'proclamation island']))",
        "pandas_eval": "True"
    },
    {
        "id": 2913,
        "statement": "the bearing of vicar island be 009\u00b0 and the bearing of wilkinson peak be 038\u00b0",
        "label": 1,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "(df[df['name'] == 'vicars island']['bearing'].values[0] == '009 degree') & (df[df['name'] == 'wilkinson peaks']['bearing'].values[0] == '038 degree')",
        "pandas_eval": "True"
    },
    {
        "id": 2914,
        "statement": "the bearing of napier mountain be 311\u00b0 and the bearing of armstrong peak be also 311\u00b0",
        "label": 1,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "(df[df['name'] == 'napier mountains']['bearing'].values[0] == '311 degree') & (df[df['name'] == 'armstrong peak']['bearing'].values[0] == '311 degree')",
        "pandas_eval": "True"
    },
    {
        "id": 2916,
        "statement": "young nunataks have a latitude of 66\u00b044.0 's while tippet nunataks have a latitude of 66\u00b044.0 's",
        "label": 1,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "(df[df['name'] == 'young nunataks']['latitude'].values[0] == \"66 degree44.0 's\") & (df[df['name'] == 'tippet nunataks']['latitude'].values[0] == \"66 degree44.0 's\")",
        "pandas_eval": "True"
    },
    {
        "id": 2917,
        "statement": "the bearing of vicar island be the same as the bearing of wilkinson peak",
        "label": 0,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "df[df['name'] == 'vicars island']['bearing'].values[0] == df[df['name'] == 'wilkinson peaks']['bearing'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 2918,
        "statement": "the bearing of young nunataks be 311\u00b0 and the bearing of vicar island be also 311\u00b0",
        "label": 0,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "(df[df['name'] == 'young nunataks']['bearing'].values[0] == '311 degree') & (df[df['name'] == 'vicars island']['bearing'].values[0] == '311 degree')",
        "pandas_eval": "False"
    },
    {
        "id": 2919,
        "statement": "there be 5 type of feature , peak , rock , island , cape and ridge",
        "label": 0,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "len(df['feature'].unique()) == 5 and set(df['feature'].unique()) == {'peak', 'rock', 'island', 'cape', 'ridge'}",
        "pandas_eval": "False"
    },
    {
        "id": 2920,
        "statement": "young nunataks and tippet nunataks have different latitude",
        "label": 0,
        "table_caption": "mount elkins",
        "table_text": "name#feature#latitude#longitude#distance#bearing\naagaard islands#island#65 degree51.0 's#53 degree40.0'e#93.4 km#346 degree\narmstrong peak#peak#66 degree24.0 's#53 degree23.0'e#45.1 km#311 degree\nbandy nunataks#nunatak#66 degree55.0 's#53 degree36.0'e#36.8 km#221 degree\nbird ridge#ridge#66 degree47.0 's#55 degree04.0'e#42.3 km#108 degree\nbratth\u00f8#peak#66 degree39.0 's#54 degree40.0'e#22.8 km#086 degree\ncape batterbee#cape#65 degree51.0 's#53 degree48.0'e#92.2 km#350 degree\nconradi peak#peak#66 degree08.0 's#54 degree35.0'e#62.4 km#018 degree\ndoyle point#point#65 degree53.0 's#54 degree52.0'e#92.8 km#021 degree\ngrimsley peaks#peak#66 degree34.0 's#53 degree40.0'e#24.1 km#297 degree\nknausen#peak#66 degree22.0 's#53 degree13.0'e#53.1 km#308 degree\nmj\u00e5kollen#peak#66 degree33.0 's#53 degree28.0'e#32.8 km#293 degree\nmount bennett#mountain#66 degree32.0 's#53 degree38.0'e#27.2 km#303 degree\nmount breckinridge#mountain#66 degree37.0 's#53 degree41.0'e#21.3 km#285 degree\nmount bride#mountain#66 degree26.0 's#53 degree57.0'e#27.4 km#341 degree\nmount gate#mountain#66 degree51.0 's#53 degree18.0'e#42.5 km#241 degree\nmount griffiths#mountain#66 degree28.0 's#54 degree01.0'e#23.0 km#345 degree\nmount maines#mountain#66 degree38.0 's#53 degree54.0'e#11.6 km#288 degree\nmount pasco#mountain#66 degree59.0 's#54 degree44.0'e#43.5 km#144 degree\nmount stadler#mountain#66 degree54.0 's#53 degree14.0'e#47.8 km#237 degree\nnapier mountains#mountain#66 degree30.0 's#53 degree40.0'e#28.3 km#311 degree\nnewman nunataks#nunatak#66 degree40.0 's#54 degree45.0'e#26.4 km#090 degree\nproclamation island#island#65 degree51.0 's#53 degree41.0'e#93.2 km#347 degree\nrabben#peak#66 degree27.0 's#54 degree07.0'e#24.1 km#356 degree\nskarvet nunatak#nunatak#66 degree26.0 's#53 degree45.0'e#31.4 km#326 degree\ns\u00f8rtoppen nunatak#nunatak#66 degree40.0 's#53 degree28.0'e#30.1 km#270 degree\ntippet nunataks#nunatak#66 degree44.0 's#53 degree15.0'e#40.3 km#259 degree\nvicars island#island#65 degree50.0 's#54 degree29.0'e#93.9 km#009 degree\nwheeler rocks#rock#66 degree17.0 's#55 degree08.0'e#61.0 km#046 degree\nwilkinson peaks#peak#66 degree37.0 's#54 degree15.0'e#7.1 km#038 degree\nyoung nunataks#nunatak#66 degree44.0 's#54 degree08.0'e#7.4 km#186 degree\n",
        "pandas_code": "df.loc[df['name'].isin(['young nunataks', 'tippet nunataks']), 'latitude'].nunique() != 1",
        "pandas_eval": "False"
    },
    {
        "id": 2921,
        "statement": "the memorial stadium be the site for 6 game",
        "label": 1,
        "table_caption": "1974 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1974#pittsburgh steelers#l 0 - 30#0 - 1#three rivers stadium#48890\n2#september 22 , 1974#green bay packers#l 13 - 20#0 - 2#memorial stadium#41252\n3#september 29 , 1974#philadelphia eagles#l 10 - 30#0 - 3#veterans stadium#64205\n4#october 6 , 1974#new england patriots#l 3 - 42#0 - 4#schaeffer stadium#59502\n5#october 13 , 1974#buffalo bills#l 14 - 27#0 - 5#memorial stadium#40626\n6#october 20 , 1974#new york jets#w 35 - 20#1 - 5#shea stadium#51745\n7#october 27 , 1974#miami dolphins#l 7 - 17#1 - 6#miami orange bowl#65868\n8#november 3 , 1974#cincinnati bengals#l 14 - 24#1 - 7#memorial stadium#36110\n9#november 10 , 1974#denver broncos#l 6 - 17#1 - 8#memorial stadium#33244\n10#november 17 , 1974#atlanta falcons#w 17 - 7#2 - 8#atlanta stadium#41278\n11#november 24 , 1974#new england patriots#l 17 - 27#2 - 9#memorial stadium#34782\n12#december 1 , 1974#buffalo bills#l 0 - 6#2 - 10#rich stadium#75325\n13#december 8 , 1974#miami dolphins#l 16 - 17#2 - 11#memorial stadium#34420\n",
        "pandas_code": "df['game site'].value_counts()['memorial stadium'] == 6",
        "pandas_eval": "True"
    },
    {
        "id": 2922,
        "statement": "there be a higher attendance on week 3 than either week 1 or 2",
        "label": 1,
        "table_caption": "1974 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1974#pittsburgh steelers#l 0 - 30#0 - 1#three rivers stadium#48890\n2#september 22 , 1974#green bay packers#l 13 - 20#0 - 2#memorial stadium#41252\n3#september 29 , 1974#philadelphia eagles#l 10 - 30#0 - 3#veterans stadium#64205\n4#october 6 , 1974#new england patriots#l 3 - 42#0 - 4#schaeffer stadium#59502\n5#october 13 , 1974#buffalo bills#l 14 - 27#0 - 5#memorial stadium#40626\n6#october 20 , 1974#new york jets#w 35 - 20#1 - 5#shea stadium#51745\n7#october 27 , 1974#miami dolphins#l 7 - 17#1 - 6#miami orange bowl#65868\n8#november 3 , 1974#cincinnati bengals#l 14 - 24#1 - 7#memorial stadium#36110\n9#november 10 , 1974#denver broncos#l 6 - 17#1 - 8#memorial stadium#33244\n10#november 17 , 1974#atlanta falcons#w 17 - 7#2 - 8#atlanta stadium#41278\n11#november 24 , 1974#new england patriots#l 17 - 27#2 - 9#memorial stadium#34782\n12#december 1 , 1974#buffalo bills#l 0 - 6#2 - 10#rich stadium#75325\n13#december 8 , 1974#miami dolphins#l 16 - 17#2 - 11#memorial stadium#34420\n",
        "pandas_code": "df.loc[df['week'] == 3, 'attendance'].values[0] > max(df.loc[df['week'].isin([1, 2]), 'attendance'])",
        "pandas_eval": "True"
    },
    {
        "id": 2923,
        "statement": "the buffalo bill be the opponent for 2 game",
        "label": 1,
        "table_caption": "1974 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1974#pittsburgh steelers#l 0 - 30#0 - 1#three rivers stadium#48890\n2#september 22 , 1974#green bay packers#l 13 - 20#0 - 2#memorial stadium#41252\n3#september 29 , 1974#philadelphia eagles#l 10 - 30#0 - 3#veterans stadium#64205\n4#october 6 , 1974#new england patriots#l 3 - 42#0 - 4#schaeffer stadium#59502\n5#october 13 , 1974#buffalo bills#l 14 - 27#0 - 5#memorial stadium#40626\n6#october 20 , 1974#new york jets#w 35 - 20#1 - 5#shea stadium#51745\n7#october 27 , 1974#miami dolphins#l 7 - 17#1 - 6#miami orange bowl#65868\n8#november 3 , 1974#cincinnati bengals#l 14 - 24#1 - 7#memorial stadium#36110\n9#november 10 , 1974#denver broncos#l 6 - 17#1 - 8#memorial stadium#33244\n10#november 17 , 1974#atlanta falcons#w 17 - 7#2 - 8#atlanta stadium#41278\n11#november 24 , 1974#new england patriots#l 17 - 27#2 - 9#memorial stadium#34782\n12#december 1 , 1974#buffalo bills#l 0 - 6#2 - 10#rich stadium#75325\n13#december 8 , 1974#miami dolphins#l 16 - 17#2 - 11#memorial stadium#34420\n",
        "pandas_code": "len(df[df['opponent'] == 'buffalo bills']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2924,
        "statement": "the last 2 game list be in december",
        "label": 1,
        "table_caption": "1974 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1974#pittsburgh steelers#l 0 - 30#0 - 1#three rivers stadium#48890\n2#september 22 , 1974#green bay packers#l 13 - 20#0 - 2#memorial stadium#41252\n3#september 29 , 1974#philadelphia eagles#l 10 - 30#0 - 3#veterans stadium#64205\n4#october 6 , 1974#new england patriots#l 3 - 42#0 - 4#schaeffer stadium#59502\n5#october 13 , 1974#buffalo bills#l 14 - 27#0 - 5#memorial stadium#40626\n6#october 20 , 1974#new york jets#w 35 - 20#1 - 5#shea stadium#51745\n7#october 27 , 1974#miami dolphins#l 7 - 17#1 - 6#miami orange bowl#65868\n8#november 3 , 1974#cincinnati bengals#l 14 - 24#1 - 7#memorial stadium#36110\n9#november 10 , 1974#denver broncos#l 6 - 17#1 - 8#memorial stadium#33244\n10#november 17 , 1974#atlanta falcons#w 17 - 7#2 - 8#atlanta stadium#41278\n11#november 24 , 1974#new england patriots#l 17 - 27#2 - 9#memorial stadium#34782\n12#december 1 , 1974#buffalo bills#l 0 - 6#2 - 10#rich stadium#75325\n13#december 8 , 1974#miami dolphins#l 16 - 17#2 - 11#memorial stadium#34420\n",
        "pandas_code": "all(df['date'].str.contains('december').tail(2))",
        "pandas_eval": "True"
    },
    {
        "id": 2925,
        "statement": "the first game against the miami dolphins be at the miami orange bowl",
        "label": 1,
        "table_caption": "1974 baltimore colts season",
        "table_text": "week#date#opponent#result#record#game site#attendance\n1#september 15 , 1974#pittsburgh steelers#l 0 - 30#0 - 1#three rivers stadium#48890\n2#september 22 , 1974#green bay packers#l 13 - 20#0 - 2#memorial stadium#41252\n3#september 29 , 1974#philadelphia eagles#l 10 - 30#0 - 3#veterans stadium#64205\n4#october 6 , 1974#new england patriots#l 3 - 42#0 - 4#schaeffer stadium#59502\n5#october 13 , 1974#buffalo bills#l 14 - 27#0 - 5#memorial stadium#40626\n6#october 20 , 1974#new york jets#w 35 - 20#1 - 5#shea stadium#51745\n7#october 27 , 1974#miami dolphins#l 7 - 17#1 - 6#miami orange bowl#65868\n8#november 3 , 1974#cincinnati bengals#l 14 - 24#1 - 7#memorial stadium#36110\n9#november 10 , 1974#denver broncos#l 6 - 17#1 - 8#memorial stadium#33244\n10#november 17 , 1974#atlanta falcons#w 17 - 7#2 - 8#atlanta stadium#41278\n11#november 24 , 1974#new england patriots#l 17 - 27#2 - 9#memorial stadium#34782\n12#december 1 , 1974#buffalo bills#l 0 - 6#2 - 10#rich stadium#75325\n13#december 8 , 1974#miami dolphins#l 16 - 17#2 - 11#memorial stadium#34420\n",
        "pandas_code": "df[(df['opponent'] == 'miami dolphins') & (df['game site'] == 'miami orange bowl')].iloc[0]['week'] == 7",
        "pandas_eval": "True"
    },
    {
        "id": 2926,
        "statement": "georg werthner be the first athlete to compete in 4 olympic decathlon",
        "label": 1,
        "table_caption": "georg werthner",
        "table_text": "year#tournament#venue#result#distance\n1975#world student games#rome , italy#5th#decathlon\n1976#olympic games#montreal , canada#16th#decathlon\n1979#world student games#mexico city , mexico#6th#decathlon\n1980#olympic games#moscow , soviet union#4th#decathlon\n1981#world student games#bucharest , romania#3rd#decathlon\n1982#european championships#athens , greece#5th#decathlon\n1983#world student games#edmonton , canada#3rd#decathlon\n1984#olympic games#los angeles , usa#9th#decathlon\n1988#olympic games#seoul , south korea#21st#decathlon\n",
        "pandas_code": "df[(df['tournament'] == 'olympic games') & (df['distance'] == 'decathlon')].groupby('year').first().reset_index().shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2927,
        "statement": "georg werthner place third 2 time in the world student game",
        "label": 1,
        "table_caption": "georg werthner",
        "table_text": "year#tournament#venue#result#distance\n1975#world student games#rome , italy#5th#decathlon\n1976#olympic games#montreal , canada#16th#decathlon\n1979#world student games#mexico city , mexico#6th#decathlon\n1980#olympic games#moscow , soviet union#4th#decathlon\n1981#world student games#bucharest , romania#3rd#decathlon\n1982#european championships#athens , greece#5th#decathlon\n1983#world student games#edmonton , canada#3rd#decathlon\n1984#olympic games#los angeles , usa#9th#decathlon\n1988#olympic games#seoul , south korea#21st#decathlon\n",
        "pandas_code": "(df[(df['tournament'] == 'world student games') & (df['result'].str.contains('3rd'))].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 2928,
        "statement": "georg werthner receive his highest place in the olympic game in 1980",
        "label": 1,
        "table_caption": "georg werthner",
        "table_text": "year#tournament#venue#result#distance\n1975#world student games#rome , italy#5th#decathlon\n1976#olympic games#montreal , canada#16th#decathlon\n1979#world student games#mexico city , mexico#6th#decathlon\n1980#olympic games#moscow , soviet union#4th#decathlon\n1981#world student games#bucharest , romania#3rd#decathlon\n1982#european championships#athens , greece#5th#decathlon\n1983#world student games#edmonton , canada#3rd#decathlon\n1984#olympic games#los angeles , usa#9th#decathlon\n1988#olympic games#seoul , south korea#21st#decathlon\n",
        "pandas_code": "(df[(df['tournament'] == 'olympic games') & (df['year'] == 1980)]['result'].str.extract('(\\\\d+)').astype(int).iloc[0] == df[df['tournament'] == 'olympic games']['result'].str.extract('(\\\\d+)').astype(int).min()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 2929,
        "statement": "georg werthne compete in a decathlon in canada on 2 separate occasion",
        "label": 1,
        "table_caption": "georg werthner",
        "table_text": "year#tournament#venue#result#distance\n1975#world student games#rome , italy#5th#decathlon\n1976#olympic games#montreal , canada#16th#decathlon\n1979#world student games#mexico city , mexico#6th#decathlon\n1980#olympic games#moscow , soviet union#4th#decathlon\n1981#world student games#bucharest , romania#3rd#decathlon\n1982#european championships#athens , greece#5th#decathlon\n1983#world student games#edmonton , canada#3rd#decathlon\n1984#olympic games#los angeles , usa#9th#decathlon\n1988#olympic games#seoul , south korea#21st#decathlon\n",
        "pandas_code": "len(df[(df['distance'] == 'decathlon') & (df['venue'].str.contains('canada', case=False))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 2930,
        "statement": "georg werthne never place higher than 3rd in a decathlon",
        "label": 1,
        "table_caption": "georg werthner",
        "table_text": "year#tournament#venue#result#distance\n1975#world student games#rome , italy#5th#decathlon\n1976#olympic games#montreal , canada#16th#decathlon\n1979#world student games#mexico city , mexico#6th#decathlon\n1980#olympic games#moscow , soviet union#4th#decathlon\n1981#world student games#bucharest , romania#3rd#decathlon\n1982#european championships#athens , greece#5th#decathlon\n1983#world student games#edmonton , canada#3rd#decathlon\n1984#olympic games#los angeles , usa#9th#decathlon\n1988#olympic games#seoul , south korea#21st#decathlon\n",
        "pandas_code": "(df[df['distance'] == 'decathlon']['result'].apply(lambda x: int(x[:-2])).min() >= 3)",
        "pandas_eval": "True"
    },
    {
        "id": 2931,
        "statement": "season 2 , 3 , and 4 all begin in 2006",
        "label": 1,
        "table_caption": "amor en custodia (tv series)",
        "table_text": "season#timeslot ( edt )#season premiere#season finale#tv season#rank ( spanish language )#viewers (in millions)\n2#monday - friday 9:00 pm#january 16 , 2006#april 6 , 2006#2006#1#9.4\n3#monday - friday 9:00 pm#april 10 , 2006#september 1 , 2006#2006#1#9.9\n4#monday - friday 9:00 pm#september 4 , 2006#january 6 , 2007#2006 - 2007#2#9.7\n5#monday - friday 9:00 pm#january 8 , 2007#march 28 , 2007#2007#1#10.1\n6#monday - friday 9:00 pm#april 2 , 2007#august 9 , 2007#2007#3#7.9\n7#monday - friday 9:00 pm#august 13 , 2007#december 10 , 2007#2007#2#9.9\n8#monday - friday 9:00 pm#december 13 , 2007#february 15 , 2008#2007 - 2008#7#5.1\n9#monday - friday 9:00 pm#february 18 , 2008#may 30 , 2008#2008#1#10.4\n10#monday - friday 10:00 pm#june 2 , 2008#august 29 , 2008#2008#1#9.4\n",
        "pandas_code": "all(df[df['season'].isin([2, 3, 4])]['season premiere'].str.contains('2006'))",
        "pandas_eval": "True"
    },
    {
        "id": 2932,
        "statement": "season 5 , 6 , 7 , and 8 all begin in 2007",
        "label": 1,
        "table_caption": "amor en custodia (tv series)",
        "table_text": "season#timeslot ( edt )#season premiere#season finale#tv season#rank ( spanish language )#viewers (in millions)\n2#monday - friday 9:00 pm#january 16 , 2006#april 6 , 2006#2006#1#9.4\n3#monday - friday 9:00 pm#april 10 , 2006#september 1 , 2006#2006#1#9.9\n4#monday - friday 9:00 pm#september 4 , 2006#january 6 , 2007#2006 - 2007#2#9.7\n5#monday - friday 9:00 pm#january 8 , 2007#march 28 , 2007#2007#1#10.1\n6#monday - friday 9:00 pm#april 2 , 2007#august 9 , 2007#2007#3#7.9\n7#monday - friday 9:00 pm#august 13 , 2007#december 10 , 2007#2007#2#9.9\n8#monday - friday 9:00 pm#december 13 , 2007#february 15 , 2008#2007 - 2008#7#5.1\n9#monday - friday 9:00 pm#february 18 , 2008#may 30 , 2008#2008#1#10.4\n10#monday - friday 10:00 pm#june 2 , 2008#august 29 , 2008#2008#1#9.4\n",
        "pandas_code": "all(df[df['season'].isin([5, 6, 7, 8])]['season premiere'].str.contains('2007'))",
        "pandas_eval": "True"
    },
    {
        "id": 2933,
        "statement": "season 9 and 10 both begin and end in 2008",
        "label": 1,
        "table_caption": "amor en custodia (tv series)",
        "table_text": "season#timeslot ( edt )#season premiere#season finale#tv season#rank ( spanish language )#viewers (in millions)\n2#monday - friday 9:00 pm#january 16 , 2006#april 6 , 2006#2006#1#9.4\n3#monday - friday 9:00 pm#april 10 , 2006#september 1 , 2006#2006#1#9.9\n4#monday - friday 9:00 pm#september 4 , 2006#january 6 , 2007#2006 - 2007#2#9.7\n5#monday - friday 9:00 pm#january 8 , 2007#march 28 , 2007#2007#1#10.1\n6#monday - friday 9:00 pm#april 2 , 2007#august 9 , 2007#2007#3#7.9\n7#monday - friday 9:00 pm#august 13 , 2007#december 10 , 2007#2007#2#9.9\n8#monday - friday 9:00 pm#december 13 , 2007#february 15 , 2008#2007 - 2008#7#5.1\n9#monday - friday 9:00 pm#february 18 , 2008#may 30 , 2008#2008#1#10.4\n10#monday - friday 10:00 pm#june 2 , 2008#august 29 , 2008#2008#1#9.4\n",
        "pandas_code": "(df.loc[df['season'] == 9, 'season premiere'].str.contains('2008').all() and df.loc[df['season'] == 9, 'season finale'].str.contains('2008').all() and df.loc[df['season'] == 10, 'season premiere'].str.contains('2008').all() and df.loc[df['season'] == 10, 'season finale'].str.contains('2008').all())",
        "pandas_eval": "True"
    },
    {
        "id": 2934,
        "statement": "season 4 and 8 both begin in 1 year and end in the next year",
        "label": 1,
        "table_caption": "amor en custodia (tv series)",
        "table_text": "season#timeslot ( edt )#season premiere#season finale#tv season#rank ( spanish language )#viewers (in millions)\n2#monday - friday 9:00 pm#january 16 , 2006#april 6 , 2006#2006#1#9.4\n3#monday - friday 9:00 pm#april 10 , 2006#september 1 , 2006#2006#1#9.9\n4#monday - friday 9:00 pm#september 4 , 2006#january 6 , 2007#2006 - 2007#2#9.7\n5#monday - friday 9:00 pm#january 8 , 2007#march 28 , 2007#2007#1#10.1\n6#monday - friday 9:00 pm#april 2 , 2007#august 9 , 2007#2007#3#7.9\n7#monday - friday 9:00 pm#august 13 , 2007#december 10 , 2007#2007#2#9.9\n8#monday - friday 9:00 pm#december 13 , 2007#february 15 , 2008#2007 - 2008#7#5.1\n9#monday - friday 9:00 pm#february 18 , 2008#may 30 , 2008#2008#1#10.4\n10#monday - friday 10:00 pm#june 2 , 2008#august 29 , 2008#2008#1#9.4\n",
        "pandas_code": "((df['season'] == 4) & (df['tv season'].str.contains('-'))).any() & ((df['season'] == 8) & (df['tv season'].str.contains('-'))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 2935,
        "statement": "the timeslot change in season 10 , from 9:00 pm 10:00 pm",
        "label": 1,
        "table_caption": "amor en custodia (tv series)",
        "table_text": "season#timeslot ( edt )#season premiere#season finale#tv season#rank ( spanish language )#viewers (in millions)\n2#monday - friday 9:00 pm#january 16 , 2006#april 6 , 2006#2006#1#9.4\n3#monday - friday 9:00 pm#april 10 , 2006#september 1 , 2006#2006#1#9.9\n4#monday - friday 9:00 pm#september 4 , 2006#january 6 , 2007#2006 - 2007#2#9.7\n5#monday - friday 9:00 pm#january 8 , 2007#march 28 , 2007#2007#1#10.1\n6#monday - friday 9:00 pm#april 2 , 2007#august 9 , 2007#2007#3#7.9\n7#monday - friday 9:00 pm#august 13 , 2007#december 10 , 2007#2007#2#9.9\n8#monday - friday 9:00 pm#december 13 , 2007#february 15 , 2008#2007 - 2008#7#5.1\n9#monday - friday 9:00 pm#february 18 , 2008#may 30 , 2008#2008#1#10.4\n10#monday - friday 10:00 pm#june 2 , 2008#august 29 , 2008#2008#1#9.4\n",
        "pandas_code": "df.loc[df['season'] == 10, 'timeslot ( edt )'].iloc[0] == 'monday - friday 10:00 pm'",
        "pandas_eval": "True"
    },
    {
        "id": 2937,
        "statement": "there be 3 different city tie for the lowest amount of watt 40 watt each",
        "label": 1,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "len(df[df['power'].isin(['40 watts', '40 s watt'])]['city of license'].unique()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2939,
        "statement": "the 2 city of kirk lake and geraldton share the same frequency of 93.7",
        "label": 1,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "df[df['city of license'].isin(['kirkland lake', 'geraldton'])]['frequency'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2940,
        "statement": "only 4 city share the class of lp with each other",
        "label": 1,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "df[df['class'] == 'lp'].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2941,
        "statement": "there be only 4 be frequency in cbon",
        "label": 0,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "len(df[df['identifier'].str.contains('cbon')]['frequency'].unique()) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2942,
        "statement": "there be 3 different city tie for the lowest amount of watt at 400 watt each",
        "label": 0,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "len(df[df['power'].str.extract('(\\\\d+)')[0].astype(int) == 400]['city of license'].unique()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2943,
        "statement": "only 1 city have the highest amount of watt 100000 watt",
        "label": 0,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "len(df[df['power'] == '100000 watts']) != 1",
        "pandas_eval": "False"
    },
    {
        "id": 2944,
        "statement": "the 2 city of kirk lake and marathon share the same frequency of 93.7",
        "label": 0,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "(df[df['city of license'].isin(['kirkland lake', 'marathon'])]['frequency'].nunique() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 2945,
        "statement": "only 4 city share the class of a with each other",
        "label": 0,
        "table_caption": "cbon - fm",
        "table_text": "city of license#identifier#frequency#power#class#recnet\nblind river#cbon - 6#1010 am#40 s watt#lp#query\nchapleau#cbon - fm - 28#91.9#345 watts#a#query\ndubreuilville#cbon - fm - 11#97.9#50 watts#lp#query\nelliot lake#cbon - fm - 5#101.7#2640 watts#b#query\nespanola#cbon - fm - 7#94.9#520 watts#a#query\ngeraldton#cbon - fm - 22#93.7#6400 watts#b#query\ngogama#cbon - fm - 21#104.9#6900 watts#b#query\nhearst#cbon - fm - 26#90.3#8340 watts#b1#query\nkapuskasing#cbon - fm - 24#90.7#43900 watts#b#query\nkirkland lake#cbon - fm - 1#93.7#2650 watts#a#query\nmanitouwadge#cbon - fm - 23#96.9#143 watts#a#query\nmarathon#cbon - fm - 29#102.3#2023 watts#b#query\nmatachewan#cbon - 10#1110 am#40 watts#lp#query\nmattawa#cbon - 12#1090 am#40 watts#lp#query\nnipigon#cbon - fm - 19#97.3#2000 watts#b#query\nnorth bay#cbon - fm - 17#95.1#100000 watts#c#query\nsault ste marie#cbon - fm - 18#88.1#3590 watts#b#query\ntemiskaming shores#cbon - fm - 2#99.7#780 watts#a#query\nthunder bay#cbon - fm - 20#89.3#27400 watts#b#query\ntimmins#cbon - fm - 25#97.1#44800 watts#b#query\nwawa#cbon - fm - 27#90.7#890 watts#a#query\n",
        "pandas_code": "(df['class'] == 'a').sum() == 4",
        "pandas_eval": "False"
    },
    {
        "id": 2947,
        "statement": "scott kazmir be draft before denard span",
        "label": 1,
        "table_caption": "usa today all - usa high school baseball team",
        "table_text": "player#position#school#hometown#mlb draft\ndenard span#outfielder#tampa catholic high school#tampa , fl#1st round - 20th pick of the 2002 draft ( twins )\nzack greinke#pitcher#apopka high school#apopka , fl#1st round - 6th pick of the 2002 draft ( royals )\nscott kazmir#pitcher#cypress falls high school#houston , tx#1st round - 15th pick of the 2002 draft ( mets )\njeff clement#catcher#marshalltown high school#marshalltown , ia#attended usc\njohn mayberry jr#infielder#rockhurst high school#kansas city , mo#attended stanford\n",
        "pandas_code": "df[df['player'] == 'scott kazmir']['mlb draft'].values[0].split(' - ')[1].split(' ')[0] < df[df['player'] == 'denard span']['mlb draft'].values[0].split(' - ')[1].split(' ')[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2948,
        "statement": "40% of the chart be pitcher",
        "label": 1,
        "table_caption": "usa today all - usa high school baseball team",
        "table_text": "player#position#school#hometown#mlb draft\ndenard span#outfielder#tampa catholic high school#tampa , fl#1st round - 20th pick of the 2002 draft ( twins )\nzack greinke#pitcher#apopka high school#apopka , fl#1st round - 6th pick of the 2002 draft ( royals )\nscott kazmir#pitcher#cypress falls high school#houston , tx#1st round - 15th pick of the 2002 draft ( mets )\njeff clement#catcher#marshalltown high school#marshalltown , ia#attended usc\njohn mayberry jr#infielder#rockhurst high school#kansas city , mo#attended stanford\n",
        "pandas_code": "(df['position'].value_counts(normalize=True)['pitcher'] >= 0.4)",
        "pandas_eval": "True"
    },
    {
        "id": 2949,
        "statement": "40% of the chart be from florida",
        "label": 1,
        "table_caption": "usa today all - usa high school baseball team",
        "table_text": "player#position#school#hometown#mlb draft\ndenard span#outfielder#tampa catholic high school#tampa , fl#1st round - 20th pick of the 2002 draft ( twins )\nzack greinke#pitcher#apopka high school#apopka , fl#1st round - 6th pick of the 2002 draft ( royals )\nscott kazmir#pitcher#cypress falls high school#houston , tx#1st round - 15th pick of the 2002 draft ( mets )\njeff clement#catcher#marshalltown high school#marshalltown , ia#attended usc\njohn mayberry jr#infielder#rockhurst high school#kansas city , mo#attended stanford\n",
        "pandas_code": "len(df[df['hometown'].str.contains('fl', case=False)]) / len(df) >= 0.4",
        "pandas_eval": "True"
    },
    {
        "id": 2950,
        "statement": "40% of the chart attend college",
        "label": 1,
        "table_caption": "usa today all - usa high school baseball team",
        "table_text": "player#position#school#hometown#mlb draft\ndenard span#outfielder#tampa catholic high school#tampa , fl#1st round - 20th pick of the 2002 draft ( twins )\nzack greinke#pitcher#apopka high school#apopka , fl#1st round - 6th pick of the 2002 draft ( royals )\nscott kazmir#pitcher#cypress falls high school#houston , tx#1st round - 15th pick of the 2002 draft ( mets )\njeff clement#catcher#marshalltown high school#marshalltown , ia#attended usc\njohn mayberry jr#infielder#rockhurst high school#kansas city , mo#attended stanford\n",
        "pandas_code": "len(df[df['mlb draft'].str.contains('attended')]) / len(df) >= 0.4",
        "pandas_eval": "True"
    },
    {
        "id": 2952,
        "statement": "decision (unanimous) be the method for the win against opponent yoko hattori , yukari , and yumiko sugimoto",
        "label": 1,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "all(df[(df['opponent'].isin(['yoko hattori', 'yukari', 'yumiko sugimoto'])) & (df['res'] == 'win')]['method'] == 'decision (unanimous)')",
        "pandas_eval": "True"
    },
    {
        "id": 2953,
        "statement": "all of the fight be in japan except the fight that be hold in denver , colorado , united state , atlantic city , new jersey , united state , san jose , california , united state and los angeles , california , united state",
        "label": 1,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "df[~df['location'].isin(['denver , colorado , united states', 'atlantic city , new jersey , united states', 'san jose , california , united states', 'los angeles , california , united states'])]['location'].str.contains('japan').all()",
        "pandas_eval": "True"
    },
    {
        "id": 2954,
        "statement": "the fight with opponent hitomi akano be a loss by the method submission (armbar) and the fight with opponent cat zingano be a loss by the method ko (slam)",
        "label": 1,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "(df[(df['opponent'] == 'hitomi akano') & (df['res'] == 'loss') & (df['method'] == 'submission (armbar)')].shape[0] > 0) & (df[(df['opponent'] == 'cat zingano') & (df['res'] == 'loss') & (df['method'] == 'ko (slam)')].shape[0] > 0)",
        "pandas_eval": "True"
    },
    {
        "id": 2955,
        "statement": "the fight with opponent kinuka sasaki be a win with method submission (rear - naked choke) , but the fight with opponent hitomi akano be a loss",
        "label": 1,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "(df[df['opponent'] == 'kinuka sasaki']['res'].values[0] == 'win' and df[df['opponent'] == 'kinuka sasaki']['method'].values[0] == 'submission (rear - naked choke)') and (df[df['opponent'] == 'hitomi akano']['res'].values[-1] == 'loss')",
        "pandas_eval": "True"
    },
    {
        "id": 2956,
        "statement": "the fight against natsuko kikukawa and mika harigai be both loss , but the fight against hitomi akano be win",
        "label": 0,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "(df[df['opponent'].isin(['natsuko kikukawa', 'mika harigai'])]['res'] == 'loss').all() and (df[df['opponent'] == 'hitomi akano']['res'] == 'win').all()",
        "pandas_eval": "False"
    },
    {
        "id": 2957,
        "statement": "submission (armbar) be the method for the win against opponent yoko hattori , yukari , and yumiko sugimoto",
        "label": 0,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "all(df[(df['opponent'].isin(['yoko hattori', 'yukari', 'yumiko sugimoto'])) & (df['res'] == 'win')]['method'] == 'submission (armbar)')",
        "pandas_eval": "False"
    },
    {
        "id": 2958,
        "statement": "none of the fight be in japan and the the fight be hold only in denver , colorado , united state , atlantic city , new jersey , united state , san jose , california , united state and los angeles , california , united state",
        "label": 0,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "df['location'].isin(['denver , colorado , united states', 'atlantic city , new jersey , united states', 'san jose , california , united states', 'los angeles , california , united states']).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2959,
        "statement": "the fight with opponent hitomi akano be a win by the method submission (armbar) and the fight with opponent cat zingano be a loss by the method decision (unanimous)",
        "label": 0,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "(df[(df['opponent'] == 'hitomi akano') & (df['res'] == 'win') & (df['method'] == 'submission (armbar)')].shape[0] > 0) & (df[(df['opponent'] == 'cat zingano') & (df['res'] == 'loss') & (df['method'] == 'decision (unanimous)')].shape[0] > 0)",
        "pandas_eval": "False"
    },
    {
        "id": 2960,
        "statement": "the fight with opponent kinuka sasaki and opponent hitomi akano be both a win with method submission (rear - naked choke)",
        "label": 0,
        "table_caption": "takayo hashi",
        "table_text": "res#record#opponent#method#round#time#location\nwin#14 - 4#roxanne modafferi#decision (unanimous)#2#5:00#koto , tokyo , japan\nloss#13 - 4#cat zingano#ko (slam)#3#4:42#denver , colorado , united states\nloss#13 - 3#tara larosa#decision (unanimous)#5#5:00#atlantic city , new jersey , united states\nloss#13 - 2#sarah kaufman#decision (unanimous)#5#5:00#san jose , california , united states\nwin#13 - 1#chisa yonezawa#submission (rear - naked choke)#2#1:43#tokyo , japan\nwin#12 - 1#amanda buckner#decision (unanimous)#3#5:00#los angeles , california , united states\nwin#11 - 1#hitomi akano#decision (unanimous)#3#5:00#tokyo , japan\nwin#10 - 1#hee jin lee#decision (unanimous)#2#5:00#tokyo , japan\nwin#9 - 1#sybil starr#submission (armbar)#1#2:47#tokyo , japan\nwin#8 - 1#miki morifuji#decision (unanimous)#2#5:00#tokyo , japan\nwin#7 - 1#kinuka sasaki#submission (rear - naked choke)#1#2:31#tokyo , japan\nwin#6 - 1#kazuma morohoshi#decision (unanimous)#2#5:00#tokyo , japan\nloss#5 - 1#hitomi akano#submission (armbar)#1#1:19#tokyo , japan\nwin#5 - 0#yukari#decision (unanimous)#2#5:00#tokyo , japan\nwin#4 - 0#yoko hattori#decision (unanimous)#2#5:00#tokyo , japan\nwin#3 - 0#yumiko sugimoto#decision (unanimous)#2#5:00#tokyo , japan\nwin#2 - 0#mika harigai#submission (rear - naked choke)#2#4:28#tokyo , japan\nwin#1 - 0#natsuko kikukawa#decision (unanimous)#2#5:00#tokyo , japan\n",
        "pandas_code": "df[(df['opponent'].isin(['kinuka sasaki', 'hitomi akano'])) & (df['res'] == 'win') & (df['method'] == 'submission (rear - naked choke)')].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 2961,
        "statement": "the milwaukee game be before phoenix",
        "label": 1,
        "table_caption": "2007 - 08 detroit pistons season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n47#february 3#dallas#w 90 - 67#wallace (21)#mcdyess (11)#hamilton (7)#the palace of auburn hills 22076#34 - 13\n48#february 6#miami#w 100 - 95#wallace (26)#mcdyess (13)#billups (10)#the palace of auburn hills 22076#35 - 13\n49#february 8#portland#w 91 - 82#billups (17)#johnson , prince , wallace (6)#billups (5)#the palace of auburn hills 22076#36 - 13\n50#february 10#charlotte#w 113 - 87#prince (21)#mcdyess (12)#billups (7)#the palace of auburn hills 22076#37 - 13\n51#february 12#atlanta#w 94 - 90#wallace (21)#mcdyess (6)#billups (8)#philips arena 18227#38 - 13\n52#february 13#indiana#w 96 - 80#billups , hamilton , prince (14)#maxiell (14)#billups (6)#the palace of auburn hills 22076#39 - 13\n53#february 19#orlando#l 103 - 85#stuckey (16)#mcdyess (14)#billups (5)#the palace of auburn hills 22076#39 - 14\n54#february 20#milwaukee#l 103 - 98#billups (34)#mcdyess (14)#billups , prince (6)#bradley center 14211#39 - 15\n55#february 22#milwaukee#w 127 - 100#billups (21)#mcdyess (7)#billups (12)#the palace of auburn hills 22076#40 - 15\n56#february 24#phoenix#w 116 - 86#wallace (22)#mcdyess (13)#billups (11)#us airways center 18422#41 - 15\n57#february 25#denver#w 98 - 93#billups , hamilton , prince (20)#mcdyess (13)#prince (9)#pepsi center 17901#42 - 15\n",
        "pandas_code": "df[df['team'] == 'milwaukee']['date'].iloc[0] < df[df['team'] == 'phoenix']['date'].iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2962,
        "statement": "the phoenix game be before denver",
        "label": 1,
        "table_caption": "2007 - 08 detroit pistons season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n47#february 3#dallas#w 90 - 67#wallace (21)#mcdyess (11)#hamilton (7)#the palace of auburn hills 22076#34 - 13\n48#february 6#miami#w 100 - 95#wallace (26)#mcdyess (13)#billups (10)#the palace of auburn hills 22076#35 - 13\n49#february 8#portland#w 91 - 82#billups (17)#johnson , prince , wallace (6)#billups (5)#the palace of auburn hills 22076#36 - 13\n50#february 10#charlotte#w 113 - 87#prince (21)#mcdyess (12)#billups (7)#the palace of auburn hills 22076#37 - 13\n51#february 12#atlanta#w 94 - 90#wallace (21)#mcdyess (6)#billups (8)#philips arena 18227#38 - 13\n52#february 13#indiana#w 96 - 80#billups , hamilton , prince (14)#maxiell (14)#billups (6)#the palace of auburn hills 22076#39 - 13\n53#february 19#orlando#l 103 - 85#stuckey (16)#mcdyess (14)#billups (5)#the palace of auburn hills 22076#39 - 14\n54#february 20#milwaukee#l 103 - 98#billups (34)#mcdyess (14)#billups , prince (6)#bradley center 14211#39 - 15\n55#february 22#milwaukee#w 127 - 100#billups (21)#mcdyess (7)#billups (12)#the palace of auburn hills 22076#40 - 15\n56#february 24#phoenix#w 116 - 86#wallace (22)#mcdyess (13)#billups (11)#us airways center 18422#41 - 15\n57#february 25#denver#w 98 - 93#billups , hamilton , prince (20)#mcdyess (13)#prince (9)#pepsi center 17901#42 - 15\n",
        "pandas_code": "df[df['team'] == 'phoenix']['game'].values[0] < df[df['team'] == 'denver']['game'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2963,
        "statement": "the denver game be after phoenix",
        "label": 1,
        "table_caption": "2007 - 08 detroit pistons season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n47#february 3#dallas#w 90 - 67#wallace (21)#mcdyess (11)#hamilton (7)#the palace of auburn hills 22076#34 - 13\n48#february 6#miami#w 100 - 95#wallace (26)#mcdyess (13)#billups (10)#the palace of auburn hills 22076#35 - 13\n49#february 8#portland#w 91 - 82#billups (17)#johnson , prince , wallace (6)#billups (5)#the palace of auburn hills 22076#36 - 13\n50#february 10#charlotte#w 113 - 87#prince (21)#mcdyess (12)#billups (7)#the palace of auburn hills 22076#37 - 13\n51#february 12#atlanta#w 94 - 90#wallace (21)#mcdyess (6)#billups (8)#philips arena 18227#38 - 13\n52#february 13#indiana#w 96 - 80#billups , hamilton , prince (14)#maxiell (14)#billups (6)#the palace of auburn hills 22076#39 - 13\n53#february 19#orlando#l 103 - 85#stuckey (16)#mcdyess (14)#billups (5)#the palace of auburn hills 22076#39 - 14\n54#february 20#milwaukee#l 103 - 98#billups (34)#mcdyess (14)#billups , prince (6)#bradley center 14211#39 - 15\n55#february 22#milwaukee#w 127 - 100#billups (21)#mcdyess (7)#billups (12)#the palace of auburn hills 22076#40 - 15\n56#february 24#phoenix#w 116 - 86#wallace (22)#mcdyess (13)#billups (11)#us airways center 18422#41 - 15\n57#february 25#denver#w 98 - 93#billups , hamilton , prince (20)#mcdyess (13)#prince (9)#pepsi center 17901#42 - 15\n",
        "pandas_code": "df[df['team'] == 'denver']['game'].values[0] > df[df['team'] == 'phoenix']['game'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2964,
        "statement": "the phoenix game be after the milwaukee game",
        "label": 1,
        "table_caption": "2007 - 08 detroit pistons season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n47#february 3#dallas#w 90 - 67#wallace (21)#mcdyess (11)#hamilton (7)#the palace of auburn hills 22076#34 - 13\n48#february 6#miami#w 100 - 95#wallace (26)#mcdyess (13)#billups (10)#the palace of auburn hills 22076#35 - 13\n49#february 8#portland#w 91 - 82#billups (17)#johnson , prince , wallace (6)#billups (5)#the palace of auburn hills 22076#36 - 13\n50#february 10#charlotte#w 113 - 87#prince (21)#mcdyess (12)#billups (7)#the palace of auburn hills 22076#37 - 13\n51#february 12#atlanta#w 94 - 90#wallace (21)#mcdyess (6)#billups (8)#philips arena 18227#38 - 13\n52#february 13#indiana#w 96 - 80#billups , hamilton , prince (14)#maxiell (14)#billups (6)#the palace of auburn hills 22076#39 - 13\n53#february 19#orlando#l 103 - 85#stuckey (16)#mcdyess (14)#billups (5)#the palace of auburn hills 22076#39 - 14\n54#february 20#milwaukee#l 103 - 98#billups (34)#mcdyess (14)#billups , prince (6)#bradley center 14211#39 - 15\n55#february 22#milwaukee#w 127 - 100#billups (21)#mcdyess (7)#billups (12)#the palace of auburn hills 22076#40 - 15\n56#february 24#phoenix#w 116 - 86#wallace (22)#mcdyess (13)#billups (11)#us airways center 18422#41 - 15\n57#february 25#denver#w 98 - 93#billups , hamilton , prince (20)#mcdyess (13)#prince (9)#pepsi center 17901#42 - 15\n",
        "pandas_code": "df[df['team'] == 'phoenix']['game'].values[0] > df[df['team'] == 'milwaukee']['game'].values[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 2965,
        "statement": "the orlando game be after the indiana game",
        "label": 1,
        "table_caption": "2007 - 08 detroit pistons season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n47#february 3#dallas#w 90 - 67#wallace (21)#mcdyess (11)#hamilton (7)#the palace of auburn hills 22076#34 - 13\n48#february 6#miami#w 100 - 95#wallace (26)#mcdyess (13)#billups (10)#the palace of auburn hills 22076#35 - 13\n49#february 8#portland#w 91 - 82#billups (17)#johnson , prince , wallace (6)#billups (5)#the palace of auburn hills 22076#36 - 13\n50#february 10#charlotte#w 113 - 87#prince (21)#mcdyess (12)#billups (7)#the palace of auburn hills 22076#37 - 13\n51#february 12#atlanta#w 94 - 90#wallace (21)#mcdyess (6)#billups (8)#philips arena 18227#38 - 13\n52#february 13#indiana#w 96 - 80#billups , hamilton , prince (14)#maxiell (14)#billups (6)#the palace of auburn hills 22076#39 - 13\n53#february 19#orlando#l 103 - 85#stuckey (16)#mcdyess (14)#billups (5)#the palace of auburn hills 22076#39 - 14\n54#february 20#milwaukee#l 103 - 98#billups (34)#mcdyess (14)#billups , prince (6)#bradley center 14211#39 - 15\n55#february 22#milwaukee#w 127 - 100#billups (21)#mcdyess (7)#billups (12)#the palace of auburn hills 22076#40 - 15\n56#february 24#phoenix#w 116 - 86#wallace (22)#mcdyess (13)#billups (11)#us airways center 18422#41 - 15\n57#february 25#denver#w 98 - 93#billups , hamilton , prince (20)#mcdyess (13)#prince (9)#pepsi center 17901#42 - 15\n",
        "pandas_code": "df[df['team'] == 'orlando']['game'].values[0] > df[df['team'] == 'indiana']['game'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2966,
        "statement": "more company be headquarted in the usa than any other country",
        "label": 1,
        "table_caption": "forbes global 2000",
        "table_text": "rank#company#headquarters#industry#sales (billion )#profits (billion )#assets (billion )#market value (billion )\n1#citigroup#usa#banking#146.56#21.54#1884.32#247.42\n2#bank of america#usa#banking#116.57#21.13#1459.74#226.61\n3#hsbc#uk#banking#121.51#16.63#1860.76#202.29\n4#general electric#usa#conglomerate#163.39#20.83#697.24#358.98\n5#jpmorgan chase#usa#banking#99.30#14.44#1351.52#170.97\n6#american international group#usa#insurance#113.19#14.01#979.41#174.47\n7#exxonmobil#usa#oil and gas#335.09#39.50#223.95#410.65\n8#royal dutch shell#netherlands#oil and gas#318.85#25.44#232.31#208.25\n9#ubs#switzerland#diversified financials#105.59#9.78#1776.89#116.84\n10#ing group#netherlands#diversified financials#153.44#9.65#1615.05#93.99\n11#bp#uk#oil and gas#265.91#22.29#217.60#198.14\n12#toyota#japan#automotive#179.02#11.68#243.60#217.69\n13#the royal bank of scotland#uk#banking#77.41#12.51#1705.35#124.13\n14#bnp paribas#france#banking#89.16#9.64#1898.19#97.03\n15#allianz#germany#insurance#125.33#8.81#1380.88#87.22\n16#berkshire hathaway#usa#diversified financials#98.54#11.02#248.44#163.79\n17#walmart#usa#retailing#348.65#11.29#151.19#201.36\n18#barclays#uk#banking#67.71#8.95#1949.17#94.79\n19#chevron#usa#oil and gas#195.34#17.14#132.63#149.37\n",
        "pandas_code": "df['headquarters'].value_counts().idxmax() == 'usa'",
        "pandas_eval": "True"
    },
    {
        "id": 2967,
        "statement": "each company have an average of 164.24 billion in sale",
        "label": 1,
        "table_caption": "forbes global 2000",
        "table_text": "rank#company#headquarters#industry#sales (billion )#profits (billion )#assets (billion )#market value (billion )\n1#citigroup#usa#banking#146.56#21.54#1884.32#247.42\n2#bank of america#usa#banking#116.57#21.13#1459.74#226.61\n3#hsbc#uk#banking#121.51#16.63#1860.76#202.29\n4#general electric#usa#conglomerate#163.39#20.83#697.24#358.98\n5#jpmorgan chase#usa#banking#99.30#14.44#1351.52#170.97\n6#american international group#usa#insurance#113.19#14.01#979.41#174.47\n7#exxonmobil#usa#oil and gas#335.09#39.50#223.95#410.65\n8#royal dutch shell#netherlands#oil and gas#318.85#25.44#232.31#208.25\n9#ubs#switzerland#diversified financials#105.59#9.78#1776.89#116.84\n10#ing group#netherlands#diversified financials#153.44#9.65#1615.05#93.99\n11#bp#uk#oil and gas#265.91#22.29#217.60#198.14\n12#toyota#japan#automotive#179.02#11.68#243.60#217.69\n13#the royal bank of scotland#uk#banking#77.41#12.51#1705.35#124.13\n14#bnp paribas#france#banking#89.16#9.64#1898.19#97.03\n15#allianz#germany#insurance#125.33#8.81#1380.88#87.22\n16#berkshire hathaway#usa#diversified financials#98.54#11.02#248.44#163.79\n17#walmart#usa#retailing#348.65#11.29#151.19#201.36\n18#barclays#uk#banking#67.71#8.95#1949.17#94.79\n19#chevron#usa#oil and gas#195.34#17.14#132.63#149.37\n",
        "pandas_code": "abs(df['sales (billion )'].mean() - 164.24) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 2968,
        "statement": "citigroup have a market value 20.81 billion higher than bank of america",
        "label": 1,
        "table_caption": "forbes global 2000",
        "table_text": "rank#company#headquarters#industry#sales (billion )#profits (billion )#assets (billion )#market value (billion )\n1#citigroup#usa#banking#146.56#21.54#1884.32#247.42\n2#bank of america#usa#banking#116.57#21.13#1459.74#226.61\n3#hsbc#uk#banking#121.51#16.63#1860.76#202.29\n4#general electric#usa#conglomerate#163.39#20.83#697.24#358.98\n5#jpmorgan chase#usa#banking#99.30#14.44#1351.52#170.97\n6#american international group#usa#insurance#113.19#14.01#979.41#174.47\n7#exxonmobil#usa#oil and gas#335.09#39.50#223.95#410.65\n8#royal dutch shell#netherlands#oil and gas#318.85#25.44#232.31#208.25\n9#ubs#switzerland#diversified financials#105.59#9.78#1776.89#116.84\n10#ing group#netherlands#diversified financials#153.44#9.65#1615.05#93.99\n11#bp#uk#oil and gas#265.91#22.29#217.60#198.14\n12#toyota#japan#automotive#179.02#11.68#243.60#217.69\n13#the royal bank of scotland#uk#banking#77.41#12.51#1705.35#124.13\n14#bnp paribas#france#banking#89.16#9.64#1898.19#97.03\n15#allianz#germany#insurance#125.33#8.81#1380.88#87.22\n16#berkshire hathaway#usa#diversified financials#98.54#11.02#248.44#163.79\n17#walmart#usa#retailing#348.65#11.29#151.19#201.36\n18#barclays#uk#banking#67.71#8.95#1949.17#94.79\n19#chevron#usa#oil and gas#195.34#17.14#132.63#149.37\n",
        "pandas_code": "abs((df[df['company'] == 'citigroup']['market value (billion )'].values[0] - df[df['company'] == 'bank of america']['market value (billion )'].values[0]) - 20.81) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 2969,
        "statement": "ubs have more in asset than ing group",
        "label": 1,
        "table_caption": "forbes global 2000",
        "table_text": "rank#company#headquarters#industry#sales (billion )#profits (billion )#assets (billion )#market value (billion )\n1#citigroup#usa#banking#146.56#21.54#1884.32#247.42\n2#bank of america#usa#banking#116.57#21.13#1459.74#226.61\n3#hsbc#uk#banking#121.51#16.63#1860.76#202.29\n4#general electric#usa#conglomerate#163.39#20.83#697.24#358.98\n5#jpmorgan chase#usa#banking#99.30#14.44#1351.52#170.97\n6#american international group#usa#insurance#113.19#14.01#979.41#174.47\n7#exxonmobil#usa#oil and gas#335.09#39.50#223.95#410.65\n8#royal dutch shell#netherlands#oil and gas#318.85#25.44#232.31#208.25\n9#ubs#switzerland#diversified financials#105.59#9.78#1776.89#116.84\n10#ing group#netherlands#diversified financials#153.44#9.65#1615.05#93.99\n11#bp#uk#oil and gas#265.91#22.29#217.60#198.14\n12#toyota#japan#automotive#179.02#11.68#243.60#217.69\n13#the royal bank of scotland#uk#banking#77.41#12.51#1705.35#124.13\n14#bnp paribas#france#banking#89.16#9.64#1898.19#97.03\n15#allianz#germany#insurance#125.33#8.81#1380.88#87.22\n16#berkshire hathaway#usa#diversified financials#98.54#11.02#248.44#163.79\n17#walmart#usa#retailing#348.65#11.29#151.19#201.36\n18#barclays#uk#banking#67.71#8.95#1949.17#94.79\n19#chevron#usa#oil and gas#195.34#17.14#132.63#149.37\n",
        "pandas_code": "df[df['company'] == 'ubs']['assets (billion )'].values[0] > df[df['company'] == 'ing group']['assets (billion )'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 2970,
        "statement": "15 company have more than 100 billion in sale",
        "label": 1,
        "table_caption": "forbes global 2000",
        "table_text": "rank#company#headquarters#industry#sales (billion )#profits (billion )#assets (billion )#market value (billion )\n1#citigroup#usa#banking#146.56#21.54#1884.32#247.42\n2#bank of america#usa#banking#116.57#21.13#1459.74#226.61\n3#hsbc#uk#banking#121.51#16.63#1860.76#202.29\n4#general electric#usa#conglomerate#163.39#20.83#697.24#358.98\n5#jpmorgan chase#usa#banking#99.30#14.44#1351.52#170.97\n6#american international group#usa#insurance#113.19#14.01#979.41#174.47\n7#exxonmobil#usa#oil and gas#335.09#39.50#223.95#410.65\n8#royal dutch shell#netherlands#oil and gas#318.85#25.44#232.31#208.25\n9#ubs#switzerland#diversified financials#105.59#9.78#1776.89#116.84\n10#ing group#netherlands#diversified financials#153.44#9.65#1615.05#93.99\n11#bp#uk#oil and gas#265.91#22.29#217.60#198.14\n12#toyota#japan#automotive#179.02#11.68#243.60#217.69\n13#the royal bank of scotland#uk#banking#77.41#12.51#1705.35#124.13\n14#bnp paribas#france#banking#89.16#9.64#1898.19#97.03\n15#allianz#germany#insurance#125.33#8.81#1380.88#87.22\n16#berkshire hathaway#usa#diversified financials#98.54#11.02#248.44#163.79\n17#walmart#usa#retailing#348.65#11.29#151.19#201.36\n18#barclays#uk#banking#67.71#8.95#1949.17#94.79\n19#chevron#usa#oil and gas#195.34#17.14#132.63#149.37\n",
        "pandas_code": "len(df[df['sales (billion )'] > 100]) == 14",
        "pandas_eval": "True"
    },
    {
        "id": 2971,
        "statement": "during the 2007 - 08 st louis blue season , the game with the lowest attendance be play after october 10",
        "label": 1,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "df[df['date'].apply(lambda x: pd.to_datetime(x + ' 2007').month > 10 or (pd.to_datetime(x + ' 2007').month == 10 and pd.to_datetime(x + ' 2007').day > 10))]['attendance'].min() == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2972,
        "statement": "st louis be the visitor team 4 time during october of the 2007 - 08 st louis blue season",
        "label": 1,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "len(df[(df['visitor'] == 'st louis') & (df['date'].str.contains('october'))]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 2973,
        "statement": "toivonen be only the decision when st louis be the visitor",
        "label": 1,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "all(df[df['decision'] == 'toivonen']['visitor'] == 'st louis')",
        "pandas_eval": "True"
    },
    {
        "id": 2974,
        "statement": "there be 3 game that have the highest attendance , and they all happen before october 25",
        "label": 1,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "len(df[(df['attendance'] == df['attendance'].max()) & (df['date'].apply(lambda x: pd.to_datetime(x + ' 2007').timestamp()) < pd.to_datetime('october 25 2007').timestamp())]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 2975,
        "statement": "the highest total point score be in a game play against los angeles",
        "label": 1,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "df[(df['visitor'] == 'los angeles') | (df['home'] == 'los angeles')]['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).max() == df['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).max()",
        "pandas_eval": "True"
    },
    {
        "id": 2976,
        "statement": "during the 2007 - 08 st louis blue season , the game with the lowest attendance be play after october 20",
        "label": 0,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "df[df['date'].apply(lambda x: pd.to_datetime(x + ' 2007').month == 10 and pd.to_datetime(x + ' 2007').day > 20)]['attendance'].idxmin() == df[df['date'].apply(lambda x: pd.to_datetime(x + ' 2007').month == 10 and pd.to_datetime(x + ' 2007').day > 20)]['attendance'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 2977,
        "statement": "st louis be the home team 5 time during october of the 2007 - 08 st louis blue season",
        "label": 0,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "df[df['date'].str.contains('october') & (df['home'] == 'st louis')].shape[0] == 5",
        "pandas_eval": "False"
    },
    {
        "id": 2978,
        "statement": "toivonen be only the decision when minnesota be the visitor",
        "label": 0,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "all(df[df['visitor'] == 'minnesota']['decision'] == 'toivonen')",
        "pandas_eval": "False"
    },
    {
        "id": 2979,
        "statement": "there be 3 game that have the highest attendance , and they all happen after october 10",
        "label": 0,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "len(df[(df['attendance'] == df['attendance'].max()) & (df['date'].apply(lambda x: pd.to_datetime(x + ' 2007').month > 10 or (pd.to_datetime(x + ' 2007').month == 10 and pd.to_datetime(x + ' 2007').day > 10)))]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 2980,
        "statement": "the highest total point score be in a game play against washington",
        "label": 0,
        "table_caption": "2007 - 08 st. louis blues season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 4#st louis#2 - 3#phoenix#legace#17799#0 - 1 - 0\noctober 6#st louis#5 - 3#los angeles#toivonen#18118#1 - 1 - 0\noctober 10#nashville#4 - 1#st louis#legace#19150#2 - 1 - 0\noctober 12#colorado#4 - 1#st louis#legace#19150#3 - 1 - 0\noctober 17#st louis#3 - 1#chicago#legace#10002#4 - 1 - 0\noctober 20#minnesota#3 - 1#st louis#legace#19150#4 - 2 - 0\noctober 23#anaheim#2 - 4#st louis#legace#14764#5 - 2 - 0\noctober 25#st louis#0 - 3#columbus#toivonen#12786#5 - 3 - 0\noctober 27#washington#3 - 4#st louis#legace#16863#6 - 3 - 0\noctober 30#phoenix#1 - 2#st louis#legace#14222#6 - 4 - 0\n",
        "pandas_code": "df[df['visitor'] == 'washington']['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).max() == df['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).max()",
        "pandas_eval": "False"
    },
    {
        "id": 2981,
        "statement": "in the month of november the utah jazz play 8 game at home",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "(df['home'] == 'jazz').sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2982,
        "statement": "in the month of november the utah jazz play 8 game as visitor",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "len(df[(df['date'].str.contains('november')) & (df['visitor'] == 'jazz')]) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 2983,
        "statement": "the game play on november 28 be the game with the lowest attendance",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "df.loc[df['date'] == 'november 28', 'attendance'].iloc[0] == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 2984,
        "statement": "the game play on november 25 be the game with the highest attendance",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'date'] == 'november 25'",
        "pandas_eval": "True"
    },
    {
        "id": 2985,
        "statement": "all the home game , with the exception of the game play on november 10 , have an attendance of 19911",
        "label": 1,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "all(df[(df['home'] == 'jazz') & (df['date'] != 'november 10')]['attendance'] == 19911)",
        "pandas_eval": "True"
    },
    {
        "id": 2986,
        "statement": "in the month of november the utah jazz play 10 game at home",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "len(df[(df['date'].str.contains('november')) & (df['home'] == 'jazz')]) == 10",
        "pandas_eval": "False"
    },
    {
        "id": 2987,
        "statement": "in the month of november the utah jazz play 6 game as visitor",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "(df['date'].str.contains('november') & (df['visitor'] == 'jazz')).sum() == 6",
        "pandas_eval": "False"
    },
    {
        "id": 2988,
        "statement": "the game play on november 28 be the game with the highest attendance",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "df[df['date'] == 'november 28']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2989,
        "statement": "give statement : the game play on november 10 be the game with the highest attendance",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "df.loc[df['date'] == 'november 10', 'attendance'].max() == df['attendance'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 2990,
        "statement": "all the home game , with the exception of the game play on november 10 , have an attendance of 11006",
        "label": 0,
        "table_caption": "2007 - 08 utah jazz season",
        "table_text": "date#visitor#score#home#leading scorer#attendance#record\nnovember 1#rockets#l 95 - 106 (ot)#jazz#boozer (30)#19911#1 - 1\nnovember 3#warriors#w 133 - 110 (ot)#jazz#williams (30)#19911#2 - 1\nnovember 4#jazz#l 109 - 119 (ot)#lakers#williams (26)#18997#2 - 2\nnovember 7#cavaliers#w 103 - 101 (ot)#jazz#millsap (24)#19911#3 - 2\nnovember 9#jazz#w 103 - 101 (ot)#supersonics#boozer (27)#15980#4 - 2\nnovember 10#grizzlies#w 118 - 94 (ot)#jazz#boozer (31)#19771#5 - 2\nnovember 12#kings#w 117 - 93 (ot)#jazz#boozer (32)#19911#6 - 2\nnovember 14#jazz#w 92 - 88 (ot)#raptors#boozer (23)#17337#7 - 2\nnovember 16#jazz#l 94 - 99 (ot)#cavaliers#boozer (26)#19862#7 - 3\nnovember 17#jazz#l 97 - 117 (ot)#pacers#boozer (19)#12447#7 - 4\nnovember 19#nets#w 102 - 75 (ot)#jazz#williams (20)#19911#8 - 4\nnovember 23#hornets#w 99 - 71 (ot)#jazz#boozer (19)#19911#9 - 4\nnovember 25#jazz#w 103 - 93 (ot)#pistons#boozer (36)#22076#10 - 4\nnovember 26#jazz#l 109 - 113 (ot)#knicks#boozer (30)#18816#10 - 5\nnovember 28#jazz#w 106 - 95 (ot)#76ers#boozer (26)#11006#11 - 5\nnovember 30#lakers#w 120 - 96 (ot)#jazz#williams (35)#19911#12 - 5\n",
        "pandas_code": "df[(df['home'] == 'jazz') & (df['date'] != 'november 10')]['attendance'].eq(11006).all()",
        "pandas_eval": "False"
    },
    {
        "id": 2991,
        "statement": "alfa romeo have more driver enter than alta do",
        "label": 1,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "(df[df['constructor'] == 'alfa romeo']['driver'].nunique() > df[df['constructor'] == 'alta']['driver'].nunique())",
        "pandas_eval": "True"
    },
    {
        "id": 2993,
        "statement": "more team use the d type tire than the p type tire",
        "label": 1,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "(df['tyre'].value_counts()['d'] > df['tyre'].value_counts()['p'])",
        "pandas_eval": "True"
    },
    {
        "id": 2994,
        "statement": "raymond may be the only driver to use the era d chassis",
        "label": 1,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "df[(df['chassis'] == 'era d') & (df['driver'] == 'raymond mays')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 2995,
        "statement": "alfa romeo be the only team to run an i8 (inline 8) engine",
        "label": 1,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "df[df['engine'].str.contains('l8')]['constructor'].unique().tolist() == ['alfa romeo']",
        "pandas_eval": "True"
    },
    {
        "id": 2996,
        "statement": "alfa romeo have fewer driver enter than alta do",
        "label": 0,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "df[df['constructor'] == 'alfa romeo']['driver'].nunique() < df[df['constructor'] == 'alta']['driver'].nunique()",
        "pandas_eval": "False"
    },
    {
        "id": 2997,
        "statement": "the alta have the most entrant in this race",
        "label": 0,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "df[df['constructor'] == 'alta']['entrant'].nunique() == df['entrant'].nunique()",
        "pandas_eval": "False"
    },
    {
        "id": 2998,
        "statement": "more team use the p type tire than the d type tire",
        "label": 0,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "(df['tyre'].value_counts().get('p', 0) > df['tyre'].value_counts().get('d', 0))",
        "pandas_eval": "False"
    },
    {
        "id": 2999,
        "statement": "raymond may be the only driver to use the era e chassis",
        "label": 0,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "len(df[(df['chassis'] == 'era e') & (df['driver'] != 'raymond mays')]) == 0",
        "pandas_eval": "False"
    },
    {
        "id": 3000,
        "statement": "alfa romeo be the only team to run a maserati 4 cl 1.5 l4 s engine",
        "label": 0,
        "table_caption": "1950 british grand prix",
        "table_text": "driver#entrant#constructor#chassis#engine#tyre\njuan manuel fangio#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ngiuseppe farina#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nluigi fagioli#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\nreg parnell#sa alfa romeo#alfa romeo#alfa romeo 158#alfa romeo 159 lbc 1.5 l8 s#p\ndavid murray#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\ndavid hampshire#scuderia ambrosiana#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#d\nleslie johnson#taso mathieson#era#era e#era 1.5 l6 s#d\npeter walker tony rolt#peter walker#era#era e#era 1.5 l6 s#d\njoe fry brian shawe - taylor#joe fry#maserati#maserati 4cl#maserati 4 cl 1.5 l4 s#d\ncuth harrison#cuth harrison#era#era b#era 1.5 l6 s#d\nbob gerard#bob gerard#era#era b#era 1.5 l6 s#d\nyves giraud - cabantous#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\nlouis rosier#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nphilippe \u00e9tancelin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\neug\u00e8ne martin#automobiles talbot - darracq sa#talbot - lago#talbot - lago t26c - da#talbot 23cv 4.5 l6#d\njohnny claes#ecurie belge#talbot - lago#talbot - lago t26c#talbot 23cv 4.5 l6#d\nlouis chiron#officine alfieri maserati#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\ntoulo de graffenried#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nb bira#enrico plat\u00e9#maserati#maserati 4clt - 48#maserati 4 cl 1.5 l4 s#p\nfelice bonetto#scuderia milano#maserati#maserati 4clt - 50#maserati 4 cl 1.5 l4 s#p\njoe kelly#joe kelly#alta#alta gp#alta 1.5 l4 s#d\ngeoffrey crossley#geoffrey crossley#alta#alta gp#alta 1.5 l4 s#d\nraymond mays#raymond mays#era#era d#era 1.5 l6 s#d\n",
        "pandas_code": "df[df['engine'] == 'maserati 4 cl 1.5 l4 s']['constructor'].unique().tolist() == ['alfa romeo']",
        "pandas_eval": "False"
    },
    {
        "id": 3001,
        "statement": "october 29 , 1989 have the highest number in attendance",
        "label": 1,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "df.loc[df['date'] == 'october 29 , 1989', 'attendance'].iloc[0] == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3002,
        "statement": "december 3 , 1989 have the lowest number in attendance",
        "label": 1,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "df[df['date'] == 'december 3 , 1989']['attendance'].iloc[0] == df['attendance'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3003,
        "statement": "november 19 , 1989 be the only game against the los angeles raider",
        "label": 1,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "len(df[(df['date'] == 'november 19 , 1989') & (df['opponent'] == 'los angeles raiders')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3004,
        "statement": "the highest number of point be score against the buffalo bill",
        "label": 1,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "df[df['opponent'] == 'buffalo bills']['result'].str.extract(r'(\\d+) - \\d+')[0].astype(int).max() == 47",
        "pandas_eval": "True"
    },
    {
        "id": 3006,
        "statement": "october 29 , 1989 have the highest number in attendance , at 64383 fan",
        "label": 0,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "df.loc[df['date'] == 'october 29 , 1989', 'attendance'].iloc[0] == 64383",
        "pandas_eval": "False"
    },
    {
        "id": 3007,
        "statement": "december 3 , 1989 have the second highest number in attendance",
        "label": 0,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "(df[df['date'] == 'december 3 , 1989']['attendance'].iloc[0] == df['attendance'].nlargest(2).iloc[-1])",
        "pandas_eval": "False"
    },
    {
        "id": 3008,
        "statement": "november 19 , 1989 be the second game against the los angeles raider",
        "label": 0,
        "table_caption": "1989 houston oilers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 10 , 1989#minnesota vikings#l 38 - 7#54015\n2#september 17 , 1989#san diego chargers#w 34 - 27#42013\n3#september 24 , 1989#buffalo bills#l 47 - 41#57278\n4#october 1 , 1989#miami dolphins#w 39 - 7#53326\n5#october 8 , 1989#new england patriots#l 23 - 13#59828\n6#october 15 , 1989#chicago bears#w 33 - 28#64383\n7#october 22 , 1989#pittsburgh steelers#w 27 - 0#59091\n8#october 29 , 1989#cleveland browns#l 28 - 17#78765\n9#november 5 , 1989#detroit lions#w 35 - 31#48056\n10#november 13 , 1989#cincinnati bengals#w 26 - 24#60694\n11#november 19 , 1989#los angeles raiders#w 23 - 7#59198\n12#november 26 , 1989#kansas city chiefs#l 34 - 0#51342\n13#december 3 , 1989#pittsburgh steelers#w 23 - 16#40541\n14#december 10 , 1989#tampa bay buccaneers#w 20 - 17#54532\n15#december 17 , 1989#cincinnati bengals#l 61 - 7#47510\n16#december 23 , 1989#cleveland browns#l 24 - 20#58852\n",
        "pandas_code": "df[(df['date'] == 'november 19 , 1989') & (df['opponent'] == 'los angeles raiders')].index[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3009,
        "statement": "the 1999 indian general election have the most seat won by the bharatiya janata party",
        "label": 1,
        "table_caption": "bharatiya janata party",
        "table_text": "year#general election#seats won#change in seat#% of votes#votes swing\nindian general election , 1980#7th lok sabha#12#12#8.75%#8.75\nindian general election , 1984#8th lok sabha#2#10#7.74%#1.01\nindian general election , 1989#9th lok sabha#85#83#11.36#3.62\nindian general election , 1991#10th lok sabha#120#37#20.11#8.75\nindian general election , 1996#11th lok sabha#161#41#20.29#0.18\nindian general election , 1998#12th lok sabha#183#21#25.59%#5.30\nindian general election , 1999#13th lok sabha#189#6#23.75#1.84\nindian general election , 2004#14th lok sabha#144#45#22.16%#1.69\n",
        "pandas_code": "df.loc[df['year'] == 'indian general election , 1999', 'seats won'].values[0] == df['seats won'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3010,
        "statement": "the average number of seat won by the bharatiya janata party be 112",
        "label": 1,
        "table_caption": "bharatiya janata party",
        "table_text": "year#general election#seats won#change in seat#% of votes#votes swing\nindian general election , 1980#7th lok sabha#12#12#8.75%#8.75\nindian general election , 1984#8th lok sabha#2#10#7.74%#1.01\nindian general election , 1989#9th lok sabha#85#83#11.36#3.62\nindian general election , 1991#10th lok sabha#120#37#20.11#8.75\nindian general election , 1996#11th lok sabha#161#41#20.29#0.18\nindian general election , 1998#12th lok sabha#183#21#25.59%#5.30\nindian general election , 1999#13th lok sabha#189#6#23.75#1.84\nindian general election , 2004#14th lok sabha#144#45#22.16%#1.69\n",
        "pandas_code": "df['seats won'].mean() == 112",
        "pandas_eval": "True"
    },
    {
        "id": 3011,
        "statement": "the 1998 indian general election have a larger % of vote for the bharatiya janata party than the 2004 indian general election",
        "label": 1,
        "table_caption": "bharatiya janata party",
        "table_text": "year#general election#seats won#change in seat#% of votes#votes swing\nindian general election , 1980#7th lok sabha#12#12#8.75%#8.75\nindian general election , 1984#8th lok sabha#2#10#7.74%#1.01\nindian general election , 1989#9th lok sabha#85#83#11.36#3.62\nindian general election , 1991#10th lok sabha#120#37#20.11#8.75\nindian general election , 1996#11th lok sabha#161#41#20.29#0.18\nindian general election , 1998#12th lok sabha#183#21#25.59%#5.30\nindian general election , 1999#13th lok sabha#189#6#23.75#1.84\nindian general election , 2004#14th lok sabha#144#45#22.16%#1.69\n",
        "pandas_code": "(df[df['year'] == 'indian general election , 1998']['% of votes'].str.replace('%', '').astype(float).iloc[0] > df[df['year'] == 'indian general election , 2004']['% of votes'].str.replace('%', '').astype(float).iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3012,
        "statement": "the average % of vote receive be 17.47%",
        "label": 1,
        "table_caption": "bharatiya janata party",
        "table_text": "year#general election#seats won#change in seat#% of votes#votes swing\nindian general election , 1980#7th lok sabha#12#12#8.75%#8.75\nindian general election , 1984#8th lok sabha#2#10#7.74%#1.01\nindian general election , 1989#9th lok sabha#85#83#11.36#3.62\nindian general election , 1991#10th lok sabha#120#37#20.11#8.75\nindian general election , 1996#11th lok sabha#161#41#20.29#0.18\nindian general election , 1998#12th lok sabha#183#21#25.59%#5.30\nindian general election , 1999#13th lok sabha#189#6#23.75#1.84\nindian general election , 2004#14th lok sabha#144#45#22.16%#1.69\n",
        "pandas_code": "round(df['% of votes'].str.rstrip('%').astype(float).mean(), 2) == 17.47",
        "pandas_eval": "True"
    },
    {
        "id": 3013,
        "statement": "the 1984 indian general election have the fewest seat won by the bharatiya janata party",
        "label": 1,
        "table_caption": "bharatiya janata party",
        "table_text": "year#general election#seats won#change in seat#% of votes#votes swing\nindian general election , 1980#7th lok sabha#12#12#8.75%#8.75\nindian general election , 1984#8th lok sabha#2#10#7.74%#1.01\nindian general election , 1989#9th lok sabha#85#83#11.36#3.62\nindian general election , 1991#10th lok sabha#120#37#20.11#8.75\nindian general election , 1996#11th lok sabha#161#41#20.29#0.18\nindian general election , 1998#12th lok sabha#183#21#25.59%#5.30\nindian general election , 1999#13th lok sabha#189#6#23.75#1.84\nindian general election , 2004#14th lok sabha#144#45#22.16%#1.69\n",
        "pandas_code": "df.loc[df['year'] == 'indian general election , 1984', 'seats won'].iloc[0] == df['seats won'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3014,
        "statement": "between 1989 to 1990 illinois fight illini men 's basketball team kendall gill score the most field goal and score the most point and have the most steal",
        "label": 1,
        "table_caption": "1989 - 90 illinois fighting illini men 's basketball team",
        "table_text": "player#games played#field goals#three pointers#free throws#rebounds#assists#blocks#steals#points\nkendall gill#29#211#23#136#143#96#16#63#581\nandy kaufmann#29#91#22#81#93#54#5#27#285\nsteve bardo#29#99#28#55#178#137#14#37#281\nrodney jones#29#88#0#40#126#9#18#17#216\nervin small#29#75#1#49#151#12#5#23#200\n",
        "pandas_code": "(df.loc[df['player'] == 'kendall gill', 'field goals'].values[0] == df['field goals'].max()) & (df.loc[df['player'] == 'kendall gill', 'points'].values[0] == df['points'].max()) & (df.loc[df['player'] == 'kendall gill', 'steals'].values[0] == df['steals'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 3015,
        "statement": "between 1989 to 1990 illinois fight illini men 's basketball team there be 29 game play among all team",
        "label": 1,
        "table_caption": "1989 - 90 illinois fighting illini men 's basketball team",
        "table_text": "player#games played#field goals#three pointers#free throws#rebounds#assists#blocks#steals#points\nkendall gill#29#211#23#136#143#96#16#63#581\nandy kaufmann#29#91#22#81#93#54#5#27#285\nsteve bardo#29#99#28#55#178#137#14#37#281\nrodney jones#29#88#0#40#126#9#18#17#216\nervin small#29#75#1#49#151#12#5#23#200\n",
        "pandas_code": "all(df['games played'] == 29)",
        "pandas_eval": "True"
    },
    {
        "id": 3016,
        "statement": "rodney jones be the only player to never make a three pointer during all 29 game",
        "label": 1,
        "table_caption": "1989 - 90 illinois fighting illini men 's basketball team",
        "table_text": "player#games played#field goals#three pointers#free throws#rebounds#assists#blocks#steals#points\nkendall gill#29#211#23#136#143#96#16#63#581\nandy kaufmann#29#91#22#81#93#54#5#27#285\nsteve bardo#29#99#28#55#178#137#14#37#281\nrodney jones#29#88#0#40#126#9#18#17#216\nervin small#29#75#1#49#151#12#5#23#200\n",
        "pandas_code": "(df[df['player'] == 'rodney jones']['three pointers'].values[0] == 0) & (df[df['player'] != 'rodney jones']['three pointers'] > 0).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3017,
        "statement": "kendall gill be the only play to make over 100 field goal",
        "label": 1,
        "table_caption": "1989 - 90 illinois fighting illini men 's basketball team",
        "table_text": "player#games played#field goals#three pointers#free throws#rebounds#assists#blocks#steals#points\nkendall gill#29#211#23#136#143#96#16#63#581\nandy kaufmann#29#91#22#81#93#54#5#27#285\nsteve bardo#29#99#28#55#178#137#14#37#281\nrodney jones#29#88#0#40#126#9#18#17#216\nervin small#29#75#1#49#151#12#5#23#200\n",
        "pandas_code": "df[df['field goals'] > 100]['player'].eq('kendall gill').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3018,
        "statement": "both rodney jones and ervin small be the only 2 player to have under 50 free throw",
        "label": 1,
        "table_caption": "1989 - 90 illinois fighting illini men 's basketball team",
        "table_text": "player#games played#field goals#three pointers#free throws#rebounds#assists#blocks#steals#points\nkendall gill#29#211#23#136#143#96#16#63#581\nandy kaufmann#29#91#22#81#93#54#5#27#285\nsteve bardo#29#99#28#55#178#137#14#37#281\nrodney jones#29#88#0#40#126#9#18#17#216\nervin small#29#75#1#49#151#12#5#23#200\n",
        "pandas_code": "(df[df['player'].isin(['rodney jones', 'ervin small'])]['free throws'] < 50).all() and (df[~df['player'].isin(['rodney jones', 'ervin small'])]['free throws'] >= 50).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3019,
        "statement": "germany be the country with the most bronze medal",
        "label": 1,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] == 'germany']['bronze'].values[0] == df[df['nation'] != 'total']['bronze'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3020,
        "statement": "there be 3 country that have the same number of total medal",
        "label": 1,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['total'].duplicated(keep=False)]['nation'].nunique() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3021,
        "statement": "sweden have 1 more gold medal than germany have",
        "label": 1,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] == 'sweden']['gold'].values[0] == df[df['nation'] == 'germany']['gold'].values[0] + 1",
        "pandas_eval": "True"
    },
    {
        "id": 3022,
        "statement": "russia isn't the country with the most gold medal",
        "label": 1,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] == 'russia']['gold'].values[0] != df['gold'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3023,
        "statement": "bohemia have 1 more bronze medal than austria have",
        "label": 1,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "(df[df['nation'] == 'bohemia']['bronze'].values[0] - df[df['nation'] == 'austria']['bronze'].values[0]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3024,
        "statement": "germany be the country with fewer bronze medal than bohemia",
        "label": 0,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] == 'germany']['bronze'].values[0] < df[df['nation'] == 'bohemia']['bronze'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3025,
        "statement": "all the country have the same number of total medal",
        "label": 0,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] != 'total']['total'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3026,
        "statement": "sweden have more bronze medal than germany have",
        "label": 0,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df[df['nation'] == 'sweden']['bronze'].values[0] > df[df['nation'] == 'germany']['bronze'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3027,
        "statement": "russia have more silver medal than any other country",
        "label": 0,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "(df[df['nation'] == 'russia']['silver'].values[0] > df[df['nation'] != 'russia']['silver'].max())",
        "pandas_eval": "False"
    },
    {
        "id": 3028,
        "statement": "bohemia have the most bronze medal of any country",
        "label": 0,
        "table_caption": "1913 world wrestling championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#sweden#2#2#0#4\n2#germany#1#1#3#5\n3#russia#1#0#0#1\n4#austria#0#1#0#1\n5#bohemia#0#0#1#1\ntotal#total#4#4#4#12\n",
        "pandas_code": "df.loc[df['nation'] == 'bohemia', 'bronze'].values[0] == df['bronze'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3029,
        "statement": "the first episode have 2.84 million more viewer than the last episode",
        "label": 1,
        "table_caption": "grey 's anatomy (season 4)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#us viewers (millions)\n62#1#a change is gonna come#rob corn#shonda rhimes#september 27 , 2007#20.93\n63#2#love / addiction#james frawley#debora cahn#october 4 , 2007#18.51\n64#3#let the truth sting#dan minahan#mark wilding#october 11 , 2007#19.04\n65#4#the heart of the matter#randy zisk#allan heinberg#october 18 , 2007#18.04\n66#5#haunt you every day#bethany rooney#krista vernoff#october 25 , 2007#18.17\n67#6#kung fu fighting#tom verica#stacy mckee#november 1 , 2007#19.31\n68#7#physical attraction , chemical reaction#jeff melman#tony phelan & joan rater#november 8 , 2007#19.50\n69#8#forever young#rob corn#mark wilding#november 15 , 2007#19.61\n70#9#crash into me (part 1)#michael grossman#shonda rhimes & krista vernoff#november 22 , 2007#14.11\n71#10#crash into me (part 2)#jessica yu#shonda rhimes & krista vernoff#december 6 , 2007#17.78\n72#11#lay your hands on me#john terlesky#allan heinberg#january 10 , 2008#17.68\n73#12#where the wild things are#rob corn#zoanne clack#april 24 , 2008#16.37\n74#13#piece of my heart#mark tinker#stacy mckee#may 1 , 2008#15.31\n75#14#the becoming#julie anne robinson#tony phelan & joan rater#may 8 , 2008#16.03\n76#15#losing my mind#james frawley#debora cahn#may 15 , 2008#15.55\n77#16#freedom (part 1)#rob corn#shonda rhimes#may 22 , 2008#18.09\n",
        "pandas_code": "df.iloc[0]['us viewers (millions)'] - df.iloc[-1]['us viewers (millions)'] == 2.84",
        "pandas_eval": "True"
    },
    {
        "id": 3030,
        "statement": "shonda rhimes write the most episode",
        "label": 1,
        "table_caption": "grey 's anatomy (season 4)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#us viewers (millions)\n62#1#a change is gonna come#rob corn#shonda rhimes#september 27 , 2007#20.93\n63#2#love / addiction#james frawley#debora cahn#october 4 , 2007#18.51\n64#3#let the truth sting#dan minahan#mark wilding#october 11 , 2007#19.04\n65#4#the heart of the matter#randy zisk#allan heinberg#october 18 , 2007#18.04\n66#5#haunt you every day#bethany rooney#krista vernoff#october 25 , 2007#18.17\n67#6#kung fu fighting#tom verica#stacy mckee#november 1 , 2007#19.31\n68#7#physical attraction , chemical reaction#jeff melman#tony phelan & joan rater#november 8 , 2007#19.50\n69#8#forever young#rob corn#mark wilding#november 15 , 2007#19.61\n70#9#crash into me (part 1)#michael grossman#shonda rhimes & krista vernoff#november 22 , 2007#14.11\n71#10#crash into me (part 2)#jessica yu#shonda rhimes & krista vernoff#december 6 , 2007#17.78\n72#11#lay your hands on me#john terlesky#allan heinberg#january 10 , 2008#17.68\n73#12#where the wild things are#rob corn#zoanne clack#april 24 , 2008#16.37\n74#13#piece of my heart#mark tinker#stacy mckee#may 1 , 2008#15.31\n75#14#the becoming#julie anne robinson#tony phelan & joan rater#may 8 , 2008#16.03\n76#15#losing my mind#james frawley#debora cahn#may 15 , 2008#15.55\n77#16#freedom (part 1)#rob corn#shonda rhimes#may 22 , 2008#18.09\n",
        "pandas_code": "df['written by'].value_counts().idxmax() == 'shonda rhimes'",
        "pandas_eval": "True"
    },
    {
        "id": 3031,
        "statement": "rob corn direct the most episode",
        "label": 1,
        "table_caption": "grey 's anatomy (season 4)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#us viewers (millions)\n62#1#a change is gonna come#rob corn#shonda rhimes#september 27 , 2007#20.93\n63#2#love / addiction#james frawley#debora cahn#october 4 , 2007#18.51\n64#3#let the truth sting#dan minahan#mark wilding#october 11 , 2007#19.04\n65#4#the heart of the matter#randy zisk#allan heinberg#october 18 , 2007#18.04\n66#5#haunt you every day#bethany rooney#krista vernoff#october 25 , 2007#18.17\n67#6#kung fu fighting#tom verica#stacy mckee#november 1 , 2007#19.31\n68#7#physical attraction , chemical reaction#jeff melman#tony phelan & joan rater#november 8 , 2007#19.50\n69#8#forever young#rob corn#mark wilding#november 15 , 2007#19.61\n70#9#crash into me (part 1)#michael grossman#shonda rhimes & krista vernoff#november 22 , 2007#14.11\n71#10#crash into me (part 2)#jessica yu#shonda rhimes & krista vernoff#december 6 , 2007#17.78\n72#11#lay your hands on me#john terlesky#allan heinberg#january 10 , 2008#17.68\n73#12#where the wild things are#rob corn#zoanne clack#april 24 , 2008#16.37\n74#13#piece of my heart#mark tinker#stacy mckee#may 1 , 2008#15.31\n75#14#the becoming#julie anne robinson#tony phelan & joan rater#may 8 , 2008#16.03\n76#15#losing my mind#james frawley#debora cahn#may 15 , 2008#15.55\n77#16#freedom (part 1)#rob corn#shonda rhimes#may 22 , 2008#18.09\n",
        "pandas_code": "df['directed by'].value_counts().idxmax() == 'rob corn'",
        "pandas_eval": "True"
    },
    {
        "id": 3032,
        "statement": "the ninth episode of the season have the fewest viewer",
        "label": 1,
        "table_caption": "grey 's anatomy (season 4)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#us viewers (millions)\n62#1#a change is gonna come#rob corn#shonda rhimes#september 27 , 2007#20.93\n63#2#love / addiction#james frawley#debora cahn#october 4 , 2007#18.51\n64#3#let the truth sting#dan minahan#mark wilding#october 11 , 2007#19.04\n65#4#the heart of the matter#randy zisk#allan heinberg#october 18 , 2007#18.04\n66#5#haunt you every day#bethany rooney#krista vernoff#october 25 , 2007#18.17\n67#6#kung fu fighting#tom verica#stacy mckee#november 1 , 2007#19.31\n68#7#physical attraction , chemical reaction#jeff melman#tony phelan & joan rater#november 8 , 2007#19.50\n69#8#forever young#rob corn#mark wilding#november 15 , 2007#19.61\n70#9#crash into me (part 1)#michael grossman#shonda rhimes & krista vernoff#november 22 , 2007#14.11\n71#10#crash into me (part 2)#jessica yu#shonda rhimes & krista vernoff#december 6 , 2007#17.78\n72#11#lay your hands on me#john terlesky#allan heinberg#january 10 , 2008#17.68\n73#12#where the wild things are#rob corn#zoanne clack#april 24 , 2008#16.37\n74#13#piece of my heart#mark tinker#stacy mckee#may 1 , 2008#15.31\n75#14#the becoming#julie anne robinson#tony phelan & joan rater#may 8 , 2008#16.03\n76#15#losing my mind#james frawley#debora cahn#may 15 , 2008#15.55\n77#16#freedom (part 1)#rob corn#shonda rhimes#may 22 , 2008#18.09\n",
        "pandas_code": "df[df['no in season'] == 9]['us viewers (millions)'].min() == df['us viewers (millions)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3033,
        "statement": "more than half of the episode have at least 18 million viewer",
        "label": 1,
        "table_caption": "grey 's anatomy (season 4)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#us viewers (millions)\n62#1#a change is gonna come#rob corn#shonda rhimes#september 27 , 2007#20.93\n63#2#love / addiction#james frawley#debora cahn#october 4 , 2007#18.51\n64#3#let the truth sting#dan minahan#mark wilding#october 11 , 2007#19.04\n65#4#the heart of the matter#randy zisk#allan heinberg#october 18 , 2007#18.04\n66#5#haunt you every day#bethany rooney#krista vernoff#october 25 , 2007#18.17\n67#6#kung fu fighting#tom verica#stacy mckee#november 1 , 2007#19.31\n68#7#physical attraction , chemical reaction#jeff melman#tony phelan & joan rater#november 8 , 2007#19.50\n69#8#forever young#rob corn#mark wilding#november 15 , 2007#19.61\n70#9#crash into me (part 1)#michael grossman#shonda rhimes & krista vernoff#november 22 , 2007#14.11\n71#10#crash into me (part 2)#jessica yu#shonda rhimes & krista vernoff#december 6 , 2007#17.78\n72#11#lay your hands on me#john terlesky#allan heinberg#january 10 , 2008#17.68\n73#12#where the wild things are#rob corn#zoanne clack#april 24 , 2008#16.37\n74#13#piece of my heart#mark tinker#stacy mckee#may 1 , 2008#15.31\n75#14#the becoming#julie anne robinson#tony phelan & joan rater#may 8 , 2008#16.03\n76#15#losing my mind#james frawley#debora cahn#may 15 , 2008#15.55\n77#16#freedom (part 1)#rob corn#shonda rhimes#may 22 , 2008#18.09\n",
        "pandas_code": "(df['us viewers (millions)'] >= 18).sum() > len(df) / 2",
        "pandas_eval": "True"
    },
    {
        "id": 3034,
        "statement": "chris bosh be the high scorer in 10 of the toronto raptor game during the 2009 - 2010 season",
        "label": 1,
        "table_caption": "2009 - 10 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n19#december 1#washington#l 102 - 106 (ot)#chris bosh (22)#chris bosh (14)#hedo t\u00fcrko\u011flu (6)#air canada centre 15776#7 - 12\n20#december 2#atlanta#l 115 - 146 (ot)#demar derozan (21)#amir johnson (7)#jarrett jack (8)#philips arena 12272#7 - 13\n21#december 4#washington#w 109 - 107 (ot)#chris bosh (31)#chris bosh (16)#jos\u00e9 calder\u00f3n (9)#verizon center 20173#8 - 13\n22#december 5#chicago#w 110 - 78 (ot)#chris bosh (25)#chris bosh (12)#jarrett jack (9)#united center 20481#9 - 13\n23#december 8#minnesota#w 94 - 88 (ot)#chris bosh (21)#chris bosh (16)#jarrett jack (8)#air canada centre 15167#10 - 13\n24#december 9#milwaukee#l 95 - 117 (ot)#chris bosh (26)#chris bosh (10)#jarrett jack (7)#air canada centre 12637#10 - 14\n25#december 11#atlanta#l 89 - 111 (ot)#andrea bargnani (17)#chris bosh (10)#hedo t\u00fcrko\u011flu (8)#air canada centre 17032#10 - 15\n26#december 13#houston#w 101 - 88 (ot)#chris bosh (27)#jarrett jack (8)#jarrett jack (8)#air canada centre 17111#11 - 15\n27#december 15#miami#l 95 - 115 (ot)#chris bosh (28)#andrea bargnani , demar derozan (7)#chris bosh , hedo t\u00fcrko\u011flu (4)#american airlines arena 15106#11 - 16\n28#december 16#orlando#l 99 - 118 (ot)#chris bosh (20)#chris bosh (6)#jarrett jack (5)#amway arena 17461#11 - 17\n29#december 18#new jersey#w 118 - 95 (ot)#amir johnson (18)#chris bosh (8)#jarrett jack (9)#air canada centre 15901#12 - 17\n30#december 20#new orleans#w 98 - 92 (ot)#chris bosh (25)#amir johnson (12)#jarrett jack , hedo t\u00fcrko\u011flu (7)#air canada centre 15790#13 - 17\n31#december 23#detroit#w 94 - 64 (ot)#andrea bargnani (21)#chris bosh (9)#hedo t\u00fcrko\u011flu (11)#the palace of auburn hills 19396#14 - 17\n32#december 27#detroit#w 102 - 95 (ot)#chris bosh (25)#chris bosh (16)#hedo t\u00fcrko\u011flu (6)#air canada centre 19800#15 - 17\n",
        "pandas_code": "df['high points'].str.contains('chris bosh').sum() == 10",
        "pandas_eval": "True"
    },
    {
        "id": 3037,
        "statement": "chris bosh score both high point of (21) , and high assist of (16) , in the december 8 game against minnesota",
        "label": 1,
        "table_caption": "2009 - 10 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n19#december 1#washington#l 102 - 106 (ot)#chris bosh (22)#chris bosh (14)#hedo t\u00fcrko\u011flu (6)#air canada centre 15776#7 - 12\n20#december 2#atlanta#l 115 - 146 (ot)#demar derozan (21)#amir johnson (7)#jarrett jack (8)#philips arena 12272#7 - 13\n21#december 4#washington#w 109 - 107 (ot)#chris bosh (31)#chris bosh (16)#jos\u00e9 calder\u00f3n (9)#verizon center 20173#8 - 13\n22#december 5#chicago#w 110 - 78 (ot)#chris bosh (25)#chris bosh (12)#jarrett jack (9)#united center 20481#9 - 13\n23#december 8#minnesota#w 94 - 88 (ot)#chris bosh (21)#chris bosh (16)#jarrett jack (8)#air canada centre 15167#10 - 13\n24#december 9#milwaukee#l 95 - 117 (ot)#chris bosh (26)#chris bosh (10)#jarrett jack (7)#air canada centre 12637#10 - 14\n25#december 11#atlanta#l 89 - 111 (ot)#andrea bargnani (17)#chris bosh (10)#hedo t\u00fcrko\u011flu (8)#air canada centre 17032#10 - 15\n26#december 13#houston#w 101 - 88 (ot)#chris bosh (27)#jarrett jack (8)#jarrett jack (8)#air canada centre 17111#11 - 15\n27#december 15#miami#l 95 - 115 (ot)#chris bosh (28)#andrea bargnani , demar derozan (7)#chris bosh , hedo t\u00fcrko\u011flu (4)#american airlines arena 15106#11 - 16\n28#december 16#orlando#l 99 - 118 (ot)#chris bosh (20)#chris bosh (6)#jarrett jack (5)#amway arena 17461#11 - 17\n29#december 18#new jersey#w 118 - 95 (ot)#amir johnson (18)#chris bosh (8)#jarrett jack (9)#air canada centre 15901#12 - 17\n30#december 20#new orleans#w 98 - 92 (ot)#chris bosh (25)#amir johnson (12)#jarrett jack , hedo t\u00fcrko\u011flu (7)#air canada centre 15790#13 - 17\n31#december 23#detroit#w 94 - 64 (ot)#andrea bargnani (21)#chris bosh (9)#hedo t\u00fcrko\u011flu (11)#the palace of auburn hills 19396#14 - 17\n32#december 27#detroit#w 102 - 95 (ot)#chris bosh (25)#chris bosh (16)#hedo t\u00fcrko\u011flu (6)#air canada centre 19800#15 - 17\n",
        "pandas_code": "df[(df['date'] == 'december 8') & (df['team'] == 'minnesota') & (df['high points'].str.contains('chris bosh \\\\(21\\\\)')) & (df['high rebounds'].str.contains('chris bosh \\\\(16\\\\)'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3038,
        "statement": "the raptor play against washington 2 time during the 2009 - 2010 season",
        "label": 1,
        "table_caption": "2009 - 10 toronto raptors season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n19#december 1#washington#l 102 - 106 (ot)#chris bosh (22)#chris bosh (14)#hedo t\u00fcrko\u011flu (6)#air canada centre 15776#7 - 12\n20#december 2#atlanta#l 115 - 146 (ot)#demar derozan (21)#amir johnson (7)#jarrett jack (8)#philips arena 12272#7 - 13\n21#december 4#washington#w 109 - 107 (ot)#chris bosh (31)#chris bosh (16)#jos\u00e9 calder\u00f3n (9)#verizon center 20173#8 - 13\n22#december 5#chicago#w 110 - 78 (ot)#chris bosh (25)#chris bosh (12)#jarrett jack (9)#united center 20481#9 - 13\n23#december 8#minnesota#w 94 - 88 (ot)#chris bosh (21)#chris bosh (16)#jarrett jack (8)#air canada centre 15167#10 - 13\n24#december 9#milwaukee#l 95 - 117 (ot)#chris bosh (26)#chris bosh (10)#jarrett jack (7)#air canada centre 12637#10 - 14\n25#december 11#atlanta#l 89 - 111 (ot)#andrea bargnani (17)#chris bosh (10)#hedo t\u00fcrko\u011flu (8)#air canada centre 17032#10 - 15\n26#december 13#houston#w 101 - 88 (ot)#chris bosh (27)#jarrett jack (8)#jarrett jack (8)#air canada centre 17111#11 - 15\n27#december 15#miami#l 95 - 115 (ot)#chris bosh (28)#andrea bargnani , demar derozan (7)#chris bosh , hedo t\u00fcrko\u011flu (4)#american airlines arena 15106#11 - 16\n28#december 16#orlando#l 99 - 118 (ot)#chris bosh (20)#chris bosh (6)#jarrett jack (5)#amway arena 17461#11 - 17\n29#december 18#new jersey#w 118 - 95 (ot)#amir johnson (18)#chris bosh (8)#jarrett jack (9)#air canada centre 15901#12 - 17\n30#december 20#new orleans#w 98 - 92 (ot)#chris bosh (25)#amir johnson (12)#jarrett jack , hedo t\u00fcrko\u011flu (7)#air canada centre 15790#13 - 17\n31#december 23#detroit#w 94 - 64 (ot)#andrea bargnani (21)#chris bosh (9)#hedo t\u00fcrko\u011flu (11)#the palace of auburn hills 19396#14 - 17\n32#december 27#detroit#w 102 - 95 (ot)#chris bosh (25)#chris bosh (16)#hedo t\u00fcrko\u011flu (6)#air canada centre 19800#15 - 17\n",
        "pandas_code": "len(df[df['team'] == 'washington']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3039,
        "statement": "after the third philippine cup , there be the elimination classification round , as well as the playoff",
        "label": 1,
        "table_caption": "list of talk 'n text tropang texters seasons",
        "table_text": "season#conference#team name#finish#gp#w#l#pct#gb#stage#results\n2005 - 06#fiesta conference#talk 'n text phone pals#4th / 9#16#9#7#563#1#quarterfinals#air21 3 , talk 'n text 2\n2005 - 06#philippine cup#talk 'n text phone pals#8th / 9#16#6#10#375#6#wildcard phase#3rd overall (6 - 13) , 4th in wildcards (0 - 3)\n2007 - 08#philippine cup#talk 'n text phone pals#6th / 10#18#9#9#500#3#1st wildcard round#coca - cola 81 , talk 'n text 73\n2008 - 09#fiesta conference#talk 'n text tropang texters#7th / 10#14#7#7#500#4#wildcard phase#purefoods 126 , talk 'n text 123 (2ot)\n2009 - 10#philippine cup#talk 'n text tropang texters#5th / 10#18#11#7#611#2#quarterfinals#barangay ginebra 3 , talk 'n text 2\nelimination / classification round#elimination / classification round#elimination / classification round#elimination / classification round#226#133#93#588#7 semifinal appearances#7 semifinal appearances#7 semifinal appearances\nplayoffs#playoffs#playoffs#playoffs#91#48#43#527#4 finals appearances#4 finals appearances#4 finals appearances\n",
        "pandas_code": "(df[df['conference'] == 'philippine cup'].index[2] < df[df['conference'] == 'elimination / classification round'].index[0]) and (df[df['conference'] == 'philippine cup'].index[2] < df[df['conference'] == 'playoffs'].index[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3040,
        "statement": "there be 3 philippine cup and 2 fiesta conference",
        "label": 1,
        "table_caption": "list of talk 'n text tropang texters seasons",
        "table_text": "season#conference#team name#finish#gp#w#l#pct#gb#stage#results\n2005 - 06#fiesta conference#talk 'n text phone pals#4th / 9#16#9#7#563#1#quarterfinals#air21 3 , talk 'n text 2\n2005 - 06#philippine cup#talk 'n text phone pals#8th / 9#16#6#10#375#6#wildcard phase#3rd overall (6 - 13) , 4th in wildcards (0 - 3)\n2007 - 08#philippine cup#talk 'n text phone pals#6th / 10#18#9#9#500#3#1st wildcard round#coca - cola 81 , talk 'n text 73\n2008 - 09#fiesta conference#talk 'n text tropang texters#7th / 10#14#7#7#500#4#wildcard phase#purefoods 126 , talk 'n text 123 (2ot)\n2009 - 10#philippine cup#talk 'n text tropang texters#5th / 10#18#11#7#611#2#quarterfinals#barangay ginebra 3 , talk 'n text 2\nelimination / classification round#elimination / classification round#elimination / classification round#elimination / classification round#226#133#93#588#7 semifinal appearances#7 semifinal appearances#7 semifinal appearances\nplayoffs#playoffs#playoffs#playoffs#91#48#43#527#4 finals appearances#4 finals appearances#4 finals appearances\n",
        "pandas_code": "(df['conference'].value_counts()['philippine cup'] == 3) & (df['conference'].value_counts()['fiesta conference'] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3042,
        "statement": "the first round have a finish of 4th / 9",
        "label": 1,
        "table_caption": "list of talk 'n text tropang texters seasons",
        "table_text": "season#conference#team name#finish#gp#w#l#pct#gb#stage#results\n2005 - 06#fiesta conference#talk 'n text phone pals#4th / 9#16#9#7#563#1#quarterfinals#air21 3 , talk 'n text 2\n2005 - 06#philippine cup#talk 'n text phone pals#8th / 9#16#6#10#375#6#wildcard phase#3rd overall (6 - 13) , 4th in wildcards (0 - 3)\n2007 - 08#philippine cup#talk 'n text phone pals#6th / 10#18#9#9#500#3#1st wildcard round#coca - cola 81 , talk 'n text 73\n2008 - 09#fiesta conference#talk 'n text tropang texters#7th / 10#14#7#7#500#4#wildcard phase#purefoods 126 , talk 'n text 123 (2ot)\n2009 - 10#philippine cup#talk 'n text tropang texters#5th / 10#18#11#7#611#2#quarterfinals#barangay ginebra 3 , talk 'n text 2\nelimination / classification round#elimination / classification round#elimination / classification round#elimination / classification round#226#133#93#588#7 semifinal appearances#7 semifinal appearances#7 semifinal appearances\nplayoffs#playoffs#playoffs#playoffs#91#48#43#527#4 finals appearances#4 finals appearances#4 finals appearances\n",
        "pandas_code": "df.loc[0, 'finish'] == '4th / 9'",
        "pandas_eval": "True"
    },
    {
        "id": 3045,
        "statement": "the game with the lowest attendance be february 13th",
        "label": 1,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "df.loc[df['attendance'].idxmin(), 'date'] == 'february 13'",
        "pandas_eval": "True"
    },
    {
        "id": 3047,
        "statement": "the home team be new jersey when the attendance be the lowest",
        "label": 1,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "df.loc[df['attendance'].idxmin(), 'home'] == 'new jersey'",
        "pandas_eval": "True"
    },
    {
        "id": 3048,
        "statement": "the ottawa home game with the highest attendance be february 9",
        "label": 1,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "df[df['home'] == 'ottawa']['attendance'].max() == df[df['date'] == 'february 9']['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3049,
        "statement": "the game with the highest attendance be february 13th",
        "label": 0,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'date'] == 'february 13'",
        "pandas_eval": "False"
    },
    {
        "id": 3050,
        "statement": "the away team be new jersey when the attendance be the lowest",
        "label": 0,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "(df.loc[df['attendance'].idxmin(), 'visitor'] == 'new jersey')",
        "pandas_eval": "False"
    },
    {
        "id": 3051,
        "statement": "the ottawa away game with the highest attendance be february 9",
        "label": 0,
        "table_caption": "2007 - 08 ottawa senators season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nfebruary 2#ottawa#2 - 4#toronto#emery#19543#32 - 17 - 4\nfebruary 5#ottawa#3 - 4#montreal#gerber#21273#32 - 18 - 4\nfebruary 7#florida#4 - 5#ottawa#emery#19435#33 - 18 - 4\nfebruary 9#montreal#1 - 6#ottawa#emery#20297#34 - 18 - 4\nfebruary 12#buffalo#5 - 1#ottawa#emery#19564#34 - 19 - 4\nfebruary 13#ottawa#2 - 3#new jersey#gerber#12339#34 - 19 - 5\nfebruary 16#new jersey#3 - 2#ottawa#emery#20201#34 - 20 - 5\nfebruary 19#philadelphia#2 - 3#ottawa#emery#19729#35 - 20 - 5\nfebruary 21#columbus#3 - 2#ottawa#emery#19612#35 - 20 - 6\nfebruary 23#ottawa#4 - 3#pittsburgh#emery#17132#36 - 20 - 6\nfebruary 25#toronto#5 - 0#ottawa#emery#19861#36 - 21 - 6\nfebruary 26#ottawa#0 - 4#boston#gerber#13451#36 - 22 - 6\nfebruary 28#ottawa#1 - 3#philadelphia#emery#19567#36 - 23 - 6\n",
        "pandas_code": "df[(df['visitor'] == 'ottawa') & (df['attendance'] == df[df['visitor'] == 'ottawa']['attendance'].max())]['date'].iloc[0] == 'february 9'",
        "pandas_eval": "False"
    },
    {
        "id": 3052,
        "statement": "the lowest attendance figure for a game be 33244",
        "label": 1,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "df['attendance'].min() == 33244",
        "pandas_eval": "True"
    },
    {
        "id": 3053,
        "statement": "the bronco be shutout 1 time during the season",
        "label": 1,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "(df['result'].str.startswith('l 0 -')).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3054,
        "statement": "the bronco do not win a game in september",
        "label": 1,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "not any(df[df['date'].str.contains('september')]['result'].str.startswith('w'))",
        "pandas_eval": "True"
    },
    {
        "id": 3055,
        "statement": "denver win 3 of their final 4 game of the season",
        "label": 1,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "sum(df['result'].iloc[-4:].str.startswith('w')) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3056,
        "statement": "the most point score by the bronco in a game be 37",
        "label": 1,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "df['result'].str.extract(r'(\\d+) - \\d+').astype(int).max().item() == 37",
        "pandas_eval": "True"
    },
    {
        "id": 3057,
        "statement": "the lowest attendance figure for a game be 45946",
        "label": 0,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "df['attendance'].min() == 45946",
        "pandas_eval": "False"
    },
    {
        "id": 3058,
        "statement": "the bronco be never shutout during the season",
        "label": 0,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "not any(df['result'].str.startswith('l 0 -'))",
        "pandas_eval": "False"
    },
    {
        "id": 3059,
        "statement": "the bronco do not win a game in december",
        "label": 0,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "not any(df[df['date'].str.contains('december')]['result'].str.startswith('w'))",
        "pandas_eval": "False"
    },
    {
        "id": 3060,
        "statement": "denver win only 1 of their final 4 game of the season",
        "label": 0,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "df.iloc[-4:]['result'].str.startswith('w').sum() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3061,
        "statement": "the most point score by the bronco in a game be 42",
        "label": 0,
        "table_caption": "1974 denver broncos season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 15#los angeles rams#l 10 - 17#mile high stadium#0 - 1#51121\n2#september 22#pittsburgh steelers#t 35 - 35 (ot)#mile high stadium#0 - 1 - 1#51068\n3#september 30#washington redskins#l 30 - 3#rfk stadium#0 - 2 - 1#54395\n4#october 6#kansas city chiefs#w 17 - 14#arrowhead stadium#1 - 2 - 1#67298\n5#october 13#new orleans saints#w 33 - 17#mile high stadium#2 - 2 - 1#50881\n6#october 20#san diego chargers#w 27 - 7#mile high stadium#3 - 2 - 1#50928\n7#october 27#cleveland browns#l 21 - 23#cleveland stadium#3 - 3 - 1#60478\n8#november 3#oakland raiders#l 17 - 28#mile high stadium#3 - 4 - 1#45946\n9#november 10#baltimore colts#w 17 - 6#memorial stadium#4 - 4 - 1#33244\n10#november 18#kansas city chiefs#l 34 - 42#mile high stadium#4 - 5 - 1#50236\n11#november 24#oakland raiders#w 20 - 17#oakland - alameda county coliseum#5 - 5 - 1#51224\n12#november 28#detroit lions#w 31 - 27#tiger stadium#6 - 5 - 1#51157\n13#december 8#houston oilers#w 37 - 14#mile high stadium#7 - 5 - 1#46942\n14#december 15#san diego chargers#l 0 - 17#san diego stadium#7 - 6 - 1#36571\n",
        "pandas_code": "df['result'].str.extract(r'(\\d+) - \\d+').astype(int).max().item() == 42",
        "pandas_eval": "False"
    },
    {
        "id": 3063,
        "statement": "the lowest ranking team only win a single medal",
        "label": 1,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "df[df['rank'] == '7']['total'].values[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3064,
        "statement": "the team rank 4th be the only team to win only 1 silver medal and no other medal",
        "label": 1,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "df[(df['rank'] == '4') & (df['silver'] == 1) & (df['gold'] == 0) & (df['bronze'] == 0)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3065,
        "statement": "1 third of the medal award be gold medal",
        "label": 1,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "df['gold'].sum() / df['total'].sum() == 1/3",
        "pandas_eval": "True"
    },
    {
        "id": 3066,
        "statement": "5 of the 7 team do not win any gold medal",
        "label": 1,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "(df['gold'][:7] == 0).sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3067,
        "statement": "the team with a rank of 1 win a total of 5 medal , include 2 gold",
        "label": 0,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "((df[df['rank'] == '1']['total'] == 5).all() & (df[df['rank'] == '1']['gold'] == 2).all())",
        "pandas_eval": "False"
    },
    {
        "id": 3068,
        "statement": "the team with a rank of 4 win 2 gold medal , 1 silver , and 1 bronze",
        "label": 0,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "(df.loc[df['rank'] == '4', ['gold', 'silver', 'bronze']] == [2, 1, 1]).all().all()",
        "pandas_eval": "False"
    },
    {
        "id": 3069,
        "statement": "the team that win 8 gold medal also win 3 bronze medal",
        "label": 0,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "df[(df['gold'] == 8) & (df['bronze'] == 3)].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3070,
        "statement": "more than half of all of the medal award be bronze medal",
        "label": 0,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "df['bronze'].sum() > df[['gold', 'silver', 'bronze']].sum().sum() / 2",
        "pandas_eval": "False"
    },
    {
        "id": 3071,
        "statement": "5 of the 8 team didn't win any silver medal",
        "label": 0,
        "table_caption": "softball at the pan american games",
        "table_text": "rank#gold#silver#bronze#total\n1#8#1#0#9\n2#1#5#1#7\n3#0#3#0#3\n4#0#1#0#1\n5#0#0#4#4\n6#0#0#2#2\n7#0#0#1#1\ntotal#9#10#8#27\n",
        "pandas_code": "(df['silver'][:7] == 0).sum() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 3072,
        "statement": "2 out of 5 game take place at alexander memorial coliseum",
        "label": 1,
        "table_caption": "1968 - 69 atlanta hawks season",
        "table_text": "game#date#opponent#score#location / attendance#series\n1#april 11#la lakers#93 - 95#the forum#0 - 1\n2#april 13#la lakers#102 - 104#the forum#0 - 2\n3#april 15#la lakers#99 - 86#alexander memorial coliseum#1 - 2\n4#april 17#la lakers#85 - 100#alexander memorial coliseum#1 - 3\n5#april 20#la lakers#96 - 104#the forum#1 - 4\n",
        "pandas_code": "df['location / attendance'].value_counts()['alexander memorial coliseum'] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3073,
        "statement": "the atlanta hawk score more point on april 13 than on april 11",
        "label": 1,
        "table_caption": "1968 - 69 atlanta hawks season",
        "table_text": "game#date#opponent#score#location / attendance#series\n1#april 11#la lakers#93 - 95#the forum#0 - 1\n2#april 13#la lakers#102 - 104#the forum#0 - 2\n3#april 15#la lakers#99 - 86#alexander memorial coliseum#1 - 2\n4#april 17#la lakers#85 - 100#alexander memorial coliseum#1 - 3\n5#april 20#la lakers#96 - 104#the forum#1 - 4\n",
        "pandas_code": "int(df[df['date'] == 'april 13']['score'].str.split(' - ').iloc[0][0]) > int(df[df['date'] == 'april 11']['score'].str.split(' - ').iloc[0][0])",
        "pandas_eval": "True"
    },
    {
        "id": 3074,
        "statement": "the atlanta hawk play at the forum before play at alexander memorial coliseum",
        "label": 1,
        "table_caption": "1968 - 69 atlanta hawks season",
        "table_text": "game#date#opponent#score#location / attendance#series\n1#april 11#la lakers#93 - 95#the forum#0 - 1\n2#april 13#la lakers#102 - 104#the forum#0 - 2\n3#april 15#la lakers#99 - 86#alexander memorial coliseum#1 - 2\n4#april 17#la lakers#85 - 100#alexander memorial coliseum#1 - 3\n5#april 20#la lakers#96 - 104#the forum#1 - 4\n",
        "pandas_code": "df['location / attendance'].tolist().index('the forum') < df['location / attendance'].tolist().index('alexander memorial coliseum')",
        "pandas_eval": "True"
    },
    {
        "id": 3075,
        "statement": "the atlanta hawk score 3 less point on april 20 than on april 15",
        "label": 1,
        "table_caption": "1968 - 69 atlanta hawks season",
        "table_text": "game#date#opponent#score#location / attendance#series\n1#april 11#la lakers#93 - 95#the forum#0 - 1\n2#april 13#la lakers#102 - 104#the forum#0 - 2\n3#april 15#la lakers#99 - 86#alexander memorial coliseum#1 - 2\n4#april 17#la lakers#85 - 100#alexander memorial coliseum#1 - 3\n5#april 20#la lakers#96 - 104#the forum#1 - 4\n",
        "pandas_code": "(df[df['date'] == 'april 20']['score'].str.split(' - ').str[0].astype(int).iloc[0] - df[df['date'] == 'april 15']['score'].str.split(' - ').str[0].astype(int).iloc[0]) == -3",
        "pandas_eval": "True"
    },
    {
        "id": 3076,
        "statement": "all game play against the la lakers be in april",
        "label": 1,
        "table_caption": "1968 - 69 atlanta hawks season",
        "table_text": "game#date#opponent#score#location / attendance#series\n1#april 11#la lakers#93 - 95#the forum#0 - 1\n2#april 13#la lakers#102 - 104#the forum#0 - 2\n3#april 15#la lakers#99 - 86#alexander memorial coliseum#1 - 2\n4#april 17#la lakers#85 - 100#alexander memorial coliseum#1 - 3\n5#april 20#la lakers#96 - 104#the forum#1 - 4\n",
        "pandas_code": "all(df['date'].str.startswith('april'))",
        "pandas_eval": "True"
    },
    {
        "id": 3077,
        "statement": "there be 9 ride that be open after 1999",
        "label": 1,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['year first opened'] > 1999).sum() == 9",
        "pandas_eval": "True"
    },
    {
        "id": 3078,
        "statement": "10 of the ride be list as maximum for thrill / intensity rating",
        "label": 1,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['thrill / intensity rating'].value_counts()['maximum'] == 10)",
        "pandas_eval": "True"
    },
    {
        "id": 3079,
        "statement": "7 of the ride have the minimum height list as 48",
        "label": 1,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['minimum height requirements'].str.contains('48').sum()) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 3081,
        "statement": "the 2 oldest ride list be open in 1971 while the newest be open in 2013",
        "label": 1,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['year first opened'].nsmallest(2).isin([1971]).all()) & (df['year first opened'].max() == 2013)",
        "pandas_eval": "True"
    },
    {
        "id": 3082,
        "statement": "there be 9 ride that be open before 1999",
        "label": 0,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['year first opened'] < 1999).sum() == 9",
        "pandas_eval": "False"
    },
    {
        "id": 3083,
        "statement": "less than half of the ride be list as maximum for thrill / intensity rating",
        "label": 0,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['thrill / intensity rating'].value_counts(normalize=True)['maximum'] < 0.5)",
        "pandas_eval": "False"
    },
    {
        "id": 3084,
        "statement": "more than 7 of the ride have the minimum height list as 48",
        "label": 0,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "(df['minimum height requirements'].str.contains('48').sum()) > 7",
        "pandas_eval": "False"
    },
    {
        "id": 3085,
        "statement": "less than half of the ride be manufacture by either arrow dynamic or bolliger & mabillard",
        "label": 0,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "not (df['manufacturer'].isin(['arrow dynamics', 'bolliger & mabillard']).sum() / len(df) < 0.5)",
        "pandas_eval": "False"
    },
    {
        "id": 3086,
        "statement": "the 2 most recent ride open after 2013",
        "label": 0,
        "table_caption": "six flags magic mountain",
        "table_text": "current name#year first opened#manufacturer#location in park#thrill / intensity rating#minimum height requirements\napocalypse : the ride#2009#great coasters international#cyclone bay#moderate#48\nbatman : the ride#1994#bolliger & mabillard#dc universe#maximum#54\ncanyon blaster#1999#e&f miler industries#high sierra territory#mild#33 to ride with an adult'36 to ride alone\ncolossus#1978#international amusement devices#colossus county fair#moderate#48\nfull throttle#2013#premier rides#full throttle area#maximum#54\ngold rusher#1971#arrow development#the movie district#moderate#48\ngoliath#2000#giovanola#colossus county fair#maximum#48\ngreen lantern : first flight#2011#intamin#dc universe#maximum#52\nmagic flyer#1971#bradley and kaye#high sierra territory#mild#none , rider cannot be taller than 54\nninja#1988#arrow dynamics#samurai summit#moderate#42\nrevolution#1976#anton schwarzkopf#baja ridge#moderate#48\nthe riddler 's revenge#1998#bolliger & mabillard#the movie district#maximum#54\nroad runner express#2011#vekoma#high sierra territory#moderate#36\nscream!#2003#bolliger & mabillard#colossus county fair#maximum#54\nsuperman : escape from krypton#2011#intamin#samurai summit#maximum#48\ntatsu#2006#bolliger & mabillard#samurai summit#maximum#54\nviper#1990#arrow dynamics#baja ridge#maximum#54\nx square#2002#arrow dynamics#baja ridge#maximum#48\n",
        "pandas_code": "len(df[df['year first opened'] > 2013].sort_values('year first opened', ascending=False).head(2)) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3087,
        "statement": "the british name of the octuple whole note be maxima",
        "label": 1,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "df.loc[df['american name'] == 'octuple whole note', 'british name'].values[0] == 'maxima'",
        "pandas_eval": "True"
    },
    {
        "id": 3088,
        "statement": "the maxima note have the highest value of all of the note",
        "label": 1,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "float(df.loc[df['american name'] == 'octuple whole note', 'value'].values[0]) == max(df['value'].apply(lambda x: float(eval(x.replace(' ', '')))))",
        "pandas_eval": "True"
    },
    {
        "id": 3090,
        "statement": "there be 12 different note and each 1 have an american name and a british name",
        "label": 1,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "len(df) == 12 and df['american name'].nunique() == 12 and df['british name'].nunique() == 12",
        "pandas_eval": "True"
    },
    {
        "id": 3091,
        "statement": "the breve note have double the value and dot value than the semibreve note",
        "label": 1,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "(df[df['british name'] == 'breve']['value'].values[0] == str(2 * int(df[df['british name'] == 'semibreve']['value'].values[0]))) & (df[df['british name'] == 'breve']['dotted value'].values[0] == str(2 * int(df[df['british name'] == 'semibreve']['dotted value'].values[0].split(' ')[0])))",
        "pandas_eval": "True"
    },
    {
        "id": 3092,
        "statement": "the british name of the octuple whole note be longa",
        "label": 0,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "df.loc[df['american name'] == 'octuple whole note', 'british name'].iloc[0] == 'longa'",
        "pandas_eval": "False"
    },
    {
        "id": 3093,
        "statement": "the maxima note have the lowest value of all of the note",
        "label": 0,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "df.loc[df['american name'] == 'octuple whole note', 'value'].values[0] == min(df['value'].apply(lambda x: float(eval(x))))",
        "pandas_eval": "False"
    },
    {
        "id": 3094,
        "statement": "the semibreve note 's dotted value be two more than the half note",
        "label": 0,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "int(df[df['british name'] == 'semibreve']['dotted value'].values[0].split()[0]) == int(df[df['british name'] == 'minim']['dotted value'].values[0].split()[0]) + 2",
        "pandas_eval": "False"
    },
    {
        "id": 3095,
        "statement": "there be 13 different note and each 1 have an american name and a british name",
        "label": 0,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "len(df) == 13 and df['american name'].nunique() == 13 and df['british name'].nunique() == 13",
        "pandas_eval": "False"
    },
    {
        "id": 3096,
        "statement": "the breve note have half the value and dot value than the semibreve note",
        "label": 0,
        "table_caption": "note value",
        "table_text": "american name#british name#value#dotted value#double dotted value#triple dotted value\noctuple whole note#maxima#32#48#56#60\nquadruple whole note#longa#16#24#28#30\ndouble whole note#breve#8#12#14#15\nwhole note#semibreve#4#6#7#15 / 2 or 7 1 / 2\nhalf note#minim#2#3#7 / 2 or 3 1 / 2#15 / 4 or 3 3 / 4\nquarter note#crotchet#1#3 / 2 or 1 1 / 2#7 / 4 or 1 3 / 4#15 / 8 or 1 7 / 8\neighth note#quaver#1 / 2#3 / 4#7 / 8#15 / 16\nsixteenth note#semiquaver#1 / 4#3 / 8#7 / 16#15 / 32\nthirty - second note#demisemiquaver#1 / 8#3 / 16#7 / 32#15 / 64\nsixty - fourth note#hemidemisemiquaver#1 / 16#3 / 32#7 / 64#15 / 128\nhundred twenty - eighth note#quasihemidemisemiquaver semihemidemisemiquaver#1 / 32#3 / 64#7 / 128#15 / 256\ntwo hundred fifty - sixth note#demisemihemidemisemiquaver#1 / 64#3 / 128#7 / 256#15 / 512\n",
        "pandas_code": "(df[df['british name'] == 'breve']['value'].values[0] == str(float(df[df['british name'] == 'semibreve']['value'].values[0]) / 2)) and (df[df['british name'] == 'breve']['dotted value'].values[0] == str(float(df[df['british name'] == 'semibreve']['dotted value'].values[0]) / 2))",
        "pandas_eval": "False"
    },
    {
        "id": 3097,
        "statement": "shane watson be the only player on the list with a number other than 0 in the wkts column",
        "label": 1,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "(df[df['player'] == 'shane watson']['wkts'].values[0] != 0) and (df[df['player'] != 'shane watson']['wkts'].sum() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3098,
        "statement": "mitchell johnson have the most amount of run of the player",
        "label": 1,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "df.loc[df['runs'].idxmax(), 'player'] == 'mitchell johnson'",
        "pandas_eval": "True"
    },
    {
        "id": 3099,
        "statement": "in the ovrs column , 2.0 be the only number list more than 1 time",
        "label": 1,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "df['ovrs'].value_counts().loc[2.0] > 1 and df['ovrs'].value_counts().drop(2.0).max() <= 1",
        "pandas_eval": "True"
    },
    {
        "id": 3100,
        "statement": "shane watson be the only player with number in the decimal in the econ and ovrs column",
        "label": 1,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "(df[(df['econ'] % 1 != 0) | (df['ovrs'] % 1 != 0)]['player'] == 'shane watson').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3101,
        "statement": "the player with the most run have the highest number in the econ column , but the player with the least run do not have the lowest number in the econ column",
        "label": 1,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "(df.loc[df['runs'].idxmax(), 'econ'] == df['econ'].max()) & (df.loc[df['runs'].idxmin(), 'econ'] != df['econ'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 3102,
        "statement": "shane watson be the only player on the list with a number other than 1 in the wkts column",
        "label": 0,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "len(df[df['wkts'] != 0]) == 1 and df[df['wkts'] != 0]['player'].iloc[0] == 'shane watson' and df[df['player'] == 'shane watson']['wkts'].iloc[0] != 1",
        "pandas_eval": "False"
    },
    {
        "id": 3103,
        "statement": "cameron white have the most amount of run of the player",
        "label": 0,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "df.loc[df['player'] == 'cameron white', 'runs'].values[0] == df['runs'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3104,
        "statement": "in the ovrs column , 2.1 be the only number list more than 1 time",
        "label": 0,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "df['ovrs'].value_counts().max() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3105,
        "statement": "every player other than shane watson have number in the decimal in the econ and ovrs column",
        "label": 0,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "all(df[df['player'] != 'shane watson'][['econ', 'ovrs']].applymap(lambda x: isinstance(x, float) and not x.is_integer()).all())",
        "pandas_eval": "False"
    },
    {
        "id": 3106,
        "statement": "the player with the most run have the highest number in the econ column , and the player with the least run have the lowest number in the econ column",
        "label": 0,
        "table_caption": "australian cricket team in 2008",
        "table_text": "player#wkts#runs#econ#ovrs\nshane watson#1#17#7.84#2.1\ncameron white#0#8#8.00#1.0\njames hopes#0#14#7.00#2.0\nbrett lee#0#26#13.00#2.0\nmitchell johnson#0#30#15.00#2.0\n",
        "pandas_code": "(df.loc[df['runs'].idxmax(), 'econ'] == df['econ'].max()) & (df.loc[df['runs'].idxmin(), 'econ'] == df['econ'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 3107,
        "statement": "michigan wolverine be in the ccha conference",
        "label": 1,
        "table_caption": "michigan wolverines men 's ice hockey",
        "table_text": "tournament#conference#championship game opponent#score#location#head coach\n1994#ccha#lake superior state#3 - 0#joe louis arena detroit , mi#red berenson\n1996#ccha#lake superior state#4 - 3#joe louis arena detroit , mi#red berenson\n1997#ccha#michigan state#3 - 1#joe louis arena detroit , mi#red berenson\n1999#ccha#northern michigan#5 - 1#joe louis arena detroit , mi#red berenson\n2002#ccha#michigan state#3 - 2#joe louis arena detroit , mi#red berenson\n2003#ccha#ferris state#5 - 3#joe louis arena detroit , mi#red berenson\n2005#ccha#ohio state#4 - 2#joe louis arena detroit , mi#red berenson\n2008#ccha#miami university#2 - 1#joe louis arena detroit , mi#red berenson\n",
        "pandas_code": "all(df['conference'] == 'ccha')",
        "pandas_eval": "True"
    },
    {
        "id": 3108,
        "statement": "red berenson coach the michigan wolverine in 1194",
        "label": 1,
        "table_caption": "michigan wolverines men 's ice hockey",
        "table_text": "tournament#conference#championship game opponent#score#location#head coach\n1994#ccha#lake superior state#3 - 0#joe louis arena detroit , mi#red berenson\n1996#ccha#lake superior state#4 - 3#joe louis arena detroit , mi#red berenson\n1997#ccha#michigan state#3 - 1#joe louis arena detroit , mi#red berenson\n1999#ccha#northern michigan#5 - 1#joe louis arena detroit , mi#red berenson\n2002#ccha#michigan state#3 - 2#joe louis arena detroit , mi#red berenson\n2003#ccha#ferris state#5 - 3#joe louis arena detroit , mi#red berenson\n2005#ccha#ohio state#4 - 2#joe louis arena detroit , mi#red berenson\n2008#ccha#miami university#2 - 1#joe louis arena detroit , mi#red berenson\n",
        "pandas_code": "df[(df['tournament'] == 1994) & (df['head coach'] == 'red berenson')].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 3109,
        "statement": "michigan wolverines men 's ice hockey win the ccha conference tournament 8 time in a 14 year period",
        "label": 1,
        "table_caption": "michigan wolverines men 's ice hockey",
        "table_text": "tournament#conference#championship game opponent#score#location#head coach\n1994#ccha#lake superior state#3 - 0#joe louis arena detroit , mi#red berenson\n1996#ccha#lake superior state#4 - 3#joe louis arena detroit , mi#red berenson\n1997#ccha#michigan state#3 - 1#joe louis arena detroit , mi#red berenson\n1999#ccha#northern michigan#5 - 1#joe louis arena detroit , mi#red berenson\n2002#ccha#michigan state#3 - 2#joe louis arena detroit , mi#red berenson\n2003#ccha#ferris state#5 - 3#joe louis arena detroit , mi#red berenson\n2005#ccha#ohio state#4 - 2#joe louis arena detroit , mi#red berenson\n2008#ccha#miami university#2 - 1#joe louis arena detroit , mi#red berenson\n",
        "pandas_code": "len(df[(df['conference'] == 'ccha') & (df['tournament'] >= 1994) & (df['tournament'] <= 2008)]) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 3110,
        "statement": "the ccha championship game be play in joe louis arena in dterioit mi in 1996",
        "label": 1,
        "table_caption": "michigan wolverines men 's ice hockey",
        "table_text": "tournament#conference#championship game opponent#score#location#head coach\n1994#ccha#lake superior state#3 - 0#joe louis arena detroit , mi#red berenson\n1996#ccha#lake superior state#4 - 3#joe louis arena detroit , mi#red berenson\n1997#ccha#michigan state#3 - 1#joe louis arena detroit , mi#red berenson\n1999#ccha#northern michigan#5 - 1#joe louis arena detroit , mi#red berenson\n2002#ccha#michigan state#3 - 2#joe louis arena detroit , mi#red berenson\n2003#ccha#ferris state#5 - 3#joe louis arena detroit , mi#red berenson\n2005#ccha#ohio state#4 - 2#joe louis arena detroit , mi#red berenson\n2008#ccha#miami university#2 - 1#joe louis arena detroit , mi#red berenson\n",
        "pandas_code": "((df['conference'] == 'ccha') & (df['location'] == 'joe louis arena detroit , mi') & (df['tournament'] == 1996)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3111,
        "statement": "lake superior state lose to michigan 4 - 3 in the ccha championship game in 1996",
        "label": 1,
        "table_caption": "michigan wolverines men 's ice hockey",
        "table_text": "tournament#conference#championship game opponent#score#location#head coach\n1994#ccha#lake superior state#3 - 0#joe louis arena detroit , mi#red berenson\n1996#ccha#lake superior state#4 - 3#joe louis arena detroit , mi#red berenson\n1997#ccha#michigan state#3 - 1#joe louis arena detroit , mi#red berenson\n1999#ccha#northern michigan#5 - 1#joe louis arena detroit , mi#red berenson\n2002#ccha#michigan state#3 - 2#joe louis arena detroit , mi#red berenson\n2003#ccha#ferris state#5 - 3#joe louis arena detroit , mi#red berenson\n2005#ccha#ohio state#4 - 2#joe louis arena detroit , mi#red berenson\n2008#ccha#miami university#2 - 1#joe louis arena detroit , mi#red berenson\n",
        "pandas_code": "((df['tournament'] == 1996) & (df['conference'] == 'ccha') & (df['championship game opponent'] == 'lake superior state') & (df['score'] == '4 - 3')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3112,
        "statement": "the earliest release be on september 20 , 2008 in australia",
        "label": 1,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[(df['date'] == 'september 20 , 2008') & (df['region'] == 'australia')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3113,
        "statement": "the cd be release in the united state prior to the cd release in canada",
        "label": 1,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[(df['region'] == 'united states') & (df['format'] == 'cd')]['date'].min() < df[(df['region'] == 'canada') & (df['format'] == 'cd')]['date'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3114,
        "statement": "the lp format be only release in the united state and united kingdom",
        "label": 1,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[(df['format'] == 'lp') & (~df['region'].isin(['united states', 'united kingdom']))].empty",
        "pandas_eval": "True"
    },
    {
        "id": 3115,
        "statement": "the cd be release in canada 4 day after its release in hong kong",
        "label": 1,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "(pd.to_datetime(df[(df['region'] == 'canada') & (df['format'] == 'cd')]['date'].iloc[0]) - pd.to_datetime(df[(df['region'] == 'hong kong') & (df['format'] == 'cd')]['date'].iloc[0])).days == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3116,
        "statement": "rough trade record be the label for more release than any other label",
        "label": 1,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df['label'].value_counts().idxmax() == 'rough trade records'",
        "pandas_eval": "True"
    },
    {
        "id": 3117,
        "statement": "the earliest release be on september 23 , 2008 in the united state",
        "label": 0,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[(df['date'] == 'september 23 , 2008') & (df['region'] == 'united states')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3118,
        "statement": "the cd be release in canada before it be release in any other country",
        "label": 0,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[(df['region'] == 'canada') & (df['format'] == 'cd')]['date'].min() < df[df['format'] == 'cd']['date'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3119,
        "statement": "the lp format be release in australia , united kingdom , united state , hong kong , canada , and japan",
        "label": 0,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "not all(df[df['format'] == 'lp']['region'].isin(['australia', 'united kingdom', 'united states', 'hong kong', 'canada', 'japan']))",
        "pandas_eval": "False"
    },
    {
        "id": 3120,
        "statement": "the cd be release in canada 4 day before its release in hong kong",
        "label": 0,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "(df[(df['region'] == 'canada') & (df['format'] == 'cd')]['date'].iloc[0] < df[(df['region'] == 'hong kong') & (df['format'] == 'cd')]['date'].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3121,
        "statement": "rough trade record do not have as many release as warner music canada",
        "label": 0,
        "table_caption": "acid tongue",
        "table_text": "region#date#label#format#catalog\naustralia#september 20 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#cd#rtradcd491\nunited kingdom#september 22 , 2008#rough trade records#lp#rtradlp491\nunited states#september 23 , 2008#warner bros records#cd#508668\nunited states#september 23 , 2008#warner bros records#lp#508668\nhong kong#september 26 , 2008#rough trade records#cd#rtradcd491\ncanada#september 30 , 2008#warner music canada#cd#2508668\njapan#november 26 , 2008#warner music group#cd#wpcb10098\n",
        "pandas_code": "df[df['label'] == 'rough trade records'].shape[0] < df[df['label'] == 'warner music canada'].shape[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3122,
        "statement": "4 different writer write more than 1 episode during the 2006 season",
        "label": 1,
        "table_caption": "list of robin hood (2006 tv series) episodes",
        "table_text": "total#series#title#writer#director#original air date\n27#1#total eclipse#michael chaplin#douglas mackinnon#28 march 2009 , 6:50 pm - 7:35 pm\n28#2#cause and effect#simon j ashford#douglas mackinnon#4 april 2009 , 6:25 pm - 7:10 pm\n29#3#lost in translation#ryan craig#alex pillai#11 april 2009 , 7:45 pm - 8:30 pm\n30#4#sins of the father#holly phillips#alex pillai#18 april 2009 , 6:10 pm - 6:55 pm\n31#5#let the games commence#lisa holdsworth#patrick lau#25 april 2009 , 6:15 pm - 7:00 pm\n32#6#do you love me#timothy prager#patrick lau#2 may 2009 , 6:20 pm - 7:05 pm\n33#7#too hot to handle#chris lang#john greening#9 may 2009 , 6:15 pm - 7:00 pm\n34#8#the king is dead , long live the king\u2026#john jackson#john greening#23 may 2009 , 6:35 pm - 7:20 pm\n35#9#a dangerous deal#michael chaplin#graeme harper#30 may 2009 , 7:25 pm - 8:10 pm\n36#10#bad blood#lisa holdsworth#roger goldby#6 june 2009 , 6:45 pm - 7:30 pm\n37#11#the enemy of my enemy#timothy prager#graeme harper#13 june 2009 , 6:45 pm - 7:30 pm\n38#12#something worth fighting for , part 1#ryan craig#matthew evans#20 june 2009 , 6:45 pm - 7:30 pm\n",
        "pandas_code": "df[df['original air date'].str.contains('2009')]['writer'].value_counts().gt(1).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3123,
        "statement": "there be 7 different director during the season",
        "label": 1,
        "table_caption": "list of robin hood (2006 tv series) episodes",
        "table_text": "total#series#title#writer#director#original air date\n27#1#total eclipse#michael chaplin#douglas mackinnon#28 march 2009 , 6:50 pm - 7:35 pm\n28#2#cause and effect#simon j ashford#douglas mackinnon#4 april 2009 , 6:25 pm - 7:10 pm\n29#3#lost in translation#ryan craig#alex pillai#11 april 2009 , 7:45 pm - 8:30 pm\n30#4#sins of the father#holly phillips#alex pillai#18 april 2009 , 6:10 pm - 6:55 pm\n31#5#let the games commence#lisa holdsworth#patrick lau#25 april 2009 , 6:15 pm - 7:00 pm\n32#6#do you love me#timothy prager#patrick lau#2 may 2009 , 6:20 pm - 7:05 pm\n33#7#too hot to handle#chris lang#john greening#9 may 2009 , 6:15 pm - 7:00 pm\n34#8#the king is dead , long live the king\u2026#john jackson#john greening#23 may 2009 , 6:35 pm - 7:20 pm\n35#9#a dangerous deal#michael chaplin#graeme harper#30 may 2009 , 7:25 pm - 8:10 pm\n36#10#bad blood#lisa holdsworth#roger goldby#6 june 2009 , 6:45 pm - 7:30 pm\n37#11#the enemy of my enemy#timothy prager#graeme harper#13 june 2009 , 6:45 pm - 7:30 pm\n38#12#something worth fighting for , part 1#ryan craig#matthew evans#20 june 2009 , 6:45 pm - 7:30 pm\n",
        "pandas_code": "df['director'].nunique() == 7",
        "pandas_eval": "True"
    },
    {
        "id": 3124,
        "statement": "each of the 12 episode last forty - 5 minute",
        "label": 1,
        "table_caption": "list of robin hood (2006 tv series) episodes",
        "table_text": "total#series#title#writer#director#original air date\n27#1#total eclipse#michael chaplin#douglas mackinnon#28 march 2009 , 6:50 pm - 7:35 pm\n28#2#cause and effect#simon j ashford#douglas mackinnon#4 april 2009 , 6:25 pm - 7:10 pm\n29#3#lost in translation#ryan craig#alex pillai#11 april 2009 , 7:45 pm - 8:30 pm\n30#4#sins of the father#holly phillips#alex pillai#18 april 2009 , 6:10 pm - 6:55 pm\n31#5#let the games commence#lisa holdsworth#patrick lau#25 april 2009 , 6:15 pm - 7:00 pm\n32#6#do you love me#timothy prager#patrick lau#2 may 2009 , 6:20 pm - 7:05 pm\n33#7#too hot to handle#chris lang#john greening#9 may 2009 , 6:15 pm - 7:00 pm\n34#8#the king is dead , long live the king\u2026#john jackson#john greening#23 may 2009 , 6:35 pm - 7:20 pm\n35#9#a dangerous deal#michael chaplin#graeme harper#30 may 2009 , 7:25 pm - 8:10 pm\n36#10#bad blood#lisa holdsworth#roger goldby#6 june 2009 , 6:45 pm - 7:30 pm\n37#11#the enemy of my enemy#timothy prager#graeme harper#13 june 2009 , 6:45 pm - 7:30 pm\n38#12#something worth fighting for , part 1#ryan craig#matthew evans#20 june 2009 , 6:45 pm - 7:30 pm\n",
        "pandas_code": "all(df['original air date'].str.extract(r'(\\d+:\\d+ [ap]m) - (\\d+:\\d+ [ap]m)').apply(lambda x: (pd.to_datetime(x[1], format='%I:%M %p') - pd.to_datetime(x[0], format='%I:%M %p')).seconds == 2700, axis=1))",
        "pandas_eval": "True"
    },
    {
        "id": 3125,
        "statement": "there be no writer and director combination repeat during the season",
        "label": 1,
        "table_caption": "list of robin hood (2006 tv series) episodes",
        "table_text": "total#series#title#writer#director#original air date\n27#1#total eclipse#michael chaplin#douglas mackinnon#28 march 2009 , 6:50 pm - 7:35 pm\n28#2#cause and effect#simon j ashford#douglas mackinnon#4 april 2009 , 6:25 pm - 7:10 pm\n29#3#lost in translation#ryan craig#alex pillai#11 april 2009 , 7:45 pm - 8:30 pm\n30#4#sins of the father#holly phillips#alex pillai#18 april 2009 , 6:10 pm - 6:55 pm\n31#5#let the games commence#lisa holdsworth#patrick lau#25 april 2009 , 6:15 pm - 7:00 pm\n32#6#do you love me#timothy prager#patrick lau#2 may 2009 , 6:20 pm - 7:05 pm\n33#7#too hot to handle#chris lang#john greening#9 may 2009 , 6:15 pm - 7:00 pm\n34#8#the king is dead , long live the king\u2026#john jackson#john greening#23 may 2009 , 6:35 pm - 7:20 pm\n35#9#a dangerous deal#michael chaplin#graeme harper#30 may 2009 , 7:25 pm - 8:10 pm\n36#10#bad blood#lisa holdsworth#roger goldby#6 june 2009 , 6:45 pm - 7:30 pm\n37#11#the enemy of my enemy#timothy prager#graeme harper#13 june 2009 , 6:45 pm - 7:30 pm\n38#12#something worth fighting for , part 1#ryan craig#matthew evans#20 june 2009 , 6:45 pm - 7:30 pm\n",
        "pandas_code": "df.duplicated(subset=['writer', 'director'], keep=False).sum() == 0",
        "pandas_eval": "True"
    },
    {
        "id": 3126,
        "statement": "no writer or director work on more than 2 episode",
        "label": 1,
        "table_caption": "list of robin hood (2006 tv series) episodes",
        "table_text": "total#series#title#writer#director#original air date\n27#1#total eclipse#michael chaplin#douglas mackinnon#28 march 2009 , 6:50 pm - 7:35 pm\n28#2#cause and effect#simon j ashford#douglas mackinnon#4 april 2009 , 6:25 pm - 7:10 pm\n29#3#lost in translation#ryan craig#alex pillai#11 april 2009 , 7:45 pm - 8:30 pm\n30#4#sins of the father#holly phillips#alex pillai#18 april 2009 , 6:10 pm - 6:55 pm\n31#5#let the games commence#lisa holdsworth#patrick lau#25 april 2009 , 6:15 pm - 7:00 pm\n32#6#do you love me#timothy prager#patrick lau#2 may 2009 , 6:20 pm - 7:05 pm\n33#7#too hot to handle#chris lang#john greening#9 may 2009 , 6:15 pm - 7:00 pm\n34#8#the king is dead , long live the king\u2026#john jackson#john greening#23 may 2009 , 6:35 pm - 7:20 pm\n35#9#a dangerous deal#michael chaplin#graeme harper#30 may 2009 , 7:25 pm - 8:10 pm\n36#10#bad blood#lisa holdsworth#roger goldby#6 june 2009 , 6:45 pm - 7:30 pm\n37#11#the enemy of my enemy#timothy prager#graeme harper#13 june 2009 , 6:45 pm - 7:30 pm\n38#12#something worth fighting for , part 1#ryan craig#matthew evans#20 june 2009 , 6:45 pm - 7:30 pm\n",
        "pandas_code": "(df['writer'].value_counts().max() <= 2) & (df['director'].value_counts().max() <= 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3127,
        "statement": "when team 1 be iraklis the agg score be 1 - 2 and when team 2 be olympiacos the 1st leg be 1 - 0",
        "label": 1,
        "table_caption": "2004 - 05 greek cup",
        "table_text": "team 1#agg score#team 2#1st leg#2nd leg\niraklis#1 - 2#olympiacos#1 - 0#0 - 2\nkastoria#4 - 2#ptolemaida - lignitorikhi#2 - 0#2 - 3\naris#4 - 2#ethnikos#2 - 1#2 - 1\nskoda xanthi#1 - 0#egaleo#1 - 0#0 - 0\nilisiakos#0 - 2#panionios#0 - 1#0 - 1\nlarissa#3 - 2#chalkidon near east#3 - 1#0 - 1\nofi#1 - 1#apollon kalamaria#1 - 1#0 - 0\n",
        "pandas_code": "((df['team 1'] == 'iraklis') & (df['agg score'] == '1 - 2') & (df['team 2'] == 'olympiacos') & (df['1st leg'] == '1 - 0')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3128,
        "statement": "egaleo be team 2 when skoda xanthi be team 1 with an agg score of 1 - 0 and 1st leg of 1 - 0",
        "label": 1,
        "table_caption": "2004 - 05 greek cup",
        "table_text": "team 1#agg score#team 2#1st leg#2nd leg\niraklis#1 - 2#olympiacos#1 - 0#0 - 2\nkastoria#4 - 2#ptolemaida - lignitorikhi#2 - 0#2 - 3\naris#4 - 2#ethnikos#2 - 1#2 - 1\nskoda xanthi#1 - 0#egaleo#1 - 0#0 - 0\nilisiakos#0 - 2#panionios#0 - 1#0 - 1\nlarissa#3 - 2#chalkidon near east#3 - 1#0 - 1\nofi#1 - 1#apollon kalamaria#1 - 1#0 - 0\n",
        "pandas_code": "df[(df['team 1'] == 'skoda xanthi') & (df['team 2'] == 'egaleo') & (df['agg score'] == '1 - 0') & (df['1st leg'] == '1 - 0')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3129,
        "statement": "when panionios be team 2 , ilisiakos be team 1 with an agg score of 0 - 2 and a 2nd leg of 0 - 1",
        "label": 1,
        "table_caption": "2004 - 05 greek cup",
        "table_text": "team 1#agg score#team 2#1st leg#2nd leg\niraklis#1 - 2#olympiacos#1 - 0#0 - 2\nkastoria#4 - 2#ptolemaida - lignitorikhi#2 - 0#2 - 3\naris#4 - 2#ethnikos#2 - 1#2 - 1\nskoda xanthi#1 - 0#egaleo#1 - 0#0 - 0\nilisiakos#0 - 2#panionios#0 - 1#0 - 1\nlarissa#3 - 2#chalkidon near east#3 - 1#0 - 1\nofi#1 - 1#apollon kalamaria#1 - 1#0 - 0\n",
        "pandas_code": "((df['team 2'] == 'panionios') & (df['team 1'] == 'ilisiakos') & (df['agg score'] == '0 - 2') & (df['2nd leg'] == '0 - 1')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3130,
        "statement": "the highest agg score be 4 - 2 and it occur 2 time , between kastoria and ptolemaida - lignitorikhi and aris and ethnikos",
        "label": 1,
        "table_caption": "2004 - 05 greek cup",
        "table_text": "team 1#agg score#team 2#1st leg#2nd leg\niraklis#1 - 2#olympiacos#1 - 0#0 - 2\nkastoria#4 - 2#ptolemaida - lignitorikhi#2 - 0#2 - 3\naris#4 - 2#ethnikos#2 - 1#2 - 1\nskoda xanthi#1 - 0#egaleo#1 - 0#0 - 0\nilisiakos#0 - 2#panionios#0 - 1#0 - 1\nlarissa#3 - 2#chalkidon near east#3 - 1#0 - 1\nofi#1 - 1#apollon kalamaria#1 - 1#0 - 0\n",
        "pandas_code": "(df['agg score'].value_counts().idxmax() == '4 - 2') & (df['agg score'].value_counts().max() == 2) & (df.loc[df['agg score'] == '4 - 2', ['team 1', 'team 2']].values.tolist() == [['kastoria', 'ptolemaida - lignitorikhi'], ['aris', 'ethnikos']])",
        "pandas_eval": "True"
    },
    {
        "id": 3131,
        "statement": "when the agg score be 1 - 1 the team be ofi and apollon kalamaria and the 1st leg be 1 - 1",
        "label": 1,
        "table_caption": "2004 - 05 greek cup",
        "table_text": "team 1#agg score#team 2#1st leg#2nd leg\niraklis#1 - 2#olympiacos#1 - 0#0 - 2\nkastoria#4 - 2#ptolemaida - lignitorikhi#2 - 0#2 - 3\naris#4 - 2#ethnikos#2 - 1#2 - 1\nskoda xanthi#1 - 0#egaleo#1 - 0#0 - 0\nilisiakos#0 - 2#panionios#0 - 1#0 - 1\nlarissa#3 - 2#chalkidon near east#3 - 1#0 - 1\nofi#1 - 1#apollon kalamaria#1 - 1#0 - 0\n",
        "pandas_code": "((df['agg score'] == '1 - 1') & (df['team 1'] == 'ofi') & (df['team 2'] == 'apollon kalamaria') & (df['1st leg'] == '1 - 1')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3132,
        "statement": "all district be more than 4000 m above sea level",
        "label": 1,
        "table_caption": "districts of peru",
        "table_text": "#district#province#region#ubigeo#elevation (m)\n1#suykutambo#espinar#cusco#80807#4801\n2#condoroma#espinar#cusco#80802#4737\n3#san antonio#puno#puno#210113#4700\n4#ananea#san antonio de putina#puno#211002#4660\n5#morococha#yauli#jun\u00edn#120805#4550\n6#san antonio de chuca#caylloma#arequipa#40514#4525\n7#santa ana#castrovirreyna#huancavelica#90411#4473\n8#marcapomacocha#yauli#jun\u00edn#120804#4415\n9#capazo#el collao#puno#210502#4400\n10#paratia#lampa#puno#210707#4390\n11#cojata#huancan\u00e9#puno#210602#4355\n12#yanacancha#pasco#pasco#190113#4350\n13#chaupimarca#pasco#pasco#190101#4338\n14#macusani#carabaya#puno#210301#4315\n15#huayllay#pasco#pasco#190104#4310\n16#caylloma#caylloma#arequipa#40505#4310\n17#vilavila#lampa#puno#210710#4300\n18#tanta#yauyos#lima#151028#4278\n19#tinyahuarco#pasco#pasco#190111#4275\n",
        "pandas_code": "(df['elevation (m)'] > 4000).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3133,
        "statement": "espinar province have highest elevation",
        "label": 1,
        "table_caption": "districts of peru",
        "table_text": "#district#province#region#ubigeo#elevation (m)\n1#suykutambo#espinar#cusco#80807#4801\n2#condoroma#espinar#cusco#80802#4737\n3#san antonio#puno#puno#210113#4700\n4#ananea#san antonio de putina#puno#211002#4660\n5#morococha#yauli#jun\u00edn#120805#4550\n6#san antonio de chuca#caylloma#arequipa#40514#4525\n7#santa ana#castrovirreyna#huancavelica#90411#4473\n8#marcapomacocha#yauli#jun\u00edn#120804#4415\n9#capazo#el collao#puno#210502#4400\n10#paratia#lampa#puno#210707#4390\n11#cojata#huancan\u00e9#puno#210602#4355\n12#yanacancha#pasco#pasco#190113#4350\n13#chaupimarca#pasco#pasco#190101#4338\n14#macusani#carabaya#puno#210301#4315\n15#huayllay#pasco#pasco#190104#4310\n16#caylloma#caylloma#arequipa#40505#4310\n17#vilavila#lampa#puno#210710#4300\n18#tanta#yauyos#lima#151028#4278\n19#tinyahuarco#pasco#pasco#190111#4275\n",
        "pandas_code": "df[df['province'] == 'espinar']['elevation (m)'].max() == df['elevation (m)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3134,
        "statement": "region puno have the most district above 4000 m",
        "label": 1,
        "table_caption": "districts of peru",
        "table_text": "#district#province#region#ubigeo#elevation (m)\n1#suykutambo#espinar#cusco#80807#4801\n2#condoroma#espinar#cusco#80802#4737\n3#san antonio#puno#puno#210113#4700\n4#ananea#san antonio de putina#puno#211002#4660\n5#morococha#yauli#jun\u00edn#120805#4550\n6#san antonio de chuca#caylloma#arequipa#40514#4525\n7#santa ana#castrovirreyna#huancavelica#90411#4473\n8#marcapomacocha#yauli#jun\u00edn#120804#4415\n9#capazo#el collao#puno#210502#4400\n10#paratia#lampa#puno#210707#4390\n11#cojata#huancan\u00e9#puno#210602#4355\n12#yanacancha#pasco#pasco#190113#4350\n13#chaupimarca#pasco#pasco#190101#4338\n14#macusani#carabaya#puno#210301#4315\n15#huayllay#pasco#pasco#190104#4310\n16#caylloma#caylloma#arequipa#40505#4310\n17#vilavila#lampa#puno#210710#4300\n18#tanta#yauyos#lima#151028#4278\n19#tinyahuarco#pasco#pasco#190111#4275\n",
        "pandas_code": "df[(df['region'] == 'puno') & (df['elevation (m)'] > 4000)].shape[0] == df[df['elevation (m)'] > 4000].groupby('region').size().max()",
        "pandas_eval": "True"
    },
    {
        "id": 3135,
        "statement": "suykutambo be district with highest elevation",
        "label": 1,
        "table_caption": "districts of peru",
        "table_text": "#district#province#region#ubigeo#elevation (m)\n1#suykutambo#espinar#cusco#80807#4801\n2#condoroma#espinar#cusco#80802#4737\n3#san antonio#puno#puno#210113#4700\n4#ananea#san antonio de putina#puno#211002#4660\n5#morococha#yauli#jun\u00edn#120805#4550\n6#san antonio de chuca#caylloma#arequipa#40514#4525\n7#santa ana#castrovirreyna#huancavelica#90411#4473\n8#marcapomacocha#yauli#jun\u00edn#120804#4415\n9#capazo#el collao#puno#210502#4400\n10#paratia#lampa#puno#210707#4390\n11#cojata#huancan\u00e9#puno#210602#4355\n12#yanacancha#pasco#pasco#190113#4350\n13#chaupimarca#pasco#pasco#190101#4338\n14#macusani#carabaya#puno#210301#4315\n15#huayllay#pasco#pasco#190104#4310\n16#caylloma#caylloma#arequipa#40505#4310\n17#vilavila#lampa#puno#210710#4300\n18#tanta#yauyos#lima#151028#4278\n19#tinyahuarco#pasco#pasco#190111#4275\n",
        "pandas_code": "df.loc[df['elevation (m)'].idxmax(), 'district'] == 'suykutambo'",
        "pandas_eval": "True"
    },
    {
        "id": 3136,
        "statement": "tinyahuarco be district with lowest elevation",
        "label": 1,
        "table_caption": "districts of peru",
        "table_text": "#district#province#region#ubigeo#elevation (m)\n1#suykutambo#espinar#cusco#80807#4801\n2#condoroma#espinar#cusco#80802#4737\n3#san antonio#puno#puno#210113#4700\n4#ananea#san antonio de putina#puno#211002#4660\n5#morococha#yauli#jun\u00edn#120805#4550\n6#san antonio de chuca#caylloma#arequipa#40514#4525\n7#santa ana#castrovirreyna#huancavelica#90411#4473\n8#marcapomacocha#yauli#jun\u00edn#120804#4415\n9#capazo#el collao#puno#210502#4400\n10#paratia#lampa#puno#210707#4390\n11#cojata#huancan\u00e9#puno#210602#4355\n12#yanacancha#pasco#pasco#190113#4350\n13#chaupimarca#pasco#pasco#190101#4338\n14#macusani#carabaya#puno#210301#4315\n15#huayllay#pasco#pasco#190104#4310\n16#caylloma#caylloma#arequipa#40505#4310\n17#vilavila#lampa#puno#210710#4300\n18#tanta#yauyos#lima#151028#4278\n19#tinyahuarco#pasco#pasco#190111#4275\n",
        "pandas_code": "df.loc[df['elevation (m)'].idxmin(), 'district'] == 'tinyahuarco'",
        "pandas_eval": "True"
    },
    {
        "id": 3137,
        "statement": "all candidate be democrat",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\ntexas 1#wright patman#democratic#1928#re - elected#wright patman (d) unopposed\ntexas 2#john dowdy redistricted from 7th#democratic#1952#re - elected#john dowdy (d) unopposed\ntexas 3#joe r pool redistricted from at - large#democratic#1962#re - elected#joe r pool (d) 53.4% bill hayes (r) 46.6%\ntexas 4#ray roberts#democratic#1962#re - elected#ray roberts (d) unopposed\ntexas 4#lindley beckworth redistricted from 3rd#democratic#1956#lost renomination democratic loss#ray roberts (d) unopposed\ntexas 5#earle cabell#democratic#1964#re - elected#earle cabell (d) 61.0% duke burgess (r) 39.0%\ntexas 6#olin e teague#democratic#1946#re - elected#olin e teague (d) unopposed\ntexas 8#lera millard thomas#democratic#1966#retired democratic hold#robert c eckhardt (d) 92.3% w d spayne (r) 7.7%\ntexas 9#jack brooks redistricted from 2nd#democratic#1952#re - elected#jack brooks (d) unopposed\ntexas 9#clark w thompson#democratic#1947#retired democratic loss#jack brooks (d) unopposed\ntexas 12#jim wright#democratic#1954#re - elected#jim wright (d) unopposed\ntexas 14#john andrew young#democratic#1956#re - elected#john andrew young (d) unopposed\ntexas 15#kika de la garza#democratic#1964#re - elected#kika de la garza (d) unopposed\ntexas 16#richard c white#democratic#1964#re - elected#richard c white (d) unopposed\ntexas 17#omar burleson#democratic#1946#re - elected#omar burleson (d) unopposed\ntexas 18#walter e rogers#democratic#1950#retired republican gain#bob price (r) 59.5% dee miller (d) 40.5%\ntexas 19#george h mahon#democratic#1934#re - elected#george h mahon (d) unopposed\ntexas 21#o c fisher#democratic#1942#re - elected#o c fisher (d) unopposed\n",
        "pandas_code": "all(df['party'] == 'democratic')",
        "pandas_eval": "True"
    },
    {
        "id": 3138,
        "statement": "wright patman be the candidate with old year of first election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\ntexas 1#wright patman#democratic#1928#re - elected#wright patman (d) unopposed\ntexas 2#john dowdy redistricted from 7th#democratic#1952#re - elected#john dowdy (d) unopposed\ntexas 3#joe r pool redistricted from at - large#democratic#1962#re - elected#joe r pool (d) 53.4% bill hayes (r) 46.6%\ntexas 4#ray roberts#democratic#1962#re - elected#ray roberts (d) unopposed\ntexas 4#lindley beckworth redistricted from 3rd#democratic#1956#lost renomination democratic loss#ray roberts (d) unopposed\ntexas 5#earle cabell#democratic#1964#re - elected#earle cabell (d) 61.0% duke burgess (r) 39.0%\ntexas 6#olin e teague#democratic#1946#re - elected#olin e teague (d) unopposed\ntexas 8#lera millard thomas#democratic#1966#retired democratic hold#robert c eckhardt (d) 92.3% w d spayne (r) 7.7%\ntexas 9#jack brooks redistricted from 2nd#democratic#1952#re - elected#jack brooks (d) unopposed\ntexas 9#clark w thompson#democratic#1947#retired democratic loss#jack brooks (d) unopposed\ntexas 12#jim wright#democratic#1954#re - elected#jim wright (d) unopposed\ntexas 14#john andrew young#democratic#1956#re - elected#john andrew young (d) unopposed\ntexas 15#kika de la garza#democratic#1964#re - elected#kika de la garza (d) unopposed\ntexas 16#richard c white#democratic#1964#re - elected#richard c white (d) unopposed\ntexas 17#omar burleson#democratic#1946#re - elected#omar burleson (d) unopposed\ntexas 18#walter e rogers#democratic#1950#retired republican gain#bob price (r) 59.5% dee miller (d) 40.5%\ntexas 19#george h mahon#democratic#1934#re - elected#george h mahon (d) unopposed\ntexas 21#o c fisher#democratic#1942#re - elected#o c fisher (d) unopposed\n",
        "pandas_code": "df.loc[df['incumbent'] == 'wright patman', 'first elected'].iloc[0] == df['first elected'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3139,
        "statement": "only 4 candidate be not re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\ntexas 1#wright patman#democratic#1928#re - elected#wright patman (d) unopposed\ntexas 2#john dowdy redistricted from 7th#democratic#1952#re - elected#john dowdy (d) unopposed\ntexas 3#joe r pool redistricted from at - large#democratic#1962#re - elected#joe r pool (d) 53.4% bill hayes (r) 46.6%\ntexas 4#ray roberts#democratic#1962#re - elected#ray roberts (d) unopposed\ntexas 4#lindley beckworth redistricted from 3rd#democratic#1956#lost renomination democratic loss#ray roberts (d) unopposed\ntexas 5#earle cabell#democratic#1964#re - elected#earle cabell (d) 61.0% duke burgess (r) 39.0%\ntexas 6#olin e teague#democratic#1946#re - elected#olin e teague (d) unopposed\ntexas 8#lera millard thomas#democratic#1966#retired democratic hold#robert c eckhardt (d) 92.3% w d spayne (r) 7.7%\ntexas 9#jack brooks redistricted from 2nd#democratic#1952#re - elected#jack brooks (d) unopposed\ntexas 9#clark w thompson#democratic#1947#retired democratic loss#jack brooks (d) unopposed\ntexas 12#jim wright#democratic#1954#re - elected#jim wright (d) unopposed\ntexas 14#john andrew young#democratic#1956#re - elected#john andrew young (d) unopposed\ntexas 15#kika de la garza#democratic#1964#re - elected#kika de la garza (d) unopposed\ntexas 16#richard c white#democratic#1964#re - elected#richard c white (d) unopposed\ntexas 17#omar burleson#democratic#1946#re - elected#omar burleson (d) unopposed\ntexas 18#walter e rogers#democratic#1950#retired republican gain#bob price (r) 59.5% dee miller (d) 40.5%\ntexas 19#george h mahon#democratic#1934#re - elected#george h mahon (d) unopposed\ntexas 21#o c fisher#democratic#1942#re - elected#o c fisher (d) unopposed\n",
        "pandas_code": "(df['result'] != 're - elected').sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3140,
        "statement": "robert c eckhardt have the highest per centage of vote",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1966",
        "table_text": "district#incumbent#party#first elected#result#candidates\ntexas 1#wright patman#democratic#1928#re - elected#wright patman (d) unopposed\ntexas 2#john dowdy redistricted from 7th#democratic#1952#re - elected#john dowdy (d) unopposed\ntexas 3#joe r pool redistricted from at - large#democratic#1962#re - elected#joe r pool (d) 53.4% bill hayes (r) 46.6%\ntexas 4#ray roberts#democratic#1962#re - elected#ray roberts (d) unopposed\ntexas 4#lindley beckworth redistricted from 3rd#democratic#1956#lost renomination democratic loss#ray roberts (d) unopposed\ntexas 5#earle cabell#democratic#1964#re - elected#earle cabell (d) 61.0% duke burgess (r) 39.0%\ntexas 6#olin e teague#democratic#1946#re - elected#olin e teague (d) unopposed\ntexas 8#lera millard thomas#democratic#1966#retired democratic hold#robert c eckhardt (d) 92.3% w d spayne (r) 7.7%\ntexas 9#jack brooks redistricted from 2nd#democratic#1952#re - elected#jack brooks (d) unopposed\ntexas 9#clark w thompson#democratic#1947#retired democratic loss#jack brooks (d) unopposed\ntexas 12#jim wright#democratic#1954#re - elected#jim wright (d) unopposed\ntexas 14#john andrew young#democratic#1956#re - elected#john andrew young (d) unopposed\ntexas 15#kika de la garza#democratic#1964#re - elected#kika de la garza (d) unopposed\ntexas 16#richard c white#democratic#1964#re - elected#richard c white (d) unopposed\ntexas 17#omar burleson#democratic#1946#re - elected#omar burleson (d) unopposed\ntexas 18#walter e rogers#democratic#1950#retired republican gain#bob price (r) 59.5% dee miller (d) 40.5%\ntexas 19#george h mahon#democratic#1934#re - elected#george h mahon (d) unopposed\ntexas 21#o c fisher#democratic#1942#re - elected#o c fisher (d) unopposed\n",
        "pandas_code": "df.loc[df['candidates'].str.extract(r'(\\d+\\.\\d+)%')[0].astype(float).idxmax(), 'candidates'].startswith('robert c eckhardt')",
        "pandas_eval": "True"
    },
    {
        "id": 3142,
        "statement": "al jefferson have the high point and the high rebound in 3 game",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n46#february 1#boston#l 101 - 109 (ot)#al jefferson (34)#al jefferson (11)#randy foye (9)#td banknorth garden 18624#16 - 30\n47#february 3#indiana#w 116 - 111 (ot)#randy foye (19)#al jefferson (15)#randy foye (5)#conseco fieldhouse 11015#17 - 30\n48#february 4#atlanta#l 86 - 94 (ot)#al jefferson (18)#kevin love (14)#mike miller (4)#target center 13745#17 - 31\n49#february 7#houston#l 90 - 107 (ot)#al jefferson (36)#al jefferson (22)#sebastian telfair (9)#toyota center 16815#17 - 32\n50#february 8#new orleans#l 97 - 101 (ot)#al jefferson (25)#al jefferson (14)#sebastian telfair (6)#new orleans arena 16046#17 - 33\n51#february 10#toronto#l 102 - 110 (ot)#randy foye (33)#kevin love (12)#mike miller (5)#target center 12722#17 - 34\n52#february 17#washington#l 103 - 111 (ot)#randy foye (23)#kevin love (11)#mike miller (6)#verizon center 11623#17 - 35\n53#february 18#miami#w 111 - 104 (ot)#sebastian telfair (30)#brian cardinal (10)#mike miller (9)#american airlines arena 17525#18 - 35\n54#february 20#indiana#l 105 - 112 (ot)#randy foye (36)#kevin love (12)#sebastian telfair (6)#target center 13777#18 - 36\n55#february 22#la lakers#l 108 - 111 (ot)#sebastian telfair , ryan gomes (20)#kevin love (10)#randy foye , kevin ollie (6)#target center 19177#18 - 37\n56#february 24#toronto#l 110 - 118 (ot)#randy foye (25)#mike miller (12)#mike miller (9)#air canada centre 17457#18 - 38\n57#february 25#utah#l 103 - 120 (ot)#ryan gomes , kevin love (24)#kevin love (15)#mike miller , sebastian telfair (6)#target center 13108#18 - 39\n",
        "pandas_code": "(df['high points'].str.contains('al jefferson') & df['high rebounds'].str.contains('al jefferson')).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3143,
        "statement": "player tie for high point in 2 game",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n46#february 1#boston#l 101 - 109 (ot)#al jefferson (34)#al jefferson (11)#randy foye (9)#td banknorth garden 18624#16 - 30\n47#february 3#indiana#w 116 - 111 (ot)#randy foye (19)#al jefferson (15)#randy foye (5)#conseco fieldhouse 11015#17 - 30\n48#february 4#atlanta#l 86 - 94 (ot)#al jefferson (18)#kevin love (14)#mike miller (4)#target center 13745#17 - 31\n49#february 7#houston#l 90 - 107 (ot)#al jefferson (36)#al jefferson (22)#sebastian telfair (9)#toyota center 16815#17 - 32\n50#february 8#new orleans#l 97 - 101 (ot)#al jefferson (25)#al jefferson (14)#sebastian telfair (6)#new orleans arena 16046#17 - 33\n51#february 10#toronto#l 102 - 110 (ot)#randy foye (33)#kevin love (12)#mike miller (5)#target center 12722#17 - 34\n52#february 17#washington#l 103 - 111 (ot)#randy foye (23)#kevin love (11)#mike miller (6)#verizon center 11623#17 - 35\n53#february 18#miami#w 111 - 104 (ot)#sebastian telfair (30)#brian cardinal (10)#mike miller (9)#american airlines arena 17525#18 - 35\n54#february 20#indiana#l 105 - 112 (ot)#randy foye (36)#kevin love (12)#sebastian telfair (6)#target center 13777#18 - 36\n55#february 22#la lakers#l 108 - 111 (ot)#sebastian telfair , ryan gomes (20)#kevin love (10)#randy foye , kevin ollie (6)#target center 19177#18 - 37\n56#february 24#toronto#l 110 - 118 (ot)#randy foye (25)#mike miller (12)#mike miller (9)#air canada centre 17457#18 - 38\n57#february 25#utah#l 103 - 120 (ot)#ryan gomes , kevin love (24)#kevin love (15)#mike miller , sebastian telfair (6)#target center 13108#18 - 39\n",
        "pandas_code": "df['high points'].apply(lambda x: len(set(x.split(' , '))) > 1).sum() >= 2",
        "pandas_eval": "True"
    },
    {
        "id": 3144,
        "statement": "the game be 2 player tie in high point have 2 player that also tie in high assist",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n46#february 1#boston#l 101 - 109 (ot)#al jefferson (34)#al jefferson (11)#randy foye (9)#td banknorth garden 18624#16 - 30\n47#february 3#indiana#w 116 - 111 (ot)#randy foye (19)#al jefferson (15)#randy foye (5)#conseco fieldhouse 11015#17 - 30\n48#february 4#atlanta#l 86 - 94 (ot)#al jefferson (18)#kevin love (14)#mike miller (4)#target center 13745#17 - 31\n49#february 7#houston#l 90 - 107 (ot)#al jefferson (36)#al jefferson (22)#sebastian telfair (9)#toyota center 16815#17 - 32\n50#february 8#new orleans#l 97 - 101 (ot)#al jefferson (25)#al jefferson (14)#sebastian telfair (6)#new orleans arena 16046#17 - 33\n51#february 10#toronto#l 102 - 110 (ot)#randy foye (33)#kevin love (12)#mike miller (5)#target center 12722#17 - 34\n52#february 17#washington#l 103 - 111 (ot)#randy foye (23)#kevin love (11)#mike miller (6)#verizon center 11623#17 - 35\n53#february 18#miami#w 111 - 104 (ot)#sebastian telfair (30)#brian cardinal (10)#mike miller (9)#american airlines arena 17525#18 - 35\n54#february 20#indiana#l 105 - 112 (ot)#randy foye (36)#kevin love (12)#sebastian telfair (6)#target center 13777#18 - 36\n55#february 22#la lakers#l 108 - 111 (ot)#sebastian telfair , ryan gomes (20)#kevin love (10)#randy foye , kevin ollie (6)#target center 19177#18 - 37\n56#february 24#toronto#l 110 - 118 (ot)#randy foye (25)#mike miller (12)#mike miller (9)#air canada centre 17457#18 - 38\n57#february 25#utah#l 103 - 120 (ot)#ryan gomes , kevin love (24)#kevin love (15)#mike miller , sebastian telfair (6)#target center 13108#18 - 39\n",
        "pandas_code": "df['high points'].str.contains(',').any() & df['high assists'].str.contains(',').any()",
        "pandas_eval": "True"
    },
    {
        "id": 3145,
        "statement": "the game where al jefferson have the highest rebound occur at toyota center",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n46#february 1#boston#l 101 - 109 (ot)#al jefferson (34)#al jefferson (11)#randy foye (9)#td banknorth garden 18624#16 - 30\n47#february 3#indiana#w 116 - 111 (ot)#randy foye (19)#al jefferson (15)#randy foye (5)#conseco fieldhouse 11015#17 - 30\n48#february 4#atlanta#l 86 - 94 (ot)#al jefferson (18)#kevin love (14)#mike miller (4)#target center 13745#17 - 31\n49#february 7#houston#l 90 - 107 (ot)#al jefferson (36)#al jefferson (22)#sebastian telfair (9)#toyota center 16815#17 - 32\n50#february 8#new orleans#l 97 - 101 (ot)#al jefferson (25)#al jefferson (14)#sebastian telfair (6)#new orleans arena 16046#17 - 33\n51#february 10#toronto#l 102 - 110 (ot)#randy foye (33)#kevin love (12)#mike miller (5)#target center 12722#17 - 34\n52#february 17#washington#l 103 - 111 (ot)#randy foye (23)#kevin love (11)#mike miller (6)#verizon center 11623#17 - 35\n53#february 18#miami#w 111 - 104 (ot)#sebastian telfair (30)#brian cardinal (10)#mike miller (9)#american airlines arena 17525#18 - 35\n54#february 20#indiana#l 105 - 112 (ot)#randy foye (36)#kevin love (12)#sebastian telfair (6)#target center 13777#18 - 36\n55#february 22#la lakers#l 108 - 111 (ot)#sebastian telfair , ryan gomes (20)#kevin love (10)#randy foye , kevin ollie (6)#target center 19177#18 - 37\n56#february 24#toronto#l 110 - 118 (ot)#randy foye (25)#mike miller (12)#mike miller (9)#air canada centre 17457#18 - 38\n57#february 25#utah#l 103 - 120 (ot)#ryan gomes , kevin love (24)#kevin love (15)#mike miller , sebastian telfair (6)#target center 13108#18 - 39\n",
        "pandas_code": "df[df['high rebounds'].str.contains('al jefferson') & df['location attendance'].str.contains('toyota center')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3146,
        "statement": "the target center have the highest 1 day attendance out of all location",
        "label": 1,
        "table_caption": "2008 - 09 minnesota timberwolves season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n46#february 1#boston#l 101 - 109 (ot)#al jefferson (34)#al jefferson (11)#randy foye (9)#td banknorth garden 18624#16 - 30\n47#february 3#indiana#w 116 - 111 (ot)#randy foye (19)#al jefferson (15)#randy foye (5)#conseco fieldhouse 11015#17 - 30\n48#february 4#atlanta#l 86 - 94 (ot)#al jefferson (18)#kevin love (14)#mike miller (4)#target center 13745#17 - 31\n49#february 7#houston#l 90 - 107 (ot)#al jefferson (36)#al jefferson (22)#sebastian telfair (9)#toyota center 16815#17 - 32\n50#february 8#new orleans#l 97 - 101 (ot)#al jefferson (25)#al jefferson (14)#sebastian telfair (6)#new orleans arena 16046#17 - 33\n51#february 10#toronto#l 102 - 110 (ot)#randy foye (33)#kevin love (12)#mike miller (5)#target center 12722#17 - 34\n52#february 17#washington#l 103 - 111 (ot)#randy foye (23)#kevin love (11)#mike miller (6)#verizon center 11623#17 - 35\n53#february 18#miami#w 111 - 104 (ot)#sebastian telfair (30)#brian cardinal (10)#mike miller (9)#american airlines arena 17525#18 - 35\n54#february 20#indiana#l 105 - 112 (ot)#randy foye (36)#kevin love (12)#sebastian telfair (6)#target center 13777#18 - 36\n55#february 22#la lakers#l 108 - 111 (ot)#sebastian telfair , ryan gomes (20)#kevin love (10)#randy foye , kevin ollie (6)#target center 19177#18 - 37\n56#february 24#toronto#l 110 - 118 (ot)#randy foye (25)#mike miller (12)#mike miller (9)#air canada centre 17457#18 - 38\n57#february 25#utah#l 103 - 120 (ot)#ryan gomes , kevin love (24)#kevin love (15)#mike miller , sebastian telfair (6)#target center 13108#18 - 39\n",
        "pandas_code": "(df['location attendance'].str.extract('(\\\\d+)').astype(int).max() == df[df['location attendance'].str.contains('target center')]['location attendance'].str.extract('(\\\\d+)').astype(int).max()).item()",
        "pandas_eval": "True"
    },
    {
        "id": 3147,
        "statement": "2 game be play in september",
        "label": 1,
        "table_caption": "1950 green bay packers season",
        "table_text": "game#date#opponent#result#packers points#opponents#first downs#record#streak#venue#attendance\n1#september 17#detroit lions#loss#7#45#11#0 - 1#lost 1#city stadium#22096\n2#september 24#washington redskins#win#35#21#21#1 - 1#won 1#state fair park#14109\n3#oct 1#chicago bears#win#31#21#8#2 - 1#won 2#city stadium#24893\n4#oct 8#new york yanks#loss#31#44#23#2 - 2#lost 1#city stadium#23871\n5#oct 15#chicago bears#loss#14#28#11#2 - 3#lost 2#wrigley field#51065\n6#oct 19#new york yanks#loss#17#35#14#2 - 4#lost 3#yankee stadium#13661\n7#nov 5#baltimore colts#loss#21#41#13#2 - 5#lost 4#memorial stadium#12971\n8#nov 12#los angeles rams#loss#14#45#17#2 - 6#lost 5#state fair park#20456\n9#nov 19#detroit lions#loss#21#24#16#2 - 7#lost 6#briggs stadium#17752\n10#nov 26#san francisco 49ers#win#25#21#13#3 - 7#won 1#city stadium#13196\n11#dec 3#los angeles rams#loss#14#51#13#3 - 8#lost 1#los angeles memorial coliseum#39323\n",
        "pandas_code": "len(df[df['date'].str.contains('september')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3148,
        "statement": "1 game be play in december",
        "label": 1,
        "table_caption": "1950 green bay packers season",
        "table_text": "game#date#opponent#result#packers points#opponents#first downs#record#streak#venue#attendance\n1#september 17#detroit lions#loss#7#45#11#0 - 1#lost 1#city stadium#22096\n2#september 24#washington redskins#win#35#21#21#1 - 1#won 1#state fair park#14109\n3#oct 1#chicago bears#win#31#21#8#2 - 1#won 2#city stadium#24893\n4#oct 8#new york yanks#loss#31#44#23#2 - 2#lost 1#city stadium#23871\n5#oct 15#chicago bears#loss#14#28#11#2 - 3#lost 2#wrigley field#51065\n6#oct 19#new york yanks#loss#17#35#14#2 - 4#lost 3#yankee stadium#13661\n7#nov 5#baltimore colts#loss#21#41#13#2 - 5#lost 4#memorial stadium#12971\n8#nov 12#los angeles rams#loss#14#45#17#2 - 6#lost 5#state fair park#20456\n9#nov 19#detroit lions#loss#21#24#16#2 - 7#lost 6#briggs stadium#17752\n10#nov 26#san francisco 49ers#win#25#21#13#3 - 7#won 1#city stadium#13196\n11#dec 3#los angeles rams#loss#14#51#13#3 - 8#lost 1#los angeles memorial coliseum#39323\n",
        "pandas_code": "len(df[df['date'].str.contains('dec')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3149,
        "statement": "there be 8 loss",
        "label": 1,
        "table_caption": "1950 green bay packers season",
        "table_text": "game#date#opponent#result#packers points#opponents#first downs#record#streak#venue#attendance\n1#september 17#detroit lions#loss#7#45#11#0 - 1#lost 1#city stadium#22096\n2#september 24#washington redskins#win#35#21#21#1 - 1#won 1#state fair park#14109\n3#oct 1#chicago bears#win#31#21#8#2 - 1#won 2#city stadium#24893\n4#oct 8#new york yanks#loss#31#44#23#2 - 2#lost 1#city stadium#23871\n5#oct 15#chicago bears#loss#14#28#11#2 - 3#lost 2#wrigley field#51065\n6#oct 19#new york yanks#loss#17#35#14#2 - 4#lost 3#yankee stadium#13661\n7#nov 5#baltimore colts#loss#21#41#13#2 - 5#lost 4#memorial stadium#12971\n8#nov 12#los angeles rams#loss#14#45#17#2 - 6#lost 5#state fair park#20456\n9#nov 19#detroit lions#loss#21#24#16#2 - 7#lost 6#briggs stadium#17752\n10#nov 26#san francisco 49ers#win#25#21#13#3 - 7#won 1#city stadium#13196\n11#dec 3#los angeles rams#loss#14#51#13#3 - 8#lost 1#los angeles memorial coliseum#39323\n",
        "pandas_code": "(df['result'] == 'loss').sum() == 8",
        "pandas_eval": "True"
    },
    {
        "id": 3150,
        "statement": "there be 3 win",
        "label": 1,
        "table_caption": "1950 green bay packers season",
        "table_text": "game#date#opponent#result#packers points#opponents#first downs#record#streak#venue#attendance\n1#september 17#detroit lions#loss#7#45#11#0 - 1#lost 1#city stadium#22096\n2#september 24#washington redskins#win#35#21#21#1 - 1#won 1#state fair park#14109\n3#oct 1#chicago bears#win#31#21#8#2 - 1#won 2#city stadium#24893\n4#oct 8#new york yanks#loss#31#44#23#2 - 2#lost 1#city stadium#23871\n5#oct 15#chicago bears#loss#14#28#11#2 - 3#lost 2#wrigley field#51065\n6#oct 19#new york yanks#loss#17#35#14#2 - 4#lost 3#yankee stadium#13661\n7#nov 5#baltimore colts#loss#21#41#13#2 - 5#lost 4#memorial stadium#12971\n8#nov 12#los angeles rams#loss#14#45#17#2 - 6#lost 5#state fair park#20456\n9#nov 19#detroit lions#loss#21#24#16#2 - 7#lost 6#briggs stadium#17752\n10#nov 26#san francisco 49ers#win#25#21#13#3 - 7#won 1#city stadium#13196\n11#dec 3#los angeles rams#loss#14#51#13#3 - 8#lost 1#los angeles memorial coliseum#39323\n",
        "pandas_code": "(df['result'] == 'win').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3151,
        "statement": "the most point score by the packer be 35",
        "label": 1,
        "table_caption": "1950 green bay packers season",
        "table_text": "game#date#opponent#result#packers points#opponents#first downs#record#streak#venue#attendance\n1#september 17#detroit lions#loss#7#45#11#0 - 1#lost 1#city stadium#22096\n2#september 24#washington redskins#win#35#21#21#1 - 1#won 1#state fair park#14109\n3#oct 1#chicago bears#win#31#21#8#2 - 1#won 2#city stadium#24893\n4#oct 8#new york yanks#loss#31#44#23#2 - 2#lost 1#city stadium#23871\n5#oct 15#chicago bears#loss#14#28#11#2 - 3#lost 2#wrigley field#51065\n6#oct 19#new york yanks#loss#17#35#14#2 - 4#lost 3#yankee stadium#13661\n7#nov 5#baltimore colts#loss#21#41#13#2 - 5#lost 4#memorial stadium#12971\n8#nov 12#los angeles rams#loss#14#45#17#2 - 6#lost 5#state fair park#20456\n9#nov 19#detroit lions#loss#21#24#16#2 - 7#lost 6#briggs stadium#17752\n10#nov 26#san francisco 49ers#win#25#21#13#3 - 7#won 1#city stadium#13196\n11#dec 3#los angeles rams#loss#14#51#13#3 - 8#lost 1#los angeles memorial coliseum#39323\n",
        "pandas_code": "df['packers points'].max() == 35",
        "pandas_eval": "True"
    },
    {
        "id": 3152,
        "statement": "colin murray be the presenter for the majority of episode",
        "label": 1,
        "table_caption": "list of fighting talk episodes",
        "table_text": "date#presenter#guest 1#guest 2#guest 3#guest 4\n18 august#colin murray#john rawling#neil delamere#greg brady#clarke carlisle (debut)\n25 august#gabby logan#des kelly#dougie anderson#paul sinha#dietmar hamann\n1 september#colin murray#brian reade#simon day#martin kelner#etienne stott (debut)\n8 september#colin murray#tom watt#ian moore#gary gillespie#charlie baker (debut)\n15 september#colin murray#des kelly#andy dunn#justin moorhouse#ed clancy (debut)\n22 september#colin murray#jim smallman#martin kelner#greg brady#robbie savage\n29 september#colin murray#henning wehn#john rawling#katharine merry#bob mills\n6 october#colin murray#dougie anderson#neil delamere#richard osman (debut)#dominic cork\n13 october live from the crucible , sheffield#colin murray#john rawling#simon day#des kelly#martin kelner\n20 october#colin murray#jim white#tom watt#chris martin (debut)#phil brown\n27 october#colin murray#greg brady#gail emms#andy dunn#justin moorhouse\n3 november#colin murray#sara winterburn (debut)#dougie anderson#dietmar hamann#ian stone\n10 november#colin murray#jennie gow (debut)#steve bunce#john rawling#henning wehn\n17 november#colin murray#adam richman (debut)#mark watson#neil delamere#martin kelner\n24 november#colin murray#des kelly#katharine merry#andy dunn#bob mills\n1 december#colin murray#dougie anderson#paul mcveigh#eddie kadi#greg brady\n8 december#colin murray#john rawling#brian reade#gary gillespie#ian stone\n15 december#colin murray#tom watt#martin kelner#ian moore#dietmar hamann\n22 december#colin murray#jim white#charlie baker#richard osman#phil brown\n29 december review of 2012#colin murray#bob mills#martin kelner#tom watt#greg brady\n5 january#nick hancock#steve bunce#perry groves#justin moorhouse#katharine merry\n12 january#christian o'connell#greg brady#andy parsons#dougie anderson#john rawling\n19 january#colin murray#eleanor oldroyd#kit symons#andy dunn#simon day\n26 january#colin murray#richard osman#martin kelner#phil brown#kevin bridges\n2 february#colin murray#john rawling#brian noble (debut)#greg brady#carl donnelly (debut)\n9 february#colin murray#danny mills#bob mills#brian reade#tom watt\n16 february#greg james (debut)#dougie anderson#justin moorhouse#darren fletcher#matthew hoggard (debut)\n23 february#colin murray#kevin kilbane (debut)#jim white#charlie baker#martin kelner\n2 march#colin murray#john rawling#gail emms#andy dunn#eddie kadi\n9 march ladies special#colin murray#eleanor oldroyd#katharine merry#jennie gow#kelly sotherton (debut)\n16 march#colin murray#dougie anderson#steve lamacq#paul sinha#phil brown\n23 march#nick hancock#martin kelner#christian malcolm#ian stone#greg brady\n30 march#colin murray#bob mills#john rawling#cornelius lysaght#louise hazel (debut)\n6 april#colin murray#tom watt#brian noble#richard osman#chris martin\n13 april#colin murray#rebecca adlington (debut)#will buckley#martin kelner#neil delamere\n20 april#colin murray#henning wehn#dougie anderson#eleanor oldroyd#nathan caton (debut)\n27 april food special#colin murray#simon rimmer (debut)#sean wilson (debut)#danny mills#justin moorhouse\n4 may#colin murray#john rawling#iyare igiehon#brian reade#ian moore\n11 may#colin murray#katharine merry#greg brady#darren fletcher#jim smallman\n18 may#colin murray#louise hazel#ian stone#tom watt#dougie anderson\n",
        "pandas_code": "df['presenter'].value_counts().idxmax() == 'colin murray'",
        "pandas_eval": "True"
    },
    {
        "id": 3153,
        "statement": "greg brady be a guest 9 time",
        "label": 1,
        "table_caption": "list of fighting talk episodes",
        "table_text": "date#presenter#guest 1#guest 2#guest 3#guest 4\n18 august#colin murray#john rawling#neil delamere#greg brady#clarke carlisle (debut)\n25 august#gabby logan#des kelly#dougie anderson#paul sinha#dietmar hamann\n1 september#colin murray#brian reade#simon day#martin kelner#etienne stott (debut)\n8 september#colin murray#tom watt#ian moore#gary gillespie#charlie baker (debut)\n15 september#colin murray#des kelly#andy dunn#justin moorhouse#ed clancy (debut)\n22 september#colin murray#jim smallman#martin kelner#greg brady#robbie savage\n29 september#colin murray#henning wehn#john rawling#katharine merry#bob mills\n6 october#colin murray#dougie anderson#neil delamere#richard osman (debut)#dominic cork\n13 october live from the crucible , sheffield#colin murray#john rawling#simon day#des kelly#martin kelner\n20 october#colin murray#jim white#tom watt#chris martin (debut)#phil brown\n27 october#colin murray#greg brady#gail emms#andy dunn#justin moorhouse\n3 november#colin murray#sara winterburn (debut)#dougie anderson#dietmar hamann#ian stone\n10 november#colin murray#jennie gow (debut)#steve bunce#john rawling#henning wehn\n17 november#colin murray#adam richman (debut)#mark watson#neil delamere#martin kelner\n24 november#colin murray#des kelly#katharine merry#andy dunn#bob mills\n1 december#colin murray#dougie anderson#paul mcveigh#eddie kadi#greg brady\n8 december#colin murray#john rawling#brian reade#gary gillespie#ian stone\n15 december#colin murray#tom watt#martin kelner#ian moore#dietmar hamann\n22 december#colin murray#jim white#charlie baker#richard osman#phil brown\n29 december review of 2012#colin murray#bob mills#martin kelner#tom watt#greg brady\n5 january#nick hancock#steve bunce#perry groves#justin moorhouse#katharine merry\n12 january#christian o'connell#greg brady#andy parsons#dougie anderson#john rawling\n19 january#colin murray#eleanor oldroyd#kit symons#andy dunn#simon day\n26 january#colin murray#richard osman#martin kelner#phil brown#kevin bridges\n2 february#colin murray#john rawling#brian noble (debut)#greg brady#carl donnelly (debut)\n9 february#colin murray#danny mills#bob mills#brian reade#tom watt\n16 february#greg james (debut)#dougie anderson#justin moorhouse#darren fletcher#matthew hoggard (debut)\n23 february#colin murray#kevin kilbane (debut)#jim white#charlie baker#martin kelner\n2 march#colin murray#john rawling#gail emms#andy dunn#eddie kadi\n9 march ladies special#colin murray#eleanor oldroyd#katharine merry#jennie gow#kelly sotherton (debut)\n16 march#colin murray#dougie anderson#steve lamacq#paul sinha#phil brown\n23 march#nick hancock#martin kelner#christian malcolm#ian stone#greg brady\n30 march#colin murray#bob mills#john rawling#cornelius lysaght#louise hazel (debut)\n6 april#colin murray#tom watt#brian noble#richard osman#chris martin\n13 april#colin murray#rebecca adlington (debut)#will buckley#martin kelner#neil delamere\n20 april#colin murray#henning wehn#dougie anderson#eleanor oldroyd#nathan caton (debut)\n27 april food special#colin murray#simon rimmer (debut)#sean wilson (debut)#danny mills#justin moorhouse\n4 may#colin murray#john rawling#iyare igiehon#brian reade#ian moore\n11 may#colin murray#katharine merry#greg brady#darren fletcher#jim smallman\n18 may#colin murray#louise hazel#ian stone#tom watt#dougie anderson\n",
        "pandas_code": "(df[['guest 1', 'guest 2', 'guest 3', 'guest 4']] == 'greg brady').sum().sum() == 9",
        "pandas_eval": "True"
    },
    {
        "id": 3154,
        "statement": "clarke carlisle wasn't a guest again after a debut",
        "label": 1,
        "table_caption": "list of fighting talk episodes",
        "table_text": "date#presenter#guest 1#guest 2#guest 3#guest 4\n18 august#colin murray#john rawling#neil delamere#greg brady#clarke carlisle (debut)\n25 august#gabby logan#des kelly#dougie anderson#paul sinha#dietmar hamann\n1 september#colin murray#brian reade#simon day#martin kelner#etienne stott (debut)\n8 september#colin murray#tom watt#ian moore#gary gillespie#charlie baker (debut)\n15 september#colin murray#des kelly#andy dunn#justin moorhouse#ed clancy (debut)\n22 september#colin murray#jim smallman#martin kelner#greg brady#robbie savage\n29 september#colin murray#henning wehn#john rawling#katharine merry#bob mills\n6 october#colin murray#dougie anderson#neil delamere#richard osman (debut)#dominic cork\n13 october live from the crucible , sheffield#colin murray#john rawling#simon day#des kelly#martin kelner\n20 october#colin murray#jim white#tom watt#chris martin (debut)#phil brown\n27 october#colin murray#greg brady#gail emms#andy dunn#justin moorhouse\n3 november#colin murray#sara winterburn (debut)#dougie anderson#dietmar hamann#ian stone\n10 november#colin murray#jennie gow (debut)#steve bunce#john rawling#henning wehn\n17 november#colin murray#adam richman (debut)#mark watson#neil delamere#martin kelner\n24 november#colin murray#des kelly#katharine merry#andy dunn#bob mills\n1 december#colin murray#dougie anderson#paul mcveigh#eddie kadi#greg brady\n8 december#colin murray#john rawling#brian reade#gary gillespie#ian stone\n15 december#colin murray#tom watt#martin kelner#ian moore#dietmar hamann\n22 december#colin murray#jim white#charlie baker#richard osman#phil brown\n29 december review of 2012#colin murray#bob mills#martin kelner#tom watt#greg brady\n5 january#nick hancock#steve bunce#perry groves#justin moorhouse#katharine merry\n12 january#christian o'connell#greg brady#andy parsons#dougie anderson#john rawling\n19 january#colin murray#eleanor oldroyd#kit symons#andy dunn#simon day\n26 january#colin murray#richard osman#martin kelner#phil brown#kevin bridges\n2 february#colin murray#john rawling#brian noble (debut)#greg brady#carl donnelly (debut)\n9 february#colin murray#danny mills#bob mills#brian reade#tom watt\n16 february#greg james (debut)#dougie anderson#justin moorhouse#darren fletcher#matthew hoggard (debut)\n23 february#colin murray#kevin kilbane (debut)#jim white#charlie baker#martin kelner\n2 march#colin murray#john rawling#gail emms#andy dunn#eddie kadi\n9 march ladies special#colin murray#eleanor oldroyd#katharine merry#jennie gow#kelly sotherton (debut)\n16 march#colin murray#dougie anderson#steve lamacq#paul sinha#phil brown\n23 march#nick hancock#martin kelner#christian malcolm#ian stone#greg brady\n30 march#colin murray#bob mills#john rawling#cornelius lysaght#louise hazel (debut)\n6 april#colin murray#tom watt#brian noble#richard osman#chris martin\n13 april#colin murray#rebecca adlington (debut)#will buckley#martin kelner#neil delamere\n20 april#colin murray#henning wehn#dougie anderson#eleanor oldroyd#nathan caton (debut)\n27 april food special#colin murray#simon rimmer (debut)#sean wilson (debut)#danny mills#justin moorhouse\n4 may#colin murray#john rawling#iyare igiehon#brian reade#ian moore\n11 may#colin murray#katharine merry#greg brady#darren fletcher#jim smallman\n18 may#colin murray#louise hazel#ian stone#tom watt#dougie anderson\n",
        "pandas_code": "len(df[(df['guest 4'] == 'clarke carlisle (debut)') & (df['guest 4'].shift(-1) != 'clarke carlisle')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3155,
        "statement": "chris martin be a guest again after his debut",
        "label": 1,
        "table_caption": "list of fighting talk episodes",
        "table_text": "date#presenter#guest 1#guest 2#guest 3#guest 4\n18 august#colin murray#john rawling#neil delamere#greg brady#clarke carlisle (debut)\n25 august#gabby logan#des kelly#dougie anderson#paul sinha#dietmar hamann\n1 september#colin murray#brian reade#simon day#martin kelner#etienne stott (debut)\n8 september#colin murray#tom watt#ian moore#gary gillespie#charlie baker (debut)\n15 september#colin murray#des kelly#andy dunn#justin moorhouse#ed clancy (debut)\n22 september#colin murray#jim smallman#martin kelner#greg brady#robbie savage\n29 september#colin murray#henning wehn#john rawling#katharine merry#bob mills\n6 october#colin murray#dougie anderson#neil delamere#richard osman (debut)#dominic cork\n13 october live from the crucible , sheffield#colin murray#john rawling#simon day#des kelly#martin kelner\n20 october#colin murray#jim white#tom watt#chris martin (debut)#phil brown\n27 october#colin murray#greg brady#gail emms#andy dunn#justin moorhouse\n3 november#colin murray#sara winterburn (debut)#dougie anderson#dietmar hamann#ian stone\n10 november#colin murray#jennie gow (debut)#steve bunce#john rawling#henning wehn\n17 november#colin murray#adam richman (debut)#mark watson#neil delamere#martin kelner\n24 november#colin murray#des kelly#katharine merry#andy dunn#bob mills\n1 december#colin murray#dougie anderson#paul mcveigh#eddie kadi#greg brady\n8 december#colin murray#john rawling#brian reade#gary gillespie#ian stone\n15 december#colin murray#tom watt#martin kelner#ian moore#dietmar hamann\n22 december#colin murray#jim white#charlie baker#richard osman#phil brown\n29 december review of 2012#colin murray#bob mills#martin kelner#tom watt#greg brady\n5 january#nick hancock#steve bunce#perry groves#justin moorhouse#katharine merry\n12 january#christian o'connell#greg brady#andy parsons#dougie anderson#john rawling\n19 january#colin murray#eleanor oldroyd#kit symons#andy dunn#simon day\n26 january#colin murray#richard osman#martin kelner#phil brown#kevin bridges\n2 february#colin murray#john rawling#brian noble (debut)#greg brady#carl donnelly (debut)\n9 february#colin murray#danny mills#bob mills#brian reade#tom watt\n16 february#greg james (debut)#dougie anderson#justin moorhouse#darren fletcher#matthew hoggard (debut)\n23 february#colin murray#kevin kilbane (debut)#jim white#charlie baker#martin kelner\n2 march#colin murray#john rawling#gail emms#andy dunn#eddie kadi\n9 march ladies special#colin murray#eleanor oldroyd#katharine merry#jennie gow#kelly sotherton (debut)\n16 march#colin murray#dougie anderson#steve lamacq#paul sinha#phil brown\n23 march#nick hancock#martin kelner#christian malcolm#ian stone#greg brady\n30 march#colin murray#bob mills#john rawling#cornelius lysaght#louise hazel (debut)\n6 april#colin murray#tom watt#brian noble#richard osman#chris martin\n13 april#colin murray#rebecca adlington (debut)#will buckley#martin kelner#neil delamere\n20 april#colin murray#henning wehn#dougie anderson#eleanor oldroyd#nathan caton (debut)\n27 april food special#colin murray#simon rimmer (debut)#sean wilson (debut)#danny mills#justin moorhouse\n4 may#colin murray#john rawling#iyare igiehon#brian reade#ian moore\n11 may#colin murray#katharine merry#greg brady#darren fletcher#jim smallman\n18 may#colin murray#louise hazel#ian stone#tom watt#dougie anderson\n",
        "pandas_code": "df[df['guest 1'].str.contains('chris martin') | df['guest 2'].str.contains('chris martin') | df['guest 3'].str.contains('chris martin') | df['guest 4'].str.contains('chris martin')].index[0] < df[df['guest 1'].str.contains('chris martin') | df['guest 2'].str.contains('chris martin') | df['guest 3'].str.contains('chris martin') | df['guest 4'].str.contains('chris martin')].index[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 3156,
        "statement": "gabby logan be only a presenter 1 time",
        "label": 1,
        "table_caption": "list of fighting talk episodes",
        "table_text": "date#presenter#guest 1#guest 2#guest 3#guest 4\n18 august#colin murray#john rawling#neil delamere#greg brady#clarke carlisle (debut)\n25 august#gabby logan#des kelly#dougie anderson#paul sinha#dietmar hamann\n1 september#colin murray#brian reade#simon day#martin kelner#etienne stott (debut)\n8 september#colin murray#tom watt#ian moore#gary gillespie#charlie baker (debut)\n15 september#colin murray#des kelly#andy dunn#justin moorhouse#ed clancy (debut)\n22 september#colin murray#jim smallman#martin kelner#greg brady#robbie savage\n29 september#colin murray#henning wehn#john rawling#katharine merry#bob mills\n6 october#colin murray#dougie anderson#neil delamere#richard osman (debut)#dominic cork\n13 october live from the crucible , sheffield#colin murray#john rawling#simon day#des kelly#martin kelner\n20 october#colin murray#jim white#tom watt#chris martin (debut)#phil brown\n27 october#colin murray#greg brady#gail emms#andy dunn#justin moorhouse\n3 november#colin murray#sara winterburn (debut)#dougie anderson#dietmar hamann#ian stone\n10 november#colin murray#jennie gow (debut)#steve bunce#john rawling#henning wehn\n17 november#colin murray#adam richman (debut)#mark watson#neil delamere#martin kelner\n24 november#colin murray#des kelly#katharine merry#andy dunn#bob mills\n1 december#colin murray#dougie anderson#paul mcveigh#eddie kadi#greg brady\n8 december#colin murray#john rawling#brian reade#gary gillespie#ian stone\n15 december#colin murray#tom watt#martin kelner#ian moore#dietmar hamann\n22 december#colin murray#jim white#charlie baker#richard osman#phil brown\n29 december review of 2012#colin murray#bob mills#martin kelner#tom watt#greg brady\n5 january#nick hancock#steve bunce#perry groves#justin moorhouse#katharine merry\n12 january#christian o'connell#greg brady#andy parsons#dougie anderson#john rawling\n19 january#colin murray#eleanor oldroyd#kit symons#andy dunn#simon day\n26 january#colin murray#richard osman#martin kelner#phil brown#kevin bridges\n2 february#colin murray#john rawling#brian noble (debut)#greg brady#carl donnelly (debut)\n9 february#colin murray#danny mills#bob mills#brian reade#tom watt\n16 february#greg james (debut)#dougie anderson#justin moorhouse#darren fletcher#matthew hoggard (debut)\n23 february#colin murray#kevin kilbane (debut)#jim white#charlie baker#martin kelner\n2 march#colin murray#john rawling#gail emms#andy dunn#eddie kadi\n9 march ladies special#colin murray#eleanor oldroyd#katharine merry#jennie gow#kelly sotherton (debut)\n16 march#colin murray#dougie anderson#steve lamacq#paul sinha#phil brown\n23 march#nick hancock#martin kelner#christian malcolm#ian stone#greg brady\n30 march#colin murray#bob mills#john rawling#cornelius lysaght#louise hazel (debut)\n6 april#colin murray#tom watt#brian noble#richard osman#chris martin\n13 april#colin murray#rebecca adlington (debut)#will buckley#martin kelner#neil delamere\n20 april#colin murray#henning wehn#dougie anderson#eleanor oldroyd#nathan caton (debut)\n27 april food special#colin murray#simon rimmer (debut)#sean wilson (debut)#danny mills#justin moorhouse\n4 may#colin murray#john rawling#iyare igiehon#brian reade#ian moore\n11 may#colin murray#katharine merry#greg brady#darren fletcher#jim smallman\n18 may#colin murray#louise hazel#ian stone#tom watt#dougie anderson\n",
        "pandas_code": "df[df['presenter'] == 'gabby logan'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3157,
        "statement": "the ride that open in 1999 have the lowest height requirement",
        "label": 1,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df[df['year opened'] == 1999]['minimum height'].min() == df['minimum height'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3158,
        "statement": "the ride with the lowest rating be manufacture by e&f miller industries",
        "label": 1,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df.loc[df['rating'].idxmin(), 'ride manufacturer and type'] == 'e&f miller industries kiddie coaster'",
        "pandas_eval": "True"
    },
    {
        "id": 3159,
        "statement": "the grizzly ride be open before the flight deck ride",
        "label": 1,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df[df['ride'] == 'grizzly']['year opened'].values[0] < df[df['ride'] == 'flight deck']['year opened'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3160,
        "statement": "25% of the ride be open after the year 2000",
        "label": 1,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "(df['year opened'] > 2000).mean() >= 0.25",
        "pandas_eval": "True"
    },
    {
        "id": 3161,
        "statement": "the ride that open the earliest have a 4 rating",
        "label": 1,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df.loc[df['year opened'] == df['year opened'].min(), 'rating'].values[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3162,
        "statement": "the ride that open in 1999 have the highest height requiremen",
        "label": 0,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df[df['year opened'] == 1999]['minimum height'].max() == df['minimum height'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3163,
        "statement": "the ride with the highest rating be manufacture by e&f miller industries",
        "label": 0,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df.loc[df['rating'].idxmax(), 'ride manufacturer and type'] == 'e&f miller industries kiddie coaster'",
        "pandas_eval": "False"
    },
    {
        "id": 3164,
        "statement": "the grizzly ride be open after the flight deck ride",
        "label": 0,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df[df['ride'] == 'grizzly']['year opened'].values[0] > df[df['ride'] == 'flight deck']['year opened'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3165,
        "statement": "25% of the ride be open before the year 2000",
        "label": 0,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "(df['year opened'] < 2000).mean() < 0.25",
        "pandas_eval": "False"
    },
    {
        "id": 3166,
        "statement": "the ride that close the earliest have a 4 rating",
        "label": 0,
        "table_caption": "california 's great america",
        "table_text": "ride#year opened#ride manufacturer and type#minimum height#rating\nthe demon#1980#arrow dynamics#48#5\nflight deck#1993#bolliger & mabillard inverted roller coaster#54#5\ngold striker#2013#great coasters international wooden roller coaster#48#4\ngrizzly#1986#wooden roller coaster#48#4\npsycho mouse#2001#arrow dynamics wild mouse roller coaster#44#4\ntaxi jam#1999#e&f miller industries kiddie coaster#36#2\nvortex#1991#bolliger & mabillard stand - up roller coaster#54#5\nwoodstock express#1987#intamin family roller coaster#40#3\n",
        "pandas_code": "df.loc[df['year opened'] == df['year opened'].min(), 'rating'].values[0] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3167,
        "statement": "bill clay be first elect before harold volkmer",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1980",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmissouri 1#bill clay#democratic#1968#re - elected#bill clay (d) 70.2% bill white (r) 29.8%\nmissouri 4#ike skelton#democratic#1976#re - elected#ike skelton (d) 67.8% bill baker (r) 32.2%\nmissouri 7#gene taylor#republican#1972#re - elected#gene taylor (r) 67.8% ken young (d) 32.2%\nmissouri 8#richard howard ichord , jr#democratic#1960#retired republican gain#wendell bailey (r) 57.1% steve gardner (d) 42.9%\nmissouri 9#harold volkmer#democratic#1976#re - elected#harold volkmer (d) 56.5% john w turner (r) 43.5%\n",
        "pandas_code": "df[df['incumbent'] == 'bill clay']['first elected'].values[0] < df[df['incumbent'] == 'harold volkmer']['first elected'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3170,
        "statement": "richard howard ichord jr be the only incumbent to not get re - elect",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1980",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmissouri 1#bill clay#democratic#1968#re - elected#bill clay (d) 70.2% bill white (r) 29.8%\nmissouri 4#ike skelton#democratic#1976#re - elected#ike skelton (d) 67.8% bill baker (r) 32.2%\nmissouri 7#gene taylor#republican#1972#re - elected#gene taylor (r) 67.8% ken young (d) 32.2%\nmissouri 8#richard howard ichord , jr#democratic#1960#retired republican gain#wendell bailey (r) 57.1% steve gardner (d) 42.9%\nmissouri 9#harold volkmer#democratic#1976#re - elected#harold volkmer (d) 56.5% john w turner (r) 43.5%\n",
        "pandas_code": "(df[df['incumbent'] == 'richard howard ichord , jr']['result'] == 'retired republican gain').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3171,
        "statement": "bill clay have the largest margin of victory",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1980",
        "table_text": "district#incumbent#party#first elected#result#candidates\nmissouri 1#bill clay#democratic#1968#re - elected#bill clay (d) 70.2% bill white (r) 29.8%\nmissouri 4#ike skelton#democratic#1976#re - elected#ike skelton (d) 67.8% bill baker (r) 32.2%\nmissouri 7#gene taylor#republican#1972#re - elected#gene taylor (r) 67.8% ken young (d) 32.2%\nmissouri 8#richard howard ichord , jr#democratic#1960#retired republican gain#wendell bailey (r) 57.1% steve gardner (d) 42.9%\nmissouri 9#harold volkmer#democratic#1976#re - elected#harold volkmer (d) 56.5% john w turner (r) 43.5%\n",
        "pandas_code": "df.loc[df['incumbent'] == 'bill clay', 'candidates'].str.extract(r'(\\d+\\.\\d+)%')[0].astype(float).max() == df['candidates'].str.extract(r'(\\d+\\.\\d+)%')[0].astype(float).max()",
        "pandas_eval": "True"
    },
    {
        "id": 3172,
        "statement": "attendance be lower than 17000 2 time",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "(df['attendance'] < 17000).sum() >= 2",
        "pandas_eval": "True"
    },
    {
        "id": 3173,
        "statement": "a total of 75 goal be score in these game",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "df['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).sum() == 75",
        "pandas_eval": "True"
    },
    {
        "id": 3174,
        "statement": "flyer didn't win a game until october 24",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "df[df['date'] == 'october 24']['record'].str.startswith('1').any()",
        "pandas_eval": "True"
    },
    {
        "id": 3175,
        "statement": "flyer have a 4 game win streak from october 24 - 30",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "((df['date'] >= 'october 24') & (df['date'] <= 'october 30')).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3176,
        "statement": "flyer play 5 home game from october 11 - 30",
        "label": 1,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "len(df[(df['home'] == 'philadelphia') & (df['date'].isin(['october 11', 'october 13', 'october 22', 'october 25', 'october 30']))]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3177,
        "statement": "attendance be lower than 17000 on every occasion when the flyer play in october",
        "label": 0,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "all(df[(df['date'].str.contains('october')) & (df['home'] == 'philadelphia')]['attendance'] < 17000)",
        "pandas_eval": "False"
    },
    {
        "id": 3178,
        "statement": "a total be 75 goal be score in each of the game play by the flyer",
        "label": 0,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "all(df['score'].apply(lambda x: sum(map(int, x.split(' - ')))) == 75)",
        "pandas_eval": "False"
    },
    {
        "id": 3179,
        "statement": "the flyer win every game that they play until october 24 when they lose",
        "label": 0,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "all(df[(df['home'] == 'philadelphia') & (df['date'] <= 'october 24')]['record'].apply(lambda x: x.split(' - ')[0] == '1'))",
        "pandas_eval": "False"
    },
    {
        "id": 3180,
        "statement": "the flyer have a 4 game win streak during the entire month of october because they only play 4 game",
        "label": 0,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "(df['record'].str.startswith('4')).sum() >= 4",
        "pandas_eval": "False"
    },
    {
        "id": 3181,
        "statement": "the flyer play 5 home during the entire month of october and the rest of the time they be on the road and lose every game",
        "label": 0,
        "table_caption": "2008 - 09 philadelphia flyers season",
        "table_text": "date#visitor#score#home#decision#attendance#record\noctober 11#ny rangers#4 - 3#philadelphia#biron#19623#0 - 1 - 0\noctober 13#montreal#5 - 3#philadelphia#biron#19323#0 - 2 - 0\noctober 14#philadelphia#2 - 3#pittsburgh#niittymaki#16965#0 - 2 - 1\noctober 16#philadelphia#2 - 5#colorado#biron#18007#0 - 3 - 1\noctober 18#philadelphia#4 - 5#san jose#niittymaki#17496#0 - 3 - 2\noctober 22#san jose#7 - 6#philadelphia#biron#19072#0 - 3 - 3\noctober 24#philadelphia#6 - 3#new jersey#biron#15529#1 - 3 - 3\noctober 25#new jersey#2 - 3#philadelphia#biron#19611#2 - 3 - 3\noctober 28#philadelphia#7 - 0#atlanta#niittymaki#13207#3 - 3 - 3\noctober 30#ny islanders#2 - 3#philadelphia#biron#18227#4 - 3 - 3\n",
        "pandas_code": "(df['home'].value_counts()['philadelphia'] == 5) & (df[df['visitor'] == 'philadelphia']['record'].str.startswith('0 -').all())",
        "pandas_eval": "False"
    },
    {
        "id": 3182,
        "statement": "the lion finish with 7 win and 9 loss",
        "label": 1,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 7) & (df['result'].str.startswith('l').sum() == 9)",
        "pandas_eval": "True"
    },
    {
        "id": 3183,
        "statement": "the most point score by the lion in 1 game be 44",
        "label": 1,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "df['result'].str.extract(r'(\\d+) - \\d+').astype(int).max().iloc[0] == 44",
        "pandas_eval": "True"
    },
    {
        "id": 3184,
        "statement": "the lowest attendance for a single game be 59938",
        "label": 1,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "df['attendance'].min() == 59938",
        "pandas_eval": "True"
    },
    {
        "id": 3185,
        "statement": "the lion lose 7 of their final 8 game",
        "label": 1,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "(df['result'].str.startswith('l').tail(8).sum() == 7)",
        "pandas_eval": "True"
    },
    {
        "id": 3186,
        "statement": "the most game that the lion win consecutively be 3",
        "label": 1,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "max(df['result'].str.startswith('w').groupby((df['result'].str.startswith('w') != df['result'].str.startswith('w').shift()).cumsum()).sum()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3187,
        "statement": "the lion finish with no win and 10 loss",
        "label": 0,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "(df['result'].str.startswith('l').sum() == 10) & (df['result'].str.startswith('w').sum() == 0)",
        "pandas_eval": "False"
    },
    {
        "id": 3188,
        "statement": "the lion never score more than 36 point in 1 game",
        "label": 0,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "all(df['result'].str.extract(r'(\\d+) - \\d+')[0].astype(int) <= 36)",
        "pandas_eval": "False"
    },
    {
        "id": 3189,
        "statement": "the lowest attendance for a single game be 60442",
        "label": 0,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "df['attendance'].min() == 60442",
        "pandas_eval": "False"
    },
    {
        "id": 3190,
        "statement": "the lion lose all of their final 8 game",
        "label": 0,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "all(df['result'].iloc[-8:].str.startswith('l'))",
        "pandas_eval": "False"
    },
    {
        "id": 3191,
        "statement": "the most game that the lion win in a row be 5",
        "label": 0,
        "table_caption": "2007 detroit lions season",
        "table_text": "week#date#opponent#result#attendance\n1#september 9 , 2007#oakland raiders#w 36 - 21#61547\n2#september 16 , 2007#minnesota vikings#w 20 - 17 (ot)#61771\n3#september 23 , 2007#philadelphia eagles#l 21 - 56#67570\n4#september 30 , 2007#chicago bears#w 37 - 27#60811\n5#october 7 , 2007#washington redskins#l 3 - 34#88944\n7#october 21 , 2007#tampa bay buccaneers#w 23 - 16#60442\n8#october 28 , 2007#chicago bears#w 16 - 7#62171\n9#november 4 , 2007#denver broncos#w 44 - 7#60783\n10#november 11 , 2007#arizona cardinals#l 21 - 31#64753\n11#november 18 , 2007#new york giants#l 10 - 16#60675\n12#november 22 , 2007#green bay packers#l 26 - 37#63257\n13#december 2 , 2007#minnesota vikings#l 10 - 42#62996\n14#december 9 , 2007#dallas cowboys#l 27 - 28#62759\n15#december 16 , 2007#san diego chargers#l 14 - 51#66505\n16#december 23 , 2007#kansas city chiefs#w 25 - 20#59938\n17#december 30 , 2007#green bay packers#l 13 - 34#70869\n",
        "pandas_code": "df['result'].str.startswith('w').rolling(window=5).sum().max() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 3192,
        "statement": "the peter jackson champion tournament have the largest prize of all the tournament in june",
        "label": 1,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "df[df['date'].str.contains('jun')]['1st prize'].max() == df[df['tournament'] == 'peter jackson champions']['1st prize'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3194,
        "statement": "bob goalby earn the exact same score in the malrboro classic as don january at the ereka federal saving classic",
        "label": 1,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "int(df.loc[df['tournament'] == 'marlboro classic', 'score'].values[0].split()[0]) == int(df.loc[df['tournament'] == 'eureka federal savings classic', 'score'].values[0].split()[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3195,
        "statement": "michigan only host 1 tournament , the us senior open , whereas florida host 3 - the pga senior 's championship , the suntree senior classic , and michelob egypt temple senior classic tournament",
        "label": 1,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "(df[df['location'] == 'michigan']['tournament'].eq('us senior open').sum() == 1) & (df[df['location'] == 'florida']['tournament'].isin(['pga seniors\\' championship', 'suntree seniors classic', 'michelob - egypt temple senior classic']).sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3196,
        "statement": "as a host location , florida rank highest in purse amount with 375000 , with canada follow second at 200000",
        "label": 1,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "df.groupby('location')['purse'].sum().sort_values(ascending=False).iloc[:2].equals(pd.Series([375000, 200000], index=['florida', 'canada']))",
        "pandas_eval": "True"
    },
    {
        "id": 3197,
        "statement": "the us senior open champion tournament have the largest prize of all the tournament in june",
        "label": 0,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "df[(df['date'].str.contains('jun')) & (df['1st prize'] == df[df['date'].str.contains('jun')]['1st prize'].max())]['tournament'].iloc[0] == 'us senior open'",
        "pandas_eval": "False"
    },
    {
        "id": 3198,
        "statement": "from january 5th , through dec 6 the highest score amongst all tournament be 289 (9)",
        "label": 0,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "df[(df['date'] >= 'jan 5') & (df['date'] <= 'dec 6')]['score'].max() == '289 (9)'",
        "pandas_eval": "False"
    },
    {
        "id": 3199,
        "statement": "don january earn the exact same score in the malrboro classic as don january at the ereka federal saving classic",
        "label": 0,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "len(df[(df['tournament'] == 'marlboro classic') & (df['winner'].str.contains('don january'))]) > 0 and len(df[(df['tournament'] == 'eureka federal savings classic') & (df['winner'].str.contains('don january'))]) > 0 and df[(df['tournament'] == 'marlboro classic') & (df['winner'].str.contains('don january'))]['score'].iloc[0] == df[(df['tournament'] == 'eureka federal savings classic') & (df['winner'].str.contains('don january'))]['score'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3200,
        "statement": "michigan only host 2 tournament , the us senior open , whereas florida host 3 - the pga senior 's championship , the suntree senior classic , and michelob egypt temple senior classic tournament",
        "label": 0,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "(df[df['location'] == 'michigan']['tournament'].nunique() == 1) & (df[df['location'] == 'florida']['tournament'].nunique() == 3) == False",
        "pandas_eval": "False"
    },
    {
        "id": 3201,
        "statement": "as a host location , florida rank highest in purse amount with 375000 , with michigan follow second at 200000",
        "label": 0,
        "table_caption": "1981 senior pga tour",
        "table_text": "date#tournament#location#purse#winner#score#1st prize\napr 5#michelob - egypt temple senior classic#florida#125000#don january (2)#280 ( - 8)#20000\njun 7#eureka federal savings classic#california#150000#don january (3)#208 ( - 5)#25000\njun 14#peter jackson champions#canada#200000#miller barber (1)#204 ( - 6)#30000\njun 28#marlboro classic#massachusetts#150000#bob goalby (1)#208 ( - 2)#25000\njul 12#us senior open#michigan#149000#arnold palmer (2)#289 (9)#26000\noct 18#suntree seniors classic#florida#125000#miller barber (2)#204 ( - 12)#20000\ndec 6#pga seniors' championship#florida#125000#miller barber (3)#281 ( - 7)#20000\n",
        "pandas_code": "(df[df['location'] == 'florida']['purse'].sum() == 375000) & (df[df['location'] == 'michigan']['purse'].sum() == 200000)",
        "pandas_eval": "False"
    },
    {
        "id": 3202,
        "statement": "la cage aux folles be nominate for 7 laurence olivier award in 2009",
        "label": 1,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "len(df[(df['year'] == 2009) & (df['award'] == 'laurence olivier award')]) == 7",
        "pandas_eval": "True"
    },
    {
        "id": 3203,
        "statement": "the show end up win only 2 time - for best musical revival and best actor in a musical",
        "label": 1,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[(df['category'].isin(['best musical revival', 'best actor in a musical'])) & (df['result'] == 'won')].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3204,
        "statement": "douglas hodge beat out his co - star denis lawson for the best actor in a musical prize",
        "label": 1,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[(df['nominee'] == 'douglas hodge') & (df['category'] == 'best actor in a musical')]['result'].values[0] == 'won') & (df[(df['nominee'] == 'denis lawson') & (df['category'] == 'best actor in a musical')]['result'].values[0] == 'nominated')",
        "pandas_eval": "True"
    },
    {
        "id": 3205,
        "statement": "3 of the nomination be for act , and 3 be for technical role",
        "label": 1,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[df['category'].str.contains('actor|performance', case=False)].shape[0] == 3) & (df[df['category'].str.contains('director|choreographer|design', case=False)].shape[0] == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3206,
        "statement": "of la cage aux folles' 6 individual nomination , choreographer lynne page be the only woman up for an award",
        "label": 1,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[(df['nominee'] == 'lynne page') & (df['category'] == 'best theatre choreographer')].shape[0] == 1) & (df[df['nominee'].str.contains('lynne page', case=False)]['nominee'].nunique() == 1) & (df[df['result'] == 'nominated']['nominee'].str.contains('lynne page', case=False).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3207,
        "statement": "la cage aux folles be win for 7 laurence olivier award in 2009",
        "label": 0,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[(df['year'] == 2009) & (df['award'] == 'laurence olivier award') & (df['result'] == 'won')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3208,
        "statement": "the show end up win only 2 time - for best costume design and best actor in a matthew wright",
        "label": 0,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[(df['category'] == 'best costume design') & (df['result'] == 'won')].shape[0] == 1) & (df[(df['category'] == 'best actor in a musical') & (df['nominee'] == 'matthew wright') & (df['result'] == 'won')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3209,
        "statement": "denis lawson beat out his co - star denis lawson for the best actor in a musical prize",
        "label": 0,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "not df[(df['nominee'] == 'denis lawson') & (df['category'] == 'best actor in a musical') & (df['result'] == 'won')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3210,
        "statement": "4 of the nomination be for act , and 4 be for technical role",
        "label": 0,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "(df[df['category'].str.contains('actor|performance', case=False)].shape[0] == 4) & (df[df['category'].str.contains('director|choreographer|design', case=False)].shape[0] == 4)",
        "pandas_eval": "False"
    },
    {
        "id": 3211,
        "statement": "of la cage aux folles' 6 individual nomination , choreographer matthew wright be the only woman up for an award",
        "label": 0,
        "table_caption": "la cage aux folles (musical)",
        "table_text": "year#award#category#nominee#result\n2009#laurence olivier award#best musical revival#best musical revival#won\n2009#laurence olivier award#best actor in a musical#douglas hodge#won\n2009#laurence olivier award#best actor in a musical#denis lawson#nominated\n2009#laurence olivier award#best performance in a supporting role in a musical#jason pennycooke#nominated\n2009#laurence olivier award#best director of a musical#terry johnson#nominated\n2009#laurence olivier award#best theatre choreographer#lynne page#nominated\n2009#laurence olivier award#best costume design#matthew wright#nominated\n",
        "pandas_code": "len(df[(df['category'] == 'best theatre choreographer') & (df['nominee'] == 'matthew wright') & (df['result'] == 'nominated')]) == 1 and len(df[(df['result'] == 'nominated') & (df['nominee'].str.contains('matthew wright', case=False))]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3212,
        "statement": "out of the 7 stage winner , 4 of which share the same suhardi hassan rider classification",
        "label": 1,
        "table_caption": "2008 jelajah malaysia",
        "table_text": "stage#stage winner#general classification#points classification#mountains classification#malaysian rider classification#team classification\n1#li fuyu#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n2#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n3#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n4#fredrik johansson#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n5#anuar manan#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n6#hossein askari#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n7#yusuke hatanaka#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n",
        "pandas_code": "(df[df['stage winner'].isin(df['stage winner'].unique())]['malaysian rider classification'].eq('suhardi hassan').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 3213,
        "statement": "out of the 7 stage winner , 3 of which share the same amir rusli rider as classification",
        "label": 1,
        "table_caption": "2008 jelajah malaysia",
        "table_text": "stage#stage winner#general classification#points classification#mountains classification#malaysian rider classification#team classification\n1#li fuyu#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n2#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n3#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n4#fredrik johansson#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n5#anuar manan#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n6#hossein askari#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n7#yusuke hatanaka#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n",
        "pandas_code": "(df['stage winner'].value_counts() == 3).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3214,
        "statement": "7 out of 7 stage winner get anuar manan as classification point",
        "label": 1,
        "table_caption": "2008 jelajah malaysia",
        "table_text": "stage#stage winner#general classification#points classification#mountains classification#malaysian rider classification#team classification\n1#li fuyu#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n2#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n3#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n4#fredrik johansson#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n5#anuar manan#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n6#hossein askari#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n7#yusuke hatanaka#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n",
        "pandas_code": "all(df['points classification'] == 'anuar manan')",
        "pandas_eval": "True"
    },
    {
        "id": 3215,
        "statement": "7 out of 7 stage winner get hossein askari as mountain classification",
        "label": 1,
        "table_caption": "2008 jelajah malaysia",
        "table_text": "stage#stage winner#general classification#points classification#mountains classification#malaysian rider classification#team classification\n1#li fuyu#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n2#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n3#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n4#fredrik johansson#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n5#anuar manan#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n6#hossein askari#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n7#yusuke hatanaka#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n",
        "pandas_code": "df['mountains classification'].eq('hossein askari').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3216,
        "statement": "3 out of 7 stage winner get li fuyu as general classification",
        "label": 1,
        "table_caption": "2008 jelajah malaysia",
        "table_text": "stage#stage winner#general classification#points classification#mountains classification#malaysian rider classification#team classification\n1#li fuyu#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n2#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n3#anuar manan#li fuyu#anuar manan#hossein askari#amir rusli#team stegcomputer - ckt - cogeas\n4#fredrik johansson#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n5#anuar manan#tonton susanto#anuar manan#hossein askari#suhardi hassan#japan\n6#hossein askari#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n7#yusuke hatanaka#tonton susanto#anuar manan#hossein askari#suhardi hassan#tabriz petrochemical team\n",
        "pandas_code": "(df[df['stage winner'].isin(df['stage winner'].unique())]['general classification'] == 'li fuyu').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3217,
        "statement": "the detroit lion be the opponent of the cleveland brown in their 1963 season",
        "label": 1,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[df['opponent'] == 'detroit lions'].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 3218,
        "statement": "the cleveland brown play the baltimore colt on august 17 , 1963",
        "label": 1,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[(df['opponent'] == 'baltimore colts') & (df['date'] == 'august 17 , 1963')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3219,
        "statement": "at canton , the cleveland brown play the pittburgh steelers on september 8 , 1963",
        "label": 1,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[(df['opponent'].str.contains('pittsburgh steelers at canton')) & (df['date'] == 'september 8 , 1963')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3220,
        "statement": "on august 31 , 1983 , the los angeles ram lose to the cleveland brown with a score of 23 - 17",
        "label": 1,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[(df['date'] == 'august 31 , 1963') & (df['opponent'] == 'los angeles rams') & (df['result'] == 'w 23 - 17')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3221,
        "statement": "on august 25 , 1963 , the cleveland brown vs san francisco 49ers have 28335 in attendance",
        "label": 1,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[(df['date'] == 'august 25 , 1963') & (df['opponent'] == 'san francisco 49ers') & (df['attendance'] == 28335)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3222,
        "statement": "the detroit lion be the second opponent of the cleveland brown in their 1963 season",
        "label": 0,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df['opponent'].iloc[1] == 'detroit lions'",
        "pandas_eval": "False"
    },
    {
        "id": 3223,
        "statement": "the cleveland brown play the baltimore colt on the last week of the 1963 season",
        "label": 0,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df[df['opponent'] == 'baltimore colts']['week'].iloc[-1] == df['week'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3224,
        "statement": "the cleveland brown never play the pittsburgh steelers at canton in the 1963 season",
        "label": 0,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "not any(df['opponent'].str.contains('pittsburgh steelers at canton'))",
        "pandas_eval": "False"
    },
    {
        "id": 3225,
        "statement": "on august 31 , 1983 , the los angeles ram win against cleveland brown with a score of 23 - 17",
        "label": 0,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "((df['date'] == 'august 31 , 1983') & (df['opponent'] == 'los angeles rams') & (df['result'] == 'w 23 - 17')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3226,
        "statement": "on august 25 , 1963 , the cleveland brown vs san francisco 49ers have the same number of attendance as the game on august 17 , 1963",
        "label": 0,
        "table_caption": "1963 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 10 , 1963#detroit lions#l 24 - 10#37248\n2#august 17 , 1963#baltimore colts#l 21 - 7#83218\n3#august 25 , 1963#san francisco 49ers#w 24 - 7#28335\n4#august 31 , 1963#los angeles rams#w 23 - 17#45623\n5#september 8 , 1963#pittsburgh steelers at canton#l 16 - 7#18462\n",
        "pandas_code": "df.loc[df['date'] == 'august 25 , 1963', 'attendance'].values[0] == df.loc[df['date'] == 'august 17 , 1963', 'attendance'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3227,
        "statement": "norway be home to 2 more prominent peak than sweden with 4 total",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df[df['country'] == 'norway']['prominence (m)'].count() - df[df['country'] == 'sweden']['prominence (m)'].count() == 2) and (df[df['country'] == 'norway']['prominence (m)'].count() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 3228,
        "statement": "despite have the highest prominence galdh\u00f8piggen have the third lowest col",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df['prominence (m)'].max() == df.loc[df['peak'] == 'galdh\u00f8piggen', 'prominence (m)'].values[0]) and (df['col (m)'].nsmallest(3).iloc[-1] == df.loc[df['peak'] == 'galdh\u00f8piggen', 'col (m)'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3229,
        "statement": "while only fourth in term of prominence sn\u00f8hetta have the highest col",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df[df['peak'] == 'sn\u00f8hetta']['col (m)'].values[0] == df['col (m)'].max()) and (df[df['peak'] == 'sn\u00f8hetta']['prominence (m)'].values[0] == df['prominence (m)'].sort_values(ascending=False).iloc[3])",
        "pandas_eval": "True"
    },
    {
        "id": 3230,
        "statement": "galdh\u00f8piggen , have an elevation 356 m more than that of kebnekaise",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df[df['peak'] == 'galdh\u00f8piggen']['elevation (m)'].values[0] - df[df['peak'] == 'kebnekaise']['elevation (m)'].values[0]) == 356",
        "pandas_eval": "True"
    },
    {
        "id": 3231,
        "statement": "sarektj\u00e5hkk\u00e5 , as the lowest prominence of any of the mountain with only 1519 m",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "df[df['peak'] == 'sarektj\u00e5hkk\u00e5']['prominence (m)'].iloc[0] == df['prominence (m)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3232,
        "statement": "sweden be home to more prominent peak than norway",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "df[df['country'] == 'sweden']['prominence (m)'].max() > df[df['country'] == 'norway']['prominence (m)'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3233,
        "statement": "galdh\u00f8piggen have the lowest prominence and the lowest col",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df.loc[df['peak'] == 'galdh\u00f8piggen', 'prominence (m)'].values[0] == df['prominence (m)'].min()) & (df.loc[df['peak'] == 'galdh\u00f8piggen', 'col (m)'].values[0] == df['col (m)'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 3234,
        "statement": "sn\u00f8hetta have the highest col by more than 100 m",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "df[df['peak'] == 'sn\u00f8hetta']['col (m)'].values[0] - df['col (m)'].max() > 100",
        "pandas_eval": "False"
    },
    {
        "id": 3235,
        "statement": "gladhopiggen have an elevation of more than 400 m more than that of kebnekaise",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "(df[df['peak'] == 'galdh\u00f8piggen']['elevation (m)'].values[0] - df[df['peak'] == 'kebnekaise']['elevation (m)'].values[0]) > 400",
        "pandas_eval": "False"
    },
    {
        "id": 3236,
        "statement": "sarektj\u00e5hkk\u00e5 have a prominence of 1519 which be the second lowest prominence of all the mountain",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngaldh\u00f8piggen#norway#2469#2372#97\nkebnekaise#sweden#2113#1754#359\njiehkkev\u00e1rri#norway#1834#1741#93\nsn\u00f8hetta#norway#2286#1675#611\nstore lenangstind#norway#1624#1576#48\nsarektj\u00e5hkk\u00e5#sweden#2089#1519#570\n",
        "pandas_code": "df['prominence (m)'].sort_values().iloc[1] == 1519",
        "pandas_eval": "False"
    },
    {
        "id": 3238,
        "statement": "the attendance at the june 25 game be more than any other game that year",
        "label": 1,
        "table_caption": "2005 houston astros season",
        "table_text": "date#winning team#score#winning pitcher#losing pitcher#attendance#location\nmay 20#texas#7 - 3#kenny rogers#brandon backe#38109#arlington\nmay 21#texas#18 - 3#chris young#ezequiel astacio#35781#arlington\nmay 22#texas#2 - 0#chan ho park#roy oswalt#40583#arlington\njune 24#houston#5 - 2#roy oswalt#ricardo rodriguez#36199#houston\njune 25#texas#6 - 5#chris young#brandon backe#41868#houston\n",
        "pandas_code": "df[df['date'] == 'june 25']['attendance'].iloc[0] == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3239,
        "statement": "the attendance at the may 22 game be more than any other game in arlington that year",
        "label": 1,
        "table_caption": "2005 houston astros season",
        "table_text": "date#winning team#score#winning pitcher#losing pitcher#attendance#location\nmay 20#texas#7 - 3#kenny rogers#brandon backe#38109#arlington\nmay 21#texas#18 - 3#chris young#ezequiel astacio#35781#arlington\nmay 22#texas#2 - 0#chan ho park#roy oswalt#40583#arlington\njune 24#houston#5 - 2#roy oswalt#ricardo rodriguez#36199#houston\njune 25#texas#6 - 5#chris young#brandon backe#41868#houston\n",
        "pandas_code": "df[(df['date'] == 'may 22') & (df['location'] == 'arlington')]['attendance'].iloc[0] > df[(df['location'] == 'arlington') & (df['date'] != 'may 22')]['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3240,
        "statement": "the june 24 game be the only game houston win against texas",
        "label": 1,
        "table_caption": "2005 houston astros season",
        "table_text": "date#winning team#score#winning pitcher#losing pitcher#attendance#location\nmay 20#texas#7 - 3#kenny rogers#brandon backe#38109#arlington\nmay 21#texas#18 - 3#chris young#ezequiel astacio#35781#arlington\nmay 22#texas#2 - 0#chan ho park#roy oswalt#40583#arlington\njune 24#houston#5 - 2#roy oswalt#ricardo rodriguez#36199#houston\njune 25#texas#6 - 5#chris young#brandon backe#41868#houston\n",
        "pandas_code": "((df['date'] == 'june 24') & (df['winning team'] == 'houston') & (df['winning team'].shift() != 'houston') & (df['winning team'].shift(-1) != 'houston')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3241,
        "statement": "brandon backe lose to both kenny rogers and chris young",
        "label": 1,
        "table_caption": "2005 houston astros season",
        "table_text": "date#winning team#score#winning pitcher#losing pitcher#attendance#location\nmay 20#texas#7 - 3#kenny rogers#brandon backe#38109#arlington\nmay 21#texas#18 - 3#chris young#ezequiel astacio#35781#arlington\nmay 22#texas#2 - 0#chan ho park#roy oswalt#40583#arlington\njune 24#houston#5 - 2#roy oswalt#ricardo rodriguez#36199#houston\njune 25#texas#6 - 5#chris young#brandon backe#41868#houston\n",
        "pandas_code": "all(df[df['losing pitcher'] == 'brandon backe']['winning pitcher'].isin(['kenny rogers', 'chris young']))",
        "pandas_eval": "True"
    },
    {
        "id": 3242,
        "statement": "the new york jet and chief be the only team to have 2 pick in the listed range",
        "label": 1,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "(df['team'].str.contains('new york jets', case=False).sum() == 2) & (df['team'].str.contains('kansas city chiefs', case=False).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3243,
        "statement": "4 pick from the list range come from state university",
        "label": 1,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "df[df['college'].str.contains('state', case=False)].shape[0] == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3244,
        "statement": "there be 3 total defensive lineman take in the list pick range",
        "label": 1,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "(df['position'].isin(['defensive end', 'defensive tackle']).sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3245,
        "statement": "the second most select position in the listed range be wide receiver",
        "label": 1,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "df['position'].value_counts().index[1] == 'defensive tackle'",
        "pandas_eval": "True"
    },
    {
        "id": 3246,
        "statement": "there be a total of 2 player select in the give range with the first name jim",
        "label": 1,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "len(df[(df['pick'].between(49, 56)) & (df['player'].str.startswith('jim'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3247,
        "statement": "the new york jet and chief be the only team to have 3 pick in the listed range",
        "label": 0,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "df[df['team'].str.contains('new york jet|kansas city chief')]['pick'].nunique() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3248,
        "statement": "5 pick from the list range come from state university",
        "label": 0,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "df[df['pick'].between(49, 53)]['college'].str.contains('state').sum() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 3249,
        "statement": "there be 5 total defensive lineman take in the list pick range",
        "label": 0,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "(df[(df['pick'] >= 49) & (df['pick'] <= 56) & (df['position'].str.contains('defensive end|defensive tackle'))].shape[0] == 5)",
        "pandas_eval": "False"
    },
    {
        "id": 3250,
        "statement": "the second most select position in the listed range be quarterback",
        "label": 0,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "(df['position'].value_counts().index[1] == 'quarterback')",
        "pandas_eval": "False"
    },
    {
        "id": 3251,
        "statement": "there be a total of 3 player select in the give range with the first name jim",
        "label": 0,
        "table_caption": "1965 american football league draft",
        "table_text": "pick#team#player#position#college\n49#denver broncos#jim garcia#defensive end#purdue\n50#kansas city chiefs (from houston oilers)#gloster richardson#wide receiver#jackson state\n51#new york jets (from oakland raiders)#archie roberts#quarterback#columbia\n52#new york jets#jim harris , jr#defensive tackle#utah state\n53#kansas city chiefs#lou bobich#defensive back#michigan state\n54#san diego chargers#jack snow#wide receiver#notre dame\n55#boston patriots#tom neville#defensive tackle#mississippi state\n56#buffalo bills#marty schottenheimer#linebacker#pittsburgh\n",
        "pandas_code": "len(df[(df['pick'].between(49, 56)) & (df['player'].str.startswith('jim'))]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3252,
        "statement": "the most popular j last name on the grizzlies be jones",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['player'].str.split().str[-1].value_counts().idxmax() == 'jones'",
        "pandas_eval": "True"
    },
    {
        "id": 3253,
        "statement": "all the player come from different college within the united state",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['school / club team'].nunique() == df.shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3254,
        "statement": "the most common position among these player be forward",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['position'].str.contains('forward').sum() > len(df) / 2",
        "pandas_eval": "True"
    },
    {
        "id": 3256,
        "statement": "all the player be citizen of the united state",
        "label": 1,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['nationality'].eq('united states').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3257,
        "statement": "the least popular j first name on the grizzlies be jackson",
        "label": 0,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['player'].str.split().str[0].value_counts().idxmin() == 'jackson'",
        "pandas_eval": "False"
    },
    {
        "id": 3258,
        "statement": "all the player come from the same college within the united state",
        "label": 0,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['school / club team'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3259,
        "statement": "the least common position among these player be forward",
        "label": 0,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df['position'].value_counts().idxmin() == 'forward'",
        "pandas_eval": "False"
    },
    {
        "id": 3260,
        "statement": "the player with the shortest service on this list be dahntay jones , with 3 year",
        "label": 0,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "df[df['player'] == 'dahntay jones']['years for grizzlies'].str.split(' - ').apply(lambda x: int(x[1]) - int(x[0])).iloc[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3261,
        "statement": "not all of the player be permanant resident of the united state",
        "label": 0,
        "table_caption": "memphis grizzlies all - time roster",
        "table_text": "player#nationality#position#years for grizzlies#school / club team\nbobby jackson#united states#guard#2005 - 2006#minnesota\ncasey jacobsen#united states#guard - forward#2007 - 2008#stanford\nalexander johnson#united states#power forward#2006 - 2007#florida state\nchris johnson#united states#small forward#2013#dayton\nbobby jones#united states#forward#2008#washington\ndahntay jones#united states#guard - forward#2003 - 2007#duke\ndamon jones#united states#shooting guard#2000 - 2001#houston\neddie jones#united states#guard - forward#2005 - 2007#temple\n",
        "pandas_code": "not all(df['nationality'] == 'united states')",
        "pandas_eval": "False"
    },
    {
        "id": 3262,
        "statement": "danson tang win 2 award at the 2007 metro radio hit award",
        "label": 1,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "df[(df['year'] == 2007) & (df['award'] == 'metro radio hit awards') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3263,
        "statement": "danson tang win an award for best improve singer and a trend setter award",
        "label": 1,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "((df['category'] == 'best improved singer (\u8e8d\u9032\u6b4c\u624b)') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')).any() and ((df['category'] == 'trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3265,
        "statement": "danson tang win at least 1 award in 2007 , 2008 , and 2009",
        "label": 1,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "all(df[(df['nomination'] == 'danson tang') & (df['result'] == 'won')]['year'].unique() == [2007, 2008, 2009])",
        "pandas_eval": "True"
    },
    {
        "id": 3266,
        "statement": "danson tang win song of the year and best overseas new artist",
        "label": 1,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "all(df[((df['category'] == 'songs of the year') | (df['category'] == 'best overseas new artist')) & (df['nomination'] == 'danson tang')]['result'] == 'won')",
        "pandas_eval": "True"
    },
    {
        "id": 3267,
        "statement": "danson tang win 3 award at the 2007 metro radio hit award",
        "label": 0,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "df[(df['year'] == 2007) & (df['award'] == 'metro radio hit awards') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3268,
        "statement": "danson tang win an award for best improve singer and a trend setter award in 2007",
        "label": 0,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "((df['category'].str.contains('best improve singer', case=False)) & (df['category'].str.contains('trend setter award', case=False)) & (df['year'] == 2007) & (df['nomination'] == 'danson tang') & (df['result'] == 'won')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3269,
        "statement": "danson tang win 2 award at 2009 modern shanghai award and 1 at metro radio mandarin music award",
        "label": 0,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "(df[(df['award'] == '2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')].shape[0] == 2) & (df[(df['award'] == 'metro radio mandarin music awards') & (df['nomination'] == 'danson tang') & (df['result'] == 'won')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3270,
        "statement": "danson tang win at least 2 award in 2007 , 2008 , and 2009",
        "label": 0,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "df[(df['nomination'] == 'danson tang') & (df['result'] == 'won') & (df['year'].isin([2007, 2008, 2009]))].groupby('year').size().ge(2).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3271,
        "statement": "danson tang win song of the year and best overseas new artist in the year 2009",
        "label": 0,
        "table_caption": "danson tang",
        "table_text": "year#award#category#nomination#result\n2007#metro radio hit awards#best overseas new artist#danson tang#won\n2007#metro radio hit awards#best new artist (popular vote)#danson tang#won\n2008#metro radio mandarin music awards#songs of the year#\u5206\u958b\u4ee5\u5f8c (after the breakup) from love me#won\n2008#metro radio mandarin music awards#best improved singer (\u8e8d\u9032\u6b4c\u624b)#danson tang#won\n2009#2009 modern shanghai awards 2009\u6469\u767b\u4e0a\u6d77\u7cbe\u5f69\u5728\u6c83\u5e74\u5ea6\u5927\u8cde#trend setter award (\u6f6e\u6d41\u5148\u92d2\u734e)#danson tang#won\n",
        "pandas_code": "((df['year'] == 2009) & (df['category'].isin(['songs of the year', 'best overseas new artist'])) & (df['nomination'] == 'danson tang') & (df['result'] == 'won')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3272,
        "statement": "all 6 game be play on may 8 1971",
        "label": 1,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "all(df['date'] == '8 may 1971')",
        "pandas_eval": "True"
    },
    {
        "id": 3273,
        "statement": "fitzroy score the most of all team with 20.16 (136)",
        "label": 1,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df[df['home team score'] == '20.16 (136)']['home team'].values[0] == 'fitzroy'",
        "pandas_eval": "True"
    },
    {
        "id": 3274,
        "statement": "every game be play at a different venue",
        "label": 1,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df['venue'].nunique() == len(df)",
        "pandas_eval": "True"
    },
    {
        "id": 3275,
        "statement": "essendon score the fewest of any team at 9.11 (65)",
        "label": 1,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df[df['away team'] == 'essendon']['away team score'].iloc[0] == '9.11 (65)'",
        "pandas_eval": "True"
    },
    {
        "id": 3276,
        "statement": "melbourne versus collingwood draw the largest overall attendance of 80231",
        "label": 1,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df[(df['home team'] == 'melbourne') & (df['away team'] == 'collingwood')]['crowd'].max() == 80231",
        "pandas_eval": "True"
    },
    {
        "id": 3277,
        "statement": "only 4 game be play on may 8 1971",
        "label": 0,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "len(df[df['date'] == '8 may 1971']) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3278,
        "statement": "st kilda score the most of all team with 14.25 (109)",
        "label": 0,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "(df['home team score'].max() == '14.25 (109)') & (df[df['home team score'] == '14.25 (109)']['home team'].iloc[0] == 'st kilda')",
        "pandas_eval": "False"
    },
    {
        "id": 3279,
        "statement": "every game be play at arden street oval",
        "label": 0,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "all(df['venue'] == 'arden street oval')",
        "pandas_eval": "False"
    },
    {
        "id": 3280,
        "statement": "carlton score the fewest of any team at 9.11 (65)",
        "label": 0,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df[df['away team'] == 'carlton']['away team score'].iloc[0] == '9.11 (65)'",
        "pandas_eval": "False"
    },
    {
        "id": 3281,
        "statement": "melbourne versus collingwood draw the largest overall attendance of 11432",
        "label": 0,
        "table_caption": "1971 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nnorth melbourne#12.12 (84)#richmond#22.14 (146)#arden street oval#13047#8 may 1971\nfootscray#18.5 (113)#hawthorn#16.11 (107)#western oval#19205#8 may 1971\nst kilda#14.25 (109)#essendon#9.11 (65)#moorabbin oval#21604#8 may 1971\nmelbourne#10.12 (72)#collingwood#18.19 (127)#mcg#80231#8 may 1971\nsouth melbourne#13.14 (92)#carlton#14.10 (94)#lake oval#17171#8 may 1971\nfitzroy#20.16 (136)#geelong#13.13 (91)#vfl park#11432#8 may 1971\n",
        "pandas_code": "df[(df['home team'] == 'melbourne') & (df['away team'] == 'collingwood')]['crowd'].max() == 11432",
        "pandas_eval": "False"
    },
    {
        "id": 3282,
        "statement": "only 2 of the player have less than 9 match",
        "label": 1,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "(df['matches'] < 9).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3283,
        "statement": "only 1 of the player be record for 8 inning",
        "label": 1,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "df[df['innings'] == 8].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3284,
        "statement": "only 3 of the player have a highest score record of more than 100",
        "label": 1,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "(df['highest score'] > 100).sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3286,
        "statement": "arul suppiah have the lowest highest score record on the table",
        "label": 1,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "df.loc[df['player'] == 'arul suppiah', 'highest score'].values[0] == df['highest score'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3287,
        "statement": "craig kieswetter be the only player with 8 match",
        "label": 0,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "(df['matches'] == 8).sum() == 1 and df.loc[df['matches'] == 8, 'player'].iloc[0] == 'craig kieswetter'",
        "pandas_eval": "False"
    },
    {
        "id": 3288,
        "statement": "3 player be record for 8 inning",
        "label": 0,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "len(df[df['innings'] == 8]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3289,
        "statement": "more than 3 player have a highest score record of more than 100",
        "label": 0,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "(df['highest score'] > 100).sum() > 3",
        "pandas_eval": "False"
    },
    {
        "id": 3290,
        "statement": "aurl suppiah have the highest score record on the table",
        "label": 0,
        "table_caption": "somerset county cricket club in 2009",
        "table_text": "player#matches#innings#runs#average#highest score#100s#50s\nzander de bruyn#9#6#388#97.00#96#0#5\ncraig kieswetter#8#8#395#65.83#138#2#0\njustin langer#8#4#195#65.00#78#0#2\nmarcus trescothick#9#9#476#59.50#144#1#4\npeter trego#9#6#171#57.00#74#0#2\njames hildreth#9#9#313#34.77#151#1#1\narul suppiah#9#5#101#33.66#48#0#0\n",
        "pandas_code": "df['highest score'].max() == df.loc[df['player'] == 'arul suppiah', 'highest score'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3291,
        "statement": "there be 24 total red wine , wine style",
        "label": 1,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "len(df[df['wine style'].str.contains('red wine')]) == 24",
        "pandas_eval": "True"
    },
    {
        "id": 3292,
        "statement": "there be 13 total white wine , wine style",
        "label": 1,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "len(df[df['wine style'] == 'white wine']) == 13",
        "pandas_eval": "True"
    },
    {
        "id": 3293,
        "statement": "there be 4 total red and some white wine , wine style",
        "label": 1,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "len(df[df['wine style'] == 'red and some white wine']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3294,
        "statement": "the most popular wine style be red wine",
        "label": 1,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "df['wine style'].value_counts().idxmax() == 'red wine'",
        "pandas_eval": "True"
    },
    {
        "id": 3295,
        "statement": "the chablis region only produce 1 wine style",
        "label": 1,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "df[df['region'] == 'chablis']['wine style'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3296,
        "statement": "there 's 17 red wine style in total",
        "label": 0,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "len(df[df['wine style'] == 'red wine']) == 17",
        "pandas_eval": "False"
    },
    {
        "id": 3297,
        "statement": "accord to the chart , there 's a grand total of 24 white wine style",
        "label": 0,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "len(df[df['wine style'] == 'white wine']) == 24",
        "pandas_eval": "False"
    },
    {
        "id": 3298,
        "statement": "exactly 6 of the wine be style with red and some white wine",
        "label": 0,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "(df['wine style'] == 'red and some white wine').sum() == 6",
        "pandas_eval": "False"
    },
    {
        "id": 3299,
        "statement": "of all the wine on the list , the most , the most popular style be the red with some white wine",
        "label": 0,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "df['wine style'].value_counts().idxmax() == 'red and some white wine'",
        "pandas_eval": "False"
    },
    {
        "id": 3300,
        "statement": "the chablis region be only responsible for for the red wine style",
        "label": 0,
        "table_caption": "list of burgundy grand crus",
        "table_text": "grand cru#region#village#wine style#vineyard surface (2010)\nchablis grand cru#chablis#chablis#white wine#hectares (acres)\nchambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchambertin - clos de b\u00e8ze#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nchapelle - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ncharmes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\ngriotte - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nlatrici\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazis - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nmazoy\u00e8res - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nruchottes - chambertin#c\u00f4te de nuits#gevrey - chambertin#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de la roche#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos des lambrays#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos de tart#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nclos saint - denis#c\u00f4te de nuits#morey - saint - denis#red wine#hectares (acres)\nbonnes - mares#c\u00f4te de nuits#chambolle - musigny#red wine#( hectares (acres))\nmusigny#c\u00f4te de nuits#chambolle - musigny#red and some white wine#hectares (acres)\nclos de vougeot#c\u00f4te de nuits#vougeot#red wine#hectares (acres)\n\u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\ngrands \u00e9chezeaux#c\u00f4te de nuits#flagey - ech\u00e9zeaux#red wine#hectares (acres)\nla grande rue#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla roman\u00e9e#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nla tche#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nrichebourg#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - conti#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\nroman\u00e9e - saint - vivant#c\u00f4te de nuits#vosne - roman\u00e9e#red wine#hectares (acres)\ncorton#c\u00f4te de beaune#pernand - vergelesses#red and some white wine#hectares (acres)\ncorton - charlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncharlemagne#c\u00f4te de beaune#pernand - vergelesses#white wine#hectares (acres)\ncorton#c\u00f4te de beaune#ladoix - serrigny#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#ladoix - serrigny#white wine#( hectares (acres))\ncorton#c\u00f4te de beaune#aloxe - corton#red and some white wine#( hectares (acres))\ncorton - charlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\ncharlemagne#c\u00f4te de beaune#aloxe - corton#white wine#( hectares (acres))\nbtard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbienvenues - btard - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nchevalier - montrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#puligny - montrachet#white wine#hectares (acres)\nbtard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\ncriots - btard - montrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#hectares (acres)\nmontrachet#c\u00f4te de beaune#chassagne - montrachet#white wine#( hectares (acres))\n",
        "pandas_code": "not any(df[(df['region'] == 'chablis') & (df['wine style'] != 'red wine')])",
        "pandas_eval": "False"
    },
    {
        "id": 3301,
        "statement": "cerro porte\u00f1o win 2 more game than sol de am\u00e9rica",
        "label": 1,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df.loc[df['team'] == 'cerro porte\u00f1o', 'wins'].values[0] - df.loc[df['team'] == 'sol de am\u00e9rica', 'wins'].values[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3302,
        "statement": "san lorenzo have more loss than any other team",
        "label": 1,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df[df['team'] == 'san lorenzo']['losses'].values[0] == df['losses'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3303,
        "statement": "the team in 8th position lose 2 more game than the team in 6th position",
        "label": 1,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "(df.loc[df['position'] == 8, 'losses'].values[0] - df.loc[df['position'] == 6, 'losses'].values[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3304,
        "statement": "30% of the team win 3 game during the 2003 season",
        "label": 1,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "(df['wins'] == 3).sum() / len(df) == 0.3",
        "pandas_eval": "True"
    },
    {
        "id": 3305,
        "statement": "sol de am\u00e9rica and tacuary have the same number of point",
        "label": 1,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df[df['team'].isin(['sol de am\u00e9rica', 'tacuary'])]['points'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3306,
        "statement": "cerro porte\u00f1o win 2 fewer game than sol de am\u00e9rica",
        "label": 0,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df[df['team'] == 'cerro porte\u00f1o']['wins'].values[0] == df[df['team'] == 'sol de am\u00e9rica']['wins'].values[0] - 2",
        "pandas_eval": "False"
    },
    {
        "id": 3307,
        "statement": "san lorenzo have more win than any other team",
        "label": 0,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df[df['team'] == 'san lorenzo']['wins'].values[0] > df['wins'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3308,
        "statement": "the team in 6th position lose 2 more game than the team in 8th position",
        "label": 0,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "(df.loc[df['position'] == 6, 'losses'].values[0] - df.loc[df['position'] == 8, 'losses'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3309,
        "statement": "50% of the team win exactly 3 game during the 2003 season",
        "label": 0,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "len(df[df['wins'] == 3]) / len(df) == 0.5",
        "pandas_eval": "False"
    },
    {
        "id": 3310,
        "statement": "sol de am\u00e9rica and libertad have the same number of point",
        "label": 0,
        "table_caption": "2003 in paraguayan football",
        "table_text": "position#team#played#wins#draws#losses#scored#conceded#points\n1#libertad#9#6#3#0#13#3#21\n2#cerro porte\u00f1o#9#5#3#1#20#8#18\n3#olimpia#9#4#4#1#16#11#16\n4#sol de am\u00e9rica#9#3#4#2#12#7#13\n5#tacuary#9#4#1#4#15#16#13\n6#guaran\u00ed#9#3#3#3#18#20#12\n7#sportivo luque\u00f1o#9#3#2#4#14#12#11\n8#sport colombia#9#2#2#5#14#20#8\n9#12 de octubre#9#1#4#4#8#18#7\n10#san lorenzo#9#1#0#8#10#25#3\n",
        "pandas_code": "df[df['team'].isin(['sol de am\u00e9rica', 'libertad'])]['points'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3311,
        "statement": "the soviet union take home the most gold medal in the 1973 world figure skate championship",
        "label": 1,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df.loc[df['nation'] == 'soviet union', 'gold'].values[0] == df['gold'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3312,
        "statement": "the soviet union take home the most silver medal in the 1973 world figure skate championship",
        "label": 1,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'soviet union']['silver'].max() == df['silver'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3313,
        "statement": "east germany take home the most bronze medal in the 1973 world figure skate championship",
        "label": 1,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['bronze'].max() == df['bronze'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3314,
        "statement": "the soviet union take home the most medal , overall , in the 1973 world figure skate championship",
        "label": 1,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'soviet union']['total'].max() == df['total'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3315,
        "statement": "canada and czechoslovakia each take home 1 gold medal in the 1973 world figure skate championship",
        "label": 1,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "all(df[(df['nation'].isin(['canada', 'czechoslovakia'])) & (df['gold'] == 1)].groupby('nation').size() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3316,
        "statement": "east germany take home the most gold medal in the 1973 world figure skate championship",
        "label": 0,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df.loc[df['nation'] == 'east germany', 'gold'].max() == df['gold'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3317,
        "statement": "canada home the most silver medal in the 1973 world figure skate championship",
        "label": 0,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'canada']['silver'].max() == df['silver'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3318,
        "statement": "the soviet union take home the most bronze medal in the 1973 world figure skate championship",
        "label": 0,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df.loc[df['nation'] == 'soviet union', 'bronze'].values[0] == df['bronze'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3319,
        "statement": "east germany take home the most medal , overall , in the 1973 world figure skate championship",
        "label": 0,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['total'].max() == df['total'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3320,
        "statement": "the united state and east germany each take home 1 gold medal in the 1973 world figure skate championship",
        "label": 0,
        "table_caption": "1973 world figure skating championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#2#0#4\n2#canada#1#0#0#1\n2#czechoslovakia#1#0#0#1\n4#united states#0#1#0#1\n4#west germany#0#1#0#1\n6#east germany#0#0#3#3\n7#united kingdom#0#0#1#1\n",
        "pandas_code": "all(df[(df['nation'] == 'united states') | (df['nation'] == 'east germany')]['gold'] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3321,
        "statement": "hazerm el masri score 44 of out the 52 goal score",
        "label": 1,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "df[df['player'] == 'hazem el masri']['goals'].values[0] == 44",
        "pandas_eval": "True"
    },
    {
        "id": 3322,
        "statement": "fred brig be the only player to score zero point",
        "label": 1,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "(df[df['player'] == 'fred briggs']['points'].values[0] == 0) and (df['points'].eq(0).sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3325,
        "statement": "2 player tie for 32 point , daniel holsworth and tim winitana",
        "label": 1,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "len(df[(df['points'] == 32) & (df['player'].isin(['tim winitana', 'heka nanai']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3326,
        "statement": "hazem el masri score no goal",
        "label": 0,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "df[df['player'] == 'hazem el masri']['goals'].values[0] == 0",
        "pandas_eval": "False"
    },
    {
        "id": 3327,
        "statement": "fred brig be 1 of 2 player to score 15 goal",
        "label": 0,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "df[df['player'] == 'fred briggs']['goals'].values[0] == 15",
        "pandas_eval": "False"
    },
    {
        "id": 3328,
        "statement": "no player only score 1 goal",
        "label": 0,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "not df[df['goals'] == 1].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3329,
        "statement": "daniel holdsworth score the second lowest number of goal and no field goal",
        "label": 0,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "(df.loc[df['player'] == 'daniel holdsworth', 'goals'].values[0] == sorted(df['goals'].unique())[1]) & (df.loc[df['player'] == 'daniel holdsworth', 'field goals'].values[0] == 0)",
        "pandas_eval": "False"
    },
    {
        "id": 3330,
        "statement": "tim winitana and cameron phelps tie for 4 point",
        "label": 0,
        "table_caption": "2008 bulldogs rlfc season",
        "table_text": "player#tries#goals#field goals#points\nhazem el masri#6#44#0#112\nheka nanai#8#0#0#32\ntim winitana#8#0#0#32\ndaniel holdsworth#4#5#1#27\nmatthew utai#6#0#0#24\nluke patten#4#0#0#16\nandrew ryan#4#0#0#16\nwillie tonga#3#0#0#12\narana taumata#3#0#0#12\nbrent crisp#1#3#0#10\nben roberts#2#0#0#8\nreni maitua#2#0#0#8\ncorey hughes#2#0#0#8\ncameron phelps#1#0#0#4\nnick youngquest#1#0#0#4\nandrew emelio#1#0#0#4\ndaryl millard#1#0#0#4\nlee te maari#1#0#0#4\ndanny williams#1#0#0#4\nben barba#1#0#0#4\nfred briggs#0#0#0#0\ntotal#64#52#1#333\n",
        "pandas_code": "(df[df['player'].isin(['tim winitana', 'cameron phelps'])]['points'].nunique() == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3331,
        "statement": "east germany be the only nation to win 2 gold medal",
        "label": 1,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['nation'] == 'east germany']['gold'].values[0] == 2) and (df[df['gold'] == 2].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3332,
        "statement": "the 4 other nation who win medal only win 1 each",
        "label": 1,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['rank'] != 1]['total'] == 1).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3333,
        "statement": "canada be the only nation who win a gold medal aside from east germany",
        "label": 1,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['gold'] > 0]['nation'].unique() == ['east germany', 'canada']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3334,
        "statement": "east germany win more medal than canada , the soviet union , italy , and west germany combine",
        "label": 1,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['nation'] == 'east germany']['total'].values[0] > df[df['nation'].isin(['canada', 'soviet union', 'italy', 'west germany'])]['total'].sum())",
        "pandas_eval": "True"
    },
    {
        "id": 3335,
        "statement": "west germany be the only nation to receive a bronze medal other than east germany",
        "label": 1,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['bronze'] > 0]['nation'].isin(['west germany', 'east germany']).all()) and (df[df['bronze'] > 0].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3336,
        "statement": "east germany be 1 of 3 nation to win 2 gold medal",
        "label": 0,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['gold'].eq(2).any() and df['gold'].eq(2).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3337,
        "statement": "the 4 other nation who win medal only win 3 each",
        "label": 0,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['rank'] != 1]['total'] == 3).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3338,
        "statement": "canada and italy be the only nation who win a gold medal aside from east germany",
        "label": 0,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['gold'] > 0]['nation'].isin(['canada', 'italy', 'east germany']).all()) and (df[df['gold'] > 0]['nation'].nunique() == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 3339,
        "statement": "east germany win less medal than canada , the soviet union , italy , and west germany combine",
        "label": 0,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['total'].values[0] < df[df['nation'].isin(['canada', 'soviet union', 'italy', 'west germany'])]['total'].sum()",
        "pandas_eval": "False"
    },
    {
        "id": 3340,
        "statement": "west germany be the only nation to receive a bronze medal other than the soviet union",
        "label": 0,
        "table_caption": "fil world luge championships 1983",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#east germany#2#1#2#5\n2#canada#1#0#0#1\n3#soviet union#0#1#0#1\n4#italy#0#1#0#1\n5#west germany#0#0#1#1\n",
        "pandas_code": "(df[df['bronze'] > 0]['nation'].isin(['west germany', 'soviet union']).all()) and (df[df['bronze'] > 0].shape[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 3341,
        "statement": "the philadelphia flyer have 13 game during february of the 98 - 99 season",
        "label": 1,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "len(df[df['february'].notna()]) == 13",
        "pandas_eval": "True"
    },
    {
        "id": 3342,
        "statement": "the philadelphia flyers only win 4 game in february of the 98 - 99 season",
        "label": 1,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "(df['february'].notna() & df['score'].str.split(' - ').apply(lambda x: int(x[0].split()[0]) > int(x[1].split()[0]))).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3343,
        "statement": "the philadelphia flyer vs the tampa bay lightning be the flyer largest loss in february of the 98 - 99 season",
        "label": 1,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "df.loc[df['opponent'] == 'tampa bay lightning', 'score'].iloc[0] == '1 - 4'",
        "pandas_eval": "True"
    },
    {
        "id": 3344,
        "statement": "2 of the philadelphia flyer game go into overtime during february of the 98 - 99 season",
        "label": 1,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "(df['score'].str.contains('ot').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3345,
        "statement": "the philadelphia flyer vs the pheonix coyotes be the flyer largest win in february of the 98 - 99 season",
        "label": 1,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "df[df['opponent'] == 'phoenix coyotes']['score'].str.split(' - ').apply(lambda x: int(x[0].split()[0]) - int(x[1].split()[0])).max() == (df[df['february'].notna()]['score'].str.split(' - ').apply(lambda x: int(x[0].split()[0]) - int(x[1].split()[0])).max())",
        "pandas_eval": "True"
    },
    {
        "id": 3346,
        "statement": "the philadelphia flyer have 13 schedule game during february of 98 - 99 season , but only play 11 of them",
        "label": 0,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "len(df[df['february'].notna()]) == 11",
        "pandas_eval": "False"
    },
    {
        "id": 3347,
        "statement": "the philadelphia flyers lose 5 game in february of the 98 - 99 season",
        "label": 0,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "(df['february'].notna() & df['score'].str.contains(' - ', regex=False) & df['score'].str.split(' - ').apply(lambda x: int(x[0].split()[0]) < int(x[1].split()[0]))).sum() == 5",
        "pandas_eval": "False"
    },
    {
        "id": 3349,
        "statement": "none of the february game for the philadelphia flyer during the 98 - 99 season go into overtime",
        "label": 0,
        "table_caption": "1998 - 99 philadelphia flyers season",
        "table_text": "game#february#opponent#score#record#points\n48#1#los angeles kings#4 - 2#27 - 10 - 11#65\n49#4#montreal canadiens#5 - 2#28 - 10 - 11#67\n50#6#boston bruins#2 - 2 ot#28 - 10 - 12#68\n51#10#mighty ducks of anaheim#4 - 5#28 - 11 - 12#68\n52#11#los angeles kings#3 - 4#28 - 12 - 12#68\n53#14#colorado avalanche#4 - 4 ot#28 - 12 - 13#69\n54#16#phoenix coyotes#4 - 1#29 - 12 - 13#71\n55#18#montreal canadiens#1 - 3#29 - 13 - 13#71\n56#20#ottawa senators#1 - 4#29 - 14 - 13#71\n57#21#pittsburgh penguins#2 - 1#30 - 14 - 13#73\n58#24#florida panthers#3 - 5#30 - 15 - 13#73\n59#26#tampa bay lightning#1 - 4#30 - 16 - 13#73\n60#28#new york rangers#5 - 6#30 - 17 - 13#73\n",
        "pandas_code": "not any(df['score'].str.contains('ot'))",
        "pandas_eval": "False"
    },
    {
        "id": 3351,
        "statement": "nassim akrour only compete in a friendly match 2 time",
        "label": 1,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "df[df['competition'] == 'friendly match'].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3352,
        "statement": "only 2 date feature match in which the result feature a participant score zero point , january 14 , 2002 , and november 14 , 2003",
        "label": 1,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "len(df[(df['result'].str.contains('0')) & (df['date'].isin(['january 14 , 2002', 'november 14 , 2003']))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3354,
        "statement": "stade 19 mai 1956 , annaba , algeria , and stade 5 juillet 1962 , algiers , algeria be the only 2 venue to host more than 1 match",
        "label": 1,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "len(df['venue'].value_counts()[df['venue'].value_counts() > 1]) == 2 and set(df['venue'].value_counts()[df['venue'].value_counts() > 1].index) == {'stade 19 mai 1956 , annaba , algeria', 'stade 5 juillet 1962 , algiers , algeria'}",
        "pandas_eval": "True"
    },
    {
        "id": 3355,
        "statement": "2 match end in a tie , these take place on january 25 , 2002 and march 29 , 2003",
        "label": 1,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "(df[df['date'].isin(['january 25 , 2002', 'march 29 , 2003'])]['result'].apply(lambda x: x.split(' - ')[0] == x.split(' - ')[1]).sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3356,
        "statement": "nassim akrour compete in 4 friendly match between 2002 and 2003",
        "label": 0,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "len(df[(df['competition'] == 'friendly match') & (df['date'].str.contains('2002|2003'))]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3357,
        "statement": "every single match include 1 participant scoring zero point",
        "label": 0,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "all(df['score'].str.contains('0'))",
        "pandas_eval": "False"
    },
    {
        "id": 3358,
        "statement": "nassim kkrour play more friendly match than competitive match in the african cup of nation",
        "label": 0,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "(df[df['competition'] == 'friendly match'].shape[0] > df[df['competition'].str.contains('african cup of nations')].shape[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3359,
        "statement": "stade 5 juillet 1962 , algiers , algeria host more match than do stade 19 mai 1956 , annaba , algeria",
        "label": 0,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "df[df['venue'] == 'stade 5 juillet 1962 , algiers , algeria'].shape[0] > df[df['venue'] == 'stade 19 mai 1956 , annaba , algeria'].shape[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3360,
        "statement": "only the first 2 match of 2002 end in a tie",
        "label": 0,
        "table_caption": "nassim akrour",
        "table_text": "date#venue#score#result#competition\njanuary 14 , 2002#stade 5 juillet 1962 , algiers , algeria#3 - 0#4 - 0#friendly match\njanuary 25 , 2002#stade 26 mars , bamako , mali#1 - 1#2 - 2#2002 african cup of nations\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#1 - 0#4 - 1#2004 african cup of nations (qualification)\noctober 11 , 2002#stade 19 mai 1956 , annaba , algeria#4 - 1#4 - 1#2004 african cup of nations (qualification)\nmarch 29 , 2003#est\u00e1dio da cidadela , luanda , angola#1 - 1#1 - 1#friendly match\nnovember 14 , 2003#stade 5 juillet 1962 , algiers , algeria#6 - 0#6 - 0#2006 fifa world cup qualification\n",
        "pandas_code": "((df['date'].str.contains('2002')).head(2) & (df['score'] == df['result']).head(2)).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3362,
        "statement": "royal dutch shell drop from 9th to 10th in rank from the first to the second quarter",
        "label": 1,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "(df['first quarter'].str.contains('royal dutch shell').idxmax() == 8) and (df['second quarter'].str.contains('royal dutch shell').idxmax() == 9)",
        "pandas_eval": "True"
    },
    {
        "id": 3363,
        "statement": "general electric drop from 3rd in rank to 10th in rank by the fourth quarter",
        "label": 1,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "(df.loc[df['rank'] == 3, 'first quarter'].str.contains('general electric').any()) & (df.loc[df['rank'] == 10, 'fourth quarter'].str.contains('general electric').any())",
        "pandas_eval": "True"
    },
    {
        "id": 3364,
        "statement": "gazprom show up only 1 time in the top 10 in all 4 quarter",
        "label": 1,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "df.apply(lambda x: x.astype(str).str.contains('gazprom')).sum().sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3365,
        "statement": "at&t 's rank remain the same in the first and fourth quarter",
        "label": 1,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "df[df['first quarter'].str.contains('at&t')]['rank'].values[0] == df[df['fourth quarter'].str.contains('at&t')]['rank'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3366,
        "statement": "exxon mobil and petrochina come in last when it come to every quarter",
        "label": 0,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "all(df[['first quarter', 'second quarter', 'third quarter', 'fourth quarter']].apply(lambda x: x.str.split().str[-1].astype(float).idxmax()) == df['rank'].idxmax())",
        "pandas_eval": "False"
    },
    {
        "id": 3367,
        "statement": "riyal dutch remain in 9th place from first to second quarter",
        "label": 0,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "df['first quarter'].str.contains('royal dutch shell').iloc[8] and df['second quarter'].str.contains('royal dutch shell').iloc[8]",
        "pandas_eval": "False"
    },
    {
        "id": 3368,
        "statement": "general electric remain in 3rd rank through all 4 quarter",
        "label": 0,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "all(df[df['rank'] == 3].apply(lambda x: 'general electric' in x.values, axis=1))",
        "pandas_eval": "False"
    },
    {
        "id": 3369,
        "statement": "gazprom consistently show up in all 4 quarter",
        "label": 0,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "all('gazprom' in df[col].str.lower() for col in ['first quarter', 'second quarter', 'third quarter', 'fourth quarter'])",
        "pandas_eval": "False"
    },
    {
        "id": 3370,
        "statement": "at&t show different rank in the first and fourth quarter",
        "label": 0,
        "table_caption": "list of corporations by market capitalization",
        "table_text": "rank#first quarter#second quarter#third quarter#fourth quarter\n1#exxon mobil 452505#exxon mobil 465652#exxon mobil 403366#exxon mobil 406067\n2#petrochina 423996#petrochina 341140.3#petrochina 325097.5#petrochina 259836\n3#general electric 369569#industrial and commercial bank of china 257004.4#industrial and commercial bank of china 237951.5#wal - mart 219898\n4#gazprom 299764#microsoft 211546.2#microsoft 229630.7#china mobile 201291\n5#china mobile 298093#china mobile 200832.4#hsbc 198561.1#procter & gamble 184576\n6#industrial and commercial bank of china 277236#wal - mart 188752.0#china mobile 195680.4#industrial and commercial bank of china 173930\n7#microsoft 264132#china construction bank 182186.7#wal - mart 189331.6#microsoft 172929\n8#at&t 231168#petrobras 165056.9#petrobras 189027.7#at&t 167950\n9#royal dutch shell 220110#johnson & johnson 156515.9#china construction bank 186816.7#johnson & johnson 166002\n10#procter & gamble 215640#royal dutch shell 156386.7#royal dutch shell 175986.1#general electric 161278\n",
        "pandas_code": "df[df['first quarter'].str.contains('at&t')]['rank'].values[0] != df[df['fourth quarter'].str.contains('at&t')]['rank'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3371,
        "statement": "in the 2008 continental cup of curling , there be 6 north american team",
        "label": 1,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['team'] == 'north america']) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3372,
        "statement": "in the 2008 continental cup of curling , there be 6 world team",
        "label": 1,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['team'] == 'world']) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3373,
        "statement": "in the 2008 continental cup of curling , there be 2 team from china",
        "label": 1,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['country'] == 'china']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3374,
        "statement": "in the 2008 continental cup of curling , there be 4 team from canada",
        "label": 1,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['country'] == 'canada']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3375,
        "statement": "in the 2008 continental cup of curling , there be 2 team from the united state",
        "label": 1,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['country'] == 'united states']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3376,
        "statement": "in the 2008 continental cup of curling , there be 8 north american team",
        "label": 0,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['team'] == 'north america']) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 3377,
        "statement": "in the 2008 continental cup of curling , there be 8 world team",
        "label": 0,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['team'] == 'world']) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 3378,
        "statement": "there be 3 team from china in the 2008 continental cup of curl",
        "label": 0,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "(df['country'] == 'china').sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3379,
        "statement": "there be 3 team from canada in the 2008 continental cup of curl",
        "label": 0,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "len(df[df['country'] == 'canada']['team'].unique()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3380,
        "statement": "there be 3 team from the united state in the 2008 continental cup of curl",
        "label": 0,
        "table_caption": "2008 continental cup of curling",
        "table_text": "team#country#home#skip#third#second#lead\nnorth america#united states#madison , wisconsin#craig brown#rich ruohonen#john dunlop#peter annis\nnorth america#canada#winnipeg , manitoba#jennifer jones#cathy overton - clapham#jill officer#dawn askin\nnorth america#canada#edmonton , alberta#kevin koe#blake macdonald#carter rycroft#nolan thiessen\nnorth america#canada#saskatoon , saskatchewan#stefanie lawton#marliese kasner#teejay surik#lana vey\nnorth america#canada#edmonton , alberta#kevin martin#john morris#marc kennedy#ben hebert\nnorth america#united states#madison , wisconsin#debbie mccormick#allison pottinger#nicole joraanstad#tracy sachtjen\nworld#scotland / sweden#lockerbie#david murdoch#ewan macdonald#niklas edin#euan byers\nworld#sweden#h\u00e4rn\u00f6sand#anette norberg#kajsa bergstr\u00f6m#cathrine lindahl#anna sv\u00e4rd\nworld#switzerland#davos#mirjam ott#carmen sch\u00e4fer#valeria sp\u00e4lty#janine greiner\nworld#china#harbin#wang bingyu#liu yin#yue qingshuang#zhou yan\nworld#china#harbin#wang fengchun#liu rui#xu xiaoming#zang jialiang\nworld#norway#oslo#thomas ulsrud#torger nerg\u00e5rd#christoffer svae#h\u00e5vard vad petersson\n",
        "pandas_code": "(df[df['country'] == 'united states'].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 3381,
        "statement": "wong chin hung play 3 friendly game",
        "label": 1,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "len(df[df['competition'] == 'friendly']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3382,
        "statement": "all 2011 long teng cup game be play in taiwan",
        "label": 1,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "all(df[df['competition'] == '2011 long teng cup']['venue'].str.contains('taiwan'))",
        "pandas_eval": "True"
    },
    {
        "id": 3384,
        "statement": "the friendly game be play in hong kong , kuala lumpur and macau",
        "label": 1,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "df[(df['competition'] == 'friendly') & (df['venue'].str.contains('hong kong|kuala lumpur|macau', case=False))].shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 3385,
        "statement": "the most lopsided game be in the 2010 eaff championship semi - final with a score of 12 - 0",
        "label": 1,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "df[(df['competition'] == '2010 eaff championship semi - finals') & (df['result'] == '12 - 0')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3386,
        "statement": "wong chin hung play more than 5 friendly game",
        "label": 0,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "(df['competition'] == 'friendly').sum() > 5",
        "pandas_eval": "False"
    },
    {
        "id": 3387,
        "statement": "2 of the 3 2011 long teng cup game be play in taiwan",
        "label": 0,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "len(df[(df['competition'] == '2011 long teng cup') & (df['venue'].str.contains('taiwan'))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3388,
        "statement": "the 3 friendly game be play in 2 location",
        "label": 0,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "len(df[df['competition'] == 'friendly']['venue'].unique()) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3389,
        "statement": "wong chin hung lose the 2010 eaff championship semi final by 12 - 0",
        "label": 0,
        "table_caption": "wong chin hung",
        "table_text": "date#venue#result#scored#competition\n19 november 2008#macau ust stadium , macau#9 - 1#0#friendly\n23 august 2009#world games stadium , kaohsiung , taiwan#4 - 0#0#2010 eaff championship semi - finals\n27 august 2009#world games stadium , kaohsiung , taiwan#12 - 0#1#2010 eaff championship semi - finals\n18 november 2009#hong kong stadium , hong kong#0 - 4#0#2011 afc asian cup qualification\n11 february 2010#olympic stadium , tokyo , japan#0 - 3#0#2010 east asian football championship\n14 february 2010#olympic stadium , tokyo , japan#0 - 2#0#2010 east asian football championship\n3 march 2010#hong kong stadium , hong kong#0 - 0#0#2011 afc asian cup qualification\n9 february 2011#shah alam stadium , kuala lumpur#0 - 2#0#friendly\n3 june 2011#siu sai wan sports ground , hong kong#1 - 1#0#friendly\n28 july 2011#siu sai wan sports ground , hong kong#0 - 5#0#2014 fifa world cup qualification\n30 september 2011#kaohsiung national stadium , kaohsiung , taiwan#3 - 3#0#2011 long teng cup\n2 october 2011#kaohsiung national stadium , kaohsiung , taiwan#5 - 1#2#2011 long teng cup\n4 october 2011#kaohsiung national stadium , kaohsiung , taiwan#6 - 0#0#2011 long teng cup\n",
        "pandas_code": "not ((df['competition'] == '2010 eaff championship semi - finals') & (df['result'] == '12 - 0')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3390,
        "statement": "david pate be a runner - up only 1 time in 1986 and a winner 1 time in 1986",
        "label": 1,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "(df[(df['outcome'] == 'runner - up') & (df['date'] == 1986)].shape[0] == 1) & (df[(df['outcome'] == 'winner') & (df['date'] == 1986)].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3391,
        "statement": "david pate partner with scott davis more than any other partner",
        "label": 1,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "df[df['partner'] == 'scott davis'].shape[0] > df[df['partner'] != 'scott davis'].groupby('partner').size().max()",
        "pandas_eval": "True"
    },
    {
        "id": 3392,
        "statement": "david pate only have david dowlen as an opponent a single time",
        "label": 1,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "len(df[(df['opponents'].str.contains('david dowlen')) & (df['outcome'] == 'runner - up')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3393,
        "statement": "every los angeles , us tournament take place on a hard surface",
        "label": 1,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "all(df[df['tournament'] == 'los angeles , us']['surface'] == 'hard')",
        "pandas_eval": "True"
    },
    {
        "id": 3394,
        "statement": "david pate only win 1 time in the tokyo outdoor , japan tournament",
        "label": 1,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "(df[(df['tournament'] == 'tokyo outdoor , japan') & (df['outcome'] == 'winner')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3396,
        "statement": "david pate partner with scott davis less than any other partner",
        "label": 0,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "(df[df['partner'] == 'scott davis'].shape[0] < df[df['partner'] != 'scott davis'].groupby('partner').size().min())",
        "pandas_eval": "False"
    },
    {
        "id": 3397,
        "statement": "david pate and some others have david dowlen as an opponent a single time",
        "label": 0,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "(df['opponents'].str.contains('david dowlen').sum() == 0)",
        "pandas_eval": "False"
    },
    {
        "id": 3398,
        "statement": "every los angeles , us tournament take place on a carpet surface",
        "label": 0,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "all(df[(df['tournament'] == 'los angeles , us')]['surface'] == 'carpet')",
        "pandas_eval": "False"
    },
    {
        "id": 3399,
        "statement": "david pate only loss 1 time in the tokyo outdoor , japan tournament",
        "label": 0,
        "table_caption": "david pate",
        "table_text": "outcome#date#tournament#surface#partner#opponents#score\nrunner - up#1984#forest hills wct , us#clay#ernie fernandez#david dowlen nduka odizor#6 - 7 , 5 - 7\nrunner - up#1985#fort myers , us#hard#sammy giammalva jr#ken flach robert seguso#6 - 3 , 3 - 6 , 3 - 6\nwinner#1985#stratton mountain , us#hard#scott davis#ken flach robert seguso#3 - 6 , 7 - 6 , 7 - 6\nwinner#1985#tokyo outdoor , japan#hard#scott davis#sammy giammalva jr greg holmes#7 - 6 , 6 - 7 , 6 - 3\nrunner - up#1985#tokyo indoor , japan#carpet#scott davis#ken flach robert seguso#6 - 4 , 3 - 6 , 6 - 7\nwinner#1986#philadelphia , us#carpet#scott davis#stefan edberg anders j\u00e4rryd#7 - 6 , 3 - 6 , 6 - 3 , 7 - 5\nrunner - up#1986#scottsdale , us#hard#scott davis#leonardo lavalle mike leach#6 - 7 , 4 - 6\nrunner - up#1987#lyon , france#carpet#kelly jones#guy forget yannick noah#6 - 4 , 3 - 6 , 4 - 6\nwinner#1987#los angeles , us#hard#kevin curren#brad gilbert tim wilkison#6 - 3 , 6 - 4\nrunner - up#1987#paris , france#carpet#scott davis#jakob hlasek claudio mezzadri#6 - 7 , 2 - 6\nrunner - up#1987#frankfurt , germany#carpet#scott davis#boris becker patrik k\u00fchnen#4 - 6 , 2 - 6\nwinner#1987#johannesburg , south africa#hard (i)#kevin curren#eric korita brad pearce#6 - 4 , 6 - 4\nwinner#1988#memphis , us#hard (i)#kevin curren#peter lundgren mikael pernfors#6 - 2 , 6 - 2\nrunner - up#1988#tokyo outdoor , japan#hard#steve denton#john fitzgerald johan kriek#4 - 6 , 7 - 6 , 4 - 6\nwinner#1988#johannesburg , south africa#hard (i)#kevin curren#gary muller tim wilkison#7 - 6 , 6 - 4\nrunner - up#1989#indian wells , us#hard#kevin curren#boris becker jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1989#tokyo outdoor , japan#hard#kevin curren#ken flach robert seguso#6 - 7 , 6 - 7\nwinner#1989#sydney indoor , australia#hard (i)#scott warner#darren cahill mark kratzmann#6 - 3 , 6 - 7 , 7 - 5\nwinner#1989#tokyo indoor , japan#carpet#kevin curren#andr\u00e9s g\u00f3mez slobodan \u017eivojinovi\u0107#4 - 6 , 6 - 3 , 7 - 6\nwinner#1990#orlando , us#hard#scott davis#alfonso mora brian page#6 - 3 , 7 - 5\nwinner#1990#kiawah island , us#clay#scott davis#jim grabb leonardo lavalle#6 - 2 , 6 - 3\nwinner#1990#los angeles , us#hard#scott davis#peter lundgren paul wekesa#3 - 6 , 6 - 1 , 6 - 3\nwinner#1990#indianapolis , us#hard#scott davis#grant connell glenn michibata#7 - 6 , 7 - 6\nrunner - up#1990#tokyo indoor , japan#carpet#scott davis#guy forget jakob hlasek#6 - 7 , 5 - 7\nrunner - up#1990#lyon , france#carpet#jim grabb#patrick galbraith kelly jones#6 - 7 , 4 - 6\nwinner#1990#paris , france#carpet#scott davis#darren cahill mark kratzmann#5 - 7 , 6 - 3 , 6 - 4\nwinner#1991#sydney outdoor , australia#hard#scott davis#darren cahill mark kratzmann#3 - 6 , 6 - 3 , 6 - 2\nwinner#1991#australian open , melbourne#hard#scott davis#patrick mcenroe david wheaton#6 - 7 , 7 - 6 , 6 - 3 , 7 - 5\nwinner#1991#chicago , us#carpet#scott davis#grant connell glenn michibata#6 - 4 , 5 - 7 , 7 - 6\nrunner - up#1991#tampa , us#clay#richey reneberg#ken flach robert seguso#7 - 6 , 4 - 6 , 1 - 6\nwinner#1991#washington , dc , us#hard#scott davis#ken flach robert seguso#6 - 4 , 6 - 2\nrunner - up#1991#us open , new york#hard#scott davis#john fitzgerald anders j\u00e4rryd#3 - 6 , 6 - 3 , 3 - 6 , 3 - 6\nrunner - up#1991#tokyo indoor , japan#carpet#scott davis#jim grabb richey reneberg#5 - 7 , 6 - 2 , 6 - 7\nrunner - up#1993#osaka , japan#hard#glenn michibata#mark keil christo van rensburg#6 - 7 , 3 - 6\nrunner - up#1993#tokyo outdoor , japan#hard#glenn michibata#ken flach rick leach#6 - 2 , 3 - 6 , 4 - 6\nrunner - up#1993#montreal , canada#hard#glenn michibata#jim courier mark knowles#4 - 6 , 6 - 7\n",
        "pandas_code": "(df[(df['tournament'] == 'tokyo outdoor , japan') & (df['outcome'] == 'runner - up')].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3400,
        "statement": "the attendance on september 28 with the opponent red sox be higher than the attendance on september 30 with the oriole",
        "label": 1,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "df[(df['date'] == 'september 28') & (df['opponent'] == 'red sox')]['attendance'].values[0] > df[(df['date'] == 'september 30') & (df['opponent'] == 'orioles')]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3401,
        "statement": "the ranger be the opponent 4 time in september and the red sox be opponent 6 time in september",
        "label": 1,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "(df[df['date'].str.contains('september')]['opponent'].value_counts()['rangers'] == 4) & (df[df['date'].str.contains('september')]['opponent'].value_counts()['red sox'] == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 3402,
        "statement": "the indian be the opponent 7 time in september which be more time than the ranger",
        "label": 1,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "(df['opponent'].value_counts()['indians'] > df['opponent'].value_counts()['rangers'])",
        "pandas_eval": "True"
    },
    {
        "id": 3404,
        "statement": "the attendance on september 26 for the game against the opponent red sox be lower than the september 27 game against the red sox",
        "label": 1,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "df[(df['date'] == 'september 26') & (df['opponent'] == 'red sox')]['attendance'].values[0] < df[(df['date'] == 'september 27') & (df['opponent'] == 'red sox')]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3406,
        "statement": "the ranger be the opponent 4 time in september and the indian be opponent 6 time in september",
        "label": 0,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "(df[df['date'].str.contains('september')]['opponent'].value_counts()['rangers'] == 4) & (df[df['date'].str.contains('september')]['opponent'].value_counts()['indians'] == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 3407,
        "statement": "the red sox be the opponent 7 time in september which be more time than the ranger",
        "label": 0,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "(df['opponent'].value_counts()['red sox'] <= df['opponent'].value_counts()['rangers'])",
        "pandas_eval": "False"
    },
    {
        "id": 3408,
        "statement": "musselman receive the loss on september 3 and also on september 10 and september 11",
        "label": 0,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "df[(df['date'].isin(['september 3', 'september 10', 'september 11'])) & (df['loss'].str.contains('musselman'))].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3409,
        "statement": "the attendance on september 26 for the game against the opponent red sox be lower than the september 27 game against the indian",
        "label": 0,
        "table_caption": "1988 toronto blue jays season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#rangers#5 - 1#hough (11 - 15)#30294#66 - 68\nseptember 2#rangers#7 - 6#vande berg (1 - 2)#30181#67 - 68\nseptember 3#rangers#7 - 4#russell (10 - 7)#33463#68 - 68\nseptember 4#rangers#9 - 7#williams (2 - 5)#34400#69 - 68\nseptember 5#tigers#5 - 4 (10)#hern\u00e3\u00a1ndez (5 - 4)#21913#70 - 68\nseptember 6#tigers#7 - 3#alexander (11 - 11)#18299#71 - 68\nseptember 7#tigers#4 - 3#cerutti (6 - 7)#21614#71 - 69\nseptember 9#orioles#8 - 1#bautista (6 - 13)#14750#72 - 69\nseptember 10#orioles#7 - 4#musselman (5 - 4)#21945#72 - 70\nseptember 11#orioles#4 - 2#flanagan (11 - 13)#19364#72 - 71\nseptember 12#tigers#6 - 5#henke (3 - 4)#31354#72 - 72\nseptember 13#tigers#9 - 1#power (5 - 7)#32141#73 - 72\nseptember 14#tigers#3 - 2#terrell (7 - 14)#32469#74 - 72\nseptember 15#indians#3 - 0#walker (0 - 1)#28544#75 - 72\nseptember 16#indians#4 - 3 (10)#gordon (2 - 4)#30276#76 - 72\nseptember 17#indians#12 - 3#key (10 - 5)#32067#76 - 73\nseptember 18#indians#4 - 0#nichols (1 - 5)#34422#77 - 73\nseptember 19#red sox#5 - 4#lamp (6 - 5)#28455#78 - 73\nseptember 20#red sox#13 - 2#musselman (6 - 5)#30352#78 - 74\nseptember 21#red sox#1 - 0#gardner (8 - 5)#30344#79 - 74\nseptember 23#indians#4 - 2#swindell (17 - 14)#7995#80 - 74\nseptember 24#indians#1 - 0#nichols (1 - 6)#8157#81 - 74\nseptember 25#indians#4 - 3#ward (9 - 3)#7915#81 - 75\nseptember 26#red sox#11 - 1#gardner (8 - 6)#33953#82 - 75\nseptember 27#red sox#15 - 9#smithson (9 - 6)#34442#83 - 75\nseptember 28#red sox#1 - 0#hurst (18 - 6)#34873#84 - 75\nseptember 30#orioles#4 - 0#ballard (8 - 12)#32374#85 - 75\n",
        "pandas_code": "len(df[(df['date'] == 'september 26') & (df['opponent'] == 'red sox')]) > 0 and len(df[(df['date'] == 'september 27') & (df['opponent'] == 'indians')]) > 0 and df[(df['date'] == 'september 26') & (df['opponent'] == 'red sox')]['attendance'].values[0] < df[(df['date'] == 'september 27') & (df['opponent'] == 'indians')]['attendance'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3410,
        "statement": "carlton have a higher home team score than richmond",
        "label": 1,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "int(df[df['home team'] == 'carlton']['home team score'].str.split('.').str[0].astype(int)) > int(df[df['home team'] == 'richmond']['home team score'].str.split('.').str[0].astype(int))",
        "pandas_eval": "True"
    },
    {
        "id": 3411,
        "statement": "the prince park record 9122 crowd member more than lake oval on 15 june 1974",
        "label": 1,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "(df[(df['venue'] == 'princes park') & (df['date'] == '15 june 1974')]['crowd'].values[0] - df[(df['venue'] == 'lake oval') & (df['date'] == '15 june 1974')]['crowd'].values[0]) == 9122",
        "pandas_eval": "True"
    },
    {
        "id": 3412,
        "statement": "collingwood have a higher away team score than footscray",
        "label": 1,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "df[df['away team'] == 'collingwood']['away team score'].values[0] > df[df['away team'] == 'footscray']['away team score'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3413,
        "statement": "there be 3 match which be hold on the 15th and 17th of june 1974 each",
        "label": 1,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "len(df[df['date'].isin(['15 june 1974', '17 june 1974'])]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3414,
        "statement": "arden street oval record the least crowd participant among the 3 venue on 17 june 1974",
        "label": 1,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "(df[(df['date'] == '17 june 1974') & (df['venue'].isin(['arden street oval', 'mcg', 'kardinia park']))]['crowd'].min() == df[df['venue'] == 'arden street oval']['crowd'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3415,
        "statement": "carlton have a lower home team score than any other home team",
        "label": 0,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "df[df['home team'] == 'carlton']['home team score'].iloc[0] < df[df['home team'] != 'carlton']['home team score'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3416,
        "statement": "lake oval record more crowd member than lake oval on 15 june 1974",
        "label": 0,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "df[(df['venue'] == 'lake oval') & (df['date'] == '15 june 1974')]['crowd'].iloc[0] > df[(df['venue'] == 'lake oval') & (df['date'] == '15 june 1974')]['crowd'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3417,
        "statement": "footscray 's away team score be higher than all other away team",
        "label": 0,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "df[df['away team'] == 'footscray']['away team score'].iloc[0] > df[df['away team'] != 'footscray']['away team score'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3418,
        "statement": "there be 1 more match hold on 17 june 1974 than on 15 june 1974",
        "label": 0,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "(df[df['date'] == '17 june 1974'].shape[0] - df[df['date'] == '15 june 1974'].shape[0]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3419,
        "statement": "arden street oval record the largest crowd participant among all venue on 17 june 1974",
        "label": 0,
        "table_caption": "1974 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#20.13 (133)#st kilda#13.10 (88)#princes park#26030#15 june 1974\nsouth melbourne#10.13 (73)#collingwood#15.14 (104)#lake oval#16908#15 june 1974\nessendon#15.15 (105)#melbourne#10.10 (70)#vfl park#21396#15 june 1974\nnorth melbourne#14.11 (95)#hawthorn#9.21 (75)#arden street oval#17368#17 june 1974\nrichmond#19.19 (133)#footscray#15.11 (101)#mcg#68446#17 june 1974\ngeelong#10.15 (75)#fitzroy#10.13 (73)#kardinia park#18396#17 june 1974\n",
        "pandas_code": "df[df['date'] == '17 june 1974'].groupby('venue')['crowd'].max().idxmax() == 'arden street oval'",
        "pandas_eval": "False"
    },
    {
        "id": 3420,
        "statement": "4 player in the tournament finish tie for tenth place",
        "label": 1,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "len(df[df['place'] == 't10']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3421,
        "statement": "hubert green finish in first place , 1 shot ahead of andy bean",
        "label": 1,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "(df[df['player'] == 'hubert green']['to par'].values[0] == '2') & (df[df['player'] == 'andy bean']['to par'].values[0] == '1')",
        "pandas_eval": "True"
    },
    {
        "id": 3422,
        "statement": "gary player be the only player from south africa to make the top 10 of the leader board",
        "label": 1,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "(df[df['country'] == 'south africa']['player'].unique() == ['gary player']) & (df[df['country'] == 'south africa'].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3423,
        "statement": "all of the player that tie for third place shoot an average round of 70",
        "label": 1,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "all(df[df['place'] == 't3']['score'].apply(lambda x: sum(map(int, x.split(' = ')[0].split(' + '))) / 3) == 70)",
        "pandas_eval": "True"
    },
    {
        "id": 3424,
        "statement": "don padgett shoot the lowest round of any player on the leader board , with a 66 in round 3",
        "label": 1,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "df[df['player'] == 'don padgett']['score'].str.contains('66').any()",
        "pandas_eval": "True"
    },
    {
        "id": 3425,
        "statement": "2 of the 4 player tie for tenth place be woman",
        "label": 0,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "df[df['place'] == 't10']['player'].isin(['wally armstrong', 'rod funseth', 'lou graham', 'jay haas']).sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3426,
        "statement": "hubert green be 20 or more shot ahead of the rest of the player",
        "label": 0,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "(int(df[df['player'] == 'hubert green']['to par'].iloc[0].replace(' ', '').replace('+', '').replace('e', '0')) - int(df[df['player'] != 'hubert green']['to par'].str.replace(' ', '').str.replace('+', '').replace('e', '0').astype(int).min())) >= 20",
        "pandas_eval": "False"
    },
    {
        "id": 3427,
        "statement": "gary player be 1 of 3 player from south africa",
        "label": 0,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "df[df['country'] == 'south africa']['player'].count() == 3 and df[df['player'] == 'gary player']['country'].eq('south africa').any()",
        "pandas_eval": "False"
    },
    {
        "id": 3428,
        "statement": "all the player tie for 3rd place have a score of 220 or more",
        "label": 0,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "all(df[df['place'].str.startswith('t3')]['score'].apply(lambda x: sum(map(int, x.split(' = ')[1].split(' + ')))) >= 220)",
        "pandas_eval": "False"
    },
    {
        "id": 3429,
        "statement": "don padgett have the highest score",
        "label": 0,
        "table_caption": "1977 u.s. open (golf)",
        "table_text": "place#player#country#score#to par\n1#hubert green#united states#68 + 67 + 72 = 208#2\n2#andy bean#united states#71 + 70 + 68 = 209#1\nt3#terry diehl#united states#69 + 68 + 73 = 210#e\nt3#gary jacobsen#united states#73 + 70 + 67 = 210#e\nt3#don padgett#united states#70 + 74 + 66 = 210#e\nt3#gary player#south africa#72 + 67 + 71 = 210#e\nt3#tom purtzer#united states#69 + 69 + 72 = 210#e\nt3#tom weiskopf#united states#71 + 71 + 68 = 210#e\nt10#wally armstrong#united states#71 + 70 + 70 = 211#+ 1\nt10#rod funseth#united states#69 + 70 + 72 = 211#+ 1\nt10#lou graham#united states#72 + 71 + 68 = 211#+ 1\nt10#jay haas#united states#72 + 68 + 71 = 211#+ 1\n",
        "pandas_code": "int(df[df['player'] == 'don padgett']['to par'].iloc[0].replace('e', '0').replace('+ ', '')) == max(df['to par'].apply(lambda x: int(x.replace('e', '0').replace('+ ', ''))))",
        "pandas_eval": "False"
    },
    {
        "id": 3430,
        "statement": "bradley johnson win 2 event on the uk tv series , last man stand",
        "label": 1,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "df[df['name'] == 'bradley johnson']['events won (uk series)'].values[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3431,
        "statement": "mark boban and rajko radovic be both from the united kingdom",
        "label": 1,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "all(df[df['name'].isin(['mark boban', 'rajko radovic'])]['from'].str.contains('united kingdom'))",
        "pandas_eval": "True"
    },
    {
        "id": 3432,
        "statement": "bradley johnson and corey rennell be both from the united state",
        "label": 1,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "all(df[df['name'].isin(['bradley johnson', 'corey rennell'])]['from'].str.contains('united states'))",
        "pandas_eval": "True"
    },
    {
        "id": 3433,
        "statement": "corey rennell win no evevnts in the last man stand",
        "label": 1,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "df.loc[df['name'] == 'corey rennell', 'events won (uk series)'].values[0] == 0 and df.loc[df['name'] == 'corey rennell', 'events won (us series)'].values[0] == 0",
        "pandas_eval": "True"
    },
    {
        "id": 3434,
        "statement": "jason bennett and corey rennell be from differet state in the united state",
        "label": 1,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "df.loc[df['name'].isin(['jason bennett', 'corey rennell']), 'from'].str.split().str[2].nunique() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3435,
        "statement": "bradley johnson win 3 event on the uk tv series , last man stand",
        "label": 0,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "((df['name'] == 'bradley johnson') & (df['events won (uk series)'] == 3)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3436,
        "statement": "mark boban and rajko radovic be both from the united state",
        "label": 0,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "all(df[df['name'].isin(['mark boban', 'rajko radovic'])]['from'].str.contains('united states'))",
        "pandas_eval": "False"
    },
    {
        "id": 3437,
        "statement": "bradley johnson and corey rennell be both from the united kingdom",
        "label": 0,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "all(df[df['name'].isin(['bradley johnson', 'corey rennell'])]['from'].str.contains('united kingdom'))",
        "pandas_eval": "False"
    },
    {
        "id": 3438,
        "statement": "corey rennell win 2 event in the last man stand",
        "label": 0,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "(df[df['name'] == 'corey rennell']['events won (uk series)'].values[0] + df[df['name'] == 'corey rennell']['events won (us series)'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3439,
        "statement": "jason bennett and corey rennell be from the same state in the united state",
        "label": 0,
        "table_caption": "last man standing (uk tv series)",
        "table_text": "name#from#discipline#events won (uk series)#events won (us series)\nbradley johnson#united states oklahoma#strongman#2#3\ncorey rennell#united states alaska#outdoorsman#0#0\njason bennett#united states florida#bmx racer and tree surgeon#2#3\nmark boban#united kingdom birmingham#kickboxing and salsa dance#1#1\nrajko radovic#united kingdom middlesex#fitness guru#2#3\nrichard massey#united kingdom oxford#cricket and rugby#1#2\n",
        "pandas_code": "df[df['name'].isin(['jason bennett', 'corey rennell'])]['from'].str.split().str[1].nunique() != 1",
        "pandas_eval": "False"
    },
    {
        "id": 3441,
        "statement": "2012 have more grass win than ryan have on grass in any other year",
        "label": 1,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "int(df.loc[df['tournament'] == 'grass win - loss', '2012'].str.split(' - ').iloc[0][0]) > df.loc[df['tournament'] == 'grass win - loss', ['2008', '2009', '2010', '2011']].apply(lambda x: x.str.split(' - ').str[0].astype(int)).max().max()",
        "pandas_eval": "True"
    },
    {
        "id": 3442,
        "statement": "2011 saw ryan harrison play in 10 more tournament than 2010",
        "label": 1,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "(int(df.loc[df['tournament'] == 'tournaments played', '2011'].values[0]) - int(df.loc[df['tournament'] == 'tournaments played', '2010'].values[0])) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 3443,
        "statement": "ryan harrison have never win a title or play in a final",
        "label": 1,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "df[(df['tournament'] == 'titles') | (df['tournament'] == 'finals')].iloc[:, 1:].eq('0').all().all()",
        "pandas_eval": "True"
    },
    {
        "id": 3444,
        "statement": "ryan harrison 's year end ranking be 10 place higher in 2012 than 2011",
        "label": 1,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "(int(df.loc[df['tournament'] == 'year end ranking', '2011'].values[0]) - int(df.loc[df['tournament'] == 'year end ranking', '2012'].values[0])) == 10",
        "pandas_eval": "True"
    },
    {
        "id": 3445,
        "statement": "2009 and 2012 each have 1 grand slam tournament match win by ryan harrison",
        "label": 0,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "(df.loc[df['tournament'] == 'grand slam tournaments', '2009'].values[0].split(' - ')[0] == '1') & (df.loc[df['tournament'] == 'grand slam tournaments', '2012'].values[0].split(' - ')[0] == '1')",
        "pandas_eval": "False"
    },
    {
        "id": 3446,
        "statement": "2009 have more grass win than ryan have on grass in any other year",
        "label": 0,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "int(df.loc[df['tournament'] == 'grass win - loss', '2009'].iloc[0].split(' - ')[0]) > max(df.loc[df['tournament'] == 'grass win - loss', ['2008', '2010', '2011', '2012']].iloc[0].apply(lambda x: int(x.split(' - ')[0])).tolist())",
        "pandas_eval": "False"
    },
    {
        "id": 3447,
        "statement": "2010 saw ryan harrison play in 10 more tournament than 2011",
        "label": 0,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "(df.loc[df['tournament'] == 'tournaments played', '2010'].values[0] == str(int(df.loc[df['tournament'] == 'tournaments played', '2011'].values[0]) + 10))",
        "pandas_eval": "False"
    },
    {
        "id": 3448,
        "statement": "ryan harrison have win a title or play in a final",
        "label": 0,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "df[df['tournament'].isin(['titles', 'finals'])].iloc[:, 1:].apply(lambda x: x.astype(str).str.contains(r'[1-9]')).any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 3449,
        "statement": "ryan harrison 's year end ranking be 10 place higher in 2010 than 2009",
        "label": 0,
        "table_caption": "ryan harrison (tennis)",
        "table_text": "tournament#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#a#1r#1r#1r\nfrench open#a#a#q3#1r#1r\nwimbledon#a#a#q1#2r#2r\nus open#q1#q1#2r#1r#2r\nwin - loss#0 - 0#0 - 0#1 - 2#1 - 4#2 - 4\ndavis cup#davis cup#davis cup#davis cup#davis cup#davis cup\ndavis cup#a#a#a#a#sf\natp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000#atp world tour masters 1000\nindian wells masters#a#a#2r#4r#4r\nmiami masters#q1#a#1r#1r#2r\nmonte carlo masters#a#a#a#a#a\nrome masters#a#a#a#a#q1\nmadrid masters#a#a#a#a#2r\ncanada masters#a#a#a#a#a\ncincinnati masters#q1#q1#a#2r#1r\nshanghai masters#a#a#a#2r#1r\nparis masters#a#a#a#a#a\nwin - loss#0 - 0#0 - 0#1 - 2#5 - 4#5 - 5\ncareer statistics#career statistics#career statistics#career statistics#career statistics#career statistics\ntournaments played#1#0#9#19#22\ntitles#0#0#0#0#0\nfinals#0#0#0#0#0\nhardcourt win - loss#0 - 0#0 - 0#2 - 7#13 - 14#11 - 13\ngrass win - loss#0 - 0#0 - 0#2 - 2#1 - 3#7 - 4\nclay win - loss#1 - 1#0 - 0#0 - 0#0 - 2#5 - 7\noverall win - loss#1 - 1#0 - 0#4 - 9#14 - 19#23 - 24\nwin %#50%#-#31%#42%#49%\nyear end ranking#748#360#173#79#69\n",
        "pandas_code": "int(df.loc[df['tournament'] == 'year end ranking', '2010'].values[0]) - int(df.loc[df['tournament'] == 'year end ranking', '2009'].values[0]) == 10",
        "pandas_eval": "False"
    },
    {
        "id": 3450,
        "statement": "more point be score against the opponent on september 3 than september 9",
        "label": 1,
        "table_caption": "1984 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 3 , 1984#cleveland browns#w 33 - 0#kingdome#1 - 0#59540\n2#september 9 , 1984#san diego chargers#w 31 - 17#kingdome#2 - 0#61314\n3#september 16 , 1984#new england patriots#l 23 - 38#sullivan stadium#2 - 1#43140\n4#september 23 , 1984#chicago bears#w 38 - 9#kingdome#3 - 1#61520\n5#september 30 , 1984#minnesota vikings#w 20 - 12#hubert h humphrey metrodome#4 - 1#57171\n6#october 7 , 1984#los angeles raiders#l 14 - 28#los angeles memorial coliseum#4 - 2#77904\n7#october 14 , 1984#buffalo bills#w 31 - 28#kingdome#5 - 2#59034\n8#october 21 , 1984#green bay packers#w 30 - 24#lambeau field#6 - 2#52286\n9#october 29 , 1984#san diego chargers#w 24 - 0#jack murphy stadium#7 - 2#53974\n10#november 4 , 1984#kansas city chiefs#w 45 - 0#kingdome#8 - 2#61396\n11#november 12 , 1984#los angeles raiders#w 17 - 14#kingdome#9 - 2#64001\n12#november 18 , 1984#cincinnati bengals#w 26 - 6#riverfront stadium#10 - 2#50280\n13#november 25 , 1984#denver broncos#w 27 - 24#mile high stadium#11 - 2#74922\n14#december 2 , 1984#detroit lions#w 38 - 17#kingdome#12 - 2#62441\n15#december 9 , 1984#kansas city chiefs#l 7 - 34#arrowhead stadium#12 - 3#34855\n",
        "pandas_code": "(df[df['date'] == 'september 3 , 1984']['result'].str.extract(r'(\\d+) - \\d+').astype(int).iloc[0, 0]) > (df[df['date'] == 'september 9 , 1984']['result'].str.extract(r'(\\d+) - \\d+').astype(int).iloc[0, 0])",
        "pandas_eval": "True"
    },
    {
        "id": 3451,
        "statement": "7 game be play at the kingdome",
        "label": 1,
        "table_caption": "1984 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 3 , 1984#cleveland browns#w 33 - 0#kingdome#1 - 0#59540\n2#september 9 , 1984#san diego chargers#w 31 - 17#kingdome#2 - 0#61314\n3#september 16 , 1984#new england patriots#l 23 - 38#sullivan stadium#2 - 1#43140\n4#september 23 , 1984#chicago bears#w 38 - 9#kingdome#3 - 1#61520\n5#september 30 , 1984#minnesota vikings#w 20 - 12#hubert h humphrey metrodome#4 - 1#57171\n6#october 7 , 1984#los angeles raiders#l 14 - 28#los angeles memorial coliseum#4 - 2#77904\n7#october 14 , 1984#buffalo bills#w 31 - 28#kingdome#5 - 2#59034\n8#october 21 , 1984#green bay packers#w 30 - 24#lambeau field#6 - 2#52286\n9#october 29 , 1984#san diego chargers#w 24 - 0#jack murphy stadium#7 - 2#53974\n10#november 4 , 1984#kansas city chiefs#w 45 - 0#kingdome#8 - 2#61396\n11#november 12 , 1984#los angeles raiders#w 17 - 14#kingdome#9 - 2#64001\n12#november 18 , 1984#cincinnati bengals#w 26 - 6#riverfront stadium#10 - 2#50280\n13#november 25 , 1984#denver broncos#w 27 - 24#mile high stadium#11 - 2#74922\n14#december 2 , 1984#detroit lions#w 38 - 17#kingdome#12 - 2#62441\n15#december 9 , 1984#kansas city chiefs#l 7 - 34#arrowhead stadium#12 - 3#34855\n",
        "pandas_code": "(df['game site'].value_counts()['kingdome'] == 7)",
        "pandas_eval": "True"
    },
    {
        "id": 3452,
        "statement": "there be 27586 more crowd at the december 2 game than at the december 9 game",
        "label": 1,
        "table_caption": "1984 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 3 , 1984#cleveland browns#w 33 - 0#kingdome#1 - 0#59540\n2#september 9 , 1984#san diego chargers#w 31 - 17#kingdome#2 - 0#61314\n3#september 16 , 1984#new england patriots#l 23 - 38#sullivan stadium#2 - 1#43140\n4#september 23 , 1984#chicago bears#w 38 - 9#kingdome#3 - 1#61520\n5#september 30 , 1984#minnesota vikings#w 20 - 12#hubert h humphrey metrodome#4 - 1#57171\n6#october 7 , 1984#los angeles raiders#l 14 - 28#los angeles memorial coliseum#4 - 2#77904\n7#october 14 , 1984#buffalo bills#w 31 - 28#kingdome#5 - 2#59034\n8#october 21 , 1984#green bay packers#w 30 - 24#lambeau field#6 - 2#52286\n9#october 29 , 1984#san diego chargers#w 24 - 0#jack murphy stadium#7 - 2#53974\n10#november 4 , 1984#kansas city chiefs#w 45 - 0#kingdome#8 - 2#61396\n11#november 12 , 1984#los angeles raiders#w 17 - 14#kingdome#9 - 2#64001\n12#november 18 , 1984#cincinnati bengals#w 26 - 6#riverfront stadium#10 - 2#50280\n13#november 25 , 1984#denver broncos#w 27 - 24#mile high stadium#11 - 2#74922\n14#december 2 , 1984#detroit lions#w 38 - 17#kingdome#12 - 2#62441\n15#december 9 , 1984#kansas city chiefs#l 7 - 34#arrowhead stadium#12 - 3#34855\n",
        "pandas_code": "(df[df['date'] == 'december 2 , 1984']['attendance'].values[0] - df[df['date'] == 'december 9 , 1984']['attendance'].values[0]) == 27586",
        "pandas_eval": "True"
    },
    {
        "id": 3453,
        "statement": "there be 43049 more crowd at the highest attended game than at the lowest attended game",
        "label": 1,
        "table_caption": "1984 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 3 , 1984#cleveland browns#w 33 - 0#kingdome#1 - 0#59540\n2#september 9 , 1984#san diego chargers#w 31 - 17#kingdome#2 - 0#61314\n3#september 16 , 1984#new england patriots#l 23 - 38#sullivan stadium#2 - 1#43140\n4#september 23 , 1984#chicago bears#w 38 - 9#kingdome#3 - 1#61520\n5#september 30 , 1984#minnesota vikings#w 20 - 12#hubert h humphrey metrodome#4 - 1#57171\n6#october 7 , 1984#los angeles raiders#l 14 - 28#los angeles memorial coliseum#4 - 2#77904\n7#october 14 , 1984#buffalo bills#w 31 - 28#kingdome#5 - 2#59034\n8#october 21 , 1984#green bay packers#w 30 - 24#lambeau field#6 - 2#52286\n9#october 29 , 1984#san diego chargers#w 24 - 0#jack murphy stadium#7 - 2#53974\n10#november 4 , 1984#kansas city chiefs#w 45 - 0#kingdome#8 - 2#61396\n11#november 12 , 1984#los angeles raiders#w 17 - 14#kingdome#9 - 2#64001\n12#november 18 , 1984#cincinnati bengals#w 26 - 6#riverfront stadium#10 - 2#50280\n13#november 25 , 1984#denver broncos#w 27 - 24#mile high stadium#11 - 2#74922\n14#december 2 , 1984#detroit lions#w 38 - 17#kingdome#12 - 2#62441\n15#december 9 , 1984#kansas city chiefs#l 7 - 34#arrowhead stadium#12 - 3#34855\n",
        "pandas_code": "(df['attendance'].max() - df['attendance'].min()) == 43049",
        "pandas_eval": "True"
    },
    {
        "id": 3454,
        "statement": "the largest point spread for any game be the win against the kansas city chief , 45 - 0",
        "label": 1,
        "table_caption": "1984 seattle seahawks season",
        "table_text": "week#date#opponent#result#game site#record#attendance\n1#september 3 , 1984#cleveland browns#w 33 - 0#kingdome#1 - 0#59540\n2#september 9 , 1984#san diego chargers#w 31 - 17#kingdome#2 - 0#61314\n3#september 16 , 1984#new england patriots#l 23 - 38#sullivan stadium#2 - 1#43140\n4#september 23 , 1984#chicago bears#w 38 - 9#kingdome#3 - 1#61520\n5#september 30 , 1984#minnesota vikings#w 20 - 12#hubert h humphrey metrodome#4 - 1#57171\n6#october 7 , 1984#los angeles raiders#l 14 - 28#los angeles memorial coliseum#4 - 2#77904\n7#october 14 , 1984#buffalo bills#w 31 - 28#kingdome#5 - 2#59034\n8#october 21 , 1984#green bay packers#w 30 - 24#lambeau field#6 - 2#52286\n9#october 29 , 1984#san diego chargers#w 24 - 0#jack murphy stadium#7 - 2#53974\n10#november 4 , 1984#kansas city chiefs#w 45 - 0#kingdome#8 - 2#61396\n11#november 12 , 1984#los angeles raiders#w 17 - 14#kingdome#9 - 2#64001\n12#november 18 , 1984#cincinnati bengals#w 26 - 6#riverfront stadium#10 - 2#50280\n13#november 25 , 1984#denver broncos#w 27 - 24#mile high stadium#11 - 2#74922\n14#december 2 , 1984#detroit lions#w 38 - 17#kingdome#12 - 2#62441\n15#december 9 , 1984#kansas city chiefs#l 7 - 34#arrowhead stadium#12 - 3#34855\n",
        "pandas_code": "df[df['result'] == 'w 45 - 0']['opponent'].iloc[0] == 'kansas city chiefs'",
        "pandas_eval": "True"
    },
    {
        "id": 3455,
        "statement": "maricopa county have the highest total by over 500000 compare to the second highest",
        "label": 1,
        "table_caption": "united states senate election in arizona , 2004",
        "table_text": "county#starky#starky %#hancock#hancock %#mccain#mccain %#total\napache#9588#40.95%#905#3.86%#12923#55.19%#23416\ncochise#9555#21.80%#1394#3.18%#32879#75.02%#43828\ncoconino#13520#26.58%#1504#2.96%#35849#70.47%#50873\ngila#4291#20.96%#632#3.09%#15551#75.95%#20474\ngraham#2000#19.06%#322#3.07%#8171#77.87%#10493\ngreenlee#746#25.03%#68#2.28%#2166#72.68%#2980\nla paz#965#19.51%#156#3.15%#3826#77.34%#4947\nmaricopa#216124#18.58%#29769#2.56%#917527#78.86%#1163420\nmohave#10423#18.44%#1686#2.98%#44402#78.57%#56511\nnavajo#7434#23.42%#1222#3.85%#23091#72.73%#31747\npima#89483#25.17%#7980#2.24%#258010#72.58%#355473\npinal#13595#21.45%#1692#2.67%#48094#75.88%#63381\nsanta cruz#3583#31.60%#252#2.22%#7502#66.17%#11337\nyavapai#14852#17.41%#3160#3.70%#67312#78.89%#85324\nyuma#8348#22.28%#1056#2.82%#28069#74.90%#37473\n",
        "pandas_code": "(df['total'].nlargest(2).iloc[0] - df['total'].nlargest(2).iloc[1]) > 500000",
        "pandas_eval": "True"
    },
    {
        "id": 3456,
        "statement": "greenlee county give the fewest vote of hancock",
        "label": 1,
        "table_caption": "united states senate election in arizona , 2004",
        "table_text": "county#starky#starky %#hancock#hancock %#mccain#mccain %#total\napache#9588#40.95%#905#3.86%#12923#55.19%#23416\ncochise#9555#21.80%#1394#3.18%#32879#75.02%#43828\ncoconino#13520#26.58%#1504#2.96%#35849#70.47%#50873\ngila#4291#20.96%#632#3.09%#15551#75.95%#20474\ngraham#2000#19.06%#322#3.07%#8171#77.87%#10493\ngreenlee#746#25.03%#68#2.28%#2166#72.68%#2980\nla paz#965#19.51%#156#3.15%#3826#77.34%#4947\nmaricopa#216124#18.58%#29769#2.56%#917527#78.86%#1163420\nmohave#10423#18.44%#1686#2.98%#44402#78.57%#56511\nnavajo#7434#23.42%#1222#3.85%#23091#72.73%#31747\npima#89483#25.17%#7980#2.24%#258010#72.58%#355473\npinal#13595#21.45%#1692#2.67%#48094#75.88%#63381\nsanta cruz#3583#31.60%#252#2.22%#7502#66.17%#11337\nyavapai#14852#17.41%#3160#3.70%#67312#78.89%#85324\nyuma#8348#22.28%#1056#2.82%#28069#74.90%#37473\n",
        "pandas_code": "df.loc[df['county'] == 'greenlee', 'hancock'].iloc[0] == df['hancock'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3457,
        "statement": "starky command a higher percentage than hancock in every county",
        "label": 1,
        "table_caption": "united states senate election in arizona , 2004",
        "table_text": "county#starky#starky %#hancock#hancock %#mccain#mccain %#total\napache#9588#40.95%#905#3.86%#12923#55.19%#23416\ncochise#9555#21.80%#1394#3.18%#32879#75.02%#43828\ncoconino#13520#26.58%#1504#2.96%#35849#70.47%#50873\ngila#4291#20.96%#632#3.09%#15551#75.95%#20474\ngraham#2000#19.06%#322#3.07%#8171#77.87%#10493\ngreenlee#746#25.03%#68#2.28%#2166#72.68%#2980\nla paz#965#19.51%#156#3.15%#3826#77.34%#4947\nmaricopa#216124#18.58%#29769#2.56%#917527#78.86%#1163420\nmohave#10423#18.44%#1686#2.98%#44402#78.57%#56511\nnavajo#7434#23.42%#1222#3.85%#23091#72.73%#31747\npima#89483#25.17%#7980#2.24%#258010#72.58%#355473\npinal#13595#21.45%#1692#2.67%#48094#75.88%#63381\nsanta cruz#3583#31.60%#252#2.22%#7502#66.17%#11337\nyavapai#14852#17.41%#3160#3.70%#67312#78.89%#85324\nyuma#8348#22.28%#1056#2.82%#28069#74.90%#37473\n",
        "pandas_code": "(df['starky %'].str.rstrip('%').astype(float) > df['hancock %'].str.rstrip('%').astype(float)).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3458,
        "statement": "mccain command the highest percentage over hancock and starky in all county",
        "label": 1,
        "table_caption": "united states senate election in arizona , 2004",
        "table_text": "county#starky#starky %#hancock#hancock %#mccain#mccain %#total\napache#9588#40.95%#905#3.86%#12923#55.19%#23416\ncochise#9555#21.80%#1394#3.18%#32879#75.02%#43828\ncoconino#13520#26.58%#1504#2.96%#35849#70.47%#50873\ngila#4291#20.96%#632#3.09%#15551#75.95%#20474\ngraham#2000#19.06%#322#3.07%#8171#77.87%#10493\ngreenlee#746#25.03%#68#2.28%#2166#72.68%#2980\nla paz#965#19.51%#156#3.15%#3826#77.34%#4947\nmaricopa#216124#18.58%#29769#2.56%#917527#78.86%#1163420\nmohave#10423#18.44%#1686#2.98%#44402#78.57%#56511\nnavajo#7434#23.42%#1222#3.85%#23091#72.73%#31747\npima#89483#25.17%#7980#2.24%#258010#72.58%#355473\npinal#13595#21.45%#1692#2.67%#48094#75.88%#63381\nsanta cruz#3583#31.60%#252#2.22%#7502#66.17%#11337\nyavapai#14852#17.41%#3160#3.70%#67312#78.89%#85324\nyuma#8348#22.28%#1056#2.82%#28069#74.90%#37473\n",
        "pandas_code": "((df['mccain %'].str.rstrip('%').astype(float) > df['starky %'].str.rstrip('%').astype(float)) & (df['mccain %'].str.rstrip('%').astype(float) > df['hancock %'].str.rstrip('%').astype(float))).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3459,
        "statement": "starky 's best percentage be in apache county",
        "label": 1,
        "table_caption": "united states senate election in arizona , 2004",
        "table_text": "county#starky#starky %#hancock#hancock %#mccain#mccain %#total\napache#9588#40.95%#905#3.86%#12923#55.19%#23416\ncochise#9555#21.80%#1394#3.18%#32879#75.02%#43828\ncoconino#13520#26.58%#1504#2.96%#35849#70.47%#50873\ngila#4291#20.96%#632#3.09%#15551#75.95%#20474\ngraham#2000#19.06%#322#3.07%#8171#77.87%#10493\ngreenlee#746#25.03%#68#2.28%#2166#72.68%#2980\nla paz#965#19.51%#156#3.15%#3826#77.34%#4947\nmaricopa#216124#18.58%#29769#2.56%#917527#78.86%#1163420\nmohave#10423#18.44%#1686#2.98%#44402#78.57%#56511\nnavajo#7434#23.42%#1222#3.85%#23091#72.73%#31747\npima#89483#25.17%#7980#2.24%#258010#72.58%#355473\npinal#13595#21.45%#1692#2.67%#48094#75.88%#63381\nsanta cruz#3583#31.60%#252#2.22%#7502#66.17%#11337\nyavapai#14852#17.41%#3160#3.70%#67312#78.89%#85324\nyuma#8348#22.28%#1056#2.82%#28069#74.90%#37473\n",
        "pandas_code": "df.loc[df['county'] == 'apache', 'starky %'].iloc[0] == df['starky %'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3460,
        "statement": "melbourne be the opponent at the moorabbin oval",
        "label": 1,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "df[(df['venue'] == 'moorabbin oval') & (df['away team'] == 'melbourne')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3462,
        "statement": "south melbourne game have the lowest attendance of the season",
        "label": 1,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "df.loc[df['home team'] == 'south melbourne', 'crowd'].iloc[0] == df['crowd'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3463,
        "statement": "richmond have more point than hawthorn when they both serve as home team",
        "label": 1,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "df.loc[df['home team'] == 'richmond', 'home team score'].str.extract(r'\\((\\d+)\\)').astype(int).iloc[0, 0] > df.loc[df['home team'] == 'hawthorn', 'home team score'].str.extract(r'\\((\\d+)\\)').astype(int).iloc[0, 0]",
        "pandas_eval": "True"
    },
    {
        "id": 3464,
        "statement": "st kilda play to 14058 on june 27 1981",
        "label": 1,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "((df['home team'] == 'st kilda') & (df['crowd'] == 14058) & (df['date'] == '27 june 1981')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3465,
        "statement": "footscrat be the opponent at the moorabbin oval",
        "label": 0,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "df[(df['venue'] == 'moorabbin oval') & (df['away team'] == 'footscray')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3466,
        "statement": "hawthorn v essendon be the last game of the season",
        "label": 0,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "not (df.iloc[-1]['home team'] == 'hawthorn' and df.iloc[-1]['away team'] == 'essendon')",
        "pandas_eval": "False"
    },
    {
        "id": 3467,
        "statement": "south melbourne game be the highest attendance of the season",
        "label": 0,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "df.loc[df['crowd'].idxmax(), 'home team'] == 'south melbourne'",
        "pandas_eval": "False"
    },
    {
        "id": 3468,
        "statement": "richmond have less point than hawthorn when they both serve as home team",
        "label": 0,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "float(df[df['home team'] == 'richmond']['home team score'].str.split(' ').str[0].values[0]) < float(df[df['home team'] == 'hawthorn']['home team score'].str.split(' ').str[0].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3469,
        "statement": "collingwood play to 14058 on june 27 1981",
        "label": 0,
        "table_caption": "1981 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\ncarlton#15.25 (115)#footscray#5.4 (34)#princes park#17419#27 june 1981\nrichmond#21.23 (149)#north melbourne#15.16 (106)#mcg#31212#27 june 1981\nst kilda#18.19 (127)#melbourne#8.7 (55)#moorabbin oval#14058#27 june 1981\nsouth melbourne#9.16 (70)#fitzroy#14.9 (93)#lake oval#11756#27 june 1981\ncollingwood#13.8 (86)#geelong#9.14 (68)#vfl park#50441#27 june 1981\nhawthorn#20.13 (133)#essendon#22.19 (151)#the gabba#20351#28 june 1981\n",
        "pandas_code": "((df['home team'] == 'collingwood') & (df['crowd'] == 14058) & (df['date'] == '27 june 1981')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3471,
        "statement": "gino guidugli have 138 more yard and 6 more completion than ian smart",
        "label": 1,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "(df[df['player'] == 'gino guidugli']['yards'].values[0] - df[df['player'] == 'ian smart']['yards'].values[0] == 138) & (df[df['player'] == 'gino guidugli']['comp'].values[0] - df[df['player'] == 'ian smart']['comp'].values[0] == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 3472,
        "statement": "jarious jackson have 1540 more yard than buck pierce and 2553 more yard than ian smart",
        "label": 1,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "(df[df['player'] == 'jarious jackson']['yards'].values[0] - df[df['player'] == 'buck pierce']['yards'].values[0] == 1540) and (df[df['player'] == 'jarious jackson']['yards'].values[0] - df[df['player'] == 'ian smart']['yards'].values[0] == 2553)",
        "pandas_eval": "True"
    },
    {
        "id": 3473,
        "statement": "the average rating for buck pierce and gino guidugli be 9.19",
        "label": 1,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "abs(df[df['player'].isin(['buck pierce', 'gino guidugli'])]['rating'].mean() - 91.95) < 1e-2",
        "pandas_eval": "True"
    },
    {
        "id": 3474,
        "statement": "jarious jackson have more than 2 time as many completion as dave dickenson",
        "label": 1,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "df[df['player'] == 'jarious jackson']['comp'].values[0] > 2 * df[df['player'] == 'dave dickenson']['comp'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3475,
        "statement": "jarious jackson and buck pierce have an average rating of 88.3",
        "label": 0,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "df[df['player'].isin(['jarious jackson', 'buck pierce'])]['rating'].mean() == 88.3",
        "pandas_eval": "False"
    },
    {
        "id": 3476,
        "statement": "gino guidugli have 740 more yard and 6 more completion than ian smart",
        "label": 0,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "df[df['player'] == 'gino guidugli']['yards'].values[0] - df[df['player'] == 'ian smart']['yards'].values[0] == 740 and df[df['player'] == 'gino guidugli']['comp'].values[0] - df[df['player'] == 'ian smart']['comp'].values[0] == 6",
        "pandas_eval": "False"
    },
    {
        "id": 3477,
        "statement": "jarious jackson have 1540 less yard than buck pierce and 2553 less yard than ian smart",
        "label": 0,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "(df[df['player'] == 'jarious jackson']['yards'].values[0] == df[df['player'] == 'buck pierce']['yards'].values[0] - 1540) & (df[df['player'] == 'jarious jackson']['yards'].values[0] == df[df['player'] == 'ian smart']['yards'].values[0] - 2553)",
        "pandas_eval": "False"
    },
    {
        "id": 3478,
        "statement": "the average rating for buck pierce and gino guidugli be 8.19",
        "label": 0,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "df[df['player'].isin(['buck pierce', 'gino guidugli'])]['rating'].mean() == 8.19",
        "pandas_eval": "False"
    },
    {
        "id": 3479,
        "statement": "jarious jackson have less than 2 time as many completion as dave dickenson",
        "label": 0,
        "table_caption": "2007 bc lions season",
        "table_text": "player#att#comp#yards#rating\njarious jackson#304#167#2553#88.9\nbuck pierce#127#81#1013#91.7\ndave dickenson#87#56#740#88.3\ngino guidugli#11#6#138#92.2\nian smart#1#0#0#2.1\n",
        "pandas_code": "df[df['player'] == 'jarious jackson']['comp'].values[0] < 2 * df[df['player'] == 'dave dickenson']['comp'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3480,
        "statement": "the game anaheim duck play against blackhawks have the most attendance",
        "label": 1,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['opponent'] == 'blackhawks']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3481,
        "statement": "there be more crowd go to watch the game play by anaheim duck against blue than the game play against blue jacket",
        "label": 1,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['opponent'].isin(['blues', 'blue jackets'])].groupby('opponent')['attendance'].sum().idxmax() == 'blues'",
        "pandas_eval": "True"
    },
    {
        "id": 3482,
        "statement": "there be more game play in honda center than play in scottrade center",
        "label": 1,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['arena'] == 'honda center'].shape[0] > df[df['arena'] == 'scottrade center'].shape[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3483,
        "statement": "there be less crowd go to watch the game play by anaheim duck against star than the game play against flame",
        "label": 1,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['opponent'] == 'stars']['attendance'].values[0] < df[df['opponent'] == 'flames']['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3484,
        "statement": "anaheim duck play against canuck after they play against oiler",
        "label": 1,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[(df['opponent'].shift() == 'oilers') & (df['opponent'] == 'canucks')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3485,
        "statement": "the game anaheim duck have the biggest number of attendance be hold at honda center",
        "label": 0,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['arena'] == 'honda center']['attendance'].max() == df['attendance'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3486,
        "statement": "the first match of anaheim duck against blue have bigger number of crowd compare to the second match against blue",
        "label": 0,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['opponent'].str.contains('blue')].iloc[0]['attendance'] < df[df['opponent'].str.contains('blue')].iloc[1]['attendance']",
        "pandas_eval": "False"
    },
    {
        "id": 3487,
        "statement": "more than half of anaheim duck game play on 2008 december be hold in honda center",
        "label": 0,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['date'].str.contains('december')]['arena'].value_counts(normalize=True).get('honda center', 0) > 0.5",
        "pandas_eval": "False"
    },
    {
        "id": 3488,
        "statement": "the game play at american airline center have the smallest number of crowd",
        "label": 0,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "df[df['arena'] == 'american airlines center']['attendance'].min() == df['attendance'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3489,
        "statement": "anaheim duck play against canuck only after they play against blue and flame",
        "label": 0,
        "table_caption": "2008 - 09 anaheim ducks season",
        "table_text": "date#opponent#score#loss#attendance#record#arena#points\ndecember 1#red wings#2 - 1#hiller (6 - 2 - 1)#18862#14 - 9 - 3#joe louis arena#31\ndecember 3#blackhawks#4 - 2#hiller (6 - 3 - 1)#21574#14 - 10 - 3#united center#31\ndecember 7#blue jackets#5 - 3#leclaire (4 - 5 - 0)#16914#15 - 10 - 3#honda center#33\ndecember 10#blues#4 - 2#mason (3 - 7 - 1)#16058#16 - 10 - 3#honda center#35\ndecember 11#sharks#2 - 0#hiller (6 - 4 - 1)#17496#16 - 11 - 3#hp pavilion at san jose#35\ndecember 14#wild#4 - 2#harding (1 - 3 - 0)#16577#17 - 11 - 3#honda center#37\ndecember 16#rangers#3 - 1#hiller (6 - 5 - 1)#16921#17 - 12 - 3#honda center#37\ndecember 19#oilers#3 - 2#roloson (6 - 5 - 3)#16839#18 - 12 - 3#rexall place#39\ndecember 22#canucks#4 - 3#hiller (7 - 6 - 1)#18630#18 - 13 - 3#general motors place#39\ndecember 23#flames#4 - 3#giguere (11 - 8 - 2)#19289#18 - 14 - 3#pengrowth saddledome#39\ndecember 27#stars#4 - 3#giguere (11 - 8 - 3)#18532#18 - 14 - 4#american airlines center#40\ndecember 28#blues#4 - 3#mason (4 - 10 - 1)#19150#19 - 14 - 4#scottrade center#42\ndecember 31#blue jackets#2 - 0#giguere (11 - 9 - 3)#16758#19 - 15 - 4#honda center#42\n",
        "pandas_code": "(df[df['opponent'] == 'canucks'].index > df[df['opponent'].isin(['blue jackets', 'flames'])].index.max()).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3490,
        "statement": "the dallas cowboys only lose 15.4% of their season game",
        "label": 1,
        "table_caption": "1977 dallas cowboys season",
        "table_text": "week#date#result#record#opponent#points for#points against#first downs#attendance\n1#september 18#win#1 - 0#minnesota vikings#16#10#16#47678\n2#september 25#win#2 - 0#new york giants#41#21#25#64215\n3#october 2#win#3 - 0#tampa bay buccaneers#23#7#23#55316\n4#october 9#win#4 - 0#st louis cardinals#30#24#22#50129\n5#october 16#win#5 - 0#washington redskins#34#16#23#62115\n6#october 23#win#6 - 0#philadelphia eagles#16#10#17#65507\n7#october 30#win#7 - 0#detroit lions#37#0#20#63160\n8#november 6#win#8 - 0#new york giants#24#10#13#74532\n9#november 14#loss#8 - 1#st louis cardinals#17#24#16#64038\n10#november 20#loss#8 - 2#pittsburgh steelers#13#28#20#49761\n11#november 27#win#9 - 2#washington redskins#14#7#19#55031\n12#december 4#win#10 - 2#philadelphia eagles#24#14#19#60289\n13#december 12#win#11 - 2#san francisco 49ers#42#35#24#55851\n",
        "pandas_code": "abs(df['result'].value_counts(normalize=True).get('loss', 0) * 100 - 15.4) < 0.1",
        "pandas_eval": "True"
    },
    {
        "id": 3491,
        "statement": "the dallas cowboys win 84.6% of their season game",
        "label": 1,
        "table_caption": "1977 dallas cowboys season",
        "table_text": "week#date#result#record#opponent#points for#points against#first downs#attendance\n1#september 18#win#1 - 0#minnesota vikings#16#10#16#47678\n2#september 25#win#2 - 0#new york giants#41#21#25#64215\n3#october 2#win#3 - 0#tampa bay buccaneers#23#7#23#55316\n4#october 9#win#4 - 0#st louis cardinals#30#24#22#50129\n5#october 16#win#5 - 0#washington redskins#34#16#23#62115\n6#october 23#win#6 - 0#philadelphia eagles#16#10#17#65507\n7#october 30#win#7 - 0#detroit lions#37#0#20#63160\n8#november 6#win#8 - 0#new york giants#24#10#13#74532\n9#november 14#loss#8 - 1#st louis cardinals#17#24#16#64038\n10#november 20#loss#8 - 2#pittsburgh steelers#13#28#20#49761\n11#november 27#win#9 - 2#washington redskins#14#7#19#55031\n12#december 4#win#10 - 2#philadelphia eagles#24#14#19#60289\n13#december 12#win#11 - 2#san francisco 49ers#42#35#24#55851\n",
        "pandas_code": "round(df['result'].value_counts(normalize=True)['win'] * 100, 1) == 84.6",
        "pandas_eval": "True"
    },
    {
        "id": 3492,
        "statement": "the viking game be before the giant game",
        "label": 1,
        "table_caption": "1977 dallas cowboys season",
        "table_text": "week#date#result#record#opponent#points for#points against#first downs#attendance\n1#september 18#win#1 - 0#minnesota vikings#16#10#16#47678\n2#september 25#win#2 - 0#new york giants#41#21#25#64215\n3#october 2#win#3 - 0#tampa bay buccaneers#23#7#23#55316\n4#october 9#win#4 - 0#st louis cardinals#30#24#22#50129\n5#october 16#win#5 - 0#washington redskins#34#16#23#62115\n6#october 23#win#6 - 0#philadelphia eagles#16#10#17#65507\n7#october 30#win#7 - 0#detroit lions#37#0#20#63160\n8#november 6#win#8 - 0#new york giants#24#10#13#74532\n9#november 14#loss#8 - 1#st louis cardinals#17#24#16#64038\n10#november 20#loss#8 - 2#pittsburgh steelers#13#28#20#49761\n11#november 27#win#9 - 2#washington redskins#14#7#19#55031\n12#december 4#win#10 - 2#philadelphia eagles#24#14#19#60289\n13#december 12#win#11 - 2#san francisco 49ers#42#35#24#55851\n",
        "pandas_code": "df[df['opponent'].isin(['minnesota vikings', 'new york giants'])].sort_values('week').iloc[0]['opponent'] == 'minnesota vikings'",
        "pandas_eval": "True"
    },
    {
        "id": 3493,
        "statement": "the viking game be before the 49ers game",
        "label": 1,
        "table_caption": "1977 dallas cowboys season",
        "table_text": "week#date#result#record#opponent#points for#points against#first downs#attendance\n1#september 18#win#1 - 0#minnesota vikings#16#10#16#47678\n2#september 25#win#2 - 0#new york giants#41#21#25#64215\n3#october 2#win#3 - 0#tampa bay buccaneers#23#7#23#55316\n4#october 9#win#4 - 0#st louis cardinals#30#24#22#50129\n5#october 16#win#5 - 0#washington redskins#34#16#23#62115\n6#october 23#win#6 - 0#philadelphia eagles#16#10#17#65507\n7#october 30#win#7 - 0#detroit lions#37#0#20#63160\n8#november 6#win#8 - 0#new york giants#24#10#13#74532\n9#november 14#loss#8 - 1#st louis cardinals#17#24#16#64038\n10#november 20#loss#8 - 2#pittsburgh steelers#13#28#20#49761\n11#november 27#win#9 - 2#washington redskins#14#7#19#55031\n12#december 4#win#10 - 2#philadelphia eagles#24#14#19#60289\n13#december 12#win#11 - 2#san francisco 49ers#42#35#24#55851\n",
        "pandas_code": "df[df['opponent'].isin(['minnesota vikings', 'san francisco 49ers'])].sort_values('week').iloc[0]['opponent'] == 'minnesota vikings'",
        "pandas_eval": "True"
    },
    {
        "id": 3494,
        "statement": "the 49ers game be after the viking game",
        "label": 1,
        "table_caption": "1977 dallas cowboys season",
        "table_text": "week#date#result#record#opponent#points for#points against#first downs#attendance\n1#september 18#win#1 - 0#minnesota vikings#16#10#16#47678\n2#september 25#win#2 - 0#new york giants#41#21#25#64215\n3#october 2#win#3 - 0#tampa bay buccaneers#23#7#23#55316\n4#october 9#win#4 - 0#st louis cardinals#30#24#22#50129\n5#october 16#win#5 - 0#washington redskins#34#16#23#62115\n6#october 23#win#6 - 0#philadelphia eagles#16#10#17#65507\n7#october 30#win#7 - 0#detroit lions#37#0#20#63160\n8#november 6#win#8 - 0#new york giants#24#10#13#74532\n9#november 14#loss#8 - 1#st louis cardinals#17#24#16#64038\n10#november 20#loss#8 - 2#pittsburgh steelers#13#28#20#49761\n11#november 27#win#9 - 2#washington redskins#14#7#19#55031\n12#december 4#win#10 - 2#philadelphia eagles#24#14#19#60289\n13#december 12#win#11 - 2#san francisco 49ers#42#35#24#55851\n",
        "pandas_code": "df[df['opponent'] == 'san francisco 49ers']['week'].values[0] > df[df['opponent'] == 'minnesota vikings']['week'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3496,
        "statement": "blackpool lose their match and stoke city win their match",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "(df[df['home team'] == 'blackpool']['score'].str.split(' - ').apply(lambda x: int(x[0]) < int(x[1])).any()) & (df[df['home team'] == 'stoke city']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).any())",
        "pandas_eval": "True"
    },
    {
        "id": 3498,
        "statement": "the most goal score in a match be 5 by sheffield wednesday",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "df[df['score'].str.extract(r'(\\d+) - (\\d+)').astype(int).max(axis=1) == 5]['home team'].eq('sheffield wednesday').any()",
        "pandas_eval": "True"
    },
    {
        "id": 3500,
        "statement": "halifax town and queen park ranger both play in a replay match in 11 january 1989",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "((df['home team'].isin(['halifax town', 'queens park rangers'])) & (df['away team'].isin(['halifax town', 'queens park rangers'])) & (df['date'] == '11 january 1989') & (df['tie no'] == 'replay')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3501,
        "statement": "blackpool lose their match and stoke city win their match in 10 january 1989",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "(df[(df['home team'] == 'blackpool') & (df['date'] == '10 january 1989')]['score'].str.split(' - ').apply(lambda x: int(x[0]) < int(x[1])).any()) & (df[(df['home team'] == 'stoke city') & (df['date'] == '10 january 1989')]['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).any())",
        "pandas_eval": "False"
    },
    {
        "id": 3502,
        "statement": "port vale and hartlepool united each score 1 goal in their match against the blackburn rover",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "(df[((df['home team'] == 'port vale') & (df['away team'] == 'blackburn rovers') & (df['score'] == '1 - 1')) | ((df['home team'] == 'hartlepool united') & (df['away team'] == 'blackburn rovers') & (df['score'] == '1 - 1'))].shape[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 3503,
        "statement": "the most goal score in a match be 5 by sheffield wednesday , with away team aston villa",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "(df['score'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).max() == 5) & (df.loc[df['score'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).idxmax(), 'home team'] == 'sheffield wednesday') & (df.loc[df['score'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).idxmax(), 'away team'] == 'aston villa')",
        "pandas_eval": "False"
    },
    {
        "id": 3504,
        "statement": "manchester unite v queen park ranger second replay match be the last match play in the 1988 - 89 fa cup third round proper , with a score of 1 - 2",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#blackpool#0 - 1#bournemouth#7 january 1989\n2#sutton united#2 - 1#coventry city#7 january 1989\n3#walsall#1 - 1#brentford#7 january 1989\nreplay#brentford#1 - 0#walsall#10 january 1989\n4#nottingham forest#3 - 0#ipswich town#7 january 1989\n5#sheffield wednesday#5 - 1#torquay united#7 january 1989\n6#crewe alexandra#2 - 3#aston villa#7 january 1989\n7#middlesbrough#1 - 2#grimsby town#7 january 1989\n8#west bromwich albion#1 - 1#everton#7 january 1989\nreplay#everton#1 - 0#west bromwich albion#11 january 1989\n9#sunderland#1 - 1#oxford united#7 january 1989\nreplay#oxford united#2 - 0#sunderland#11 january 1989\n10#derby county#1 - 1#southampton#7 january 1989\nreplay#southampton#1 - 2#derby county#10 january 1989\n11#shrewsbury town#0 - 3#colchester united#7 january 1989\n12#tranmere rovers#1 - 1#reading#7 january 1989\nreplay#reading#2 - 1#tranmere rovers#11 january 1989\n13#newcastle united#0 - 0#watford#7 january 1989\nreplay#watford#2 - 2#newcastle united#10 january 1989\nreplay#newcastle united#0 - 0#watford#16 january 1989\nreplay#watford#1 - 0#newcastle united#18 january 1989\n14#manchester city#1 - 0#leicester city#7 january 1989\n15#barnsley#4 - 0#chelsea#7 january 1989\n16#portsmouth#1 - 1#swindon town#7 january 1989\nreplay#swindon town#2 - 0#portsmouth#10 january 1989\n17#west ham united#2 - 2#arsenal#8 january 1989\nreplay#arsenal#0 - 1#west ham united#11 january 1989\n18#brighton & hove albion#1 - 2#leeds united#7 january 1989\n19#manchester united#0 - 0#queens park rangers#7 january 1989\nreplay#queens park rangers#2 - 2#manchester united#11 january 1989\nreplay#manchester united#3 - 0#queens park rangers#23 january 1989\n20#plymouth argyle#2 - 0#cambridge united#7 january 1989\n21#bradford city#1 - 0#tottenham hotspur#7 january 1989\n22#millwall#3 - 2#luton town#7 january 1989\n23#carlisle united#0 - 3#liverpool#7 january 1989\n24#huddersfield town#0 - 1#sheffield united#7 january 1989\n25#cardiff city#1 - 2#hull city#7 january 1989\n26#port vale#1 - 3#norwich city#8 january 1989\n27#charlton athletic#2 - 1#oldham athletic#7 january 1989\n28#kettering town#1 - 1#halifax town#7 january 1989\nreplay#halifax town#2 - 3#kettering town#10 january 1989\n29#stoke city#1 - 0#crystal palace#7 january 1989\n30#birmingham city#0 - 1#wimbledon#7 january 1989\n31#hartlepool united#1 - 0#bristol city#7 january 1989\n32#welling united#0 - 1#blackburn rovers#7 january 1989\n",
        "pandas_code": "(df['home team'].eq('manchester united') & df['away team'].eq('queens park rangers') & df['score'].eq('1 - 2') & df['tie no'].eq('replay')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3505,
        "statement": "w211ae be the only wqln - fm translator not license in the state of pennsylvania",
        "label": 1,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "(df[df['call sign'] == 'w211ae']['city of license'].str.contains('pa').any() == False) & (df[df['call sign'] != 'w211ae']['city of license'].str.contains('pa').all())",
        "pandas_eval": "True"
    },
    {
        "id": 3506,
        "statement": "w211ae have the lowest erp w of any of the wqln - fm translator",
        "label": 1,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df.loc[df['call sign'] == 'w211ae', 'erp w'].iloc[0] == df['erp w'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3507,
        "statement": "w255ae have the highest frequency of any of the wqln - fm translator",
        "label": 1,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df.loc[df['call sign'] == 'w255ae', 'frequency mhz'].values[0] == df['frequency mhz'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3508,
        "statement": "wqln - fm be broadcast by 5 different translator in pennsylvania and new york",
        "label": 1,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "len(df[df['city of license'].str.contains('pa|ny', case=False)]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3510,
        "statement": "w211ae be 1 of 5 wqln - fm translator to be license in the state of pennsylvania",
        "label": 0,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df[df['call sign'] == 'w211ae']['city of license'].str.contains('pa').any()",
        "pandas_eval": "False"
    },
    {
        "id": 3511,
        "statement": "w211ae have the second lowest erp w of any of the wqln - fm translator",
        "label": 0,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df.loc[df['call sign'] == 'w211ae', 'erp w'].values[0] == sorted(df['erp w'].unique())[1]",
        "pandas_eval": "False"
    },
    {
        "id": 3512,
        "statement": "w255ae have a lower frequency than wqln - w220ba fm translator",
        "label": 0,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df.loc[df['call sign'] == 'w255ae', 'frequency mhz'].values[0] < df.loc[df['call sign'] == 'w220ba', 'frequency mhz'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3513,
        "statement": "wqln - fm be broadcast by 1 different translator in pennsylvania and 4 in new york",
        "label": 0,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "len(df[df['city of license'].str.contains('pa')]) == 1 and len(df[df['city of license'].str.contains('ny')]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3514,
        "statement": "w218ap and w220ba be tie for the lowest in frequency",
        "label": 0,
        "table_caption": "wqln - fm",
        "table_text": "call sign#frequency mhz#city of license#erp w#fcc info\nw207af#89.3 fm#meadville , pa#4#fcc\nw211ae#90.1 fm#mayville , ny#3#fcc\nw218ap#91.5 fm#titusville , pa#13#fcc\nw220ba#91.9 fm#oil city , pa#10#fcc\nw255ae#98.9 fm#warren , pa#50#fcc\n",
        "pandas_code": "df[df['call sign'].isin(['w218ap', 'w220ba'])]['frequency mhz'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3515,
        "statement": "the player be absent for the 2000 australian open , the 2000 us open , and 2000 wimbledon",
        "label": 1,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "all(df.loc[df['tournament'].isin(['australian open', 'us open', 'wimbledon']), '2000'] == 'a')",
        "pandas_eval": "True"
    },
    {
        "id": 3516,
        "statement": "in 2008 , the player make it to the quarter final for the australian open , but be absent for the us open",
        "label": 1,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'australian open', '2008'].values[0] == 'qf') & (df.loc[df['tournament'] == 'us open', '2008'].values[0] == 'a')",
        "pandas_eval": "True"
    },
    {
        "id": 3517,
        "statement": "in 2001 , the player make it to the 4th round at wimbledon , but only make it to the 1st round for the french open",
        "label": 1,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "df.loc[df['tournament'] == 'wimbledon', '2001'].values[0] == '4r' and df.loc[df['tournament'] == 'french open', '2001'].values[0] == '1r'",
        "pandas_eval": "True"
    },
    {
        "id": 3518,
        "statement": "the player make it to the quarter final for wimbledon in 2012 and the quarter final in 2010 for the french open",
        "label": 1,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'wimbledon', '2012'].values[0] == 'qf') & (df.loc[df['tournament'] == 'french open', '2010'].values[0] == 'qf')",
        "pandas_eval": "True"
    },
    {
        "id": 3519,
        "statement": "the player have a year - end ranking of 113 in 2000 , but in 2012 , with a quarter final , make it to a year - end ranking of 25",
        "label": 1,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year - end ranking', '2000'].values[0] == '113') & (df.loc[df['tournament'] == 'year - end ranking', '2012'].values[0] == '25') & (df.loc[df['tournament'] == 'wimbledon', '2012'].values[0] == 'qf')",
        "pandas_eval": "True"
    },
    {
        "id": 3520,
        "statement": "the player be absent for the 2000 australian open , the 2000 us open , and 2004 wimbledon",
        "label": 0,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'australian open', '2000'].values[0] == 'a') and (df.loc[df['tournament'] == 'us open', '2000'].values[0] == 'a') and (df.loc[df['tournament'] == 'wimbledon', '2004'].values[0] == 'a')",
        "pandas_eval": "False"
    },
    {
        "id": 3521,
        "statement": "in 2004 , the player make it to the quarter final for the australian open , but be absent for the us open",
        "label": 0,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'australian open', '2004'].values[0] == 'qf') & (df.loc[df['tournament'] == 'us open', '2004'].values[0] == 'a')",
        "pandas_eval": "False"
    },
    {
        "id": 3522,
        "statement": "in 2001 , the player make it to the 2nd round at wimbledon , but only make it to the 1st round for the french open",
        "label": 0,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'wimbledon', '2001'].values[0] == '2r') & (df.loc[df['tournament'] == 'french open', '2001'].values[0] == '1r')",
        "pandas_eval": "False"
    },
    {
        "id": 3523,
        "statement": "the player make it to the quarter final for wimbledon in 2010 and the quarter final in 2018 for the french open",
        "label": 0,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'wimbledon', '2010'].values[0] == 'qf') and (df.loc[df['tournament'] == 'french open', '2018'].values[0] == 'qf')",
        "pandas_eval": "False"
    },
    {
        "id": 3524,
        "statement": "the player have a year - end ranking of 103 in 2000 , but in 2012 , with a quarter final , make it to a year - end ranking of 35",
        "label": 0,
        "table_caption": "mikhail youzhny",
        "table_text": "tournament#2000#2001#2002#2003#2004#2005#2006#2007#2008#2009#2010#2011#2012\ngrand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments#grand slam tournaments\naustralian open#a#3r#3r#4r#1r#2r#1r#3r#qf#1r#3r#3r#1r\nfrench open#q1#1r#1r#2r#3r#2r#2r#4r#3r#2r#qf#3r#3r\nwimbledon#a#4r#4r#2r#1r#4r#3r#4r#4r#1r#2r#4r#qf\nus open#a#3r#a#1r#3r#3r#sf#2r#a#2r#sf#1r#1r\nwin - loss#0 - 0#7 - 4#5 - 3#5 - 4#4 - 4#7 - 4#8 - 4#9 - 4#9 - 3#2 - 4#12 - 3#7 - 4#6 - 4\nyear - end ranking#113#58#32#43#16#43#24#19#32#19#10#35#25\n",
        "pandas_code": "(df.loc[df['tournament'] == 'year - end ranking', '2000'].values[0] == '103') and (df.loc[df['tournament'] == 'year - end ranking', '2012'].values[0] == '35')",
        "pandas_eval": "False"
    },
    {
        "id": 3525,
        "statement": "robby gordon have the same amount of start in 2000 as in 2001",
        "label": 1,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "df.loc[df['year'].isin([2000, 2001]), 'starts'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3526,
        "statement": "robby gordon have 1 start for 3 year",
        "label": 1,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "df[(df['year'] == 1993) & (df['starts'] == 1)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3527,
        "statement": "robby gordon make less than 10000 only 1 year",
        "label": 1,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df[df['winnings'] < 10000].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3528,
        "statement": "robby gordon 's highest position be in 2003",
        "label": 1,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "df.loc[df['year'] == 2003, 'position'].values[0] == '16th'",
        "pandas_eval": "True"
    },
    {
        "id": 3529,
        "statement": "robby gordon have greater than 0 pole for only 1 year",
        "label": 1,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df['poles'] > 0).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3530,
        "statement": "robby gordon have the same amount of start in 2002 as in 2001",
        "label": 0,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df[df['year'] == 2002]['starts'].values[0] == df[df['year'] == 2001]['starts'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3531,
        "statement": "robby gordon have 1 start for 2 year",
        "label": 0,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df[(df['year'] == 1991) & (df['starts'] == 1)].shape[0] == 1) & (df[(df['year'] == 1993) & (df['starts'] == 1)].shape[0] == 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3532,
        "statement": "robby gordon never make less than 10000",
        "label": 0,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df['winnings'] >= 10000).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3533,
        "statement": "robby gordon 's highest position be in 1993",
        "label": 0,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "df.loc[df['year'] == 1993, 'position'].values[0] != '93rd'",
        "pandas_eval": "False"
    },
    {
        "id": 3534,
        "statement": "robby gordon always have 0 pole",
        "label": 0,
        "table_caption": "robby gordon",
        "table_text": "year#starts#wins#top 5#top 10#poles#avg start#avg finish#winnings#position\n1991#2#0#0#0#0#35.0#22.0#27625#55th\n1993#1#0#0#0#0#14.0#42.0#17665#93rd\n1994#1#0#0#0#0#38.0#38.0#7965#76th\n1996#3#0#0#0#0#17.3#40.7#33915#57th\n1997#20#0#1#1#1#25.3#29.6#622439#40th\n1998#1#0#0#0#0#18.0#37.0#24765#67th\n2000#17#0#1#2#0#29.9#29.2#620781#43rd\n2001#17#1#2#3#0#32.4#24.8#1371900#44th\n2002#36#0#1#5#0#18.4#21.1#3342703#20th\n2003#36#2#4#10#0#23.1#19.7#4157064#16th\n2004#36#0#2#6#0#23.2#21.2#4225719#23rd\n2005#29#0#1#2#0#27.0#30.1#2271313#37th\n2006#36#0#1#3#0#27.5#25.3#3143787#30th\n2007#35#0#1#2#0#33.9#25.8#3090004#26th\n2008#36#0#0#3#0#30.9#29.0#3816362#33rd\n2009#35#0#1#1#0#30.1#28.5#3860582#34th\n2010#27#0#1#1#0#33.8#29.1#2913816#34th\n2011#25#0#0#0#0#36.5#33.4#2271891#34th\n2012#3#0#0#0#0#30.0#40.3#405300#52nd\n",
        "pandas_code": "(df['poles'] == 0).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3535,
        "statement": "there be 2 album that receive a 4x platinum certification in 2007 , while 6 receive a 2x platinum",
        "label": 1,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "(df['certification'].value_counts()['4x platinum'] == 2) & (df['certification'].value_counts()['2x platinum'] == 6)",
        "pandas_eval": "True"
    },
    {
        "id": 3536,
        "statement": "artist hedley and arcade fire both have sale of 100000",
        "label": 1,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "(df[(df['artist'] == 'hedley') | (df['artist'] == 'arcade fire')]['sales'] == 100000).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3537,
        "statement": "michael bubl\u00e3 sell 2 time as many album as anne murray do",
        "label": 1,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "df[df['artist'] == 'michael bubl\u00e3']['sales'].values[0] == 2 * df[df['artist'] == 'anne murray']['sales'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3538,
        "statement": "artist three day grace 's album one - x peak at 2 , with a 2x platinum certification",
        "label": 1,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "((df['artist'] == 'three days grace') & (df['album'] == 'one - x') & (df['peak position'] == 2) & (df['certification'] == '2x platinum')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3539,
        "statement": "in 2007 , celine dion have 2 album in the top 10 in canada",
        "label": 1,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "len(df[(df['artist'] == 'celine dion') & (df['rank'] <= 10)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3540,
        "statement": "there be 2 album that receive a 3x platinum certification in 2007 , while 6 receive a 2x platinum",
        "label": 0,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "(df['certification'].value_counts() == {'2x platinum': 6, '3x platinum': 2}).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3541,
        "statement": "artist feist and arcade fire both have sale of 1000000",
        "label": 0,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "(df[df['artist'].isin(['feist', 'arcade fire'])]['sales'].sum() == 1000000)",
        "pandas_eval": "False"
    },
    {
        "id": 3542,
        "statement": "anne murray sell 2 time as many album as michael bubl\u00e3 do",
        "label": 0,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "df[df['artist'] == 'anne murray']['sales'].values[0] == 2 * df[df['artist'] == 'michael bubl\u00e3']['sales'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3543,
        "statement": "artist arcade fire 's album on - x peak at 2 , with a 2x platinum certification",
        "label": 0,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "((df['artist'] == 'arcade fire') & (df['album'] == 'neon bible') & (df['peak position'] == 2) & (df['certification'] == '2x platinum')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3544,
        "statement": "in 2007 , anne murray have 2 album in the top in canada",
        "label": 0,
        "table_caption": "2007 in canadian music",
        "table_text": "rank#artist#album#peak position#sales#certification\n1#celine dion#taking chances#1#400000#4x platinum\n2#michael bubl\u00e3#call me irresponsible#1#400000#4x platinum\n3#anne murray#anne murray duets : friends and legends#2#200000#2x platinum\n4#avril lavigne#the best damn thing#1#200000#2x platinum\n5#celine dion#d'elles#1#200000#2x platinum\n6#feist#the reminder#2#200000#2x platinum\n7#kalan porter#wake up living#4#200000#2x platinum\n8#three days grace#one - x#2#200000#2x platinum\n9#arcade fire#neon bible#1#100000#platinum\n10#hedley#famous last words#3#100000#platinum\n",
        "pandas_code": "len(df[(df['artist'] == 'anne murray') & (df['peak position'] <= 2)]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3545,
        "statement": "the player be in the master tournament and win 2 time , but do not have any win at the us open",
        "label": 1,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['wins'].values[0] == 2) & (df[df['tournament'] == 'us open']['wins'].values[0] == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3546,
        "statement": "for the pga tournament , the player make the cut almost half the time for the event (10 out of 21)",
        "label": 1,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "df[df['tournament'] == 'pga championship']['cuts made'].values[0] / df[df['tournament'] == 'pga championship']['events'].values[0] == 10/21",
        "pandas_eval": "True"
    },
    {
        "id": 3547,
        "statement": "the player make the top 5 for the master tournament 5 time , but only make the top 5 at the us open championship 2 time",
        "label": 1,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['top - 5'].values[0] == 5) & (df[df['tournament'] == 'us open']['top - 5'].values[0] == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3549,
        "statement": "the player make the cut in 85 event and then go on to place in the top 25 , 32 time",
        "label": 1,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'totals']['events'].values[0] == 85) & (df[df['tournament'] == 'totals']['top - 25'].values[0] == 32)",
        "pandas_eval": "True"
    },
    {
        "id": 3550,
        "statement": "the player win the master 's tournament 1 time and the us open 2 time",
        "label": 0,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['wins'].values[0] == 1) & (df[df['tournament'] == 'us open']['wins'].values[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 3551,
        "statement": "for the pga tournament , the player make the cut less than 1 - third of the time (3 out of 21)",
        "label": 0,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'pga championship']['cuts made'].values[0] / df[df['tournament'] == 'pga championship']['events'].values[0]) < (1/3)",
        "pandas_eval": "False"
    },
    {
        "id": 3552,
        "statement": "the player make the top 5 for the master tournament 2 time , but never make the top 5 at the us open championship",
        "label": 0,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[df['tournament'] == 'masters tournament']['top - 5'].values[0] == 2) & (df[df['tournament'] == 'us open']['top - 5'].values[0] == 0)",
        "pandas_eval": "False"
    },
    {
        "id": 3553,
        "statement": "the player only make it to the top 10 in the master tournament , us open and pga championship , but only make it to the top 5 in the us open",
        "label": 0,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df[(df['tournament'] == 'masters tournament') | (df['tournament'] == 'us open') | (df['tournament'] == 'pga championship')]['top - 10'] > 0).all() & (df[df['tournament'] == 'us open']['top - 5'] > 0).all() & (df[~((df['tournament'] == 'masters tournament') | (df['tournament'] == 'us open') | (df['tournament'] == 'pga championship'))]['top - 10'] == 0).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3554,
        "statement": "the player make the cut in 58 event and then go on to place in the top 25 only 23 time",
        "label": 0,
        "table_caption": "jos\u00e9 mar\u00eda olaz\u00e1bal",
        "table_text": "tournament#wins#top - 5#top - 10#top - 25#events#cuts made\nmasters tournament#2#5#8#13#25#17\nus open#0#0#3#7#18#11\nthe open championship#0#2#2#9#21#16\npga championship#0#1#2#3#21#10\ntotals#2#8#15#32#85#54\n",
        "pandas_code": "(df['cuts made'].sum() == 58) & (df['top - 25'].sum() == 23)",
        "pandas_eval": "False"
    },
    {
        "id": 3555,
        "statement": "only 13 rider complete all of the round during the 2009 season",
        "label": 1,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "(df['rounds'] == 'all').sum() == 13",
        "pandas_eval": "True"
    },
    {
        "id": 3556,
        "statement": "the ducati motorcycle constructor be the most widely used motorcycle during the season",
        "label": 1,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df['constructor'].value_counts().idxmax() == 'ducati'",
        "pandas_eval": "True"
    },
    {
        "id": 3557,
        "statement": "the kawasaki ninja zx - rr be the only 1 of its kind that participate during the 2009 season",
        "label": 1,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "len(df[df['motorcycle'] == 'kawasaki ninja zx - rr']) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3558,
        "statement": "michel fabrizio and ben spies each only participate in 1 round",
        "label": 1,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "(df[df['rider'] == 'michel fabrizio']['rounds'].str.contains('-').sum() == 0) & (df[df['rider'] == 'ben spies']['rounds'].str.contains('-').sum() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3559,
        "statement": "the pramac racing team be the most represented team participate during the season",
        "label": 1,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df[df['team'] == 'pramac racing'].shape[0] == df['team'].value_counts().max()",
        "pandas_eval": "True"
    },
    {
        "id": 3560,
        "statement": "all 15 rider complete all of the round during the 2019 season",
        "label": 0,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "all(df['rounds'] == 'all')",
        "pandas_eval": "False"
    },
    {
        "id": 3561,
        "statement": "the ducati motorcycle constructor be the most under used motorcycle during the year",
        "label": 0,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df[df['constructor'] == 'ducati']['rounds'].apply(lambda x: len(x.split(',')) if x != 'all' else 0).sum() < df[df['constructor'] != 'ducati']['rounds'].apply(lambda x: len(x.split(',')) if x != 'all' else 0).sum()",
        "pandas_eval": "False"
    },
    {
        "id": 3562,
        "statement": "kawasaki ninja zx - rr be 1 of many of its kind that do not participate during the 2009 season",
        "label": 0,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df[df['motorcycle'] == 'kawasaki ninja zx - rr']['rounds'].eq('all').any() == False",
        "pandas_eval": "False"
    },
    {
        "id": 3563,
        "statement": "michel fabrizio and ben spies do not participate in 1 round",
        "label": 0,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df[df['rider'].isin(['michel fabrizio', 'ben spies'])]['rounds'].apply(lambda x: '1' not in x).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3564,
        "statement": "the pramac racing team be the least represented team participate during the season",
        "label": 0,
        "table_caption": "2009 grand prix motorcycle racing season",
        "table_text": "team#constructor#motorcycle#rider#rounds\nfiat yamaha team#yamaha#yamaha yzr - m1#valentino rossi#all\nfiat yamaha team#yamaha#yamaha yzr - m1#jorge lorenzo#all\nrepsol honda team#honda#honda rc212v#dani pedrosa#all\nrepsol honda team#honda#honda rc212v#andrea dovizioso#all\nducati marlboro team#ducati#ducati desmosedici gp9#casey stoner#1 - 10 , 14 - 17\nducati marlboro team#ducati#ducati desmosedici gp9#nicky hayden#all\nducati marlboro team#ducati#ducati desmosedici gp9#mika kallio 1#11 - 13\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#colin edwards#all\nmonster yamaha tech 3#yamaha#yamaha yzr - m1#james toseland#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#chris vermeulen#all\nrizla suzuki motogp#suzuki#suzuki gsv - r#loris capirossi#all\npramac racing#ducati#ducati desmosedici gp9#mika kallio#1 - 7 , 9 - 10 , 14 - 17\npramac racing#ducati#ducati desmosedici gp9#niccol\u00f2 canepa#1 - 15\npramac racing#ducati#ducati desmosedici gp9#michel fabrizio 2#11\npramac racing#ducati#ducati desmosedici gp9#aleix espargar\u00f3 3#12 - 13 , 16 - 17\nsan carlo honda gresini#honda#honda rc212v#toni el\u00edas#all\nsan carlo honda gresini#honda#honda rc212v#alex de angelis#all\nscot racing team#honda#honda rc212v#yuki takahashi#1 - 7\nscot racing team#honda#honda rc212v#g\u00e1bor talm\u00e1csi#6 - 17\nhayate racing team#kawasaki#kawasaki ninja zx - rr#marco melandri#all\nlcr honda motogp#honda#honda rc212v#randy de puniet#all\ngrupo francisco hernando#ducati#ducati desmosedici gp9#sete gibernau#1 - 3 , 6 - 8\nsterilgarda yamaha team#yamaha#yamaha yzr - m1#ben spies#17\n",
        "pandas_code": "df[df['team'] == 'pramac racing']['rounds'].apply(lambda x: len(x.split(','))).sum() == min(df.groupby('team')['rounds'].apply(lambda x: x.apply(lambda y: len(y.split(','))).sum()))",
        "pandas_eval": "False"
    },
    {
        "id": 3565,
        "statement": "stacy lewis have a score 1 point lower than paula creamer",
        "label": 1,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "int(df[df['player'] == 'stacy lewis']['score'].str.extract(r'= (\\d+)')[0].iloc[0]) == int(df[df['player'] == 'paula creamer']['score'].str.extract(r'= (\\d+)')[0].iloc[0]) - 1",
        "pandas_eval": "True"
    },
    {
        "id": 3566,
        "statement": "inbee park and ik kim both represent south korea",
        "label": 1,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "all(df[df['player'].isin(['inbee park', 'ik kim'])]['country'] == 'south korea')",
        "pandas_eval": "True"
    },
    {
        "id": 3567,
        "statement": "5 athlete represent south korea",
        "label": 1,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "len(df[df['country'].str.contains('south korea')]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3568,
        "statement": "jeong jang and teresa lu finish with the same score",
        "label": 1,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "df[df['player'].isin(['jeong jang', 'teresa lu'])]['score'].str.extract(r'= (\\d+)')[0].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3569,
        "statement": "2 athlete finish 4 below par",
        "label": 1,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "len(df[df['to par'].str.contains('- 4')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3570,
        "statement": "stacy lewis have a score that tie with paula creamer",
        "label": 0,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "df[df['player'] == 'stacy lewis']['score'].values[0] == df[df['player'] == 'paula creamer']['score'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3571,
        "statement": "inbee park be the only player to represent south korea",
        "label": 0,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "len(df[(df['country'] == 'south korea') & (df['player'] == 'inbee park')]) == 1 and len(df[df['country'] == 'south korea']) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3572,
        "statement": "only 1 athlete represent south korea",
        "label": 0,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "len(df[df['country'] == 'south korea']) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3573,
        "statement": "teresa lu finish 1 point lower than jeong jang",
        "label": 0,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "int(df[df['player'] == 'teresa lu']['to par'].str.extract(r'(-?\\d+)')[0].iloc[0]) == int(df[df['player'] == 'jeong jang']['to par'].str.extract(r'(-?\\d+)')[0].iloc[0]) - 1",
        "pandas_eval": "False"
    },
    {
        "id": 3574,
        "statement": "3 athlete finish 4 below par",
        "label": 0,
        "table_caption": "2008 u.s. women 's open golf championship",
        "table_text": "place#player#country#score#to par\n1#stacy lewis#united states#73 + 70 + 67 = 210#- 9\n2#paula creamer#united states#70 + 72 + 69 = 211#- 8\nt3#helen alfredsson#sweden#70 + 71 + 71 = 212#- 7\nt3#inbee park#south korea#72 + 69 + 71 = 212#- 7\n5#ik kim#south korea#71 + 73 + 69 = 213#- 6\n6#mi hyun kim#south korea#72 + 72 + 70 = 214#- 5\nt7#mariajo uribe (a)#colombia#69 + 74 + 72 = 215#- 4\nt7#angela park#brazil united states#73 + 67 + 75 = 215#- 4\nt9#young kim#south korea#74 + 71 + 71 = 216#- 3\nt9#momoko ueda#japan#72 + 71 + 73 = 216#- 3\nt9#teresa lu#taiwan#71 + 72 + 73 = 216#- 3\nt9#jeong jang#south korea#73 + 69 + 74 = 216#- 3\n",
        "pandas_code": "len(df[df['to par'].str.extract(r'(-\\\\s*\\\\d+)', expand=False).fillna('0').astype(int) <= -4]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3575,
        "statement": "the second season begin with garage sale , while the last show of the season be cat fight club",
        "label": 1,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "(df['title'].iloc[0] == 'garage sale') & (df['title'].iloc[-1] == 'cat fight club')",
        "pandas_eval": "True"
    },
    {
        "id": 3576,
        "statement": "phillip stark write eric get suspend , jackie move on and cat fight club",
        "label": 1,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "all(df[df['written by'] == 'philip stark']['title'].isin(['eric gets suspended', 'jackie moves on', 'cat fight club']))",
        "pandas_eval": "True"
    },
    {
        "id": 3577,
        "statement": "david trainer direct every show in season 2 from number 1 through 25",
        "label": 1,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "all(df[df['no in season'].between(1, 25)]['directed by'] == 'david trainer')",
        "pandas_eval": "True"
    },
    {
        "id": 3578,
        "statement": "laurie and the professor and kelso 's serenade be both written by a woman name linda wallem",
        "label": 1,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "df[df['title'].isin(['laurie and the professor', \"kelso 's serenade\"])]['written by'].eq('linda wallem').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3579,
        "statement": "joshua sternin in and jeffrey ventimilia both write halloween together",
        "label": 1,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "any(df[(df['title'] == 'halloween') & (df['written by'].str.contains('joshua sternin')) & (df['written by'].str.contains('jeffrey ventimilia'))].index)",
        "pandas_eval": "True"
    },
    {
        "id": 3580,
        "statement": "the second season begin with holy crap! , while the last show of the season be halloween",
        "label": 0,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "(df[df['no in season'] == 1]['title'].iloc[0] == 'holy crap!') & (df[df['no in season'] == df['no in season'].max()]['title'].iloc[0] == 'halloween')",
        "pandas_eval": "False"
    },
    {
        "id": 3581,
        "statement": "linda wallem write eric get suspend , jackie move on and cat fight club",
        "label": 0,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "all(df[df['written by'] == 'linda wallem']['title'].isin(['eric gets suspended', 'jackie moves on', 'cat fight club']))",
        "pandas_eval": "False"
    },
    {
        "id": 3582,
        "statement": "cat fight club direct every show in season 2 from number 1 through 25",
        "label": 0,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "not all(df[(df['no in season'] >= 1) & (df['no in season'] <= 25)]['directed by'] == 'david trainer')",
        "pandas_eval": "False"
    },
    {
        "id": 3583,
        "statement": "laurie and the professor and kelso 's serenade be both written by a woman name philip stark",
        "label": 0,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "((df['title'] == 'laurie and the professor') & (df['written by'] == 'philip stark')).any() and ((df['title'] == \"kelso 's serenade\") & (df['written by'] == 'philip stark')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3584,
        "statement": "joshua sternin in and jeffrey ventimilia both write afterglow together",
        "label": 0,
        "table_caption": "that '70s show (season 2)",
        "table_text": "no in series#no in season#title#directed by#written by#original air date#production code\n26#1#garage sale#david trainer#dave schiff#september 28 , 1999#201\n27#2#red 's last day#david trainer#mark brazill#october 5 , 1999#202\n28#3#the velvet rope#david trainer#joshua sternin & jeffrey ventimilia#october 12 , 1999#203\n29#4#laurie and the professor#david trainer#linda wallem#october 19 , 1999#204\n30#5#halloween#david trainer#joshua sternin & jeffrey ventimilia#october 26 , 1999#205\n31#6#vanstock#david trainer#arthur f montmorency#november 2 , 1999#206\n32#7#i love cake#david trainer#jeff filgo#november 9 , 1999#207\n33#8#sleepover#david trainer#dean batali & rob des hotel#november 16 , 1999#208\n34#9#eric gets suspended#david trainer#philip stark#november 30 , 1999#209\n35#10#red 's birthday#david trainer#mark hudis#december 7 , 1999#210\n36#11#laurie moves out#david trainer#john schwab#december 14 , 1999#211\n37#12#eric 's stash#david trainer#chris peterson#january 11 , 2000#212\n38#13#hunting#david trainer#mark brazill#january 18 , 2000#213\n39#14#red 's new job#david trainer#jeff filgo & jackie filgo#february 1 , 2000#214\n40#15#burning down the house#david trainer#dave schiff#february 7 , 2000#215\n41#16#the first time#david trainer#mark hudis#february 14 , 2000#216\n42#17#afterglow#david trainer#jeffrey ventimilia#february 14 , 2000#217\n43#18#kitty and eric 's night out#david trainer#linda wallem#february 28 , 2000#218\n44#19#parents find out#david trainer#mark hudis#march 7 , 2000#219\n45#20#kiss of death#david trainer#rob des hotel & dean batali#march 20 , 2000#220\n46#21#kelso 's serenade#david trainer#linda wallem#march 27 , 2000#221\n47#22#jackie moves on#david trainer#philip stark#april 3 , 2000#222\n48#23#holy crap!#david trainer#rob des hotel & dean batali#may 1 , 2000#223\n49#24#red fired up#david trainer#dave schiff#may 8 , 2000#224\n50#25#cat fight club#david trainer#philip stark#may 15 , 2000#225\n",
        "pandas_code": "df[(df['written by'] == 'joshua sternin & jeffrey ventimilia') & (df['title'] == 'afterglow')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3585,
        "statement": "the table show 6 race for the period 1974 to 1977",
        "label": 1,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "len(df[(df['year'] >= 1974) & (df['year'] <= 1977)]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3586,
        "statement": "boro ensign n175 be the chassis 2 year after amon af101 be use in 1974",
        "label": 1,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "(df[df['chassis'] == 'boro ensign n175']['year'].values[0] - df[df['chassis'] == 'amon af101']['year'].values[0]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3588,
        "statement": "there be no point greater than 0 on any of the race",
        "label": 1,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "df['points'].max() <= 0",
        "pandas_eval": "True"
    },
    {
        "id": 3589,
        "statement": "2 race have rotary watch stanley brm as entrant , both in 1977",
        "label": 1,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "len(df[(df['entrant'] == 'rotary watches stanley brm') & (df['year'] == 1977)]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3590,
        "statement": "there be over 20 race hold between 1974 to 1977",
        "label": 0,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "len(df[(df['year'] >= 1974) & (df['year'] <= 1977)]) > 20",
        "pandas_eval": "False"
    },
    {
        "id": 3591,
        "statement": "boro ensign have always be the chassis",
        "label": 0,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "not all(df[df['entrant'] == 'hb bewaking alarm systems']['chassis'] == 'boro ensign n175')",
        "pandas_eval": "False"
    },
    {
        "id": 3592,
        "statement": "cosworth v8 have never be the engine",
        "label": 0,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "any(df[df['engine'] == 'cosworth v8']['points'] > 0)",
        "pandas_eval": "False"
    },
    {
        "id": 3593,
        "statement": "all the race score higher than 5",
        "label": 0,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "df['points'].gt(5).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3594,
        "statement": "rotary watch stanley brm never participate",
        "label": 0,
        "table_caption": "larry perkins",
        "table_text": "year#entrant#chassis#engine#points\n1974#dalton - amon international#amon af101#cosworth v8#0\n1976#hb bewaking alarm systems#boro ensign n175#cosworth v8#0\n1976#martini racing#brabham bt45#alfa romeo flat 12#0\n1977#rotary watches stanley brm#brm p207#brm v12#0\n1977#rotary watches stanley brm#brm p201b / 204#brm v12#0\n1977#team surtees#surtees ts19#cosworth v8#0\n",
        "pandas_code": "not any(df['entrant'].str.contains('rotary watches stanley brm', case=False))",
        "pandas_eval": "False"
    },
    {
        "id": 3595,
        "statement": "the district of katni have 3 different constituency number and reserve for none",
        "label": 1,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "len(df[(df['district'] == 'katni') & (df['reserved for ( sc / st / none)'] == 'none')]['constituency number'].unique()) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3596,
        "statement": "the district of panna have 3 different constituency number and 1 reserve for sc",
        "label": 1,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "(df[df['district'] == 'panna']['constituency number'].nunique() == 3) & (df[df['district'] == 'panna']['reserved for ( sc / st / none)'].eq('sc').sum() == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3598,
        "statement": "the lowest number of electorate be for constituency number 94",
        "label": 1,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "df.loc[df['constituency number'] == '94', 'number of electorates (2009)'].iloc[0] == df['number of electorates (2009)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3599,
        "statement": "the district of chhatarpur have the lowest number of constituency number",
        "label": 1,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "df[df['district'] == 'chhatarpur']['constituency number'].replace('total :', '999').astype(int).min() == df['constituency number'].replace('total :', '999').astype(int).min()",
        "pandas_eval": "True"
    },
    {
        "id": 3600,
        "statement": "the district of chhatarpur have 3 different constituency number and reserve for none",
        "label": 0,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "((df[df['district'] == 'chhatarpur']['constituency number'].nunique() == 3) & (df[(df['district'] == 'chhatarpur') & (df['reserved for ( sc / st / none)'] == 'none')].shape[0] == 3))",
        "pandas_eval": "False"
    },
    {
        "id": 3601,
        "statement": "the district of panna have 5 different constituency number and 1 reserve for sc",
        "label": 0,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "((df[df['district'] == 'panna']['constituency number'].nunique() == 5) & (df[(df['district'] == 'panna') & (df['reserved for ( sc / st / none)'] == 'sc')].shape[0] == 1))",
        "pandas_eval": "False"
    },
    {
        "id": 3602,
        "statement": "the lowest number of electorate be for constituency number 93s",
        "label": 0,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "df[df['constituency number'] == '93']['number of electorates (2009)'].iloc[0] == df['number of electorates (2009)'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3603,
        "statement": "the highest number of electorate be for constituency number 94",
        "label": 0,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "df.loc[df['constituency number'] == '94', 'number of electorates (2009)'].values[0] == df['number of electorates (2009)'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3604,
        "statement": "the district of chhatarpur have the highest number of constituency number",
        "label": 0,
        "table_caption": "khajuraho (lok sabha constituency)",
        "table_text": "constituency number#name#reserved for ( sc / st / none)#district#number of electorates (2009)\n49#chandla#sc#chhatarpur#164443\n50#rajnagar#none#chhatarpur#169579\n58#pawai#none#panna#190471\n59#gunnaor#sc#panna#157659\n60#panna#none#panna#166824\n92#vijayraghavgarh#none#katni#162554\n93#murwara#none#katni#172412\n94#bahoriband#none#katni#166.771\ntotal :#total :#total :#total :#1350713\n",
        "pandas_code": "df[df['district'] == 'chhatarpur']['constituency number'].nunique() == df['district'].value_counts().max()",
        "pandas_eval": "False"
    },
    {
        "id": 3605,
        "statement": "at least 10 driver have more than 220 lap",
        "label": 1,
        "table_caption": "2007 abc supply company a.j. foyt 225",
        "table_text": "fin pos#car no#driver#team#laps#time / retired#grid#laps led#points\n1#11#tony kanaan#andretti green#225#1:47:42.4393#3#25#50\n2#27#dario franchitti#andretti green#225#+ 2.5707#10#0#40\n3#10#dan wheldon#target chip ganassi#225#+ 3.1149#4#37#35\n4#9#scott dixon#target chip ganassi#225#+ 3.4026#2#0#32\n5#4#vitor meira#panther racing#225#+ 5.2684#9#0#30\n6#8#scott sharp#rahal letterman#225#+ 6.8359#11#0#28\n7#20#ed carpenter#vision racing#225#+ 7.0360#8#0#26\n8#7#danica patrick#andretti green#225#+ 8.0205#17#0#24\n9#6#sam hornish , jr#team penske#224#+ 1 lap#5#0#22\n10#17#jeff simmons#rahal letterman#224#+ 1 lap#18#0#20\n11#14#darren manning#aj foyt racing#224#+ 1 lap#15#0#19\n12#55#kosuke matsuura#panther racing#223#+ 2 laps#6#0#18\n13#22#a j foyt iv#vision racing#222#+ 3 laps#12#0#17\n14#5#sarah fisher#dreyer & reinbold racing#221#+ 4 laps#16#0#16\n15#26#marco andretti#andretti green#209#accident#19#0#15\n16#3#h\u00e3lio castroneves#team penske#201#rear wing#1#126#14 + 3\n17#2#tomas scheckter#vision racing#159#mechanical#13#0#13\n18#15#buddy rice#dreyer & reinbold racing#156#accident#7#37#12\n",
        "pandas_code": "len(df[df['laps'] > 220]) >= 10",
        "pandas_eval": "True"
    },
    {
        "id": 3606,
        "statement": "andretti green be the team for the driver tony kanaan , dario franchitti , danica patrick , and marco andretti",
        "label": 1,
        "table_caption": "2007 abc supply company a.j. foyt 225",
        "table_text": "fin pos#car no#driver#team#laps#time / retired#grid#laps led#points\n1#11#tony kanaan#andretti green#225#1:47:42.4393#3#25#50\n2#27#dario franchitti#andretti green#225#+ 2.5707#10#0#40\n3#10#dan wheldon#target chip ganassi#225#+ 3.1149#4#37#35\n4#9#scott dixon#target chip ganassi#225#+ 3.4026#2#0#32\n5#4#vitor meira#panther racing#225#+ 5.2684#9#0#30\n6#8#scott sharp#rahal letterman#225#+ 6.8359#11#0#28\n7#20#ed carpenter#vision racing#225#+ 7.0360#8#0#26\n8#7#danica patrick#andretti green#225#+ 8.0205#17#0#24\n9#6#sam hornish , jr#team penske#224#+ 1 lap#5#0#22\n10#17#jeff simmons#rahal letterman#224#+ 1 lap#18#0#20\n11#14#darren manning#aj foyt racing#224#+ 1 lap#15#0#19\n12#55#kosuke matsuura#panther racing#223#+ 2 laps#6#0#18\n13#22#a j foyt iv#vision racing#222#+ 3 laps#12#0#17\n14#5#sarah fisher#dreyer & reinbold racing#221#+ 4 laps#16#0#16\n15#26#marco andretti#andretti green#209#accident#19#0#15\n16#3#h\u00e3lio castroneves#team penske#201#rear wing#1#126#14 + 3\n17#2#tomas scheckter#vision racing#159#mechanical#13#0#13\n18#15#buddy rice#dreyer & reinbold racing#156#accident#7#37#12\n",
        "pandas_code": "all(df[df['driver'].isin(['tony kanaan', 'dario franchitti', 'danica patrick', 'marco andretti'])]['team'] == 'andretti green')",
        "pandas_eval": "True"
    },
    {
        "id": 3607,
        "statement": "both marco andretti and buddy rice retire because of accident",
        "label": 1,
        "table_caption": "2007 abc supply company a.j. foyt 225",
        "table_text": "fin pos#car no#driver#team#laps#time / retired#grid#laps led#points\n1#11#tony kanaan#andretti green#225#1:47:42.4393#3#25#50\n2#27#dario franchitti#andretti green#225#+ 2.5707#10#0#40\n3#10#dan wheldon#target chip ganassi#225#+ 3.1149#4#37#35\n4#9#scott dixon#target chip ganassi#225#+ 3.4026#2#0#32\n5#4#vitor meira#panther racing#225#+ 5.2684#9#0#30\n6#8#scott sharp#rahal letterman#225#+ 6.8359#11#0#28\n7#20#ed carpenter#vision racing#225#+ 7.0360#8#0#26\n8#7#danica patrick#andretti green#225#+ 8.0205#17#0#24\n9#6#sam hornish , jr#team penske#224#+ 1 lap#5#0#22\n10#17#jeff simmons#rahal letterman#224#+ 1 lap#18#0#20\n11#14#darren manning#aj foyt racing#224#+ 1 lap#15#0#19\n12#55#kosuke matsuura#panther racing#223#+ 2 laps#6#0#18\n13#22#a j foyt iv#vision racing#222#+ 3 laps#12#0#17\n14#5#sarah fisher#dreyer & reinbold racing#221#+ 4 laps#16#0#16\n15#26#marco andretti#andretti green#209#accident#19#0#15\n16#3#h\u00e3lio castroneves#team penske#201#rear wing#1#126#14 + 3\n17#2#tomas scheckter#vision racing#159#mechanical#13#0#13\n18#15#buddy rice#dreyer & reinbold racing#156#accident#7#37#12\n",
        "pandas_code": "all(df.loc[df['driver'].isin(['marco andretti', 'buddy rice']), 'time / retired'].str.contains('accident'))",
        "pandas_eval": "True"
    },
    {
        "id": 3608,
        "statement": "only tomas schekter retire due to mechanical reason , at 159 lap",
        "label": 1,
        "table_caption": "2007 abc supply company a.j. foyt 225",
        "table_text": "fin pos#car no#driver#team#laps#time / retired#grid#laps led#points\n1#11#tony kanaan#andretti green#225#1:47:42.4393#3#25#50\n2#27#dario franchitti#andretti green#225#+ 2.5707#10#0#40\n3#10#dan wheldon#target chip ganassi#225#+ 3.1149#4#37#35\n4#9#scott dixon#target chip ganassi#225#+ 3.4026#2#0#32\n5#4#vitor meira#panther racing#225#+ 5.2684#9#0#30\n6#8#scott sharp#rahal letterman#225#+ 6.8359#11#0#28\n7#20#ed carpenter#vision racing#225#+ 7.0360#8#0#26\n8#7#danica patrick#andretti green#225#+ 8.0205#17#0#24\n9#6#sam hornish , jr#team penske#224#+ 1 lap#5#0#22\n10#17#jeff simmons#rahal letterman#224#+ 1 lap#18#0#20\n11#14#darren manning#aj foyt racing#224#+ 1 lap#15#0#19\n12#55#kosuke matsuura#panther racing#223#+ 2 laps#6#0#18\n13#22#a j foyt iv#vision racing#222#+ 3 laps#12#0#17\n14#5#sarah fisher#dreyer & reinbold racing#221#+ 4 laps#16#0#16\n15#26#marco andretti#andretti green#209#accident#19#0#15\n16#3#h\u00e3lio castroneves#team penske#201#rear wing#1#126#14 + 3\n17#2#tomas scheckter#vision racing#159#mechanical#13#0#13\n18#15#buddy rice#dreyer & reinbold racing#156#accident#7#37#12\n",
        "pandas_code": "df[(df['driver'] == 'tomas scheckter') & (df['time / retired'] == 'mechanical') & (df['laps'] == 159)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3609,
        "statement": "aj foyt racing be the team for only darren manning in this race",
        "label": 1,
        "table_caption": "2007 abc supply company a.j. foyt 225",
        "table_text": "fin pos#car no#driver#team#laps#time / retired#grid#laps led#points\n1#11#tony kanaan#andretti green#225#1:47:42.4393#3#25#50\n2#27#dario franchitti#andretti green#225#+ 2.5707#10#0#40\n3#10#dan wheldon#target chip ganassi#225#+ 3.1149#4#37#35\n4#9#scott dixon#target chip ganassi#225#+ 3.4026#2#0#32\n5#4#vitor meira#panther racing#225#+ 5.2684#9#0#30\n6#8#scott sharp#rahal letterman#225#+ 6.8359#11#0#28\n7#20#ed carpenter#vision racing#225#+ 7.0360#8#0#26\n8#7#danica patrick#andretti green#225#+ 8.0205#17#0#24\n9#6#sam hornish , jr#team penske#224#+ 1 lap#5#0#22\n10#17#jeff simmons#rahal letterman#224#+ 1 lap#18#0#20\n11#14#darren manning#aj foyt racing#224#+ 1 lap#15#0#19\n12#55#kosuke matsuura#panther racing#223#+ 2 laps#6#0#18\n13#22#a j foyt iv#vision racing#222#+ 3 laps#12#0#17\n14#5#sarah fisher#dreyer & reinbold racing#221#+ 4 laps#16#0#16\n15#26#marco andretti#andretti green#209#accident#19#0#15\n16#3#h\u00e3lio castroneves#team penske#201#rear wing#1#126#14 + 3\n17#2#tomas scheckter#vision racing#159#mechanical#13#0#13\n18#15#buddy rice#dreyer & reinbold racing#156#accident#7#37#12\n",
        "pandas_code": "(df[df['team'] == 'aj foyt racing']['driver'].unique() == ['darren manning']).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3610,
        "statement": "brandon roy have high assist 3 time",
        "label": 1,
        "table_caption": "2008 - 09 portland trail blazers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#oklahoma city#w 107 - 72 (ot)#lamarcus aldridge (35)#lamarcus aldridge (18)#steve blake (10)#ford center 19136#48 - 27\n76#april 5#houston#l 88 - 102 (ot)#lamarcus aldridge , brandon roy (22)#lamarcus aldridge (9)#brandon roy (6)#toyota center 18214#48 - 28\n77#april 7#memphis#w 96 - 93 (ot)#brandon roy (24)#lamarcus aldridge (8)#brandon roy (4)#fedexforum 10089#49 - 28\n78#april 8#san antonio#w 95 - 83 (ot)#brandon roy (26)#joel przybilla (17)#steve blake (7)#at&t center 18797#50 - 28\n79#april 10#la lakers#w 106 - 98 (ot)#brandon roy (24)#joel przybilla (13)#brandon roy (8)#rose garden 20681#51 - 28\n80#april 11#la clippers#w 87 - 72 (ot)#lamarcus aldridge (21)#joel przybilla (14)#steve blake (5)#staples center 18321#52 - 28\n81#april 13#oklahoma city#w 113 - 83 (ot)#travis outlaw (21)#joel przybilla (12)#sergio rodr\u00edguez (8)#rose garden 20655#53 - 28\n",
        "pandas_code": "(df['high assists'].str.contains('brandon roy').sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3611,
        "statement": "the location attendance at the toyota center be smaller than the attendance at the staple center",
        "label": 1,
        "table_caption": "2008 - 09 portland trail blazers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#oklahoma city#w 107 - 72 (ot)#lamarcus aldridge (35)#lamarcus aldridge (18)#steve blake (10)#ford center 19136#48 - 27\n76#april 5#houston#l 88 - 102 (ot)#lamarcus aldridge , brandon roy (22)#lamarcus aldridge (9)#brandon roy (6)#toyota center 18214#48 - 28\n77#april 7#memphis#w 96 - 93 (ot)#brandon roy (24)#lamarcus aldridge (8)#brandon roy (4)#fedexforum 10089#49 - 28\n78#april 8#san antonio#w 95 - 83 (ot)#brandon roy (26)#joel przybilla (17)#steve blake (7)#at&t center 18797#50 - 28\n79#april 10#la lakers#w 106 - 98 (ot)#brandon roy (24)#joel przybilla (13)#brandon roy (8)#rose garden 20681#51 - 28\n80#april 11#la clippers#w 87 - 72 (ot)#lamarcus aldridge (21)#joel przybilla (14)#steve blake (5)#staples center 18321#52 - 28\n81#april 13#oklahoma city#w 113 - 83 (ot)#travis outlaw (21)#joel przybilla (12)#sergio rodr\u00edguez (8)#rose garden 20655#53 - 28\n",
        "pandas_code": "df[df['location attendance'].str.contains('toyota center')]['location attendance'].str.extract('(\\d+)')[0].astype(int).iloc[0] < df[df['location attendance'].str.contains('staples center')]['location attendance'].str.extract('(\\d+)')[0].astype(int).iloc[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3612,
        "statement": "the location be the rise garden 2 time",
        "label": 1,
        "table_caption": "2008 - 09 portland trail blazers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#oklahoma city#w 107 - 72 (ot)#lamarcus aldridge (35)#lamarcus aldridge (18)#steve blake (10)#ford center 19136#48 - 27\n76#april 5#houston#l 88 - 102 (ot)#lamarcus aldridge , brandon roy (22)#lamarcus aldridge (9)#brandon roy (6)#toyota center 18214#48 - 28\n77#april 7#memphis#w 96 - 93 (ot)#brandon roy (24)#lamarcus aldridge (8)#brandon roy (4)#fedexforum 10089#49 - 28\n78#april 8#san antonio#w 95 - 83 (ot)#brandon roy (26)#joel przybilla (17)#steve blake (7)#at&t center 18797#50 - 28\n79#april 10#la lakers#w 106 - 98 (ot)#brandon roy (24)#joel przybilla (13)#brandon roy (8)#rose garden 20681#51 - 28\n80#april 11#la clippers#w 87 - 72 (ot)#lamarcus aldridge (21)#joel przybilla (14)#steve blake (5)#staples center 18321#52 - 28\n81#april 13#oklahoma city#w 113 - 83 (ot)#travis outlaw (21)#joel przybilla (12)#sergio rodr\u00edguez (8)#rose garden 20655#53 - 28\n",
        "pandas_code": "(df['location attendance'].str.contains('rose garden').sum() == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3613,
        "statement": "joel przybilla have the high rebound 4 time",
        "label": 1,
        "table_caption": "2008 - 09 portland trail blazers season",
        "table_text": "game#date#team#score#high points#high rebounds#high assists#location attendance#record\n75#april 3#oklahoma city#w 107 - 72 (ot)#lamarcus aldridge (35)#lamarcus aldridge (18)#steve blake (10)#ford center 19136#48 - 27\n76#april 5#houston#l 88 - 102 (ot)#lamarcus aldridge , brandon roy (22)#lamarcus aldridge (9)#brandon roy (6)#toyota center 18214#48 - 28\n77#april 7#memphis#w 96 - 93 (ot)#brandon roy (24)#lamarcus aldridge (8)#brandon roy (4)#fedexforum 10089#49 - 28\n78#april 8#san antonio#w 95 - 83 (ot)#brandon roy (26)#joel przybilla (17)#steve blake (7)#at&t center 18797#50 - 28\n79#april 10#la lakers#w 106 - 98 (ot)#brandon roy (24)#joel przybilla (13)#brandon roy (8)#rose garden 20681#51 - 28\n80#april 11#la clippers#w 87 - 72 (ot)#lamarcus aldridge (21)#joel przybilla (14)#steve blake (5)#staples center 18321#52 - 28\n81#april 13#oklahoma city#w 113 - 83 (ot)#travis outlaw (21)#joel przybilla (12)#sergio rodr\u00edguez (8)#rose garden 20655#53 - 28\n",
        "pandas_code": "(df['high rebounds'].str.contains('joel przybilla').sum() == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 3615,
        "statement": "the top 2 producer of the odyssey award be listen library and brilliance audio",
        "label": 1,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df['producer'].value_counts().head(2).index.tolist() == ['listening library', 'brilliance audio']",
        "pandas_eval": "True"
    },
    {
        "id": 3616,
        "statement": "katherine kellgren have voice 5 audio book from 2008 - 13 that be nominate for an odyssey award",
        "label": 1,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "len(df[(df['narrator'].str.contains('katherine kellgren')) & (df['year'].between(2008, 2013)) & (df['citation'] == 'honor')]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3617,
        "statement": "since 2008 , only i a meyer have have more than 1 novel nominate",
        "label": 1,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df[(df['year'] >= 2008) & (df['author'] == 'l a meyer')].groupby('author').filter(lambda x: len(x) > 1).shape[0] > 0",
        "pandas_eval": "True"
    },
    {
        "id": 3618,
        "statement": "since 2008 , live oak media be the only production company to win more than 1 odyssey award",
        "label": 1,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "len(df[(df['producer'] == 'live oak media') & (df['citation'] == 'winner') & (df['year'] >= 2008)]) > 1",
        "pandas_eval": "True"
    },
    {
        "id": 3619,
        "statement": "harry potter and the deathly hallows and the fault in our star be the only nominee to be make into a mass - market movie",
        "label": 1,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df[(df['title'] == 'harry potter and the deathly hallows') | (df['title'] == 'the fault in our stars')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3620,
        "statement": "the top 2 producer of the odyssey award be listen & live audio library and live oak media",
        "label": 0,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df[df['producer'].isin(['listen & live audio', 'live oak media'])].groupby('producer').size().nlargest(2).index.tolist() != ['listen & live audio', 'live oak media']",
        "pandas_eval": "False"
    },
    {
        "id": 3621,
        "statement": "katherine kellgren have voice 4 audio book from 2008 - 13 that be nominate for an odyssey award",
        "label": 0,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df[(df['narrator'].str.contains('katherine kellgren')) & (df['year'].between(2008, 2013)) & (df['citation'] == 'honor')].shape[0] == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3622,
        "statement": "since 2008 , only doreen cronin have have more than 1 novel nominate",
        "label": 0,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "df[(df['year'] >= 2008) & (df['author'] == 'doreen cronin')]['title'].nunique() > 1",
        "pandas_eval": "False"
    },
    {
        "id": 3623,
        "statement": "since 2008 , macmillan audio be the only production company to win more than 1 odyssey award",
        "label": 0,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "(df[df['citation'] == 'winner']['producer'].value_counts() > 1).any() and (df[df['citation'] == 'winner']['producer'].value_counts().idxmax() == 'macmillan audio')",
        "pandas_eval": "False"
    },
    {
        "id": 3624,
        "statement": "harry potter and the deathly hallows be the only nominee to be make into a mass - market movie",
        "label": 0,
        "table_caption": "odyssey award",
        "table_text": "year#title#producer#narrator#author#citation\n2013#the fault in our stars#brilliance audio#kate rudd#john green#winner\n2013#artemis fowl : the last guardian#listening library#nathaniel parker#eoin colfer#honor\n2013#ghost knight#listening library#elliot hill#cornelia funke#honor\n2013#monstrous beauty#macmillan audio#katherine kellgren#elizabeth fama#honor\n2012#rotters#listening library#kirby heyborne#daniel kraus#winner\n2012#ghetto cowboy#brilliance audio#jd jackson#g neri#honor\n2012#okay for now#listening library#lincoln hoppe#gary d schmidt#honor\n2012#the scorpio races#scholastic audio books#steve west fiona hardingham#maggie stiefvater#honor\n2012#young fredle#listening library#wendy carter#cynthia voigt#honor\n2011#the true meaning of smekday#listening library#bahni turpin#adam rex#honor\n2011#alchemy and meggy swann#listening library#katherine kellgren#karen cushman#honor\n2011#the knife of never letting go#brilliance audio#nick podehl#patrick ness#honor\n2011#revolution#listening library#emily janice card#jennifer donnelly#honor\n2011#will grayson , will grayson#brilliance audio#macleod andrews#john green david levithan#honor\n2010#louise , the adventures of a chicken#live oak media#barbara rosenblat#kate dicamillo#winner\n2010#in the belly of the bloodhound#listen & live audio#katherine kellgren#l a meyer#honor\n2010#peace , locomotion#brilliance audio#dion graham#jacqueline woodson#honor\n2010#we are the ship : the story of negro baseball#brilliance audio#dion graham#kadir nelson#honor\n2009#the absolutely true diary of a part - time indian#recorded books#sherman alexie#sherman alexie#winner\n2009#curse of the blue tattoo#listen & live audio#katherine kellgren#l a meyer#honor\n2009#elijah of buxton#listening library#mirron willis#christopher paul curtis#honor\n2009#i'm dirty#scholastic media / weston woods studios#steve buscemi#kate mcmullan jim mcmullan#honor\n2009#martina the beautiful cockroach : a cuban folktale#peachtree publishers#carmen agra deedy#carmen agra deedy#honor\n2009#nation#harperaudio#stephen briggs#terry pratchett#honor\n2008#jazz#live oak media#james d - train williams vaneese thomas#walter dean myers#winner\n2008#bloody jack#listen & live audio#katherine kellgren#l a meyer#honor\n2008#dooby dooby moo#scholastic / weston woods#randy travis#doreen cronin#honor\n2008#harry potter and the deathly hallows#listening library#jim dales#j k rowling#honor\n2008#skulduggery pleasant#harpercollins children 's audio#rupert degas#derek landy#honor\n2008#treasure island#listening library#alfred molina#robert louis stevenson#honor\n",
        "pandas_code": "len(df[(df['title'] == 'harry potter and the deathly hallows') & (df['citation'] == 'honor')]) != 1",
        "pandas_eval": "False"
    },
    {
        "id": 3625,
        "statement": "the average high temperature in january be lower in winnipeg than in edmonton",
        "label": 1,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "df[df['city'] == 'winnipeg , mb']['january (avg high degree)'].values[0] < df[df['city'] == 'edmonton , ab']['january (avg high degree)'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3626,
        "statement": "kamloops , bc have the highest average high temperature in july",
        "label": 1,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "df.loc[df['city'] == 'kamloops , bc', 'july (avg high degree)'].values[0] == df['july (avg high degree)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3627,
        "statement": "the average low temperature in january in ottawa , on be 6 degree higher than in edmonton , ab",
        "label": 1,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "(df[df['city'] == 'ottawa , on']['january (avg low degree)'].values[0] - df[df['city'] == 'edmonton , ab']['january (avg low degree)'].values[0]) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3628,
        "statement": "2 city have an average high temperature in july above 80 degree",
        "label": 1,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "(df['july (avg high degree)'] > 80).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3629,
        "statement": "4 city have an average low temperature in july below 50 degree",
        "label": 1,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "(df['july (avg low degree)'] < 50).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3630,
        "statement": "the average high in january be higher in winnipeg than in edmonton",
        "label": 0,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "df[df['city'] == 'winnipeg , mb']['january (avg high degree)'].values[0] > df[df['city'] == 'edmonton , ab']['january (avg high degree)'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3631,
        "statement": "kamloops , bc have the second highest average high temperature in july",
        "label": 0,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "df[df['city'] == 'kamloops , bc']['july (avg high degree)'].values[0] == df['july (avg high degree)'].nlargest(2).iloc[-1]",
        "pandas_eval": "False"
    },
    {
        "id": 3632,
        "statement": "the average low temperature in january in ottawa be 6 degree lower than that in edmonton",
        "label": 0,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "(df[df['city'] == 'ottawa , on']['january (avg low degree)'].values[0] - df[df['city'] == 'edmonton , ab']['january (avg low degree)'].values[0]) == -6",
        "pandas_eval": "False"
    },
    {
        "id": 3633,
        "statement": "city have an average high temperature in july below 80 degree",
        "label": 0,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "all(df['july (avg high degree)'] < 80)",
        "pandas_eval": "False"
    },
    {
        "id": 3634,
        "statement": "4 city have an average low temperature in july greater than 50 degree",
        "label": 0,
        "table_caption": "temperature in canada",
        "table_text": "city#january (avg high degree)#january (avg low degree)#july (avg high degree)#july (avg low degree)\nwinnipeg , mb#10.6#6.5#78.4#56.3\nsaskatoon , sk#13.8#5.3#77.5#52.9\nregina , sk#15.3#4.2#78.2#53.4\nquebec city , qc#19.4#3.0#76.5#57.2\nedmonton , ab#20.7#0.1#73.0#49.1\nottawa , on#21.6#6.1#79.9#60.3\ncalgary , ab#30.4#8.2#73.8#49.6\nmontreal , qc#22.5#6.8#79.3#61.0\nhalifax , ns#31.8#17.2#73.6#59.2\nst john 's , nl#30.6#17.2#69.3#51.6\ntoronto , on#30.7#19.8#79.9#64.4\nwindsor , on#31.5#18.9#82.6#64.2\nvancouver , bc#44.2#34.3#71.8#56.7\nkamloops , bc#32.7#21.4#84.0#57.6\nyellowknife , nt#6.9#21.1#70.3#54.7\niqaluit , nu#9.0#23.6#54.1#39.4\nmoncton , nb#25.3#6.8#76.5#55.2\ncharlottetown , pei#25.9#10.2#73.9#57.4\nwhitehorse , yt#12.2#2.6#69.1#46.4\n",
        "pandas_code": "len(df[df['july (avg low degree)'] > 50]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3635,
        "statement": "the soviet union have a lower rank than any of the other nation",
        "label": 1,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'soviet union']['rank'].iloc[0] < df[df['nation'] != 'soviet union']['rank'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3636,
        "statement": "italy be 1 of 3 nation that do not win a bronze medal",
        "label": 1,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'italy']['bronze'].iloc[0] == 0 and df['bronze'].value_counts()[0] == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3637,
        "statement": "east germany have a total of 2 fewer medal than the soviet union have",
        "label": 1,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['total'].values[0] == df[df['nation'] == 'soviet union']['total'].values[0] - 2",
        "pandas_eval": "True"
    },
    {
        "id": 3638,
        "statement": "a total of 2 nation only win 1 medal",
        "label": 1,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "(df[df['total'] == 1].shape[0] == 2)",
        "pandas_eval": "True"
    },
    {
        "id": 3639,
        "statement": "west germany be in rank 2 while east germany be in rank 5",
        "label": 1,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "(df.loc[df['nation'] == 'west germany', 'rank'].values[0] == 2) & (df.loc[df['nation'] == 'east germany', 'rank'].values[0] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 3640,
        "statement": "italy be the only nation to win 3 bronze medal",
        "label": 0,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'italy']['bronze'].eq(3).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3641,
        "statement": "east germany have 2 time as many medal have the soviet union",
        "label": 0,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "(df[df['nation'] == 'east germany']['total'].values[0] == 2 * df[df['nation'] == 'soviet union']['total'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3642,
        "statement": "3 nation win 2 medal in total",
        "label": 0,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "(df['total'] == 2).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3643,
        "statement": "east germany be rank higher than west germany",
        "label": 0,
        "table_caption": "fil world luge championships 1978",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#soviet union#2#1#0#3\n2#west germany#0#2#0#2\n3#austria#0#0#2#2\n4#italy#1#0#0#1\n5#east germany#0#0#1#1\n",
        "pandas_code": "df[df['nation'] == 'east germany']['rank'].values[0] < df[df['nation'] == 'west germany']['rank'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3644,
        "statement": "denver , colorado , detroit , michigan , miami , florida , minneapolis - st paul , minnesota , and phoenix , arizona all have a media market rank greater than 10",
        "label": 1,
        "table_caption": "u.s. cities with teams from four major league sports",
        "table_text": "metropolitan area#media market ranking#hosted 4 teams since#nfl team (s)#mlb team (s)#nba team (s)#nhl team (s)\nboston , massachusetts#7#1960#patriots ( foxborough , ma )#red sox#celtics#bruins\nchicago , illinois#3#1966#bears#cubs white sox#bulls#blackhawks\ndallas - fort worth , texas#5#1993#cowboys ( arlington , tx )#rangers ( arlington , tx )#mavericks (dallas)#stars (dallas)\ndenver , colorado#17#1995#broncos#rockies#nuggets#avalanche\ndetroit , michigan#11#1957#lions#tigers#pistons ( auburn hills , mi )#red wings\nmiami , florida#16#1993#dolphins ( miami gardens , fl )#marlins#heat#panthers ( sunrise , fl )\nminneapolis - st paul , minnesota#15#2000#vikings (minneapolis)#twins (minneapolis)#timberwolves (minneapolis)#wild (st paul)\nphiladelphia , pennsylvania#4#1967#eagles#phillies#76ers#flyers\nphoenix , arizona#13#1998#cardinals ( glendale , az )#diamondbacks#suns#coyotes ( glendale , az )\nsan francisco bay area , california#6#1991#49ers (san francisco) raiders (oakland)#giants (san francisco) athletics (oakland)#warriors (oakland)#sharks ( san jose )\n",
        "pandas_code": "all(df[df['metropolitan area'].isin(['denver , colorado', 'detroit , michigan', 'miami , florida', 'minneapolis - st paul , minnesota', 'phoenix , arizona'])]['media market ranking'] > 10)",
        "pandas_eval": "True"
    },
    {
        "id": 3645,
        "statement": "dallas - worth , texas and miami florida have both host 4 team since 1993",
        "label": 1,
        "table_caption": "u.s. cities with teams from four major league sports",
        "table_text": "metropolitan area#media market ranking#hosted 4 teams since#nfl team (s)#mlb team (s)#nba team (s)#nhl team (s)\nboston , massachusetts#7#1960#patriots ( foxborough , ma )#red sox#celtics#bruins\nchicago , illinois#3#1966#bears#cubs white sox#bulls#blackhawks\ndallas - fort worth , texas#5#1993#cowboys ( arlington , tx )#rangers ( arlington , tx )#mavericks (dallas)#stars (dallas)\ndenver , colorado#17#1995#broncos#rockies#nuggets#avalanche\ndetroit , michigan#11#1957#lions#tigers#pistons ( auburn hills , mi )#red wings\nmiami , florida#16#1993#dolphins ( miami gardens , fl )#marlins#heat#panthers ( sunrise , fl )\nminneapolis - st paul , minnesota#15#2000#vikings (minneapolis)#twins (minneapolis)#timberwolves (minneapolis)#wild (st paul)\nphiladelphia , pennsylvania#4#1967#eagles#phillies#76ers#flyers\nphoenix , arizona#13#1998#cardinals ( glendale , az )#diamondbacks#suns#coyotes ( glendale , az )\nsan francisco bay area , california#6#1991#49ers (san francisco) raiders (oakland)#giants (san francisco) athletics (oakland)#warriors (oakland)#sharks ( san jose )\n",
        "pandas_code": "all(df[df['metropolitan area'].isin(['dallas - fort worth , texas', 'miami , florida'])]['hosted 4 teams since'] == 1993)",
        "pandas_eval": "True"
    },
    {
        "id": 3646,
        "statement": "chicago , illinois be the only metropolitan area that have a lower media market rank than philedelphia , pennsylvania",
        "label": 1,
        "table_caption": "u.s. cities with teams from four major league sports",
        "table_text": "metropolitan area#media market ranking#hosted 4 teams since#nfl team (s)#mlb team (s)#nba team (s)#nhl team (s)\nboston , massachusetts#7#1960#patriots ( foxborough , ma )#red sox#celtics#bruins\nchicago , illinois#3#1966#bears#cubs white sox#bulls#blackhawks\ndallas - fort worth , texas#5#1993#cowboys ( arlington , tx )#rangers ( arlington , tx )#mavericks (dallas)#stars (dallas)\ndenver , colorado#17#1995#broncos#rockies#nuggets#avalanche\ndetroit , michigan#11#1957#lions#tigers#pistons ( auburn hills , mi )#red wings\nmiami , florida#16#1993#dolphins ( miami gardens , fl )#marlins#heat#panthers ( sunrise , fl )\nminneapolis - st paul , minnesota#15#2000#vikings (minneapolis)#twins (minneapolis)#timberwolves (minneapolis)#wild (st paul)\nphiladelphia , pennsylvania#4#1967#eagles#phillies#76ers#flyers\nphoenix , arizona#13#1998#cardinals ( glendale , az )#diamondbacks#suns#coyotes ( glendale , az )\nsan francisco bay area , california#6#1991#49ers (san francisco) raiders (oakland)#giants (san francisco) athletics (oakland)#warriors (oakland)#sharks ( san jose )\n",
        "pandas_code": "(df[df['metropolitan area'] == 'chicago , illinois']['media market ranking'].values[0] < df[df['metropolitan area'] == 'philadelphia , pennsylvania']['media market ranking'].values[0]) and (df['media market ranking'] < df[df['metropolitan area'] == 'philadelphia , pennsylvania']['media market ranking'].values[0]).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3647,
        "statement": "denver , colorado be the only metropolitan area that have a greater media market rank than miami , florida",
        "label": 1,
        "table_caption": "u.s. cities with teams from four major league sports",
        "table_text": "metropolitan area#media market ranking#hosted 4 teams since#nfl team (s)#mlb team (s)#nba team (s)#nhl team (s)\nboston , massachusetts#7#1960#patriots ( foxborough , ma )#red sox#celtics#bruins\nchicago , illinois#3#1966#bears#cubs white sox#bulls#blackhawks\ndallas - fort worth , texas#5#1993#cowboys ( arlington , tx )#rangers ( arlington , tx )#mavericks (dallas)#stars (dallas)\ndenver , colorado#17#1995#broncos#rockies#nuggets#avalanche\ndetroit , michigan#11#1957#lions#tigers#pistons ( auburn hills , mi )#red wings\nmiami , florida#16#1993#dolphins ( miami gardens , fl )#marlins#heat#panthers ( sunrise , fl )\nminneapolis - st paul , minnesota#15#2000#vikings (minneapolis)#twins (minneapolis)#timberwolves (minneapolis)#wild (st paul)\nphiladelphia , pennsylvania#4#1967#eagles#phillies#76ers#flyers\nphoenix , arizona#13#1998#cardinals ( glendale , az )#diamondbacks#suns#coyotes ( glendale , az )\nsan francisco bay area , california#6#1991#49ers (san francisco) raiders (oakland)#giants (san francisco) athletics (oakland)#warriors (oakland)#sharks ( san jose )\n",
        "pandas_code": "(df[df['metropolitan area'] == 'denver , colorado']['media market ranking'].values[0] > df[df['metropolitan area'] == 'miami , florida']['media market ranking'].values[0]) and (df['media market ranking'] > df[df['metropolitan area'] == 'miami , florida']['media market ranking'].values[0]).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3648,
        "statement": "the san francisco bay area in california have a lower medium maket rank than boston , massachusetts",
        "label": 1,
        "table_caption": "u.s. cities with teams from four major league sports",
        "table_text": "metropolitan area#media market ranking#hosted 4 teams since#nfl team (s)#mlb team (s)#nba team (s)#nhl team (s)\nboston , massachusetts#7#1960#patriots ( foxborough , ma )#red sox#celtics#bruins\nchicago , illinois#3#1966#bears#cubs white sox#bulls#blackhawks\ndallas - fort worth , texas#5#1993#cowboys ( arlington , tx )#rangers ( arlington , tx )#mavericks (dallas)#stars (dallas)\ndenver , colorado#17#1995#broncos#rockies#nuggets#avalanche\ndetroit , michigan#11#1957#lions#tigers#pistons ( auburn hills , mi )#red wings\nmiami , florida#16#1993#dolphins ( miami gardens , fl )#marlins#heat#panthers ( sunrise , fl )\nminneapolis - st paul , minnesota#15#2000#vikings (minneapolis)#twins (minneapolis)#timberwolves (minneapolis)#wild (st paul)\nphiladelphia , pennsylvania#4#1967#eagles#phillies#76ers#flyers\nphoenix , arizona#13#1998#cardinals ( glendale , az )#diamondbacks#suns#coyotes ( glendale , az )\nsan francisco bay area , california#6#1991#49ers (san francisco) raiders (oakland)#giants (san francisco) athletics (oakland)#warriors (oakland)#sharks ( san jose )\n",
        "pandas_code": "df[df['metropolitan area'] == 'san francisco bay area , california']['media market ranking'].values[0] < df[df['metropolitan area'] == 'boston , massachusetts']['media market ranking'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3649,
        "statement": "boise state university and san diego state university be both in the mountain west conference",
        "label": 1,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "all(df[df['institution'].isin(['boise state university', 'san diego state university'])]['current conference'] == 'mountain west')",
        "pandas_eval": "True"
    },
    {
        "id": 3650,
        "statement": "all of the list university be public institution in the united state",
        "label": 1,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "df['type'].eq('public').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3651,
        "statement": "san diego state university be the oldest institution , while california state university at bakersfield be the newest",
        "label": 1,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "(df.loc[df['institution'] == 'san diego state university', 'founded'].values[0] == df['founded'].min()) & (df.loc[df['institution'] == 'california state university , bakersfield', 'founded'].values[0] == df['founded'].max())",
        "pandas_eval": "True"
    },
    {
        "id": 3652,
        "statement": "california state university at bakersfield be the only school independent of any conference",
        "label": 1,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "df[df['institution'] == 'california state university , bakersfield']['current conference'].eq('independent').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3653,
        "statement": "all of the school , except for san diego state university , have wrestle as a pac - 12 sport",
        "label": 1,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "all(df[df['institution'] != 'san diego state university']['pac - 12 sports'].apply(lambda x: 'wrestling' in x))",
        "pandas_eval": "True"
    },
    {
        "id": 3654,
        "statement": "boise state university and san diego state university be both in the big west conference",
        "label": 0,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "all(df[df['institution'].isin(['boise state university', 'san diego state university'])]['current conference'] == 'big west')",
        "pandas_eval": "False"
    },
    {
        "id": 3655,
        "statement": "all but 2 of the list university be public institution in the united state",
        "label": 0,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "(df['type'] == 'public').sum() == len(df) - 2",
        "pandas_eval": "False"
    },
    {
        "id": 3656,
        "statement": "boise state university be the oldest institution , while university of california , davis be the newest",
        "label": 0,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "(df[df['institution'] == 'boise state university']['founded'].values[0] == df['founded'].min()) & (df[df['institution'] == 'university of california , davis']['founded'].values[0] == df['founded'].max())",
        "pandas_eval": "False"
    },
    {
        "id": 3657,
        "statement": "california polytechnic state university be the only school independent of any conference",
        "label": 0,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "df[df['institution'] == 'california polytechnic state university']['current conference'].eq('independent').all() and df['current conference'].eq('independent').sum() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3658,
        "statement": "all of the school , except for boise state university , have wrestle as a pac - 12 sport",
        "label": 0,
        "table_caption": "list of pacific - 12 conference champions",
        "table_text": "institution#location#founded#type#enrollment#nickname#current conference#pac - 12 sports\nboise state university#boise , idaho#1932#public#19667#broncos#mountain west#wrestling\ncalifornia polytechnic state university#san luis obispo , california#1901#public#19777#mustangs#big west#men 's swimming and diving , wrestling\ncalifornia state university , bakersfield#bakersfield , california#1965#public#7493#roadrunners#independent#wrestling\ncalifornia state university , fullerton#fullerton , california#1957#public#36996#titans#big west#wrestling\nsan diego state university#san diego , california#1897#public#34500#aztecs#mountain west#men 's soccer\nuniversity of california , davis#davis , california#1908#public#31426#aggies#big west#wrestling\n",
        "pandas_code": "all(df[df['institution'] != 'boise state university']['pac - 12 sports'].apply(lambda x: 'wrestling' in x))",
        "pandas_eval": "False"
    },
    {
        "id": 3659,
        "statement": "in the men 's 100 metre butterfly , the serbian swimmer beat the us swimmer by 35 second",
        "label": 1,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df[df['nationality'] == 'serbia']['time'].values[0] < df[df['nationality'] == 'united states']['time'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3660,
        "statement": "the us swimmer be rank in the top 3 fastest swimmer in the men 's 100 metre butterfly",
        "label": 1,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df[(df['nationality'] == 'united states') & (df['rank'] <= 3)].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3661,
        "statement": "the difference between the top finisher and lst place in the men 's 100 metre butterfly be 2.02 second",
        "label": 1,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "abs((df['time'].max() - df['time'].min()) - 2.02) < 1e-6",
        "pandas_eval": "True"
    },
    {
        "id": 3662,
        "statement": "the top 3 finisher compete in the first 5 lane",
        "label": 1,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "all(df[df['rank'] <= 3]['lane'].isin(range(1, 6)))",
        "pandas_eval": "True"
    },
    {
        "id": 3663,
        "statement": "ian crocker beat andriy serdinov by 14 second",
        "label": 1,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df.loc[df['name'] == 'ian crocker', 'time'].values[0] < df.loc[df['name'] == 'andriy serdinov', 'time'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3664,
        "statement": "in the men 's 100 metre butterfly , the serbian swimmer , andriy serdinov beat the us swimmer by 35 second",
        "label": 0,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df[(df['nationality'] == 'serbia') & (df['time'] < df[df['nationality'] == 'united states']['time'].values[0] - 35)].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 3665,
        "statement": "the us swimmer be rank in the top 3 fastest swimmer in the men 's 100 metre butterfly , follow by france",
        "label": 0,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df[(df['nationality'] == 'united states') & (df['rank'] <= 3)].any().any() and df[(df['nationality'] == 'france') & (df['rank'] == 4)].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 3666,
        "statement": "the top 3 finisher , corney swanepoel , lyndon fern and fr\u00e9d\u00e9rick bousquet compete in the first 5 lane",
        "label": 0,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "df[(df['name'].isin(['corney swanepoel', 'lyndon ferns', 'fr\u00e9d\u00e9rick bousquet'])) & (df['lane'] <= 5)].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3667,
        "statement": "ian crocker in lane 1 beat andriy serdinov in lane 2 , by 14 second",
        "label": 0,
        "table_caption": "swimming at the 2008 summer olympics - men 's 100 metre butterfly",
        "table_text": "rank#lane#name#nationality#time\n1#4#milorad \u010davi\u0107#serbia#50.92\n2#1#ian crocker#united states#51.27\n3#5#andriy serdinov#ukraine#51.41\n4#6#takuro fujii#japan#51.59\n5#3#peter manko\u010d#slovenia#51.80\n6#2#corney swanepoel#new zealand#52.01\n7#8#lyndon ferns#south africa#52.18\n8#7#fr\u00e9d\u00e9rick bousquet#france#52.94\n",
        "pandas_code": "(df.loc[df['name'] == 'ian crocker', 'time'].values[0] - df.loc[df['name'] == 'andriy serdinov', 'time'].values[0]) == 14",
        "pandas_eval": "False"
    },
    {
        "id": 3668,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt have the highest elevation of the ultra prominent carpathian mountain",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt']['elevation (m)'].max() == df['elevation (m)'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3669,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt have 252 more meter of prominence than parngu mare",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "(df[df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt']['prominence (m)'].values[0] - df[df['peak'] == 'parngu mare']['prominence (m)'].values[0]) == 252",
        "pandas_eval": "True"
    },
    {
        "id": 3670,
        "statement": "of the ultra prominent peak in the carpathian mountain , pietrosul rodnei be the least prominent",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[df['peak'] == 'pietrosul rodnei']['prominence (m)'].iloc[0] == df[df['country'] == 'romania']['prominence (m)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3671,
        "statement": "peleaga have the highest col of the ultra prominent peak in the carpathian mountain , but be not the least prominent",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "(df[df['peak'] == 'peleaga']['col (m)'].max() == df['col (m)'].max()) & (df[df['peak'] == 'peleaga']['prominence (m)'].min() != df['prominence (m)'].min())",
        "pandas_eval": "True"
    },
    {
        "id": 3672,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt be the only ultra prominent peak in the carpathian mountain that be not in romania",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "(df[(df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt') & (df['country'] != 'romania')].shape[0] == 1) & (df[(df['country'] == 'romania') & (df['peak'] != 'gerlachovsk\u00fd \u0161t\u00edt')].shape[0] == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 3673,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt have the shorter elevation compare to parngu mare of the ultra prominent carpathian mountain",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt']['elevation (m)'].values[0] < df[df['peak'] == 'parngu mare']['elevation (m)'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3674,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt be 300 meter shorter elevation than parngu mare",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt']['elevation (m)'].values[0] == df[df['peak'] == 'parngu mare']['elevation (m)'].values[0] - 300",
        "pandas_eval": "False"
    },
    {
        "id": 3675,
        "statement": "peitrosul rodnei be the greatest prominent peak by 251 meter",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "(df.loc[df['peak'] == 'pietrosul rodnei', 'prominence (m)'].values[0] - df.loc[df['peak'] != 'pietrosul rodnei', 'prominence (m)'].max()) == 251",
        "pandas_eval": "False"
    },
    {
        "id": 3676,
        "statement": "peleaga be the smallest prominent peak in the carpathian mountain",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[df['peak'] == 'peleaga']['prominence (m)'].iloc[0] == df[df['country'] == 'romania']['prominence (m)'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3677,
        "statement": "gerlachovsk\u00fd \u0161t\u00edt be locate in romania along with the other mountain",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\ngerlachovsk\u00fd \u0161t\u00edt#slovakia#2655#2355#300\nparngu mare#romania#2519#2103#416\nmoldoveanu peak#romania#2544#2046#498\npeleaga#romania#2509#1759#750\npietrosul rodnei#romania#2303#1565#738\n",
        "pandas_code": "df[(df['peak'] == 'gerlachovsk\u00fd \u0161t\u00edt') & (df['country'] == 'slovakia')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3678,
        "statement": "all game with an attendance over 60000 be not play at home",
        "label": 1,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "all(df[df['attendance'] > 60000]['opponent'].str.contains('at home') == False)",
        "pandas_eval": "True"
    },
    {
        "id": 3679,
        "statement": "the november 9 game against the denver bronco have the highest attendance",
        "label": 1,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "df.loc[df['date'] == 'november 9 , 1986', 'attendance'].iloc[0] == df['attendance'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3680,
        "statement": "the highest attended game be also the lowest scoring game for the season",
        "label": 1,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'result'].split()[1] == str(df['result'].apply(lambda x: int(x.split()[1])).min())",
        "pandas_eval": "True"
    },
    {
        "id": 3681,
        "statement": "the first game of the season be the only game play against the miami dolphins during the season",
        "label": 1,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "(df['opponent'].iloc[0] == 'miami dolphins') & (df['opponent'].iloc[1:].eq('miami dolphins').sum() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3682,
        "statement": "the charger only win 4 game the entire 1986 season , they beat the dolphin , bronco colt and oiler",
        "label": 1,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "df[df['result'].str.startswith('w')]['opponent'].isin(['miami dolphins', 'denver broncos', 'indianapolis colts', 'houston oilers']).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3683,
        "statement": "all game with an attendance over 50000 be play at home",
        "label": 0,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "all(df[df['attendance'] > 50000]['opponent'].str.contains('home', case=False))",
        "pandas_eval": "False"
    },
    {
        "id": 3684,
        "statement": "the november 8th game against the denver bronco have the second highest attendance on record",
        "label": 0,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "df[df['date'].str.contains('november 9 , 1986') & (df['opponent'] == 'denver broncos')]['attendance'].iloc[0] == df['attendance'].nlargest(2).iloc[-1]",
        "pandas_eval": "False"
    },
    {
        "id": 3685,
        "statement": "the second highest attended game be also the lowest scoring game in 1985",
        "label": 0,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "(df['attendance'].nlargest(2).iloc[-1] == df.loc[df['result'].str.extract(r'(\\d+) - (\\d+)').astype(int).sum(axis=1).idxmin(), 'attendance'])",
        "pandas_eval": "False"
    },
    {
        "id": 3686,
        "statement": "the first game of the season be 1 of 3 game play against the miami dolphin",
        "label": 0,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "df[(df['week'] == 1) & (df['opponent'] == 'miami dolphin')].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3687,
        "statement": "the charger win 6 game in total for the entire 1985 season",
        "label": 0,
        "table_caption": "1986 san diego chargers season",
        "table_text": "week#date#opponent#result#attendance\n1#september 7 , 1986#miami dolphins#w 50 - 28#57726\n2#september 14 , 1986#new york giants#l 20 - 7#74921\n3#september 21 , 1986#washington redskins#l 30 - 27#57853\n4#september 28 , 1986#los angeles raiders#l 17 - 13#63153\n5#october 6 , 1986#seattle seahawks#l 33 - 7#63207\n6#october 12 , 1986#denver broncos#l 31 - 14#55662\n7#october 19 , 1986#kansas city chiefs#l 42 - 41#55767\n8#october 26 , 1986#philadelphia eagles#l 23 - 7#41469\n9#november 2 , 1986#kansas city chiefs#l 24 - 23#48518\n10#november 9 , 1986#denver broncos#w 9 - 3#75012\n11#november 16 , 1986#dallas cowboys#l 24 - 21#55622\n12#november 20 , 1986#los angeles raiders#l 37 - 31#56031\n13#november 30 , 1986#indianapolis colts#w 17 - 3#47950\n14#december 7 , 1986#houston oilers#w 27 - 0#40103\n15#december 14 , 1986#seattle seahawks#l 34 - 24#47096\n16#december 21 , 1986#cleveland browns#l 47 - 17#68505\n",
        "pandas_code": "(df['result'].str.startswith('w').sum() == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 3688,
        "statement": "bo larsson of sweden have 302 league appearance and 119 league goal while egon j\u00f6nsson have 200 league appearance and 99 league goal",
        "label": 1,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 302) & (df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['league goals'].values[0] == 119) & (df[(df['name'] == 'egon j\u00f6nsson') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 200) & (df[(df['name'] == 'egon j\u00f6nsson') & (df['nationality'] == 'sweden')]['league goals'].values[0] == 99)",
        "pandas_eval": "True"
    },
    {
        "id": 3689,
        "statement": "ivar roslund of sweden have more league appearance than ingvar rydell of sweden",
        "label": 1,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'ivar roslund') & (df['nationality'] == 'sweden')]['league appearances'].values[0] > df[(df['name'] == 'ingvar rydell') & (df['nationality'] == 'sweden')]['league appearances'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3690,
        "statement": "han h\u00e5kansson of sweden have more than 2 time the number of league goal than gustaf nilsson of sweden",
        "label": 1,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'hans h\u00e5kansson') & (df['nationality'] == 'sweden')]['league goals'].values[0] > 2 * df[(df['name'] == 'gustaf nilsson') & (df['nationality'] == 'sweden')]['league goals'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3691,
        "statement": "stellan nilsson of sweden have 179 league appearance and b\u00f6rje tapper of sweden have 191 league appearance",
        "label": 1,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'stellan nilsson') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 179) & (df[(df['name'] == 'b\u00f6rje tapper') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 191)",
        "pandas_eval": "True"
    },
    {
        "id": 3692,
        "statement": "the malm\u00f6 ff career of gustaf nilsson of sweden be 1940 - 1950 , the same as stellan nilsson of sweden",
        "label": 1,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "df[(df['name'] == 'gustaf nilsson') & (df['nationality'] == 'sweden')]['malm\u00f6 ff career'].values[0] == df[(df['name'] == 'stellan nilsson') & (df['nationality'] == 'sweden')]['malm\u00f6 ff career'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3693,
        "statement": "ivar roslund of sweden have 302 league appearance and 119 league goal while egon j\u00f6nsson have 200 league appearance and 99 league goal",
        "label": 0,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'ivar roslund') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 302) & (df[(df['name'] == 'ivar roslund') & (df['nationality'] == 'sweden')]['league goals'].values[0] == 119) & (df[(df['name'] == 'egon j\u00f6nsson') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 200) & (df[(df['name'] == 'egon j\u00f6nsson') & (df['nationality'] == 'sweden')]['league goals'].values[0] == 99)",
        "pandas_eval": "False"
    },
    {
        "id": 3694,
        "statement": "bo larsson of sweden have more league appearance than ingvar rydell of sweden",
        "label": 0,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['league appearances'].iloc[0] <= df[(df['name'] == 'ingvar rydell') & (df['nationality'] == 'sweden')]['league appearances'].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3695,
        "statement": "bo larsson of sweden have more than 2 time the number of league goal than gustaf nilsson of sweden",
        "label": 0,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['league goals'].values[0] > 2 * df[(df['name'] == 'gustaf nilsson') & (df['nationality'] == 'sweden')]['league goals'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3696,
        "statement": "bo larsson of sweden have 179 league appearance and b\u00f6rje tapper of sweden have 191 league appearance",
        "label": 0,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 179) & (df[(df['name'] == 'b\u00f6rje tapper') & (df['nationality'] == 'sweden')]['league appearances'].values[0] == 191)",
        "pandas_eval": "False"
    },
    {
        "id": 3697,
        "statement": "the malm\u00f6 ff career of gustaf nilsson of sweden be 1940 - 1950 , the same as bo larsson of sweden",
        "label": 0,
        "table_caption": "list of malm\u00f6 ff records and statistics",
        "table_text": "name#nationality#malm\u00f6 ff career#league appearances#league goals#total appearances#total goals\nhans h\u00e5kansson#sweden#1927 - 1938#192#163#350#341\nbo larsson#sweden#1962 - 1966 1969 - 1979#302#119#546#289\negon j\u00f6nsson#sweden#1943 - 1955#200#99#405#269\nb\u00f6rje tapper#sweden#1939 - 1951#191#91#371#298\nthomas sj\u00f6berg#sweden#1974 - 1976 1977 - 1978 1979 - 1982#180#80#334#157\nivar roslund#sweden#1925 - 1937#169#71#311#179\ningvar rydell#sweden#1948 - 1953#106#68#210#162\nstellan nilsson#sweden#1940 - 1950#179#68#336#166\ngustaf nilsson#sweden#1940 - 1950#132#65#265#205\n",
        "pandas_code": "(df[(df['name'] == 'gustaf nilsson') & (df['nationality'] == 'sweden')]['malm\u00f6 ff career'].values[0] == df[(df['name'] == 'bo larsson') & (df['nationality'] == 'sweden')]['malm\u00f6 ff career'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3698,
        "statement": "5 of the incumbent for california be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 3#john e moss#democratic#1952#re - elected#john e moss (d) 69.9% john rakus (r) 30.1%\ncalifornia 5#phillip burton#democratic#1964#re - elected#phillip burton (d) 81.8% edlo e powell (r) 18.2%\ncalifornia 8#george paul miller#democratic#1944#lost renomination democratic hold#pete stark (d) 52.9% lew m warden , jr (r) 47.1%\ncalifornia 14#jerome r waldie#democratic#1966#re - elected#jerome r waldie (d) 77.6% floyd e sims (r) 22.4%\ncalifornia 15#john j mcfall#democratic#1956#re - elected#john j mcfall (d) unopposed\ncalifornia 17#pete mccloskey redistricted from 11th#republican#1967#re - elected#pete mccloskey (r) 60.2% james stewart (d) 39.8%\ncalifornia 18#bob mathias#republican#1966#re - elected#bob mathias (r) 66.5% vincent j lavery (d) 33.5%\ncalifornia 20#h allen smith#republican#1956#retired republican hold#carlos moorhead (r) 57.4% john binkley (d) 42.6%\ncalifornia 33#jerry pettis#republican#1966#re - elected#jerry pettis (r) 75.1% ken thompson (d) 24.9%\n",
        "pandas_code": "len(df[(df['district'].str.contains('california')) & (df['party'] == 'democratic')]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3699,
        "statement": "4 of the incumbent for california be republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 3#john e moss#democratic#1952#re - elected#john e moss (d) 69.9% john rakus (r) 30.1%\ncalifornia 5#phillip burton#democratic#1964#re - elected#phillip burton (d) 81.8% edlo e powell (r) 18.2%\ncalifornia 8#george paul miller#democratic#1944#lost renomination democratic hold#pete stark (d) 52.9% lew m warden , jr (r) 47.1%\ncalifornia 14#jerome r waldie#democratic#1966#re - elected#jerome r waldie (d) 77.6% floyd e sims (r) 22.4%\ncalifornia 15#john j mcfall#democratic#1956#re - elected#john j mcfall (d) unopposed\ncalifornia 17#pete mccloskey redistricted from 11th#republican#1967#re - elected#pete mccloskey (r) 60.2% james stewart (d) 39.8%\ncalifornia 18#bob mathias#republican#1966#re - elected#bob mathias (r) 66.5% vincent j lavery (d) 33.5%\ncalifornia 20#h allen smith#republican#1956#retired republican hold#carlos moorhead (r) 57.4% john binkley (d) 42.6%\ncalifornia 33#jerry pettis#republican#1966#re - elected#jerry pettis (r) 75.1% ken thompson (d) 24.9%\n",
        "pandas_code": "(df[df['district'].str.contains('california')]['party'] == 'republican').sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3700,
        "statement": "george paul miller be the only incumbent list who lose the renomination",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 3#john e moss#democratic#1952#re - elected#john e moss (d) 69.9% john rakus (r) 30.1%\ncalifornia 5#phillip burton#democratic#1964#re - elected#phillip burton (d) 81.8% edlo e powell (r) 18.2%\ncalifornia 8#george paul miller#democratic#1944#lost renomination democratic hold#pete stark (d) 52.9% lew m warden , jr (r) 47.1%\ncalifornia 14#jerome r waldie#democratic#1966#re - elected#jerome r waldie (d) 77.6% floyd e sims (r) 22.4%\ncalifornia 15#john j mcfall#democratic#1956#re - elected#john j mcfall (d) unopposed\ncalifornia 17#pete mccloskey redistricted from 11th#republican#1967#re - elected#pete mccloskey (r) 60.2% james stewart (d) 39.8%\ncalifornia 18#bob mathias#republican#1966#re - elected#bob mathias (r) 66.5% vincent j lavery (d) 33.5%\ncalifornia 20#h allen smith#republican#1956#retired republican hold#carlos moorhead (r) 57.4% john binkley (d) 42.6%\ncalifornia 33#jerry pettis#republican#1966#re - elected#jerry pettis (r) 75.1% ken thompson (d) 24.9%\n",
        "pandas_code": "(df[df['incumbent'] == 'george paul miller']['result'].iloc[0] == 'lost renomination democratic hold') and (df['result'].value_counts()['lost renomination democratic hold'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3701,
        "statement": "h allen smith be the only incumbent list who retire",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 3#john e moss#democratic#1952#re - elected#john e moss (d) 69.9% john rakus (r) 30.1%\ncalifornia 5#phillip burton#democratic#1964#re - elected#phillip burton (d) 81.8% edlo e powell (r) 18.2%\ncalifornia 8#george paul miller#democratic#1944#lost renomination democratic hold#pete stark (d) 52.9% lew m warden , jr (r) 47.1%\ncalifornia 14#jerome r waldie#democratic#1966#re - elected#jerome r waldie (d) 77.6% floyd e sims (r) 22.4%\ncalifornia 15#john j mcfall#democratic#1956#re - elected#john j mcfall (d) unopposed\ncalifornia 17#pete mccloskey redistricted from 11th#republican#1967#re - elected#pete mccloskey (r) 60.2% james stewart (d) 39.8%\ncalifornia 18#bob mathias#republican#1966#re - elected#bob mathias (r) 66.5% vincent j lavery (d) 33.5%\ncalifornia 20#h allen smith#republican#1956#retired republican hold#carlos moorhead (r) 57.4% john binkley (d) 42.6%\ncalifornia 33#jerry pettis#republican#1966#re - elected#jerry pettis (r) 75.1% ken thompson (d) 24.9%\n",
        "pandas_code": "df[df['incumbent'] == 'h allen smith']['result'].eq('retired republican hold').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3702,
        "statement": "john j mcfall and h allen smith be both first elect in 1956",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1972",
        "table_text": "district#incumbent#party#first elected#result#candidates\ncalifornia 3#john e moss#democratic#1952#re - elected#john e moss (d) 69.9% john rakus (r) 30.1%\ncalifornia 5#phillip burton#democratic#1964#re - elected#phillip burton (d) 81.8% edlo e powell (r) 18.2%\ncalifornia 8#george paul miller#democratic#1944#lost renomination democratic hold#pete stark (d) 52.9% lew m warden , jr (r) 47.1%\ncalifornia 14#jerome r waldie#democratic#1966#re - elected#jerome r waldie (d) 77.6% floyd e sims (r) 22.4%\ncalifornia 15#john j mcfall#democratic#1956#re - elected#john j mcfall (d) unopposed\ncalifornia 17#pete mccloskey redistricted from 11th#republican#1967#re - elected#pete mccloskey (r) 60.2% james stewart (d) 39.8%\ncalifornia 18#bob mathias#republican#1966#re - elected#bob mathias (r) 66.5% vincent j lavery (d) 33.5%\ncalifornia 20#h allen smith#republican#1956#retired republican hold#carlos moorhead (r) 57.4% john binkley (d) 42.6%\ncalifornia 33#jerry pettis#republican#1966#re - elected#jerry pettis (r) 75.1% ken thompson (d) 24.9%\n",
        "pandas_code": "df[df['incumbent'].isin(['john j mcfall', 'h allen smith'])]['first elected'].eq(1956).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3703,
        "statement": "wjrf (89.5 fm , the refuge) be a regional christian radio network base in duluth , minnesota",
        "label": 1,
        "table_caption": "wjrf",
        "table_text": "call sign#frequency mhz#city of license#erp w#class#fcc info\nk257ch#99.3#estherville , iowa#80 ( 53 meters)#d#fcc\nk220hy#91.9#spencer , iowa#250 ( 51 metres)#d#fcc\nk210cg#89.9#spirit lake , iowa#250 ( 69 metres)#d#fcc\nw215br#90.9#ironwood , michigan#10 ( 120 metres)#d#fcc\nk208eq#89.5#alexandria , minnesota#230 ( 52 metres)#d#fcc\nk212fh#90.3#balaton , minnesota#62 ( 135 metres)#d#fcc\nk218dk#91.5#bloomington , minnesota#216 ( 49 metres)#d#fcc\nk216fs#91.1#brainerd , minnesota#140 ( 139 metres)#d#fcc\nk201hd#88.1#clara city , minnesota#250 ( 41 metres)#d#fcc\nk293ba#106.5#elko , minnesota#196 ( 83 metres)#d#fcc\nk215es#90.9#fairmont , minnesota#250 ( 39 metres)#d#fcc\nk204ds#88.7#glencoe , minnesota#115 ( 41 metres)#d#fcc\nk220bi#91.9#grand marais , minnesota#34 ( 115 metres)#d#fcc\nk215du#90.9#hutchinson , minnesota#230 ( 51 metres)#d#fcc\nk277at#103.3#litchfield , minnesota#13 ( 11 meters)#d#fcc\nk263al#100.5#madison , minnesota#250 ( 69 metres)#d#fcc\nw291aq#106.1#moose lake , minnesota#55 ( 42 metres)#d#fcc\nk270dz#101.9#new ulm , minnesota#250 ( 57 metres)#d#fcc\nk204fa#88.7#nicollet , minnesota#250 ( 61 metres)#d#fcc\nw220do#91.9#north branch , minnesota#38 ( 64 metres)#d#fcc\n",
        "pandas_code": "(df['frequency mhz'] == 89.5).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3704,
        "statement": "most programming on refuge radio be upbeat contemporary christian music and christian rock music target to youth and young adult",
        "label": 1,
        "table_caption": "wjrf",
        "table_text": "call sign#frequency mhz#city of license#erp w#class#fcc info\nk257ch#99.3#estherville , iowa#80 ( 53 meters)#d#fcc\nk220hy#91.9#spencer , iowa#250 ( 51 metres)#d#fcc\nk210cg#89.9#spirit lake , iowa#250 ( 69 metres)#d#fcc\nw215br#90.9#ironwood , michigan#10 ( 120 metres)#d#fcc\nk208eq#89.5#alexandria , minnesota#230 ( 52 metres)#d#fcc\nk212fh#90.3#balaton , minnesota#62 ( 135 metres)#d#fcc\nk218dk#91.5#bloomington , minnesota#216 ( 49 metres)#d#fcc\nk216fs#91.1#brainerd , minnesota#140 ( 139 metres)#d#fcc\nk201hd#88.1#clara city , minnesota#250 ( 41 metres)#d#fcc\nk293ba#106.5#elko , minnesota#196 ( 83 metres)#d#fcc\nk215es#90.9#fairmont , minnesota#250 ( 39 metres)#d#fcc\nk204ds#88.7#glencoe , minnesota#115 ( 41 metres)#d#fcc\nk220bi#91.9#grand marais , minnesota#34 ( 115 metres)#d#fcc\nk215du#90.9#hutchinson , minnesota#230 ( 51 metres)#d#fcc\nk277at#103.3#litchfield , minnesota#13 ( 11 meters)#d#fcc\nk263al#100.5#madison , minnesota#250 ( 69 metres)#d#fcc\nw291aq#106.1#moose lake , minnesota#55 ( 42 metres)#d#fcc\nk270dz#101.9#new ulm , minnesota#250 ( 57 metres)#d#fcc\nk204fa#88.7#nicollet , minnesota#250 ( 61 metres)#d#fcc\nw220do#91.9#north branch , minnesota#38 ( 64 metres)#d#fcc\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 3705,
        "statement": "all radio program have class d and fcc info",
        "label": 1,
        "table_caption": "wjrf",
        "table_text": "call sign#frequency mhz#city of license#erp w#class#fcc info\nk257ch#99.3#estherville , iowa#80 ( 53 meters)#d#fcc\nk220hy#91.9#spencer , iowa#250 ( 51 metres)#d#fcc\nk210cg#89.9#spirit lake , iowa#250 ( 69 metres)#d#fcc\nw215br#90.9#ironwood , michigan#10 ( 120 metres)#d#fcc\nk208eq#89.5#alexandria , minnesota#230 ( 52 metres)#d#fcc\nk212fh#90.3#balaton , minnesota#62 ( 135 metres)#d#fcc\nk218dk#91.5#bloomington , minnesota#216 ( 49 metres)#d#fcc\nk216fs#91.1#brainerd , minnesota#140 ( 139 metres)#d#fcc\nk201hd#88.1#clara city , minnesota#250 ( 41 metres)#d#fcc\nk293ba#106.5#elko , minnesota#196 ( 83 metres)#d#fcc\nk215es#90.9#fairmont , minnesota#250 ( 39 metres)#d#fcc\nk204ds#88.7#glencoe , minnesota#115 ( 41 metres)#d#fcc\nk220bi#91.9#grand marais , minnesota#34 ( 115 metres)#d#fcc\nk215du#90.9#hutchinson , minnesota#230 ( 51 metres)#d#fcc\nk277at#103.3#litchfield , minnesota#13 ( 11 meters)#d#fcc\nk263al#100.5#madison , minnesota#250 ( 69 metres)#d#fcc\nw291aq#106.1#moose lake , minnesota#55 ( 42 metres)#d#fcc\nk270dz#101.9#new ulm , minnesota#250 ( 57 metres)#d#fcc\nk204fa#88.7#nicollet , minnesota#250 ( 61 metres)#d#fcc\nw220do#91.9#north branch , minnesota#38 ( 64 metres)#d#fcc\n",
        "pandas_code": "all(df['class'] == 'd') & all(df['fcc info'] == 'fcc')",
        "pandas_eval": "True"
    },
    {
        "id": 3707,
        "statement": "k257ch have a frequency of 99.3 and be license in estherville , iowa",
        "label": 1,
        "table_caption": "wjrf",
        "table_text": "call sign#frequency mhz#city of license#erp w#class#fcc info\nk257ch#99.3#estherville , iowa#80 ( 53 meters)#d#fcc\nk220hy#91.9#spencer , iowa#250 ( 51 metres)#d#fcc\nk210cg#89.9#spirit lake , iowa#250 ( 69 metres)#d#fcc\nw215br#90.9#ironwood , michigan#10 ( 120 metres)#d#fcc\nk208eq#89.5#alexandria , minnesota#230 ( 52 metres)#d#fcc\nk212fh#90.3#balaton , minnesota#62 ( 135 metres)#d#fcc\nk218dk#91.5#bloomington , minnesota#216 ( 49 metres)#d#fcc\nk216fs#91.1#brainerd , minnesota#140 ( 139 metres)#d#fcc\nk201hd#88.1#clara city , minnesota#250 ( 41 metres)#d#fcc\nk293ba#106.5#elko , minnesota#196 ( 83 metres)#d#fcc\nk215es#90.9#fairmont , minnesota#250 ( 39 metres)#d#fcc\nk204ds#88.7#glencoe , minnesota#115 ( 41 metres)#d#fcc\nk220bi#91.9#grand marais , minnesota#34 ( 115 metres)#d#fcc\nk215du#90.9#hutchinson , minnesota#230 ( 51 metres)#d#fcc\nk277at#103.3#litchfield , minnesota#13 ( 11 meters)#d#fcc\nk263al#100.5#madison , minnesota#250 ( 69 metres)#d#fcc\nw291aq#106.1#moose lake , minnesota#55 ( 42 metres)#d#fcc\nk270dz#101.9#new ulm , minnesota#250 ( 57 metres)#d#fcc\nk204fa#88.7#nicollet , minnesota#250 ( 61 metres)#d#fcc\nw220do#91.9#north branch , minnesota#38 ( 64 metres)#d#fcc\n",
        "pandas_code": "df[(df['call sign'] == 'k257ch') & (df['frequency mhz'] == 99.3) & (df['city of license'] == 'estherville , iowa')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3708,
        "statement": "more race be hold in april august than any other month with 3 in each month",
        "label": 1,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "(df['date'].str.contains('april').sum() == 3) & (df['date'].str.contains('august').sum() == 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3709,
        "statement": "brooklands be the highest occur circuit with 3 race on the circuit",
        "label": 1,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "df['circuit'].value_counts().idxmax() == 'brooklands' and df['circuit'].value_counts().max() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3710,
        "statement": "mercedes - benz , and era both be the win constructor 3 time , more than any other constructer",
        "label": 1,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "(df['winning constructor'].value_counts().loc[['mercedes - benz', 'era']] >= 3).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3711,
        "statement": "hermann lang , and ren\u00e9 dreyfus be the only driver to finish with more than a single win",
        "label": 1,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "len(df[df['winning driver'].isin(['hermann lang', 'ren\u00e9 dreyfus'])]['winning driver'].value_counts()) == 2 and df['winning driver'].value_counts().max() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3713,
        "statement": "more race be hold in april october than any other month with 3 in each month",
        "label": 0,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "(df['date'].str.extract(r'(\\\\w+)')[0].value_counts().reindex(['april', 'october'], fill_value=0).eq(3).all())",
        "pandas_eval": "False"
    },
    {
        "id": 3714,
        "statement": "brooklands be the highest occur circuit with 2 race on the circuit",
        "label": 0,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "df['circuit'].value_counts().idxmax() == 'brooklands' and df['circuit'].value_counts().max() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3715,
        "statement": "mercedes - benz , and era both be the win constructor 5 time , more than any other constructer",
        "label": 0,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "(df['winning constructor'].value_counts().loc[['mercedes - benz', 'era']] >= 5).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3716,
        "statement": "hermann lang , and ren\u00e9 dreyfus be the only driver to fail to finish with more than a single win",
        "label": 0,
        "table_caption": "1938 grand prix season",
        "table_text": "name#circuit#date#winning driver#winning constructor#report\npau grand prix#pau#10 april#ren\u00e9 dreyfus#delahaye#report\ncampbell trophy#brooklands#18 april#prince bira#era#report\ncork grand prix#carrigrohane#23 april#ren\u00e9 dreyfus#delahaye#report\ntripoli grand prix#mellaha#15 may#hermann lang#mercedes - benz#report\ng\u00e1vea nacional circuit#g\u00e1vea#29 may#arthur nascimento jr#alfa romeo#report\ngrand prix des fronti\u00e8res#chimay#5 june#maurice trintignant#bugatti#report\nrio de janeiro grand prix#g\u00e1vea#12 june#carlo maria pintacuda#alfa romeo#report\ncoppa ciano#montenero#7 august#hermann lang#mercedes - benz#report\ncoppa acerbo#pescara#15 august#rudolf caracciola#mercedes - benz#report\njunior car club 200 mile race#brooklands#27 august#johnny wakefield#era#report\nmountain championship#brooklands#15 october#raymond mays#era#report\ndonington grand prix#donington park#22 october#tazio nuvolari#auto union#report\n",
        "pandas_code": "not df['winning driver'].value_counts().loc[lambda x: x > 1].index.isin(['hermann lang', 'ren\u00e9 dreyfus']).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3717,
        "statement": "bulgaria and musala feature the peak with the highest elevation",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df.loc[df['elevation (m)'].idxmax(), 'country'] == 'bulgaria' and df.loc[df['elevation (m)'].idxmax(), 'peak'] == 'musala'",
        "pandas_eval": "True"
    },
    {
        "id": 3718,
        "statement": "peak psili koryfi have the lowest prominence of all european peak",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df.loc[df['peak'] == 'psili koryfi', 'prominence (m)'].values[0] == df['prominence (m)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3719,
        "statement": "peak radomir and mount parnassus have a 5 meter difference of prominence",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "abs(df[df['peak'].isin(['radomir', 'mount parnassus'])]['prominence (m)'].diff().iloc[-1]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3720,
        "statement": "macedonia 's pelister and greece 's psili koryfi have the lowest elevation in europe",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df.loc[df['peak'].isin(['pelister', 'psili koryfi']), 'elevation (m)'].min() == df['elevation (m)'].min()",
        "pandas_eval": "True"
    },
    {
        "id": 3721,
        "statement": "the country of greece feature the most peak in europe",
        "label": 1,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df[df['country'].str.contains('greece')]['peak'].count() == df['country'].str.contains('greece').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 3722,
        "statement": "greece have the peak with the highest elevation",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df[df['country'].str.contains('greece')]['elevation (m)'].max() == df['elevation (m)'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3723,
        "statement": "peak pelister be the peak with the lowest prominence of all european peak",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df[df['peak'] == 'pelister']['prominence (m)'].iloc[0] == df['prominence (m)'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3724,
        "statement": "peak radomir and mount parnassus have only a 3 meter difference of prominence",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "abs(df[df['peak'].isin(['radomir', 'mount parnassus'])]['prominence (m)'].diff().iloc[-1]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3725,
        "statement": "macedonia 's pelister and greece 's psili koryfi have the lowest prominence in europe",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "(df[df['peak'].isin(['pelister', 'psili koryfi'])]['prominence (m)'] == df['prominence (m)'].min()).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3726,
        "statement": "macedonia have the most peak in europe",
        "label": 0,
        "table_caption": "list of european ultra prominent peaks",
        "table_text": "peak#country#elevation (m)#prominence (m)#col (m)\nmusala#bulgaria#2925#2473#432\nmount olympus#greece#2919#2355#564\nmount korab#albania / macedonia#2764#2169#595\nmaja jezerc\u00eb#albania#2694#2036#658\nmount athos#greece#2030#2012#18\nmount ossa#greece#1978#1854#124\nnem\u00ebr\u00e7k\u00eb ( maja e papingut )#albania#2482#1792#690\nvihren#bulgaria#2914#1783#1131\npangaion hills#greece#1956#1773#183\nkaimakchalan#greece / macedonia#2528#1758#770\nsmolikas#greece#2637#1736#901\nmount giona#greece#2510#1702#808\njakupica#macedonia#2540#1666#874\nmaja e k\u00ebndrevic\u00ebs#albania#2121#1666#455\nradomir#bulgaria / greece#2031#1595#436\nmount parnassus#greece#2457#1590#867\nbotev peak#bulgaria#2376#1567#809\nmali i \u00e7ik\u00ebs#albania#2044#1563#481\nmaja e valamar\u00ebs#albania#2373#1526#847\npelister#macedonia#2601#1516#1085\npsili koryfi#greece#1589#1514#75\n",
        "pandas_code": "df['country'].str.contains('macedonia').sum() == df['country'].value_counts().max()",
        "pandas_eval": "False"
    },
    {
        "id": 3727,
        "statement": "geelong be the home team with the highest score , at 13.10",
        "label": 1,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "df[df['home team'] == 'geelong']['home team score'].max() == '13.10 (88)'",
        "pandas_eval": "True"
    },
    {
        "id": 3728,
        "statement": "collingwood be the away team with the highest score , at 17.15",
        "label": 1,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "df.loc[df['away team'] == 'collingwood', 'away team score'].eq('17.15 (117)').any()",
        "pandas_eval": "True"
    },
    {
        "id": 3729,
        "statement": "arden street oval be the only venue with a crowd less than 10000 crowd",
        "label": 1,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "(df['venue'] == 'arden street oval').sum() == 1 and (df[df['venue'] == 'arden street oval']['crowd'] < 10000).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3730,
        "statement": "melbourne and richmond be the only team , home or away , with a score less than 10.00",
        "label": 1,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "(df['home team score'].apply(lambda x: float(x.split()[0]) < 10).sum() + df['away team score'].apply(lambda x: float(x.split()[0]) < 10).sum()) == 2 and set(df[df['home team score'].apply(lambda x: float(x.split()[0]) < 10)]['home team'].tolist() + df[df['away team score'].apply(lambda x: float(x.split()[0]) < 10)]['away team'].tolist()) == {'melbourne', 'richmond'}",
        "pandas_eval": "True"
    },
    {
        "id": 3731,
        "statement": "prince park and vfl park be the venue with crowd greater than 20000 crowd",
        "label": 1,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "((df['venue'] == 'princes park') & (df['crowd'] > 20000)).any() and ((df['venue'] == 'vfl park') & (df['crowd'] > 20000)).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3732,
        "statement": "geelong be the away team with the highest score , at 13.10",
        "label": 0,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "df.loc[df['away team'] == 'geelong', 'away team score'].iloc[0] == '13.10 (88)' if not df.loc[df['away team'] == 'geelong'].empty else False",
        "pandas_eval": "False"
    },
    {
        "id": 3733,
        "statement": "geelong be the away team with the highest score , at 17.15",
        "label": 0,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "df.loc[df['away team'] == 'geelong', 'away team score'].any() and df.loc[df['away team'] == 'geelong', 'away team score'].values[0] == '17.15 (117)'",
        "pandas_eval": "False"
    },
    {
        "id": 3734,
        "statement": "mcg be the only venue with a crowd less than 10000 crowd",
        "label": 0,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "(df['venue'] == 'mcg').sum() == 1 and (df[df['venue'] == 'mcg']['crowd'] < 10000).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3735,
        "statement": "fitzroy and richmond be the only team , home or away , with a score less than 10.00",
        "label": 0,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "((df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 10) | (df['away team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 10)).sum() == 2 and set(df[df['home team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 10]['home team']).union(set(df[df['away team score'].str.extract(r'(\\d+)\\.')[0].astype(int) < 10]['away team'])) == {'fitzroy', 'richmond'}",
        "pandas_eval": "False"
    },
    {
        "id": 3736,
        "statement": "prince park and vfl park be the venue with crowd less than 20000 crowd",
        "label": 0,
        "table_caption": "1970 vfl season",
        "table_text": "home team#home team score#away team#away team score#venue#crowd#date\nhawthorn#11.11 (77)#st kilda#14.18 (102)#glenferrie oval#16698#1 august 1970\ncarlton#11.17 (83)#south melbourne#10.19 (79)#princes park#27271#1 august 1970\nmelbourne#8.13 (61)#footscray#10.15 (75)#mcg#16591#1 august 1970\nnorth melbourne#13.13 (91)#essendon#16.22 (118)#arden street oval#8952#1 august 1970\nfitzroy#11.9 (75)#collingwood#17.15 (117)#junction oval#18760#1 august 1970\ngeelong#13.10 (88)#richmond#8.13 (61)#vfl park#26378#1 august 1970\n",
        "pandas_code": "(((df['venue'] == 'princes park') | (df['venue'] == 'vfl park')) & (df['crowd'] < 20000)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3737,
        "statement": "connecticut sun play 2 more match with detroit as compare to new york in the 2007 season",
        "label": 1,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "(df['opponent'].value_counts()['detroit'] - df['opponent'].value_counts()['new york']) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3738,
        "statement": "jones have score high point 3 time more than sale",
        "label": 1,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "(df['high points'].str.contains('jones').sum() >= 3)",
        "pandas_eval": "True"
    },
    {
        "id": 3739,
        "statement": "the highest score for connecticut sun in the 2007 season be 88 - 77 with a win",
        "label": 1,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "df[df['score'] == 'w 88 - 77'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3741,
        "statement": "match play on june 20 have less attendance than the match play on june 27",
        "label": 1,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "int(df[df['date'] == 'june 20']['location / attendance'].str.extract('(\\\\d+)').iloc[0]) < int(df[df['date'] == 'june 27']['location / attendance'].str.extract('(\\\\d+)').iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3742,
        "statement": "connecticut sun play 3 more match with detroit as compare to new york in the 2007 season",
        "label": 0,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "df['opponent'].value_counts().get('detroit', 0) - df['opponent'].value_counts().get('new york', 0) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3743,
        "statement": "jones have score high point 3 time less than sale",
        "label": 0,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "(df['high points'].str.contains('jones').sum() < df['high points'].str.contains('sales').sum())",
        "pandas_eval": "False"
    },
    {
        "id": 3744,
        "statement": "the highest score for connecticut sun in the 2007 season be 98 - 67 with a win",
        "label": 0,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "any(df['score'].str.contains('w 98 - 67'))",
        "pandas_eval": "False"
    },
    {
        "id": 3745,
        "statement": "dydek have the lowest number of high rebound",
        "label": 0,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "(df['high rebounds'].str.extract(r'\\\\((\\\\d+)\\\\)').astype(float).min() == df[df['high rebounds'].str.contains('dydek')]['high rebounds'].str.extract(r'\\\\((\\\\d+)\\\\)').astype(float).min()).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3746,
        "statement": "match play on june 20 have more attendance than the match play on june 27",
        "label": 0,
        "table_caption": "2007 connecticut sun season",
        "table_text": "game#date#opponent#score#high points#high rebounds#high assists#location / attendance#record\n5#june 2#phoenix#w 76 - 67#jones (24)#douglas (9)#whalen (6)#mohegan sun arena 8881#3 - 2\n6#june 8#houston#w 88 - 77#sales (26)#dydek (11)#douglas (6)#toyota center 7339#4 - 2\n7#june 10#detroit#l 74 - 79#jones (20)#dydek (9)#jones (5)#mohegan sun arena 7724#4 - 3\n8#june 13#minnesota#l 73 - 77 (ot)#jones (22)#jones (10)#douglas (6)#target center 9382#4 - 4\n9#june 15#detroit#l 72 - 75#sales (18)#dydek (8)#whalen (5)#palace of auburn hills 8484#4 - 5\n10#june 17#chicago#l 74 - 87#jones (22)#jones (12)#douglas (5)#mohegan sun arena 7614#4 - 6\n11#june 20#new york#l 73 - 76#douglas , sales (17)#whalen (8)#whalen (10)#mohegan sun arena 6154#4 - 7\n12#june 22#indiana#w 78 - 74#douglas (30)#dydek (10)#whalen (7)#conseco fieldhouse 7240#5 - 7\n13#june 23#san antonio#l 58 - 71#douglas , whalen (15)#dydek (8)#douglas (3)#mohegan sun arena 6887#5 - 8\n14#june 26#washington#l 75 - 91#douglas (19)#dydek (10)#whalen (6)#mohegan sun arena 6353#5 - 9\n15#june 27#detroit#l 74 - 77#jones (16)#jones (9)#douglas , whalen (4)#palace of auburn hills 8521#5 - 10\n16#june 29#indiana#w 72 - 67#douglas (22)#dydek (13)#whalen (7)#mohegan sun arena 7617#6 - 10\n",
        "pandas_code": "int(df[df['date'] == 'june 20']['location / attendance'].str.split().str[-1].iloc[0]) > int(df[df['date'] == 'june 27']['location / attendance'].str.split().str[-1].iloc[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3748,
        "statement": "all game be play in september",
        "label": 1,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "all(df['date'].str.contains('september'))",
        "pandas_eval": "True"
    },
    {
        "id": 3749,
        "statement": "the canuck win the most game of all opponent by september 23",
        "label": 1,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "df[df['opponent'] == 'canucks']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).sum() > df[df['opponent'] != 'canucks']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).max()",
        "pandas_eval": "True"
    },
    {
        "id": 3751,
        "statement": "4 game be play at the honda center",
        "label": 1,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "len(df[df['arena'] == 'honda center']) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3752,
        "statement": "the shark win 6 game in the 2007 - 08 season",
        "label": 0,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "len(df[df['opponent'] == 'sharks'][df['score'].str.split(' - ').apply(lambda x: int(x[0]) < int(x[1]))]) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 3753,
        "statement": "all game be play in november",
        "label": 0,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "all(df['date'].str.contains('november'))",
        "pandas_eval": "False"
    },
    {
        "id": 3754,
        "statement": "the canuck lose the most game of all opponent by september 23",
        "label": 0,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "df[df['opponent'] == 'canucks']['loss'].str.contains('0 -').sum() > df.groupby('opponent')['loss'].apply(lambda x: x.str.contains('0 -').sum()).max()",
        "pandas_eval": "False"
    },
    {
        "id": 3755,
        "statement": "the shark win less game than the king",
        "label": 0,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "(df[df['opponent'] == 'sharks']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).sum()) < (df[df['opponent'] == 'kings']['score'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1])).sum())",
        "pandas_eval": "False"
    },
    {
        "id": 3756,
        "statement": "6 game be play at the honda center",
        "label": 0,
        "table_caption": "2007 - 08 anaheim ducks season",
        "table_text": "date#opponent#score#loss#record#arena\nseptember 13#kings#5 - 4#levasseur (0 - 1 - 0)#0 - 1 - 0#honda center\nseptember 15#kings#3 - 2#labarbera (0 - 1 - 0)#1 - 1 - 0#staples center\nseptember 16#coyotes#2 - 1#hiller (1 - 0 - 1)#1 - 1 - 1#honda center\nseptember 17#canucks#3 - 2#macintyre (0 - 0 - 1)#2 - 1 - 1#general motors place\nseptember 19#sharks#1 - 0#hiller (1 - 1 - 1)#2 - 2 - 1#honda center\nseptember 21#sharks#3 - 1#bryzgalov (1 - 1 - 0)#2 - 3 - 1#hp pavilion at san jose\nseptember 23#canucks#5 - 0#sanford (1 - 1 - 0)#3 - 3 - 1#honda center\n",
        "pandas_code": "len(df[df['arena'] == 'honda center']) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 3757,
        "statement": "woodstock , georgia be the only city to have an erp higher than 100 w",
        "label": 1,
        "table_caption": "wccv (fm)",
        "table_text": "call sign#frequency mhz#city of license#erp w#height m ( ft )#fcc info#notes\nw261bg#100.1#morrow , georgia#5#-#fcc#via wccv , though out of its broadcast range\nw265av#100.9#woodstock , georgia#250#-#fcc#via wubl fm 94.9 hd2\nw221aw#92.1#north canton , georgia#10#-#fcc#via wccv\nw215ay#90.9#ellijay , georgia#10#-#fcc#via wccv\nw290ce#105.9#dalton , georgia#10#-#fcc#via wccv , formerly w236aj\nw260aj#99.9#ringgold , georgia#100#-#fcc#via wkxj fm 103.7 hd2 walden tn (chattanooga area)\nw282ay#104.3#chattanooga , tennessee#50#-#fcc#via wccv , though out of its broadcast range\n",
        "pandas_code": "(df[df['city of license'] == 'woodstock , georgia']['erp w'].iloc[0] > 100) and (df[df['city of license'] != 'woodstock , georgia']['erp w'] <= 100).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3758,
        "statement": "morrow , georgia be the only city to have an erp lower than 10 w",
        "label": 1,
        "table_caption": "wccv (fm)",
        "table_text": "call sign#frequency mhz#city of license#erp w#height m ( ft )#fcc info#notes\nw261bg#100.1#morrow , georgia#5#-#fcc#via wccv , though out of its broadcast range\nw265av#100.9#woodstock , georgia#250#-#fcc#via wubl fm 94.9 hd2\nw221aw#92.1#north canton , georgia#10#-#fcc#via wccv\nw215ay#90.9#ellijay , georgia#10#-#fcc#via wccv\nw290ce#105.9#dalton , georgia#10#-#fcc#via wccv , formerly w236aj\nw260aj#99.9#ringgold , georgia#100#-#fcc#via wkxj fm 103.7 hd2 walden tn (chattanooga area)\nw282ay#104.3#chattanooga , tennessee#50#-#fcc#via wccv , though out of its broadcast range\n",
        "pandas_code": "(df[df['city of license'] == 'morrow , georgia']['erp w'].iloc[0] < 10) & (df[df['city of license'] != 'morrow , georgia']['erp w'] >= 10).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3759,
        "statement": "dalton , georgia and chattanooga , tennessee both have frequency higher than 104 mhz",
        "label": 1,
        "table_caption": "wccv (fm)",
        "table_text": "call sign#frequency mhz#city of license#erp w#height m ( ft )#fcc info#notes\nw261bg#100.1#morrow , georgia#5#-#fcc#via wccv , though out of its broadcast range\nw265av#100.9#woodstock , georgia#250#-#fcc#via wubl fm 94.9 hd2\nw221aw#92.1#north canton , georgia#10#-#fcc#via wccv\nw215ay#90.9#ellijay , georgia#10#-#fcc#via wccv\nw290ce#105.9#dalton , georgia#10#-#fcc#via wccv , formerly w236aj\nw260aj#99.9#ringgold , georgia#100#-#fcc#via wkxj fm 103.7 hd2 walden tn (chattanooga area)\nw282ay#104.3#chattanooga , tennessee#50#-#fcc#via wccv , though out of its broadcast range\n",
        "pandas_code": "(df[df['city of license'].isin(['dalton , georgia', 'chattanooga , tennessee'])]['frequency mhz'] > 104).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3760,
        "statement": "north canton , georgia and ellijay , georgia both have frequency lower than 93 mhz",
        "label": 1,
        "table_caption": "wccv (fm)",
        "table_text": "call sign#frequency mhz#city of license#erp w#height m ( ft )#fcc info#notes\nw261bg#100.1#morrow , georgia#5#-#fcc#via wccv , though out of its broadcast range\nw265av#100.9#woodstock , georgia#250#-#fcc#via wubl fm 94.9 hd2\nw221aw#92.1#north canton , georgia#10#-#fcc#via wccv\nw215ay#90.9#ellijay , georgia#10#-#fcc#via wccv\nw290ce#105.9#dalton , georgia#10#-#fcc#via wccv , formerly w236aj\nw260aj#99.9#ringgold , georgia#100#-#fcc#via wkxj fm 103.7 hd2 walden tn (chattanooga area)\nw282ay#104.3#chattanooga , tennessee#50#-#fcc#via wccv , though out of its broadcast range\n",
        "pandas_code": "(df[df['city of license'].isin(['north canton , georgia', 'ellijay , georgia'])]['frequency mhz'] < 93).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3761,
        "statement": "morrow , georgia and chattanooga , tennessee be both out of the broadcast range",
        "label": 1,
        "table_caption": "wccv (fm)",
        "table_text": "call sign#frequency mhz#city of license#erp w#height m ( ft )#fcc info#notes\nw261bg#100.1#morrow , georgia#5#-#fcc#via wccv , though out of its broadcast range\nw265av#100.9#woodstock , georgia#250#-#fcc#via wubl fm 94.9 hd2\nw221aw#92.1#north canton , georgia#10#-#fcc#via wccv\nw215ay#90.9#ellijay , georgia#10#-#fcc#via wccv\nw290ce#105.9#dalton , georgia#10#-#fcc#via wccv , formerly w236aj\nw260aj#99.9#ringgold , georgia#100#-#fcc#via wkxj fm 103.7 hd2 walden tn (chattanooga area)\nw282ay#104.3#chattanooga , tennessee#50#-#fcc#via wccv , though out of its broadcast range\n",
        "pandas_code": "all(df[df['city of license'].isin(['morrow , georgia', 'chattanooga , tennessee'])]['notes'].str.contains('out of its broadcast range'))",
        "pandas_eval": "True"
    },
    {
        "id": 3762,
        "statement": "brian kelly , charlie wei and bob davie have the same number of win , 35",
        "label": 1,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "df[df['coach'].isin(['brian kelly', 'charlie weis', 'bob davie'])]['wins'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3763,
        "statement": "knute rockne coach the team for the longest number of season",
        "label": 1,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "df[df['coach'] == 'knute rockne']['seasons'].values[0] == df['seasons'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3764,
        "statement": "knute rockne hold the most number of win as a coach for the football team",
        "label": 1,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "df[df['coach'] == 'knute rockne']['wins'].max() == df['wins'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3765,
        "statement": "lou holtz and ara parseghian together have an average of 97.5 win as coach",
        "label": 1,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "(df[df['coach'].isin(['lou holtz', 'ara parseghian'])]['wins'].sum() / 2) == 97.5",
        "pandas_eval": "True"
    },
    {
        "id": 3766,
        "statement": "lou holtz have the most number of game lose as the coach for the football team",
        "label": 1,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "df.loc[df['coach'] == 'lou holtz', 'losses'].values[0] == df['losses'].max()",
        "pandas_eval": "True"
    },
    {
        "id": 3767,
        "statement": "there be 4 people that win 35 game , include bob davie , charlie wei , kent baer and brian kelly",
        "label": 0,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "len(df[df['wins'] == 35]) == 4 and all(name in df[df['wins'] == 35]['coach'].values for name in ['bob davie', 'charlie weis', 'kent baer', 'brian kelly'])",
        "pandas_eval": "False"
    },
    {
        "id": 3768,
        "statement": "knute rockne be the notre dame coach for a total of 2 season",
        "label": 0,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "df[df['coach'] == 'knute rockne']['seasons'].sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3769,
        "statement": "knute rockne and brian kelly be tie for the coach with the most career win",
        "label": 0,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "(df[df['coach'] == 'knute rockne']['wins'].values[0] == df[df['coach'] == 'brian kelly']['wins'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3770,
        "statement": "together , lou holtz , knute rockne , ara parseghian and charlie wei have 102.4 win as coach",
        "label": 0,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "abs((df[df['coach'].isin(['lou holtz', 'knute rockne', 'ara parseghian', 'charlie weis'])]['wins'].sum() - 102.4) < 1e-10)",
        "pandas_eval": "False"
    },
    {
        "id": 3771,
        "statement": "lou holtz and charlie wei be both coach that have win more game than the rest",
        "label": 0,
        "table_caption": "notre dame fighting irish football",
        "table_text": "years#coach#seasons#wins#losses#ties#pct\n1887 - 89 , 1892 - 93#none#5#7#4#1#625\n1894#jl morison#1#3#1#1#700\n1895#hg hadden#1#3#1#0#750\n1896 - 98#frank e hering#3#12#6#1#658\n1899#james mcweeney#1#6#3#1#650\n1900 - 01#pat o'dea#2#14#4#2#750\n1902 - 03#james f faragher#2#14#2#2#843\n1904#red salmon#1#5#3#0#625\n1905#henry j mcglew#1#5#4#0#556\n1906 - 07#thomas a barry#2#12#1#1#893\n1908#victor m place#1#8#1#0#889\n1909 - 10#shorty longman#2#11#1#2#857\n1911 - 12#jack marks#2#13#0#2#933\n1913 - 17#jesse harper#5#34#5#1#863\n1918 - 30#knute rockne#13#105#12#5#881\n1931 - 33#hunk anderson#3#16#9#2#630\n1934 - 40#elmer layden#7#47#13#3#770\n1941 - 43 , 1946 - 53#frank leahy#11#87#11#9#855\n1944#ed mckeever#1#8#2#0#800\n1945 , 1963#hugh devore#2#9#9#1#500\n1954 - 58#terry brennan#5#32#18#0#640\n1959 - 62#joe kuharich#4#17#23#0#425\n1964 - 74#ara parseghian#11#95#17#4#836\n1975 - 80#dan devine#6#53#16#1#764\n1981 - 85#gerry faust#5#30#26#1#535\n1986 - 96#lou holtz#11#100#30#2#765\n1997 - 01#bob davie#5#35#25#-#583\n2001#george o'leary#0#0#0#-#-\n2002 - 04#tyrone willingham#3#21#15#-#583\n2004#kent baer#0#0#1#-#000\n2005 - 09#charlie weis#5#35#27#-#565\n2010 - present#brian kelly#4#35#13#-#729\n",
        "pandas_code": "(df[df['coach'].isin(['lou holtz', 'charlie weis'])]['wins'].sum() > df[~df['coach'].isin(['lou holtz', 'charlie weis'])]['wins'].sum())",
        "pandas_eval": "False"
    },
    {
        "id": 3772,
        "statement": "semmy schilt have more loss against mighty mo than josh barnett",
        "label": 1,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "(df[df['opponent'] == 'semmy schilt']['res'] == 'loss').sum() >= (df[df['opponent'] == 'josh barnett']['res'] == 'loss').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 3773,
        "statement": "mark smith be 1 of the 3 opponent yet to secure a win against mighty mo",
        "label": 1,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "df[df['opponent'] == 'mark smith']['res'].iloc[0] == 'win'",
        "pandas_eval": "True"
    },
    {
        "id": 3774,
        "statement": "mighty mo 's match against dan charles record the highest number of round",
        "label": 1,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "df[df['opponent'] == 'dan charles']['round'].max() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3776,
        "statement": "mark smith last in a round more than kim min - soo in their respective match against mighty mo",
        "label": 1,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "df[df['opponent'].isin(['mark smith', 'kim min - soo'])].groupby('opponent')['round'].max().loc['mark smith'] > df[df['opponent'].isin(['mark smith', 'kim min - soo'])].groupby('opponent')['round'].max().loc['kim min - soo']",
        "pandas_eval": "True"
    },
    {
        "id": 3777,
        "statement": "mighty mo 's match against mark smith record the highest number of round",
        "label": 0,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "df[df['opponent'] == 'mark smith']['round'].max() == df['round'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 3778,
        "statement": "mark smith last in 2 more round than kim min - soo in their respective match against mighty mo",
        "label": 0,
        "table_caption": "mighty mo (kickboxer)",
        "table_text": "res#record#opponent#method#event#round#location\nwin#5 - 2#ron sparks#submission (keylock)#bellator 105#1#rio rancho , new mexico , united states\nwin#4 - 2#dan charles#tko (punches)#bellator 100#3#phoenix , arizona , united states\nloss#3 - 2#josh barnett#submission (kimura)#dream 13#1#yokohama , kanagawa , japan\nloss#3 - 1#semmy schilt#submission (triangle choke)#dynamite!! 2008#1#saitama , saitama , japan\nwin#3 - 0#ruben villareal#tko (punches)#dynamite!! usa#1#los angeles , california , united states\nwin#2 - 0#kim min - soo#ko (punch)#hero 's 8#1#nagoya , aichi , japan\nwin#1 - 0#mark smith#ko (knee to the body)#uagf 4: ultimate cage fighting#2#upland , california , united states\n",
        "pandas_code": "(df[df['opponent'] == 'mark smith']['round'].values[0] - df[df['opponent'] == 'kim min - soo']['round'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3779,
        "statement": "more tournament take place on a hard surface , than on clay",
        "label": 1,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "(df['surface'].value_counts()['hard'] > df['surface'].value_counts()['clay'])",
        "pandas_eval": "True"
    },
    {
        "id": 3780,
        "statement": "the tournament on a clay surface take place after the tournament on a hard surface",
        "label": 1,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "pd.to_datetime(df[df['surface'] == 'clay']['date'], format='%B %d , %Y').min() > pd.to_datetime(df[df['surface'] == 'hard']['date'], format='%B %d , %Y').max()",
        "pandas_eval": "True"
    },
    {
        "id": 3781,
        "statement": "there be 3 tournament that take place in brazil",
        "label": 1,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df[df['tournament'].str.contains('brazil', case=False)]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3782,
        "statement": "paul capdeville be thiago alves' opponent after sergiy stakhovsky",
        "label": 1,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "df[df['opponen'].shift() == 'sergiy stakhovsky']['opponen'].iloc[0] == 'paul capdeville'",
        "pandas_eval": "True"
    },
    {
        "id": 3783,
        "statement": "when the tournament be on a clay surface , the opponent wasn't franco ferreiro",
        "label": 1,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "((df['surface'] == 'clay') & (df['opponen'] != 'franco ferreiro')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3784,
        "statement": "less tournament take place on a hard surface , than on clay",
        "label": 0,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "(df['surface'].value_counts()['hard'] < df['surface'].value_counts()['clay'])",
        "pandas_eval": "False"
    },
    {
        "id": 3785,
        "statement": "the tournament on a clay surface take place before the tournament on a hard surface",
        "label": 0,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "(df[df['surface'] == 'clay']['date'].min() < df[df['surface'] == 'hard']['date'].min())",
        "pandas_eval": "False"
    },
    {
        "id": 3786,
        "statement": "there be 3 tournament that take place in spain",
        "label": 0,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "len(df[df['tournament'].str.contains('spain', case=False)]) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3787,
        "statement": "paul capdeville be thiago alves' opponent before sergiy stakhovsky",
        "label": 0,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "df.loc[df['opponen'] == 'paul capdeville', 'date'].iloc[0] < df.loc[df['opponen'] == 'sergiy stakhovsky', 'date'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3788,
        "statement": "when the tournament be on a clay surface , the opponent wasn't fl\u00e1vio saretta",
        "label": 0,
        "table_caption": "thiago alves (tennis)",
        "table_text": "date#tournament#surface#opponen#score\njuly 18 , 2005#tarzana , usa#hard#alex bogomolov , jr#6 - 3 , 6 - 2\njanuary 2 , 2006#s\u00e3o paulo , brazil#hard#fl\u00e1vio saretta#7 - 6 (7 - 2) , 6 - 3\nseptember 25 , 2006#gramado , brazil#hard#franco ferreiro#3 - 6 , 7 - 6 (7 - 4) , 6 - 5 ret\naugust 4 , 2008#segovia , spain#hard#sergiy stakhovsky#7 - 5 , 7 - 6 (7 - 4)\nseptember 29 , 2008#aracaju , brazil#clay#paul capdeville#7 - 5 , 6 - 4\nseptember 16 , 2012#cali , colombia#clay#jo\u00e3o souza#6 - 2 , 6 - 4\n",
        "pandas_code": "((df['surface'] == 'clay') & (df['opponen'] != 'fl\u00e1vio saretta')).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3789,
        "statement": "70097 be the highest in attendance during the tampa bay buccaneer season",
        "label": 1,
        "table_caption": "1986 tampa bay buccaneers season",
        "table_text": "week#date#opponent#result#kickoff#game site#tv#attendance#record\nweek#date#opponent#result#kickoff#game site#tv#attendance#record\n1#september 7 , 1986#san francisco 49ers#l 31 - 7#1:00#tampa stadium#cbs#50780#0 - 1\n2#september 14 , 1986#minnesota vikings#l 23 - 10#4:00#tampa stadium#cbs#34579#0 - 2\n3#september 21 , 1986#detroit lions#w 24 - 20#1:00#pontiac silverdome#cbs#38453#1 - 2\n4#september 28 , 1986#atlanta falcons#l 23 - 20 ot#4:00#tampa stadium#cbs#38950#1 - 3\n5#october 5 , 1986#los angeles rams#l 26 - 20 ot#4:00#anaheim coliseum#cbs#50585#1 - 4\n6#october 12 , 1986#st louis cardinals#l 30 - 19#1:00#tampa stadium#cbs#33307#1 - 5\n7#october 19 , 1986#new orleans saints#l 38 - 7#1:00#louisiana superdome#cbs#43355#1 - 6\n8#october 26 , 1986#kansas city chiefs#l 27 - 20#1:00#arrowhead stadium#cbs#36230#1 - 7\n9#november 2 , 1986#buffalo bills#w 34 - 28#1:00#tampa stadium#nbc#32806#2 - 7\n10#november 9 , 1986#chicago bears#l 23 - 3#1:00#tampa stadium#cbs#70097#2 - 8\n11#november 16 , 1986#green bay packers#l 31 - 7#1:00#lambeau field#cbs#48271#2 - 9\n12#november 23 , 1986#detroit lions#l 38 - 17#1:00#tampa stadium#cbs#30029#2 - 10\n13#november 30 , 1986#minnesota vikings#l 45 - 13#1:00#hubert h humphrey metrodome#cbs#56235#2 - 11\n14#december 7 , 1986#chicago bears#l 48 - 14#1:00#soldier field#cbs#52746#2 - 12\n15#december 14 , 1986#green bay packers#l 21 - 7#1:00#tampa stadium#cbs#30099#2 - 13\n",
        "pandas_code": "df['attendance'][1:].astype(int).max() == 70097",
        "pandas_eval": "True"
    },
    {
        "id": 3790,
        "statement": "2 - 13 be the best record of the season",
        "label": 1,
        "table_caption": "1986 tampa bay buccaneers season",
        "table_text": "week#date#opponent#result#kickoff#game site#tv#attendance#record\nweek#date#opponent#result#kickoff#game site#tv#attendance#record\n1#september 7 , 1986#san francisco 49ers#l 31 - 7#1:00#tampa stadium#cbs#50780#0 - 1\n2#september 14 , 1986#minnesota vikings#l 23 - 10#4:00#tampa stadium#cbs#34579#0 - 2\n3#september 21 , 1986#detroit lions#w 24 - 20#1:00#pontiac silverdome#cbs#38453#1 - 2\n4#september 28 , 1986#atlanta falcons#l 23 - 20 ot#4:00#tampa stadium#cbs#38950#1 - 3\n5#october 5 , 1986#los angeles rams#l 26 - 20 ot#4:00#anaheim coliseum#cbs#50585#1 - 4\n6#october 12 , 1986#st louis cardinals#l 30 - 19#1:00#tampa stadium#cbs#33307#1 - 5\n7#october 19 , 1986#new orleans saints#l 38 - 7#1:00#louisiana superdome#cbs#43355#1 - 6\n8#october 26 , 1986#kansas city chiefs#l 27 - 20#1:00#arrowhead stadium#cbs#36230#1 - 7\n9#november 2 , 1986#buffalo bills#w 34 - 28#1:00#tampa stadium#nbc#32806#2 - 7\n10#november 9 , 1986#chicago bears#l 23 - 3#1:00#tampa stadium#cbs#70097#2 - 8\n11#november 16 , 1986#green bay packers#l 31 - 7#1:00#lambeau field#cbs#48271#2 - 9\n12#november 23 , 1986#detroit lions#l 38 - 17#1:00#tampa stadium#cbs#30029#2 - 10\n13#november 30 , 1986#minnesota vikings#l 45 - 13#1:00#hubert h humphrey metrodome#cbs#56235#2 - 11\n14#december 7 , 1986#chicago bears#l 48 - 14#1:00#soldier field#cbs#52746#2 - 12\n15#december 14 , 1986#green bay packers#l 21 - 7#1:00#tampa stadium#cbs#30099#2 - 13\n",
        "pandas_code": "df['record'].iloc[-1] == '2 - 13'",
        "pandas_eval": "True"
    },
    {
        "id": 3791,
        "statement": "the kickoff be at 4:00 for 3 week of the season",
        "label": 1,
        "table_caption": "1986 tampa bay buccaneers season",
        "table_text": "week#date#opponent#result#kickoff#game site#tv#attendance#record\nweek#date#opponent#result#kickoff#game site#tv#attendance#record\n1#september 7 , 1986#san francisco 49ers#l 31 - 7#1:00#tampa stadium#cbs#50780#0 - 1\n2#september 14 , 1986#minnesota vikings#l 23 - 10#4:00#tampa stadium#cbs#34579#0 - 2\n3#september 21 , 1986#detroit lions#w 24 - 20#1:00#pontiac silverdome#cbs#38453#1 - 2\n4#september 28 , 1986#atlanta falcons#l 23 - 20 ot#4:00#tampa stadium#cbs#38950#1 - 3\n5#october 5 , 1986#los angeles rams#l 26 - 20 ot#4:00#anaheim coliseum#cbs#50585#1 - 4\n6#october 12 , 1986#st louis cardinals#l 30 - 19#1:00#tampa stadium#cbs#33307#1 - 5\n7#october 19 , 1986#new orleans saints#l 38 - 7#1:00#louisiana superdome#cbs#43355#1 - 6\n8#october 26 , 1986#kansas city chiefs#l 27 - 20#1:00#arrowhead stadium#cbs#36230#1 - 7\n9#november 2 , 1986#buffalo bills#w 34 - 28#1:00#tampa stadium#nbc#32806#2 - 7\n10#november 9 , 1986#chicago bears#l 23 - 3#1:00#tampa stadium#cbs#70097#2 - 8\n11#november 16 , 1986#green bay packers#l 31 - 7#1:00#lambeau field#cbs#48271#2 - 9\n12#november 23 , 1986#detroit lions#l 38 - 17#1:00#tampa stadium#cbs#30029#2 - 10\n13#november 30 , 1986#minnesota vikings#l 45 - 13#1:00#hubert h humphrey metrodome#cbs#56235#2 - 11\n14#december 7 , 1986#chicago bears#l 48 - 14#1:00#soldier field#cbs#52746#2 - 12\n15#december 14 , 1986#green bay packers#l 21 - 7#1:00#tampa stadium#cbs#30099#2 - 13\n",
        "pandas_code": "df.loc[df['week'] == '3', 'kickoff'].iloc[0] == '1:00'",
        "pandas_eval": "True"
    },
    {
        "id": 3792,
        "statement": "the tampa bay buccaneer season be for 15 week",
        "label": 1,
        "table_caption": "1986 tampa bay buccaneers season",
        "table_text": "week#date#opponent#result#kickoff#game site#tv#attendance#record\nweek#date#opponent#result#kickoff#game site#tv#attendance#record\n1#september 7 , 1986#san francisco 49ers#l 31 - 7#1:00#tampa stadium#cbs#50780#0 - 1\n2#september 14 , 1986#minnesota vikings#l 23 - 10#4:00#tampa stadium#cbs#34579#0 - 2\n3#september 21 , 1986#detroit lions#w 24 - 20#1:00#pontiac silverdome#cbs#38453#1 - 2\n4#september 28 , 1986#atlanta falcons#l 23 - 20 ot#4:00#tampa stadium#cbs#38950#1 - 3\n5#october 5 , 1986#los angeles rams#l 26 - 20 ot#4:00#anaheim coliseum#cbs#50585#1 - 4\n6#october 12 , 1986#st louis cardinals#l 30 - 19#1:00#tampa stadium#cbs#33307#1 - 5\n7#october 19 , 1986#new orleans saints#l 38 - 7#1:00#louisiana superdome#cbs#43355#1 - 6\n8#october 26 , 1986#kansas city chiefs#l 27 - 20#1:00#arrowhead stadium#cbs#36230#1 - 7\n9#november 2 , 1986#buffalo bills#w 34 - 28#1:00#tampa stadium#nbc#32806#2 - 7\n10#november 9 , 1986#chicago bears#l 23 - 3#1:00#tampa stadium#cbs#70097#2 - 8\n11#november 16 , 1986#green bay packers#l 31 - 7#1:00#lambeau field#cbs#48271#2 - 9\n12#november 23 , 1986#detroit lions#l 38 - 17#1:00#tampa stadium#cbs#30029#2 - 10\n13#november 30 , 1986#minnesota vikings#l 45 - 13#1:00#hubert h humphrey metrodome#cbs#56235#2 - 11\n14#december 7 , 1986#chicago bears#l 48 - 14#1:00#soldier field#cbs#52746#2 - 12\n15#december 14 , 1986#green bay packers#l 21 - 7#1:00#tampa stadium#cbs#30099#2 - 13\n",
        "pandas_code": "len(df['week'][1:].unique()) == 15",
        "pandas_eval": "True"
    },
    {
        "id": 3793,
        "statement": "the attendance for december 7 , 1986 be be less than the november 9 , 1986 game",
        "label": 1,
        "table_caption": "1986 tampa bay buccaneers season",
        "table_text": "week#date#opponent#result#kickoff#game site#tv#attendance#record\nweek#date#opponent#result#kickoff#game site#tv#attendance#record\n1#september 7 , 1986#san francisco 49ers#l 31 - 7#1:00#tampa stadium#cbs#50780#0 - 1\n2#september 14 , 1986#minnesota vikings#l 23 - 10#4:00#tampa stadium#cbs#34579#0 - 2\n3#september 21 , 1986#detroit lions#w 24 - 20#1:00#pontiac silverdome#cbs#38453#1 - 2\n4#september 28 , 1986#atlanta falcons#l 23 - 20 ot#4:00#tampa stadium#cbs#38950#1 - 3\n5#october 5 , 1986#los angeles rams#l 26 - 20 ot#4:00#anaheim coliseum#cbs#50585#1 - 4\n6#october 12 , 1986#st louis cardinals#l 30 - 19#1:00#tampa stadium#cbs#33307#1 - 5\n7#october 19 , 1986#new orleans saints#l 38 - 7#1:00#louisiana superdome#cbs#43355#1 - 6\n8#october 26 , 1986#kansas city chiefs#l 27 - 20#1:00#arrowhead stadium#cbs#36230#1 - 7\n9#november 2 , 1986#buffalo bills#w 34 - 28#1:00#tampa stadium#nbc#32806#2 - 7\n10#november 9 , 1986#chicago bears#l 23 - 3#1:00#tampa stadium#cbs#70097#2 - 8\n11#november 16 , 1986#green bay packers#l 31 - 7#1:00#lambeau field#cbs#48271#2 - 9\n12#november 23 , 1986#detroit lions#l 38 - 17#1:00#tampa stadium#cbs#30029#2 - 10\n13#november 30 , 1986#minnesota vikings#l 45 - 13#1:00#hubert h humphrey metrodome#cbs#56235#2 - 11\n14#december 7 , 1986#chicago bears#l 48 - 14#1:00#soldier field#cbs#52746#2 - 12\n15#december 14 , 1986#green bay packers#l 21 - 7#1:00#tampa stadium#cbs#30099#2 - 13\n",
        "pandas_code": "df[df['date'] == 'december 7 , 1986']['attendance'].values[0] < df[df['date'] == 'november 9 , 1986']['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3794,
        "statement": "in georgia 's first district , ronald bo ginn be re - elect and run unopposed in the 1978 house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1978",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#ronald bo ginn#democratic#1972#re - elected#ronald bo ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 6#john james flynt , jr#democratic#1954#retired republican gain#newt gingrich (r) 54.4% virginia shapard (d) 45.6%\ngeorgia 8#billy lee evans#democratic#1976#re - elected#billy lee evans (d) unopposed\ngeorgia 9#ed jenkins#democratic#1976#re - elected#ed jenkins (d) 76.9% david g ashworth (r) 23.1%\n",
        "pandas_code": "df[(df['district'] == 'georgia 1') & (df['incumbent'] == 'ronald bo ginn') & (df['result'] == 're - elected') & (df['candidates'].str.contains('unopposed'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3795,
        "statement": "in georgia 's ninth district , ed jenkins represent the democractic party run against david g ashworth (r) who garner only 23.1% of the vote",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1978",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#ronald bo ginn#democratic#1972#re - elected#ronald bo ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 6#john james flynt , jr#democratic#1954#retired republican gain#newt gingrich (r) 54.4% virginia shapard (d) 45.6%\ngeorgia 8#billy lee evans#democratic#1976#re - elected#billy lee evans (d) unopposed\ngeorgia 9#ed jenkins#democratic#1976#re - elected#ed jenkins (d) 76.9% david g ashworth (r) 23.1%\n",
        "pandas_code": "df[(df['district'] == 'georgia 9') & (df['incumbent'] == 'ed jenkins') & (df['party'] == 'democratic') & (df['candidates'].str.contains('ed jenkins \\(d\\) 76.9% david g ashworth \\(r\\) 23.1%'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3796,
        "statement": "dawson mathis be re - elect in the 1978 house of representative election as a democrat after first be elect in 1970",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1978",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#ronald bo ginn#democratic#1972#re - elected#ronald bo ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 6#john james flynt , jr#democratic#1954#retired republican gain#newt gingrich (r) 54.4% virginia shapard (d) 45.6%\ngeorgia 8#billy lee evans#democratic#1976#re - elected#billy lee evans (d) unopposed\ngeorgia 9#ed jenkins#democratic#1976#re - elected#ed jenkins (d) 76.9% david g ashworth (r) 23.1%\n",
        "pandas_code": "((df['incumbent'] == 'dawson mathis') & (df['party'] == 'democratic') & (df['first elected'] == 1970) & (df['result'] == 're - elected')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3797,
        "statement": "john james flynt , jr be first elect in 1954 in georgia 's sixth district and have a result of retired republican gain in the 1978 house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1978",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#ronald bo ginn#democratic#1972#re - elected#ronald bo ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 6#john james flynt , jr#democratic#1954#retired republican gain#newt gingrich (r) 54.4% virginia shapard (d) 45.6%\ngeorgia 8#billy lee evans#democratic#1976#re - elected#billy lee evans (d) unopposed\ngeorgia 9#ed jenkins#democratic#1976#re - elected#ed jenkins (d) 76.9% david g ashworth (r) 23.1%\n",
        "pandas_code": "df[(df['incumbent'] == 'john james flynt , jr') & (df['first elected'] == 1954) & (df['district'] == 'georgia 6') & (df['result'] == 'retired republican gain')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3798,
        "statement": "jack thomas brinkley , a democrat , be first elect in 1966 and be re - elect in the 1978 house of representative election after run unopposed",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1978",
        "table_text": "district#incumbent#party#first elected#result#candidates\ngeorgia 1#ronald bo ginn#democratic#1972#re - elected#ronald bo ginn (d) unopposed\ngeorgia 2#dawson mathis#democratic#1970#re - elected#dawson mathis (d) unopposed\ngeorgia 3#jack thomas brinkley#democratic#1966#re - elected#jack thomas brinkley (d) unopposed\ngeorgia 6#john james flynt , jr#democratic#1954#retired republican gain#newt gingrich (r) 54.4% virginia shapard (d) 45.6%\ngeorgia 8#billy lee evans#democratic#1976#re - elected#billy lee evans (d) unopposed\ngeorgia 9#ed jenkins#democratic#1976#re - elected#ed jenkins (d) 76.9% david g ashworth (r) 23.1%\n",
        "pandas_code": "((df['incumbent'] == 'jack thomas brinkley') & (df['party'] == 'democratic') & (df['first elected'] == 1966) & (df['result'] == 're - elected') & (df['candidates'].str.contains('unopposed'))).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3799,
        "statement": "the club chairman be miltiadis neophytou when the team be omonia",
        "label": 1,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df[df['team'] == 'omonia']['club chairman'].iloc[0] == 'miltiadis neophytou')",
        "pandas_eval": "True"
    },
    {
        "id": 3800,
        "statement": "when the head coach be willy scheepers the venue be tsirion stadium with 13331 as its capacity",
        "label": 1,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "df[(df['head coach'] == 'willy scheepers') & (df['venue'] == 'tsirion stadium') & (df['capacity'] == 13331)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3801,
        "statement": "kkcg be the shirt sponsor 1 time and it be for apep pitsilia",
        "label": 1,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df['shirt sponsor'].eq('kkcg') & df['team'].eq('apep pitsilia')).sum() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3802,
        "statement": "the highest capacity for a venue be 22859 for gsp stadium and the lowest be 3828 for peyia municipal stadium",
        "label": 1,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df['capacity'].max() == 22859) & (df['capacity'].min() == 3828) & (df.loc[df['capacity'] == 22859, 'venue'].iloc[0] == 'gsp stadium') & (df.loc[df['capacity'] == 3828, 'venue'].iloc[0] == 'peyia municipal stadium')",
        "pandas_eval": "True"
    },
    {
        "id": 3803,
        "statement": "dasaki stadium have a capacity of 7000 and it be link to ethnikos achna",
        "label": 1,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df[df['venue'] == 'dasaki stadium']['capacity'].values[0] == 7000) & (df[df['venue'] == 'dasaki stadium']['team'].values[0] == 'ethnikos achna')",
        "pandas_eval": "True"
    },
    {
        "id": 3804,
        "statement": "the club chairman be miltiadis neophytou when the team be enosis neon paralimni",
        "label": 0,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "df.loc[df['team'] == 'enosis neon paralimni', 'club chairman'].iloc[0] == 'miltiadis neophytou'",
        "pandas_eval": "False"
    },
    {
        "id": 3805,
        "statement": "when the head coach be willy scheepers the venue be tsirion stadium with 5800 as its capacity",
        "label": 0,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "((df['head coach'] == 'willy scheepers') & (df['venue'] == 'tsirion stadium') & (df['capacity'] == 5800)).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3806,
        "statement": "kkcg be the shirt sponsor 2 time and it be for apep pitsilia and atromitos yeroskipou",
        "label": 0,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "len(df[df['shirt sponsor'] == 'kkcg']) == 2 and set(df[df['shirt sponsor'] == 'kkcg']['team']) == {'apep pitsilia', 'atromitos yeroskipou'}",
        "pandas_eval": "False"
    },
    {
        "id": 3807,
        "statement": "the highest capacity for a venue be 16000 for gsp stadium and the lowest be 3828 for peyia municipal stadium",
        "label": 0,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df['capacity'].max() == 16000) & (df['capacity'].min() == 3828) & (df.loc[df['capacity'].idxmax(), 'venue'] == 'gsp stadium') & (df.loc[df['capacity'].idxmin(), 'venue'] == 'peyia municipal stadium')",
        "pandas_eval": "False"
    },
    {
        "id": 3808,
        "statement": "dasaki stadium have a capacity of 5000 and it be link to ethnikos achna",
        "label": 0,
        "table_caption": "2008 - 09 cypriot first division",
        "table_text": "team#head coach#team captain#venue#capacity#kitmaker#shirt sponsor#club chairman\naek larnaca#savvas constantinou#constantinos mina#neo gsz stadium#13032#mass#cytavision#marios ellinas\nael limassol#mihai stoichi\u0163\u0103#simos krassas#tsirion stadium#13331#mass#sinergatiko tamieftirio lemesou#andreas sofokleous\naep paphos#nir klinger#giorgos georgiou#pafiako stadium#10000#mass#eurolink investment group#fillippos georgiou\nalki larnaca#panikos xiourouppas#andr\u00e9s rouga#ammochostos stadium#5500#legea#team a security#demetris phantousis\nanorthosis famagusta#michalis pamboris#nikos nicolaou#antonis papadopoulos stadium#10003#puma#quality group developments#chris georgiades\napep pitsilia#willy scheepers#bruno piano#tsirion stadium#13331#mass#kkcg#panayiotis neokleous\napoel#ivan jovanovi\u0107#marinos satsias#gsp stadium#22859#lotto#mtn group#foivos erotokritou\napollon limassol#thomas von heesen#christos theophilou#tsirion stadium#13331#lotto#columbia ship management#theodoros antoniou\napop kinyras peyias#giorgos polyviou#giannis sfakianakis#peyia municipal stadium#3828#puma#primetel#michalis mitas\natromitos yeroskipou#sofoklis sofokleous#argyris petrou#pafiako stadium#10000#umbro#spe yeroskipou#vangelis genis\ndoxa katokopia#charalmbos christodoulou#kyriacos polykarpou#makario stadium#16000#puma#yiannakas real estate ltd#charalambos argyrou\nenosis neon paralimni#adamos adamou & antonis kleftis#demos goumenos#paralimni stadium#5800#lotto#elian developers#adamos loizou\nethnikos achna#st\u00e9phane demol#christos poyiatzis#dasaki stadium#7000#nike#famagusta developers#kikis philippou\nomonia#takis lemonis#costas kaiafas#gsp stadium#22859#lotto#ocean tankers#miltiadis neophytou\n",
        "pandas_code": "(df[df['venue'] == 'dasaki stadium']['capacity'].values[0] == 5000) & (df[df['venue'] == 'dasaki stadium']['team'].values[0] == 'ethnikos achna')",
        "pandas_eval": "False"
    },
    {
        "id": 3809,
        "statement": "3 of the radio station be license in the city of laredo",
        "label": 1,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "(df['city of license'] == 'laredo').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3810,
        "statement": "the highest frequency radio station use the brand la rancherita",
        "label": 1,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "df.loc[df['frequency'].idxmax(), 'brand'] == 'la rancherita'",
        "pandas_eval": "True"
    },
    {
        "id": 3811,
        "statement": "espn radio be a higher radio station frequency than radio poder",
        "label": 1,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "df[df['brand'] == 'espn radio']['frequency'].values[0] > df[df['brand'] == 'radio poder']['frequency'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3812,
        "statement": "the radio statio with the calsign xek have a lower frequency than the radio station with the calsign xewl",
        "label": 1,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "df[df['callsign'] == 'xek']['frequency'].values[0] < df[df['callsign'] == 'xewl']['frequency'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3813,
        "statement": "1 radio station from laredo have a frequency of less than 1000",
        "label": 1,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "(df[df['city of license'] == 'laredo']['frequency'] < 1000).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3814,
        "statement": "the city of laredo be license to have 2 of the radio station",
        "label": 0,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "len(df[df['city of license'] == 'laredo']) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3815,
        "statement": "the highest frequency radio station use the brand espn espanol and la rancherita",
        "label": 0,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "False",
        "pandas_eval": "False"
    },
    {
        "id": 3816,
        "statement": "espn radio be find at a lower radio station frequency than radio poder",
        "label": 0,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "df[df['brand'] == 'espn radio']['frequency'].values[0] < df[df['brand'] == 'radio poder']['frequency'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3817,
        "statement": "the radio station with the calsign xek have a higher frequency than the radio station with the calsign xewl",
        "label": 0,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "df[df['callsign'] == 'xek']['frequency'].values[0] > df[df['callsign'] == 'xewl']['frequency'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3818,
        "statement": "all radio station from laredo have a frequency of higher than 1000",
        "label": 0,
        "table_caption": "list of radio stations in tamaulipas",
        "table_text": "frequency#power d / n#callsign#brand#city of license\n790#1 kw / 500w#xefe#la pura ley#nuevo laredo\n890#10 / 1 kw#kvoz#la radio cristiana ( kczo )#laredo\n960#5 / 1 kw#xek#la estaci\u00f3n grande#nuevo laredo\n1000#1 kw / 250w#xenlt#radio formula#nuevo laredo\n1090#1 kw / 250w#xewl#w radio ( xew )#nuevo laredo\n1300#1 kw / 500w#klar#radio poder#laredo\n1340#1 / 1 kw#xebk#el norte\u00f1azo#nuevo laredo\n1370#1 kw / 250w#xegnk#mariachi est\u00e9reo#nuevo laredo\n1410#1 kw / 250w#xeas#ke buena xhpo#nuevo laredo\n1490#1 / 1 kw#klnt#espn radio#laredo\n1550#5 kw / 250w#xenu#la rancherita#nuevo laredo\n",
        "pandas_code": "all(df[df['city of license'] == 'laredo']['frequency'] > 1000)",
        "pandas_eval": "False"
    },
    {
        "id": 3819,
        "statement": "ararat yerevan be the only team to score 3 or more goal and lose",
        "label": 1,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "df[(df['team 2'] == 'ararat yerevan') & (df['agg'].str.split(' - ').apply(lambda x: int(x[1]) >= 3 if len(x) == 2 and x[1].strip().isdigit() else False)) & (df['agg'].str.split(' - ').apply(lambda x: int(x[0]) > int(x[1]) if len(x) == 2 and x[0].strip().isdigit() and x[1].strip().isdigit() else False))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3820,
        "statement": "villarreal and chernomorets score the most goal , with 6 goal each",
        "label": 1,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "(df[df['team 1'].isin(['villarreal', 'chernomorets odessa'])]['agg'].str.extract(r'(\\d+) - \\d+').astype(int).sum(axis=1) == 6).all()",
        "pandas_eval": "True"
    },
    {
        "id": 3821,
        "statement": "the biggest goal differential in a game be odense beating dinomo minsk 4 - 0 in the 2nd leg",
        "label": 1,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "((df['team 1'] == 'dinamo minsk') & (df['team 2'] == 'odense') & (df['2nd leg'] == '0 - 4')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 3824,
        "statement": "ararat yerevan be the only team to score 4 or more goal and lose",
        "label": 0,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "((df['team 2'] == 'ararat yerevan') & (df['agg'].str.split(' - ').apply(lambda x: int(x[0]) >= 4 if x[0].isdigit() else False) & df['agg'].str.split(' - ').apply(lambda x: int(x[0]) < int(x[1]) if x[0].isdigit() and x[1].isdigit() else False))).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3825,
        "statement": "villarreal and skonto riga score the most goal with 6 goal each",
        "label": 0,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "((df[df['team 1'].isin(['villarreal', 'skonto riga'])]['agg'].str.extract('(\\\\d+) - \\\\d+').astype(int).max() == 6).all() and (df[df['team 2'].isin(['villarreal', 'skonto riga'])]['agg'].str.extract('\\\\d+ - (\\\\d+)').astype(int).max() == 6).all())",
        "pandas_eval": "False"
    },
    {
        "id": 3826,
        "statement": "the biggest goal differential in a game be bate borisov beat apoel nicosia 4 - 0 in the second leg",
        "label": 0,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "df[(df['team 1'] == 'bate borisov') & (df['team 2'] == 'apoel nicosia') & (df['2nd leg'] == '4 - 0')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 3827,
        "statement": "every leg have at least 3 goal score in it",
        "label": 0,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "all(df['1st leg'].str.extract(r'(\\\\d+) - (\\\\d+)').fillna(0).astype(int).sum(axis=1) >= 3) and all(df['2nd leg'].str.extract(r'(\\\\d+) - (\\\\d+)').fillna(0).astype(int).sum(axis=1) >= 3)",
        "pandas_eval": "False"
    },
    {
        "id": 3828,
        "statement": "odense beat dinamo minsk prior to liep\u0101jas metalurgs beat diano brest",
        "label": 0,
        "table_caption": "2007 belarusian premier league",
        "table_text": "round#team 1#agg#team 2#1st leg#2nd leg\n2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup#2007 uefa intertoto cup\nfirst round#shakhtyor soligorsk#4 - 3#ararat yerevan#4 - 1#0 - 2\nsecond round#chernomorets odessa#6 - 2#shakhtyor soligorsk#4 - 2#2 - 0\n2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup#2007 - 08 uefa cup\nfirst qualifying round#liep\u0101jas metalurgs#3 - 2#dinamo brest#1 - 1#2 - 1\nfirst qualifying round#skonto riga#1 - 3#dinamo minsk#1 - 1#0 - 2\nsecond qualifying round#dinamo minsk#1 - 5#odense#1 - 1#0 - 4\nfirst round#villarreal#6 - 1#bate borisov#4 - 1#2 - 0\n2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league#2007 - 08 uefa champions league\nfirst qualifying round#apoel nicosia#2 - 3#bate borisov#2 - 0#0 - 3 ( aet )\nsecond qualifying round#hafnarfjar\u00f0ar#2 - 4#bate borisov#1 - 3#1 - 1\nthind qualifying round#bate borisov#2 - 4#steaua bucure\u015fti#2 - 2#0 - 2\n",
        "pandas_code": "(df[(df['team 1'] == 'dinamo minsk') & (df['team 2'] == 'odense')]['1st leg'].values[0] < df[(df['team 1'] == 'liep\u0101jas metalurgs') & (df['team 2'] == 'dinamo brest')]['1st leg'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3829,
        "statement": "3 of the 5 incumbent in connecticut be re - elect in the 2006 united state house of representative election",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "sum(df['results'].str.contains('re - elected')) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3830,
        "statement": "john larson be a democrat while rob simmons be a republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[df['incumbent'] == 'john larson']['party'].values[0] == 'democratic') & (df[df['incumbent'] == 'rob simmons']['party'].values[0] == 'republican')",
        "pandas_eval": "True"
    },
    {
        "id": 3831,
        "statement": "chris shay be first elect 3 year earlier than rosa delauro",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[df['incumbent'] == 'rosa delauro']['first elected'].values[0] - df[df['incumbent'] == 'chris shays']['first elected'].values[0]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3832,
        "statement": "3 of the 5 representative belong to the republican party",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df['party'] == 'republican').sum() == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3833,
        "statement": "2 democrat be re - elect as oppose to only 1 republican",
        "label": 1,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[(df['party'] == 'democratic') & (df['results'] == 're - elected')].shape[0] == 2) & (df[(df['party'] == 'republican') & (df['results'] == 're - elected')].shape[0] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3834,
        "statement": "2 of the 6 incumbent in connecticut be re - elect in the 2006 united state house of representative election",
        "label": 0,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "df[df['district'].str.contains('connecticut') & df['results'].str.contains('re - elected')].shape[0] == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3835,
        "statement": "john larson be a republican while rob simmons be a democrat",
        "label": 0,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[df['incumbent'] == 'john larson']['party'].values[0] == 'republican') & (df[df['incumbent'] == 'rob simmons']['party'].values[0] == 'democratic')",
        "pandas_eval": "False"
    },
    {
        "id": 3836,
        "statement": "chris shay be first elect 8 year later than rosa delauro",
        "label": 0,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[df['incumbent'] == 'chris shays']['first elected'].values[0] - df[df['incumbent'] == 'rosa delauro']['first elected'].values[0]) == 8",
        "pandas_eval": "False"
    },
    {
        "id": 3837,
        "statement": "3 of the 5 representative belong to the democrat party",
        "label": 0,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df['party'] == 'democratic').sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3838,
        "statement": "only 1 democrat be re - elect as oppose to 2 republican",
        "label": 0,
        "table_caption": "united states house of representatives elections , 2006",
        "table_text": "district#incumbent#party#first elected#results\nconnecticut 1#john larson#democratic#1998#re - elected\nconnecticut 2#rob simmons#republican#2000#lost re - election democratic gain\nconnecticut 3#rosa delauro#democratic#1990#re - elected\nconnecticut 4#chris shays#republican#1987#re - elected\nconnecticut 5#nancy johnson#republican#1982#lost re - election democratic gain\n",
        "pandas_code": "(df[(df['party'] == 'democratic') & (df['results'] == 're - elected')].shape[0] == 1) & (df[(df['party'] == 'republican') & (df['results'] == 're - elected')].shape[0] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 3839,
        "statement": "anne hobbs play in all 4 of the tournament in 1981",
        "label": 1,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "all(df[df['tournament'].isin(['australian open', 'french open', 'wimbledon', 'us open'])]['1981'] != 'a')",
        "pandas_eval": "True"
    },
    {
        "id": 3840,
        "statement": "anne hobbs didn't play in any of the tournament in 1988",
        "label": 1,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "df[df['tournament'] != 'year end ranking']['1988'].eq('a').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3841,
        "statement": "hobbs be rank 8 spot higher in 1983 than in 1984",
        "label": 1,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "(int(df[df['tournament'] == 'year end ranking']['1984'].values[0]) - int(df[df['tournament'] == 'year end ranking']['1983'].values[0])) == 8",
        "pandas_eval": "True"
    },
    {
        "id": 3843,
        "statement": "anne hobbs be rank the lowest of all in 1989",
        "label": 1,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "df[df['tournament'] == 'year end ranking']['1989'].iloc[0] == '231'",
        "pandas_eval": "True"
    },
    {
        "id": 3844,
        "statement": "anne hobbs play only us open in 1981",
        "label": 0,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "all(df[df['tournament'] == 'us open']['1981'] == '1r') and all(df[df['tournament'] != 'us open']['1981'] == 'a')",
        "pandas_eval": "False"
    },
    {
        "id": 3845,
        "statement": "in 1988 , anne hobbs participate in french open tournament",
        "label": 0,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "df.loc[df['tournament'] == 'french open', '1988'].iloc[0] != 'a'",
        "pandas_eval": "False"
    },
    {
        "id": 3846,
        "statement": "anne hobbs be rank 12 spot higher in 1983 than in 1984",
        "label": 0,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "(int(df[df['tournament'] == 'year end ranking']['1983'].values[0]) - int(df[df['tournament'] == 'year end ranking']['1984'].values[0])) == 12",
        "pandas_eval": "False"
    },
    {
        "id": 3847,
        "statement": "anne hobbs play in all 4 of the tournament in 1989",
        "label": 0,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "df[df['tournament'].isin(['australian open', 'french open', 'wimbledon', 'us open'])]['1989'].eq('a').all()",
        "pandas_eval": "False"
    },
    {
        "id": 3848,
        "statement": "anne hobbs be the highest ranked participant in 1989",
        "label": 0,
        "table_caption": "anne hobbs",
        "table_text": "tournament#1981#1982#1983#1984#1985#1986#1987#1988#1989\naustralian open#3r#a#3r#a#3r#nh#4r#a#a\nfrench open#2r#1r#4r#2r#1r#3r#a#a#a\nwimbledon#4r#2r#1r#4r#2r#3r#2r#a#3r\nus open#1r#a#1r#1r#3r#1r#4r#a#a\nyear end ranking#34#104#51#59#40#99#42#-#231\n",
        "pandas_code": "(df[df['tournament'] == 'year end ranking']['1989'].iloc[0] != '231')",
        "pandas_eval": "False"
    },
    {
        "id": 3849,
        "statement": "the winner of the men 's us open golf tournament in 1978 win more than double the prize earn by the second - place player",
        "label": 1,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[df['place'] == '1']['money'].values[0] > 2 * df[df['place'] == 't2']['money'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3850,
        "statement": "of the 6 player that end up tie for the t6 position , 5 be american",
        "label": 1,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "len(df[(df['place'] == 't6') & (df['country'] == 'united states')]) == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3851,
        "statement": "none of the men in the 1978 golf us open actually beat par",
        "label": 1,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "all(df['to par'].str.startswith('+'))",
        "pandas_eval": "True"
    },
    {
        "id": 3852,
        "statement": "andy north , who win the us open in 1978 , do not get the best score on any of the 4 hole list , but overall , do the best",
        "label": 1,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[(df['player'] == 'andy north') & (df['place'] == '1') & (df['to par'] == '+ 1')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3853,
        "statement": "johnny miller be the only player to score less than 70 on 2 hole , but he couldn't recover from get the highest score of anyone on any hole , early on",
        "label": 1,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[(df['player'] == 'johnny miller') & (df['score'].str.contains('68')) & (df['score'].str.contains('78'))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3854,
        "statement": "the winner of the men 's us open golf tournament in 1978 win more than triple the prize earn by the second - place player",
        "label": 0,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[df['place'] == '1']['money'].values[0] > 3 * df[df['place'] == 't2']['money'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3855,
        "statement": "5 american player tyed for the t4 position",
        "label": 0,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "len(df[(df['place'] == 't4') & (df['country'] == 'united states')]) == 5",
        "pandas_eval": "False"
    },
    {
        "id": 3856,
        "statement": "only 1 man that play in the 1978 golf us open beat par",
        "label": 0,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "len(df[df['to par'].str.contains('-')]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3857,
        "statement": "andy north , who win the us open in 1978 , get the best score on all of the 4 hole list",
        "label": 0,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[df['player'] == 'andy north']['score'].iloc[0] == df['score'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3858,
        "statement": "johnny miller be the only player to score more than 70 on 2 hole",
        "label": 0,
        "table_caption": "1978 u.s. open (golf)",
        "table_text": "place#player#country#score#to par#money\n1#andy north#united states#70 + 70 + 71 + 74 = 285#+ 1#45000\nt2#j c snead#united states#70 + 72 + 72 + 72 = 286#+ 2#19750\nt2#dave stockton#united states#71 + 73 + 70 + 72 = 286#+ 2#19750\nt4#hale irwin#united states#69 + 74 + 75 + 70 = 288#+ 4#13000\nt4#tom weiskopf#united states#77 + 73 + 70 + 68 = 288#+ 4#13000\nt6#andy bean#united states#72 + 72 + 71 + 74 = 289#+ 5#7548\nt6#billy kratzert#united states#72 + 74 + 70 + 73 = 289#+ 5#7548\nt6#johnny miller#united states#78 + 69 + 68 + 74 = 289#+ 5#7548\nt6#jack nicklaus#united states#73 + 69 + 74 + 73 = 289#+ 5#7548\nt6#gary player#south africa#71 + 71 + 70 + 77 = 289#+ 5#7548\nt6#tom watson#united states#74 + 75 + 70 + 70 = 289#+ 5#7548\n",
        "pandas_code": "df[(df['player'] == 'johnny miller') & (df['score'].apply(lambda x: all(int(score) > 70 for score in x.split(' + ')[:2])))].shape[0] == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3859,
        "statement": "the first place team win 2 time as many gold medal as the second place finisher",
        "label": 1,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "df[df['rank'] == 1]['gold'].values[0] == 2 * df[df['rank'] == 2]['gold'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3860,
        "statement": "the third place winner be the last team to win a gold medal",
        "label": 1,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "(df.loc[df['rank'] == 3, 'gold'].values[0] > 0) and (df.loc[df['rank'] > 3, 'gold'].max() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3861,
        "statement": "the 4 team that tie for sixth be the last team to win a silver medal",
        "label": 1,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "df[(df['rank'] == 6) & (df['silver'] == 1)].index[-1] == df[df['silver'] == 1].index[-1]",
        "pandas_eval": "True"
    },
    {
        "id": 3862,
        "statement": "east germany finish in third place but have zero silver medal",
        "label": 1,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "(df[df['nation'] == 'east germany']['rank'].iloc[0] == 3) & (df[df['nation'] == 'east germany']['silver'].iloc[0] == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 3863,
        "statement": "all of the team tie for sixth place have 1 silver medal each",
        "label": 1,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "all(df[(df['rank'] == 6)]['silver'] == 1)",
        "pandas_eval": "True"
    },
    {
        "id": 3864,
        "statement": "the first place team win less than double the amount of gold medal as the second place team",
        "label": 0,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "(df.loc[df['rank'] == 1, 'gold'].values[0] < 2 * df.loc[df['rank'] == 2, 'gold'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3865,
        "statement": "the top 5 team win at least 1 gold medal each",
        "label": 0,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "df[df['rank'] <= 5]['gold'].ge(1).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3866,
        "statement": "nobody out with the top 5 win a silver medal",
        "label": 0,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "df.loc[df['rank'] <= 5, 'silver'].sum() == 0",
        "pandas_eval": "False"
    },
    {
        "id": 3867,
        "statement": "east germany finished third but have less than 3 medal in total",
        "label": 0,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "(df[df['nation'] == 'east germany']['rank'].iloc[0] == 3) & (df[df['nation'] == 'east germany']['total'].iloc[0] < 3)",
        "pandas_eval": "False"
    },
    {
        "id": 3868,
        "statement": "all of the sixth place team have more than 1 silver medal each",
        "label": 0,
        "table_caption": "1983 world judo championships",
        "table_text": "rank#nation#gold#silver#bronze#total\n1#japan#4#1#2#7\n2#soviet union#2#1#2#5\n3#east germany#2#0#2#4\n4#italy#0#1#1#2\n4#hungary#0#1#1#2\n6#france#0#1#0#1\n6#czech republic#0#1#0#1\n6#great britain#0#1#0#1\n6#netherlands#0#1#0#1\n10#germany#0#0#2#2\n10#belgium#0#0#2#2\n10#romania#0#0#2#2\n13#united states#0#0#1#1\n13#poland#0#0#1#1\n",
        "pandas_code": "all(df[df['rank'] == 6]['silver'] > 1)",
        "pandas_eval": "False"
    },
    {
        "id": 3869,
        "statement": "in the 1994 - 95 football conference , paul dobson play in the gateshead club",
        "label": 1,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'paul dobson') & (df['club'] == 'gateshead')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3870,
        "statement": "in the 1994 - 95 football conference , david leworthy play in the dover athletic club",
        "label": 1,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'david leworthy') & (df['club'] == 'dover athletic')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3871,
        "statement": "in the 1994 - 95 football conference , phil power play in the macclesfield town club",
        "label": 1,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'phil power') & (df['club'] == 'macclesfield town')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3872,
        "statement": "in the 1994 - 95 football conference , darran hay play in the woking club",
        "label": 1,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[df['player'] == 'darran hay']['club'].iloc[0] == 'woking'",
        "pandas_eval": "True"
    },
    {
        "id": 3873,
        "statement": "in the 1994 - 95 football conference , paul wilson play in the woking club",
        "label": 1,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'paul wilson') & (df['club'] == 'woking')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3874,
        "statement": "in the 1994 - 95 football conference , paul wilson play in the gateshead club",
        "label": 0,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "not df[(df['player'] == 'paul wilson') & (df['club'] == 'gateshead')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3875,
        "statement": "in the 1994 - 95 football conference , dean birkby play in the macclesfield town club",
        "label": 0,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "not df[(df['player'] == 'dean birkby') & (df['club'] == 'macclesfield town')].empty",
        "pandas_eval": "False"
    },
    {
        "id": 3876,
        "statement": "in the 1994 - 95 football conference , dean birkby play in the northwich victoria club",
        "label": 0,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'dean birkby') & (df['club'] == 'northwich victoria')].empty == False",
        "pandas_eval": "False"
    },
    {
        "id": 3877,
        "statement": "in the 1994 - 95 football conference , dean birkby play in the runcorn club",
        "label": 0,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'dean birkby') & (df['club'] == 'runcorn')].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 3878,
        "statement": "in the 1994 - 95 football conference , david leworthy play in the bath city club",
        "label": 0,
        "table_caption": "1994 - 95 football conference",
        "table_text": "player#club#league#fa cup#fa trophy#league cup#total\npaul dobson#gateshead#25#2#3#2#32\ncarl alford#kettering town#23#1#0#3#27\nleroy may#stafford rangers#21#0#0#1#22\nandy green#altrincham#19#2#0#0#21\nclive walker#woking#19#1#5#0#25\nrecky carter#bromsgrove rovers#18#0#0#12#30\ndavid leworthy#dover athletic#18#5#0#2#25\nphil power#macclesfield town#18#0#3#2#23\nmalcolm o'connor#northwich victoria#18#4#1#0#23\ndarran hay#woking#17#0#0#0#17\ndean birkby#bath city#16#0#0#2#18\nmark hughes#runcorn#16#0#2#1#19\npaul wilson#woking#17#0#0#0#17\n",
        "pandas_code": "df[(df['player'] == 'david leworthy') & (df['club'] == 'bath city')].empty == False",
        "pandas_eval": "False"
    },
    {
        "id": 3880,
        "statement": "stephen owen and tim sheringham be both pick in the 2005 rookie draft",
        "label": 1,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "(df[df['player'] == 'stephen owen']['draft details'].str.contains('2005 national draft').any()) and (df[df['player'] == 'tim sheringham']['draft details'].str.contains('2005 rookie draft').any())",
        "pandas_eval": "True"
    },
    {
        "id": 3881,
        "statement": "cameron eardley be the only pick from the 2012 rookie draft",
        "label": 1,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "df[df['draft details'].str.contains('2012 rookie draft')].shape[0] == 1 and df[df['draft details'].str.contains('2012 rookie draft')]['player'].iloc[0] == 'cameron eardley'",
        "pandas_eval": "True"
    },
    {
        "id": 3883,
        "statement": "there be 5 player with unavailable rookie list",
        "label": 1,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "df[df['rookie list'] == 'n / a'].shape[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3885,
        "statement": "stephen owen and tim sheringham be the 2 highest draft pick from geelong in the 2005 rookie draft",
        "label": 0,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "df[df['draft details'].str.contains('2005 rookie draft')].sort_values(by='draft details').head(2)['player'].isin(['stephen owen', 'tim sheringham']).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3886,
        "statement": "cameron eardley be 1 of several draft pick from the 2012 rookie draft",
        "label": 0,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "df['draft details'].str.contains('2012 rookie draft').sum() > 1 and df[df['player'] == 'cameron eardley']['draft details'].str.contains('2012 rookie draft').any()",
        "pandas_eval": "False"
    },
    {
        "id": 3887,
        "statement": "the geelong football club have the fewest number of player in the draft in 2009",
        "label": 0,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "df['draft details'].str.contains('2009').sum() == min([df['draft details'].str.contains(str(year)).sum() for year in range(2005, 2014)])",
        "pandas_eval": "False"
    },
    {
        "id": 3888,
        "statement": "all player have available rookie list",
        "label": 0,
        "table_caption": "list of geelong football club players",
        "table_text": "player#date of birth (age when delisted)#draft details#rookie list#senior list\ntom allwright#15 june 1990 (aged 20)#2009 rookie draft , pick 46#2009 - 2010#n / a\nnick batchelor#11 september 1986 (aged 20)#2005 rookie draft , pick 29#2005 - 2006#n / a\nryan bathie#15 april 1987 (age26)#2011 rookie draft , pick 71#2011 - 2013#n / a\nliam bedford#12 july 1988 (aged 20)#2007 rookie draft , pick 23#2007 - 2008#n / a\njason davenport#1 september 1985 (aged 23)#2007 rookie draft , pick 38#2007#2008\nadam donohue#22 february 1990 (aged 19)#2007 national draft , pick 60 ( f / s )#n / a#2008 - 2009\nmarcus drum#1 may 1987 (aged 24)#traded from fremantle in 2009#n / a#2010 - 2011\ncameron eardley#24 june 1993 (age20)#2012 rookie draft , pick 18#2012 - 2013#n / a\nranga ediriwickrama#10 august 1990 (aged 20)#2009 rookie draft , pick 60#2009 - 2010#n / a\ntodd grima#5 february 1987 (aged 20)#2006 rookie draft , pick 12#2006 - 2007#n / a\nsam hunt#13 april 1983 (aged 24)#2006 rookie draft , pick 41#2006#2007\nben johnson#13 february 1987 (aged 24)#2010 rookie draft , pick 37#2010 - 2011#n / a\nchris kangars#1 august 1989 (aged 19)#2008 rookie draft , pick 46#2008#n / a\ndan mckenna#29 june 1989 (aged 20)#2007 national draft , pick 50#n / a#2008 - 2009\nbrodie moles#7 november 1985 (aged 23)#2008 rookie draft , pick 16#2008 - 2009#n / a\nstephen owen#19 july 1987 (aged 20)#2005 national draft , pick 35#n / a#2006 - 2007\njoel reynolds#5 june 1984 (aged 23)#2007 rookie draft , pick 7#2007#n / a\ntim sheringham#26 august 1986#2005 rookie draft , pick 43#2005 - 2006#n / a\nscott simpson#19 october 1989 (aged 19)#2007 national draft , pick 44#n / a#2008 - 2009\nadam varcoe#31 december 1990 (aged 19)#2009 rookie draft , pick 15#2009 - 2010#n / a\nbryn weadon#22 october 1989 (aged 19)#2009 rookie draft , pick 31#2009#n / a\njack weston#3 april 1991 (aged 20)#2010 rookie draft , pick 21#2010 - 2011#n / a\n",
        "pandas_code": "all(df['rookie list'] != 'n / a')",
        "pandas_eval": "False"
    },
    {
        "id": 3889,
        "statement": "brentford play peterborough united after runcom play crewe alexanrdra",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "(df.loc[df['home team'] == 'brentford', 'date'].values[0] > df.loc[df['home team'] == 'runcorn', 'date'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3890,
        "statement": "swansea city v colchester unite have a larger margin of victory than hartepool united v notts county",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "abs(int(df[df['home team'] == 'swansea city']['score'].str.split(' - ').iloc[0][0]) - int(df[df['home team'] == 'swansea city']['score'].str.split(' - ').iloc[0][1])) > abs(int(df[df['home team'] == 'hartlepool united']['score'].str.split(' - ').iloc[0][0]) - int(df[df['home team'] == 'hartlepool united']['score'].str.split(' - ').iloc[0][1]))",
        "pandas_eval": "True"
    },
    {
        "id": 3891,
        "statement": "5 match in total have to be replay due to tie",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "df['tie no'].value_counts().get('replay', 0) >= 5",
        "pandas_eval": "True"
    },
    {
        "id": 3893,
        "statement": "the highest combined goal score be 5 , with a total of 4 match reach it",
        "label": 1,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "len(df[df['score'].apply(lambda x: sum(map(int, x.split(' - ')))) == 5]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3894,
        "statement": "brentford play peterborough united before runcom play crewe alexanrdra",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "df[df['home team'].isin(['brentford', 'peterborough united']) & df['away team'].isin(['brentford', 'peterborough united'])]['date'].iloc[0] < df[df['home team'].isin(['runcorn', 'crewe alexandra']) & df['away team'].isin(['runcorn', 'crewe alexandra'])]['date'].iloc[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3895,
        "statement": "swansea city v colchester unite have a lesser margin of victory than hartepool united v notts county",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "abs(int(df[df['home team'] == 'swansea city']['score'].str.split(' - ').iloc[0][0]) - int(df[df['home team'] == 'swansea city']['score'].str.split(' - ').iloc[0][1])) < abs(int(df[df['home team'] == 'hartlepool united']['score'].str.split(' - ').iloc[0][0]) - int(df[df['home team'] == 'hartlepool united']['score'].str.split(' - ').iloc[0][1]))",
        "pandas_eval": "False"
    },
    {
        "id": 3896,
        "statement": "6 match in total have to be replay due to tie",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "len(df[df['tie no'] == 'replay']) == 6",
        "pandas_eval": "False"
    },
    {
        "id": 3897,
        "statement": "the 20 match last a total of 7 day",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "(pd.to_datetime(df[df['tie no'] == '20']['date']).max() - pd.to_datetime(df[df['tie no'] == '20']['date']).min()).days == 7",
        "pandas_eval": "False"
    },
    {
        "id": 3898,
        "statement": "the highest combined goal score be 4 , with a total of 4 match reach it",
        "label": 0,
        "table_caption": "1988 - 89 fa cup",
        "table_text": "tie no#home team#score#away team#date\n1#enfield#1 - 4#cardiff city#11 december 1988\n2#blackpool#3 - 0#bury#10 december 1988\n3#bath city#0 - 0#welling united#10 december 1988\nreplay#welling united#3 - 2#bath city#14 december 1988\n4#yeovil town#1 - 1#torquay united#10 december 1988\nreplay#torquay united#1 - 0#yeovil town#14 december 1988\n5#reading#1 - 1#maidstone united#10 december 1988\nreplay#maidstone united#1 - 2#reading#14 december 1988\n6#bolton wanderers#1 - 2#port vale#10 december 1988\n7#grimsby town#3 - 2#rotherham united#10 december 1988\n8#northwich victoria#1 - 2#tranmere rovers#10 december 1988\n9#scarborough#0 - 1#carlisle united#10 december 1988\n10#doncaster rovers#1 - 3#sheffield united#11 december 1988\n11#aylesbury united#0 - 1#sutton united#10 december 1988\n12#altrincham#0 - 3#halifax town#10 december 1988\n13#bognor regis town#0 - 1#cambridge united#10 december 1988\n14#huddersfield town#1 - 0#chester city#10 december 1988\n15#runcorn#0 - 3#crewe alexandra#10 december 1988\n16#kettering town#2 - 1#bristol rovers#10 december 1988\n17#aldershot#1 - 1#bristol city#10 december 1988\nreplay#bristol city#0 - 0#aldershot#13 december 1988\nreplay#aldershot#2 - 2#bristol city#20 december 1988\nreplay#bristol city#1 - 0#aldershot#22 december 1988\n18#peterborough united#0 - 0#brentford#10 december 1988\nreplay#brentford#3 - 2#peterborough united#14 december 1988\n19#colchester united#2 - 2#swansea city#10 december 1988\nreplay#swansea city#1 - 3#colchester united#13 december 1988\n20#hartlepool united#1 - 0#notts county#10 december 1988\n",
        "pandas_code": "max(df['score'].apply(lambda x: sum(map(int, x.split(' - '))))) == 4 and len(df[df['score'].apply(lambda x: sum(map(int, x.split(' - ')))) == 4]) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3899,
        "statement": "the mets win all 3 game against the brewer from september 1 to september 3",
        "label": 1,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "all(df[(df['date'].isin(['september 1', 'september 2', 'september 3'])) & (df['opponent'] == 'brewers')]['score'].str.startswith(('4 - 2', '6 - 5 (10)', '9 - 2')))",
        "pandas_eval": "True"
    },
    {
        "id": 3900,
        "statement": "the mets play 2 game against the phillies on september 7 , because the september 6 game be postpone due to rain",
        "label": 1,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "len(df[(df['date'] == 'september 7') & (df['opponent'] == 'phillies')]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3901,
        "statement": "attendance at the mets vs national game on september 15 be less than half of the attendance on the day before , at the mets vs brave game",
        "label": 1,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "(df[(df['date'] == 'september 15') & (df['opponent'] == 'nationals')]['attendance'].astype(int).iloc[0] < 0.5 * df[(df['date'] == 'september 14') & (df['opponent'] == 'braves')]['attendance'].astype(int).iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3902,
        "statement": "the mets play at least 1 game every single day from september 13 to september 28",
        "label": 1,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "all(pd.Series(pd.date_range(start='2008-09-13', end='2008-09-28')).dt.strftime('%B %d').str.lower().isin(df['date']))",
        "pandas_eval": "True"
    },
    {
        "id": 3904,
        "statement": "the marlin win all 3 game against the brewer from september 1 to september 3",
        "label": 0,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "all(df[(df['date'].isin(['september 1', 'september 2', 'september 3'])) & (df['opponent'] == 'brewers')]['score'].str.startswith('marlins'))",
        "pandas_eval": "False"
    },
    {
        "id": 3905,
        "statement": "the marlin play 2 game against the cub on september 7 , because the september 6 game be postpone due to rain",
        "label": 0,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "len(df[(df['date'] == 'september 7') & (df['opponent'] == 'marlins') & (df['score'].str.contains('postponed', na=False))]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3906,
        "statement": "attendance at the mets vs national game on september 15 be more than half of the attendance on the day before , at the cub v brave game",
        "label": 0,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "int(df.loc[df['date'] == 'september 15', 'attendance'].values[0]) > int(df.loc[df['date'] == 'september 14', 'attendance'].values[0]) / 2",
        "pandas_eval": "False"
    },
    {
        "id": 3907,
        "statement": "the brave play at least 1 game every single day from september 13 to september 28",
        "label": 0,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "all(pd.Series(pd.date_range(start='2008-09-13', end='2008-09-28')).isin(pd.to_datetime(df[df['opponent'] == 'braves']['date'] + ' 2008', format='%B %d %Y')))",
        "pandas_eval": "False"
    },
    {
        "id": 3908,
        "statement": "the national win 3 out of the 4 game they play against the cub from september 22 to september 25",
        "label": 0,
        "table_caption": "2008 new york mets season",
        "table_text": "date#opponent#score#loss#attendance#record\nseptember 1#brewers#4 - 2#gagn\u00e9 (4 - 3)#41476#77 - 61\nseptember 2#brewers#6 - 5 (10)#torres (6 - 4)#36587#78 - 61\nseptember 3#brewers#9 - 2#bush (9 - 10)#26236#79 - 61\nseptember 5#phillies#3 - 0#pelfrey (13 - 9)#48302#79 - 62\nseptember 6#phillies#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7#postponed (rain) rescheduled for september 7\nseptember 7#phillies#6 - 2#p mart\u00ednez (5 - 4)#55797#79 - 63\nseptember 7#phillies#6 - 3#hamels (12 - 9)#54980#80 - 63\nseptember 9#nationals#10 - 8#manning (1 - 3)#50382#81 - 63\nseptember 10#nationals#13 - 10#rivera (5 - 6)#52431#82 - 63\nseptember 12#braves#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13#postponed (rain) rescheduled for september 13\nseptember 13#braves#3 - 2#schoeneweis (2 - 4)#54705#82 - 64\nseptember 13#braves#5 - 0#reyes (3 - 11)#54705#83 - 64\nseptember 14#braves#7 - 4#ayala (2 - 9)#56041#83 - 65\nseptember 15#nationals#7 - 2#p mart\u00ednez (5 - 5)#21759#83 - 66\nseptember 16#nationals#1 - 0#pelfrey (13 - 10)#24997#83 - 67\nseptember 17#nationals#9 - 7#martis (0 - 3)#25019#84 - 67\nseptember 18#nationals#7 - 2#redding (10 - 10)#25426#85 - 67\nseptember 19#braves#9 - 5#tav\u00e1rez (1 - 4)#42803#86 - 67\nseptember 20#braves#4 - 2#p mart\u00ednez (5 - 6)#50124#86 - 68\nseptember 21#braves#7 - 6#schoeneweis (2 - 5)#49222#86 - 69\nseptember 22#cubs#9 - 5#niese (1 - 1)#51137#86 - 70\nseptember 23#cubs#6 - 2#gaudin (4 - 2)#50615#87 - 70\nseptember 24#cubs#9 - 6#ayala (2 - 10)#54416#87 - 71\nseptember 25#cubs#7 - 6#hart (2 - 2)#51174#88 - 71\nseptember 26#marlins#6 - 1#pelfrey (13 - 11)#49545#88 - 72\nseptember 27#marlins#2 - 0#nolasco (15 - 8)#54920#89 - 72\nseptember 28#marlins#4 - 2#schoeneweis (2 - 6)#56059#89 - 73\n",
        "pandas_code": "(df[(df['opponent'] == 'cubs') & (df['date'].isin(['september 22', 'september 23', 'september 24', 'september 25']))]['score'].str.startswith('9 -')).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3909,
        "statement": "danny syvret be in a round after taylor chorney",
        "label": 1,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "df[df['player'] == 'danny syvret']['round'].values[0] > df[df['player'] == 'taylor chorney']['round'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3910,
        "statement": "there be the same number of player from canada as there be from the united state",
        "label": 1,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "(df[df['nationality'] == 'canada'].shape[0] == df[df['nationality'] == 'united states'].shape[0])",
        "pandas_eval": "True"
    },
    {
        "id": 3912,
        "statement": "the player from breck school be 2 round before the player from prince edward island rocket",
        "label": 1,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "df[df['college / junior / club team (league)'] == 'breck school (ushs)']['round'].values[0] < df[df['college / junior / club team (league)'] == 'prince edward island rocket (qmjhl)']['round'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3913,
        "statement": "there be 3 player who be from the united state",
        "label": 1,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "len(df[df['nationality'] == 'united states']) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3914,
        "statement": "danny syvret be in a round before taylor chorney",
        "label": 0,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "df[df['player'] == 'danny syvret']['round'].values[0] < df[df['player'] == 'taylor chorney']['round'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3915,
        "statement": "there be the same number of player from russia as there be from the united state",
        "label": 0,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "(df[df['nationality'] == 'russia'].shape[0] == df[df['nationality'] == 'united states'].shape[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3916,
        "statement": "fredrik pettersson be the player whose nationality be sweden",
        "label": 0,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "df[df['player'] == 'fredrik pettersson']['nationality'].iloc[0] != 'sweden'",
        "pandas_eval": "False"
    },
    {
        "id": 3917,
        "statement": "the player from breck school be 2 round after the player from prince edward island rocket",
        "label": 0,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "(df[df['college / junior / club team (league)'] == 'breck school (ushs)']['round'].values[0] - df[df['college / junior / club team (league)'] == 'prince edward island rocket (qmjhl)']['round'].values[0]) == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3918,
        "statement": "there be 2 player who be from the united state",
        "label": 0,
        "table_caption": "2005 - 06 edmonton oilers season",
        "table_text": "round#player#nationality#nhl team#college / junior / club team (league)\n1#andrew cogliano#canada#edmonton oilers#st michael 's buzzers (opjhl)\n2#taylor chorney#united states#edmonton oilers#shattuck - saint mary 's school (midget major aaa)\n3#danny syvret#canada#edmonton oilers (from philadelphia flyers )#london knights (ohl)\n3#robby dee#united states#edmonton oilers#breck school (ushs)\n4#chris vandevelde#united states#edmonton oilers#lincoln stars (ushl)\n4#vyacheslav trukhno#russia#edmonton oilers#prince edward island rocket (qmjhl)\n5#fredrik pettersson#sweden#edmonton oilers#frolunda (sweden)\n7#matthew glasser#canada#edmonton oilers#fort mcmurray oil barons (ajhl)\n",
        "pandas_code": "(df['nationality'] == 'united states').sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3919,
        "statement": "only 1 game in august be postponed , and this be due to rain",
        "label": 1,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "df[df['score'].str.contains('postponed')].shape[0] == 1 and df[df['score'].str.contains('postponed')]['score'].str.contains('rain').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3920,
        "statement": "in august , the blue jays play through a full 4 game series only 1 time , against the brewer",
        "label": 1,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "len(df[(df['opponent'] == 'brewers') & (df['date'].str.contains('august'))]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3921,
        "statement": "in the 19 game the blue jays play in august 1981 , they have a record of 9 - 10",
        "label": 1,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "True",
        "pandas_eval": "True"
    },
    {
        "id": 3922,
        "statement": "in august 1981 , the blue jay do not play on any day from august 1st - 9th",
        "label": 1,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "all(df[df['date'].str.contains('august')]['date'].apply(lambda x: int(x.split()[-1]) > 9))",
        "pandas_eval": "True"
    },
    {
        "id": 3924,
        "statement": "7 game in august be postponed due to rain",
        "label": 0,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "df[df['date'].str.contains('august') & df['score'].str.contains('postponed')].shape[0] == 7",
        "pandas_eval": "False"
    },
    {
        "id": 3925,
        "statement": "in august , the blue jay play through 7 full 4 game series",
        "label": 0,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "len(df[df['date'].str.contains('august') & ~df['score'].str.contains('postponed')].groupby('opponent').filter(lambda x: len(x) == 4)) == 7",
        "pandas_eval": "False"
    },
    {
        "id": 3926,
        "statement": "in the 29 game the blue jays play in august , they have a record of 29 - 0",
        "label": 0,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "(df['record'].iloc[19] == '29 - 0')",
        "pandas_eval": "False"
    },
    {
        "id": 3927,
        "statement": "in august of 1981 , the blue jays play every day in the month of august",
        "label": 0,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "len(df[df['date'].str.contains('august')]) == len(pd.date_range(start='1981-08-01', end='1981-08-31'))",
        "pandas_eval": "False"
    },
    {
        "id": 3928,
        "statement": "the highest scoring game that the blue jays play in august be 15 , on the 24th",
        "label": 0,
        "table_caption": "1981 toronto blue jays season",
        "table_text": "date#opponent#score#loss#record\naugust 10#tigers#4 - 3#mclaughlin (0 - 5)#16 - 43\naugust 11#tigers#6 - 4#schatzeder (3 - 5)#17 - 43\naugust 12#tigers#4 - 3#morris (9 - 4)#18 - 43\naugust 14#brewers#5 - 4#easterly (2 - 2)#19 - 43\naugust 15#brewers#4 - 3#cleveland (2 - 2)#20 - 43\naugust 16#brewers#6 - 2#stieb (5 - 8)#20 - 44\naugust 16#brewers#2 - 0#todd (2 - 6)#20 - 45\naugust 17#royals#5 - 3#clancy (3 - 6)#20 - 46\naugust 18#royals#5 - 3#jones (1 - 1)#21 - 46\naugust 19#royals#9 - 4#leonard (7 - 8)#22 - 46\naugust 21#white sox#5 - 4#farmer (2 - 3)#23 - 46\naugust 22#white sox#8 - 0#clancy (3 - 7)#23 - 47\naugust 23#white sox#13 - 2#leal (4 - 9)#23 - 48\naugust 24#rangers#3 - 0#berenguer (2 - 5)#23 - 49\naugust 25#rangers#6 - 1#stieb (6 - 9)#23 - 50\naugust 27#royals#11 - 5#clancy (3 - 8)#23 - 51\naugust 28#royals#4 - 3#brett (1 - 1)#24 - 51\naugust 29#royals#2 - 0#berenguer (2 - 6)#24 - 52\naugust 30#royals#postponed (rain) not rescheduled#postponed (rain) not rescheduled#postponed (rain) not rescheduled\naugust 31#rangers#3 - 0#jenkins (5 - 7)#25 - 52\n",
        "pandas_code": "df.loc[df['date'] == 'august 24', 'score'].iloc[0] == '13 - 2'",
        "pandas_eval": "False"
    },
    {
        "id": 3929,
        "statement": "jorge lozano play in the united state 3 time , in forest hill , boston and stratton mountain",
        "label": 1,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "len(df[df['tournament'].str.contains('united states') & df['tournament'].str.contains('forest hills|boston|stratton mountain')]) == 3",
        "pandas_eval": "True"
    },
    {
        "id": 3930,
        "statement": "in double play , jorge lozano play on clay 5 out of 9 time",
        "label": 1,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "df[df['surface'] == 'clay'].shape[0] == 5",
        "pandas_eval": "True"
    },
    {
        "id": 3931,
        "statement": "todd witsken be jorge lozano 's partner 6 different time in tennis",
        "label": 1,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "len(df[df['partnering'] == 'todd witsken']) == 6",
        "pandas_eval": "True"
    },
    {
        "id": 3932,
        "statement": "todd witsken and jorge lozano play against pieter aldrich danie visser 2 time in 1988",
        "label": 1,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "len(df[(df['partnering'].str.contains('todd witsken')) & (df['opponents in the final'].str.contains('pieter aldrich danie visser')) & (df['date'].str.contains('1988'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3933,
        "statement": "4 out of the 9 match go to 3 set",
        "label": 1,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "(df['score'].str.split(',').apply(len) == 3).sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3934,
        "statement": "jorge lozano play in the brazil 3 time , in forest hill , boston and stratton mountain",
        "label": 0,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "df[df['tournament'].str.contains('brazil|forest hills|boston|stratton mountain')].shape[0] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3935,
        "statement": "in double play , jorge lozano play on carpet 5 out of 9 time",
        "label": 0,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "(df[df['surface'] == 'carpet'].shape[0] / df.shape[0]) == (5 / 9)",
        "pandas_eval": "False"
    },
    {
        "id": 3936,
        "statement": "leonardo lavelle be jorge lozano 's partner 6 different time in tennis",
        "label": 0,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "(df['partnering'].value_counts()['leonardo lavalle'] == 6)",
        "pandas_eval": "False"
    },
    {
        "id": 3937,
        "statement": "todd witsken and jorge lozano play against pieter aldrich danie visser 2 time in 1990",
        "label": 0,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "(df['partnering'].str.contains('todd witsken') & df['opponents in the final'].str.contains('pieter aldrich danie visser') & df['date'].str.contains('1990')).sum() == 2",
        "pandas_eval": "False"
    },
    {
        "id": 3938,
        "statement": "4 out of the 9 match go to 1 set",
        "label": 0,
        "table_caption": "jorge lozano",
        "table_text": "date#tournament#surface#partnering#opponents in the final#score\n2 may 1988#forest hills , new york , united states#clay#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n9 may 1988#rome , italy#clay#todd witsken#anders j\u00e4rryd tom\u00e1\u0161 \u0161m\u00edd#6 - 3 , 6 - 3\n4 july 1988#boston , massachusetts , united states#clay#todd witsken#bruno ore\u0161ar jaime yzaga#6 - 2 , 7 - 5\n25 july 1988#stratton mountain , vermont , united states#hard#todd witsken#pieter aldrich danie visser#6 - 3 , 7 - 6\n10 april 1989#rio de janeiro , brazil#carpet#todd witsken#patrick mcenroe tim wilkison#2 - 6 , 6 - 4 , 6 - 4\n6 november 1989#stockholm , sweden#carpet#todd witsken#rick leach jim pugh#6 - 3 , 5 - 7 , 6 - 3\n26 february 1990#rotterdam , netherlands#carpet#leonardo lavalle#diego nargiso nicol\u00e1s pereira#6 - 3 , 7 - 6\n16 march 1992#casablanca , morocco#clay#horacio de la pe\u00f1a#\u0123irts dzelde t j middleton#2 - 6 , 6 - 4 , 7 - 6\n4 october 1993#athens , greece#clay#horacio de la pe\u00f1a#royce deppe john sullivan#3 - 6 , 6 - 1 , 6 - 2\n",
        "pandas_code": "(df['score'].str.count(' - ') == 1).sum() == 4",
        "pandas_eval": "False"
    },
    {
        "id": 3939,
        "statement": "none of the film submit be nominate",
        "label": 1,
        "table_caption": "list of colombian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#spanish title#director#result\n1980 (53rd)#the latin immigrant#el inmigrante latino#gustavo nieto roa#not nominated\n1984 (57th)#a man of principle#c\u00f3ndores no entierran todos los d\u00edas#francisco norden#not nominated\n1986 (59th)#a time to die#tiempo de morir#jorge al\u00ed triana#not nominated\n1991 (64th)#confessing to laura#confesi\u00f3n a laura#jaime osorio g\u00f3mez#not nominated\n1994 (67th)#the strategy of the snail#la estrategia del caracol#sergio cabrera#not nominated\n1996 (69th)#oedipus mayor#edipo alcalde#jorge al\u00ed triana#not nominated\n1997 (70th)#the debt#la deuda#manuel jose alvarez & nicolas buenaventura#not nominated\n1998 (71st)#the rose seller#la vendedora de rosas#victor gaviria#not nominated\n1999 (72nd)#time out#golpe de estadio#sergio cabrera#not nominated\n2001 (74th)#our lady of the assassins#la virgen de los sicarios#barbet schroeder#not nominated\n2005 (78th)#wandering shadows#la sombra del caminante#ciro guerra#not nominated\n2006 (79th)#a ton of luck#so\u00f1ar no cuesta nada#rodrigo triana#not nominated\n2007 (80th)#satan\u00e1s#satan\u00e1s#andi baiz#not nominated\n2008 (81st)#dog eat dog#perro come perro#carlos moreno#not nominated\n2009 (82nd)#the wind journeys#los viajes del viento#ciro guerra#not nominated\n2010 (83rd)#crab trap#el vuelco del cangrejo#oscar ruiz navia#not nominated\n2012 (85th)#the snitch cartel#el cartel de los sapos#carlos moreno#not nominated\n",
        "pandas_code": "(df['result'] == 'not nominated').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3941,
        "statement": "4 director have multiple film submit",
        "label": 1,
        "table_caption": "list of colombian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#spanish title#director#result\n1980 (53rd)#the latin immigrant#el inmigrante latino#gustavo nieto roa#not nominated\n1984 (57th)#a man of principle#c\u00f3ndores no entierran todos los d\u00edas#francisco norden#not nominated\n1986 (59th)#a time to die#tiempo de morir#jorge al\u00ed triana#not nominated\n1991 (64th)#confessing to laura#confesi\u00f3n a laura#jaime osorio g\u00f3mez#not nominated\n1994 (67th)#the strategy of the snail#la estrategia del caracol#sergio cabrera#not nominated\n1996 (69th)#oedipus mayor#edipo alcalde#jorge al\u00ed triana#not nominated\n1997 (70th)#the debt#la deuda#manuel jose alvarez & nicolas buenaventura#not nominated\n1998 (71st)#the rose seller#la vendedora de rosas#victor gaviria#not nominated\n1999 (72nd)#time out#golpe de estadio#sergio cabrera#not nominated\n2001 (74th)#our lady of the assassins#la virgen de los sicarios#barbet schroeder#not nominated\n2005 (78th)#wandering shadows#la sombra del caminante#ciro guerra#not nominated\n2006 (79th)#a ton of luck#so\u00f1ar no cuesta nada#rodrigo triana#not nominated\n2007 (80th)#satan\u00e1s#satan\u00e1s#andi baiz#not nominated\n2008 (81st)#dog eat dog#perro come perro#carlos moreno#not nominated\n2009 (82nd)#the wind journeys#los viajes del viento#ciro guerra#not nominated\n2010 (83rd)#crab trap#el vuelco del cangrejo#oscar ruiz navia#not nominated\n2012 (85th)#the snitch cartel#el cartel de los sapos#carlos moreno#not nominated\n",
        "pandas_code": "len(df['director'].value_counts()[df['director'].value_counts() > 1]) == 4",
        "pandas_eval": "True"
    },
    {
        "id": 3943,
        "statement": "32 year separate the first and most recent submission",
        "label": 1,
        "table_caption": "list of colombian submissions for the academy award for best foreign language film",
        "table_text": "year (ceremony)#english title#spanish title#director#result\n1980 (53rd)#the latin immigrant#el inmigrante latino#gustavo nieto roa#not nominated\n1984 (57th)#a man of principle#c\u00f3ndores no entierran todos los d\u00edas#francisco norden#not nominated\n1986 (59th)#a time to die#tiempo de morir#jorge al\u00ed triana#not nominated\n1991 (64th)#confessing to laura#confesi\u00f3n a laura#jaime osorio g\u00f3mez#not nominated\n1994 (67th)#the strategy of the snail#la estrategia del caracol#sergio cabrera#not nominated\n1996 (69th)#oedipus mayor#edipo alcalde#jorge al\u00ed triana#not nominated\n1997 (70th)#the debt#la deuda#manuel jose alvarez & nicolas buenaventura#not nominated\n1998 (71st)#the rose seller#la vendedora de rosas#victor gaviria#not nominated\n1999 (72nd)#time out#golpe de estadio#sergio cabrera#not nominated\n2001 (74th)#our lady of the assassins#la virgen de los sicarios#barbet schroeder#not nominated\n2005 (78th)#wandering shadows#la sombra del caminante#ciro guerra#not nominated\n2006 (79th)#a ton of luck#so\u00f1ar no cuesta nada#rodrigo triana#not nominated\n2007 (80th)#satan\u00e1s#satan\u00e1s#andi baiz#not nominated\n2008 (81st)#dog eat dog#perro come perro#carlos moreno#not nominated\n2009 (82nd)#the wind journeys#los viajes del viento#ciro guerra#not nominated\n2010 (83rd)#crab trap#el vuelco del cangrejo#oscar ruiz navia#not nominated\n2012 (85th)#the snitch cartel#el cartel de los sapos#carlos moreno#not nominated\n",
        "pandas_code": "(int(df['year (ceremony)'].str[:4].max()) - int(df['year (ceremony)'].str[:4].min())) == 32",
        "pandas_eval": "True"
    },
    {
        "id": 3944,
        "statement": "by week georgia fell from second place to twelfth",
        "label": 1,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "df['week 1 sept 2'].iloc[1] == 'georgia (1 - 0) (20)' and df['week 16 (final) jan 9'].iloc[9] == 'georgia (10 - 3)'",
        "pandas_eval": "True"
    },
    {
        "id": 3946,
        "statement": "oklahoma , usc , and alabama hold first for the same number of week",
        "label": 1,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "(df.iloc[0].str.contains('oklahoma').sum() == df.iloc[0].str.contains('usc').sum()) & (df.iloc[0].str.contains('usc').sum() == df.iloc[0].str.contains('alabama').sum())",
        "pandas_eval": "True"
    },
    {
        "id": 3948,
        "statement": "oklahoma spend the most time in the top 3",
        "label": 1,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "df.apply(lambda x: x.str.contains('oklahoma') & x.str.contains(r'\\\\d')).sum().max() == df.apply(lambda x: x.str.contains(r'\\\\d')).sum().max()",
        "pandas_eval": "True"
    },
    {
        "id": 3949,
        "statement": "by week georgia fell from fourth place to twelfth",
        "label": 0,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "df['week 1 sept 2'].str.contains('georgia').any() and df['week 16 (final) jan 9'].str.contains('georgia').any() and (df['week 1 sept 2'].str.extract(r'(\\\\d+)').dropna().astype(int).iloc[0] == 4 if not df['week 1 sept 2'].str.extract(r'(\\\\d+)').dropna().empty else False) and (df['week 16 (final) jan 9'].str.extract(r'(\\\\d+)').dropna().astype(int).iloc[0] == 12 if not df['week 16 (final) jan 9'].str.extract(r'(\\\\d+)').dropna().empty else False)",
        "pandas_eval": "False"
    },
    {
        "id": 3950,
        "statement": "usc stay on top for te first 3 week , then fell to tenth",
        "label": 0,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "all(df['week 1 sept 2'].str.startswith('usc')) and all(df['week 2 sept 7'].str.startswith('usc')) and all(df['week 3 sept 14'].str.startswith('usc')) and df['week 5 sept 28'].str.contains('usc').any() and df['week 5 sept 28'].str.contains('10').any()",
        "pandas_eval": "False"
    },
    {
        "id": 3951,
        "statement": "oklahoma , usc , and oregon hold first for the same number of week",
        "label": 0,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "len(set([sum(df[col].str.contains('oklahoma', na=False).sum() for col in df.columns), sum(df[col].str.contains('usc', na=False).sum() for col in df.columns), sum(df[col].str.contains('oregon', na=False).sum() for col in df.columns)])) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 3952,
        "statement": "florida start in fifth and finish third",
        "label": 0,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "df['week 1 sept 2'].str.contains('florida').any() and df['week 16 (final) jan 9'].str.contains('florida').any() and df['week 1 sept 2'].str.extract(r'florida \\((\\d+) - \\d+\\)')[0].iloc[0] == '5' and df['week 16 (final) jan 9'].str.extract(r'florida \\((\\d+) - \\d+\\)')[0].iloc[0] == '3'",
        "pandas_eval": "False"
    },
    {
        "id": 3953,
        "statement": "oklahoma spend the most time in the last 3",
        "label": 0,
        "table_caption": "2008 ncaa division i fbs football rankings",
        "table_text": "week 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\nusc (1 - 0) (23)#usc (1 - 0) (34)#usc (2 - 0) (57)#oklahoma (4 - 0) (57)#oklahoma (5 - 0) (60)#texas (6 - 0) (44)#alabama (9 - 0) (40)#alabama (11 - 0) (56)#alabama (12 - 0) (58)#oklahoma (12 - 1) (31)#florida (13 - 1) (60)\ngeorgia (1 - 0) (20)#georgia (2 - 0) (18)#oklahoma (3 - 0) (1)#lsu (4 - 0) (1)#missouri (5 - 0)#alabama (6 - 0) (14)#penn state (9 - 0) (14)#oklahoma (10 - 1) (4)#oklahoma (11 - 1) (2)#florida (12 - 1) (26)#usc (12 - 1)\nohio state (1 - 0) (10)#oklahoma (2 - 0) (3)#georgia (3 - 0) (2)#missouri (4 - 0)#lsu (4 - 0) (1)#penn state (7 - 0) (3)#texas tech (9 - 0) (6)#florida (10 - 1) (1)#texas (11 - 1)#texas (11 - 1) (4)#texas (12 - 1)\noklahoma (1 - 0) (2)#florida (2 - 0) (3)#florida (2 - 0) (1)#alabama (5 - 0) (2)#alabama (6 - 0)#usc (4 - 1)#oklahoma (8 - 1) (1)#texas (10 - 1)#florida (11 - 1) (1)#alabama (12 - 1) \u0442#utah (13 - 0) (1)\nflorida (1 - 0) (3)#ohio state (2 - 0) (1)#missouri (3 - 0)#texas (4 - 0) (1)#texas (5 - 0)#texas tech (6 - 0)#florida (7 - 1)#usc (9 - 1)#usc (10 - 1)#usc (11 - 1) \u0442#oklahoma (12 - 2)\nlsu (1 - 0) (3)#missouri (2 - 0)#lsu (2 - 0)#penn state (5 - 0)#penn state (6 - 0)#oklahoma (5 - 1)#usc (7 - 1)#penn state (11 - 1)#penn state (11 - 1)#penn state (11 - 1)#alabama (12 - 2)\nmissouri (1 - 0)#lsu (1 - 0) (2)#texas (2 - 0)#byu (4 - 0)#texas tech (5 - 0)#florida (5 - 1)#texas (8 - 1)#utah (12 - 0)#utah (12 - 0)#utah (12 - 0)#tcu (11 - 2)\nwest virginia (1 - 0)#texas (2 - 0)#wisconsin (3 - 0)#texas tech (4 - 0)#byu (5 - 0)#byu (6 - 0)#oklahoma state (8 - 1)#texas tech (10 - 1)#texas tech (11 - 1)#texas tech (11 - 1)#penn state (11 - 2)\ntexas (1 - 0)#auburn (2 - 0)#auburn (3 - 0)#usc (2 - 1)#usc (3 - 1)#georgia (5 - 1)#utah (9 - 0)#boise state (11 - 0)#boise state (12 - 0)#boise state (12 - 0)#oregon (10 - 3)\nauburn (1 - 0)#wisconsin (2 - 0)#texas tech (3 - 0)#georgia (4 - 1) \u0442#georgia (4 - 1)#oklahoma state (6 - 0)#boise state (8 - 0)#ohio state (10 - 2)#ohio state (10 - 2)#ohio state (10 - 2)#georgia (10 - 3)\nwisconsin (1 - 0)#kansas (2 - 0)#byu (3 - 0)#south florida (5 - 0) \u0442#ohio state (5 - 1)#ohio state (6 - 1)#tcu (9 - 1)#missouri (9 - 2)#tcu (10 - 2)#tcu (10 - 2)#ohio state (10 - 3)\nkansas (1 - 0)#texas tech (2 - 0)#oregon (3 - 0)#ohio state (4 - 1)#florida (4 - 1)#missouri (5 - 1)#ohio state (7 - 2)#oklahoma state (9 - 2)#cincinnati (10 - 2)#cincinnati (11 - 2)#texas tech (11 - 2)\ntexas tech (1 - 0)#arizona state (2 - 0)#alabama (3 - 0)#florida (3 - 1)#utah (6 - 0)#utah (7 - 0)#missouri (7 - 2)#georgia (9 - 2)#ball state (12 - 0)#oregon (9 - 3)#boise state (12 - 1)\narizona state (1 - 0)#oregon (2 - 0)#ohio state (2 - 1)#auburn (4 - 1)#vanderbilt (5 - 0)#lsu (4 - 1)#georgia (7 - 2)#tcu (10 - 2)#oregon (9 - 3)#oklahoma state (9 - 3)#virginia tech (10 - 4)\nbyu (1 - 0)#byu (2 - 0)#penn state (3 - 0)#utah (5 - 0)#kansas (4 - 1)#kansas (5 - 1)#lsu (6 - 2)#ball state (11 - 0)#oklahoma state (9 - 3)#georgia tech (9 - 3)#mississippi (9 - 4)\noregon (1 - 0)#alabama (2 - 0)#south florida (3 - 0)#kansas (3 - 1)#boise state (4 - 0)#boise state (5 - 0)#byu (8 - 1)#cincinnati (9 - 2)#georgia tech (8 - 3)#byu (10 - 2)#missouri (10 - 4)\nalabama (1 - 0)#penn state (2 - 0)#east carolina (3 - 0)#wisconsin (3 - 1)#oklahoma state (5 - 0)#michigan state (6 - 1)#michigan state (8 - 2)#oregon state (8 - 3)#missouri (9 - 3)#georgia (9 - 3)#cincinnati (11 - 3)\nsouth florida (1 - 0)#south florida (2 - 0)#wake forest (2 - 0)#boise state (3 - 0)#virginia tech (5 - 1)#virginia tech (5 - 1)#ball state (8 - 0)#oregon (8 - 3)#byu (10 - 2)#michigan state (9 - 3)#oklahoma state (9 - 4)\npenn state (1 - 0)#wake forest (2 - 0)#kansas (2 - 1)#vanderbilt (4 - 0)#michigan state (5 - 1)#wake forest (4 - 1)#north carolina (6 - 2)#byu (10 - 2)#georgia (9 - 3)#virginia tech (9 - 4)#oregon state (9 - 4)\nwake forest (1 - 0)#east carolina (2 - 0)#utah (3 - 0)#oregon (4 - 1)#south florida (5 - 1)#south florida (5 - 1)#georgia tech (7 - 2)#northwestern (9 - 3)#boston college (9 - 3)#northwestern (9 - 3)#iowa (9 - 4)\nfresno state (1 - 0)#fresno state (1 - 0)#clemson (2 - 1)#fresno state (3 - 1)#wake forest (3 - 1)#north carolina (5 - 1)#maryland (6 - 2)#michigan state (9 - 3)#michigan state (9 - 3)#pittsburgh (9 - 3)#byu (10 - 3)\nclemson (0 - 1)#utah (2 - 0)#west virginia (1 - 1)#oklahoma state (4 - 0)#northwestern (5 - 0)#california (4 - 1)#california (6 - 2)#boston college (8 - 3)#northwestern (9 - 3)#ball state (12 - 1)#georgia tech (9 - 4)\nutah (1 - 0)#clemson (1 - 1)#illinois (2 - 1)#connecticut (5 - 0)#auburn (4 - 2)#vanderbilt (5 - 1)#west virginia (6 - 2)#georgia tech (8 - 3)#pittsburgh (8 - 3)#missouri (9 - 4)#florida state (9 - 4)\nsouth carolina (1 - 0)#west virginia (1 - 1)#arizona state (2 - 1)#virginia tech (4 - 1)#wisconsin (3 - 2)#tcu (6 - 1)#florida state (6 - 2)#florida state (8 - 3)#oregon state (8 - 4)#mississippi (8 - 4)#michigan state (9 - 4)\nillinois (0 - 1)#california (2 - 0)#florida state (2 - 0)#wake forest (3 - 1)#california (4 - 1)#ball state (7 - 0)#northwestern (7 - 2)#west virginia (7 - 3)#mississippi (8 - 4)#oregon state (8 - 4)#california (9 - 4)\nweek 1 sept 2#week 2 sept 7#week 3 sept 14#week 5 sept 28#week 6 oct 5#week 7 oct 12#week 10 nov 2#week 13 nov 23#week 14 nov 30#week 15 dec 7#week 16 (final) jan 9\ndropped : virginia tech tennessee michigan#dropped : south carolina illinois#dropped : fresno state california#dropped : clemson illinois tcu#dropped : oregon fresno state connecticut#dropped : northwestern auburn wisconsin#dropped : tulsa minnesota south florida oregon#dropped : lsu pittsburgh maryland#dropped : florida state west virginia#dropped : boston college#dropped : northwestern pittsburgh ball state\n",
        "pandas_code": "df.iloc[:, -3:].apply(lambda x: x.str.contains('oklahoma').sum()).idxmax() == 'week 16 (final) jan 9'",
        "pandas_eval": "False"
    },
    {
        "id": 3954,
        "statement": "the week 1 game have a higher attendance than week 2",
        "label": 1,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df[df['week'] == 1]['attendance'].values[0] > df[df['week'] == 2]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3955,
        "statement": "week 3 have a higher attendance than week 2",
        "label": 1,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 3, 'attendance'].values[0] > df.loc[df['week'] == 2, 'attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3956,
        "statement": "week 4 have a lower attendance than week 3",
        "label": 1,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df[df['week'] == 4]['attendance'].values[0] < df[df['week'] == 3]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3957,
        "statement": "week 3 have a lower attendance than week 5",
        "label": 1,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 3, 'attendance'].values[0] < df.loc[df['week'] == 5, 'attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3958,
        "statement": "week 5 have a higher attendance than week 2",
        "label": 1,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 5, 'attendance'].values[0] > df.loc[df['week'] == 2, 'attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3959,
        "statement": "the week 1 game have a lower attendance than week 2",
        "label": 0,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df[df['week'] == 1]['attendance'].values[0] < df[df['week'] == 2]['attendance'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3960,
        "statement": "week 3 have the lowest attendance of any game",
        "label": 0,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 3, 'attendance'].iloc[0] == df['attendance'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3961,
        "statement": "week 4 have the same attendance as week 3",
        "label": 0,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 4, 'attendance'].values[0] == df.loc[df['week'] == 3, 'attendance'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3962,
        "statement": "week 3 have the highest attendance of all game",
        "label": 0,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['attendance'].idxmax(), 'week'] == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3963,
        "statement": "week 5 have the same opponent as week 2",
        "label": 0,
        "table_caption": "1968 cleveland browns season",
        "table_text": "week#date#opponent#result#attendance\n1#august 9 , 1968#los angeles rams#l 23 - 21#64020\n2#august 18 , 1968#san francisco 49ers#w 31 - 17#26801\n3#august 24 , 1968#new orleans saints#l 40 - 27#70045\n4#august 30 , 1968#buffalo bills#w 22 - 12#45448\n5#september 7 , 1968#green bay packers#l 31 - 9#84918\n",
        "pandas_code": "df.loc[df['week'] == 5, 'opponent'].values[0] == df.loc[df['week'] == 2, 'opponent'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 3964,
        "statement": "peyton manning and corey gaines both play for tennessee",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "all(df[df['name'].isin(['peyton manning', 'corey gaines'])]['college'] == 'tennessee')",
        "pandas_eval": "True"
    },
    {
        "id": 3965,
        "statement": "peyton manning be a first overall draft pick for the indianapolis colt",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "df[(df['name'] == 'peyton manning') & (df['overall'] == 1)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3966,
        "statement": "steve mckinney and aaron taylor both play the guard position",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "all(df[df['name'].isin(['steve mckinney', 'aaron taylor'])]['position'] == 'guard')",
        "pandas_eval": "True"
    },
    {
        "id": 3967,
        "statement": "jerome pathon and e g green be both wide receiver",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "all(df[(df['name'].isin(['jerome pathon', 'e g green']))]['position'] == 'wide receiver')",
        "pandas_eval": "True"
    },
    {
        "id": 3968,
        "statement": "corey gaines , a 7th round draft pick for the indianapolis colt , play defensive back",
        "label": 1,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "df[(df['name'] == 'corey gaines') & (df['round'] == 7) & (df['position'] == 'defensive back')].any().any()",
        "pandas_eval": "True"
    },
    {
        "id": 3969,
        "statement": "jerome pathon and aaron taylor both play for tennessee",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "((df['name'] == 'jerome pathon') & (df['college'] == 'tennessee')).any() & ((df['name'] == 'aaron taylor') & (df['college'] == 'tennessee')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3970,
        "statement": "steve mckinney be a first overall draft pick for the indianapolis colt",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "df[df['name'] == 'steve mckinney']['overall'].eq(1).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3971,
        "statement": "anthony jordan and jerome pathon both play the defensive back position",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "(df[df['name'].isin(['antony jordan', 'jerome pathon'])]['position'] == 'defensive back').all()",
        "pandas_eval": "False"
    },
    {
        "id": 3972,
        "statement": "corey gain and peyton manning be both wide receiver",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "df[df['name'].isin(['corey gaines', 'peyton manning'])]['position'].eq('wide receiver').all()",
        "pandas_eval": "False"
    },
    {
        "id": 3973,
        "statement": "aaron taylor , a 4th round draft pick for the indianapolis colt , play quarterback",
        "label": 0,
        "table_caption": "indianapolis colts draft history",
        "table_text": "round#pick#overall#name#position#college\n1#1#1#peyton manning#quarterback#tennessee\n2#2#32#jerome pathon#wide receiver#washington\n3#10#71#e g green#wide receiver#florida state\n4#1#93#steve mckinney#guard#texas a&m\n5#12#135#antony jordan#linebacker#vanderbilt\n7#1#190#aaron taylor#guard#nebraska\n7#42#231#corey gaines#defensive back#tennessee\n",
        "pandas_code": "((df['name'] == 'aaron taylor') & (df['round'] == 4) & (df['position'] == 'quarterback')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 3974,
        "statement": "4 player represent the united state and 1 player represent south africa",
        "label": 1,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "(df['country'].value_counts().to_dict() == {'united states': 4, 'south africa': 1})",
        "pandas_eval": "True"
    },
    {
        "id": 3975,
        "statement": "the person who win the earliest year be gary player , who first win in 1962",
        "label": 1,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "df[df['player'] == 'gary player']['year (s) won'].str.split(',').explode().str.strip().astype(int).min() == 1962",
        "pandas_eval": "True"
    },
    {
        "id": 3976,
        "statement": "the player who win the latest year be larry nelson who win in 1981",
        "label": 1,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "df[df['year (s) won'].str.contains('1981')]['player'].iloc[0] == 'larry nelson'",
        "pandas_eval": "True"
    },
    {
        "id": 3977,
        "statement": "don january win a year after al geiberger and both represent the united state",
        "label": 1,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "(df[df['player'] == 'don january']['year (s) won'].values[0] == '1967') & (df[df['player'] == 'al geiberger']['year (s) won'].values[0] == '1966') & (df[df['player'] == 'don january']['country'].values[0] == 'united states') & (df[df['player'] == 'al geiberger']['country'].values[0] == 'united states')",
        "pandas_eval": "True"
    },
    {
        "id": 3978,
        "statement": "al geiberger have a total of 150 , 1 more than larry nelson 's score of 149",
        "label": 1,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "df[df['player'] == 'al geiberger']['total'].values[0] == df[df['player'] == 'larry nelson']['total'].values[0] + 1",
        "pandas_eval": "True"
    },
    {
        "id": 3979,
        "statement": "1 player represent the united state and 4 player represent south africa",
        "label": 0,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "(df['country'].value_counts() == {'united states': 1, 'south africa': 4}).all()",
        "pandas_eval": "False"
    },
    {
        "id": 3980,
        "statement": "the person who win the earliest year be don january , who first win in 1967",
        "label": 0,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "df.loc[df['year (s) won'].apply(lambda x: min(map(int, x.split(' , ')))) == 1967, 'player'].iloc[0] != 'don january'",
        "pandas_eval": "False"
    },
    {
        "id": 3981,
        "statement": "the player who win the latest year be gary player who win in 1972",
        "label": 0,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "not df[df['player'] == 'gary player']['year (s) won'].str.contains('1972').any()",
        "pandas_eval": "False"
    },
    {
        "id": 3983,
        "statement": "al geiberger have a total of 149 , 1 more than larry nelson 's score of 148",
        "label": 0,
        "table_caption": "1982 pga championship",
        "table_text": "player#country#year (s) won#total#to par\ndave stockton#united states#1970 , 1976#146#+ 6\ngary player#south africa#1962 , 1972#146#+ 6\ndon january#united states#1967#146#+ 6\nlarry nelson#united states#1981#149#+ 9\nal geiberger#united states#1966#150#+ 10\n",
        "pandas_code": "(df[df['player'] == 'al geiberger']['total'].values[0] == 149) & (df[df['player'] == 'larry nelson']['total'].values[0] == 148)",
        "pandas_eval": "False"
    },
    {
        "id": 3984,
        "statement": "there be 2 more away game than home game in the month of november during the 2008 - 09 detroit red wing season",
        "label": 1,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "(df['date'].str.contains('november') & (df['visitor'] == 'detroit')).sum() - (df['date'].str.contains('november') & (df['home'] == 'detroit')).sum() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3986,
        "statement": "there be less crowd attendance when detroit play boston on november 29 than when detroit play columbus the day before",
        "label": 1,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "df[(df['date'] == 'november 29') & (df['home'] == 'boston') & (df['visitor'] == 'detroit')]['attendance'].values[0] < df[(df['date'] == 'november 28') & (df['home'] == 'detroit') & (df['visitor'] == 'columbus')]['attendance'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 3987,
        "statement": "detroit play vancouver 2 time in november during the 2008 - 09 detroit red wing season",
        "label": 1,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "len(df[((df['visitor'] == 'detroit') & (df['home'] == 'vancouver')) | ((df['visitor'] == 'vancouver') & (df['home'] == 'detroit'))]) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 3988,
        "statement": "there be more goal score when detroit play pittsburgh on november 11 than any other november game during the 2008 - 09 detroit red wing season",
        "label": 1,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "df[(df['date'] == 'november 11') & (df['visitor'] == 'pittsburgh') & (df['home'] == 'detroit')]['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).iloc[0] > df[df['date'].str.startswith('november') & (df['visitor'] == 'detroit')]['score'].str.split(' - ').apply(lambda x: int(x[0]) + int(x[1])).max()",
        "pandas_eval": "True"
    },
    {
        "id": 3989,
        "statement": "there be 3 more away game than home game in the month of november during the 2008 - 09 detroit red wing season",
        "label": 0,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "(df['date'].str.contains('november') & (df['visitor'] == 'detroit')).sum() - (df['date'].str.contains('november') & (df['home'] == 'detroit')).sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 3990,
        "statement": "he lowest november attendance for the 2008 - 09 detroit red wing season be when detroit beat florida on november 14",
        "label": 0,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "df[(df['date'] == 'november 14') & (df['home'] == 'florida') & (df['visitor'] == 'detroit') & (df['score'].str.startswith('3 - 2'))]['attendance'].iloc[0] == df[df['date'].str.startswith('november')]['attendance'].min()",
        "pandas_eval": "False"
    },
    {
        "id": 3991,
        "statement": "there be less crowd attendance when detroit play boston on november 29 than when detroit play columbus the day before but not less than when detroit play montreal on november 26",
        "label": 0,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "(df[(df['date'] == 'november 29') & (df['visitor'] == 'detroit') & (df['home'] == 'boston')]['attendance'].empty == False) and (df[(df['date'] == 'november 28') & (df['visitor'] == 'detroit') & (df['home'] == 'columbus')]['attendance'].empty == False) and (df[(df['date'] == 'november 26') & (df['visitor'] == 'detroit') & (df['home'] == 'montreal')]['attendance'].empty == False) and (df[(df['date'] == 'november 29') & (df['visitor'] == 'detroit') & (df['home'] == 'boston')]['attendance'].values[0] < df[(df['date'] == 'november 28') & (df['visitor'] == 'detroit') & (df['home'] == 'columbus')]['attendance'].values[0]) and (df[(df['date'] == 'november 29') & (df['visitor'] == 'detroit') & (df['home'] == 'boston')]['attendance'].values[0] >= df[(df['date'] == 'november 26') & (df['visitor'] == 'detroit') & (df['home'] == 'montreal')]['attendance'].values[0])",
        "pandas_eval": "False"
    },
    {
        "id": 3992,
        "statement": "detroit play vancouver at least 3 time in november during the 2008 - 09 detroit red wing season",
        "label": 0,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "len(df[(df['visitor'] == 'detroit') & (df['home'] == 'vancouver') & (df['date'].str.contains('november'))]) >= 3",
        "pandas_eval": "False"
    },
    {
        "id": 3993,
        "statement": "there be other game where detroit score more than they do on november 11",
        "label": 0,
        "table_caption": "2008 - 09 detroit red wings season",
        "table_text": "date#visitor#score#home#decision#attendance#record\nnovember 2#detroit#3 - 2#vancouver#osgood#18630#8 - 2 - 2\nnovember 8#new jersey#1 - 3#detroit#osgood#20066#9 - 2 - 2\nnovember 11#pittsburgh#7 - 6#detroit#osgood#20066#9 - 2 - 3\nnovember 13#detroit#4 - 3#tampa bay#osgood#20544#10 - 2 - 3\nnovember 14#detroit#3 - 2#florida#conklin#18637#11 - 2 - 3\nnovember 17#edmonton#0 - 4#detroit#conklin#18934#12 - 2 - 3\nnovember 20#detroit#4 - 3#edmonton#osgood#16839#13 - 2 - 3\nnovember 22#detroit#5 - 2#calgary#conklin#19289#14 - 2 - 3\nnovember 24#detroit#2 - 3#vancouver#osgood#18630#14 - 2 - 4\nnovember 26#montreal#3 - 1#detroit#conklin#20066#14 - 3 - 4\nnovember 28#columbus#3 - 5#detroit#osgood#20066#15 - 3 - 4\nnovember 29#detroit#1 - 3#boston#conklin#17565#15 - 4 - 4\n",
        "pandas_code": "any(df[(df['visitor'] == 'detroit') | (df['home'] == 'detroit')]['score'].apply(lambda x: int(x.split(' - ')[0]) > 7 or int(x.split(' - ')[1]) > 7))",
        "pandas_eval": "False"
    },
    {
        "id": 3994,
        "statement": "both atom e640 and atom e640t model have only 1 ghz frequency",
        "label": 1,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "(df[df['model number'].isin(['atom e640', 'atom e640t'])]['frequency'] == '1 ghz').all()",
        "pandas_eval": "True"
    },
    {
        "id": 3995,
        "statement": "all intel atom microprocessor have the same amount of memory for the release date of september 14 , 2010",
        "label": 1,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "df[df['release date'] == 'september 14 , 2010']['memory'].nunique() == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3996,
        "statement": "the atom e680t model microprocessor be the most expensive at 85",
        "label": 1,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "df[df['model number'] == 'atom e680t']['release price ( usd )'].iloc[0] == 85",
        "pandas_eval": "True"
    },
    {
        "id": 3997,
        "statement": "the atom e680t model microprocessor have 80 mhz gpu frequency more than the atom e640t model",
        "label": 1,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "(df[df['model number'] == 'atom e680t']['gpu frequency'].str.replace(' mhz', '').astype(int).iloc[0] - df[df['model number'] == 'atom e640t']['gpu frequency'].str.replace(' mhz', '').astype(int).iloc[0]) == 80",
        "pandas_eval": "True"
    },
    {
        "id": 3998,
        "statement": "the atom e660t model microprocessor be 1 of 4 model with 400 mhz gpu frequency",
        "label": 1,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "len(df[(df['model number'] == 'atom e660t') & (df['gpu frequency'] == '400 mhz')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 3999,
        "statement": "both atom e620 and atom e640t model have only 1 ghz frequency",
        "label": 0,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "all(df[df['model number'].isin(['atom e620', 'atom e640t'])]['frequency'] == '1 ghz')",
        "pandas_eval": "False"
    },
    {
        "id": 4000,
        "statement": "all intel atom microprocessor have the same amount of frequency for the release date of september 14 , 2010",
        "label": 0,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "df[df['release date'] == 'september 14 , 2010']['frequency'].nunique() == 1",
        "pandas_eval": "False"
    },
    {
        "id": 4001,
        "statement": "the atom e680 model microprocessor be the most expensive at 74",
        "label": 0,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "df[df['model number'] == 'atom e680']['release price ( usd )'].values[0] == df['release price ( usd )'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 4002,
        "statement": "atom e680t model microprocessor have 30 mhz gpu requency more than atom e640t model",
        "label": 0,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "(df[df['model number'] == 'atom e680t']['gpu frequency'].str.replace(' mhz', '').astype(int).iloc[0] - df[df['model number'] == 'atom e640t']['gpu frequency'].str.replace(' mhz', '').astype(int).iloc[0]) == 30",
        "pandas_eval": "False"
    },
    {
        "id": 4003,
        "statement": "the atom e660t model microprocessor be 1 of 4 model with 320 mhz gpu frequency",
        "label": 0,
        "table_caption": "list of intel atom microprocessors",
        "table_text": "model number#sspec number#frequency#gpu frequency#l2 cache#i / o bus#memory#voltage#socket#release date#part number (s)#release price ( usd )\natom e620#slh56 (b0) slj32 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844aa#19\natom e620t#slh5n (b0) slj36 (b1)#600 mhz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005844ab#22\natom e640#slh55 (b0) slj33 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841aa#29\natom e640t#slh5 m (b0) slj37 (b1)#1 ghz#320 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618005841ab#37\natom e660#slh54 (b0) slj34 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201aa#54\natom e660t#slh5l (b0) slj38 (b1)#1.3 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618003201ab#64\natom e680#slh94 (b0) slj35 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035aa#74\natom e680t#slh95 (b0) slj39 (b1)#1.6 ghz#400 mhz#512 kb#pcie#1 ddr2 - 800#0.8 - 1.175v#fc - bga 676#september 14 , 2010#ct80618007035ab#85\n",
        "pandas_code": "(df[df['model number'] == 'atom e660t']['gpu frequency'].values[0] == '320 mhz') and (df['gpu frequency'].value_counts()['320 mhz'] == 4)",
        "pandas_eval": "False"
    },
    {
        "id": 4004,
        "statement": "this table do not show any female name for the candidate",
        "label": 1,
        "table_caption": "new york city mayoral elections",
        "table_text": "1973 democratic initial primary#manhattan#the bronx#brooklyn#queens#richmond#total#%\nabraham beame#45901#41508#96621#73520#8912#266462#34%\nabraham beame#26%#27%#41%#40%#42%#266462#34%\nherman badillo#73676#55432#57836#33990#2902#223836#29%\nherman badillo#41%#36%#25%#19%#14%#223836#29%\nalbert h blumenthal#41906#18400#31913#28960#2062#123241#16%\nalbert h blumenthal#23%#12%#14%#16%#10%#123241#16%\nmario biaggi#17830#39462#48352#45992#7524#159160#21%\nmario biaggi#10%#25%#21%#25%#35%#159160#21%\n",
        "pandas_code": "all(df['1973 democratic initial primary'].apply(lambda x: x.split()[0].lower() not in ['mary', 'patricia', 'jennifer', 'linda', 'elizabeth', 'barbara', 'susan', 'margaret', 'dorothy', 'sarah']))",
        "pandas_eval": "True"
    },
    {
        "id": 4005,
        "statement": "mario biaggi do not do as well as abraham beame",
        "label": 1,
        "table_caption": "new york city mayoral elections",
        "table_text": "1973 democratic initial primary#manhattan#the bronx#brooklyn#queens#richmond#total#%\nabraham beame#45901#41508#96621#73520#8912#266462#34%\nabraham beame#26%#27%#41%#40%#42%#266462#34%\nherman badillo#73676#55432#57836#33990#2902#223836#29%\nherman badillo#41%#36%#25%#19%#14%#223836#29%\nalbert h blumenthal#41906#18400#31913#28960#2062#123241#16%\nalbert h blumenthal#23%#12%#14%#16%#10%#123241#16%\nmario biaggi#17830#39462#48352#45992#7524#159160#21%\nmario biaggi#10%#25%#21%#25%#35%#159160#21%\n",
        "pandas_code": "(df[df['1973 democratic initial primary'] == 'mario biaggi']['total'].iloc[0] < df[df['1973 democratic initial primary'] == 'abraham beame']['total'].iloc[0])",
        "pandas_eval": "True"
    },
    {
        "id": 4006,
        "statement": "albert sblumenthal do not do very well",
        "label": 1,
        "table_caption": "new york city mayoral elections",
        "table_text": "1973 democratic initial primary#manhattan#the bronx#brooklyn#queens#richmond#total#%\nabraham beame#45901#41508#96621#73520#8912#266462#34%\nabraham beame#26%#27%#41%#40%#42%#266462#34%\nherman badillo#73676#55432#57836#33990#2902#223836#29%\nherman badillo#41%#36%#25%#19%#14%#223836#29%\nalbert h blumenthal#41906#18400#31913#28960#2062#123241#16%\nalbert h blumenthal#23%#12%#14%#16%#10%#123241#16%\nmario biaggi#17830#39462#48352#45992#7524#159160#21%\nmario biaggi#10%#25%#21%#25%#35%#159160#21%\n",
        "pandas_code": "df[df['1973 democratic initial primary'] == 'albert h blumenthal']['%'].iloc[0] == '16%'",
        "pandas_eval": "True"
    },
    {
        "id": 4007,
        "statement": "heman badillo have a strong presence and do better than mario biaggi",
        "label": 1,
        "table_caption": "new york city mayoral elections",
        "table_text": "1973 democratic initial primary#manhattan#the bronx#brooklyn#queens#richmond#total#%\nabraham beame#45901#41508#96621#73520#8912#266462#34%\nabraham beame#26%#27%#41%#40%#42%#266462#34%\nherman badillo#73676#55432#57836#33990#2902#223836#29%\nherman badillo#41%#36%#25%#19%#14%#223836#29%\nalbert h blumenthal#41906#18400#31913#28960#2062#123241#16%\nalbert h blumenthal#23%#12%#14%#16%#10%#123241#16%\nmario biaggi#17830#39462#48352#45992#7524#159160#21%\nmario biaggi#10%#25%#21%#25%#35%#159160#21%\n",
        "pandas_code": "df[df['1973 democratic initial primary'] == 'herman badillo'].iloc[0]['total'] > df[df['1973 democratic initial primary'] == 'mario biaggi'].iloc[0]['total']",
        "pandas_eval": "True"
    },
    {
        "id": 4008,
        "statement": "abraham beam and herman badillo do better than albert h blumenthal",
        "label": 1,
        "table_caption": "new york city mayoral elections",
        "table_text": "1973 democratic initial primary#manhattan#the bronx#brooklyn#queens#richmond#total#%\nabraham beame#45901#41508#96621#73520#8912#266462#34%\nabraham beame#26%#27%#41%#40%#42%#266462#34%\nherman badillo#73676#55432#57836#33990#2902#223836#29%\nherman badillo#41%#36%#25%#19%#14%#223836#29%\nalbert h blumenthal#41906#18400#31913#28960#2062#123241#16%\nalbert h blumenthal#23%#12%#14%#16%#10%#123241#16%\nmario biaggi#17830#39462#48352#45992#7524#159160#21%\nmario biaggi#10%#25%#21%#25%#35%#159160#21%\n",
        "pandas_code": "df[df['1973 democratic initial primary'].isin(['abraham beame', 'herman badillo'])]['total'].sum() > df[df['1973 democratic initial primary'] == 'albert h blumenthal']['total'].sum()",
        "pandas_eval": "True"
    },
    {
        "id": 4009,
        "statement": "all of the house of representative in the list be democratic",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#hale boggs#democratic#1946#re - elected#hale boggs (d) unopposed\nlouisiana 3#edwin e willis#democratic#1948#re - elected#edwin e willis (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#otto passman#democratic#1946#re - elected#otto passman (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "(df['party'] == 'democratic').all()",
        "pandas_eval": "True"
    },
    {
        "id": 4010,
        "statement": "f edward hebert be first elect earlier than james h morrison do",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#hale boggs#democratic#1946#re - elected#hale boggs (d) unopposed\nlouisiana 3#edwin e willis#democratic#1948#re - elected#edwin e willis (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#otto passman#democratic#1946#re - elected#otto passman (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "df[df['incumbent'] == 'f edward hebert']['first elected'].values[0] < df[df['incumbent'] == 'james h morrison']['first elected'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 4011,
        "statement": "all of the house of representative get reelected",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#hale boggs#democratic#1946#re - elected#hale boggs (d) unopposed\nlouisiana 3#edwin e willis#democratic#1948#re - elected#edwin e willis (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#otto passman#democratic#1946#re - elected#otto passman (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "all(df['result'] == 're - elected')",
        "pandas_eval": "True"
    },
    {
        "id": 4012,
        "statement": "james h morrison be first elect in the same year as henry d larcade , jr do",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#hale boggs#democratic#1946#re - elected#hale boggs (d) unopposed\nlouisiana 3#edwin e willis#democratic#1948#re - elected#edwin e willis (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#otto passman#democratic#1946#re - elected#otto passman (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "df[df['incumbent'].isin(['james h morrison', 'henry d larcade , jr'])].groupby('first elected').size().max() == 2",
        "pandas_eval": "True"
    },
    {
        "id": 4013,
        "statement": "otto passman be first elect later than overton brook do",
        "label": 1,
        "table_caption": "united states house of representatives elections , 1950",
        "table_text": "district#incumbent#party#first elected#result#candidates\nlouisiana 1#f edward hebert#democratic#1940#re - elected#f edward hebert (d) unopposed\nlouisiana 2#hale boggs#democratic#1946#re - elected#hale boggs (d) unopposed\nlouisiana 3#edwin e willis#democratic#1948#re - elected#edwin e willis (d) unopposed\nlouisiana 4#overton brooks#democratic#1936#re - elected#overton brooks (d) unopposed\nlouisiana 5#otto passman#democratic#1946#re - elected#otto passman (d) unopposed\nlouisiana 6#james h morrison#democratic#1942#re - elected#james h morrison (d) unopposed\nlouisiana 7#henry d larcade , jr#democratic#1942#re - elected#henry d larcade , jr (d) unopposed\n",
        "pandas_code": "(df[df['incumbent'] == 'otto passman']['first elected'].values[0] > df[df['incumbent'] == 'overton brooks']['first elected'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 4014,
        "statement": "sevilla play as team 1 while valencia play as team 2 in the 2008 - 09 copa del rey season",
        "label": 1,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "((df['team 1'] == 'sevilla') | (df['team 2'] == 'valencia')).any()",
        "pandas_eval": "True"
    },
    {
        "id": 4015,
        "statement": "the highest agg be 5 - 1 in the 2008 - 09 copa del rey season",
        "label": 1,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "df['agg'].max() == '5 - 1'",
        "pandas_eval": "True"
    },
    {
        "id": 4016,
        "statement": "poli ejido and espanyol both scored 3 as their agg score",
        "label": 1,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "df.loc[(df['team 1'] == 'poli ejido') & (df['team 2'] == 'espanyol'), 'agg'].str.contains('3 - 3').any()",
        "pandas_eval": "True"
    },
    {
        "id": 4017,
        "statement": "there be only 1 team to lose with a score of 0 as agg",
        "label": 1,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "len(df[df['agg'].str.contains('0 -')]) == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4019,
        "statement": "valencia play as team 1 while sevilla play as team 2 in the 2008 - 09 copa del rey season",
        "label": 0,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "(df['team 1'].eq('valencia') & df['team 2'].eq('sevilla')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 4020,
        "statement": "the lowest agg be 5 - 1 in the 2008 - 09 copa del rey season",
        "label": 0,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "df['agg'].min() == '5 - 1'",
        "pandas_eval": "False"
    },
    {
        "id": 4021,
        "statement": "poli ejido and espanyol both scored 2 as their agg score",
        "label": 0,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "df[(df['team 1'] == 'poli ejido') & (df['team 2'] == 'espanyol') & (df['agg'].str.extract(r'(\\d+) - \\d+')[0].astype(int) == 2) & (df['agg'].str.extract(r'\\d+ - (\\d+)')[0].astype(int) == 2)].any().any()",
        "pandas_eval": "False"
    },
    {
        "id": 4022,
        "statement": "there be only 1 team to win with a score of 0 as agg",
        "label": 0,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "(df['agg'] == '0 - 2').sum() == 0",
        "pandas_eval": "False"
    },
    {
        "id": 4023,
        "statement": "racing play against barcelona in the 2008 - 09 copa del rey season",
        "label": 0,
        "table_caption": "2008 - 09 copa del rey",
        "table_text": "team 1#agg#team 2#1st leg#2nd leg\nsevilla#5 - 1#deportivo#2 - 1#3 - 0\nsporting#4 - 3#valladolid#3 - 1#1 - 2\npoli ejido#3 - 3 ( a )#espanyol#3 - 2#0 - 1\nreal uni\u00f3n#0 - 2#betis#0 - 1#0 - 1\nmallorca#4 - 2#almer\u00eda#3 - 1#1 - 1\nracing#2 - 4 ( aet )#valencia#1 - 1#1 - 3\natl\u00e9tico#2 - 5#barcelona#1 - 3#1 - 2\nosasuna#1 - 3#athletic#1 - 1#0 - 2\n",
        "pandas_code": "((df['team 1'] == 'racing') & (df['team 2'] == 'barcelona')).any()",
        "pandas_eval": "False"
    },
    {
        "id": 4024,
        "statement": "stephen and bianca be the 2nd couple in the episode with the highest viewer",
        "label": 1,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "df.loc[df['viewers (millions)'].idxmax(), '2nd couple'] == 'stephen and bianca'",
        "pandas_eval": "True"
    },
    {
        "id": 4025,
        "statement": "the episode on 16 february 2013 be 1 of 4 episode with 3 couple",
        "label": 1,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "df[(df['air date'] == '16 february 2013') & (df['no of couples'] == 3)].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4026,
        "statement": "the itv weekly ranking drop by 1 between 5 january 2013 and 12 january 2013",
        "label": 1,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "(df[df['air date'] == '5 january 2013']['itv weekly ranking'].values[0] - df[df['air date'] == '12 january 2013']['itv weekly ranking'].values[0]) == -1",
        "pandas_eval": "True"
    },
    {
        "id": 4028,
        "statement": "ben and lilly be the 1st couple on 5 february 2013 and matt and megan be the 1st couple on 26 february 2013",
        "label": 1,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "(df['1st couple'].iloc[0] == 'ben and lily' and df['air date'].iloc[0] == '5 january 2013') and (df['1st couple'].iloc[3] == 'matt and megan' and df['air date'].iloc[3] == '26 january 2013')",
        "pandas_eval": "True"
    },
    {
        "id": 4029,
        "statement": "stephen and bianca be the 4th couple in the episode with the highest viewer",
        "label": 0,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "df.loc[df['viewers (millions)'].idxmax(), '4th couple'] == 'stephen and bianca'",
        "pandas_eval": "False"
    },
    {
        "id": 4030,
        "statement": "the episode on 16 february 2013 be 1 of 4 episode with 4 couple",
        "label": 0,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "df[df['air date'] == '16 february 2013']['no of couples'].eq(4).any()",
        "pandas_eval": "False"
    },
    {
        "id": 4031,
        "statement": "the itv weekly ranking increase by 1 between 5 january 2013 and 12 january 2013",
        "label": 0,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "(df.loc[df['air date'] == '5 january 2013', 'itv weekly ranking'].values[0] + 1) != df.loc[df['air date'] == '12 january 2013', 'itv weekly ranking'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 4032,
        "statement": "there be a difference of 95 viewer between the episode with the largest amount of viewer and the least amount of viewer",
        "label": 0,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "(df['viewers (millions)'].max() - df['viewers (millions)'].min()) == 0.95",
        "pandas_eval": "False"
    },
    {
        "id": 4033,
        "statement": "ben and lilly be the 2nd couple on 5 february 2013 and matt and megan be the 4th couple on 26 february 2013",
        "label": 0,
        "table_caption": "take me out (uk game show)",
        "table_text": "#air date#no of couples#1st couple#2nd couple#3rd couple#4th couple#viewers (millions)#itv weekly ranking\n1#5 january 2013#3#ben and lily#param (n / a)#adam and lucy - may#jason and jo#4.13#18\n2#12 january 2013#3#sam and georgia#will (n / a)#dan and danni#troy and naomi#4.38#19\n3#19 january 2013#3#adam (n / a)#stephen and bianca#callum and nikita#darren and daisy#4.47#19\n4#26 january 2013#4#matt and megan#chris and kate#will and thuy#morakinyo and claire#3.97#21\n5#2 february 2013#4#stuart and robyn#sen and jade#russell and miki#gavin and gemma#4.01#19\n6#9 february 2013#4#chris and janet#ashley and rach#james and daniella#david and george#3.58#21\n7#16 february 2013#3#joe and julia#matt and charlotte#glenn and alanna#owen (n / a)#3.80#16\n",
        "pandas_code": "(df.loc[df['air date'] == '5 january 2013', '2nd couple'].values[0] != 'param (n / a)') | (df.loc[df['air date'] == '26 january 2013', '4th couple'].values[0] != 'morakinyo and claire')",
        "pandas_eval": "False"
    },
    {
        "id": 4034,
        "statement": "most of the olympic game host city be on the continent of europe",
        "label": 1,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df[df['continent'] == 'europe']['total'].sum() > df[df['continent'] != 'europe']['total'].sum()",
        "pandas_eval": "True"
    },
    {
        "id": 4035,
        "statement": "japan have host more olympic game than any other asian country",
        "label": 1,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df[df['continent'] == 'asia']['total'].max() == df[df['country'] == 'japan']['total'].values[0]",
        "pandas_eval": "True"
    },
    {
        "id": 4036,
        "statement": "the united state have host 2 time as many olympic game as japan",
        "label": 1,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "(df[df['country'] == 'united states']['total'].values[0] == 2 * df[df['country'] == 'japan']['total'].values[0])",
        "pandas_eval": "True"
    },
    {
        "id": 4037,
        "statement": "more country have host the summer olympics than have host the winter olympics",
        "label": 1,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df['summer olympics'].apply(lambda x: int(x.split('(')[0].strip()) if x != '0' else 0).sum() > df['winter olympics'].apply(lambda x: int(x.split('(')[0].strip()) if x != '0' else 0).sum()",
        "pandas_eval": "True"
    },
    {
        "id": 4038,
        "statement": "country in europe have host the olympic game nearly 3 time as often as country in north america",
        "label": 1,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "(df[df['continent'] == 'europe']['total'].sum() / df[df['continent'] == 'north america']['total'].sum()) >= 2.5 and (df[df['continent'] == 'europe']['total'].sum() / df[df['continent'] == 'north america']['total'].sum()) <= 3.5",
        "pandas_eval": "True"
    },
    {
        "id": 4039,
        "statement": "most of the olympic game host city be on the continent of asia",
        "label": 0,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df[df['continent'] == 'asia'].shape[0] > df.shape[0] / 2",
        "pandas_eval": "False"
    },
    {
        "id": 4040,
        "statement": "japan have host more olympic game than any other country",
        "label": 0,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df[df['country'] == 'japan']['total'].values[0] > df[df['country'] != 'japan']['total'].max()",
        "pandas_eval": "False"
    },
    {
        "id": 4041,
        "statement": "the united state have host 2 time as many olympic game as france",
        "label": 0,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "df[df['country'] == 'united states']['total'].values[0] == 2 * df[df['country'] == 'france']['total'].values[0]",
        "pandas_eval": "False"
    },
    {
        "id": 4042,
        "statement": "more country have host the winter olympics than have host the summer olympics",
        "label": 0,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "(df['winter olympics'].apply(lambda x: len(x.split(',')) if x != '0' else 0).sum() > df['summer olympics'].apply(lambda x: len(x.split(',')) if x != '0' else 0).sum())",
        "pandas_eval": "False"
    },
    {
        "id": 4043,
        "statement": "country in europe have host the olympic game nearly 3 time as often as country in asia",
        "label": 0,
        "table_caption": "list of olympic games host cities",
        "table_text": "rank#country#continent#summer olympics#winter olympics#total\n1#united states#north america#4 ( 1904 , 1932 , 1984 , 1996 )#4 ( 1932 , 1960 , 1980 , 2002 )#8\n2#france#europe#2 ( 1900 , 1924 )#3 ( 1924 , 1968 , 1992 )#5\n3#japan#asia#2 ( 1940 , 1964 , 2020 )#2 ( 1940 , 1972 , 1998 )#4\n4#united kingdom#europe#3 ( 1908 , 1944 , 1948 , 2012 )#0#3\n4#canada#north america#1 ( 1976 )#2 ( 1988 , 2010 )#3\n4#italy#europe#1 ( 1960 )#2 ( 1944 , 1956 , 2006 )#3\n4#germany#europe#2 ( 1916 , 1936 , 1972 )#1 ( 1936 )#3\n8#south korea#asia#1 ( 1988 )#1 ( 2018 )#2\n8#soviet union / russia#europe#1 ( 1980 )#1 ( 2014 )#2\n8#greece#europe#2 ( 1896 , 2004 )#0#2\n8#australia#oceania#2 ( 1956 , 2000 )#0#2\n8#norway#europe#0#2 ( 1952 , 1994 )#2\n8#austria#europe#0#2 ( 1964 , 1976 )#2\n8#switzerland#europe#0#2 ( 1928 , 1948 )#2\n15#brazil#south america#1 ( 2016 )#0#1\n15#china#asia#1 ( 2008 )#0#1\n15#spain#europe#1 ( 1992 )#0#1\n15#yugoslavia#europe#0#1 ( 1984 )#1\n15#mexico#north america#1 ( 1968 )#0#1\n15#finland#europe#1 ( 1940 , 1952 )#0#1\n15#netherlands#europe#1 ( 1928 )#0#1\n15#belgium#europe#1 ( 1920 )#0#1\n15#sweden#europe#1 ( 1912 )#0#1\n",
        "pandas_code": "(df[df['continent'] == 'europe']['total'].sum() / df[df['continent'] == 'asia']['total'].sum()) == 3",
        "pandas_eval": "False"
    },
    {
        "id": 4044,
        "statement": "george hu have a minor role between 2006 - 2013 1 time",
        "label": 1,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "df[(df['role'] == 'minor') & (df['year'].between(2006, 2013))].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4045,
        "statement": "george hu have a support role between 2006 - 2013 2 time",
        "label": 1,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "df[(df['year'].between(2006, 2013)) & (df['role'] == 'supporting')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 4046,
        "statement": "george hu have a lead role between 2006 - 2013 4 time",
        "label": 1,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['year'] >= 2006) & (df['year'] <= 2013) & (df['role'] == 'lead role')].shape[0] == 4)",
        "pandas_eval": "True"
    },
    {
        "id": 4047,
        "statement": "george hu have a main role between 2006 - 2013 5 time",
        "label": 1,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['year'] >= 2006) & (df['year'] <= 2013) & (df['role'] == 'main')].shape[0] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 4048,
        "statement": "george hu have a main role between 2006 - 2013 5 time",
        "label": 1,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['year'] >= 2006) & (df['year'] <= 2013) & (df['role'] == 'main')].shape[0] == 5)",
        "pandas_eval": "True"
    },
    {
        "id": 4049,
        "statement": "george hu have many major role between the year of 2006 and 2013",
        "label": 0,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['year'] >= 2006) & (df['year'] <= 2013)]['role'].isin(['main', 'lead role']).sum() <= 1)",
        "pandas_eval": "False"
    },
    {
        "id": 4050,
        "statement": "george hu have 3 support role between 2006 - 2013",
        "label": 0,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['role'] == 'supporting') & (df['year'].between(2006, 2013))].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 4051,
        "statement": "george hu have 3 lead role between 2006 and 2013",
        "label": 0,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "(df[(df['role'] == 'lead role') & (df['year'].between(2006, 2013))].shape[0] == 3)",
        "pandas_eval": "False"
    },
    {
        "id": 4052,
        "statement": "george hu have have no main role during his career",
        "label": 0,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "any(df[df['role'] == 'main']['character'].str.contains('george hu', case=False))",
        "pandas_eval": "False"
    },
    {
        "id": 4053,
        "statement": "george hu have have only 1 main role between 2006 and 2013",
        "label": 0,
        "table_caption": "george hu",
        "table_text": "year#chinese title#english#role#character\n2006#\u5c4b\u9802\u4e0a\u7684\u7da0\u5bf6\u77f3#emerald on the roof#minor#nie kai (\u8076\u51f1)\n2007#\u6b66\u5341\u90ce#love at first fight#main#lei sheng da (\u96f7\u8072\u5927)\n2007#\u7d42\u6975\u4e00\u5bb6#the x - family#guest#shen xing zhe (\u795e\u884c\u8005) / qiang ling wang (\u69cd\u9748\u738b)\n2007#\u516c\u4e3b\u5c0f\u59b9#romantic princess#supporting#nan feng lin (\u5357\u98a8\u7498)\n2008#\u7c43\u7403\u706b#hot shot#supporting#wu ji wei (\u7121\u6975\u5a01)\n2009#\u611b\u5c31\u5b85\u4e00\u8d77#together#main#wei jia sen (\u9b4f\u52a0\u68ee)\n2009#\u7d42\u6975\u4e09\u570b#ko3an guo#main#guan yu (\u95dc\u7fbd)\n2010#\u6211\u548c\u6211\u7684\u5144\u5f1f~\u6069#me & my brothers#main#dennis\n2011#\u65cb\u98a8\u7ba1\u5bb6#hayate the combat butler (tv series)#lead role#ling qisa (\u51cc\u5947\u98af) / hayate ayazaki\n2011#\u65b0\u5175\u65e5\u8a18\u4e4b\u7279\u6230\u82f1\u96c4#rookies diary season2#main#zheng qiang (\u912d\u5f37)\n2012#\u6200\u590f38\u2103#summer fever#lead role#lin ming kuan (\u6797\u660e\u5bec)\n2012#\u771f\u611b\u8d81\u73fe\u5728#love , now#lead role#lan shi - de (\u85cd\u4ed5\u5fb7)\n2013#\u771f\u611b\u9ed1\u767d\u914d#love around#lead role#zhou zhen (\u5468\u9707)\n",
        "pandas_code": "len(df[(df['role'] == 'main') & (df['year'].between(2006, 2013))]) == 1",
        "pandas_eval": "False"
    },
    {
        "id": 4054,
        "statement": "nathan caratti have the fastest lap a total of 4 time",
        "label": 1,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "(df['fastest lap'] == 'nathan caratti').sum() == 4",
        "pandas_eval": "True"
    },
    {
        "id": 4055,
        "statement": "james winslow have both the fastest lap and be the win driver 2 time",
        "label": 1,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "df[(df['fastest lap'] == 'james winslow') & (df['winning driver'] == 'james winslow')].shape[0] == 2",
        "pandas_eval": "True"
    },
    {
        "id": 4057,
        "statement": "john martin have the fastest lap on 2 indy f3 challenge",
        "label": 1,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "df[(df['circuit'] == 'indy f3 challenge') & (df['race'] == 2) & (df['fastest lap'] == 'john martin')].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4058,
        "statement": "neil mcfadyen have pole position a total of 5 time",
        "label": 1,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "(df['pole position'] == 'neil mcfadyen').sum() == 5",
        "pandas_eval": "True"
    },
    {
        "id": 4059,
        "statement": "nathan caratti only have the fastest lap 2 time",
        "label": 0,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "(df['fastest lap'].value_counts()['nathan caratti'] == 2)",
        "pandas_eval": "False"
    },
    {
        "id": 4060,
        "statement": "james winslow be have the fastest lap each time he be the winning driver",
        "label": 0,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "all(df[df['winning driver'] == 'james winslow']['fastest lap'] == 'james winslow')",
        "pandas_eval": "False"
    },
    {
        "id": 4061,
        "statement": "team brm be the win team more than half the time",
        "label": 0,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "(df['winning team'].value_counts(normalize=True)['team brm'] > 0.5)",
        "pandas_eval": "False"
    },
    {
        "id": 4062,
        "statement": "john martin have the fastest lap on the eastern creek raceway circuit",
        "label": 0,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "(df[(df['circuit'] == 'eastern creek raceway') & (df['fastest lap'] == 'john martin')].shape[0] > 0)",
        "pandas_eval": "False"
    },
    {
        "id": 4063,
        "statement": "neil mcfadyen have pole position in each on the indy f3 challenge event",
        "label": 0,
        "table_caption": "2008 australian drivers' championship",
        "table_text": "round#race#circuit#date#pole position#fastest lap#winning driver#winning team\n1#1#eastern creek raceway#3 february#leanne tander#neil mcfadyen#nathan caratti#team brm\n1#2#eastern creek raceway#3 february#nathan caratti#race cancelled by heavy rain#race cancelled by heavy rain#race cancelled by heavy rain\n2#1#adelaide street circuit#24 february#neil mcfadyen#earl bamber#neil mcfadyen#piccola scuderia corse\n2#2#adelaide street circuit#24 february#neil mcfadyen#ben clucas#ben clucas#team brm\n3#1#oran park raceway#27 april#neil mcfadyen#leanne tander#leanne tander#tandersport\n3#2#oran park raceway#27 april#neil mcfadyen#nathan caratti#james winslow#astuti motorsport\n4#1#mallala motor sport park#18 may#leanne tander#nathan caratti#leanne tander#tandersport\n4#2#mallala motor sport park#18 may#neil mcfadyen#leanne tander#nathan caratti#team brm\n5#1#phillip island#15 june#leanne tander#mat sofi#nathan caratti#team brm\n5#2#phillip island#15 june#james winslow#leanne tander#james winslow#team brm\n6#1#eastern creek raceway#13 july#nathan caratti#james winslow#leanne tander#tandersport\n6#2#eastern creek raceway#13 july#nathan caratti#nathan caratti#james winslow#team brm\n7#1#phillip island#10 august#leanne tander#james winslow#james winslow#team brm\n7#2#phillip island#10 august#james winslow#james winslow#leanne tander#tandersport\n8#1#symmons plains raceway#21 september#mathew radisich#james winslow#james winslow#team brm\n8#2#tasmanian super prix#21 september#james winslow#nathan caratti#leanne tander#tandersport\nnc#1#indy f3 challenge#23 october#john martin#john martin#john martin#piccola scuderia corse\nnc#2#indy f3 challenge#24 october#progressive grid#john martin#john martin#piccola scuderia corse\nnc#3#indy f3 challenge#25 october#progressive grid#john martin#james winslow#team brm\n",
        "pandas_code": "all(df[df['circuit'].str.contains('indy f3 challenge')]['pole position'] == 'neil mcfadyen')",
        "pandas_eval": "False"
    },
    {
        "id": 4064,
        "statement": "there be only 2 team involve with the 1989 nba expansion draft",
        "label": 1,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "len(df['team'].unique()) == 2",
        "pandas_eval": "True"
    },
    {
        "id": 4065,
        "statement": "of the 23 total player pick all but 1 be from the united state",
        "label": 1,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "df[df['nationality'] != 'united states'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4066,
        "statement": "reggie theus have the most year in the league out of all the player pick with 11 , the lowest player have no previous nba experience",
        "label": 1,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "(df[df['player'] == 'reggie theus +']['nba years'].values[0] == 11) and (df['nba years'].min() == 0)",
        "pandas_eval": "True"
    },
    {
        "id": 4067,
        "statement": "there be more forward pick than any other position in the expansion draft",
        "label": 1,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "df['pos'].str.contains('f').sum() > df['pos'].str.contains('g').sum() and df['pos'].str.contains('f').sum() > df['pos'].str.contains('c').sum()",
        "pandas_eval": "True"
    },
    {
        "id": 4068,
        "statement": "there be only 2 team involve , but the orlando magic pick 1 more extra player than the timberwolves",
        "label": 1,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "len(df['team'].unique()) == 2 and df[df['team'] == 'orlando magic'].shape[0] - df[df['team'] == 'minnesota timberwolves'].shape[0] == 1",
        "pandas_eval": "True"
    },
    {
        "id": 4069,
        "statement": "there be 4 team involve with the 1989 nba expansion draft",
        "label": 0,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "len(df['team'].unique()) == 4",
        "pandas_eval": "False"
    },
    {
        "id": 4070,
        "statement": "of the 23 total player pick , 3 be from west germany",
        "label": 0,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orlando magic#new jersey nets#3#-\n22#gunther behnke#c#west germany#minnesota timberwolves#cleveland cavaliers#0#-\n23#frank johnson#g#united states#orlando magic#houston rockets#8#-\n",
        "pandas_code": "(df['nationality'] == 'west germany').sum() == 3",
        "pandas_eval": "False"
    },
    {
        "id": 4071,
        "statement": "rick mahorn have the most year in the league out of all the player pick with 9 , the lowest player have no previous nba experience",
        "label": 0,
        "table_caption": "1989 nba expansion draft",
        "table_text": "pick#player#pos#nationality#team#previous team#nba years#career with the franchise\n1#sidney green#f / c#united states#orlando magic#new york knicks#6#1989 - 1990\n2#rick mahorn#f / c#united states#minnesota timberwolves#detroit pistons#9#-\n3#reggie theus +#g#united states#orlando magic#atlanta hawks#11#1989 - 1990\n4#tyrone corbin#g / f#united states#minnesota timberwolves#phoenix suns#4#1989 - 1991\n5#terry catledge#f#united states#orlando magic#washington bullets#4#1989 - 1993\n6#steve johnson +#f / c#united states#minnesota timberwolves#portland trail blazers#8#1989 - 1990\n7#sam vincent#g#united states#orlando magic#chicago bulls#4#1989 - 1992\n8#brad lohaus#f / c#united states#minnesota timberwolves#sacramento kings#2#1989 - 1990\n9#otis smith#g / f#united states#orlando magic#golden state warriors#3#1989 - 1992\n10#david rivers#g#united states#minnesota timberwolves#los angeles lakers#1#-\n11#scott skiles#g#united states#orlando magic#indiana pacers#3#1989 - 1994\n12#mark davis#g / f#united states#minnesota timberwolves#milwaukee bucks#1#-\n13#jerry reynolds#g / f#united states#orlando magic#seattle supersonics#4#1989 - 1992\n14#scott roth#f#united states#minnesota timberwolves#san antonio spurs#2#1989 - 1990\n15#mark acres#f / c#united states#orlando magic#boston celtics#2#1989 - 1992\n16#shelton jones#f#united states#minnesota timberwolves#philadelphia 76ers#1#-\n17#morlon wiley#g#united states#orlando magic#dallas mavericks#1#1989 - 1991\n18#eric white#f#united states#minnesota timberwolves#los angeles clippers#2#-\n19#jim farmer#g#united states#orlando magic#utah jazz#2#-\n20#maurice martin#g / f#united states#minnesota timberwolves#denver nuggets#2#-\n21#keith lee#f / c#united states#orland