niryuu/sni-each-converted huggingface dataset:
select the rows whose name record fuzzily matches to willie adams . take the pick record of this row . select the rows whose name record fuzzily matches to bob reed . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; willie adams } ; pick } ; hop { filter_eq { all_rows ; name ; bob reed } ; pick } }
select the rows whose year left record is greater than or equal to 1970 . among these rows , select the rows whose conference joined record fuzzily matches to hoosier heartland . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater_eq { all_rows ; year left ; 1970 } ; conference joined ; hoosier heartland } } ; 2 }
select the row whose attendance record of all rows is maximum . the date record of this row is 1970 - 10 - 04 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; 1970 - 10 - 04 }
the average of the weight record of all rows is 90 .
Output: round_eq { avg { all_rows ; weight } ; 90 }
the average of the crowd record of all rows is 25000-27000 .
Output: round_eq { avg { all_rows ; crowd } ; 25000-27000 }
select the rows whose new channel ( s ) record fuzzily matches to same channel as original . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; new channel ( s ) ; same channel as original } } ; 3 }
select the rows whose school / club team record fuzzily matches to oklahoma state . there is only one such row in the table . the player record of this unqiue row is tony allen .
Output: and { only { filter_eq { all_rows ; school / club team ; oklahoma state } } ; eq { hop { filter_eq { all_rows ; school / club team ; oklahoma state } ; player } ; tony allen } }
select the rows whose high rebounds record fuzzily matches to greg monroe . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; high rebounds ; greg monroe } } ; 4 }
select the rows whose women 's singles record fuzzily matches to gabriela zabavníková . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; women 's singles ; gabriela zabavníková } } ; 3 }
select the row whose viewers ( millions ) record of all rows is maximum . the airdate record of this row is sunday , june 1 , 2008 .
Output: eq { hop { argmax { all_rows ; viewers ( millions ) } ; airdate } ; sunday , june 1 , 2008 }
select the rows whose position record fuzzily matches to hb . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; position ; hb } } ; 4 }
select the rows whose date record fuzzily matches to 1948 . the sum of the round record of these rows is 4 .
Output: round_eq { sum { filter_eq { all_rows ; date ; 1948 } ; round } ; 4 }
select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 } } ; 3 }
select the rows whose shirt printing record fuzzily matches to bluewings . there is only one such row in the table . the year record of this unqiue row is 1996 .
Output: and { only { filter_eq { all_rows ; shirt printing ; bluewings } } ; eq { hop { filter_eq { all_rows ; shirt printing ; bluewings } ; year } ; 1996 } }
for the agg records of all rows , most of them are not equal to 0 .
Output: most_not_eq { all_rows ; agg ; 0 }
select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } }
select the rows whose competition record fuzzily matches to 1994 fifa world cup qualification . for the venue records of these rows , most of them fuzzily match to estadio cuscatlán , san salvador , el salvador .
Output: most_eq { filter_eq { all_rows ; competition ; 1994 fifa world cup qualification } ; venue ; estadio cuscatlán , san salvador , el salvador }
select the rows whose club record is arbitrary . the number of such rows is 8 .
Output: eq { count { filter_all { all_rows ; club } } ; 8 }
select the rows whose directed by record fuzzily matches to dearbhla walsh . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; directed by ; dearbhla walsh } } ; 3 }
select the rows whose record fuzzily matches to soybean oil . take the polyunsaturated fat record of this row . select the rows whose record fuzzily matches to rice bran oil . take the polyunsaturated fat record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ; soybean oil } ; polyunsaturated fat } ; hop { filter_eq { all_rows ; ; rice bran oil } ; polyunsaturated fat } }
select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 17 .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 17 }
select the row whose time record of all rows is minimum . the nationality record of this row is zimbabwe .
Output: eq { hop { argmin { all_rows ; time } ; nationality } ; zimbabwe }
select the rows whose score record is equal to 0 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; score ; 0 } } ; 7 }
for the date records of all rows , all of them fuzzily match to september .
Output: all_eq { all_rows ; date ; september }
select the rows whose score record fuzzily matches to 1 - 0 . among these rows , select the rows whose attendance record fuzzily matches to february . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; score ; 1 - 0 } ; attendance ; february } } ; 2 }
the 2nd minimum launch date record of all rows is 25 january 1958 . the name record of the row with 2nd minimum launch date record is rta tv - 5 .
Output: and { eq { nth_min { all_rows ; launch date ; 2 } ; 25 january 1958 } ; eq { hop { nth_argmin { all_rows ; launch date ; 2 } ; name } ; rta tv - 5 } }
the sum of the round record of all rows is 19 .
Output: round_eq { sum { all_rows ; round } ; 19 }
the sum of the money record of all rows is 1497 .
Output: round_eq { sum { all_rows ; money } ; 1497 }
the average of the points record of all rows is 1.4 .
Output: round_eq { avg { all_rows ; points } ; 1.4 }
select the rows whose high assists record fuzzily matches to steve nash . among these rows , select the rows whose location attendance record fuzzily matches to us airways center . the number of such rows is 6 .
Output: eq { count { filter_eq { filter_eq { all_rows ; high assists ; steve nash } ; location attendance ; us airways center } } ; 6 }