list of amusement park rankings
in the rankings for amusement parks , water country usa was ranked two positions lower than siam water park .
{'row_1': '20', 'row_2': '18', 'col': '1', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'yes', 'diff_result': {'diff_value': '2', 'bigger': 'row1'}}
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'water park', 'water country usa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose water park record fuzzily matches to water country usa .', 'tostr': 'filter_eq { all_rows ; water park ; water country usa }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; water park ; water country usa } ; rank }', 'tointer': 'select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'water park', 'siam water park'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose water park record fuzzily matches to siam water park .', 'tostr': 'filter_eq { all_rows ; water park ; siam water park }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; water park ; siam water park } ; rank }', 'tointer': 'select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row .'}], 'result': '2', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } }'}, '2'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } } ; 2 }', 'tointer': 'select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row . select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row . the first record is 2 larger than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'water park', 'water country usa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose water park record fuzzily matches to water country usa .', 'tostr': 'filter_eq { all_rows ; water park ; water country usa }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; water park ; water country usa } ; rank }', 'tointer': 'select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row .'}, '20'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 }', 'tointer': 'the rank record of the first row is 20 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'water park', 'siam water park'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose water park record fuzzily matches to siam water park .', 'tostr': 'filter_eq { all_rows ; water park ; siam water park }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; water park ; siam water park } ; rank }', 'tointer': 'select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row .'}, '18'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 }', 'tointer': 'the rank record of the second row is 18 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 } ; eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 } }', 'tointer': 'the rank record of the first row is 20 . the rank record of the second row is 18 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 } ; eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 } } } = true', 'tointer': 'select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row . select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row . the first record is 2 larger than the second record . the rank record of the first row is 20 . the rank record of the second row is 18 .'}
and { eq { diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 } ; eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 } } } = true
select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row . select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row . the first record is 2 larger than the second record . the rank record of the first row is 20 . the rank record of the second row is 18 .
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['rank', 'water park', 'location', '2011', '2012']
[['1', 'typhoon lagoon at walt disney world resort', 'lake buena vista , florida , usa', '2058000', '2100000'], ['2', 'chime - long water park', 'guangzhou , china', '1900000', '2021000'], ['3', 'blizzard beach at walt disney world resort', 'lake buena vista , florida , usa', '1891000', '1929000'], ['4', 'ocean world', 'gangwon - do , south korea', '1726000', '1720000'], ['5', 'aquatica', 'orlando , florida , usa', '1500000', '1538000'], ['6', 'caribbean bay at everland resort', 'gyeonggi - do , south korea', '1497000', '1508000'], ['7', 'aquaventure', 'dubai , united arab emirates', '1200000', '1300000'], ['8', "wet 'n wild orlando", 'orlando , florida , usa', '1223000', '1247000'], ['9', "wet 'n' wild water world", 'gold coast , queensland , australia', '1200000', '1200000'], ['10', 'sunway lagoon', 'kuala lumpur , malaysia', '1040000', '1200000'], ['11', 'resom spa castle', 'chungcheongnam - do , south korea', '1034000', '1158000'], ['12', 'schlitterbahn', 'new braunfels , texas , usa', '982000', '1017000'], ['13', 'atlantis water adventure', 'jakarta , indonesia', '950000', '1000000'], ['14', 'summerland', 'tokyo , japan', '850000', '990000'], ['15', 'happy magic water cube', 'beijing , china', '768000', '968000'], ['16', 'the jungle water adventure', 'bogor , indonesia', '871000', '951000'], ['17', 'wild wadi water park', 'dubai , united arab emirates', '890000', '860000'], ['18', 'siam water park', 'tenerife , spain', '800000', '800000'], ['19', 'ocean park water adventure', 'jakarta , indonesia', '600000', '750000'], ['20', 'water country usa', 'williamsburg , virginia , usa', '723000', '748000']]