list of how it 's made episodes
the netflix tv series segment three wheeled vehicles recorded 66 episodes while the netflix tv series segment air filters recorded 70 episodes .
{'row_1': '1', 'row_2': '5', 'col': '2', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None}
{'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'three wheeled vehicles'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles .', 'tostr': 'filter_eq { all_rows ; segment a ; three wheeled vehicles }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'air filters'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to air filters .', 'tostr': 'filter_eq { all_rows ; segment a ; air filters }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; air filters } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'three wheeled vehicles'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles .', 'tostr': 'filter_eq { all_rows ; segment a ; three wheeled vehicles }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row .'}, '66'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 }', 'tointer': 'the episode record of the first row is 66 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'air filters'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to air filters .', 'tostr': 'filter_eq { all_rows ; segment a ; air filters }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; air filters } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row .'}, '70'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 }', 'tointer': 'the episode record of the second row is 70 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } }', 'tointer': 'the episode record of the first row is 66 . the episode record of the second row is 70 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } } = true', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .'}
and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } } = true
select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .
{'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'segment a_11': 'segment a', 'three wheeled vehicles_12': 'three wheeled vehicles', 'episode_13': 'episode', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'segment a_15': 'segment a', 'air filters_16': 'air filters', 'episode_17': 'episode', 'and_7': 'and', 'eq_5': 'eq', '66_18': '66', 'eq_6': 'eq', '70_19': '70'}
['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d']
[['6 - 01', '66', 's03e14', 'three wheeled vehicles', 'baseball bats', 'artificial bonsai', 's trombone'], ['6 - 02', '67', 's03e15', 's spring', 's paver', 's piano ( part 1 )', 's piano ( part 2 )'], ['6 - 03', '68', 's03e16', 's rope', 's billiard table', 's sailboard', 's cymbal'], ['6 - 04', '69', 's03e17', 's seatbelt', 's window', 'wax figurines', 'hot air balloons'], ['6 - 05', '70', 's03e18', 'air filters', 'billiard cues', 'ice sculptures', 's suit'], ['6 - 06', '71', 's03e19', 'escalator s handrail', 's highlighter', 'guitar s string', 'wigs'], ['6 - 07', '72', 's03e20', 'traditional bows', 's coffee machine', 's mascot', 's hammock'], ['6 - 08', '73', 's03e21', 'fibreglass insulation', 's wooden duck', 'gumball machines', 'exhaust systems'], ['6 - 09', '74', 's03e22', 's chain', 's bagel', 'vinyl records ( part 1 )', 'vinyl records ( part 2 )'], ['6 - 10', '75', 's03e23', 's windshield', 'english saddles', 'butter', 'post clocks'], ['6 - 11', '76', 's03e24', 'individual transporters', 'cedar canoes', 'electric guitars ( part 1 )', 'electric guitars ( part 2 )'], ['6 - 12', '77', 's03e25', 'residential water heaters', 'air bags', 'jelly beans', 'ice resurfacers']]