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SELECT count(*) FROM catalog_contents
[ "Catalog_Contents" ]
[ "{\"columns\":[\"catalog_entry_id\",\"catalog_level_number\",\"parent_entry_id\",\"previous_entry_id\",\"next_entry_id\",\"catalog_entry_name\",\"product_stock_number\",\"price_in_dollars\",\"price_in_euros\",\"price_in_pounds\",\"capacity\",\"length\",\"height\",\"width\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,1,5,9,7,\"Cola\",\"89 cp\",200.78,159.84,172.17,\"1\",\"3\",\"9\",\"5\"],[2,8,6,9,8,\"Root beer\",\"37 hq\",687.59,590.11,471.78,\"8\",\"6\",\"5\",\"6\"],[3,8,6,6,1,\"Cream Soda\",\"52 ee\",360.5,202.32,110.32,\"5\",\"9\",\"7\",\"8\"],[4,1,7,8,6,\"Carbonated Water\",\"15 mr\",667.89,458.45,349.01,\"8\",\"6\",\"2\",\"1\"],[5,9,4,7,6,\"Ginger Beer\",\"42 cp\",616.22,537.66,405.75,\"5\",\"5\",\"7\",\"9\"],[6,1,3,4,8,\"Tizer\",\"61 py\",642.37,434.21,331.43,\"6\",\"6\",\"7\",\"1\"],[7,9,7,3,3,\"Vimto\",\"01 ap\",745.02,510.32,497.4,\"6\",\"9\",\"6\",\"5\"],[8,8,6,5,3,\"Ramune\",\"53 bg\",574.35,441.82,440.52,\"4\",\"4\",\"7\",\"5\"],[9,1,7,9,9,\"Sprite Lemo\",\"24 ec\",952.37,703.17,433.82,\"8\",\"7\",\"1\",\"3\"],[10,8,5,6,6,\"Dr Pepper\",\"26 op\",777.41,616.54,572.41,\"1\",\"6\",\"1\",\"6\"],[11,1,3,6,9,\"Diet Pepsi\",\"49 jg\",808.31,643.77,515.62,\"9\",\"8\",\"3\",\"3\"],[12,8,4,5,3,\"Diet Mountain Dew\",\"96 zx\",883.43,752.87,678.01,\"8\",\"8\",\"1\",\"3\"],[13,1,5,9,1,\"Mountain Dew\",\"49 cz\",475.79,457.4,335.63,\"7\",\"8\",\"4\",\"5\"],[14,9,3,5,8,\"Fenta Orange\",\"65 wc\",415.92,385.85,371.9,\"7\",\"4\",\"3\",\"7\"],[15,1,6,8,9,\"Wanglaoji\",\"51 kr\",533.6,498.62,422.71,\"4\",\"5\",\"8\",\"8\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[15]]}
SELECT count(*) FROM catalog_contents <table_name> : Catalog_Contents col : catalog_entry_id | catalog_level_number | parent_entry_id | previous_entry_id | next_entry_id | catalog_entry_name | product_stock_number | price_in_dollars | price_in_euros | price_in_pounds | capacity | length | height | width row 1 : 1 | 1 | 5 | 9 | 7 | Cola | 89 cp | 200.78 | 159.84 | 172.17 | 1 | 3 | 9 | 5 row 2 : 2 | 8 | 6 | 9 | 8 | Root beer | 37 hq | 687.59 | 590.11 | 471.78 | 8 | 6 | 5 | 6 row 3 : 3 | 8 | 6 | 6 | 1 | Cream Soda | 52 ee | 360.5 | 202.32 | 110.32 | 5 | 9 | 7 | 8 row 4 : 4 | 1 | 7 | 8 | 6 | Carbonated Water | 15 mr | 667.89 | 458.45 | 349.01 | 8 | 6 | 2 | 1 row 5 : 5 | 9 | 4 | 7 | 6 | Ginger Beer | 42 cp | 616.22 | 537.66 | 405.75 | 5 | 5 | 7 | 9 row 6 : 6 | 1 | 3 | 4 | 8 | Tizer | 61 py | 642.37 | 434.21 | 331.43 | 6 | 6 | 7 | 1 row 7 : 7 | 9 | 7 | 3 | 3 | Vimto | 01 ap | 745.02 | 510.32 | 497.4 | 6 | 9 | 6 | 5 row 8 : 8 | 8 | 6 | 5 | 3 | Ramune | 53 bg | 574.35 | 441.82 | 440.52 | 4 | 4 | 7 | 5 row 9 : 9 | 1 | 7 | 9 | 9 | Sprite Lemo | 24 ec | 952.37 | 703.17 | 433.82 | 8 | 7 | 1 | 3 row 10 : 10 | 8 | 5 | 6 | 6 | Dr Pepper | 26 op | 777.41 | 616.54 | 572.41 | 1 | 6 | 1 | 6 row 11 : 11 | 1 | 3 | 6 | 9 | Diet Pepsi | 49 jg | 808.31 | 643.77 | 515.62 | 9 | 8 | 3 | 3 row 12 : 12 | 8 | 4 | 5 | 3 | Diet Mountain Dew | 96 zx | 883.43 | 752.87 | 678.01 | 8 | 8 | 1 | 3 row 13 : 13 | 1 | 5 | 9 | 1 | Mountain Dew | 49 cz | 475.79 | 457.4 | 335.63 | 7 | 8 | 4 | 5 row 14 : 14 | 9 | 3 | 5 | 8 | Fenta Orange | 65 wc | 415.92 | 385.85 | 371.9 | 7 | 4 | 3 | 7 row 15 : 15 | 1 | 6 | 8 | 9 | Wanglaoji | 51 kr | 533.6 | 498.62 | 422.71 | 4 | 5 | 8 | 8
col : count(*) row 1 : 15
SELECT catalog_entry_name FROM catalog_contents WHERE next_entry_id > 8
[ "Catalog_Contents" ]
[ "{\"columns\":[\"catalog_entry_id\",\"catalog_level_number\",\"parent_entry_id\",\"previous_entry_id\",\"next_entry_id\",\"catalog_entry_name\",\"product_stock_number\",\"price_in_dollars\",\"price_in_euros\",\"price_in_pounds\",\"capacity\",\"length\",\"height\",\"width\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,1,5,9,7,\"Cola\",\"89 cp\",200.78,159.84,172.17,\"1\",\"3\",\"9\",\"5\"],[2,8,6,9,8,\"Root beer\",\"37 hq\",687.59,590.11,471.78,\"8\",\"6\",\"5\",\"6\"],[3,8,6,6,1,\"Cream Soda\",\"52 ee\",360.5,202.32,110.32,\"5\",\"9\",\"7\",\"8\"],[4,1,7,8,6,\"Carbonated Water\",\"15 mr\",667.89,458.45,349.01,\"8\",\"6\",\"2\",\"1\"],[5,9,4,7,6,\"Ginger Beer\",\"42 cp\",616.22,537.66,405.75,\"5\",\"5\",\"7\",\"9\"],[6,1,3,4,8,\"Tizer\",\"61 py\",642.37,434.21,331.43,\"6\",\"6\",\"7\",\"1\"],[7,9,7,3,3,\"Vimto\",\"01 ap\",745.02,510.32,497.4,\"6\",\"9\",\"6\",\"5\"],[8,8,6,5,3,\"Ramune\",\"53 bg\",574.35,441.82,440.52,\"4\",\"4\",\"7\",\"5\"],[9,1,7,9,9,\"Sprite Lemo\",\"24 ec\",952.37,703.17,433.82,\"8\",\"7\",\"1\",\"3\"],[10,8,5,6,6,\"Dr Pepper\",\"26 op\",777.41,616.54,572.41,\"1\",\"6\",\"1\",\"6\"],[11,1,3,6,9,\"Diet Pepsi\",\"49 jg\",808.31,643.77,515.62,\"9\",\"8\",\"3\",\"3\"],[12,8,4,5,3,\"Diet Mountain Dew\",\"96 zx\",883.43,752.87,678.01,\"8\",\"8\",\"1\",\"3\"],[13,1,5,9,1,\"Mountain Dew\",\"49 cz\",475.79,457.4,335.63,\"7\",\"8\",\"4\",\"5\"],[14,9,3,5,8,\"Fenta Orange\",\"65 wc\",415.92,385.85,371.9,\"7\",\"4\",\"3\",\"7\"],[15,1,6,8,9,\"Wanglaoji\",\"51 kr\",533.6,498.62,422.71,\"4\",\"5\",\"8\",\"8\"]]}" ]
{"columns":["catalog_entry_name"],"index":[0,1,2],"data":[["Sprite Lemo"],["Diet Pepsi"],["Wanglaoji"]]}
SELECT catalog_entry_name FROM catalog_contents WHERE next_entry_id > 8 <table_name> : Catalog_Contents col : catalog_entry_id | catalog_level_number | parent_entry_id | previous_entry_id | next_entry_id | catalog_entry_name | product_stock_number | price_in_dollars | price_in_euros | price_in_pounds | capacity | length | height | width row 1 : 1 | 1 | 5 | 9 | 7 | Cola | 89 cp | 200.78 | 159.84 | 172.17 | 1 | 3 | 9 | 5 row 2 : 2 | 8 | 6 | 9 | 8 | Root beer | 37 hq | 687.59 | 590.11 | 471.78 | 8 | 6 | 5 | 6 row 3 : 3 | 8 | 6 | 6 | 1 | Cream Soda | 52 ee | 360.5 | 202.32 | 110.32 | 5 | 9 | 7 | 8 row 4 : 4 | 1 | 7 | 8 | 6 | Carbonated Water | 15 mr | 667.89 | 458.45 | 349.01 | 8 | 6 | 2 | 1 row 5 : 5 | 9 | 4 | 7 | 6 | Ginger Beer | 42 cp | 616.22 | 537.66 | 405.75 | 5 | 5 | 7 | 9 row 6 : 6 | 1 | 3 | 4 | 8 | Tizer | 61 py | 642.37 | 434.21 | 331.43 | 6 | 6 | 7 | 1 row 7 : 7 | 9 | 7 | 3 | 3 | Vimto | 01 ap | 745.02 | 510.32 | 497.4 | 6 | 9 | 6 | 5 row 8 : 8 | 8 | 6 | 5 | 3 | Ramune | 53 bg | 574.35 | 441.82 | 440.52 | 4 | 4 | 7 | 5 row 9 : 9 | 1 | 7 | 9 | 9 | Sprite Lemo | 24 ec | 952.37 | 703.17 | 433.82 | 8 | 7 | 1 | 3 row 10 : 10 | 8 | 5 | 6 | 6 | Dr Pepper | 26 op | 777.41 | 616.54 | 572.41 | 1 | 6 | 1 | 6 row 11 : 11 | 1 | 3 | 6 | 9 | Diet Pepsi | 49 jg | 808.31 | 643.77 | 515.62 | 9 | 8 | 3 | 3 row 12 : 12 | 8 | 4 | 5 | 3 | Diet Mountain Dew | 96 zx | 883.43 | 752.87 | 678.01 | 8 | 8 | 1 | 3 row 13 : 13 | 1 | 5 | 9 | 1 | Mountain Dew | 49 cz | 475.79 | 457.4 | 335.63 | 7 | 8 | 4 | 5 row 14 : 14 | 9 | 3 | 5 | 8 | Fenta Orange | 65 wc | 415.92 | 385.85 | 371.9 | 7 | 4 | 3 | 7 row 15 : 15 | 1 | 6 | 8 | 9 | Wanglaoji | 51 kr | 533.6 | 498.62 | 422.71 | 4 | 5 | 8 | 8
col : catalog_entry_name row 1 : Sprite Lemo row 2 : Diet Pepsi row 3 : Wanglaoji
SELECT count(*) FROM Aircraft
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[16]]}
SELECT count(*) FROM Aircraft <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : count(*) row 1 : 16
SELECT name , distance FROM Aircraft
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name","distance"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],"data":[["Boeing 747-400",8430],["Boeing 737-800",3383],["Airbus A340-300",7120],["British Aerospace Jetstream 41",1502],["Embraer ERJ-145",1530],["SAAB 340",2128],["Piper Archer III",520],["Tupolev 154",4103],["Schwitzer 2-33",30],["Lockheed L1011",6900],["Boeing 757-300",4010],["Boeing 777-300",6441],["Boeing 767-400ER",6475],["Airbus A320",2605],["Airbus A319",1805],["Boeing 727",1504]]}
SELECT name , distance FROM Aircraft <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name | distance row 1 : Boeing 747-400 | 8430 row 2 : Boeing 737-800 | 3383 row 3 : Airbus A340-300 | 7120 row 4 : British Aerospace Jetstream 41 | 1502 row 5 : Embraer ERJ-145 | 1530 row 6 : SAAB 340 | 2128 row 7 : Piper Archer III | 520 row 8 : Tupolev 154 | 4103 row 9 : Schwitzer 2-33 | 30 row 10 : Lockheed L1011 | 6900 row 11 : Boeing 757-300 | 4010 row 12 : Boeing 777-300 | 6441 row 13 : Boeing 767-400ER | 6475 row 14 : Airbus A320 | 2605 row 15 : Airbus A319 | 1805 row 16 : Boeing 727 | 1504
SELECT aid FROM Aircraft WHERE distance > 1000
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["aid"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],"data":[[1],[2],[3],[4],[5],[6],[8],[9],[10],[11],[12],[13],[14],[15]]}
SELECT aid FROM Aircraft WHERE distance > 1000 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : aid row 1 : 1 row 2 : 2 row 3 : 3 row 4 : 4 row 5 : 5 row 6 : 6 row 7 : 8 row 8 : 9 row 9 : 10 row 10 : 11 row 11 : 12 row 12 : 13 row 13 : 14 row 14 : 15
SELECT count(*) FROM Aircraft WHERE distance BETWEEN 1000 AND 5000
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[9]]}
SELECT count(*) FROM Aircraft WHERE distance BETWEEN 1000 AND 5000 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : count(*) row 1 : 9
SELECT name , distance FROM Aircraft WHERE aid = 12
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name","distance"],"index":[0],"data":[["Boeing 767-400ER",6475]]}
SELECT name , distance FROM Aircraft WHERE aid = 12 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name | distance row 1 : Boeing 767-400ER | 6475
SELECT min(distance) , avg(distance) , max(distance) FROM Aircraft
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["min(distance)","avg(distance)","max(distance)"],"index":[0],"data":[[30,3655.375,8430]]}
SELECT min(distance) , avg(distance) , max(distance) FROM Aircraft <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : min(distance) | avg(distance) | max(distance) row 1 : 30 | 3655.375 | 8430
SELECT aid , name FROM Aircraft ORDER BY distance DESC LIMIT 1
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["aid","name"],"index":[0],"data":[[1,"Boeing 747-400"]]}
SELECT aid , name FROM Aircraft ORDER BY distance DESC LIMIT 1 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : aid | name row 1 : 1 | Boeing 747-400
SELECT name FROM Aircraft ORDER BY distance LIMIT 3
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name"],"index":[0,1,2],"data":[["Schwitzer 2-33"],["Piper Archer III"],["British Aerospace Jetstream 41"]]}
SELECT name FROM Aircraft ORDER BY distance LIMIT 3 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name row 1 : Schwitzer 2-33 row 2 : Piper Archer III row 3 : British Aerospace Jetstream 41
SELECT name FROM Aircraft WHERE distance > (SELECT avg(distance) FROM Aircraft)
[ "aircraft" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name"],"index":[0,1,2,3,4,5,6],"data":[["Boeing 747-400"],["Airbus A340-300"],["Tupolev 154"],["Lockheed L1011"],["Boeing 757-300"],["Boeing 777-300"],["Boeing 767-400ER"]]}
SELECT name FROM Aircraft WHERE distance > (SELECT avg(distance) FROM Aircraft) <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name row 1 : Boeing 747-400 row 2 : Airbus A340-300 row 3 : Tupolev 154 row 4 : Lockheed L1011 row 5 : Boeing 757-300 row 6 : Boeing 777-300 row 7 : Boeing 767-400ER
SELECT count(*) FROM Employee
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[31]]}
SELECT count(*) FROM Employee <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : count(*) row 1 : 31
SELECT name , salary FROM Employee ORDER BY salary
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["name","salary"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],"data":[["Milo Brooks",20],["Donald King",18050],["Richard Jackson",23980],["Patricia Jones",24450],["Linda Davis",27984],["Elizabeth Taylor",32021],["Haywood Kelly",32899],["Chad Stewart",33546],["David Anderson",43001],["Barbara Wilson",43723],["Robert Brown",44740],["Michael Miller",48090],["William Moore",48250],["Jennifer Thomas",54921],["William Ward",84476],["Michael Miller",99890],["Larry West",101745],["William Jones",105743],["Eric Cooper",114323],["James Smith",120433],["Dorthy Lewis",152013],["John Williams",153972],["Mary Johnson",178345],["Karen Scott",205187],["Mark Young",205187],["Lawrence Sperry",212156],["Angela Martinez",212156],["Joseph Thompson",212156],["Betty Adams",227489],["Lisa Walker",256481],["George Wright",289950]]}
SELECT name , salary FROM Employee ORDER BY salary <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : name | salary row 1 : Milo Brooks | 20 row 2 : Donald King | 18050 row 3 : Richard Jackson | 23980 row 4 : Patricia Jones | 24450 row 5 : Linda Davis | 27984 row 6 : Elizabeth Taylor | 32021 row 7 : Haywood Kelly | 32899 row 8 : Chad Stewart | 33546 row 9 : David Anderson | 43001 row 10 : Barbara Wilson | 43723 row 11 : Robert Brown | 44740 row 12 : Michael Miller | 48090 row 13 : William Moore | 48250 row 14 : Jennifer Thomas | 54921 row 15 : William Ward | 84476 row 16 : Michael Miller | 99890 row 17 : Larry West | 101745 row 18 : William Jones | 105743 row 19 : Eric Cooper | 114323 row 20 : James Smith | 120433 row 21 : Dorthy Lewis | 152013 row 22 : John Williams | 153972 row 23 : Mary Johnson | 178345 row 24 : Karen Scott | 205187 row 25 : Mark Young | 205187 row 26 : Lawrence Sperry | 212156 row 27 : Angela Martinez | 212156 row 28 : Joseph Thompson | 212156 row 29 : Betty Adams | 227489 row 30 : Lisa Walker | 256481 row 31 : George Wright | 289950
SELECT eid FROM Employee WHERE salary > 100000
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["eid"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],"data":[[242518965],[141582651],[11564812],[567354612],[552455318],[550156548],[390487451],[355548984],[310454876],[142519864],[269734834],[552455348],[556784565],[573284895],[574489456]]}
SELECT eid FROM Employee WHERE salary > 100000 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : eid row 1 : 242518965 row 2 : 141582651 row 3 : 11564812 row 4 : 567354612 row 5 : 552455318 row 6 : 550156548 row 7 : 390487451 row 8 : 355548984 row 9 : 310454876 row 10 : 142519864 row 11 : 269734834 row 12 : 552455348 row 13 : 556784565 row 14 : 573284895 row 15 : 574489456
SELECT count(*) FROM Employee WHERE salary BETWEEN 100000 AND 200000
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[7]]}
SELECT count(*) FROM Employee WHERE salary BETWEEN 100000 AND 200000 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : count(*) row 1 : 7
SELECT name , salary FROM Employee WHERE eid = 242518965
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["name","salary"],"index":[0],"data":[["James Smith",120433]]}
SELECT name , salary FROM Employee WHERE eid = 242518965 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : name | salary row 1 : James Smith | 120433
SELECT avg(salary) , max(salary) FROM Employee
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["avg(salary)","max(salary)"],"index":[0],"data":[[109915.3870967742,289950]]}
SELECT avg(salary) , max(salary) FROM Employee <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : avg(salary) | max(salary) row 1 : 109915.3870967742 | 289950
SELECT eid , name FROM Employee ORDER BY salary DESC LIMIT 1
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["eid","name"],"index":[0],"data":[[269734834,"George Wright"]]}
SELECT eid , name FROM Employee ORDER BY salary DESC LIMIT 1 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : eid | name row 1 : 269734834 | George Wright
SELECT name FROM Employee ORDER BY salary ASC LIMIT 3
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["name"],"index":[0,1,2],"data":[["Milo Brooks"],["Donald King"],["Richard Jackson"]]}
SELECT name FROM Employee ORDER BY salary ASC LIMIT 3 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : name row 1 : Milo Brooks row 2 : Donald King row 3 : Richard Jackson
SELECT name FROM Employee WHERE salary > (SELECT avg(salary) FROM Employee)
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["name"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12],"data":[["James Smith"],["Mary Johnson"],["John Williams"],["Lisa Walker"],["Karen Scott"],["Lawrence Sperry"],["Angela Martinez"],["Joseph Thompson"],["Betty Adams"],["George Wright"],["Dorthy Lewis"],["Mark Young"],["Eric Cooper"]]}
SELECT name FROM Employee WHERE salary > (SELECT avg(salary) FROM Employee) <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : name row 1 : James Smith row 2 : Mary Johnson row 3 : John Williams row 4 : Lisa Walker row 5 : Karen Scott row 6 : Lawrence Sperry row 7 : Angela Martinez row 8 : Joseph Thompson row 9 : Betty Adams row 10 : George Wright row 11 : Dorthy Lewis row 12 : Mark Young row 13 : Eric Cooper
SELECT eid , salary FROM Employee WHERE name = 'Mark Young'
[ "employee" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}" ]
{"columns":["eid","salary"],"index":[0],"data":[[556784565,205187]]}
SELECT eid , salary FROM Employee WHERE name = 'Mark Young' <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20
col : eid | salary row 1 : 556784565 | 205187
SELECT count(*) FROM Flight
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[10]]}
SELECT count(*) FROM Flight <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : count(*) row 1 : 10
SELECT flno , origin , destination FROM Flight ORDER BY origin
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["flno","origin","destination"],"index":[0,1,2,3,4,5,6,7,8,9],"data":[[76,"Chicago","Los Angeles"],[68,"Chicago","New York"],[99,"Los Angeles","Washington D.C."],[13,"Los Angeles","Chicago"],[346,"Los Angeles","Dallas"],[387,"Los Angeles","Boston"],[7,"Los Angeles","Sydney"],[2,"Los Angeles","Tokyo"],[33,"Los Angeles","Honolulu"],[34,"Los Angeles","Honolulu"]]}
SELECT flno , origin , destination FROM Flight ORDER BY origin <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : flno | origin | destination row 1 : 76 | Chicago | Los Angeles row 2 : 68 | Chicago | New York row 3 : 99 | Los Angeles | Washington D.C. row 4 : 13 | Los Angeles | Chicago row 5 : 346 | Los Angeles | Dallas row 6 : 387 | Los Angeles | Boston row 7 : 7 | Los Angeles | Sydney row 8 : 2 | Los Angeles | Tokyo row 9 : 33 | Los Angeles | Honolulu row 10 : 34 | Los Angeles | Honolulu
SELECT flno FROM Flight WHERE origin = "Los Angeles"
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["flno"],"index":[0,1,2,3,4,5,6,7],"data":[[99],[13],[346],[387],[7],[2],[33],[34]]}
SELECT flno FROM Flight WHERE origin = "Los Angeles" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : flno row 1 : 99 row 2 : 13 row 3 : 346 row 4 : 387 row 5 : 7 row 6 : 2 row 7 : 33 row 8 : 34
SELECT origin FROM Flight WHERE destination = "Honolulu"
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["origin"],"index":[0,1],"data":[["Los Angeles"],["Los Angeles"]]}
SELECT origin FROM Flight WHERE destination = "Honolulu" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : origin row 1 : Los Angeles row 2 : Los Angeles
SELECT departure_date , arrival_date FROM Flight WHERE origin = "Los Angeles" AND destination = "Honolulu"
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["departure_date","arrival_date"],"index":[0,1],"data":[["04\/12\/2005 09:15","04\/12\/2005 11:15"],["04\/12\/2005 12:45","04\/12\/2005 03:18"]]}
SELECT departure_date , arrival_date FROM Flight WHERE origin = "Los Angeles" AND destination = "Honolulu" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : departure_date | arrival_date row 1 : 04/12/2005 09:15 | 04/12/2005 11:15 row 2 : 04/12/2005 12:45 | 04/12/2005 03:18
SELECT flno FROM Flight WHERE distance > 2000
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["flno"],"index":[0,1,2,3,4,5],"data":[[99],[387],[7],[2],[33],[34]]}
SELECT flno FROM Flight WHERE distance > 2000 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : flno row 1 : 99 row 2 : 387 row 3 : 7 row 4 : 2 row 5 : 33 row 6 : 34
SELECT avg(price) FROM Flight WHERE origin = "Los Angeles" AND destination = "Honolulu"
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["avg(price)"],"index":[0],"data":[[400.605]]}
SELECT avg(price) FROM Flight WHERE origin = "Los Angeles" AND destination = "Honolulu" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : avg(price) row 1 : 400.605
SELECT origin , destination FROM Flight WHERE price > 300
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["origin","destination"],"index":[0,1,2],"data":[["Los Angeles","Tokyo"],["Los Angeles","Honolulu"],["Los Angeles","Honolulu"]]}
SELECT origin , destination FROM Flight WHERE price > 300 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : origin | destination row 1 : Los Angeles | Tokyo row 2 : Los Angeles | Honolulu row 3 : Los Angeles | Honolulu
SELECT flno , distance FROM Flight ORDER BY price DESC LIMIT 1
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["flno","distance"],"index":[0],"data":[[2,5478]]}
SELECT flno , distance FROM Flight ORDER BY price DESC LIMIT 1 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : flno | distance row 1 : 2 | 5478
SELECT flno FROM Flight ORDER BY distance ASC LIMIT 3
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["flno"],"index":[0,1,2],"data":[[68],[346],[13]]}
SELECT flno FROM Flight ORDER BY distance ASC LIMIT 3 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : flno row 1 : 68 row 2 : 346 row 3 : 13
SELECT avg(distance) , avg(price) FROM Flight WHERE origin = "Los Angeles"
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["avg(distance)","avg(price)"],"index":[0],"data":[[3247.625,345.16]]}
SELECT avg(distance) , avg(price) FROM Flight WHERE origin = "Los Angeles" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : avg(distance) | avg(price) row 1 : 3247.625 | 345.16
SELECT origin , count(*) FROM Flight GROUP BY origin
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["origin","count(*)"],"index":[0,1],"data":[["Chicago",2],["Los Angeles",8]]}
SELECT origin , count(*) FROM Flight GROUP BY origin <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : origin | count(*) row 1 : Chicago | 2 row 2 : Los Angeles | 8
SELECT destination , count(*) FROM Flight GROUP BY destination
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["destination","count(*)"],"index":[0,1,2,3,4,5,6,7,8],"data":[["Boston",1],["Chicago",1],["Dallas",1],["Honolulu",2],["Los Angeles",1],["New York",1],["Sydney",1],["Tokyo",1],["Washington D.C.",1]]}
SELECT destination , count(*) FROM Flight GROUP BY destination <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : destination | count(*) row 1 : Boston | 1 row 2 : Chicago | 1 row 3 : Dallas | 1 row 4 : Honolulu | 2 row 5 : Los Angeles | 1 row 6 : New York | 1 row 7 : Sydney | 1 row 8 : Tokyo | 1 row 9 : Washington D.C. | 1
SELECT origin FROM Flight GROUP BY origin ORDER BY count(*) DESC LIMIT 1
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["origin"],"index":[0],"data":[["Los Angeles"]]}
SELECT origin FROM Flight GROUP BY origin ORDER BY count(*) DESC LIMIT 1 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : origin row 1 : Los Angeles
SELECT destination FROM Flight GROUP BY destination ORDER BY count(*) LIMIT 1
[ "flight" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}" ]
{"columns":["destination"],"index":[0],"data":[["Boston"]]}
SELECT destination FROM Flight GROUP BY destination ORDER BY count(*) LIMIT 1 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10
col : destination row 1 : Boston
SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid WHERE T1.flno = 99
[ "flight", "aircraft" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}", "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name"],"index":[0],"data":[["Boeing 747-400"]]}
SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid WHERE T1.flno = 99 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name row 1 : Boeing 747-400
SELECT T1.flno FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid WHERE T2.name = "Airbus A340-300"
[ "flight", "aircraft" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}", "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["flno"],"index":[0,1],"data":[[13],[7]]}
SELECT T1.flno FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid WHERE T2.name = "Airbus A340-300" <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : flno row 1 : 13 row 2 : 7
SELECT T2.name , count(*) FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid GROUP BY T1.aid
[ "flight", "aircraft" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}", "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name","count(*)"],"index":[0,1,2,3,4,5,6,7],"data":[["Boeing 747-400",1],["Boeing 737-800",1],["Airbus A340-300",2],["Embraer ERJ-145",1],["SAAB 340",1],["Piper Archer III",1],["Lockheed L1011",2],["Boeing 757-300",1]]}
SELECT T2.name , count(*) FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid GROUP BY T1.aid <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name | count(*) row 1 : Boeing 747-400 | 1 row 2 : Boeing 737-800 | 1 row 3 : Airbus A340-300 | 2 row 4 : Embraer ERJ-145 | 1 row 5 : SAAB 340 | 1 row 6 : Piper Archer III | 1 row 7 : Lockheed L1011 | 2 row 8 : Boeing 757-300 | 1
SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid GROUP BY T1.aid HAVING count(*) >= 2
[ "flight", "aircraft" ]
[ "{\"columns\":[\"flno\",\"origin\",\"destination\",\"distance\",\"departure_date\",\"arrival_date\",\"price\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[99,\"Los Angeles\",\"Washington D.C.\",2308,\"04\\/12\\/2005 09:30\",\"04\\/12\\/2005 09:40\",235.98,1],[13,\"Los Angeles\",\"Chicago\",1749,\"04\\/12\\/2005 08:45\",\"04\\/12\\/2005 08:45\",220.98,3],[346,\"Los Angeles\",\"Dallas\",1251,\"04\\/12\\/2005 11:50\",\"04\\/12\\/2005 07:05\",182.0,2],[387,\"Los Angeles\",\"Boston\",2606,\"04\\/12\\/2005 07:03\",\"04\\/12\\/2005 05:03\",261.56,6],[7,\"Los Angeles\",\"Sydney\",7487,\"04\\/12\\/2005 05:30\",\"04\\/12\\/2005 11:10\",278.56,3],[2,\"Los Angeles\",\"Tokyo\",5478,\"04\\/12\\/2005 06:30\",\"04\\/12\\/2005 03:55\",780.99,9],[33,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 09:15\",\"04\\/12\\/2005 11:15\",375.23,7],[34,\"Los Angeles\",\"Honolulu\",2551,\"04\\/12\\/2005 12:45\",\"04\\/12\\/2005 03:18\",425.98,5],[76,\"Chicago\",\"Los Angeles\",1749,\"04\\/12\\/2005 08:32\",\"04\\/12\\/2005 10:03\",220.98,9],[68,\"Chicago\",\"New York\",802,\"04\\/12\\/2005 09:00\",\"04\\/12\\/2005 12:02\",202.45,10]]}", "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}" ]
{"columns":["name"],"index":[0,1],"data":[["Airbus A340-300"],["Lockheed L1011"]]}
SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid = T2.aid GROUP BY T1.aid HAVING count(*) >= 2 <table_name> : flight col : flno | origin | destination | distance | departure_date | arrival_date | price | aid row 1 : 99 | Los Angeles | Washington D.C. | 2308 | 04/12/2005 09:30 | 04/12/2005 09:40 | 235.98 | 1 row 2 : 13 | Los Angeles | Chicago | 1749 | 04/12/2005 08:45 | 04/12/2005 08:45 | 220.98 | 3 row 3 : 346 | Los Angeles | Dallas | 1251 | 04/12/2005 11:50 | 04/12/2005 07:05 | 182.0 | 2 row 4 : 387 | Los Angeles | Boston | 2606 | 04/12/2005 07:03 | 04/12/2005 05:03 | 261.56 | 6 row 5 : 7 | Los Angeles | Sydney | 7487 | 04/12/2005 05:30 | 04/12/2005 11:10 | 278.56 | 3 row 6 : 2 | Los Angeles | Tokyo | 5478 | 04/12/2005 06:30 | 04/12/2005 03:55 | 780.99 | 9 row 7 : 33 | Los Angeles | Honolulu | 2551 | 04/12/2005 09:15 | 04/12/2005 11:15 | 375.23 | 7 row 8 : 34 | Los Angeles | Honolulu | 2551 | 04/12/2005 12:45 | 04/12/2005 03:18 | 425.98 | 5 row 9 : 76 | Chicago | Los Angeles | 1749 | 04/12/2005 08:32 | 04/12/2005 10:03 | 220.98 | 9 row 10 : 68 | Chicago | New York | 802 | 04/12/2005 09:00 | 04/12/2005 12:02 | 202.45 | 10 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504
col : name row 1 : Airbus A340-300 row 2 : Lockheed L1011
SELECT count(DISTINCT eid) FROM Certificate
[ "certificate" ]
[ "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["count(DISTINCT eid)"],"index":[0],"data":[[20]]}
SELECT count(DISTINCT eid) FROM Certificate <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : count(DISTINCT eid) row 1 : 20
SELECT eid FROM Employee EXCEPT SELECT eid FROM Certificate
[ "employee", "certificate" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["eid"],"index":[0,1,2,3,4,5,6,7,8,9,10],"data":[[15645489],[248965255],[254099823],[287321212],[310454877],[348121549],[486512566],[489221823],[489456522],[552455348],[619023588]]}
SELECT eid FROM Employee EXCEPT SELECT eid FROM Certificate <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : eid row 1 : 15645489 row 2 : 248965255 row 3 : 254099823 row 4 : 287321212 row 5 : 310454877 row 6 : 348121549 row 7 : 486512566 row 8 : 489221823 row 9 : 489456522 row 10 : 552455348 row 11 : 619023588
SELECT T3.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T1.name = "John Williams"
[ "aircraft", "employee", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0,1],"data":[["Boeing 737-800"],["Boeing 757-300"]]}
SELECT T3.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T1.name = "John Williams" <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : Boeing 737-800 row 2 : Boeing 757-300
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800"
[ "aircraft", "employee", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0,1,2,3,4,5,6,7],"data":[["John Williams"],["Mary Johnson"],["Betty Adams"],["James Smith"],["George Wright"],["Larry West"],["Mark Young"],["Lisa Walker"]]}
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800" <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : John Williams row 2 : Mary Johnson row 3 : Betty Adams row 4 : James Smith row 5 : George Wright row 6 : Larry West row 7 : Mark Young row 8 : Lisa Walker
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800" INTERSECT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Airbus A340-300"
[ "aircraft", "employee", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0,1,2,3],"data":[["Betty Adams"],["George Wright"],["Lisa Walker"],["Mark Young"]]}
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800" INTERSECT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Airbus A340-300" <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : Betty Adams row 2 : George Wright row 3 : Lisa Walker row 4 : Mark Young
SELECT name FROM Employee EXCEPT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800"
[ "aircraft", "employee", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21],"data":[["Angela Martinez"],["Barbara Wilson"],["Chad Stewart"],["David Anderson"],["Donald King"],["Dorthy Lewis"],["Elizabeth Taylor"],["Eric Cooper"],["Haywood Kelly"],["Jennifer Thomas"],["Joseph Thompson"],["Karen Scott"],["Lawrence Sperry"],["Linda Davis"],["Michael Miller"],["Milo Brooks"],["Patricia Jones"],["Richard Jackson"],["Robert Brown"],["William Jones"],["William Moore"],["William Ward"]]}
SELECT name FROM Employee EXCEPT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800" <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : Angela Martinez row 2 : Barbara Wilson row 3 : Chad Stewart row 4 : David Anderson row 5 : Donald King row 6 : Dorthy Lewis row 7 : Elizabeth Taylor row 8 : Eric Cooper row 9 : Haywood Kelly row 10 : Jennifer Thomas row 11 : Joseph Thompson row 12 : Karen Scott row 13 : Lawrence Sperry row 14 : Linda Davis row 15 : Michael Miller row 16 : Milo Brooks row 17 : Patricia Jones row 18 : Richard Jackson row 19 : Robert Brown row 20 : William Jones row 21 : William Moore row 22 : William Ward
SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid = T1.aid GROUP BY T1.aid ORDER BY count(*) DESC LIMIT 1
[ "aircraft", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0],"data":[["Boeing 737-800"]]}
SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid = T1.aid GROUP BY T1.aid ORDER BY count(*) DESC LIMIT 1 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : Boeing 737-800
SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid = T1.aid WHERE T2.distance > 5000 GROUP BY T1.aid ORDER BY count(*) >= 5
[ "aircraft", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0,1,2,3,4],"data":[["Boeing 747-400"],["Lockheed L1011"],["Boeing 777-300"],["Airbus A340-300"],["Boeing 767-400ER"]]}
SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid = T1.aid WHERE T2.distance > 5000 GROUP BY T1.aid ORDER BY count(*) >= 5 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : Boeing 747-400 row 2 : Lockheed L1011 row 3 : Boeing 777-300 row 4 : Airbus A340-300 row 5 : Boeing 767-400ER
SELECT T1.name , T1.salary FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1
[ "employee", "certificate" ]
[ "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name","salary"],"index":[0],"data":[["George Wright",289950]]}
SELECT T1.name , T1.salary FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name | salary row 1 : George Wright | 289950
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.distance > 5000 GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1
[ "aircraft", "employee", "certificate" ]
[ "{\"columns\":[\"aid\",\"name\",\"distance\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],\"data\":[[1,\"Boeing 747-400\",8430],[2,\"Boeing 737-800\",3383],[3,\"Airbus A340-300\",7120],[4,\"British Aerospace Jetstream 41\",1502],[5,\"Embraer ERJ-145\",1530],[6,\"SAAB 340\",2128],[7,\"Piper Archer III\",520],[8,\"Tupolev 154\",4103],[16,\"Schwitzer 2-33\",30],[9,\"Lockheed L1011\",6900],[10,\"Boeing 757-300\",4010],[11,\"Boeing 777-300\",6441],[12,\"Boeing 767-400ER\",6475],[13,\"Airbus A320\",2605],[14,\"Airbus A319\",1805],[15,\"Boeing 727\",1504]]}", "{\"columns\":[\"eid\",\"name\",\"salary\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\"data\":[[242518965,\"James Smith\",120433],[141582651,\"Mary Johnson\",178345],[11564812,\"John Williams\",153972],[567354612,\"Lisa Walker\",256481],[552455318,\"Larry West\",101745],[550156548,\"Karen Scott\",205187],[390487451,\"Lawrence Sperry\",212156],[274878974,\"Michael Miller\",99890],[254099823,\"Patricia Jones\",24450],[356187925,\"Robert Brown\",44740],[355548984,\"Angela Martinez\",212156],[310454876,\"Joseph Thompson\",212156],[489456522,\"Linda Davis\",27984],[489221823,\"Richard Jackson\",23980],[548977562,\"William Ward\",84476],[310454877,\"Chad Stewart\",33546],[142519864,\"Betty Adams\",227489],[269734834,\"George Wright\",289950],[287321212,\"Michael Miller\",48090],[552455348,\"Dorthy Lewis\",152013],[248965255,\"Barbara Wilson\",43723],[159542516,\"William Moore\",48250],[348121549,\"Haywood Kelly\",32899],[90873519,\"Elizabeth Taylor\",32021],[486512566,\"David Anderson\",43001],[619023588,\"Jennifer Thomas\",54921],[15645489,\"Donald King\",18050],[556784565,\"Mark Young\",205187],[573284895,\"Eric Cooper\",114323],[574489456,\"William Jones\",105743],[574489457,\"Milo Brooks\",20]]}", "{\"columns\":[\"eid\",\"aid\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68],\"data\":[[11564812,2],[11564812,10],[90873519,6],[141582651,2],[141582651,10],[141582651,12],[142519864,1],[142519864,2],[142519864,3],[142519864,7],[142519864,10],[142519864,11],[142519864,12],[142519864,13],[159542516,5],[159542516,7],[242518965,2],[242518965,10],[269734834,1],[269734834,2],[269734834,3],[269734834,4],[269734834,5],[269734834,6],[269734834,7],[269734834,8],[269734834,9],[269734834,10],[269734834,11],[269734834,12],[269734834,13],[269734834,14],[269734834,15],[274878974,10],[274878974,12],[310454876,8],[310454876,9],[355548984,8],[355548984,9],[356187925,6],[390487451,3],[390487451,13],[390487451,14],[548977562,7],[550156548,1],[550156548,12],[552455318,2],[552455318,7],[552455318,14],[556784565,2],[556784565,3],[556784565,5],[567354612,1],[567354612,2],[567354612,3],[567354612,4],[567354612,5],[567354612,7],[567354612,9],[567354612,10],[567354612,11],[567354612,12],[567354612,15],[573284895,3],[573284895,4],[573284895,5],[574489456,6],[574489456,8],[574489457,7]]}" ]
{"columns":["name"],"index":[0],"data":[["George Wright"]]}
SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.distance > 5000 GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1 <table_name> : aircraft col : aid | name | distance row 1 : 1 | Boeing 747-400 | 8430 row 2 : 2 | Boeing 737-800 | 3383 row 3 : 3 | Airbus A340-300 | 7120 row 4 : 4 | British Aerospace Jetstream 41 | 1502 row 5 : 5 | Embraer ERJ-145 | 1530 row 6 : 6 | SAAB 340 | 2128 row 7 : 7 | Piper Archer III | 520 row 8 : 8 | Tupolev 154 | 4103 row 9 : 16 | Schwitzer 2-33 | 30 row 10 : 9 | Lockheed L1011 | 6900 row 11 : 10 | Boeing 757-300 | 4010 row 12 : 11 | Boeing 777-300 | 6441 row 13 : 12 | Boeing 767-400ER | 6475 row 14 : 13 | Airbus A320 | 2605 row 15 : 14 | Airbus A319 | 1805 row 16 : 15 | Boeing 727 | 1504 <table_name> : employee col : eid | name | salary row 1 : 242518965 | James Smith | 120433 row 2 : 141582651 | Mary Johnson | 178345 row 3 : 11564812 | John Williams | 153972 row 4 : 567354612 | Lisa Walker | 256481 row 5 : 552455318 | Larry West | 101745 row 6 : 550156548 | Karen Scott | 205187 row 7 : 390487451 | Lawrence Sperry | 212156 row 8 : 274878974 | Michael Miller | 99890 row 9 : 254099823 | Patricia Jones | 24450 row 10 : 356187925 | Robert Brown | 44740 row 11 : 355548984 | Angela Martinez | 212156 row 12 : 310454876 | Joseph Thompson | 212156 row 13 : 489456522 | Linda Davis | 27984 row 14 : 489221823 | Richard Jackson | 23980 row 15 : 548977562 | William Ward | 84476 row 16 : 310454877 | Chad Stewart | 33546 row 17 : 142519864 | Betty Adams | 227489 row 18 : 269734834 | George Wright | 289950 row 19 : 287321212 | Michael Miller | 48090 row 20 : 552455348 | Dorthy Lewis | 152013 row 21 : 248965255 | Barbara Wilson | 43723 row 22 : 159542516 | William Moore | 48250 row 23 : 348121549 | Haywood Kelly | 32899 row 24 : 90873519 | Elizabeth Taylor | 32021 row 25 : 486512566 | David Anderson | 43001 row 26 : 619023588 | Jennifer Thomas | 54921 row 27 : 15645489 | Donald King | 18050 row 28 : 556784565 | Mark Young | 205187 row 29 : 573284895 | Eric Cooper | 114323 row 30 : 574489456 | William Jones | 105743 row 31 : 574489457 | Milo Brooks | 20 <table_name> : certificate col : eid | aid row 1 : 11564812 | 2 row 2 : 11564812 | 10 row 3 : 90873519 | 6 row 4 : 141582651 | 2 row 5 : 141582651 | 10 row 6 : 141582651 | 12 row 7 : 142519864 | 1 row 8 : 142519864 | 2 row 9 : 142519864 | 3 row 10 : 142519864 | 7 row 11 : 142519864 | 10 row 12 : 142519864 | 11 row 13 : 142519864 | 12 row 14 : 142519864 | 13 row 15 : 159542516 | 5 row 16 : 159542516 | 7 row 17 : 242518965 | 2 row 18 : 242518965 | 10 row 19 : 269734834 | 1 row 20 : 269734834 | 2 row 21 : 269734834 | 3 row 22 : 269734834 | 4 row 23 : 269734834 | 5 row 24 : 269734834 | 6 row 25 : 269734834 | 7 row 26 : 269734834 | 8 row 27 : 269734834 | 9 row 28 : 269734834 | 10 row 29 : 269734834 | 11 row 30 : 269734834 | 12 row 31 : 269734834 | 13 row 32 : 269734834 | 14 row 33 : 269734834 | 15 row 34 : 274878974 | 10 row 35 : 274878974 | 12 row 36 : 310454876 | 8 row 37 : 310454876 | 9 row 38 : 355548984 | 8 row 39 : 355548984 | 9 row 40 : 356187925 | 6 row 41 : 390487451 | 3 row 42 : 390487451 | 13 row 43 : 390487451 | 14 row 44 : 548977562 | 7 row 45 : 550156548 | 1 row 46 : 550156548 | 12 row 47 : 552455318 | 2 row 48 : 552455318 | 7 row 49 : 552455318 | 14 row 50 : 556784565 | 2 row 51 : 556784565 | 3 row 52 : 556784565 | 5 row 53 : 567354612 | 1 row 54 : 567354612 | 2 row 55 : 567354612 | 3 row 56 : 567354612 | 4 row 57 : 567354612 | 5 row 58 : 567354612 | 7 row 59 : 567354612 | 9 row 60 : 567354612 | 10 row 61 : 567354612 | 11 row 62 : 567354612 | 12 row 63 : 567354612 | 15 row 64 : 573284895 | 3 row 65 : 573284895 | 4 row 66 : 573284895 | 5 row 67 : 574489456 | 6 row 68 : 574489456 | 8 row 69 : 574489457 | 7
col : name row 1 : George Wright
SELECT count(DISTINCT allergy) FROM Allergy_type
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["count(DISTINCT allergy)"],"index":[0],"data":[[14]]}
SELECT count(DISTINCT allergy) FROM Allergy_type <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : count(DISTINCT allergy) row 1 : 14
SELECT count(DISTINCT allergytype) FROM Allergy_type
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["count(DISTINCT allergytype)"],"index":[0],"data":[[3]]}
SELECT count(DISTINCT allergytype) FROM Allergy_type <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : count(DISTINCT allergytype) row 1 : 3
SELECT DISTINCT allergytype FROM Allergy_type
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["AllergyType"],"index":[0,1,2],"data":[["food"],["environmental"],["animal"]]}
SELECT DISTINCT allergytype FROM Allergy_type <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : AllergyType row 1 : food row 2 : environmental row 3 : animal
SELECT allergy , allergytype FROM Allergy_type
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["Allergy","AllergyType"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],"data":[["Eggs","food"],["Nuts","food"],["Milk","food"],["Shellfish","food"],["Anchovies","food"],["Wheat","food"],["Soy","food"],["Ragweed","environmental"],["Tree Pollen","environmental"],["Grass Pollen","environmental"],["Cat","animal"],["Dog","animal"],["Rodent","animal"],["Bee Stings","animal"]]}
SELECT allergy , allergytype FROM Allergy_type <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
SELECT DISTINCT allergy FROM Allergy_type WHERE allergytype = "food"
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["Allergy"],"index":[0,1,2,3,4,5,6],"data":[["Anchovies"],["Eggs"],["Milk"],["Nuts"],["Shellfish"],["Soy"],["Wheat"]]}
SELECT DISTINCT allergy FROM Allergy_type WHERE allergytype = "food" <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : Allergy row 1 : Anchovies row 2 : Eggs row 3 : Milk row 4 : Nuts row 5 : Shellfish row 6 : Soy row 7 : Wheat
SELECT allergytype FROM Allergy_type WHERE allergy = "Cat"
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["AllergyType"],"index":[0],"data":[["animal"]]}
SELECT allergytype FROM Allergy_type WHERE allergy = "Cat" <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : AllergyType row 1 : animal
SELECT count(*) FROM Allergy_type WHERE allergytype = "animal"
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[4]]}
SELECT count(*) FROM Allergy_type WHERE allergytype = "animal" <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : count(*) row 1 : 4
SELECT allergytype , count(*) FROM Allergy_type GROUP BY allergytype
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["AllergyType","count(*)"],"index":[0,1,2],"data":[["animal",4],["environmental",3],["food",7]]}
SELECT allergytype , count(*) FROM Allergy_type GROUP BY allergytype <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : AllergyType | count(*) row 1 : animal | 4 row 2 : environmental | 3 row 3 : food | 7
SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) DESC LIMIT 1
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["AllergyType"],"index":[0],"data":[["food"]]}
SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) DESC LIMIT 1 <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : AllergyType row 1 : food
SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) ASC LIMIT 1
[ "Allergy_Type" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}" ]
{"columns":["AllergyType"],"index":[0],"data":[["environmental"]]}
SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) ASC LIMIT 1 <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal
col : AllergyType row 1 : environmental
SELECT count(*) FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[34]]}
SELECT count(*) FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 34
SELECT Fname , Lname FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","LName"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"data":[["Linda","Smith"],["Tracy","Kim"],["Shiela","Jones"],["Dinesh","Kumar"],["Paul","Gompers"],["Andy","Schultz"],["Lisa","Apap"],["Jandy","Nelson"],["Eric","Tai"],["Derek","Lee"],["David","Adams"],["Steven","Davis"],["Charles","Norris"],["Susan","Lee"],["Mark","Schwartz"],["Bruce","Wilson"],["Michael","Leighton"],["Arthur","Pang"],["Ian","Thornton"],["George","Andreou"],["Michael","Woods"],["David","Shieber"],["Stacy","Prater"],["Mark","Goldman"],["Eric","Pang"],["Paul","Brody"],["Eric","Rugh"],["Jun","Han"],["Lisa","Cheng"],["Sarah","Smith"],["Eric","Brown"],["William","Simms"],["Eric","Epp"],["Sarah","Schmidt"]]}
SELECT Fname , Lname FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | LName row 1 : Linda | Smith row 2 : Tracy | Kim row 3 : Shiela | Jones row 4 : Dinesh | Kumar row 5 : Paul | Gompers row 6 : Andy | Schultz row 7 : Lisa | Apap row 8 : Jandy | Nelson row 9 : Eric | Tai row 10 : Derek | Lee row 11 : David | Adams row 12 : Steven | Davis row 13 : Charles | Norris row 14 : Susan | Lee row 15 : Mark | Schwartz row 16 : Bruce | Wilson row 17 : Michael | Leighton row 18 : Arthur | Pang row 19 : Ian | Thornton row 20 : George | Andreou row 21 : Michael | Woods row 22 : David | Shieber row 23 : Stacy | Prater row 24 : Mark | Goldman row 25 : Eric | Pang row 26 : Paul | Brody row 27 : Eric | Rugh row 28 : Jun | Han row 29 : Lisa | Cheng row 30 : Sarah | Smith row 31 : Eric | Brown row 32 : William | Simms row 33 : Eric | Epp row 34 : Sarah | Schmidt
SELECT count(DISTINCT advisor) FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(DISTINCT advisor)"],"index":[0],"data":[[18]]}
SELECT count(DISTINCT advisor) FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(DISTINCT advisor) row 1 : 18
SELECT DISTINCT Major FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Major"],"index":[0,1,2,3,4,5],"data":[[600],[520],[540],[550],[100],[50]]}
SELECT DISTINCT Major FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Major row 1 : 600 row 2 : 520 row 3 : 540 row 4 : 550 row 5 : 100 row 6 : 50
SELECT DISTINCT city_code FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["city_code"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],"data":[["BAL"],["HKG"],["WAS"],["CHI"],["YYZ"],["PIT"],["HOU"],["PHL"],["DAL"],["DET"],["LON"],["NYC"],["LOS"],["ROC"],["PEK"],["SFO"],["ATL"],["NAR"],["BOS"]]}
SELECT DISTINCT city_code FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : city_code row 1 : BAL row 2 : HKG row 3 : WAS row 4 : CHI row 5 : YYZ row 6 : PIT row 7 : HOU row 8 : PHL row 9 : DAL row 10 : DET row 11 : LON row 12 : NYC row 13 : LOS row 14 : ROC row 15 : PEK row 16 : SFO row 17 : ATL row 18 : NAR row 19 : BOS
SELECT Fname , Lname , Age FROM Student WHERE Sex = 'F'
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","LName","Age"],"index":[0,1,2,3,4,5,6,7,8,9],"data":[["Linda","Smith",18],["Tracy","Kim",19],["Shiela","Jones",21],["Lisa","Apap",18],["Jandy","Nelson",20],["Susan","Lee",16],["Stacy","Prater",18],["Lisa","Cheng",21],["Sarah","Smith",20],["Sarah","Schmidt",26]]}
SELECT Fname , Lname , Age FROM Student WHERE Sex = 'F' <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | LName | Age row 1 : Linda | Smith | 18 row 2 : Tracy | Kim | 19 row 3 : Shiela | Jones | 21 row 4 : Lisa | Apap | 18 row 5 : Jandy | Nelson | 20 row 6 : Susan | Lee | 16 row 7 : Stacy | Prater | 18 row 8 : Lisa | Cheng | 21 row 9 : Sarah | Smith | 20 row 10 : Sarah | Schmidt | 26
SELECT StuID FROM Student WHERE Sex = 'M'
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["StuID"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"data":[[1004],[1005],[1006],[1009],[1010],[1011],[1012],[1014],[1016],[1017],[1018],[1019],[1020],[1021],[1022],[1023],[1025],[1026],[1027],[1028],[1029],[1032],[1033],[1034]]}
SELECT StuID FROM Student WHERE Sex = 'M' <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : StuID row 1 : 1004 row 2 : 1005 row 3 : 1006 row 4 : 1009 row 5 : 1010 row 6 : 1011 row 7 : 1012 row 8 : 1014 row 9 : 1016 row 10 : 1017 row 11 : 1018 row 12 : 1019 row 13 : 1020 row 14 : 1021 row 15 : 1022 row 16 : 1023 row 17 : 1025 row 18 : 1026 row 19 : 1027 row 20 : 1028 row 21 : 1029 row 22 : 1032 row 23 : 1033 row 24 : 1034
SELECT count(*) FROM Student WHERE age = 18
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[10]]}
SELECT count(*) FROM Student WHERE age = 18 <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 10
SELECT StuID FROM Student WHERE age > 20
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["StuID"],"index":[0,1,2,3,4,5,6],"data":[[1003],[1005],[1011],[1017],[1020],[1030],[1035]]}
SELECT StuID FROM Student WHERE age > 20 <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : StuID row 1 : 1003 row 2 : 1005 row 3 : 1011 row 4 : 1017 row 5 : 1020 row 6 : 1030 row 7 : 1035
SELECT city_code FROM Student WHERE LName = "Kim"
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["city_code"],"index":[0],"data":[["HKG"]]}
SELECT city_code FROM Student WHERE LName = "Kim" <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : city_code row 1 : HKG
SELECT Advisor FROM Student WHERE StuID = 1004
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Advisor"],"index":[0],"data":[[8423]]}
SELECT Advisor FROM Student WHERE StuID = 1004 <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Advisor row 1 : 8423
SELECT count(*) FROM Student WHERE city_code = "HKG" OR city_code = "CHI"
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[4]]}
SELECT count(*) FROM Student WHERE city_code = "HKG" OR city_code = "CHI" <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 4
SELECT min(age) , avg(age) , max(age) FROM Student
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["min(age)","avg(age)","max(age)"],"index":[0],"data":[[16,19.5588235294,27]]}
SELECT min(age) , avg(age) , max(age) FROM Student <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : min(age) | avg(age) | max(age) row 1 : 16 | 19.5588235294 | 27
SELECT LName FROM Student WHERE age = (SELECT min(age) FROM Student)
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["LName"],"index":[0],"data":[["Lee"]]}
SELECT LName FROM Student WHERE age = (SELECT min(age) FROM Student) <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : LName row 1 : Lee
SELECT StuID FROM Student WHERE age = (SELECT max(age) FROM Student)
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["StuID"],"index":[0],"data":[[1017]]}
SELECT StuID FROM Student WHERE age = (SELECT max(age) FROM Student) <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : StuID row 1 : 1017
SELECT major , count(*) FROM Student GROUP BY major
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Major","count(*)"],"index":[0,1,2,3,4,5],"data":[[50,2],[100,1],[520,6],[540,2],[550,5],[600,18]]}
SELECT major , count(*) FROM Student GROUP BY major <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Major | count(*) row 1 : 50 | 2 row 2 : 100 | 1 row 3 : 520 | 6 row 4 : 540 | 2 row 5 : 550 | 5 row 6 : 600 | 18
SELECT major FROM Student GROUP BY major ORDER BY count(*) DESC LIMIT 1
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Major"],"index":[0],"data":[[600]]}
SELECT major FROM Student GROUP BY major ORDER BY count(*) DESC LIMIT 1 <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Major row 1 : 600
SELECT age , count(*) FROM Student GROUP BY age
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Age","count(*)"],"index":[0,1,2,3,4,5,6,7,8],"data":[[16,1],[17,4],[18,10],[19,4],[20,8],[21,2],[22,2],[26,2],[27,1]]}
SELECT age , count(*) FROM Student GROUP BY age <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Age | count(*) row 1 : 16 | 1 row 2 : 17 | 4 row 3 : 18 | 10 row 4 : 19 | 4 row 5 : 20 | 8 row 6 : 21 | 2 row 7 : 22 | 2 row 8 : 26 | 2 row 9 : 27 | 1
SELECT avg(age) , sex FROM Student GROUP BY sex
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["avg(age)","Sex"],"index":[0,1],"data":[[19.7,"F"],[19.5,"M"]]}
SELECT avg(age) , sex FROM Student GROUP BY sex <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : avg(age) | Sex row 1 : 19.7 | F row 2 : 19.5 | M
SELECT city_code , count(*) FROM Student GROUP BY city_code
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["city_code","count(*)"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],"data":[["ATL",1],["BAL",4],["BOS",1],["CHI",1],["DAL",1],["DET",1],["HKG",3],["HOU",1],["LON",1],["LOS",1],["NAR",1],["NYC",3],["PEK",1],["PHL",3],["PIT",4],["ROC",1],["SFO",1],["WAS",3],["YYZ",2]]}
SELECT city_code , count(*) FROM Student GROUP BY city_code <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : city_code | count(*) row 1 : ATL | 1 row 2 : BAL | 4 row 3 : BOS | 1 row 4 : CHI | 1 row 5 : DAL | 1 row 6 : DET | 1 row 7 : HKG | 3 row 8 : HOU | 1 row 9 : LON | 1 row 10 : LOS | 1 row 11 : NAR | 1 row 12 : NYC | 3 row 13 : PEK | 1 row 14 : PHL | 3 row 15 : PIT | 4 row 16 : ROC | 1 row 17 : SFO | 1 row 18 : WAS | 3 row 19 : YYZ | 2
SELECT advisor , count(*) FROM Student GROUP BY advisor
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Advisor","count(*)"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],"data":[[1121,3],[1148,3],[2192,4],[2311,3],[5718,2],[7134,2],[7271,2],[7712,1],[7723,1],[7792,1],[8423,1],[8721,1],[8722,3],[8723,1],[8741,1],[8772,3],[8918,1],[9172,1]]}
SELECT advisor , count(*) FROM Student GROUP BY advisor <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Advisor | count(*) row 1 : 1121 | 3 row 2 : 1148 | 3 row 3 : 2192 | 4 row 4 : 2311 | 3 row 5 : 5718 | 2 row 6 : 7134 | 2 row 7 : 7271 | 2 row 8 : 7712 | 1 row 9 : 7723 | 1 row 10 : 7792 | 1 row 11 : 8423 | 1 row 12 : 8721 | 1 row 13 : 8722 | 3 row 14 : 8723 | 1 row 15 : 8741 | 1 row 16 : 8772 | 3 row 17 : 8918 | 1 row 18 : 9172 | 1
SELECT advisor FROM Student GROUP BY advisor ORDER BY count(*) DESC LIMIT 1
[ "Student" ]
[ "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Advisor"],"index":[0],"data":[[2192]]}
SELECT advisor FROM Student GROUP BY advisor ORDER BY count(*) DESC LIMIT 1 <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Advisor row 1 : 2192
SELECT count(*) FROM Has_allergy WHERE Allergy = "Cat"
[ "Has_Allergy" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[4]]}
SELECT count(*) FROM Has_allergy WHERE Allergy = "Cat" <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : count(*) row 1 : 4
SELECT StuID FROM Has_allergy GROUP BY StuID HAVING count(*) >= 2
[ "Has_Allergy" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["StuID"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[[1002],[1005],[1007],[1010],[1015],[1016],[1018],[1022],[1023],[1024],[1029],[1031]]}
SELECT StuID FROM Has_allergy GROUP BY StuID HAVING count(*) >= 2 <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : StuID row 1 : 1002 row 2 : 1005 row 3 : 1007 row 4 : 1010 row 5 : 1015 row 6 : 1016 row 7 : 1018 row 8 : 1022 row 9 : 1023 row 10 : 1024 row 11 : 1029 row 12 : 1031
SELECT StuID FROM Student EXCEPT SELECT StuID FROM Has_allergy
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["StuID"],"index":[0,1,2,3,4],"data":[[1008],[1032],[1033],[1034],[1035]]}
SELECT StuID FROM Student EXCEPT SELECT StuID FROM Has_allergy <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : StuID row 1 : 1008 row 2 : 1032 row 3 : 1033 row 4 : 1034 row 5 : 1035
SELECT count(*) FROM has_allergy AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.sex = "F" AND T1.allergy = "Milk" OR T1.allergy = "Eggs"
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[4]]}
SELECT count(*) FROM has_allergy AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.sex = "F" AND T1.allergy = "Milk" OR T1.allergy = "Eggs" <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 4
SELECT count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy = T2.allergy WHERE T2.allergytype = "food"
[ "Allergy_Type", "Has_Allergy" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[28]]}
SELECT count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy = T2.allergy WHERE T2.allergytype = "food" <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : count(*) row 1 : 28
SELECT Allergy FROM Has_allergy GROUP BY Allergy ORDER BY count(*) DESC LIMIT 1
[ "Has_Allergy" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["Allergy"],"index":[0],"data":[["Tree Pollen"]]}
SELECT Allergy FROM Has_allergy GROUP BY Allergy ORDER BY count(*) DESC LIMIT 1 <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : Allergy row 1 : Tree Pollen
SELECT Allergy , count(*) FROM Has_allergy GROUP BY Allergy
[ "Has_Allergy" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["Allergy","count(*)"],"index":[0,1,2,3,4,5,6,7,8,9,10,11],"data":[["Anchovies",3],["Cat",4],["Dog",3],["Eggs",3],["Grass Pollen",4],["Milk",3],["Nuts",11],["Ragweed",6],["Rodent",1],["Shellfish",4],["Soy",4],["Tree Pollen",13]]}
SELECT Allergy , count(*) FROM Has_allergy GROUP BY Allergy <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : Allergy | count(*) row 1 : Anchovies | 3 row 2 : Cat | 4 row 3 : Dog | 3 row 4 : Eggs | 3 row 5 : Grass Pollen | 4 row 6 : Milk | 3 row 7 : Nuts | 11 row 8 : Ragweed | 6 row 9 : Rodent | 1 row 10 : Shellfish | 4 row 11 : Soy | 4 row 12 : Tree Pollen | 13
SELECT T2.allergytype , count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy = T2.allergy GROUP BY T2.allergytype
[ "Allergy_Type", "Has_Allergy" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}" ]
{"columns":["AllergyType","count(*)"],"index":[0,1,2],"data":[["animal",8],["environmental",23],["food",28]]}
SELECT T2.allergytype , count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy = T2.allergy GROUP BY T2.allergytype <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy
col : AllergyType | count(*) row 1 : animal | 8 row 2 : environmental | 23 row 3 : food | 28
SELECT lname , age FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Milk" INTERSECT SELECT StuID FROM Has_allergy WHERE Allergy = "Cat")
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["LName","Age"],"index":[0,1],"data":[["Apap",18],["Lee",17]]}
SELECT lname , age FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Milk" INTERSECT SELECT StuID FROM Has_allergy WHERE Allergy = "Cat") <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : LName | Age row 1 : Apap | 18 row 2 : Lee | 17
SELECT T1.Allergy , T1.AllergyType FROM Allergy_type AS T1 JOIN Has_allergy AS T2 ON T1.Allergy = T2.Allergy JOIN Student AS T3 ON T3.StuID = T2.StuID WHERE T3.Fname = "Lisa" ORDER BY T1.Allergy
[ "Allergy_Type", "Has_Allergy", "Student" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Allergy","AllergyType"],"index":[0,1,2,3,4,5,6,7,8,9],"data":[["Anchovies","food"],["Cat","animal"],["Dog","animal"],["Eggs","food"],["Grass Pollen","environmental"],["Grass Pollen","environmental"],["Milk","food"],["Ragweed","environmental"],["Shellfish","food"],["Tree Pollen","environmental"]]}
SELECT T1.Allergy , T1.AllergyType FROM Allergy_type AS T1 JOIN Has_allergy AS T2 ON T1.Allergy = T2.Allergy JOIN Student AS T3 ON T3.StuID = T2.StuID WHERE T3.Fname = "Lisa" ORDER BY T1.Allergy <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Allergy | AllergyType row 1 : Anchovies | food row 2 : Cat | animal row 3 : Dog | animal row 4 : Eggs | food row 5 : Grass Pollen | environmental row 6 : Grass Pollen | environmental row 7 : Milk | food row 8 : Ragweed | environmental row 9 : Shellfish | food row 10 : Tree Pollen | environmental
SELECT fname , sex FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Milk" EXCEPT SELECT StuID FROM Has_allergy WHERE Allergy = "Cat")
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","Sex"],"index":[0],"data":[["Mark","M"]]}
SELECT fname , sex FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Milk" EXCEPT SELECT StuID FROM Has_allergy WHERE Allergy = "Cat") <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | Sex row 1 : Mark | M
SELECT avg(age) FROM Student WHERE StuID IN ( SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food" INTERSECT SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "animal")
[ "Allergy_Type", "Has_Allergy", "Student" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["avg(age)"],"index":[0],"data":[[18.3333333333]]}
SELECT avg(age) FROM Student WHERE StuID IN ( SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food" INTERSECT SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "animal") <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : avg(age) row 1 : 18.3333333333
SELECT fname , lname FROM Student WHERE StuID NOT IN (SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food")
[ "Allergy_Type", "Has_Allergy", "Student" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","LName"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],"data":[["Linda","Smith"],["Shiela","Jones"],["Jandy","Nelson"],["Eric","Tai"],["David","Adams"],["Steven","Davis"],["Bruce","Wilson"],["Arthur","Pang"],["Ian","Thornton"],["George","Andreou"],["Stacy","Prater"],["Mark","Goldman"],["Eric","Pang"],["Paul","Brody"],["Eric","Rugh"],["Lisa","Cheng"],["Eric","Brown"],["William","Simms"],["Eric","Epp"],["Sarah","Schmidt"]]}
SELECT fname , lname FROM Student WHERE StuID NOT IN (SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food") <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | LName row 1 : Linda | Smith row 2 : Shiela | Jones row 3 : Jandy | Nelson row 4 : Eric | Tai row 5 : David | Adams row 6 : Steven | Davis row 7 : Bruce | Wilson row 8 : Arthur | Pang row 9 : Ian | Thornton row 10 : George | Andreou row 11 : Stacy | Prater row 12 : Mark | Goldman row 13 : Eric | Pang row 14 : Paul | Brody row 15 : Eric | Rugh row 16 : Lisa | Cheng row 17 : Eric | Brown row 18 : William | Simms row 19 : Eric | Epp row 20 : Sarah | Schmidt
SELECT count(*) FROM Student WHERE sex = "M" AND StuID IN (SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food")
[ "Allergy_Type", "Has_Allergy", "Student" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[10]]}
SELECT count(*) FROM Student WHERE sex = "M" AND StuID IN (SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food") <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 10
SELECT DISTINCT T1.fname , T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid = T2.stuid WHERE T2.Allergy = "Milk" OR T2.Allergy = "Cat"
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","city_code"],"index":[0,1,2,3,4],"data":[["Linda","BAL"],["Lisa","PIT"],["Derek","HOU"],["Mark","DET"],["David","NYC"]]}
SELECT DISTINCT T1.fname , T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid = T2.stuid WHERE T2.Allergy = "Milk" OR T2.Allergy = "Cat" <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | city_code row 1 : Linda | BAL row 2 : Lisa | PIT row 3 : Derek | HOU row 4 : Mark | DET row 5 : David | NYC
SELECT count(*) FROM Student WHERE age > 18 AND StuID NOT IN ( SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food" OR T2.allergytype = "animal")
[ "Allergy_Type", "Has_Allergy", "Student" ]
[ "{\"columns\":[\"Allergy\",\"AllergyType\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13],\"data\":[[\"Eggs\",\"food\"],[\"Nuts\",\"food\"],[\"Milk\",\"food\"],[\"Shellfish\",\"food\"],[\"Anchovies\",\"food\"],[\"Wheat\",\"food\"],[\"Soy\",\"food\"],[\"Ragweed\",\"environmental\"],[\"Tree Pollen\",\"environmental\"],[\"Grass Pollen\",\"environmental\"],[\"Cat\",\"animal\"],[\"Dog\",\"animal\"],[\"Rodent\",\"animal\"],[\"Bee Stings\",\"animal\"]]}", "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["count(*)"],"index":[0],"data":[[12]]}
SELECT count(*) FROM Student WHERE age > 18 AND StuID NOT IN ( SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food" OR T2.allergytype = "animal") <table_name> : Allergy_Type col : Allergy | AllergyType row 1 : Eggs | food row 2 : Nuts | food row 3 : Milk | food row 4 : Shellfish | food row 5 : Anchovies | food row 6 : Wheat | food row 7 : Soy | food row 8 : Ragweed | environmental row 9 : Tree Pollen | environmental row 10 : Grass Pollen | environmental row 11 : Cat | animal row 12 : Dog | animal row 13 : Rodent | animal row 14 : Bee Stings | animal <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : count(*) row 1 : 12
SELECT fname , major FROM Student WHERE StuID NOT IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Soy")
[ "Has_Allergy", "Student" ]
[ "{\"columns\":[\"StuID\",\"Allergy\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58],\"data\":[[1001,\"Cat\"],[1002,\"Shellfish\"],[1002,\"Tree Pollen\"],[1003,\"Dog\"],[1004,\"Nuts\"],[1005,\"Nuts\"],[1005,\"Tree Pollen\"],[1006,\"Nuts\"],[1007,\"Ragweed\"],[1007,\"Tree Pollen\"],[1007,\"Grass Pollen\"],[1007,\"Eggs\"],[1007,\"Milk\"],[1007,\"Shellfish\"],[1007,\"Anchovies\"],[1007,\"Cat\"],[1007,\"Dog\"],[1009,\"Tree Pollen\"],[1010,\"Ragweed\"],[1010,\"Tree Pollen\"],[1010,\"Grass Pollen\"],[1010,\"Eggs\"],[1010,\"Milk\"],[1010,\"Shellfish\"],[1010,\"Anchovies\"],[1010,\"Cat\"],[1010,\"Dog\"],[1011,\"Ragweed\"],[1012,\"Ragweed\"],[1013,\"Ragweed\"],[1014,\"Nuts\"],[1015,\"Nuts\"],[1015,\"Soy\"],[1016,\"Nuts\"],[1016,\"Milk\"],[1017,\"Tree Pollen\"],[1018,\"Nuts\"],[1018,\"Soy\"],[1019,\"Tree Pollen\"],[1020,\"Tree Pollen\"],[1021,\"Tree Pollen\"],[1022,\"Nuts\"],[1022,\"Anchovies\"],[1023,\"Rodent\"],[1023,\"Cat\"],[1023,\"Nuts\"],[1024,\"Ragweed\"],[1024,\"Tree Pollen\"],[1025,\"Tree Pollen\"],[1026,\"Grass Pollen\"],[1027,\"Tree Pollen\"],[1028,\"Tree Pollen\"],[1029,\"Soy\"],[1029,\"Nuts\"],[1029,\"Eggs\"],[1030,\"Grass Pollen\"],[1031,\"Nuts\"],[1031,\"Shellfish\"],[1031,\"Soy\"]]}", "{\"columns\":[\"StuID\",\"LName\",\"Fname\",\"Age\",\"Sex\",\"Major\",\"Advisor\",\"city_code\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],\"data\":[[1001,\"Smith\",\"Linda\",18,\"F\",600,1121,\"BAL\"],[1002,\"Kim\",\"Tracy\",19,\"F\",600,7712,\"HKG\"],[1003,\"Jones\",\"Shiela\",21,\"F\",600,7792,\"WAS\"],[1004,\"Kumar\",\"Dinesh\",20,\"M\",600,8423,\"CHI\"],[1005,\"Gompers\",\"Paul\",26,\"M\",600,1121,\"YYZ\"],[1006,\"Schultz\",\"Andy\",18,\"M\",600,1148,\"BAL\"],[1007,\"Apap\",\"Lisa\",18,\"F\",600,8918,\"PIT\"],[1008,\"Nelson\",\"Jandy\",20,\"F\",600,9172,\"BAL\"],[1009,\"Tai\",\"Eric\",19,\"M\",600,2192,\"YYZ\"],[1010,\"Lee\",\"Derek\",17,\"M\",600,2192,\"HOU\"],[1011,\"Adams\",\"David\",22,\"M\",600,1148,\"PHL\"],[1012,\"Davis\",\"Steven\",20,\"M\",600,7723,\"PIT\"],[1014,\"Norris\",\"Charles\",18,\"M\",600,8741,\"DAL\"],[1015,\"Lee\",\"Susan\",16,\"F\",600,8721,\"HKG\"],[1016,\"Schwartz\",\"Mark\",17,\"M\",600,2192,\"DET\"],[1017,\"Wilson\",\"Bruce\",27,\"M\",600,1148,\"LON\"],[1018,\"Leighton\",\"Michael\",20,\"M\",600,1121,\"PIT\"],[1019,\"Pang\",\"Arthur\",18,\"M\",600,2192,\"WAS\"],[1020,\"Thornton\",\"Ian\",22,\"M\",520,7271,\"NYC\"],[1021,\"Andreou\",\"George\",19,\"M\",520,8722,\"NYC\"],[1022,\"Woods\",\"Michael\",17,\"M\",540,8722,\"PHL\"],[1023,\"Shieber\",\"David\",20,\"M\",520,8722,\"NYC\"],[1024,\"Prater\",\"Stacy\",18,\"F\",540,7271,\"BAL\"],[1025,\"Goldman\",\"Mark\",18,\"M\",520,7134,\"PIT\"],[1026,\"Pang\",\"Eric\",19,\"M\",520,7134,\"HKG\"],[1027,\"Brody\",\"Paul\",18,\"M\",520,8723,\"LOS\"],[1028,\"Rugh\",\"Eric\",20,\"M\",550,2311,\"ROC\"],[1029,\"Han\",\"Jun\",17,\"M\",100,2311,\"PEK\"],[1030,\"Cheng\",\"Lisa\",21,\"F\",550,2311,\"SFO\"],[1031,\"Smith\",\"Sarah\",20,\"F\",550,8772,\"PHL\"],[1032,\"Brown\",\"Eric\",20,\"M\",550,8772,\"ATL\"],[1033,\"Simms\",\"William\",18,\"M\",550,8772,\"NAR\"],[1034,\"Epp\",\"Eric\",18,\"M\",50,5718,\"BOS\"],[1035,\"Schmidt\",\"Sarah\",26,\"F\",50,5718,\"WAS\"]]}" ]
{"columns":["Fname","Major"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29],"data":[["Linda",600],["Tracy",600],["Shiela",600],["Dinesh",600],["Paul",600],["Andy",600],["Lisa",600],["Jandy",600],["Eric",600],["Derek",600],["David",600],["Steven",600],["Charles",600],["Mark",600],["Bruce",600],["Arthur",600],["Ian",520],["George",520],["Michael",540],["David",520],["Stacy",540],["Mark",520],["Eric",520],["Paul",520],["Eric",550],["Lisa",550],["Eric",550],["William",550],["Eric",50],["Sarah",50]]}
SELECT fname , major FROM Student WHERE StuID NOT IN (SELECT StuID FROM Has_allergy WHERE Allergy = "Soy") <table_name> : Has_Allergy col : StuID | Allergy row 1 : 1001 | Cat row 2 : 1002 | Shellfish row 3 : 1002 | Tree Pollen row 4 : 1003 | Dog row 5 : 1004 | Nuts row 6 : 1005 | Nuts row 7 : 1005 | Tree Pollen row 8 : 1006 | Nuts row 9 : 1007 | Ragweed row 10 : 1007 | Tree Pollen row 11 : 1007 | Grass Pollen row 12 : 1007 | Eggs row 13 : 1007 | Milk row 14 : 1007 | Shellfish row 15 : 1007 | Anchovies row 16 : 1007 | Cat row 17 : 1007 | Dog row 18 : 1009 | Tree Pollen row 19 : 1010 | Ragweed row 20 : 1010 | Tree Pollen row 21 : 1010 | Grass Pollen row 22 : 1010 | Eggs row 23 : 1010 | Milk row 24 : 1010 | Shellfish row 25 : 1010 | Anchovies row 26 : 1010 | Cat row 27 : 1010 | Dog row 28 : 1011 | Ragweed row 29 : 1012 | Ragweed row 30 : 1013 | Ragweed row 31 : 1014 | Nuts row 32 : 1015 | Nuts row 33 : 1015 | Soy row 34 : 1016 | Nuts row 35 : 1016 | Milk row 36 : 1017 | Tree Pollen row 37 : 1018 | Nuts row 38 : 1018 | Soy row 39 : 1019 | Tree Pollen row 40 : 1020 | Tree Pollen row 41 : 1021 | Tree Pollen row 42 : 1022 | Nuts row 43 : 1022 | Anchovies row 44 : 1023 | Rodent row 45 : 1023 | Cat row 46 : 1023 | Nuts row 47 : 1024 | Ragweed row 48 : 1024 | Tree Pollen row 49 : 1025 | Tree Pollen row 50 : 1026 | Grass Pollen row 51 : 1027 | Tree Pollen row 52 : 1028 | Tree Pollen row 53 : 1029 | Soy row 54 : 1029 | Nuts row 55 : 1029 | Eggs row 56 : 1030 | Grass Pollen row 57 : 1031 | Nuts row 58 : 1031 | Shellfish row 59 : 1031 | Soy <table_name> : Student col : StuID | LName | Fname | Age | Sex | Major | Advisor | city_code row 1 : 1001 | Smith | Linda | 18 | F | 600 | 1121 | BAL row 2 : 1002 | Kim | Tracy | 19 | F | 600 | 7712 | HKG row 3 : 1003 | Jones | Shiela | 21 | F | 600 | 7792 | WAS row 4 : 1004 | Kumar | Dinesh | 20 | M | 600 | 8423 | CHI row 5 : 1005 | Gompers | Paul | 26 | M | 600 | 1121 | YYZ row 6 : 1006 | Schultz | Andy | 18 | M | 600 | 1148 | BAL row 7 : 1007 | Apap | Lisa | 18 | F | 600 | 8918 | PIT row 8 : 1008 | Nelson | Jandy | 20 | F | 600 | 9172 | BAL row 9 : 1009 | Tai | Eric | 19 | M | 600 | 2192 | YYZ row 10 : 1010 | Lee | Derek | 17 | M | 600 | 2192 | HOU row 11 : 1011 | Adams | David | 22 | M | 600 | 1148 | PHL row 12 : 1012 | Davis | Steven | 20 | M | 600 | 7723 | PIT row 13 : 1014 | Norris | Charles | 18 | M | 600 | 8741 | DAL row 14 : 1015 | Lee | Susan | 16 | F | 600 | 8721 | HKG row 15 : 1016 | Schwartz | Mark | 17 | M | 600 | 2192 | DET row 16 : 1017 | Wilson | Bruce | 27 | M | 600 | 1148 | LON row 17 : 1018 | Leighton | Michael | 20 | M | 600 | 1121 | PIT row 18 : 1019 | Pang | Arthur | 18 | M | 600 | 2192 | WAS row 19 : 1020 | Thornton | Ian | 22 | M | 520 | 7271 | NYC row 20 : 1021 | Andreou | George | 19 | M | 520 | 8722 | NYC row 21 : 1022 | Woods | Michael | 17 | M | 540 | 8722 | PHL row 22 : 1023 | Shieber | David | 20 | M | 520 | 8722 | NYC row 23 : 1024 | Prater | Stacy | 18 | F | 540 | 7271 | BAL row 24 : 1025 | Goldman | Mark | 18 | M | 520 | 7134 | PIT row 25 : 1026 | Pang | Eric | 19 | M | 520 | 7134 | HKG row 26 : 1027 | Brody | Paul | 18 | M | 520 | 8723 | LOS row 27 : 1028 | Rugh | Eric | 20 | M | 550 | 2311 | ROC row 28 : 1029 | Han | Jun | 17 | M | 100 | 2311 | PEK row 29 : 1030 | Cheng | Lisa | 21 | F | 550 | 2311 | SFO row 30 : 1031 | Smith | Sarah | 20 | F | 550 | 8772 | PHL row 31 : 1032 | Brown | Eric | 20 | M | 550 | 8772 | ATL row 32 : 1033 | Simms | William | 18 | M | 550 | 8772 | NAR row 33 : 1034 | Epp | Eric | 18 | M | 50 | 5718 | BOS row 34 : 1035 | Schmidt | Sarah | 26 | F | 50 | 5718 | WAS
col : Fname | Major row 1 : Linda | 600 row 2 : Tracy | 600 row 3 : Shiela | 600 row 4 : Dinesh | 600 row 5 : Paul | 600 row 6 : Andy | 600 row 7 : Lisa | 600 row 8 : Jandy | 600 row 9 : Eric | 600 row 10 : Derek | 600 row 11 : David | 600 row 12 : Steven | 600 row 13 : Charles | 600 row 14 : Mark | 600 row 15 : Bruce | 600 row 16 : Arthur | 600 row 17 : Ian | 520 row 18 : George | 520 row 19 : Michael | 540 row 20 : David | 520 row 21 : Stacy | 540 row 22 : Mark | 520 row 23 : Eric | 520 row 24 : Paul | 520 row 25 : Eric | 550 row 26 : Lisa | 550 row 27 : Eric | 550 row 28 : William | 550 row 29 : Eric | 50 row 30 : Sarah | 50
SELECT billing_country , COUNT(*) FROM invoices GROUP BY billing_country ORDER BY count(*) DESC LIMIT 5;
[ "invoices" ]
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Brigadeiro Faria Lima, 2170\",\"S\\u00c3\\u00a3o Jos\\u00c3\\u00a9 dos Campos\",\"SP\",\"Brazil\",\"12227-000\",8.91],[383,10,\"2011-08-12 00:00:00\",\"Rua Dr. Falc\\u00c3\\u00a3o Filho, 155\",\"S\\u00c3\\u00a3o Paulo\",\"SP\",\"Brazil\",\"01007-010\",13.86],[384,24,\"2011-08-20 00:00:00\",\"162 E Superior Street\",\"Chicago\",\"IL\",\"USA\",\"60611\",0.99],[385,25,\"2011-09-02 00:00:00\",\"319 N. Frances Street\",\"Madison\",\"WI\",\"USA\",\"53703\",1.98],[386,27,\"2011-09-02 00:00:00\",\"1033 N Park Ave\",\"Tucson\",\"AZ\",\"USA\",\"85719\",1.98],[387,29,\"2011-09-03 00:00:00\",\"796 Dundas Street West\",\"Toronto\",\"ON\",\"Canada\",\"M6J 1V1\",3.96],[388,33,\"2011-09-04 00:00:00\",\"5112 48 Street\",\"Yellowknife\",\"NT\",\"Canada\",\"X1A 1N6\",5.94],[389,39,\"2011-09-07 00:00:00\",\"4, Rue Milton\",\"Paris\",null,\"France\",\"75009\",8.91],[390,48,\"2011-09-12 00:00:00\",\"Lijnbaansgracht 120bg\",\"Amsterdam\",\"VV\",\"Netherlands\",\"1016\",13.86],[391,3,\"2011-09-20 00:00:00\",\"1498 rue B\\u00c3\\u00a9langer\",\"Montr\\u00c3\\u00a9al\",\"QC\",\"Canada\",\"H2G 1A7\",0.99],[392,4,\"2011-10-03 00:00:00\",\"Ullev\\u00c3\\u00a5lsveien 14\",\"Oslo\",null,\"Norway\",\"0171\",1.98],[393,6,\"2011-10-03 00:00:00\",\"Rilsk\\u00c3\\u00a1 3174\\/6\",\"Prague\",null,\"Czech Republic\",\"14300\",1.98],[394,8,\"2011-10-04 00:00:00\",\"Gr\\u00c3\\u00a9trystraat 63\",\"Brussels\",null,\"Belgium\",\"1000\",3.96],[395,12,\"2011-10-05 00:00:00\",\"Pra\\u00c3\\u00a7a Pio X, 119\",\"Rio de Janeiro\",\"RJ\",\"Brazil\",\"20040-020\",5.94],[396,18,\"2011-10-08 00:00:00\",\"627 Broadway\",\"New York\",\"NY\",\"USA\",\"10012-2612\",8.91],[397,27,\"2011-10-13 00:00:00\",\"1033 N Park Ave\",\"Tucson\",\"AZ\",\"USA\",\"85719\",13.86],[398,41,\"2011-10-21 00:00:00\",\"11, Place Bellecour\",\"Lyon\",null,\"France\",\"69002\",0.99],[399,42,\"2011-11-03 00:00:00\",\"9, Place Louis Barthou\",\"Bordeaux\",null,\"France\",\"33000\",1.98],[400,44,\"2011-11-03 00:00:00\",\"Porthaninkatu 9\",\"Helsinki\",null,\"Finland\",\"00530\",1.98],[401,46,\"2011-11-04 00:00:00\",\"3 Chatham Street\",\"Dublin\",\"Dublin\",\"Ireland\",null,3.96],[402,50,\"2011-11-05 00:00:00\",\"C\\/ San Bernardo 85\",\"Madrid\",null,\"Spain\",\"28015\",5.94],[403,56,\"2011-11-08 00:00:00\",\"307 Macacha G\\u00c3\\u00bcemes\",\"Buenos Aires\",null,\"Argentina\",\"1106\",8.91],[404,6,\"2011-11-13 00:00:00\",\"Rilsk\\u00c3\\u00a1 3174\\/6\",\"Prague\",null,\"Czech Republic\",\"14300\",25.86],[405,20,\"2011-11-21 00:00:00\",\"541 Del Medio Avenue\",\"Mountain View\",\"CA\",\"USA\",\"94040-111\",0.99],[406,21,\"2011-12-04 00:00:00\",\"801 W 4th Street\",\"Reno\",\"NV\",\"USA\",\"89503\",1.98],[407,23,\"2011-12-04 00:00:00\",\"69 Salem Street\",\"Boston\",\"MA\",\"USA\",\"2113\",1.98],[408,25,\"2011-12-05 00:00:00\",\"319 N. Frances Street\",\"Madison\",\"WI\",\"USA\",\"53703\",3.96],[409,29,\"2011-12-06 00:00:00\",\"796 Dundas Street West\",\"Toronto\",\"ON\",\"Canada\",\"M6J 1V1\",5.94],[410,35,\"2011-12-09 00:00:00\",\"Rua dos Campe\\u00c3\\u00b5es Europeus de Viena, 4350\",\"Porto\",null,\"Portugal\",null,8.91],[411,44,\"2011-12-14 00:00:00\",\"Porthaninkatu 9\",\"Helsinki\",null,\"Finland\",\"00530\",13.86],[412,58,\"2011-12-22 00:00:00\",\"12,Community Centre\",\"Delhi\",null,\"India\",\"110017\",1.99]]}" ]
{"columns":["billing_country","COUNT(*)"],"index":[0,1,2,3,4],"data":[["USA",91],["Canada",56],["Brazil",35],["France",35],["Germany",28]]}
SELECT billing_country , COUNT(*) FROM invoices GROUP BY billing_country ORDER BY count(*) DESC LIMIT 5; <table_name> : invoices col : id | customer_id | invoice_date | billing_address | billing_city | billing_state | billing_country | billing_postal_code | total row 1 : 1 | 2 | 2007-01-01 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 1.98 row 2 : 2 | 4 | 2007-01-02 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 3.96 row 3 : 3 | 8 | 2007-01-03 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 5.94 row 4 : 4 | 14 | 2007-01-06 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 8.91 row 5 : 5 | 23 | 2007-01-11 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 13.86 row 6 : 6 | 37 | 2007-01-19 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 0.99 row 7 : 7 | 38 | 2007-02-01 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 1.98 row 8 : 8 | 40 | 2007-02-01 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 1.98 row 9 : 9 | 42 | 2007-02-02 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 3.96 row 10 : 10 | 46 | 2007-02-03 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 5.94 row 11 : 11 | 52 | 2007-02-06 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 8.91 row 12 : 12 | 2 | 2007-02-11 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 13.86 row 13 : 13 | 16 | 2007-02-19 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 0.99 row 14 : 14 | 17 | 2007-03-04 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 1.98 row 15 : 15 | 19 | 2007-03-04 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 1.98 row 16 : 16 | 21 | 2007-03-05 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 3.96 row 17 : 17 | 25 | 2007-03-06 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 5.94 row 18 : 18 | 31 | 2007-03-09 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 8.91 row 19 : 19 | 40 | 2007-03-14 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 13.86 row 20 : 20 | 54 | 2007-03-22 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 0.99 row 21 : 21 | 55 | 2007-04-04 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 1.98 row 22 : 22 | 57 | 2007-04-04 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 1.98 row 23 : 23 | 59 | 2007-04-05 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 3.96 row 24 : 24 | 4 | 2007-04-06 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 5.94 row 25 : 25 | 10 | 2007-04-09 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 8.91 row 26 : 26 | 19 | 2007-04-14 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 13.86 row 27 : 27 | 33 | 2007-04-22 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 0.99 row 28 : 28 | 34 | 2007-05-05 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 1.98 row 29 : 29 | 36 | 2007-05-05 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 1.98 row 30 : 30 | 38 | 2007-05-06 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 3.96 row 31 : 31 | 42 | 2007-05-07 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 5.94 row 32 : 32 | 48 | 2007-05-10 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 8.91 row 33 : 33 | 57 | 2007-05-15 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 13.86 row 34 : 34 | 12 | 2007-05-23 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 0.99 row 35 : 35 | 13 | 2007-06-05 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 1.98 row 36 : 36 | 15 | 2007-06-05 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 1.98 row 37 : 37 | 17 | 2007-06-06 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 3.96 row 38 : 38 | 21 | 2007-06-07 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 5.94 row 39 : 39 | 27 | 2007-06-10 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 8.91 row 40 : 40 | 36 | 2007-06-15 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 13.86 row 41 : 41 | 50 | 2007-06-23 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 0.99 row 42 : 42 | 51 | 2007-07-06 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 1.98 row 43 : 43 | 53 | 2007-07-06 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 1.98 row 44 : 44 | 55 | 2007-07-07 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 3.96 row 45 : 45 | 59 | 2007-07-08 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 5.94 row 46 : 46 | 6 | 2007-07-11 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 8.91 row 47 : 47 | 15 | 2007-07-16 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 13.86 row 48 : 48 | 29 | 2007-07-24 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 0.99 row 49 : 49 | 30 | 2007-08-06 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 1.98 row 50 : 50 | 32 | 2007-08-06 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 1.98 row 51 : 51 | 34 | 2007-08-07 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 3.96 row 52 : 52 | 38 | 2007-08-08 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 5.94 row 53 : 53 | 44 | 2007-08-11 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 8.91 row 54 : 54 | 53 | 2007-08-16 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 13.86 row 55 : 55 | 8 | 2007-08-24 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 0.99 row 56 : 56 | 9 | 2007-09-06 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 1.98 row 57 : 57 | 11 | 2007-09-06 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 1.98 row 58 : 58 | 13 | 2007-09-07 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 3.96 row 59 : 59 | 17 | 2007-09-08 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 5.94 row 60 : 60 | 23 | 2007-09-11 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 8.91 row 61 : 61 | 32 | 2007-09-16 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 13.86 row 62 : 62 | 46 | 2007-09-24 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 0.99 row 63 : 63 | 47 | 2007-10-07 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 1.98 row 64 : 64 | 49 | 2007-10-07 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 1.98 row 65 : 65 | 51 | 2007-10-08 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 3.96 row 66 : 66 | 55 | 2007-10-09 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 5.94 row 67 : 67 | 2 | 2007-10-12 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 8.91 row 68 : 68 | 11 | 2007-10-17 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 13.86 row 69 : 69 | 25 | 2007-10-25 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 0.99 row 70 : 70 | 26 | 2007-11-07 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 1.98 row 71 : 71 | 28 | 2007-11-07 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 1.98 row 72 : 72 | 30 | 2007-11-08 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 3.96 row 73 : 73 | 34 | 2007-11-09 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 5.94 row 74 : 74 | 40 | 2007-11-12 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 8.91 row 75 : 75 | 49 | 2007-11-17 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 13.86 row 76 : 76 | 4 | 2007-11-25 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 0.99 row 77 : 77 | 5 | 2007-12-08 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 1.98 row 78 : 78 | 7 | 2007-12-08 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 1.98 row 79 : 79 | 9 | 2007-12-09 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 3.96 row 80 : 80 | 13 | 2007-12-10 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 5.94 row 81 : 81 | 19 | 2007-12-13 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 8.91 row 82 : 82 | 28 | 2007-12-18 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 13.86 row 83 : 83 | 42 | 2007-12-26 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 0.99 row 84 : 84 | 43 | 2008-01-08 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 1.98 row 85 : 85 | 45 | 2008-01-08 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 1.98 row 86 : 86 | 47 | 2008-01-09 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 3.96 row 87 : 87 | 51 | 2008-01-10 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 6.94 row 88 : 88 | 57 | 2008-01-13 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 17.91 row 89 : 89 | 7 | 2008-01-18 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 18.86 row 90 : 90 | 21 | 2008-01-26 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 0.99 row 91 : 91 | 22 | 2008-02-08 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 1.98 row 92 : 92 | 24 | 2008-02-08 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 1.98 row 93 : 93 | 26 | 2008-02-09 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 3.96 row 94 : 94 | 30 | 2008-02-10 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 5.94 row 95 : 95 | 36 | 2008-02-13 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 8.91 row 96 : 96 | 45 | 2008-02-18 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 21.86 row 97 : 97 | 59 | 2008-02-26 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 1.99 row 98 : 98 | 1 | 2008-03-10 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 3.98 row 99 : 99 | 3 | 2008-03-10 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 3.98 row 100 : 100 | 5 | 2008-03-11 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 3.96 row 101 : 101 | 9 | 2008-03-12 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 5.94 row 102 : 102 | 15 | 2008-03-15 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 9.91 row 103 : 103 | 24 | 2008-03-20 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 15.86 row 104 : 104 | 38 | 2008-03-28 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 0.99 row 105 : 105 | 39 | 2008-04-10 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 1.98 row 106 : 106 | 41 | 2008-04-10 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 1.98 row 107 : 107 | 43 | 2008-04-11 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 3.96 row 108 : 108 | 47 | 2008-04-12 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 5.94 row 109 : 109 | 53 | 2008-04-15 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 8.91 row 110 : 110 | 3 | 2008-04-20 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 13.86 row 111 : 111 | 17 | 2008-04-28 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 0.99 row 112 : 112 | 18 | 2008-05-11 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 1.98 row 113 : 113 | 20 | 2008-05-11 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 1.98 row 114 : 114 | 22 | 2008-05-12 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 3.96 row 115 : 115 | 26 | 2008-05-13 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 5.94 row 116 : 116 | 32 | 2008-05-16 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 8.91 row 117 : 117 | 41 | 2008-05-21 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 13.86 row 118 : 118 | 55 | 2008-05-29 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 0.99 row 119 : 119 | 56 | 2008-06-11 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 1.98 row 120 : 120 | 58 | 2008-06-11 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 1.98 row 121 : 121 | 1 | 2008-06-12 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 3.96 row 122 : 122 | 5 | 2008-06-13 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 5.94 row 123 : 123 | 11 | 2008-06-16 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 8.91 row 124 : 124 | 20 | 2008-06-21 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 13.86 row 125 : 125 | 34 | 2008-06-29 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 0.99 row 126 : 126 | 35 | 2008-07-12 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 1.98 row 127 : 127 | 37 | 2008-07-12 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 1.98 row 128 : 128 | 39 | 2008-07-13 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 3.96 row 129 : 129 | 43 | 2008-07-14 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 5.94 row 130 : 130 | 49 | 2008-07-17 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 8.91 row 131 : 131 | 58 | 2008-07-22 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 13.86 row 132 : 132 | 13 | 2008-07-30 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 0.99 row 133 : 133 | 14 | 2008-08-12 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 1.98 row 134 : 134 | 16 | 2008-08-12 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 1.98 row 135 : 135 | 18 | 2008-08-13 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 3.96 row 136 : 136 | 22 | 2008-08-14 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 5.94 row 137 : 137 | 28 | 2008-08-17 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 8.91 row 138 : 138 | 37 | 2008-08-22 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 13.86 row 139 : 139 | 51 | 2008-08-30 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 0.99 row 140 : 140 | 52 | 2008-09-12 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 1.98 row 141 : 141 | 54 | 2008-09-12 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 1.98 row 142 : 142 | 56 | 2008-09-13 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 3.96 row 143 : 143 | 1 | 2008-09-14 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 5.94 row 144 : 144 | 7 | 2008-09-17 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 8.91 row 145 : 145 | 16 | 2008-09-22 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 13.86 row 146 : 146 | 30 | 2008-09-30 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 0.99 row 147 : 147 | 31 | 2008-10-13 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 1.98 row 148 : 148 | 33 | 2008-10-13 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 1.98 row 149 : 149 | 35 | 2008-10-14 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 3.96 row 150 : 150 | 39 | 2008-10-15 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 5.94 row 151 : 151 | 45 | 2008-10-18 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 8.91 row 152 : 152 | 54 | 2008-10-23 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 13.86 row 153 : 153 | 9 | 2008-10-31 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 0.99 row 154 : 154 | 10 | 2008-11-13 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 1.98 row 155 : 155 | 12 | 2008-11-13 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 1.98 row 156 : 156 | 14 | 2008-11-14 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 3.96 row 157 : 157 | 18 | 2008-11-15 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 5.94 row 158 : 158 | 24 | 2008-11-18 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 8.91 row 159 : 159 | 33 | 2008-11-23 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 13.86 row 160 : 160 | 47 | 2008-12-01 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 0.99 row 161 : 161 | 48 | 2008-12-14 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 1.98 row 162 : 162 | 50 | 2008-12-14 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 1.98 row 163 : 163 | 52 | 2008-12-15 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 3.96 row 164 : 164 | 56 | 2008-12-16 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 5.94 row 165 : 165 | 3 | 2008-12-19 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 8.91 row 166 : 166 | 12 | 2008-12-24 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 13.86 row 167 : 167 | 26 | 2009-01-01 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 0.99 row 168 : 168 | 27 | 2009-01-14 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 1.98 row 169 : 169 | 29 | 2009-01-14 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 1.98 row 170 : 170 | 31 | 2009-01-15 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 3.96 row 171 : 171 | 35 | 2009-01-16 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 5.94 row 172 : 172 | 41 | 2009-01-19 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 8.91 row 173 : 173 | 50 | 2009-01-24 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 13.86 row 174 : 174 | 5 | 2009-02-01 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 0.99 row 175 : 175 | 6 | 2009-02-14 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 1.98 row 176 : 176 | 8 | 2009-02-14 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 1.98 row 177 : 177 | 10 | 2009-02-15 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 3.96 row 178 : 178 | 14 | 2009-02-16 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 5.94 row 179 : 179 | 20 | 2009-02-19 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 8.91 row 180 : 180 | 29 | 2009-02-24 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 13.86 row 181 : 181 | 43 | 2009-03-04 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 0.99 row 182 : 182 | 44 | 2009-03-17 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 1.98 row 183 : 183 | 46 | 2009-03-17 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 1.98 row 184 : 184 | 48 | 2009-03-18 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 3.96 row 185 : 185 | 52 | 2009-03-19 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 5.94 row 186 : 186 | 58 | 2009-03-22 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 8.91 row 187 : 187 | 8 | 2009-03-27 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 13.86 row 188 : 188 | 22 | 2009-04-04 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 0.99 row 189 : 189 | 23 | 2009-04-17 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 1.98 row 190 : 190 | 25 | 2009-04-17 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 1.98 row 191 : 191 | 27 | 2009-04-18 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 3.96 row 192 : 192 | 31 | 2009-04-19 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 5.94 row 193 : 193 | 37 | 2009-04-22 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 14.91 row 194 : 194 | 46 | 2009-04-27 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 21.86 row 195 : 195 | 1 | 2009-05-05 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 0.99 row 196 : 196 | 2 | 2009-05-18 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 1.98 row 197 : 197 | 4 | 2009-05-18 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 1.98 row 198 : 198 | 6 | 2009-05-19 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 3.96 row 199 : 199 | 10 | 2009-05-20 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 5.94 row 200 : 200 | 16 | 2009-05-23 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 8.91 row 201 : 201 | 25 | 2009-05-28 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 18.86 row 202 : 202 | 39 | 2009-06-05 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 1.99 row 203 : 203 | 40 | 2009-06-18 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 2.98 row 204 : 204 | 42 | 2009-06-18 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 3.98 row 205 : 205 | 44 | 2009-06-19 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 7.96 row 206 : 206 | 48 | 2009-06-20 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 8.94 row 207 : 207 | 54 | 2009-06-23 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 8.91 row 208 : 208 | 4 | 2009-06-28 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 15.86 row 209 : 209 | 18 | 2009-07-06 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 0.99 row 210 : 210 | 19 | 2009-07-19 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 1.98 row 211 : 211 | 21 | 2009-07-19 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 1.98 row 212 : 212 | 23 | 2009-07-20 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 3.96 row 213 : 213 | 27 | 2009-07-21 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 5.94 row 214 : 214 | 33 | 2009-07-24 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 8.91 row 215 : 215 | 42 | 2009-07-29 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 13.86 row 216 : 216 | 56 | 2009-08-06 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 0.99 row 217 : 217 | 57 | 2009-08-19 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 1.98 row 218 : 218 | 59 | 2009-08-19 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 1.98 row 219 : 219 | 2 | 2009-08-20 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 3.96 row 220 : 220 | 6 | 2009-08-21 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 5.94 row 221 : 221 | 12 | 2009-08-24 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 8.91 row 222 : 222 | 21 | 2009-08-29 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 13.86 row 223 : 223 | 35 | 2009-09-06 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 0.99 row 224 : 224 | 36 | 2009-09-19 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 1.98 row 225 : 225 | 38 | 2009-09-19 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 1.98 row 226 : 226 | 40 | 2009-09-20 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 3.96 row 227 : 227 | 44 | 2009-09-21 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 5.94 row 228 : 228 | 50 | 2009-09-24 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 8.91 row 229 : 229 | 59 | 2009-09-29 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 13.86 row 230 : 230 | 14 | 2009-10-07 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 0.99 row 231 : 231 | 15 | 2009-10-20 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 1.98 row 232 : 232 | 17 | 2009-10-20 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 1.98 row 233 : 233 | 19 | 2009-10-21 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 3.96 row 234 : 234 | 23 | 2009-10-22 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 5.94 row 235 : 235 | 29 | 2009-10-25 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 8.91 row 236 : 236 | 38 | 2009-10-30 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 13.86 row 237 : 237 | 52 | 2009-11-07 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 0.99 row 238 : 238 | 53 | 2009-11-20 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 1.98 row 239 : 239 | 55 | 2009-11-20 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 1.98 row 240 : 240 | 57 | 2009-11-21 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 3.96 row 241 : 241 | 2 | 2009-11-22 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 5.94 row 242 : 242 | 8 | 2009-11-25 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 8.91 row 243 : 243 | 17 | 2009-11-30 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 13.86 row 244 : 244 | 31 | 2009-12-08 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 0.99 row 245 : 245 | 32 | 2009-12-21 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 1.98 row 246 : 246 | 34 | 2009-12-21 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 1.98 row 247 : 247 | 36 | 2009-12-22 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 3.96 row 248 : 248 | 40 | 2009-12-23 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 5.94 row 249 : 249 | 46 | 2009-12-26 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 8.91 row 250 : 250 | 55 | 2009-12-31 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 13.86 row 251 : 251 | 10 | 2010-01-08 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 0.99 row 252 : 252 | 11 | 2010-01-21 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 1.98 row 253 : 253 | 13 | 2010-01-21 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 1.98 row 254 : 254 | 15 | 2010-01-22 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 3.96 row 255 : 255 | 19 | 2010-01-23 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 5.94 row 256 : 256 | 25 | 2010-01-26 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 8.91 row 257 : 257 | 34 | 2010-01-31 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 13.86 row 258 : 258 | 48 | 2010-02-08 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 0.99 row 259 : 259 | 49 | 2010-02-21 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 1.98 row 260 : 260 | 51 | 2010-02-21 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 1.98 row 261 : 261 | 53 | 2010-02-22 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 3.96 row 262 : 262 | 57 | 2010-02-23 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 5.94 row 263 : 263 | 4 | 2010-02-26 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 8.91 row 264 : 264 | 13 | 2010-03-03 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 13.86 row 265 : 265 | 27 | 2010-03-11 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 0.99 row 266 : 266 | 28 | 2010-03-24 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 1.98 row 267 : 267 | 30 | 2010-03-24 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 1.98 row 268 : 268 | 32 | 2010-03-25 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 3.96 row 269 : 269 | 36 | 2010-03-26 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 5.94 row 270 : 270 | 42 | 2010-03-29 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 8.91 row 271 : 271 | 51 | 2010-04-03 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 13.86 row 272 : 272 | 6 | 2010-04-11 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 0.99 row 273 : 273 | 7 | 2010-04-24 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 1.98 row 274 : 274 | 9 | 2010-04-24 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 1.98 row 275 : 275 | 11 | 2010-04-25 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 3.96 row 276 : 276 | 15 | 2010-04-26 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 5.94 row 277 : 277 | 21 | 2010-04-29 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 8.91 row 278 : 278 | 30 | 2010-05-04 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 13.86 row 279 : 279 | 44 | 2010-05-12 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 0.99 row 280 : 280 | 45 | 2010-05-25 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 1.98 row 281 : 281 | 47 | 2010-05-25 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 1.98 row 282 : 282 | 49 | 2010-05-26 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 3.96 row 283 : 283 | 53 | 2010-05-27 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 5.94 row 284 : 284 | 59 | 2010-05-30 00:00:00 | 3,Raj Bhavan Road | Bangalore | | India | 560001 | 8.91 row 285 : 285 | 9 | 2010-06-04 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 13.86 row 286 : 286 | 23 | 2010-06-12 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 0.99 row 287 : 287 | 24 | 2010-06-25 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 1.98 row 288 : 288 | 26 | 2010-06-25 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 1.98 row 289 : 289 | 28 | 2010-06-26 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 3.96 row 290 : 290 | 32 | 2010-06-27 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 5.94 row 291 : 291 | 38 | 2010-06-30 00:00:00 | Barbarossastraße 19 | Berlin | | Germany | 10779 | 8.91 row 292 : 292 | 47 | 2010-07-05 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 13.86 row 293 : 293 | 2 | 2010-07-13 00:00:00 | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | 0.99 row 294 : 294 | 3 | 2010-07-26 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 1.98 row 295 : 295 | 5 | 2010-07-26 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 1.98 row 296 : 296 | 7 | 2010-07-27 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 3.96 row 297 : 297 | 11 | 2010-07-28 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 5.94 row 298 : 298 | 17 | 2010-07-31 00:00:00 | 1 Microsoft Way | Redmond | WA | USA | 98052-8300 | 10.91 row 299 : 299 | 26 | 2010-08-05 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 23.86 row 300 : 300 | 40 | 2010-08-13 00:00:00 | 8, Rue Hanovre | Paris | | France | 75002 | 0.99 row 301 : 301 | 41 | 2010-08-26 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 1.98 row 302 : 302 | 43 | 2010-08-26 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 1.98 row 303 : 303 | 45 | 2010-08-27 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 3.96 row 304 : 304 | 49 | 2010-08-28 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 5.94 row 305 : 305 | 55 | 2010-08-31 00:00:00 | 421 Bourke Street | Sidney | NSW | Australia | 2010 | 8.91 row 306 : 306 | 5 | 2010-09-05 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 16.86 row 307 : 307 | 19 | 2010-09-13 00:00:00 | 1 Infinite Loop | Cupertino | CA | USA | 95014 | 1.99 row 308 : 308 | 20 | 2010-09-26 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 3.98 row 309 : 309 | 22 | 2010-09-26 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 3.98 row 310 : 310 | 24 | 2010-09-27 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 7.96 row 311 : 311 | 28 | 2010-09-28 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 11.94 row 312 : 312 | 34 | 2010-10-01 00:00:00 | Rua da Assunção 53 | Lisbon | | Portugal | | 10.91 row 313 : 313 | 43 | 2010-10-06 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 16.86 row 314 : 314 | 57 | 2010-10-14 00:00:00 | Calle Lira, 198 | Santiago | | Chile | | 0.99 row 315 : 315 | 58 | 2010-10-27 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 1.98 row 316 : 316 | 1 | 2010-10-27 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 1.98 row 317 : 317 | 3 | 2010-10-28 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 3.96 row 318 : 318 | 7 | 2010-10-29 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 5.94 row 319 : 319 | 13 | 2010-11-01 00:00:00 | Qe 7 Bloco G | Brasília | DF | Brazil | 71020-677 | 8.91 row 320 : 320 | 22 | 2010-11-06 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 13.86 row 321 : 321 | 36 | 2010-11-14 00:00:00 | Tauentzienstraße 8 | Berlin | | Germany | 10789 | 0.99 row 322 : 322 | 37 | 2010-11-27 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 1.98 row 323 : 323 | 39 | 2010-11-27 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 1.98 row 324 : 324 | 41 | 2010-11-28 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 3.96 row 325 : 325 | 45 | 2010-11-29 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 5.94 row 326 : 326 | 51 | 2010-12-02 00:00:00 | Celsiusg. 9 | Stockholm | | Sweden | 11230 | 8.91 row 327 : 327 | 1 | 2010-12-07 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 13.86 row 328 : 328 | 15 | 2010-12-15 00:00:00 | 700 W Pender Street | Vancouver | BC | Canada | V6C 1G8 | 0.99 row 329 : 329 | 16 | 2010-12-28 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 1.98 row 330 : 330 | 18 | 2010-12-28 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 1.98 row 331 : 331 | 20 | 2010-12-29 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 3.96 row 332 : 332 | 24 | 2010-12-30 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 5.94 row 333 : 333 | 30 | 2011-01-02 00:00:00 | 230 Elgin Street | Ottawa | ON | Canada | K2P 1L7 | 8.91 row 334 : 334 | 39 | 2011-01-07 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 13.86 row 335 : 335 | 53 | 2011-01-15 00:00:00 | 113 Lupus St | London | | United Kingdom | SW1V 3EN | 0.99 row 336 : 336 | 54 | 2011-01-28 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 1.98 row 337 : 337 | 56 | 2011-01-28 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 1.98 row 338 : 338 | 58 | 2011-01-29 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 3.96 row 339 : 339 | 3 | 2011-01-30 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 5.94 row 340 : 340 | 9 | 2011-02-02 00:00:00 | Sønder Boulevard 51 | Copenhagen | | Denmark | 1720 | 8.91 row 341 : 341 | 18 | 2011-02-07 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 13.86 row 342 : 342 | 32 | 2011-02-15 00:00:00 | 696 Osborne Street | Winnipeg | MB | Canada | R3L 2B9 | 0.99 row 343 : 343 | 33 | 2011-02-28 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 1.98 row 344 : 344 | 35 | 2011-02-28 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 1.98 row 345 : 345 | 37 | 2011-03-01 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 3.96 row 346 : 346 | 41 | 2011-03-02 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 5.94 row 347 : 347 | 47 | 2011-03-05 00:00:00 | Via Degli Scipioni, 43 | Rome | RM | Italy | 00192 | 8.91 row 348 : 348 | 56 | 2011-03-10 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 13.86 row 349 : 349 | 11 | 2011-03-18 00:00:00 | Av. Paulista, 2022 | São Paulo | SP | Brazil | 01310-200 | 0.99 row 350 : 350 | 12 | 2011-03-31 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 1.98 row 351 : 351 | 14 | 2011-03-31 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 1.98 row 352 : 352 | 16 | 2011-04-01 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 3.96 row 353 : 353 | 20 | 2011-04-02 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 5.94 row 354 : 354 | 26 | 2011-04-05 00:00:00 | 2211 W Berry Street | Fort Worth | TX | USA | 76110 | 8.91 row 355 : 355 | 35 | 2011-04-10 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 13.86 row 356 : 356 | 49 | 2011-04-18 00:00:00 | Ordynacka 10 | Warsaw | | Poland | 00-358 | 0.99 row 357 : 357 | 50 | 2011-05-01 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 1.98 row 358 : 358 | 52 | 2011-05-01 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 1.98 row 359 : 359 | 54 | 2011-05-02 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 3.96 row 360 : 360 | 58 | 2011-05-03 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 5.94 row 361 : 361 | 5 | 2011-05-06 00:00:00 | Klanova 9/506 | Prague | | Czech Republic | 14700 | 8.91 row 362 : 362 | 14 | 2011-05-11 00:00:00 | 8210 111 ST NW | Edmonton | AB | Canada | T6G 2C7 | 13.86 row 363 : 363 | 28 | 2011-05-19 00:00:00 | 302 S 700 E | Salt Lake City | UT | USA | 84102 | 0.99 row 364 : 364 | 29 | 2011-06-01 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 1.98 row 365 : 365 | 31 | 2011-06-01 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 1.98 row 366 : 366 | 33 | 2011-06-02 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 3.96 row 367 : 367 | 37 | 2011-06-03 00:00:00 | Berger Straße 10 | Frankfurt | | Germany | 60316 | 5.94 row 368 : 368 | 43 | 2011-06-06 00:00:00 | 68, Rue Jouvence | Dijon | | France | 21000 | 8.91 row 369 : 369 | 52 | 2011-06-11 00:00:00 | 202 Hoxton Street | London | | United Kingdom | N1 5LH | 13.86 row 370 : 370 | 7 | 2011-06-19 00:00:00 | Rotenturmstraße 4, 1010 Innere Stadt | Vienne | | Austria | 1010 | 0.99 row 371 : 371 | 8 | 2011-07-02 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 1.98 row 372 : 372 | 10 | 2011-07-02 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 1.98 row 373 : 373 | 12 | 2011-07-03 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 3.96 row 374 : 374 | 16 | 2011-07-04 00:00:00 | 1600 Amphitheatre Parkway | Mountain View | CA | USA | 94043-1351 | 5.94 row 375 : 375 | 22 | 2011-07-07 00:00:00 | 120 S Orange Ave | Orlando | FL | USA | 32801 | 8.91 row 376 : 376 | 31 | 2011-07-12 00:00:00 | 194A Chain Lake Drive | Halifax | NS | Canada | B3S 1C5 | 13.86 row 377 : 377 | 45 | 2011-07-20 00:00:00 | Erzsébet krt. 58. | Budapest | | Hungary | H-1073 | 0.99 row 378 : 378 | 46 | 2011-08-02 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 1.98 row 379 : 379 | 48 | 2011-08-02 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 1.98 row 380 : 380 | 50 | 2011-08-03 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 3.96 row 381 : 381 | 54 | 2011-08-04 00:00:00 | 110 Raeburn Pl | Edinburgh | | United Kingdom | EH4 1HH | 5.94 row 382 : 382 | 1 | 2011-08-07 00:00:00 | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | 8.91 row 383 : 383 | 10 | 2011-08-12 00:00:00 | Rua Dr. Falcão Filho, 155 | São Paulo | SP | Brazil | 01007-010 | 13.86 row 384 : 384 | 24 | 2011-08-20 00:00:00 | 162 E Superior Street | Chicago | IL | USA | 60611 | 0.99 row 385 : 385 | 25 | 2011-09-02 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 1.98 row 386 : 386 | 27 | 2011-09-02 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 1.98 row 387 : 387 | 29 | 2011-09-03 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 3.96 row 388 : 388 | 33 | 2011-09-04 00:00:00 | 5112 48 Street | Yellowknife | NT | Canada | X1A 1N6 | 5.94 row 389 : 389 | 39 | 2011-09-07 00:00:00 | 4, Rue Milton | Paris | | France | 75009 | 8.91 row 390 : 390 | 48 | 2011-09-12 00:00:00 | Lijnbaansgracht 120bg | Amsterdam | VV | Netherlands | 1016 | 13.86 row 391 : 391 | 3 | 2011-09-20 00:00:00 | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | 0.99 row 392 : 392 | 4 | 2011-10-03 00:00:00 | Ullevålsveien 14 | Oslo | | Norway | 0171 | 1.98 row 393 : 393 | 6 | 2011-10-03 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 1.98 row 394 : 394 | 8 | 2011-10-04 00:00:00 | Grétrystraat 63 | Brussels | | Belgium | 1000 | 3.96 row 395 : 395 | 12 | 2011-10-05 00:00:00 | Praça Pio X, 119 | Rio de Janeiro | RJ | Brazil | 20040-020 | 5.94 row 396 : 396 | 18 | 2011-10-08 00:00:00 | 627 Broadway | New York | NY | USA | 10012-2612 | 8.91 row 397 : 397 | 27 | 2011-10-13 00:00:00 | 1033 N Park Ave | Tucson | AZ | USA | 85719 | 13.86 row 398 : 398 | 41 | 2011-10-21 00:00:00 | 11, Place Bellecour | Lyon | | France | 69002 | 0.99 row 399 : 399 | 42 | 2011-11-03 00:00:00 | 9, Place Louis Barthou | Bordeaux | | France | 33000 | 1.98 row 400 : 400 | 44 | 2011-11-03 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 1.98 row 401 : 401 | 46 | 2011-11-04 00:00:00 | 3 Chatham Street | Dublin | Dublin | Ireland | | 3.96 row 402 : 402 | 50 | 2011-11-05 00:00:00 | C/ San Bernardo 85 | Madrid | | Spain | 28015 | 5.94 row 403 : 403 | 56 | 2011-11-08 00:00:00 | 307 Macacha Güemes | Buenos Aires | | Argentina | 1106 | 8.91 row 404 : 404 | 6 | 2011-11-13 00:00:00 | Rilská 3174/6 | Prague | | Czech Republic | 14300 | 25.86 row 405 : 405 | 20 | 2011-11-21 00:00:00 | 541 Del Medio Avenue | Mountain View | CA | USA | 94040-111 | 0.99 row 406 : 406 | 21 | 2011-12-04 00:00:00 | 801 W 4th Street | Reno | NV | USA | 89503 | 1.98 row 407 : 407 | 23 | 2011-12-04 00:00:00 | 69 Salem Street | Boston | MA | USA | 2113 | 1.98 row 408 : 408 | 25 | 2011-12-05 00:00:00 | 319 N. Frances Street | Madison | WI | USA | 53703 | 3.96 row 409 : 409 | 29 | 2011-12-06 00:00:00 | 796 Dundas Street West | Toronto | ON | Canada | M6J 1V1 | 5.94 row 410 : 410 | 35 | 2011-12-09 00:00:00 | Rua dos Campeões Europeus de Viena, 4350 | Porto | | Portugal | | 8.91 row 411 : 411 | 44 | 2011-12-14 00:00:00 | Porthaninkatu 9 | Helsinki | | Finland | 00530 | 13.86 row 412 : 412 | 58 | 2011-12-22 00:00:00 | 12,Community Centre | Delhi | | India | 110017 | 1.99
col : billing_country | COUNT(*) row 1 : USA | 91 row 2 : Canada | 56 row 3 : Brazil | 35 row 4 : France | 35 row 5 : Germany | 28