vaishali/multitabqa-base
Table Question Answering
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SELECT count(*) FROM head WHERE age > 56 | How many heads of the departments are older than 56 ? | [
"head"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[5]]} | How many heads of the departments are older than 56 ? <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 | col : count(*) row 1 : 5 |
SELECT name , born_state , age FROM head ORDER BY age | List the name, born state and age of the heads of departments ordered by age. | [
"head"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}"
] | {"columns":["name","born_state","age"],"index":[0,1,2,3,4,5,6,7,8,9],"data":[["P\u00c3\u00a1draig Harrington","Connecticut",43.0],["Stewart Cink","Florida",50.0],["Dudley Hart","California",52.0],["Jeff Maggert","Delaware",53.0],["Nick Faldo","California",56.0],["Tiger Woods","Alabama",67.0],["Franklin Langham","Connecticut",67.0],["Sergio Garc\u00c3\u00ada","California",68.0],["K. J. Choi","Alabama",69.0],["Billy Mayfair","California",69.0]]} | List the name, born state and age of the heads of departments ordered by age. <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 | col : name | born_state | age row 1 : Pádraig Harrington | Connecticut | 43 row 2 : Stewart Cink | Florida | 50 row 3 : Dudley Hart | California | 52 row 4 : Jeff Maggert | Delaware | 53 row 5 : Nick Faldo | California | 56 row 6 : Tiger Woods | Alabama | 67 row 7 : Franklin Langham | Connecticut | 67 row 8 : Sergio GarcÃa | California | 68 row 9 : K. J. Choi | Alabama | 69 row 10 : Billy Mayfair | California | 69 |
SELECT creation , name , budget_in_billions FROM department | List the creation year, name and budget of each department. | [
"department"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}"
] | {"columns":["Creation","Name","Budget_in_Billions"],"index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],"data":[["1789","State",9.96],["1789","Treasury",11.1],["1947","Defense",439.3],["1870","Justice",23.4],["1849","Interior",10.7],["1889","Agriculture",77.6],["1903","Commerce",6.2],["1913","Labor",59.7],["1953","Health and Human Services",543.2],["1965","Housing and Urban Development",46.2],["1966","Transportation",58.0],["1977","Energy",21.5],["1979","Education",62.8],["1989","Veterans Affairs",73.2],["2002","Homeland Security",44.6]]} | List the creation year, name and budget of each department. <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 | col : Creation | Name | Budget_in_Billions row 1 : 1789 | State | 9.96 row 2 : 1789 | Treasury | 11.1 row 3 : 1947 | Defense | 439.3 row 4 : 1870 | Justice | 23.4 row 5 : 1849 | Interior | 10.7 row 6 : 1889 | Agriculture | 77.6 row 7 : 1903 | Commerce | 6.2 row 8 : 1913 | Labor | 59.7 row 9 : 1953 | Health and Human Services | 543.2 row 10 : 1965 | Housing and Urban Development | 46.2 row 11 : 1966 | Transportation | 58.0 row 12 : 1977 | Energy | 21.5 row 13 : 1979 | Education | 62.8 row 14 : 1989 | Veterans Affairs | 73.2 row 15 : 2002 | Homeland Security | 44.6 |
SELECT max(budget_in_billions) , min(budget_in_billions) FROM department | What are the maximum and minimum budget of the departments? | [
"department"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}"
] | {"columns":["max(budget_in_billions)","min(budget_in_billions)"],"index":[0],"data":[[543.2,6.2]]} | What are the maximum and minimum budget of the departments? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 | col : max(budget_in_billions) | min(budget_in_billions) row 1 : 543.2 | 6.2 |
SELECT avg(num_employees) FROM department WHERE ranking BETWEEN 10 AND 15 | What is the average number of employees of the departments whose rank is between 10 and 15? | [
"department"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}"
] | {"columns":["avg(num_employees)"],"index":[0],"data":[[105468.1666666667]]} | What is the average number of employees of the departments whose rank is between 10 and 15? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 | col : avg(num_employees) row 1 : 105468.1666666667 |
SELECT name FROM head WHERE born_state != 'California' | What are the names of the heads who are born outside the California state? | [
"head"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}"
] | {"columns":["name"],"index":[0,1,2,3,4,5],"data":[["Tiger Woods"],["K. J. Choi"],["Jeff Maggert"],["Stewart Cink"],["P\u00c3\u00a1draig Harrington"],["Franklin Langham"]]} | What are the names of the heads who are born outside the California state? <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 | col : name row 1 : Tiger Woods row 2 : K. J. Choi row 3 : Jeff Maggert row 4 : Stewart Cink row 5 : Pádraig Harrington row 6 : Franklin Langham |
SELECT DISTINCT T1.creation FROM department AS T1 JOIN management AS T2 ON T1.department_id = T2.department_id JOIN head AS T3 ON T2.head_id = T3.head_id WHERE T3.born_state = 'Alabama' | What are the distinct creation years of the departments managed by a secretary born in state 'Alabama'? | [
"department",
"head",
"management"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}",
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["Creation"],"index":[0],"data":[["1903"]]} | What are the distinct creation years of the departments managed by a secretary born in state 'Alabama'? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : Creation row 1 : 1903 |
SELECT born_state FROM head GROUP BY born_state HAVING count(*) >= 3 | What are the names of the states where at least 3 heads were born? | [
"head"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}"
] | {"columns":["born_state"],"index":[0],"data":[["California"]]} | What are the names of the states where at least 3 heads were born? <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 | col : born_state row 1 : California |
SELECT creation FROM department GROUP BY creation ORDER BY count(*) DESC LIMIT 1 | In which year were most departments established? | [
"department"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}"
] | {"columns":["Creation"],"index":[0],"data":[["1789"]]} | In which year were most departments established? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 | col : Creation row 1 : 1789 |
SELECT T1.name , T1.num_employees FROM department AS T1 JOIN management AS T2 ON T1.department_id = T2.department_id WHERE T2.temporary_acting = 'Yes' | Show the name and number of employees for the departments managed by heads whose temporary acting value is 'Yes'? | [
"department",
"management"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["Name","Num_Employees"],"index":[0,1,2],"data":[["Treasury",115897.0],["Homeland Security",208000.0],["Treasury",115897.0]]} | Show the name and number of employees for the departments managed by heads whose temporary acting value is 'Yes'? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : Name | Num_Employees row 1 : Treasury | 115897 row 2 : Homeland Security | 208000 row 3 : Treasury | 115897 |
SELECT count(DISTINCT temporary_acting) FROM management | How many acting statuses are there? | [
"management"
] | [
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["count(DISTINCT temporary_acting)"],"index":[0],"data":[[2]]} | How many acting statuses are there? <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : count(DISTINCT temporary_acting) row 1 : 2 |
SELECT count(*) FROM department WHERE department_id NOT IN (SELECT department_id FROM management); | How many departments are led by heads who are not mentioned? | [
"department",
"management"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[11]]} | How many departments are led by heads who are not mentioned? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : count(*) row 1 : 11 |
SELECT DISTINCT T1.age FROM management AS T2 JOIN head AS T1 ON T1.head_id = T2.head_id WHERE T2.temporary_acting = 'Yes' | What are the distinct ages of the heads who are acting? | [
"head",
"management"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["age"],"index":[0,1,2],"data":[[53.0],[52.0],[69.0]]} | What are the distinct ages of the heads who are acting? <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : age row 1 : 53 row 2 : 52 row 3 : 69 |
SELECT T3.born_state FROM department AS T1 JOIN management AS T2 ON T1.department_id = T2.department_id JOIN head AS T3 ON T2.head_id = T3.head_id WHERE T1.name = 'Treasury' INTERSECT SELECT T3.born_state FROM department AS T1 JOIN management AS T2 ON T1.department_id = T2.department_id JOIN head AS T3 ON T2.head_id = T3.head_id WHERE T1.name = 'Homeland Security' | List the states where both the secretary of 'Treasury' department and the secretary of 'Homeland Security' were born. | [
"department",
"head",
"management"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}",
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["born_state"],"index":[0],"data":[["California"]]} | List the states where both the secretary of 'Treasury' department and the secretary of 'Homeland Security' were born. <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : born_state row 1 : California |
SELECT T1.department_id , T1.name , count(*) FROM management AS T2 JOIN department AS T1 ON T1.department_id = T2.department_id GROUP BY T1.department_id HAVING count(*) > 1 | Which department has more than 1 head at a time? List the id, name and the number of heads. | [
"department",
"management"
] | [
"{\"columns\":[\"Department_ID\",\"Name\",\"Creation\",\"Ranking\",\"Budget_in_Billions\",\"Num_Employees\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[1,\"State\",\"1789\",1,9.96,30266.0],[2,\"Treasury\",\"1789\",2,11.1,115897.0],[3,\"Defense\",\"1947\",3,439.3,3000000.0],[4,\"Justice\",\"1870\",4,23.4,112557.0],[5,\"Interior\",\"1849\",5,10.7,71436.0],[6,\"Agriculture\",\"1889\",6,77.6,109832.0],[7,\"Commerce\",\"1903\",7,6.2,36000.0],[8,\"Labor\",\"1913\",8,59.7,17347.0],[9,\"Health and Human Services\",\"1953\",9,543.2,67000.0],[10,\"Housing and Urban Development\",\"1965\",10,46.2,10600.0],[11,\"Transportation\",\"1966\",11,58.0,58622.0],[12,\"Energy\",\"1977\",12,21.5,116100.0],[13,\"Education\",\"1979\",13,62.8,4487.0],[14,\"Veterans Affairs\",\"1989\",14,73.2,235000.0],[15,\"Homeland Security\",\"2002\",15,44.6,208000.0]]}",
"{\"columns\":[\"department_ID\",\"head_ID\",\"temporary_acting\"],\"index\":[0,1,2,3,4],\"data\":[[2,5,\"Yes\"],[15,4,\"Yes\"],[2,6,\"Yes\"],[7,3,\"No\"],[11,10,\"No\"]]}"
] | {"columns":["Department_ID","Name","count(*)"],"index":[0],"data":[[2,"Treasury",2]]} | Which department has more than 1 head at a time? List the id, name and the number of heads. <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No | col : Department_ID | Name | count(*) row 1 : 2 | Treasury | 2 |
SELECT head_id , name FROM head WHERE name LIKE '%Ha%' | Which head's name has the substring 'Ha'? List the id and name. | [
"head"
] | [
"{\"columns\":[\"head_ID\",\"name\",\"born_state\",\"age\"],\"index\":[0,1,2,3,4,5,6,7,8,9],\"data\":[[1,\"Tiger Woods\",\"Alabama\",67.0],[2,\"Sergio Garc\\u00c3\\u00ada\",\"California\",68.0],[3,\"K. J. Choi\",\"Alabama\",69.0],[4,\"Dudley Hart\",\"California\",52.0],[5,\"Jeff Maggert\",\"Delaware\",53.0],[6,\"Billy Mayfair\",\"California\",69.0],[7,\"Stewart Cink\",\"Florida\",50.0],[8,\"Nick Faldo\",\"California\",56.0],[9,\"P\\u00c3\\u00a1draig Harrington\",\"Connecticut\",43.0],[10,\"Franklin Langham\",\"Connecticut\",67.0]]}"
] | {"columns":["head_ID","name"],"index":[0,1,2],"data":[[4,"Dudley Hart"],[9,"P\u00c3\u00a1draig Harrington"],[10,"Franklin Langham"]]} | Which head's name has the substring 'Ha'? List the id and name. <table_name> : head col : head_ID | name | born_state | age row 1 : 1 | Tiger Woods | Alabama | 67 row 2 : 2 | Sergio GarcÃa | California | 68 row 3 : 3 | K. J. Choi | Alabama | 69 row 4 : 4 | Dudley Hart | California | 52 row 5 : 5 | Jeff Maggert | Delaware | 53 row 6 : 6 | Billy Mayfair | California | 69 row 7 : 7 | Stewart Cink | Florida | 50 row 8 : 8 | Nick Faldo | California | 56 row 9 : 9 | Pádraig Harrington | Connecticut | 43 row 10 : 10 | Franklin Langham | Connecticut | 67 | col : head_ID | name row 1 : 4 | Dudley Hart row 2 : 9 | Pádraig Harrington row 3 : 10 | Franklin Langham |
SELECT count(*) FROM farm | How many farms are there? | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[8]]} | How many farms are there? <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : count(*) row 1 : 8 |
SELECT count(*) FROM farm | Count the number of farms. | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[8]]} | Count the number of farms. <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : count(*) row 1 : 8 |
SELECT Total_Horses FROM farm ORDER BY Total_Horses ASC | List the total number of horses on farms in ascending order. | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["Total_Horses"],"index":[0,1,2,3,4,5,6,7],"data":[[2546.9],[2604.8],[3658.9],[4781.3],[5056.5],[5308.2],[5486.9],[5607.5]]} | List the total number of horses on farms in ascending order. <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : Total_Horses row 1 : 2546.9 row 2 : 2604.8 row 3 : 3658.9 row 4 : 4781.3 row 5 : 5056.5 row 6 : 5308.2 row 7 : 5486.9 row 8 : 5607.5 |
SELECT Total_Horses FROM farm ORDER BY Total_Horses ASC | What is the total horses record for each farm, sorted ascending? | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["Total_Horses"],"index":[0,1,2,3,4,5,6,7],"data":[[2546.9],[2604.8],[3658.9],[4781.3],[5056.5],[5308.2],[5486.9],[5607.5]]} | What is the total horses record for each farm, sorted ascending? <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : Total_Horses row 1 : 2546.9 row 2 : 2604.8 row 3 : 3658.9 row 4 : 4781.3 row 5 : 5056.5 row 6 : 5308.2 row 7 : 5486.9 row 8 : 5607.5 |
SELECT Hosts FROM farm_competition WHERE Theme != 'Aliens' | What are the hosts of competitions whose theme is not "Aliens"? | [
"farm_competition"
] | [
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Hosts"],"index":[0,1,2,3,4],"data":[["Miley Cyrus Jared Leto and Karen Mok"],["Leehom Wang and Kelly Rowland"],["Alicia Keys"],["Vanness Wu and Michelle Branch"],["Shaggy and Coco Lee"]]} | What are the hosts of competitions whose theme is not "Aliens"? <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Hosts row 1 : Miley Cyrus Jared Leto and Karen Mok row 2 : Leehom Wang and Kelly Rowland row 3 : Alicia Keys row 4 : Vanness Wu and Michelle Branch row 5 : Shaggy and Coco Lee |
SELECT Hosts FROM farm_competition WHERE Theme != 'Aliens' | Return the hosts of competitions for which the theme is not Aliens? | [
"farm_competition"
] | [
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Hosts"],"index":[0,1,2,3,4],"data":[["Miley Cyrus Jared Leto and Karen Mok"],["Leehom Wang and Kelly Rowland"],["Alicia Keys"],["Vanness Wu and Michelle Branch"],["Shaggy and Coco Lee"]]} | Return the hosts of competitions for which the theme is not Aliens? <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Hosts row 1 : Miley Cyrus Jared Leto and Karen Mok row 2 : Leehom Wang and Kelly Rowland row 3 : Alicia Keys row 4 : Vanness Wu and Michelle Branch row 5 : Shaggy and Coco Lee |
SELECT Theme FROM farm_competition ORDER BY YEAR ASC | What are the themes of farm competitions sorted by year in ascending order? | [
"farm_competition"
] | [
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Theme"],"index":[0,1,2,3,4,5],"data":[["Aliens"],["MTV Cube"],["Valentine's Day"],["MTV Asia Aid"],["Codehunters"],["Carnival M is back!"]]} | What are the themes of farm competitions sorted by year in ascending order? <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Theme row 1 : Aliens row 2 : MTV Cube row 3 : Valentine's Day row 4 : MTV Asia Aid row 5 : Codehunters row 6 : Carnival M is back! |
SELECT Theme FROM farm_competition ORDER BY YEAR ASC | Return the themes of farm competitions, sorted by year ascending. | [
"farm_competition"
] | [
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Theme"],"index":[0,1,2,3,4,5],"data":[["Aliens"],["MTV Cube"],["Valentine's Day"],["MTV Asia Aid"],["Codehunters"],["Carnival M is back!"]]} | Return the themes of farm competitions, sorted by year ascending. <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Theme row 1 : Aliens row 2 : MTV Cube row 3 : Valentine's Day row 4 : MTV Asia Aid row 5 : Codehunters row 6 : Carnival M is back! |
SELECT avg(Working_Horses) FROM farm WHERE Total_Horses > 5000 | What is the average number of working horses of farms with more than 5000 total number of horses? | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["avg(Working_Horses)"],"index":[0],"data":[[3977.75]]} | What is the average number of working horses of farms with more than 5000 total number of horses? <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : avg(Working_Horses) row 1 : 3977.75 |
SELECT avg(Working_Horses) FROM farm WHERE Total_Horses > 5000 | Give the average number of working horses on farms with more than 5000 total horses. | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["avg(Working_Horses)"],"index":[0],"data":[[3977.75]]} | Give the average number of working horses on farms with more than 5000 total horses. <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : avg(Working_Horses) row 1 : 3977.75 |
SELECT max(Cows) , min(Cows) FROM farm | What are the maximum and minimum number of cows across all farms. | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["max(Cows)","min(Cows)"],"index":[0],"data":[[3987.0,2407.2]]} | What are the maximum and minimum number of cows across all farms. <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : max(Cows) | min(Cows) row 1 : 3987 | 2407.2 |
SELECT max(Cows) , min(Cows) FROM farm | Return the maximum and minimum number of cows across all farms. | [
"farm"
] | [
"{\"columns\":[\"Farm_ID\",\"Year\",\"Total_Horses\",\"Working_Horses\",\"Total_Cattle\",\"Oxen\",\"Bulls\",\"Cows\",\"Pigs\",\"Sheep_and_Goats\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[1,1927,5056.5,3900.1,8374.5,805.5,31.6,3852.1,4412.4,7956.3],[2,1928,5486.9,4090.5,8604.8,895.3,32.8,3987.0,6962.9,8112.2],[3,1929,5607.5,4198.8,7611.0,593.7,26.9,3873.0,4161.2,7030.8],[4,1930,5308.2,3721.6,6274.1,254.8,49.6,3471.6,3171.8,4533.4],[5,1931,4781.3,3593.7,6189.5,113.8,40.0,3377.0,3373.3,3364.8],[6,1932,3658.9,3711.6,5006.7,105.2,71.6,2739.5,2623.7,2109.5],[7,1933,2604.8,3711.2,4446.3,116.9,37.6,2407.2,2089.2,2004.7],[8,1934,2546.9,2197.3,5277.5,156.5,46.7,2518.0,4236.7,2197.1]]}"
] | {"columns":["max(Cows)","min(Cows)"],"index":[0],"data":[[3987.0,2407.2]]} | Return the maximum and minimum number of cows across all farms. <table_name> : farm col : Farm_ID | Year | Total_Horses | Working_Horses | Total_Cattle | Oxen | Bulls | Cows | Pigs | Sheep_and_Goats row 1 : 1 | 1927 | 5056.5 | 3900.1 | 8374.5 | 805.5 | 31.6 | 3852.1 | 4412.4 | 7956.3 row 2 : 2 | 1928 | 5486.9 | 4090.5 | 8604.8 | 895.3 | 32.8 | 3987.0 | 6962.9 | 8112.2 row 3 : 3 | 1929 | 5607.5 | 4198.8 | 7611.0 | 593.7 | 26.9 | 3873.0 | 4161.2 | 7030.8 row 4 : 4 | 1930 | 5308.2 | 3721.6 | 6274.1 | 254.8 | 49.6 | 3471.6 | 3171.8 | 4533.4 row 5 : 5 | 1931 | 4781.3 | 3593.7 | 6189.5 | 113.8 | 40.0 | 3377.0 | 3373.3 | 3364.8 row 6 : 6 | 1932 | 3658.9 | 3711.6 | 5006.7 | 105.2 | 71.6 | 2739.5 | 2623.7 | 2109.5 row 7 : 7 | 1933 | 2604.8 | 3711.2 | 4446.3 | 116.9 | 37.6 | 2407.2 | 2089.2 | 2004.7 row 8 : 8 | 1934 | 2546.9 | 2197.3 | 5277.5 | 156.5 | 46.7 | 2518.0 | 4236.7 | 2197.1 | col : max(Cows) | min(Cows) row 1 : 3987 | 2407.2 |
SELECT count(DISTINCT Status) FROM city | How many different statuses do cities have? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["count(DISTINCT Status)"],"index":[0],"data":[[2]]} | How many different statuses do cities have? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : count(DISTINCT Status) row 1 : 2 |
SELECT count(DISTINCT Status) FROM city | Count the number of different statuses. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["count(DISTINCT Status)"],"index":[0],"data":[[2]]} | Count the number of different statuses. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : count(DISTINCT Status) row 1 : 2 |
SELECT Official_Name FROM city ORDER BY Population DESC | List official names of cities in descending order of population. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name"],"index":[0,1,2,3,4],"data":[["Grand Falls\/Grand-Sault"],["Perth-Andover"],["Plaster Rock"],["Drummond"],["Aroostook"]]} | List official names of cities in descending order of population. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name row 1 : Grand Falls/Grand-Sault row 2 : Perth-Andover row 3 : Plaster Rock row 4 : Drummond row 5 : Aroostook |
SELECT Official_Name FROM city ORDER BY Population DESC | What are the official names of cities, ordered descending by population? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name"],"index":[0,1,2,3,4],"data":[["Grand Falls\/Grand-Sault"],["Perth-Andover"],["Plaster Rock"],["Drummond"],["Aroostook"]]} | What are the official names of cities, ordered descending by population? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name row 1 : Grand Falls/Grand-Sault row 2 : Perth-Andover row 3 : Plaster Rock row 4 : Drummond row 5 : Aroostook |
SELECT Official_Name , Status FROM city ORDER BY Population DESC LIMIT 1 | List the official name and status of the city with the largest population. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name","Status"],"index":[0],"data":[["Grand Falls\/Grand-Sault","Town"]]} | List the official name and status of the city with the largest population. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name | Status row 1 : Grand Falls/Grand-Sault | Town |
SELECT Official_Name , Status FROM city ORDER BY Population DESC LIMIT 1 | What is the official name and status of the city with the most residents? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name","Status"],"index":[0],"data":[["Grand Falls\/Grand-Sault","Town"]]} | What is the official name and status of the city with the most residents? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name | Status row 1 : Grand Falls/Grand-Sault | Town |
SELECT T2.Year , T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID | Show the years and the official names of the host cities of competitions. | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Year","Official_Name"],"index":[0,1,2,3,4,5],"data":[[2013,"Grand Falls\/Grand-Sault"],[2006,"Perth-Andover"],[2005,"Plaster Rock"],[2004,"Drummond"],[2003,"Aroostook"],[2002,"Aroostook"]]} | Show the years and the official names of the host cities of competitions. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Year | Official_Name row 1 : 2013 | Grand Falls/Grand-Sault row 2 : 2006 | Perth-Andover row 3 : 2005 | Plaster Rock row 4 : 2004 | Drummond row 5 : 2003 | Aroostook row 6 : 2002 | Aroostook |
SELECT T2.Year , T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID | Give the years and official names of the cities of each competition. | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Year","Official_Name"],"index":[0,1,2,3,4,5],"data":[[2013,"Grand Falls\/Grand-Sault"],[2006,"Perth-Andover"],[2005,"Plaster Rock"],[2004,"Drummond"],[2003,"Aroostook"],[2002,"Aroostook"]]} | Give the years and official names of the cities of each competition. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Year | Official_Name row 1 : 2013 | Grand Falls/Grand-Sault row 2 : 2006 | Perth-Andover row 3 : 2005 | Plaster Rock row 4 : 2004 | Drummond row 5 : 2003 | Aroostook row 6 : 2002 | Aroostook |
SELECT T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID GROUP BY T2.Host_city_ID HAVING COUNT(*) > 1 | Show the official names of the cities that have hosted more than one competition. | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Official_Name"],"index":[0],"data":[["Aroostook"]]} | Show the official names of the cities that have hosted more than one competition. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Official_Name row 1 : Aroostook |
SELECT T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID GROUP BY T2.Host_city_ID HAVING COUNT(*) > 1 | What are the official names of cities that have hosted more than one competition? | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Official_Name"],"index":[0],"data":[["Aroostook"]]} | What are the official names of cities that have hosted more than one competition? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Official_Name row 1 : Aroostook |
SELECT T1.Status FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID GROUP BY T2.Host_city_ID ORDER BY COUNT(*) DESC LIMIT 1 | Show the status of the city that has hosted the greatest number of competitions. | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | Show the status of the city that has hosted the greatest number of competitions. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Status row 1 : Village |
SELECT T1.Status FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID GROUP BY T2.Host_city_ID ORDER BY COUNT(*) DESC LIMIT 1 | What is the status of the city that has hosted the most competitions? | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | What is the status of the city that has hosted the most competitions? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Status row 1 : Village |
SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID WHERE T1.Population > 1000 | Please show the themes of competitions with host cities having populations larger than 1000. | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Theme"],"index":[0,1,2],"data":[["Carnival M is back!"],["Codehunters"],["MTV Asia Aid"]]} | Please show the themes of competitions with host cities having populations larger than 1000. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Theme row 1 : Carnival M is back! row 2 : Codehunters row 3 : MTV Asia Aid |
SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID WHERE T1.Population > 1000 | What are the themes of competitions that have corresponding host cities with more than 1000 residents? | [
"city",
"farm_competition"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}",
"{\"columns\":[\"Competition_ID\",\"Year\",\"Theme\",\"Host_city_ID\",\"Hosts\"],\"index\":[0,1,2,3,4,5],\"data\":[[1,2013,\"Carnival M is back!\",1,\"Miley Cyrus Jared Leto and Karen Mok\"],[2,2006,\"Codehunters\",2,\"Leehom Wang and Kelly Rowland\"],[3,2005,\"MTV Asia Aid\",3,\"Alicia Keys\"],[4,2004,\"Valentine's Day\",4,\"Vanness Wu and Michelle Branch\"],[5,2003,\"MTV Cube\",5,\"Shaggy and Coco Lee\"],[6,2002,\"Aliens\",5,\"Mandy Moore and Ronan Keating\"]]}"
] | {"columns":["Theme"],"index":[0,1,2],"data":[["Carnival M is back!"],["Codehunters"],["MTV Asia Aid"]]} | What are the themes of competitions that have corresponding host cities with more than 1000 residents? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 <table_name> : farm_competition col : Competition_ID | Year | Theme | Host_city_ID | Hosts row 1 : 1 | 2013 | Carnival M is back! | 1 | Miley Cyrus Jared Leto and Karen Mok row 2 : 2 | 2006 | Codehunters | 2 | Leehom Wang and Kelly Rowland row 3 : 3 | 2005 | MTV Asia Aid | 3 | Alicia Keys row 4 : 4 | 2004 | Valentine's Day | 4 | Vanness Wu and Michelle Branch row 5 : 5 | 2003 | MTV Cube | 5 | Shaggy and Coco Lee row 6 : 6 | 2002 | Aliens | 5 | Mandy Moore and Ronan Keating | col : Theme row 1 : Carnival M is back! row 2 : Codehunters row 3 : MTV Asia Aid |
SELECT Status , avg(Population) FROM city GROUP BY Status | Please show the different statuses of cities and the average population of cities with each status. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status","avg(Population)"],"index":[0,1],"data":[["Town",5706.0],["Village",1009.75]]} | Please show the different statuses of cities and the average population of cities with each status. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status | avg(Population) row 1 : Town | 5706.0 row 2 : Village | 1009.75 |
SELECT Status , avg(Population) FROM city GROUP BY Status | What are the statuses and average populations of each city? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status","avg(Population)"],"index":[0,1],"data":[["Town",5706.0],["Village",1009.75]]} | What are the statuses and average populations of each city? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status | avg(Population) row 1 : Town | 5706.0 row 2 : Village | 1009.75 |
SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) ASC | Please show the different statuses, ordered by the number of cities that have each. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0,1],"data":[["Town"],["Village"]]} | Please show the different statuses, ordered by the number of cities that have each. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Town row 2 : Village |
SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) ASC | Return the different statuses of cities, ascending by frequency. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0,1],"data":[["Town"],["Village"]]} | Return the different statuses of cities, ascending by frequency. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Town row 2 : Village |
SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1 | List the most common type of Status across cities. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | List the most common type of Status across cities. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Village |
SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1 | What is the most common status across all cities? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | What is the most common status across all cities? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Village |
SELECT Status FROM city WHERE Population > 1500 INTERSECT SELECT Status FROM city WHERE Population < 500 | Show the status shared by cities with population bigger than 1500 and smaller than 500. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | Show the status shared by cities with population bigger than 1500 and smaller than 500. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Village |
SELECT Status FROM city WHERE Population > 1500 INTERSECT SELECT Status FROM city WHERE Population < 500 | Which statuses correspond to both cities that have a population over 1500 and cities that have a population lower than 500? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Status"],"index":[0],"data":[["Village"]]} | Which statuses correspond to both cities that have a population over 1500 and cities that have a population lower than 500? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Status row 1 : Village |
SELECT Official_Name FROM city WHERE Population > 1500 OR Population < 500 | Find the official names of cities with population bigger than 1500 or smaller than 500. | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name"],"index":[0,1,2],"data":[["Grand Falls\/Grand-Sault"],["Perth-Andover"],["Aroostook"]]} | Find the official names of cities with population bigger than 1500 or smaller than 500. <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name row 1 : Grand Falls/Grand-Sault row 2 : Perth-Andover row 3 : Aroostook |
SELECT Official_Name FROM city WHERE Population > 1500 OR Population < 500 | What are the official names of cities that have population over 1500 or less than 500? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Official_Name"],"index":[0,1,2],"data":[["Grand Falls\/Grand-Sault"],["Perth-Andover"],["Aroostook"]]} | What are the official names of cities that have population over 1500 or less than 500? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Official_Name row 1 : Grand Falls/Grand-Sault row 2 : Perth-Andover row 3 : Aroostook |
SELECT Census_Ranking FROM city WHERE Status != "Village" | Show the census ranking of cities whose status are not "Village". | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Census_Ranking"],"index":[0],"data":[["636 of 5008"]]} | Show the census ranking of cities whose status are not "Village". <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Census_Ranking row 1 : 636 of 5008 |
SELECT Census_Ranking FROM city WHERE Status != "Village" | What are the census rankings of cities that do not have the status "Village"? | [
"city"
] | [
"{\"columns\":[\"City_ID\",\"Official_Name\",\"Status\",\"Area_km_2\",\"Population\",\"Census_Ranking\"],\"index\":[0,1,2,3,4],\"data\":[[1,\"Grand Falls\\/Grand-Sault\",\"Town\",18.06,5706.0,\"636 of 5008\"],[2,\"Perth-Andover\",\"Village\",8.89,1778.0,\"1442 of 5,008\"],[3,\"Plaster Rock\",\"Village\",3.09,1135.0,\"1936 of 5,008\"],[4,\"Drummond\",\"Village\",8.91,775.0,\"2418 of 5008\"],[5,\"Aroostook\",\"Village\",2.24,351.0,\"3460 of 5008\"]]}"
] | {"columns":["Census_Ranking"],"index":[0],"data":[["636 of 5008"]]} | What are the census rankings of cities that do not have the status "Village"? <table_name> : city col : City_ID | Official_Name | Status | Area_km_2 | Population | Census_Ranking row 1 : 1 | Grand Falls/Grand-Sault | Town | 18.06 | 5706 | 636 of 5008 row 2 : 2 | Perth-Andover | Village | 8.89 | 1778 | 1442 of 5,008 row 3 : 3 | Plaster Rock | Village | 3.09 | 1135 | 1936 of 5,008 row 4 : 4 | Drummond | Village | 8.91 | 775 | 2418 of 5008 row 5 : 5 | Aroostook | Village | 2.24 | 351 | 3460 of 5008 | col : Census_Ranking row 1 : 636 of 5008 |
SELECT T1.course_name FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_Id GROUP BY T1.course_id ORDER BY count(*) DESC LIMIT 1 | which course has most number of registered students? | [
"Courses",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["course_name"],"index":[0],"data":[["statistics"]]} | which course has most number of registered students? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : course_name row 1 : statistics |
SELECT T1.course_name FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_Id GROUP BY T1.course_id ORDER BY count(*) DESC LIMIT 1 | What is the name of the course with the most registered students? | [
"Courses",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["course_name"],"index":[0],"data":[["statistics"]]} | What is the name of the course with the most registered students? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : course_name row 1 : statistics |
SELECT student_id FROM student_course_registrations GROUP BY student_id ORDER BY count(*) LIMIT 1 | what is id of students who registered some courses but the least number of courses in these students? | [
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_id"],"index":[0],"data":[[111]]} | what is id of students who registered some courses but the least number of courses in these students? <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_id row 1 : 111 |
SELECT student_id FROM student_course_registrations GROUP BY student_id ORDER BY count(*) LIMIT 1 | What are the ids of the students who registered for some courses but had the least number of courses for all students? | [
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_id"],"index":[0],"data":[[111]]} | What are the ids of the students who registered for some courses but had the least number of courses for all students? <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_id row 1 : 111 |
SELECT T2.first_name , T2.last_name FROM candidates AS T1 JOIN people AS T2 ON T1.candidate_id = T2.person_id | what are the first name and last name of all candidates? | [
"People",
"Candidates"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}"
] | {"columns":["first_name","last_name"],"index":[0,1,2,3,4,5,6,7],"data":[["Shannon","Senger"],["Virginie","Hartmann"],["Dariana","Bednar"],["Verna","Grant"],["Hoyt","Wintheiser"],["Mayra","Hartmann"],["Lizeth","Bartoletti"],["Nova","Feest"]]} | what are the first name and last name of all candidates? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy | col : first_name | last_name row 1 : Shannon | Senger row 2 : Virginie | Hartmann row 3 : Dariana | Bednar row 4 : Verna | Grant row 5 : Hoyt | Wintheiser row 6 : Mayra | Hartmann row 7 : Lizeth | Bartoletti row 8 : Nova | Feest |
SELECT T2.first_name , T2.last_name FROM candidates AS T1 JOIN people AS T2 ON T1.candidate_id = T2.person_id | What are the first and last names of all the candidates? | [
"People",
"Candidates"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}"
] | {"columns":["first_name","last_name"],"index":[0,1,2,3,4,5,6,7],"data":[["Shannon","Senger"],["Virginie","Hartmann"],["Dariana","Bednar"],["Verna","Grant"],["Hoyt","Wintheiser"],["Mayra","Hartmann"],["Lizeth","Bartoletti"],["Nova","Feest"]]} | What are the first and last names of all the candidates? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy | col : first_name | last_name row 1 : Shannon | Senger row 2 : Virginie | Hartmann row 3 : Dariana | Bednar row 4 : Verna | Grant row 5 : Hoyt | Wintheiser row 6 : Mayra | Hartmann row 7 : Lizeth | Bartoletti row 8 : Nova | Feest |
SELECT student_id FROM students WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance) | List the id of students who never attends courses? | [
"Students",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1],"data":[[131],[181]]} | List the id of students who never attends courses? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 131 row 2 : 181 |
SELECT student_id FROM students WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance) | What are the ids of every student who has never attended a course? | [
"Students",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1],"data":[[131],[181]]} | What are the ids of every student who has never attended a course? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 131 row 2 : 181 |
SELECT student_id FROM student_course_attendance | List the id of students who attended some courses? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1,2,3,4,5,6,7],"data":[[111],[121],[121],[141],[141],[151],[161],[171]]} | List the id of students who attended some courses? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 111 row 2 : 121 row 3 : 121 row 4 : 141 row 5 : 141 row 6 : 151 row 7 : 161 row 8 : 171 |
SELECT student_id FROM student_course_attendance | What are the ids of all students who have attended at least one course? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1,2,3,4,5,6,7],"data":[[111],[121],[121],[141],[141],[151],[161],[171]]} | What are the ids of all students who have attended at least one course? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 111 row 2 : 121 row 3 : 121 row 4 : 141 row 5 : 141 row 6 : 151 row 7 : 161 row 8 : 171 |
SELECT T1.student_id , T2.course_name FROM student_course_registrations AS T1 JOIN courses AS T2 ON T1.course_id = T2.course_id | What are the ids of all students for courses and what are the names of those courses? | [
"Courses",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_id","course_name"],"index":[0,1,2,3,4,5,6,7,8],"data":[[111,"statistics"],[121,"statistics"],[141,"statistics"],[171,"statistics"],[141,"English"],[161,"English"],[121,"French"],[131,"French"],[151,"data structure"]]} | What are the ids of all students for courses and what are the names of those courses? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_id | course_name row 1 : 111 | statistics row 2 : 121 | statistics row 3 : 141 | statistics row 4 : 171 | statistics row 5 : 141 | English row 6 : 161 | English row 7 : 121 | French row 8 : 131 | French row 9 : 151 | data structure |
SELECT T2.student_details FROM student_course_registrations AS T1 JOIN students AS T2 ON T1.student_id = T2.student_id ORDER BY T1.registration_date DESC LIMIT 1 | What is detail of the student who most recently registered course? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_details"],"index":[0],"data":[["Martin"]]} | What is detail of the student who most recently registered course? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_details row 1 : Martin |
SELECT T2.student_details FROM student_course_registrations AS T1 JOIN students AS T2 ON T1.student_id = T2.student_id ORDER BY T1.registration_date DESC LIMIT 1 | What details do we have on the students who registered for courses most recently? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_details"],"index":[0],"data":[["Martin"]]} | What details do we have on the students who registered for courses most recently? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_details row 1 : Martin |
SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = "English" | How many students attend course English? | [
"Courses",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[2]]} | How many students attend course English? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : count(*) row 1 : 2 |
SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = "English" | How many students are attending English courses? | [
"Courses",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[2]]} | How many students are attending English courses? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : count(*) row 1 : 2 |
SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T2.student_id = 171 | How many courses do the student whose id is 171 attend? | [
"Courses",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[1]]} | How many courses do the student whose id is 171 attend? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : count(*) row 1 : 1 |
SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T2.student_id = 171 | How many courses does the student with id 171 actually attend? | [
"Courses",
"Student_Course_Attendance"
] | [
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["count(*)"],"index":[0],"data":[[1]]} | How many courses does the student with id 171 actually attend? <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : count(*) row 1 : 1 |
SELECT T2.candidate_id FROM people AS T1 JOIN candidates AS T2 ON T1.person_id = T2.candidate_id WHERE T1.email_address = "[email protected]" | Find id of the candidate whose email is [email protected]? | [
"People",
"Candidates"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}"
] | {"columns":["candidate_id"],"index":[0],"data":[[151]]} | Find id of the candidate whose email is [email protected]? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy | col : candidate_id row 1 : 151 |
SELECT T2.candidate_id FROM people AS T1 JOIN candidates AS T2 ON T1.person_id = T2.candidate_id WHERE T1.email_address = "[email protected]" | What is the id of the candidate whose email is [email protected]? | [
"People",
"Candidates"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}"
] | {"columns":["candidate_id"],"index":[0],"data":[[151]]} | What is the id of the candidate whose email is [email protected]? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy | col : candidate_id row 1 : 151 |
SELECT candidate_id FROM candidate_assessments ORDER BY assessment_date DESC LIMIT 1 | Find id of the candidate who most recently accessed the course? | [
"Candidate_Assessments"
] | [
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["candidate_id"],"index":[0],"data":[[121]]} | Find id of the candidate who most recently accessed the course? <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : candidate_id row 1 : 121 |
SELECT candidate_id FROM candidate_assessments ORDER BY assessment_date DESC LIMIT 1 | What is the id of the candidate who most recently accessed the course? | [
"Candidate_Assessments"
] | [
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["candidate_id"],"index":[0],"data":[[121]]} | What is the id of the candidate who most recently accessed the course? <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : candidate_id row 1 : 121 |
SELECT T1.student_details FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1 | What is detail of the student who registered the most number of courses? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_details"],"index":[0],"data":[["Martin"]]} | What is detail of the student who registered the most number of courses? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_details row 1 : Martin |
SELECT T1.student_details FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1 | What are the details of the student who registered for the most number of courses? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_details"],"index":[0],"data":[["Martin"]]} | What are the details of the student who registered for the most number of courses? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_details row 1 : Martin |
SELECT T1.student_id , count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id | List the id of students who registered some courses and the number of their registered courses? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_id","count(*)"],"index":[0,1,2,3,4,5,6],"data":[[111,1],[121,2],[131,1],[141,2],[151,1],[161,1],[171,1]]} | List the id of students who registered some courses and the number of their registered courses? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_id | count(*) row 1 : 111 | 1 row 2 : 121 | 2 row 3 : 131 | 1 row 4 : 141 | 2 row 5 : 151 | 1 row 6 : 161 | 1 row 7 : 171 | 1 |
SELECT T1.student_id , count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id | For every student who is registered for some course, how many courses are they registered for? | [
"Students",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["student_id","count(*)"],"index":[0,1,2,3,4,5,6],"data":[[111,1],[121,2],[131,1],[141,2],[151,1],[161,1],[171,1]]} | For every student who is registered for some course, how many courses are they registered for? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : student_id | count(*) row 1 : 111 | 1 row 2 : 121 | 2 row 3 : 131 | 1 row 4 : 141 | 2 row 5 : 151 | 1 row 6 : 161 | 1 row 7 : 171 | 1 |
SELECT T3.course_name , count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id JOIN courses AS T3 ON T2.course_id = T3.course_id GROUP BY T2.course_id | How many registed students do each course have? List course name and the number of their registered students? | [
"Students",
"Courses",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["course_name","count(*)"],"index":[0,1,2,3],"data":[["statistics",4],["English",2],["French",2],["data structure",1]]} | How many registed students do each course have? List course name and the number of their registered students? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : course_name | count(*) row 1 : statistics | 4 row 2 : English | 2 row 3 : French | 2 row 4 : data structure | 1 |
SELECT T3.course_name , count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id JOIN courses AS T3 ON T2.course_id = T3.course_id GROUP BY T2.course_id | For each course id, how many students are registered and what are the course names? | [
"Students",
"Courses",
"Student_Course_Registrations"
] | [
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"course_id\",\"course_name\",\"course_description\",\"other_details\"],\"index\":[0,1,2,3,4,5],\"data\":[[\"301\",\"statistics\",\"statistics\",null],[\"302\",\"English\",\"English\",null],[\"303\",\"French\",\"French\",null],[\"304\",\"database\",\"database\",null],[\"305\",\"data structure\",\"data structure\",null],[\"306\",\"Art history\",\"Art history\",null]]}",
"{\"columns\":[\"student_id\",\"course_id\",\"registration_date\"],\"index\":[0,1,2,3,4,5,6,7,8],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2008-10-04 10:35:13\"],[121,303,\"2008-11-14 10:35:13\"],[131,303,\"2008-11-05 10:35:13\"],[141,302,\"2008-11-06 10:35:13\"],[151,305,\"2008-11-07 10:35:13\"],[161,302,\"2008-11-07 10:35:13\"],[171,301,\"2008-11-07 10:35:13\"],[141,301,\"2008-11-08 10:35:13\"]]}"
] | {"columns":["course_name","count(*)"],"index":[0,1,2,3],"data":[["statistics",4],["English",2],["French",2],["data structure",1]]} | For each course id, how many students are registered and what are the course names? <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : Courses col : course_id | course_name | course_description | other_details row 1 : 301 | statistics | statistics | row 2 : 302 | English | English | row 3 : 303 | French | French | row 4 : 304 | database | database | row 5 : 305 | data structure | data structure | row 6 : 306 | Art history | Art history | <table_name> : Student_Course_Registrations col : student_id | course_id | registration_date row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2008-10-04 10:35:13 row 3 : 121 | 303 | 2008-11-14 10:35:13 row 4 : 131 | 303 | 2008-11-05 10:35:13 row 5 : 141 | 302 | 2008-11-06 10:35:13 row 6 : 151 | 305 | 2008-11-07 10:35:13 row 7 : 161 | 302 | 2008-11-07 10:35:13 row 8 : 171 | 301 | 2008-11-07 10:35:13 row 9 : 141 | 301 | 2008-11-08 10:35:13 | col : course_name | count(*) row 1 : statistics | 4 row 2 : English | 2 row 3 : French | 2 row 4 : data structure | 1 |
SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = "Pass" | Find id of candidates whose assessment code is "Pass"? | [
"Candidate_Assessments"
] | [
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["candidate_id"],"index":[0,1,2,3],"data":[[111],[121],[141],[151]]} | Find id of candidates whose assessment code is "Pass"? <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : candidate_id row 1 : 111 row 2 : 121 row 3 : 141 row 4 : 151 |
SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = "Pass" | What are the ids of the candidates that have an outcome code of Pass? | [
"Candidate_Assessments"
] | [
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["candidate_id"],"index":[0,1,2,3],"data":[[111],[121],[141],[151]]} | What are the ids of the candidates that have an outcome code of Pass? <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : candidate_id row 1 : 111 row 2 : 121 row 3 : 141 row 4 : 151 |
SELECT T3.cell_mobile_number FROM candidates AS T1 JOIN candidate_assessments AS T2 ON T1.candidate_id = T2.candidate_id JOIN people AS T3 ON T1.candidate_id = T3.person_id WHERE T2.asessment_outcome_code = "Fail" | Find the cell mobile number of the candidates whose assessment code is "Fail"? | [
"People",
"Candidates",
"Candidate_Assessments"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}",
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["cell_mobile_number"],"index":[0],"data":[["(262)347-9364x516"]]} | Find the cell mobile number of the candidates whose assessment code is "Fail"? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : cell_mobile_number row 1 : (262)347-9364x516 |
SELECT T3.cell_mobile_number FROM candidates AS T1 JOIN candidate_assessments AS T2 ON T1.candidate_id = T2.candidate_id JOIN people AS T3 ON T1.candidate_id = T3.person_id WHERE T2.asessment_outcome_code = "Fail" | What are the cell phone numbers of the candidates that received an assessment code of "Fail"? | [
"People",
"Candidates",
"Candidate_Assessments"
] | [
"{\"columns\":[\"person_id\",\"first_name\",\"middle_name\",\"last_name\",\"cell_mobile_number\",\"email_address\",\"login_name\",\"password\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Shannon\",\"Elissa\",\"Senger\",\"01955267735\",\"[email protected]\",\"pgub\",\"5e4ff49a61b3544da3ad7dc7e2cf28847564c64c\"],[121,\"Virginie\",\"Jasmin\",\"Hartmann\",\"(508)319-2970x043\",\"[email protected]\",\"bkkv\",\"b063331ea8116befaa7b84c59c6a22200f5f8caa\"],[131,\"Dariana\",\"Hayley\",\"Bednar\",\"(262)347-9364x516\",\"[email protected]\",\"zops\",\"b20b6a9f24aadeda70d54e410c3219f61fb063fb\"],[141,\"Verna\",\"Arielle\",\"Grant\",\"1-372-548-7538x314\",\"[email protected]\",\"uuol\",\"7be9c03d5467d563555c51ebb3eb78e7f90832ec\"],[151,\"Hoyt\",\"Mercedes\",\"Wintheiser\",\"1-603-110-0647\",\"[email protected]\",\"bnto\",\"c55795df86182959094b83e27900f7cf44ced570\"],[161,\"Mayra\",\"Haley\",\"Hartmann\",\"724-681-4161x51632\",\"[email protected]\",\"rzxu\",\"ecae473cb54601e01457078ac0cdf4a1ced837bb\"],[171,\"Lizeth\",\"Bell\",\"Bartoletti\",\"812.228.0645x91481\",\"[email protected]\",\"mkou\",\"76a93d1d3b7becc932d203beac61d064bd54e947\"],[181,\"Nova\",\"Amiya\",\"Feest\",\"766-272-9964\",\"[email protected]\",\"qrwl\",\"7dce9b688636ee212294c257dd2f6b85c7f65f2e\"]]}",
"{\"columns\":[\"candidate_id\",\"candidate_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Jane\"],[121,\"Robert\"],[131,\"Alex\"],[141,\"Tao\"],[151,\"Jack\"],[161,\"Leo\"],[171,\"Robin\"],[181,\"Cindy\"]]}",
"{\"columns\":[\"candidate_id\",\"qualification\",\"assessment_date\",\"asessment_outcome_code\"],\"index\":[0,1,2,3,4],\"data\":[[111,\"A\",\"2010-04-07 11:44:34\",\"Pass\"],[121,\"B\",\"2010-04-17 11:44:34\",\"Pass\"],[131,\"D\",\"2010-04-05 11:44:34\",\"Fail\"],[141,\"C\",\"2010-04-06 11:44:34\",\"Pass\"],[151,\"B\",\"2010-04-09 11:44:34\",\"Pass\"]]}"
] | {"columns":["cell_mobile_number"],"index":[0],"data":[["(262)347-9364x516"]]} | What are the cell phone numbers of the candidates that received an assessment code of "Fail"? <table_name> : People col : person_id | first_name | middle_name | last_name | cell_mobile_number | email_address | login_name | password row 1 : 111 | Shannon | Elissa | Senger | 01955267735 | [email protected] | pgub | 5e4ff49a61b3544da3ad7dc7e2cf28847564c64c row 2 : 121 | Virginie | Jasmin | Hartmann | (508)319-2970x043 | [email protected] | bkkv | b063331ea8116befaa7b84c59c6a22200f5f8caa row 3 : 131 | Dariana | Hayley | Bednar | (262)347-9364x516 | [email protected] | zops | b20b6a9f24aadeda70d54e410c3219f61fb063fb row 4 : 141 | Verna | Arielle | Grant | 1-372-548-7538x314 | [email protected] | uuol | 7be9c03d5467d563555c51ebb3eb78e7f90832ec row 5 : 151 | Hoyt | Mercedes | Wintheiser | 1-603-110-0647 | [email protected] | bnto | c55795df86182959094b83e27900f7cf44ced570 row 6 : 161 | Mayra | Haley | Hartmann | 724-681-4161x51632 | [email protected] | rzxu | ecae473cb54601e01457078ac0cdf4a1ced837bb row 7 : 171 | Lizeth | Bell | Bartoletti | 812.228.0645x91481 | [email protected] | mkou | 76a93d1d3b7becc932d203beac61d064bd54e947 row 8 : 181 | Nova | Amiya | Feest | 766-272-9964 | [email protected] | qrwl | 7dce9b688636ee212294c257dd2f6b85c7f65f2e <table_name> : Candidates col : candidate_id | candidate_details row 1 : 111 | Jane row 2 : 121 | Robert row 3 : 131 | Alex row 4 : 141 | Tao row 5 : 151 | Jack row 6 : 161 | Leo row 7 : 171 | Robin row 8 : 181 | Cindy <table_name> : Candidate_Assessments col : candidate_id | qualification | assessment_date | asessment_outcome_code row 1 : 111 | A | 2010-04-07 11:44:34 | Pass row 2 : 121 | B | 2010-04-17 11:44:34 | Pass row 3 : 131 | D | 2010-04-05 11:44:34 | Fail row 4 : 141 | C | 2010-04-06 11:44:34 | Pass row 5 : 151 | B | 2010-04-09 11:44:34 | Pass | col : cell_mobile_number row 1 : (262)347-9364x516 |
SELECT student_id FROM student_course_attendance WHERE course_id = 301 | What are the id of students who registered course 301? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1,2,3],"data":[[111],[121],[141],[171]]} | What are the id of students who registered course 301? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 111 row 2 : 121 row 3 : 141 row 4 : 171 |
SELECT student_id FROM student_course_attendance WHERE course_id = 301 | What are the ids of the students who registered for course 301? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0,1,2,3],"data":[[111],[121],[141],[171]]} | What are the ids of the students who registered for course 301? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 111 row 2 : 121 row 3 : 141 row 4 : 171 |
SELECT student_id FROM student_course_attendance WHERE course_id = 301 ORDER BY date_of_attendance DESC LIMIT 1 | What is the id of the student who most recently registered course 301? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0],"data":[[171]]} | What is the id of the student who most recently registered course 301? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 171 |
SELECT student_id FROM student_course_attendance WHERE course_id = 301 ORDER BY date_of_attendance DESC LIMIT 1 | What are the ids of the students who registered for course 301 most recently? | [
"Student_Course_Attendance"
] | [
"{\"columns\":[\"student_id\",\"course_id\",\"date_of_attendance\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,301,\"2008-11-04 10:35:13\"],[121,301,\"2012-04-09 11:44:34\"],[121,303,\"2014-04-09 11:44:34\"],[141,302,\"2013-04-09 11:44:34\"],[171,301,\"2015-04-09 11:44:34\"],[161,302,\"2014-01-09 11:44:34\"],[151,305,\"2012-05-09 11:44:34\"],[141,301,\"2012-09-09 11:44:34\"]]}"
] | {"columns":["student_id"],"index":[0],"data":[[171]]} | What are the ids of the students who registered for course 301 most recently? <table_name> : Student_Course_Attendance col : student_id | course_id | date_of_attendance row 1 : 111 | 301 | 2008-11-04 10:35:13 row 2 : 121 | 301 | 2012-04-09 11:44:34 row 3 : 121 | 303 | 2014-04-09 11:44:34 row 4 : 141 | 302 | 2013-04-09 11:44:34 row 5 : 171 | 301 | 2015-04-09 11:44:34 row 6 : 161 | 302 | 2014-01-09 11:44:34 row 7 : 151 | 305 | 2012-05-09 11:44:34 row 8 : 141 | 301 | 2012-09-09 11:44:34 | col : student_id row 1 : 171 |
SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id | Find distinct cities of addresses of people? | [
"Addresses",
"People_Addresses"
] | [
"{\"columns\":[\"address_id\",\"line_1\",\"line_2\",\"city\",\"zip_postcode\",\"state_province_county\",\"country\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[5,\"0900 Roderick Oval\\nNew Albina, WA 19200-7914\",\"Suite 096\",\"Linnealand\",\"862\",\"Montana\",\"USA\"],[9,\"966 Dach Ports Apt. 322\\nLake Harmonyhaven, VA 65235\",\"Apt. 163\",\"South Minnie\",\"716\",\"Texas\",\"USA\"],[29,\"28550 Broderick Underpass Suite 667\\nZakaryhaven, WY 22945-1534\",\"Apt. 419\",\"North Trystanborough\",\"112\",\"Vermont\",\"USA\"],[30,\"83706 Ana Trafficway Apt. 992\\nWest Jarret, MI 01112\",\"Apt. 884\",\"Lake Kaley\",\"431\",\"Washington\",\"USA\"],[43,\"69165 Beatty Station\\nHaleighstad, MS 55164\",\"Suite 333\",\"Stephaniemouth\",\"559\",\"Massachusetts\",\"USA\"],[45,\"242 Pacocha Streets\\nEast Isabellashire, ND 03506\",\"Suite 370\",\"O'Connellview\",\"514\",\"NewMexico\",\"USA\"],[55,\"801 Modesto Island Suite 306\\nLacyville, VT 34059\",\"Suite 764\",\"New Alta\",\"176\",\"Mississippi\",\"USA\"],[63,\"0177 Fisher Dam\\nBerniershire, KS 00038-7574\",\"Apt. 903\",\"South Keenan\",\"613\",\"Michigan\",\"USA\"],[68,\"09471 Hickle Light\\nPort Maxime, NJ 91550-5409\",\"Suite 903\",\"Hannahside\",\"354\",\"Connecticut\",\"USA\"],[73,\"67831 Lavonne Lodge\\nOlsontown, DC 20894\",\"Apt. 756\",\"Alizeshire\",\"687\",\"NewMexico\",\"USA\"],[82,\"228 Fahey Land\\nBaileymouth, FL 06297-5606\",\"Suite 087\",\"South Naomibury\",\"079\",\"Ohio\",\"USA\"],[88,\"1770 Adriel Ramp Apt. 397\\nWest Ashlynnchester, UT 91968\",\"Apt. 617\",\"East Tavaresburgh\",\"179\",\"SouthDakota\",\"USA\"],[92,\"8760 Eldon Squares Suite 260\\nMarquisestad, GA 38537\",\"Apt. 435\",\"Lake Devon\",\"244\",\"SouthDakota\",\"USA\"],[94,\"8263 Abbott Crossing Apt. 066\\nOberbrunnerbury, LA 67451\",\"Apt. 626\",\"Boyleshire\",\"536\",\"Kansas\",\"USA\"],[99,\"521 Paucek Field\\nNorth Oscartown, WI 31527\",\"Apt. 849\",\"Terencetown\",\"979\",\"Michigan\",\"USA\"]]}",
"{\"columns\":[\"person_address_id\",\"person_id\",\"address_id\",\"date_from\",\"date_to\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[122,111,9,\"2012-09-26 13:21:00\",\"2018-03-21 09:46:30\"],[257,121,5,\"2008-07-31 02:17:25\",\"2018-03-09 02:11:12\"],[269,131,88,\"2008-05-26 20:43:41\",\"2018-03-11 20:26:41\"],[276,141,99,\"2014-05-10 00:32:31\",\"2018-03-08 06:16:47\"],[281,151,92,\"2010-11-26 05:21:12\",\"2018-03-12 21:10:02\"],[340,161,45,\"2017-05-01 17:32:26\",\"2018-03-09 08:45:06\"],[363,171,55,\"2015-05-24 16:14:12\",\"2018-02-23 22:44:18\"],[396,181,82,\"2013-12-26 16:57:01\",\"2018-03-03 16:06:17\"]]}"
] | {"columns":["city"],"index":[0,1,2,3,4,5,6,7],"data":[["South Minnie"],["Linnealand"],["East Tavaresburgh"],["Terencetown"],["Lake Devon"],["O'Connellview"],["New Alta"],["South Naomibury"]]} | Find distinct cities of addresses of people? <table_name> : Addresses col : address_id | line_1 | line_2 | city | zip_postcode | state_province_county | country row 1 : 5 | 0900 Roderick Oval
New Albina, WA 19200-7914 | Suite 096 | Linnealand | 862 | Montana | USA row 2 : 9 | 966 Dach Ports Apt. 322
Lake Harmonyhaven, VA 65235 | Apt. 163 | South Minnie | 716 | Texas | USA row 3 : 29 | 28550 Broderick Underpass Suite 667
Zakaryhaven, WY 22945-1534 | Apt. 419 | North Trystanborough | 112 | Vermont | USA row 4 : 30 | 83706 Ana Trafficway Apt. 992
West Jarret, MI 01112 | Apt. 884 | Lake Kaley | 431 | Washington | USA row 5 : 43 | 69165 Beatty Station
Haleighstad, MS 55164 | Suite 333 | Stephaniemouth | 559 | Massachusetts | USA row 6 : 45 | 242 Pacocha Streets
East Isabellashire, ND 03506 | Suite 370 | O'Connellview | 514 | NewMexico | USA row 7 : 55 | 801 Modesto Island Suite 306
Lacyville, VT 34059 | Suite 764 | New Alta | 176 | Mississippi | USA row 8 : 63 | 0177 Fisher Dam
Berniershire, KS 00038-7574 | Apt. 903 | South Keenan | 613 | Michigan | USA row 9 : 68 | 09471 Hickle Light
Port Maxime, NJ 91550-5409 | Suite 903 | Hannahside | 354 | Connecticut | USA row 10 : 73 | 67831 Lavonne Lodge
Olsontown, DC 20894 | Apt. 756 | Alizeshire | 687 | NewMexico | USA row 11 : 82 | 228 Fahey Land
Baileymouth, FL 06297-5606 | Suite 087 | South Naomibury | 79 | Ohio | USA row 12 : 88 | 1770 Adriel Ramp Apt. 397
West Ashlynnchester, UT 91968 | Apt. 617 | East Tavaresburgh | 179 | SouthDakota | USA row 13 : 92 | 8760 Eldon Squares Suite 260
Marquisestad, GA 38537 | Apt. 435 | Lake Devon | 244 | SouthDakota | USA row 14 : 94 | 8263 Abbott Crossing Apt. 066
Oberbrunnerbury, LA 67451 | Apt. 626 | Boyleshire | 536 | Kansas | USA row 15 : 99 | 521 Paucek Field
North Oscartown, WI 31527 | Apt. 849 | Terencetown | 979 | Michigan | USA <table_name> : People_Addresses col : person_address_id | person_id | address_id | date_from | date_to row 1 : 122 | 111 | 9 | 2012-09-26 13:21:00 | 2018-03-21 09:46:30 row 2 : 257 | 121 | 5 | 2008-07-31 02:17:25 | 2018-03-09 02:11:12 row 3 : 269 | 131 | 88 | 2008-05-26 20:43:41 | 2018-03-11 20:26:41 row 4 : 276 | 141 | 99 | 2014-05-10 00:32:31 | 2018-03-08 06:16:47 row 5 : 281 | 151 | 92 | 2010-11-26 05:21:12 | 2018-03-12 21:10:02 row 6 : 340 | 161 | 45 | 2017-05-01 17:32:26 | 2018-03-09 08:45:06 row 7 : 363 | 171 | 55 | 2015-05-24 16:14:12 | 2018-02-23 22:44:18 row 8 : 396 | 181 | 82 | 2013-12-26 16:57:01 | 2018-03-03 16:06:17 | col : city row 1 : South Minnie row 2 : Linnealand row 3 : East Tavaresburgh row 4 : Terencetown row 5 : Lake Devon row 6 : O'Connellview row 7 : New Alta row 8 : South Naomibury |
SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id | What are the different cities where people live? | [
"Addresses",
"People_Addresses"
] | [
"{\"columns\":[\"address_id\",\"line_1\",\"line_2\",\"city\",\"zip_postcode\",\"state_province_county\",\"country\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[5,\"0900 Roderick Oval\\nNew Albina, WA 19200-7914\",\"Suite 096\",\"Linnealand\",\"862\",\"Montana\",\"USA\"],[9,\"966 Dach Ports Apt. 322\\nLake Harmonyhaven, VA 65235\",\"Apt. 163\",\"South Minnie\",\"716\",\"Texas\",\"USA\"],[29,\"28550 Broderick Underpass Suite 667\\nZakaryhaven, WY 22945-1534\",\"Apt. 419\",\"North Trystanborough\",\"112\",\"Vermont\",\"USA\"],[30,\"83706 Ana Trafficway Apt. 992\\nWest Jarret, MI 01112\",\"Apt. 884\",\"Lake Kaley\",\"431\",\"Washington\",\"USA\"],[43,\"69165 Beatty Station\\nHaleighstad, MS 55164\",\"Suite 333\",\"Stephaniemouth\",\"559\",\"Massachusetts\",\"USA\"],[45,\"242 Pacocha Streets\\nEast Isabellashire, ND 03506\",\"Suite 370\",\"O'Connellview\",\"514\",\"NewMexico\",\"USA\"],[55,\"801 Modesto Island Suite 306\\nLacyville, VT 34059\",\"Suite 764\",\"New Alta\",\"176\",\"Mississippi\",\"USA\"],[63,\"0177 Fisher Dam\\nBerniershire, KS 00038-7574\",\"Apt. 903\",\"South Keenan\",\"613\",\"Michigan\",\"USA\"],[68,\"09471 Hickle Light\\nPort Maxime, NJ 91550-5409\",\"Suite 903\",\"Hannahside\",\"354\",\"Connecticut\",\"USA\"],[73,\"67831 Lavonne Lodge\\nOlsontown, DC 20894\",\"Apt. 756\",\"Alizeshire\",\"687\",\"NewMexico\",\"USA\"],[82,\"228 Fahey Land\\nBaileymouth, FL 06297-5606\",\"Suite 087\",\"South Naomibury\",\"079\",\"Ohio\",\"USA\"],[88,\"1770 Adriel Ramp Apt. 397\\nWest Ashlynnchester, UT 91968\",\"Apt. 617\",\"East Tavaresburgh\",\"179\",\"SouthDakota\",\"USA\"],[92,\"8760 Eldon Squares Suite 260\\nMarquisestad, GA 38537\",\"Apt. 435\",\"Lake Devon\",\"244\",\"SouthDakota\",\"USA\"],[94,\"8263 Abbott Crossing Apt. 066\\nOberbrunnerbury, LA 67451\",\"Apt. 626\",\"Boyleshire\",\"536\",\"Kansas\",\"USA\"],[99,\"521 Paucek Field\\nNorth Oscartown, WI 31527\",\"Apt. 849\",\"Terencetown\",\"979\",\"Michigan\",\"USA\"]]}",
"{\"columns\":[\"person_address_id\",\"person_id\",\"address_id\",\"date_from\",\"date_to\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[122,111,9,\"2012-09-26 13:21:00\",\"2018-03-21 09:46:30\"],[257,121,5,\"2008-07-31 02:17:25\",\"2018-03-09 02:11:12\"],[269,131,88,\"2008-05-26 20:43:41\",\"2018-03-11 20:26:41\"],[276,141,99,\"2014-05-10 00:32:31\",\"2018-03-08 06:16:47\"],[281,151,92,\"2010-11-26 05:21:12\",\"2018-03-12 21:10:02\"],[340,161,45,\"2017-05-01 17:32:26\",\"2018-03-09 08:45:06\"],[363,171,55,\"2015-05-24 16:14:12\",\"2018-02-23 22:44:18\"],[396,181,82,\"2013-12-26 16:57:01\",\"2018-03-03 16:06:17\"]]}"
] | {"columns":["city"],"index":[0,1,2,3,4,5,6,7],"data":[["South Minnie"],["Linnealand"],["East Tavaresburgh"],["Terencetown"],["Lake Devon"],["O'Connellview"],["New Alta"],["South Naomibury"]]} | What are the different cities where people live? <table_name> : Addresses col : address_id | line_1 | line_2 | city | zip_postcode | state_province_county | country row 1 : 5 | 0900 Roderick Oval
New Albina, WA 19200-7914 | Suite 096 | Linnealand | 862 | Montana | USA row 2 : 9 | 966 Dach Ports Apt. 322
Lake Harmonyhaven, VA 65235 | Apt. 163 | South Minnie | 716 | Texas | USA row 3 : 29 | 28550 Broderick Underpass Suite 667
Zakaryhaven, WY 22945-1534 | Apt. 419 | North Trystanborough | 112 | Vermont | USA row 4 : 30 | 83706 Ana Trafficway Apt. 992
West Jarret, MI 01112 | Apt. 884 | Lake Kaley | 431 | Washington | USA row 5 : 43 | 69165 Beatty Station
Haleighstad, MS 55164 | Suite 333 | Stephaniemouth | 559 | Massachusetts | USA row 6 : 45 | 242 Pacocha Streets
East Isabellashire, ND 03506 | Suite 370 | O'Connellview | 514 | NewMexico | USA row 7 : 55 | 801 Modesto Island Suite 306
Lacyville, VT 34059 | Suite 764 | New Alta | 176 | Mississippi | USA row 8 : 63 | 0177 Fisher Dam
Berniershire, KS 00038-7574 | Apt. 903 | South Keenan | 613 | Michigan | USA row 9 : 68 | 09471 Hickle Light
Port Maxime, NJ 91550-5409 | Suite 903 | Hannahside | 354 | Connecticut | USA row 10 : 73 | 67831 Lavonne Lodge
Olsontown, DC 20894 | Apt. 756 | Alizeshire | 687 | NewMexico | USA row 11 : 82 | 228 Fahey Land
Baileymouth, FL 06297-5606 | Suite 087 | South Naomibury | 79 | Ohio | USA row 12 : 88 | 1770 Adriel Ramp Apt. 397
West Ashlynnchester, UT 91968 | Apt. 617 | East Tavaresburgh | 179 | SouthDakota | USA row 13 : 92 | 8760 Eldon Squares Suite 260
Marquisestad, GA 38537 | Apt. 435 | Lake Devon | 244 | SouthDakota | USA row 14 : 94 | 8263 Abbott Crossing Apt. 066
Oberbrunnerbury, LA 67451 | Apt. 626 | Boyleshire | 536 | Kansas | USA row 15 : 99 | 521 Paucek Field
North Oscartown, WI 31527 | Apt. 849 | Terencetown | 979 | Michigan | USA <table_name> : People_Addresses col : person_address_id | person_id | address_id | date_from | date_to row 1 : 122 | 111 | 9 | 2012-09-26 13:21:00 | 2018-03-21 09:46:30 row 2 : 257 | 121 | 5 | 2008-07-31 02:17:25 | 2018-03-09 02:11:12 row 3 : 269 | 131 | 88 | 2008-05-26 20:43:41 | 2018-03-11 20:26:41 row 4 : 276 | 141 | 99 | 2014-05-10 00:32:31 | 2018-03-08 06:16:47 row 5 : 281 | 151 | 92 | 2010-11-26 05:21:12 | 2018-03-12 21:10:02 row 6 : 340 | 161 | 45 | 2017-05-01 17:32:26 | 2018-03-09 08:45:06 row 7 : 363 | 171 | 55 | 2015-05-24 16:14:12 | 2018-02-23 22:44:18 row 8 : 396 | 181 | 82 | 2013-12-26 16:57:01 | 2018-03-03 16:06:17 | col : city row 1 : South Minnie row 2 : Linnealand row 3 : East Tavaresburgh row 4 : Terencetown row 5 : Lake Devon row 6 : O'Connellview row 7 : New Alta row 8 : South Naomibury |
SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id JOIN students AS T3 ON T2.person_id = T3.student_id | Find distinct cities of address of students? | [
"Addresses",
"Students",
"People_Addresses"
] | [
"{\"columns\":[\"address_id\",\"line_1\",\"line_2\",\"city\",\"zip_postcode\",\"state_province_county\",\"country\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],\"data\":[[5,\"0900 Roderick Oval\\nNew Albina, WA 19200-7914\",\"Suite 096\",\"Linnealand\",\"862\",\"Montana\",\"USA\"],[9,\"966 Dach Ports Apt. 322\\nLake Harmonyhaven, VA 65235\",\"Apt. 163\",\"South Minnie\",\"716\",\"Texas\",\"USA\"],[29,\"28550 Broderick Underpass Suite 667\\nZakaryhaven, WY 22945-1534\",\"Apt. 419\",\"North Trystanborough\",\"112\",\"Vermont\",\"USA\"],[30,\"83706 Ana Trafficway Apt. 992\\nWest Jarret, MI 01112\",\"Apt. 884\",\"Lake Kaley\",\"431\",\"Washington\",\"USA\"],[43,\"69165 Beatty Station\\nHaleighstad, MS 55164\",\"Suite 333\",\"Stephaniemouth\",\"559\",\"Massachusetts\",\"USA\"],[45,\"242 Pacocha Streets\\nEast Isabellashire, ND 03506\",\"Suite 370\",\"O'Connellview\",\"514\",\"NewMexico\",\"USA\"],[55,\"801 Modesto Island Suite 306\\nLacyville, VT 34059\",\"Suite 764\",\"New Alta\",\"176\",\"Mississippi\",\"USA\"],[63,\"0177 Fisher Dam\\nBerniershire, KS 00038-7574\",\"Apt. 903\",\"South Keenan\",\"613\",\"Michigan\",\"USA\"],[68,\"09471 Hickle Light\\nPort Maxime, NJ 91550-5409\",\"Suite 903\",\"Hannahside\",\"354\",\"Connecticut\",\"USA\"],[73,\"67831 Lavonne Lodge\\nOlsontown, DC 20894\",\"Apt. 756\",\"Alizeshire\",\"687\",\"NewMexico\",\"USA\"],[82,\"228 Fahey Land\\nBaileymouth, FL 06297-5606\",\"Suite 087\",\"South Naomibury\",\"079\",\"Ohio\",\"USA\"],[88,\"1770 Adriel Ramp Apt. 397\\nWest Ashlynnchester, UT 91968\",\"Apt. 617\",\"East Tavaresburgh\",\"179\",\"SouthDakota\",\"USA\"],[92,\"8760 Eldon Squares Suite 260\\nMarquisestad, GA 38537\",\"Apt. 435\",\"Lake Devon\",\"244\",\"SouthDakota\",\"USA\"],[94,\"8263 Abbott Crossing Apt. 066\\nOberbrunnerbury, LA 67451\",\"Apt. 626\",\"Boyleshire\",\"536\",\"Kansas\",\"USA\"],[99,\"521 Paucek Field\\nNorth Oscartown, WI 31527\",\"Apt. 849\",\"Terencetown\",\"979\",\"Michigan\",\"USA\"]]}",
"{\"columns\":[\"student_id\",\"student_details\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[111,\"Marry\"],[121,\"Martin\"],[131,\"Barry\"],[141,\"Nikhil\"],[151,\"John\"],[161,\"Sarah\"],[171,\"Joe\"],[181,\"Nancy\"]]}",
"{\"columns\":[\"person_address_id\",\"person_id\",\"address_id\",\"date_from\",\"date_to\"],\"index\":[0,1,2,3,4,5,6,7],\"data\":[[122,111,9,\"2012-09-26 13:21:00\",\"2018-03-21 09:46:30\"],[257,121,5,\"2008-07-31 02:17:25\",\"2018-03-09 02:11:12\"],[269,131,88,\"2008-05-26 20:43:41\",\"2018-03-11 20:26:41\"],[276,141,99,\"2014-05-10 00:32:31\",\"2018-03-08 06:16:47\"],[281,151,92,\"2010-11-26 05:21:12\",\"2018-03-12 21:10:02\"],[340,161,45,\"2017-05-01 17:32:26\",\"2018-03-09 08:45:06\"],[363,171,55,\"2015-05-24 16:14:12\",\"2018-02-23 22:44:18\"],[396,181,82,\"2013-12-26 16:57:01\",\"2018-03-03 16:06:17\"]]}"
] | {"columns":["city"],"index":[0,1,2,3,4,5,6,7],"data":[["South Minnie"],["Linnealand"],["East Tavaresburgh"],["Terencetown"],["Lake Devon"],["O'Connellview"],["New Alta"],["South Naomibury"]]} | Find distinct cities of address of students? <table_name> : Addresses col : address_id | line_1 | line_2 | city | zip_postcode | state_province_county | country row 1 : 5 | 0900 Roderick Oval
New Albina, WA 19200-7914 | Suite 096 | Linnealand | 862 | Montana | USA row 2 : 9 | 966 Dach Ports Apt. 322
Lake Harmonyhaven, VA 65235 | Apt. 163 | South Minnie | 716 | Texas | USA row 3 : 29 | 28550 Broderick Underpass Suite 667
Zakaryhaven, WY 22945-1534 | Apt. 419 | North Trystanborough | 112 | Vermont | USA row 4 : 30 | 83706 Ana Trafficway Apt. 992
West Jarret, MI 01112 | Apt. 884 | Lake Kaley | 431 | Washington | USA row 5 : 43 | 69165 Beatty Station
Haleighstad, MS 55164 | Suite 333 | Stephaniemouth | 559 | Massachusetts | USA row 6 : 45 | 242 Pacocha Streets
East Isabellashire, ND 03506 | Suite 370 | O'Connellview | 514 | NewMexico | USA row 7 : 55 | 801 Modesto Island Suite 306
Lacyville, VT 34059 | Suite 764 | New Alta | 176 | Mississippi | USA row 8 : 63 | 0177 Fisher Dam
Berniershire, KS 00038-7574 | Apt. 903 | South Keenan | 613 | Michigan | USA row 9 : 68 | 09471 Hickle Light
Port Maxime, NJ 91550-5409 | Suite 903 | Hannahside | 354 | Connecticut | USA row 10 : 73 | 67831 Lavonne Lodge
Olsontown, DC 20894 | Apt. 756 | Alizeshire | 687 | NewMexico | USA row 11 : 82 | 228 Fahey Land
Baileymouth, FL 06297-5606 | Suite 087 | South Naomibury | 79 | Ohio | USA row 12 : 88 | 1770 Adriel Ramp Apt. 397
West Ashlynnchester, UT 91968 | Apt. 617 | East Tavaresburgh | 179 | SouthDakota | USA row 13 : 92 | 8760 Eldon Squares Suite 260
Marquisestad, GA 38537 | Apt. 435 | Lake Devon | 244 | SouthDakota | USA row 14 : 94 | 8263 Abbott Crossing Apt. 066
Oberbrunnerbury, LA 67451 | Apt. 626 | Boyleshire | 536 | Kansas | USA row 15 : 99 | 521 Paucek Field
North Oscartown, WI 31527 | Apt. 849 | Terencetown | 979 | Michigan | USA <table_name> : Students col : student_id | student_details row 1 : 111 | Marry row 2 : 121 | Martin row 3 : 131 | Barry row 4 : 141 | Nikhil row 5 : 151 | John row 6 : 161 | Sarah row 7 : 171 | Joe row 8 : 181 | Nancy <table_name> : People_Addresses col : person_address_id | person_id | address_id | date_from | date_to row 1 : 122 | 111 | 9 | 2012-09-26 13:21:00 | 2018-03-21 09:46:30 row 2 : 257 | 121 | 5 | 2008-07-31 02:17:25 | 2018-03-09 02:11:12 row 3 : 269 | 131 | 88 | 2008-05-26 20:43:41 | 2018-03-11 20:26:41 row 4 : 276 | 141 | 99 | 2014-05-10 00:32:31 | 2018-03-08 06:16:47 row 5 : 281 | 151 | 92 | 2010-11-26 05:21:12 | 2018-03-12 21:10:02 row 6 : 340 | 161 | 45 | 2017-05-01 17:32:26 | 2018-03-09 08:45:06 row 7 : 363 | 171 | 55 | 2015-05-24 16:14:12 | 2018-02-23 22:44:18 row 8 : 396 | 181 | 82 | 2013-12-26 16:57:01 | 2018-03-03 16:06:17 | col : city row 1 : South Minnie row 2 : Linnealand row 3 : East Tavaresburgh row 4 : Terencetown row 5 : Lake Devon row 6 : O'Connellview row 7 : New Alta row 8 : South Naomibury |
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Dataset Card for "spider-tableQA"
Usage
import pandas as pd
from datasets import load_dataset
spider_tableQA = load_dataset("vaishali/spider-tableQA")
for sample in spider_tableQA['train']:
question = sample['question']
sql_query = sample['query']
input_table_names = sample["table_names"]
input_tables = [pd.read_json(table, orient='split') for table in sample['tables']]
answer = pd.read_json(sample['answer'], orient='split')
# flattened input/output
input_to_model = sample["source"]
target = sample["target"]
BibTeX entry and citation info
@inproceedings{pal-etal-2023-multitabqa,
title = "{M}ulti{T}ab{QA}: Generating Tabular Answers for Multi-Table Question Answering",
author = "Pal, Vaishali and
Yates, Andrew and
Kanoulas, Evangelos and
de Rijke, Maarten",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.348",
doi = "10.18653/v1/2023.acl-long.348",
pages = "6322--6334",
abstract = "Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world queries are complex in nature, often over multiple tables in a relational database or web page. Single table questions do not involve common table operations such as set operations, Cartesian products (joins), or nested queries. Furthermore, multi-table operations often result in a tabular output, which necessitates table generation capabilities of tabular QA models. To fill this gap, we propose a new task of answering questions over multiple tables. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers. To enable effective training, we build a pre-training dataset comprising of 132,645 SQL queries and tabular answers. Further, we evaluate the generated tables by introducing table-specific metrics of varying strictness assessing various levels of granularity of the table structure. MultiTabQA outperforms state-of-the-art single table QA models adapted to a multi-table QA setting by finetuning on three datasets: Spider, Atis and GeoQuery.",
}
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