input stringlengths 667 7.62k | output stringlengths 41 600 | db_id stringlengths 3 31 | query stringlengths 18 577 | question stringlengths 3 224 | schema stringlengths 177 6.14k | primary_keys stringlengths 16 545 | foreign_keys stringlengths 16 1.48k |
|---|---|---|---|---|---|---|---|
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many different types of sports do we offer?</question>
[SQL]:
| <SQL_QUERY>SELECT count(DISTINCT sportname) FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT count(DISTINCT sportname) FROM Sportsinfo | How many different types of sports do we offer? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many students play sports?</question>
[SQL]:
| <SQL_QUERY>SELECT count(DISTINCT StuID) FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT count(DISTINCT StuID) FROM Sportsinfo | How many students play sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many different students are involved in sports?</question>
[SQL]:
| <SQL_QUERY>SELECT count(DISTINCT StuID) FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT count(DISTINCT StuID) FROM Sportsinfo | How many different students are involved in sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>List ids for all student who are on scholarship.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y' | List ids for all student who are on scholarship. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids for all sporty students who are on scholarship?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y' | What are the ids for all sporty students who are on scholarship? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show last names for all student who are on scholarship.</question>
[SQL]:
| <SQL_QUERY>SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.onscholarship = 'Y' | Show last names for all student who are on scholarship. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the last names for all scholarship students?</question>
[SQL]:
| <SQL_QUERY>SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.onscholarship = 'Y' | What are the last names for all scholarship students? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many games are played for all students?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(gamesplayed) FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT sum(gamesplayed) FROM Sportsinfo | How many games are played for all students? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the total number of games played?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(gamesplayed) FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT sum(gamesplayed) FROM Sportsinfo | What is the total number of games played? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many games are played for all football games by students on scholarship?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname = "Football" AND onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname = "Football" AND onscholarship = 'Y' | How many games are played for all football games by students on scholarship? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the total number of all football games played by scholarship students?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname = "Football" AND onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname = "Football" AND onscholarship = 'Y' | What is the total number of all football games played by scholarship students? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all sport name and the number of students.</question>
[SQL]:
| <SQL_QUERY>SELECT sportname , count(*) FROM Sportsinfo GROUP BY sportname</SQL_QUERY> | game_1 | SELECT sportname , count(*) FROM Sportsinfo GROUP BY sportname | Show all sport name and the number of students. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many students play each sport?</question>
[SQL]:
| <SQL_QUERY>SELECT sportname , count(*) FROM Sportsinfo GROUP BY sportname</SQL_QUERY> | game_1 | SELECT sportname , count(*) FROM Sportsinfo GROUP BY sportname | How many students play each sport? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all student IDs with the number of sports and total number of games played</question>
[SQL]:
| <SQL_QUERY>SELECT StuID , count(*) , sum(gamesplayed) FROM Sportsinfo GROUP BY StuID</SQL_QUERY> | game_1 | SELECT StuID , count(*) , sum(gamesplayed) FROM Sportsinfo GROUP BY StuID | Show all student IDs with the number of sports and total number of games played | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all students along with how many sports and games did they play?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID , count(*) , sum(gamesplayed) FROM Sportsinfo GROUP BY StuID</SQL_QUERY> | game_1 | SELECT StuID , count(*) , sum(gamesplayed) FROM Sportsinfo GROUP BY StuID | What are the ids of all students along with how many sports and games did they play? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all student IDs with more than total 10 hours per week on all sports played.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek) > 10</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek) > 10 | Show all student IDs with more than total 10 hours per week on all sports played. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the student IDs for everybody who worked for more than 10 hours per week on all sports?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek) > 10</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek) > 10 | What are the student IDs for everybody who worked for more than 10 hours per week on all sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the first name and last name of the student who have most number of sports?</question>
[SQL]:
| <SQL_QUERY>SELECT T2.Fname , T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT T2.Fname , T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1 | What is the first name and last name of the student who have most number of sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the first and last name of the student who played the most sports?</question>
[SQL]:
| <SQL_QUERY>SELECT T2.Fname , T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT T2.Fname , T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1 | What is the first and last name of the student who played the most sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which sport has most number of students on scholarship?</question>
[SQL]:
| <SQL_QUERY>SELECT sportname FROM Sportsinfo WHERE onscholarship = 'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT sportname FROM Sportsinfo WHERE onscholarship = 'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1 | Which sport has most number of students on scholarship? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the sport with the most scholarship students?</question>
[SQL]:
| <SQL_QUERY>SELECT sportname FROM Sportsinfo WHERE onscholarship = 'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT sportname FROM Sportsinfo WHERE onscholarship = 'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1 | What is the sport with the most scholarship students? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show student ids who don't have any sports.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo | Show student ids who don't have any sports. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all students who don't play sports?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo</SQL_QUERY> | game_1 | SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo | What are the ids of all students who don't play sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show student ids who are on scholarship and have major 600.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE major = 600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE major = 600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y' | Show student ids who are on scholarship and have major 600. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the student ids for those on scholarship in major number 600?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE major = 600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y'</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE major = 600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship = 'Y' | What are the student ids for those on scholarship in major number 600? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show student ids who are female and play football.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE sex = 'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname = "Football"</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE sex = 'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname = "Football" | Show student ids who are female and play football. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all female students who play football?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE sex = 'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname = "Football"</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE sex = 'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname = "Football" | What are the ids of all female students who play football? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all male student ids who don't play football.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE sex = 'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname = "Football"</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE sex = 'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname = "Football" | Show all male student ids who don't play football. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all male students who do not play football?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student WHERE sex = 'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname = "Football"</SQL_QUERY> | game_1 | SELECT StuID FROM Student WHERE sex = 'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname = "Football" | What are the ids of all male students who do not play football? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show total hours per week and number of games played for student David Shieber.</question>
[SQL]:
| <SQL_QUERY>SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.Fname = "David" AND T2.Lname = "Shieber"</SQL_QUERY> | game_1 | SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.Fname = "David" AND T2.Lname = "Shieber" | Show total hours per week and number of games played for student David Shieber. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the total number of hours per work and number of games played by David Shieber?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.Fname = "David" AND T2.Lname = "Shieber"</SQL_QUERY> | game_1 | SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.Fname = "David" AND T2.Lname = "Shieber" | What is the total number of hours per work and number of games played by David Shieber? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show total hours per week and number of games played for students under 20.</question>
[SQL]:
| <SQL_QUERY>SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.age < 20</SQL_QUERY> | game_1 | SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.age < 20 | Show total hours per week and number of games played for students under 20. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the total number of hours per week and number of games played by students under 20?</question>
[SQL]:
| <SQL_QUERY>SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.age < 20</SQL_QUERY> | game_1 | SELECT sum(hoursperweek) , sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T2.age < 20 | What is the total number of hours per week and number of games played by students under 20? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many students play video games?</question>
[SQL]:
| <SQL_QUERY>SELECT count(DISTINCT StuID) FROM Plays_games</SQL_QUERY> | game_1 | SELECT count(DISTINCT StuID) FROM Plays_games | How many students play video games? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many different students play games?</question>
[SQL]:
| <SQL_QUERY>SELECT count(DISTINCT StuID) FROM Plays_games</SQL_QUERY> | game_1 | SELECT count(DISTINCT StuID) FROM Plays_games | How many different students play games? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show ids of students who don't play video game.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games</SQL_QUERY> | game_1 | SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games | Show ids of students who don't play video game. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all students who are not video game players?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games</SQL_QUERY> | game_1 | SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games | What are the ids of all students who are not video game players? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show ids of students who play video game and play sports.</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games | Show ids of students who play video game and play sports. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all students who played video games and sports?</question>
[SQL]:
| <SQL_QUERY>SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games</SQL_QUERY> | game_1 | SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games | What are the ids of all students who played video games and sports? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all game ids and the number of hours played.</question>
[SQL]:
| <SQL_QUERY>SELECT gameid , sum(hours_played) FROM Plays_games GROUP BY gameid</SQL_QUERY> | game_1 | SELECT gameid , sum(hours_played) FROM Plays_games GROUP BY gameid | Show all game ids and the number of hours played. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are ids and total number of hours played for each game?</question>
[SQL]:
| <SQL_QUERY>SELECT gameid , sum(hours_played) FROM Plays_games GROUP BY gameid</SQL_QUERY> | game_1 | SELECT gameid , sum(hours_played) FROM Plays_games GROUP BY gameid | What are ids and total number of hours played for each game? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all student ids and the number of hours played.</question>
[SQL]:
| <SQL_QUERY>SELECT Stuid , sum(hours_played) FROM Plays_games GROUP BY Stuid</SQL_QUERY> | game_1 | SELECT Stuid , sum(hours_played) FROM Plays_games GROUP BY Stuid | Show all student ids and the number of hours played. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the ids of all students and number of hours played?</question>
[SQL]:
| <SQL_QUERY>SELECT Stuid , sum(hours_played) FROM Plays_games GROUP BY Stuid</SQL_QUERY> | game_1 | SELECT Stuid , sum(hours_played) FROM Plays_games GROUP BY Stuid | What are the ids of all students and number of hours played? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show the game name that has most number of hours played.</question>
[SQL]:
| <SQL_QUERY>SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1 | Show the game name that has most number of hours played. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the name of the game that has been played the most?</question>
[SQL]:
| <SQL_QUERY>SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1</SQL_QUERY> | game_1 | SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1 | What is the name of the game that has been played the most? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all game names played by at least 1000 hours.</question>
[SQL]:
| <SQL_QUERY>SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid HAVING sum(hours_played) >= 1000</SQL_QUERY> | game_1 | SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid HAVING sum(hours_played) >= 1000 | Show all game names played by at least 1000 hours. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of all the games that have been played for at least 1000 hours?</question>
[SQL]:
| <SQL_QUERY>SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid HAVING sum(hours_played) >= 1000</SQL_QUERY> | game_1 | SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid GROUP BY T1.gameid HAVING sum(hours_played) >= 1000 | What are the names of all the games that have been played for at least 1000 hours? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Show all game names played by Linda Smith</question>
[SQL]:
| <SQL_QUERY>SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid JOIN Student AS T3 ON T3.Stuid = T1.Stuid WHERE T3.Lname = "Smith" AND T3.Fname = "Linda"</SQL_QUERY> | game_1 | SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid JOIN Student AS T3 ON T3.Stuid = T1.Stuid WHERE T3.Lname = "Smith" AND T3.Fname = "Linda" | Show all game names played by Linda Smith | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of all games played by Linda Smith?</question>
[SQL]:
| <SQL_QUERY>SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid JOIN Student AS T3 ON T3.Stuid = T1.Stuid WHERE T3.Lname = "Smith" AND T3.Fname = "Linda"</SQL_QUERY> | game_1 | SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid = T2.gameid JOIN Student AS T3 ON T3.Stuid = T1.Stuid WHERE T3.Lname = "Smith" AND T3.Fname = "Linda" | What are the names of all games played by Linda Smith? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the last and first name of students who are playing Football or Lacrosse.</question>
[SQL]:
| <SQL_QUERY>SELECT T2.lname , T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.SportName = "Football" OR T1.SportName = "Lacrosse"</SQL_QUERY> | game_1 | SELECT T2.lname , T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.SportName = "Football" OR T1.SportName = "Lacrosse" | Find the last and first name of students who are playing Football or Lacrosse. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the first and last name of all students who play Football or Lacrosse?</question>
[SQL]:
| <SQL_QUERY>SELECT T2.lname , T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.SportName = "Football" OR T1.SportName = "Lacrosse"</SQL_QUERY> | game_1 | SELECT T2.lname , T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID = T2.StuID WHERE T1.SportName = "Football" OR T1.SportName = "Lacrosse" | What is the first and last name of all students who play Football or Lacrosse? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the first name and age of the students who are playing both Football and Lacrosse.</question>
[SQL]:
| <SQL_QUERY>SELECT fname , age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName = "Football" INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName = "Lacrosse")</SQL_QUERY> | game_1 | SELECT fname , age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName = "Football" INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName = "Lacrosse") | Find the first name and age of the students who are playing both Football and Lacrosse. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the first names and ages of all students who are playing both Football and Lacrosse?</question>
[SQL]:
| <SQL_QUERY>SELECT fname , age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName = "Football" INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName = "Lacrosse")</SQL_QUERY> | game_1 | SELECT fname , age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName = "Football" INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName = "Lacrosse") | What are the first names and ages of all students who are playing both Football and Lacrosse? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the last name and gender of the students who are playing both Call of Destiny and Works of Widenius games.</question>
[SQL]:
| <SQL_QUERY>SELECT lname , sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Call of Destiny" INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Works of Widenius")</SQL_QUERY> | game_1 | SELECT lname , sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Call of Destiny" INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Works of Widenius") | Find the last name and gender of the students who are playing both Call of Destiny and Works of Widenius games. | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number);</schema>"
<primary_keys>[Primary Keys]: student : stuid, video_games : gameid</primary_keys>"
<foreign_keys>[Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid</foreign_keys>"
Answer the following question with a SQL Statement:<question>what is the last name and gender of all students who played both Call of Destiny and Works of Widenius?</question>
[SQL]:
| <SQL_QUERY>SELECT lname , sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Call of Destiny" INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Works of Widenius")</SQL_QUERY> | game_1 | SELECT lname , sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Call of Destiny" INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Works of Widenius") | what is the last name and gender of all students who played both Call of Destiny and Works of Widenius? | [Schema (values) (types)]: | game_1 | Student : stuid (text) , lname (number) , fname (text) , age (text) , sex (number) , major (text) , advisor (number) , city_code (number) | Video_Games : gameid (text) , gname (number) , gtype (text) | Plays_Games : stuid (text) , gameid (number) , hours_played (text) | SportsInfo : stuid (text) , sportname (number) , hoursperweek (text) , gamesplayed (text) , onscholarship (number); | [Primary Keys]: student : stuid, video_games : gameid | [Foreign Keys]: plays_games : stuid = student : stuid | plays_games : gameid = video_games : gameid | sportsinfo : stuid = student : stuid |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the name of all customers.</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers | Find the name of all customers. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of all the customers?</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers | What are the names of all the customers? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many customers are there?</question>
[SQL]:
| <SQL_QUERY>SELECT count(*) FROM customers</SQL_QUERY> | customers_and_addresses | SELECT count(*) FROM customers | How many customers are there? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Return the total number of distinct customers.</question>
[SQL]:
| <SQL_QUERY>SELECT count(*) FROM customers</SQL_QUERY> | customers_and_addresses | SELECT count(*) FROM customers | Return the total number of distinct customers. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the average amount of items ordered in each order?</question>
[SQL]:
| <SQL_QUERY>SELECT avg(order_quantity) FROM order_items</SQL_QUERY> | customers_and_addresses | SELECT avg(order_quantity) FROM order_items | What is the average amount of items ordered in each order? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the average order quantity per order.</question>
[SQL]:
| <SQL_QUERY>SELECT avg(order_quantity) FROM order_items</SQL_QUERY> | customers_and_addresses | SELECT avg(order_quantity) FROM order_items | Find the average order quantity per order. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of customers who use payment method "Cash"?</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE payment_method = "Cash"</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE payment_method = "Cash" | What are the names of customers who use payment method "Cash"? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which customers use "Cash" for payment method? Return the customer names.</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE payment_method = "Cash"</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE payment_method = "Cash" | Which customers use "Cash" for payment method? Return the customer names. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the "date became customers" of the customers whose ID is between 10 and 20.</question>
[SQL]:
| <SQL_QUERY>SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20</SQL_QUERY> | customers_and_addresses | SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20 | Find the "date became customers" of the customers whose ID is between 10 and 20. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the dates when customers with ids between 10 and 20 became customers?</question>
[SQL]:
| <SQL_QUERY>SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20</SQL_QUERY> | customers_and_addresses | SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20 | What are the dates when customers with ids between 10 and 20 became customers? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which payment method is used by most customers?</question>
[SQL]:
| <SQL_QUERY>SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | customers_and_addresses | SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1 | Which payment method is used by most customers? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the payment method that is used most frequently.</question>
[SQL]:
| <SQL_QUERY>SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | customers_and_addresses | SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1 | Find the payment method that is used most frequently. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of customers using the most popular payment method?</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE payment_method = (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1)</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE payment_method = (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1) | What are the names of customers using the most popular payment method? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the name of the customers who use the most frequently used payment method.</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE payment_method = (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1)</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE payment_method = (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1) | Find the name of the customers who use the most frequently used payment method. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are all the payment methods?</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT payment_method FROM customers</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT payment_method FROM customers | What are all the payment methods? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Return all the distinct payment methods used by customers.</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT payment_method FROM customers</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT payment_method FROM customers | Return all the distinct payment methods used by customers. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the details of all products?</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT product_details FROM products</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT product_details FROM products | What are the details of all products? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Return the the details of all products.</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT product_details FROM products</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT product_details FROM products | Return the the details of all products. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the name of all customers whose name contains "Alex".</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE customer_name LIKE "%Alex%"</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE customer_name LIKE "%Alex%" | Find the name of all customers whose name contains "Alex". | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which customer's name contains "Alex"? Find the full name.</question>
[SQL]:
| <SQL_QUERY>SELECT customer_name FROM customers WHERE customer_name LIKE "%Alex%"</SQL_QUERY> | customers_and_addresses | SELECT customer_name FROM customers WHERE customer_name LIKE "%Alex%" | Which customer's name contains "Alex"? Find the full name. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the detail of products whose detail contains the word "Latte" or the word "Americano"</question>
[SQL]:
| <SQL_QUERY>SELECT product_details FROM products WHERE product_details LIKE "%Latte%" OR product_details LIKE "%Americano%"</SQL_QUERY> | customers_and_addresses | SELECT product_details FROM products WHERE product_details LIKE "%Latte%" OR product_details LIKE "%Americano%" | Find the detail of products whose detail contains the word "Latte" or the word "Americano" | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which product's detail contains the word "Latte" or "Americano"? Return the full detail.</question>
[SQL]:
| <SQL_QUERY>SELECT product_details FROM products WHERE product_details LIKE "%Latte%" OR product_details LIKE "%Americano%"</SQL_QUERY> | customers_and_addresses | SELECT product_details FROM products WHERE product_details LIKE "%Latte%" OR product_details LIKE "%Americano%" | Which product's detail contains the word "Latte" or "Americano"? Return the full detail. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the address content of the customer named "Maudie Kertzmann"?</question>
[SQL]:
| <SQL_QUERY>SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t1.customer_name = "Maudie Kertzmann"</SQL_QUERY> | customers_and_addresses | SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t1.customer_name = "Maudie Kertzmann" | What is the address content of the customer named "Maudie Kertzmann"? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Return the address content for the customer whose name is "Maudie Kertzmann".</question>
[SQL]:
| <SQL_QUERY>SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t1.customer_name = "Maudie Kertzmann"</SQL_QUERY> | customers_and_addresses | SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t1.customer_name = "Maudie Kertzmann" | Return the address content for the customer whose name is "Maudie Kertzmann". | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>How many customers are living in city "Lake Geovannyton"?</question>
[SQL]:
| <SQL_QUERY>SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.city = "Lake Geovannyton"</SQL_QUERY> | customers_and_addresses | SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.city = "Lake Geovannyton" | How many customers are living in city "Lake Geovannyton"? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the number of customers who live in the city called Lake Geovannyton.</question>
[SQL]:
| <SQL_QUERY>SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.city = "Lake Geovannyton"</SQL_QUERY> | customers_and_addresses | SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.city = "Lake Geovannyton" | Find the number of customers who live in the city called Lake Geovannyton. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the name of customers who are living in Colorado?</question>
[SQL]:
| <SQL_QUERY>SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.state_province_county = "Colorado"</SQL_QUERY> | customers_and_addresses | SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.state_province_county = "Colorado" | Find the name of customers who are living in Colorado? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the names of customers who live in Colorado state?</question>
[SQL]:
| <SQL_QUERY>SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.state_province_county = "Colorado"</SQL_QUERY> | customers_and_addresses | SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id WHERE t3.state_province_county = "Colorado" | What are the names of customers who live in Colorado state? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the list of cities that no customer is living in.</question>
[SQL]:
| <SQL_QUERY>SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id)</SQL_QUERY> | customers_and_addresses | SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id) | Find the list of cities that no customer is living in. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the cities no customers live in?</question>
[SQL]:
| <SQL_QUERY>SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id)</SQL_QUERY> | customers_and_addresses | SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id) | What are the cities no customers live in? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which city has the most customers living in?</question>
[SQL]:
| <SQL_QUERY>SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | customers_and_addresses | SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1 | Which city has the most customers living in? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the city where the most customers live.</question>
[SQL]:
| <SQL_QUERY>SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1</SQL_QUERY> | customers_and_addresses | SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1 | Find the city where the most customers live. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Retrieve the list of all cities.</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT city FROM addresses</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT city FROM addresses | Retrieve the list of all cities. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>List all the distinct cities</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT city FROM addresses</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT city FROM addresses | List all the distinct cities | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the city with post code 255.</question>
[SQL]:
| <SQL_QUERY>SELECT city FROM addresses WHERE zip_postcode = 255</SQL_QUERY> | customers_and_addresses | SELECT city FROM addresses WHERE zip_postcode = 255 | Find the city with post code 255. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which city is post code 255 located in?</question>
[SQL]:
| <SQL_QUERY>SELECT city FROM addresses WHERE zip_postcode = 255</SQL_QUERY> | customers_and_addresses | SELECT city FROM addresses WHERE zip_postcode = 255 | Which city is post code 255 located in? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the state and country of all cities with post code starting with 4.</question>
[SQL]:
| <SQL_QUERY>SELECT state_province_county , country FROM addresses WHERE zip_postcode LIKE "4%"</SQL_QUERY> | customers_and_addresses | SELECT state_province_county , country FROM addresses WHERE zip_postcode LIKE "4%" | Find the state and country of all cities with post code starting with 4. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What are the state and country of all the cities that have post codes starting with 4.\</question>
[SQL]:
| <SQL_QUERY>SELECT state_province_county , country FROM addresses WHERE zip_postcode LIKE "4%"</SQL_QUERY> | customers_and_addresses | SELECT state_province_county , country FROM addresses WHERE zip_postcode LIKE "4%" | What are the state and country of all the cities that have post codes starting with 4.\ | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>List the countries having more than 4 addresses listed.</question>
[SQL]:
| <SQL_QUERY>SELECT country FROM addresses GROUP BY country HAVING count(address_id) > 4</SQL_QUERY> | customers_and_addresses | SELECT country FROM addresses GROUP BY country HAVING count(address_id) > 4 | List the countries having more than 4 addresses listed. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>For which countries are there more than four distinct addresses listed?</question>
[SQL]:
| <SQL_QUERY>SELECT country FROM addresses GROUP BY country HAVING count(address_id) > 4</SQL_QUERY> | customers_and_addresses | SELECT country FROM addresses GROUP BY country HAVING count(address_id) > 4 | For which countries are there more than four distinct addresses listed? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>List all the contact channel codes that were used less than 5 times.</question>
[SQL]:
| <SQL_QUERY>SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id) < 5</SQL_QUERY> | customers_and_addresses | SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id) < 5 | List all the contact channel codes that were used less than 5 times. | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which contact channel codes were used less than 5 times?</question>
[SQL]:
| <SQL_QUERY>SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id) < 5</SQL_QUERY> | customers_and_addresses | SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id) < 5 | Which contact channel codes were used less than 5 times? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Which contact channel has been used by the customer with name "Tillman Ernser"?</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser"</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser" | Which contact channel has been used by the customer with name "Tillman Ernser"? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>Find the contact channel code that was used by the customer named "Tillman Ernser".</question>
[SQL]:
| <SQL_QUERY>SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser"</SQL_QUERY> | customers_and_addresses | SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser" | Find the contact channel code that was used by the customer named "Tillman Ernser". | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
This is a task converting text into SQL statements. We will first give the dataset schema and column types, primary keys and foreign keys and then ask a question in text. You are asked to generate SQL statement in SQLite.
<schema>[Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text);</schema>"
<primary_keys>[Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id</primary_keys>"
<foreign_keys>[Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id</foreign_keys>"
Answer the following question with a SQL Statement:<question>What is the "active to date" of the latest contact channel used by "Tillman Ernser"?</question>
[SQL]:
| <SQL_QUERY>SELECT max(t2.active_to_date) FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser"</SQL_QUERY> | customers_and_addresses | SELECT max(t2.active_to_date) FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser" | What is the "active to date" of the latest contact channel used by "Tillman Ernser"? | [Schema (values) (types)]: | customers_and_addresses | Addresses : address_id (text) , address_content (number) , city (text) , zip_postcode (text) , state_province_county (text) , country (text) , other_address_details (text) | Products : product_id (text) , product_details (number) | Customers : customer_id (text) , payment_method (number) , customer_name (text) , date_became_customer (text) , other_customer_details (text) | Customer_Addresses : customer_id (text) , address_id (number) , date_address_from (text) , address_type (text) , date_address_to (text) | Customer_Contact_Channels : customer_id (text) , channel_code (number) , active_from_date (text) , active_to_date (text) , contact_number (text) | Customer_Orders : order_id (text) , customer_id (number) , order_status (text) , order_date (text) , order_details (text) | Order_Items : order_id (text) , product_id (number) , order_quantity (text); | [Primary Keys]: addresses : address_id, products : product_id, customers : customer_id, customer_addresses : order_id | [Foreign Keys]: customer_addresses : customer_id = customers : customer_id | customer_addresses : address_id = addresses : address_id | customer_contact_channels : customer_id = customers : customer_id | customer_orders : customer_id = customers : customer_id | order_items : order_id = customer_orders : order_id | order_items : product_id = products : product_id |
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