File size: 19,866 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | You are SQL Tutor that fixes the student query. Given a database schema, a question, and SQL query generated by student, its response in database and the feedback on the correctness of the query. Based on the Feedback, generate a fixed sql that correctly aligns with the intent of question.
Examples:
database schema :
table movies , columns = [ movies.director_name ( text | values : Esteban Sapir , Oskar Roehler ) , movies.movie_popularity ( integer | values : 105 , 23 ) , movies.movie_id ( integer | primary key | values : 1 , 2 ) , movies.movie_title ( text | values : La Antena , Elementary Particles ) , movies.director_id ( text | values : 131 , 73 ) , movies.movie_release_year ( integer | values : 2007 , 2006 ) , movies.movie_url ( text ) , movies.movie_title_language ( text | values : en ) , movies.director_url ( text ) , movies.movie_image_url ( text ) ]
table ratings , columns = [ ratings.movie_id ( integer | values : 1066 , 1067 ) , ratings.rating_score ( integer | values : 3 , 2 ) , ratings.rating_id ( integer | values : 15610495 , 10704606 ) , ratings.user_id ( integer | values : 41579158 , 85981819 ) , ratings.critic ( text ) , ratings.rating_url ( text ) , ratings.rating_timestamp_utc ( text | values : 2017-06-10 12:38:33 , 2014-08-15 23:42:31 ) , ratings.critic_likes ( integer | values : 0 , 1 ) , ratings.critic_comments ( integer | values : 0 , 2 ) , ratings.user_trialist ( integer | values : 0 , 1 ) ]
table lists , columns = [ lists.list_title ( text | values : Headscratchers ) , lists.list_movie_number ( integer | values : 5 , 3 ) , lists.list_description ( text ) , lists.list_id ( integer | primary key | values : 1 , 2 ) , lists.user_id ( integer | values : 88260493 , 45204418 ) , lists.list_comments ( integer | values : 3 , 2 ) , lists.list_url ( text ) , lists.list_followers ( integer | values : 5 , 1 ) , lists.list_third_image_url ( text ) , lists.list_cover_image_url ( text ) ]
table ratings_users , columns = [ ratings_users.user_id ( integer | values : 41579158 , 68654088 ) , ratings_users.user_subscriber ( integer | values : 0 , 1 ) , ratings_users.user_trialist ( integer | values : 0 , 1 ) , ratings_users.user_has_payment_method ( integer | values : 0 , 1 ) , ratings_users.user_eligible_for_trial ( integer | values : 1 , 0 ) , ratings_users.rating_date_utc ( text | values : 2017-06-10 , 2012-10-02 ) , ratings_users.user_cover_image_url ( text ) , ratings_users.user_avatar_image_url ( text ) ]
table lists_users , columns = [ lists_users.list_id ( integer | primary key | values : 192287 , 192313 ) , lists_users.user_id ( integer | primary key | values : 2385 , 15264 ) , lists_users.user_trialist ( integer | values : 1 , 0 ) , lists_users.user_has_payment_method ( text | values : 1 , 0 ) , lists_users.user_subscriber ( integer | values : 1 , 0 ) , lists_users.user_eligible_for_trial ( text | values : 0 , 1 ) , lists_users.user_avatar_image_url ( text ) , lists_users.user_cover_image_url ( text ) , lists_users.list_creation_date_utc ( text | values : 2009-12-18 , 2010-01-30 ) , lists_users.list_update_date_utc ( text | values : 2019-11-26 , 2020-05-01 ) ]
foreign keys :
lists.user_id = lists_users.user_id
ratings_users.user_id = lists_users.user_id
lists_users.user_id = lists.user_id
lists_users.list_id = lists.list_id
ratings.user_id = ratings_users.user_id
ratings.rating_id = ratings.rating_id
ratings.user_id = lists_users.user_id
ratings.movie_id = movies.movie_id
Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
movies.director_name ( Christopher Nolan )
movies.movie_title ( The Average , Score )
ratings.critic ( average , AVERAGE , Christopher! , score , Nolan. )
lists.list_title ( CHRISTOPHER NOLAN , christopher nolan , Christopher nolan , Christopher Nolan , Christopher NOLAN , Directed By , average , rating , Nolan , Numbers , Score , movies )
Question: What is the average popularity of each movie that was directed by Christopher Nolan? Indicate which movie directed by him has received the highest number of 5 rating scores.
SQL query: SELECT avg(movies.movie_popularity), movies.movie_title FROM ratings INNER JOIN movies ON ratings.movie_id = movies.movie_id WHERE movies.director_name = 'Christopher Nolan' GROUP BY movies.movie_title ORDER BY count(ratings.rating_score) DESC LIMIT 1
Feedback:
SELECT.
1. Based on the SQL query, the SQL query selects: [movies.movie_popularity, movies.movie_title]
2. The question asks for ['the average popularity of each movie']
3. Based on the question, the query should select: [movies.movie_popularity]
4. Compare 1. and 3., the SQL query selects unnecessary columns [movies.movie_title].
5. Conclude: incorrect.
FIXED SQL: SELECT avg(movies.movie_popularity) FROM ratings INNER JOIN movies ON ratings.movie_id = movies.movie_id WHERE movies.director_name = 'Christopher Nolan' GROUP BY movies.movie_title ORDER BY count(ratings.rating_score) DESC LIMIT 1
=========
database schema :
table movies , columns = [ movies.director_name ( text | values : Esteban Sapir , Oskar Roehler ) , movies.movie_popularity ( integer | values : 105 , 23 ) , movies.movie_release_year ( integer | values : 2007 , 2006 ) , movies.movie_title ( text | values : La Antena , Elementary Particles ) , movies.movie_id ( integer | primary key | values : 1 , 2 ) , movies.director_id ( text | values : 131 , 73 ) , movies.movie_url ( text ) , movies.director_url ( text ) , movies.movie_title_language ( text | values : en ) , movies.movie_image_url ( text ) ]
table lists , columns = [ lists.list_title ( text | values : Headscratchers ) , lists.list_movie_number ( integer | values : 5 , 3 ) , lists.list_description ( text ) , lists.list_id ( integer | primary key | values : 1 , 2 ) , lists.user_id ( integer | values : 88260493 , 45204418 ) , lists.list_url ( text ) , lists.list_comments ( integer | values : 3 , 2 ) , lists.list_followers ( integer | values : 5 , 1 ) , lists.list_third_image_url ( text ) , lists.list_second_image_url ( text ) ]
table ratings_users , columns = [ ratings_users.user_id ( integer | values : 41579158 , 68654088 ) , ratings_users.user_trialist ( integer | values : 0 , 1 ) , ratings_users.user_subscriber ( integer | values : 0 , 1 ) , ratings_users.user_has_payment_method ( integer | values : 0 , 1 ) , ratings_users.user_eligible_for_trial ( integer | values : 1 , 0 ) , ratings_users.user_cover_image_url ( text ) , ratings_users.rating_date_utc ( text | values : 2017-06-10 , 2012-10-02 ) , ratings_users.user_avatar_image_url ( text ) ]
table lists_users , columns = [ lists_users.list_id ( integer | primary key | values : 192287 , 192313 ) , lists_users.user_id ( integer | primary key | values : 2385 , 15264 ) , lists_users.user_trialist ( integer | values : 1 , 0 ) , lists_users.user_subscriber ( integer | values : 1 , 0 ) , lists_users.user_has_payment_method ( text | values : 1 , 0 ) , lists_users.user_avatar_image_url ( text ) , lists_users.user_eligible_for_trial ( text | values : 0 , 1 ) , lists_users.user_cover_image_url ( text ) , lists_users.list_update_date_utc ( text | values : 2019-11-26 , 2020-05-01 ) , lists_users.list_creation_date_utc ( text | values : 2009-12-18 , 2010-01-30 ) ]
table ratings , columns = [ ratings.movie_id ( integer | values : 1066 , 1067 ) , ratings.critic ( text ) , ratings.rating_id ( integer | values : 15610495 , 10704606 ) , ratings.user_id ( integer | values : 41579158 , 85981819 ) , ratings.rating_score ( integer | values : 3 , 2 ) , ratings.critic_likes ( integer | values : 0 , 1 ) , ratings.rating_url ( text ) , ratings.critic_comments ( integer | values : 0 , 2 ) , ratings.user_trialist ( integer | values : 0 , 1 ) , ratings.user_subscriber ( integer | values : 0 , 1 ) ]
foreign keys :
lists.user_id = lists_users.user_id
ratings_users.user_id = lists_users.user_id
lists_users.user_id = lists.user_id
lists_users.list_id = lists.list_id
ratings.user_id = ratings_users.user_id
ratings.rating_id = ratings.rating_id
ratings.user_id = lists_users.user_id
ratings.movie_id = movies.movie_id
Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
movies.director_name ( Steven Spielberg )
movies.movie_release_year ( 2021 )
movies.movie_title ( Spielberg , Release )
movies.movie_id ( 2021 )
lists.list_title ( STEVEN SPIELBERG , steven spielberg , Steven spielberg , Directed By , spielberg , 2021 , Movies released in 2012 , Spielberg! , Released in 2012 , movies! )
ratings.movie_id ( 2021 )
ratings.rating_id ( 2021 )
Question: What are the movie popularity of the movies released in 2021 that were directed by Steven Spielberg? List the names of the movies and their corresponding popularity.
SQL query: SELECT movie_popularity, movie_title FROM movies WHERE movie_release_year = 2021 AND director_name = 'Steven Spielberg'
Feedback:
SELECT.
1. Based on the SQL query, the query selects: [movie_popularity, movie_title]
2. The question asks for ['names of the movies', 'their corresponding popularity']
3. Based on the question, the query should select: [movie_title, movie_popularity]
4. Compare 1. and 3., The SQL query selects correct columns but in wrong order.
5. Conclude: incorrect.
FIXED SQL: SELECT movie_title, movie_popularity FROM movies WHERE movie_release_year = 2021 AND director_name = 'Steven Spielberg'
=========
database schema :
table master , columns = [ master.firstnhl ( text | comment : first nhl season | values : 1997 , 1943 ) , master.birthcountry ( text | values : Finland , Canada ) , master.playerid ( text | values : aaltoan01 , abbeybr01 ) , master.namegiven ( text | values : Antti , Bruce ) , master.lastname ( text | values : Aalto , Abbey ) , master.birthyear ( text | values : 1975 , 1951 ) , master.namenick ( text | comment : nickname | values : Preacher , Taffy ) , master.firstname ( text | values : Antti , Bruce ) , master.lastnhl ( text | comment : last nhl season | values : 2000 , 1943 ) , master.birthday ( text | values : 4 , 18 ) ]
table scoring , columns = [ scoring.playerid ( text | values : aaltoan01 , abbeybr01 ) , scoring.g ( integer | comment : goals | values : 0 , 3 ) , scoring.tmid ( text | comment : team id | values : ANA , CIN ) , scoring.year ( integer | values : 1997 , 1998 ) , scoring.lgid ( text | comment : league id | values : NHL , WHA ) , scoring.gp ( integer | comment : game played | values : 3 , 73 ) , scoring.pos ( text | comment : position | values : C , D ) , scoring.stint ( integer | values : 1 , 2 ) , scoring.pts ( integer | comment : points | values : 0 , 8 ) , scoring.gwg ( text | comment : game-winning goals | values : 0 , 1 ) ]
table teamshalf , columns = [ teamshalf.g ( integer | comment : games | values : 10 , 4 ) , teamshalf.year ( integer | primary key | values : 1916 , 1917 ) , teamshalf.tmid ( text | primary key | comment : team id | values : MOC , MOW ) , teamshalf.lgid ( text | comment : league id | values : NHA , NHL ) , teamshalf.rank ( integer | values : 1 , 3 ) , teamshalf.half ( integer | primary key | values : 1 , 2 ) , teamshalf.w ( integer | comment : wins | values : 7 , 3 ) , teamshalf.gf ( integer | comment : goals for | values : 58 , 31 ) , teamshalf.l ( integer | comment : loses | values : 3 , 7 ) , teamshalf.t ( integer | comment : ties | values : 0 ) ]
table scoringsc , columns = [ scoringsc.playerid ( text | values : adamsbi01 , adamsja01 ) , scoringsc.tmid ( text | comment : team id | values : VML , CAT ) , scoringsc.year ( integer | values : 1920 , 1921 ) , scoringsc.g ( integer | comment : goals | values : 0 , 2 ) , scoringsc.lgid ( text | comment : league id | values : PCHA , WCHL ) , scoringsc.gp ( integer | comment : games played | values : 4 , 5 ) , scoringsc.pts ( integer | comment : points | values : 0 , 3 ) , scoringsc.pos ( text | comment : position | values : R , C ) , scoringsc.a ( integer | comment : assists | values : 0 , 1 ) , scoringsc.pim ( integer | comment : penalty minutes | values : 0 , 6 ) ]
table scoringshootout , columns = [ scoringshootout.playerid ( text | values : adamske01 , afanadm01 ) , scoringshootout.tmid ( text | comment : team id | values : PHO , TBL ) , scoringshootout.g ( integer | comment : goals | values : 0 , 1 ) , scoringshootout.year ( integer | values : 2006 , 2005 ) , scoringshootout.stint ( integer | values : 1 , 2 ) , scoringshootout.gdg ( integer | comment : game deciding goals | values : 0 , 1 ) , scoringshootout.s ( integer | comment : shots | values : 1 , 2 ) ]
table teamssc , columns = [ teamssc.g ( integer | comment : games | values : 3 , 5 ) , teamssc.tmid ( text | primary key | comment : team id | values : QU1 , VA1 ) , teamssc.year ( integer | primary key | values : 1912 , 1913 ) , teamssc.lgid ( text | comment : league id | values : NHA , PCHA ) , teamssc.gf ( integer | comment : goals for | values : 12 , 16 ) , teamssc.w ( integer | comment : wins | values : 1 , 2 ) , teamssc.ga ( integer | comment : goals against | values : 16 , 12 ) , teamssc.t ( integer | comment : ties | values : 0 , 1 ) , teamssc.l ( integer | comment : loses | values : 2 , 1 ) , teamssc.pim ( text | comment : penalty minutes | values : 24 , 20 ) ]
foreign keys :
scoring.playerid = master.playerid
scoringsc.playerid = master.playerid
scoringshootout.playerid = master.playerid
Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
master.birthcountry ( Canada )
master.namenick ( Mean )
Question: How many Canadian players, between the ages of 18 and 24 when they initially played their first NHL, had a cumulative goal total of no more than 5? Indicate their complete names, the year, and the team for which they scored the specified amount of goals.
SQL query: SELECT master.firstname, master.lastname, scoring.tmid, scoring.year FROM master INNER JOIN scoring ON master.playerid = scoring.playerid WHERE master.birthcountry = 'Canada' AND (scoring.g < 5 OR (scoring.g = 5 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) < 18 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) > 24)) GROUP BY master.playerid, master.firstname, master.lastname, scoring.year, scoring.tmid
Feedback:
SELECT.
1. Based on the SQL query, the query selects: [master.firstname, master.lastname, scoring.tmid, scoring.year]
2. The question asks for ['complete names', 'year', 'team']
3. Based on the question, the query should select: [master.firstname, master.lastname, scoring.year, scoring.tmid]
4. Compare 1. and 3., The SQL query selects correct columns but in wrong order.
5. Conclude: incorrect.
FIXED SQL: SELECT master.firstname, master.lastname, scoring.year, scoring.tmid FROM master INNER JOIN scoring ON master.playerid = scoring.playerid WHERE master.birthcountry = 'Canada' AND (scoring.g < 5 OR (scoring.g = 5 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) < 18 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) > 24)) GROUP BY master.playerid, master.firstname, master.lastname, scoring.year, scoring.tmid
=========
database schema :
table salesorderheader , columns = [ salesorderheader.territoryid ( integer | values : 5 , 6 ) , salesorderheader.salesorderid ( integer | primary key | values : 71821 , 44088 ) , salesorderheader.salespersonid ( integer | values : 279 , 282 ) , salesorderheader.salesordernumber ( text | values : SO43659 , SO43660 ) , salesorderheader.customerid ( integer | values : 29825 , 29672 ) , salesorderheader.status ( integer | values : 5 ) , salesorderheader.comment ( text ) , salesorderheader.freight ( real | values : 616.0984 , 38.8276 ) , salesorderheader.purchaseordernumber ( text | values : PO522145787 , PO18850127500 ) , salesorderheader.taxamt ( real | comment : tax amount | values : 1971.5149 , 124.2483 ) ]
table salesperson , columns = [ salesperson.saleslastyear ( real | values : 0.0 , 1750406.4785 ) , salesperson.salesytd ( real | comment : sales year to date | values : 559697.5639 , 3763178.1787 ) , salesperson.territoryid ( integer | values : 2 , 4 ) , salesperson.businessentityid ( integer | primary key | values : 287 , 275 ) , salesperson.commissionpct ( real | comment : commission percentage | values : 0.0 , 0.012 ) , salesperson.salesquota ( real | values : 300000.0 , 250000.0 ) , salesperson.rowguid ( text ) , salesperson.modifieddate ( datetime | values : 2010-12-28 00:00:00.0 , 2011-05-24 00:00:00.0 ) , salesperson.bonus ( real | values : 0.0 , 4100.0 ) ]
table salesterritory , columns = [ salesterritory.saleslastyear ( real | values : 3298694.4938 , 3607148.9371 ) , salesterritory.salesytd ( real | comment : sales year to date | values : 7887186.7882 , 2402176.8476 ) , salesterritory.countryregioncode ( text | values : US , CA ) , salesterritory.name ( text | values : Australia , Canada ) , salesterritory.territoryid ( integer | primary key | values : 2 , 10 ) , salesterritory.costytd ( real | comment : cost year to date | values : 0.0 ) , salesterritory.group ( text | values : North America , Europe ) , salesterritory.costlastyear ( real | values : 0.0 ) , salesterritory.modifieddate ( datetime | values : 2008-04-30 00:00:00.0 ) , salesterritory.rowguid ( text ) ]
table stateprovince , columns = [ stateprovince.territoryid ( integer | values : 6 , 1 ) , stateprovince.countryregioncode ( text | values : FR , CA ) , stateprovince.name ( text | values : Ain , Aisne ) , stateprovince.stateprovinceid ( integer | primary key | values : 103 , 101 ) , stateprovince.stateprovincecode ( text | values : 01 , 02 ) , stateprovince.isonlystateprovinceflag ( integer | values : 0 , 1 ) , stateprovince.rowguid ( text ) , stateprovince.modifieddate ( datetime | values : 2014-02-08 10:17:21.0 , 2008-04-30 00:00:00.0 ) ]
table customer , columns = [ customer.territoryid ( integer ) , customer.personid ( integer ) , customer.storeid ( integer ) , customer.customerid ( integer | primary key ) , customer.rowguid ( text ) , customer.accountnumber ( text ) , customer.modifieddate ( datetime ) ]
table address , columns = [ address.addressid ( integer | primary key | values : 18089 , 23192 ) , address.city ( text | values : Ottawa , Burnaby ) , address.postalcode ( text | values : K4B 1S2 , V5A 4X1 ) , address.stateprovinceid ( integer | values : 57 , 7 ) , address.addressline1 ( text | values : #500-75 O'Connor Street , #9900 2700 Production Way ) , address.rowguid ( text ) , address.addressline2 ( text | values : Space 55 , Unit B-105 ) , address.spatiallocation ( text ) , address.modifieddate ( datetime | values : 2007-12-04 00:00:00.0 , 2008-11-30 00:00:00.0 ) ]
foreign keys :
salesperson.territoryid = salesterritory.territoryid
customer.territoryid = salesterritory.territoryid
Matched contents are written in this format table.column (some values can be found in that column)
matched contents : None
Question: Which territory has the greatest difference in sales from previous year to this year? Indicate the difference, as well as the name and country of the region.
SQL query: SELECT saleslastyear - salesytd, countryregioncode, name FROM salesterritory ORDER BY saleslastyear - salesytd DESC LIMIT 1
Execution response [written in pandas format]:
0 1 2
0 1.386404e+06 Southeast US
Feedback:
SELECT.
1. Based on the SQL query, the query selects: [saleslastyear - salesytd, countryregioncode, name]
2. The question asks for ['difference in sales', 'name of the region', 'country of the region']
3. Based on the question, the query should select: [saleslastyear - salesytd, name, countryregioncode]
4. Compare 1. and 3., The SQL query selects correct columns but in wrong order.
5. Conclude: incorrect.
FIXED SQL: SELECT saleslastyear - salesytd, name, countryregioncode FROM salesterritory ORDER BY saleslastyear - salesytd DESC LIMIT 1
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