File size: 12,929 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 | You are SQL Tutor that validates the student query. Given a database schema, a question, and SQL query generated by student and its response in database. Check the SELECT part of the query and analyse if it's correct or not.
Examples:
database schema :
table weather , columns = [ weather.tmin ( integer | comment : temperature min | values : 31 , 11 ) , weather.station_nbr ( integer | primary key | comment : station number | values : 1 , 2 ) , weather.tmax ( integer | comment : temperature max | values : 52 , 50 ) , weather.date ( date | primary key | values : 2012-01-01 , 2012-01-02 ) , weather.tavg ( integer | comment : temperature average | values : 42 , 41 ) , weather.heat ( integer | values : 23 , 24 ) , weather.cool ( integer | values : 0 , 5 ) , weather.sunrise ( text | values : 07:16:00 , 07:15:00 ) , weather.sunset ( text | values : 16:26:00 , 16:27:00 ) , weather.depart ( integer | comment : departure from normal | values : 16 , 12 ) ]
table relation , columns = [ relation.station_nbr ( integer | comment : station number | values : 1 , 14 ) , relation.store_nbr ( integer | primary key | comment : store number | values : 1 , 2 ) ]
table sales_in_weather , columns = [ sales_in_weather.date ( date | primary key | values : 2012-01-01 , 2012-01-02 ) , sales_in_weather.store_nbr ( integer | primary key | comment : store number | values : 1 , 2 ) , sales_in_weather.units ( integer | values : 0 , 29 ) , sales_in_weather.item_nbr ( integer | primary key | comment : item number | values : 1 , 2 ) ]
foreign keys :
relation.station_nbr = weather.station_nbr
relation.store_nbr = sales_in_weather.store_nbr
Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
weather.date ( 2014-04-28 )
weather.cool ( 7 )
weather.depart ( 7 )
relation.station_nbr ( 7 )
relation.store_nbr ( 7 )
sales_in_weather.date ( 2014-04-28 )
sales_in_weather.store_nbr ( 7 )
sales_in_weather.units ( 7 )
sales_in_weather.item_nbr ( 7 )
Question: Tell the temperature range of the home weather station of store no.7 on 2014/4/28.
SQL query: SELECT tmax - tmin FROM weather INNER JOIN relation ON weather.station_nbr = relation.station_nbr WHERE relation.store_nbr = 7 AND weather.`date` = '2014-04-28'
Execution response [written in pandas format]:
0
0 34
Feedback:
SELECT.
1. Based on the SQL query, the query selects: [tmax, tmin]
2. The question asks for ['the temperature range']
3. Based on the question, the query should select: [weather.tmax, weather.tmin]
4. Compare 1. and 3., The SQL query selects correct columns, and in correct order.
5. Conclude: correct.
=========
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
Execution response [written in pandas format]:
0 1
0 6776.0 The Dark Knight
Feedback:
SELECT.
1. Based on the SQL query, the SQL query selects: [movies.movie_popularity, movies.movie_title]
2. The question asks for ['movie popularity']
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.
=========
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
Execution response [written in pandas format]:
0 1 2 3 4
0 1 Bruce Abbey 1975 CIN
1 1 George Abbott 1943 BOS
2 1 Reg Abbott 1952 MTL
3 1 Sid Abel 1938 DET
4 1 Sid Abel 1939 DET
... .. ... ... ... ...
19065 1 Jerry Zrymiak 1976 MF2
19066 1 Mike Zuke 1976 IND
19067 1 Mike Zuke 1984 HAR
19068 1 Mike Zuke 1985 HAR
19069 1 Wayne Zuk 1973 EDO
[19070 rows x 5 columns]
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. |