QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    Which movies have received the greatest ratings from female users whose occupations fall within the category of 3? Female users mean that u_gender = 'F'

    【Evidence】
    Female users mean that u_gender = 'F'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.movieid FROM users AS T1 INNER JOIN u2base AS T2 ON T1.userid = T2.userid INNER JOIN movies AS T3 ON T2.movieid = T3.movieid WHERE T1.u_gender = 'F' AND T1.occupation = 3 AND T2.rating = 5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    How many female actresses appeared in the movie 2312852, what country was it in, and what was it's running time? female actresses mean that a_gender = 'F'

    【Evidence】
    female actresses mean that a_gender = 'F'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(IIF(T1.a_gender = 'F', 1, 0)) , T3.country, T3.runningtime FROM actors AS T1 INNER JOIN movies2actors AS T2 ON T1.actorid = T2.actorid INNER JOIN movies AS T3 ON T2.movieid = T3.movieid WHERE T2.movieid = 2312852 GROUP BY T3.country, T3.runningtime
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    How many horror movies were made by the worst directors? d_quality = 5 refers to direct the best, d_quality = 0 refers to direct the worst

    【Evidence】
    d_quality = 5 refers to direct the best, d_quality = 0 refers to direct the worst

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T1.movieid) FROM movies2directors AS T1 INNER JOIN movies AS T2 ON T1.movieid = T2.movieid INNER JOIN directors AS T3 ON T1.directorid = T3.directorid WHERE T1.genre = 'horror' AND T3.d_quality = 0
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    What are the genres of all the English-language foreign films having a runtime of two hours or less? List each one. isEnglish = 'T' means films in English; Film and movie share the same meaning

    【Evidence】
    isEnglish = 'T' means films in English; Film and movie share the same meaning

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.genre FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid WHERE T1.runningtime <= 2 AND T1.isEnglish = 'T' AND T1.country = 'other'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    Among the English comedy movies produced in the UK, how many movies with a running time of 3 was rated the highest by users between the age 45-50? Indicate the movie names. UK is a country

    【Evidence】
    UK is a country

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT T1.movieid FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid INNER JOIN u2base AS T3 ON T1.movieid = T3.movieid INNER JOIN users AS T4 ON T3.userid = T4.userid WHERE T1.country = 'UK' AND T2.genre = 'Comedy' AND T1.runningtime = 3 AND T3.rating = 5 AND T4.age BETWEEN 45 AND 50 AND T1.isEnglish = 'T'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    What is the percentage difference of English and non-English-language crime movies in other countries in year 3? non-English-language refers to isEnglish = 'F'; The percentage difference can be computed by [count(isEnglish = 'T' in movies) - count(isEnglish = 'F' in movies) / (all movies)] * 100%

    【Evidence】
    non-English-language refers to isEnglish = 'F'; The percentage difference can be computed by [count(isEnglish = 'T' in movies) - count(isEnglish = 'F' in movies) / (all movies)] * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T1.isEnglish = 'T', 1, 0)) - SUM(IIF(T1.isEnglish = 'F', 1, 0)) AS REAL) * 100 / COUNT(T1.movieid) FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid WHERE T1.country = 'other' AND T1.year = 3
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movielens
【Schema】
# Table: movies2actors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(actorid:INTEGER, Primary Key, Examples: [88796, 88848, 121878]),
(cast_num:INTEGER, Examples: [0, 1, 2])
]
# Table: users
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(age:TEXT, Examples: [1, 56, 25]),
(u_gender:TEXT, Examples: [F, M]),
(occupation:TEXT, Examples: [2, 3, 4])
]
# Table: directors
[
(directorid:INTEGER, Primary Key, Examples: [7387, 12945, 15725]),
(d_quality:INTEGER, Examples: [0, 1, 2]),
(avg_revenue:INTEGER, Examples: [0, 1, 2])
]
# Table: movies
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(year:INTEGER, Examples: [3, 4, 1]),
(isEnglish:TEXT, Examples: [T, F]),
(country:TEXT, Examples: [other, USA, France]),
(runningtime:INTEGER, Examples: [2, 3, 0])
]
# Table: movies2directors
[
(movieid:INTEGER, Primary Key, Examples: [1672052, 1672111, 1672580]),
(directorid:INTEGER, Primary Key, Examples: [22397, 54934, 297253]),
(genre:TEXT, Examples: [Action, Adventure, Animation])
]
# Table: u2base
[
(userid:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movieid:INTEGER, Primary Key, Examples: [1684486, 1685309, 1685493]),
(rating:TEXT, Examples: [1, 2, 3])
]
# Table: actors
[
(actorid:INTEGER, Primary Key, Examples: [4, 16, 28]),
(a_gender:TEXT, Examples: [M, F]),
(a_quality:INTEGER, Examples: [4, 0, 3])
]
【Foreign keys】
movies2actors.actorid=actors.actorid
movies2actors.movieid=movies.movieid
movies2directors.directorid=directors.directorid
movies2directors.movieid=movies.movieid
u2base.movieid=movies.movieid
u2base.userid=users.userid

    【Question】
    What is the total amount male and female actors who were casted in movie ID 1684910 and what is the proportion between the highest quality actors against the worst quality of actors? Indicate your answer in percentage. List the the director as well as the genre. Female actors mean that a_gender = 'F'; Male actors mean that a_gender = 'M'; a_quality = 5 refers to act the best, a_quality = 0 refers to act the worst

    【Evidence】
    Female actors mean that a_gender = 'F'; Male actors mean that a_gender = 'M'; a_quality = 5 refers to act the best, a_quality = 0 refers to act the worst

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(IIF(a_gender = 'M', 1, 0)) , SUM(IIF(a_gender = 'F', 1, 0)) , CAST(SUM(IIF(a_quality = 5, 1, 0)) AS REAL) * 100 / COUNT(*) , CAST(SUM(IIF(a_quality = 0, 1, 0)) AS REAL) * 100 / COUNT(*), ( SELECT directorid FROM movies2directors WHERE movieid = 1684910 ) , ( SELECT genre FROM movies2directors WHERE movieid = 1684910 ) FROM actors WHERE actorid IN ( SELECT actorid FROM movies2actors WHERE movieid = 1684910 )
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 shooting
【Schema】
# Table: officers
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, W, B]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Patino, Fillingim, Padilla]),
(first_name:TEXT, Examples: [Michael, Brian, Gilbert]),
(full_name:TEXT, Examples: [Patino, Michael, Fillingim, Brian, Padilla, Gilbert])
]
# Table: incidents
[
(case_number:TEXT, Primary Key, Examples: [031347-2015, 072458-2016, 089985-2016]),
(date:DATE),
(location:TEXT, Examples: [7400 Bonnie View Road]),
(subject_statuses:TEXT, Examples: [Deceased, Shoot and Miss, Injured]),
(subject_weapon:TEXT, Examples: [Vehicle, Shotgun, Handgun]),
(subjects:TEXT, Examples: [Luster, Desmond Dwayne B/M]),
(subject_count:INTEGER, Examples: [1, 2, 0]),
(officers:TEXT, Examples: [Tollerton, Aaron W/M, Cardenas, Steven L/M, Ruben, Fredirick W/M])
]
# Table: subjects
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, B, W]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Curry, Chavez, Salinas]),
(first_name:TEXT, Examples: [James, Gabriel, Nick]),
(full_name:TEXT, Examples: [Curry, James, Chavez, Gabriel, Salinas, Nick])
]
【Foreign keys】
officers.case_number=incidents.case_number
subjects.case_number=incidents.case_number

    【Question】
    Among all the male officers, what is the percentage of them are White? male refers to gender = 'M'; white refers to race = 'W'; percentage = divide(count(officers where race = 'W'), count(officers)) where gender = 'M' * 100%

    【Evidence】
    male refers to gender = 'M'; white refers to race = 'W'; percentage = divide(count(officers where race = 'W'), count(officers)) where gender = 'M' * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(race = 'W', 1, 0)) AS REAL) * 100 / COUNT(case_number) FROM officers WHERE gender = 'M'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 shooting
【Schema】
# Table: officers
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, W, B]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Patino, Fillingim, Padilla]),
(first_name:TEXT, Examples: [Michael, Brian, Gilbert]),
(full_name:TEXT, Examples: [Patino, Michael, Fillingim, Brian, Padilla, Gilbert])
]
# Table: incidents
[
(case_number:TEXT, Primary Key, Examples: [031347-2015, 072458-2016, 089985-2016]),
(date:DATE),
(location:TEXT, Examples: [7400 Bonnie View Road]),
(subject_statuses:TEXT, Examples: [Deceased, Shoot and Miss, Injured]),
(subject_weapon:TEXT, Examples: [Vehicle, Shotgun, Handgun]),
(subjects:TEXT, Examples: [Luster, Desmond Dwayne B/M]),
(subject_count:INTEGER, Examples: [1, 2, 0]),
(officers:TEXT, Examples: [Tollerton, Aaron W/M, Cardenas, Steven L/M, Ruben, Fredirick W/M])
]
# Table: subjects
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, B, W]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Curry, Chavez, Salinas]),
(first_name:TEXT, Examples: [James, Gabriel, Nick]),
(full_name:TEXT, Examples: [Curry, James, Chavez, Gabriel, Salinas, Nick])
]
【Foreign keys】
officers.case_number=incidents.case_number
subjects.case_number=incidents.case_number

    【Question】
    What is the percentage of the cases involved more than 3 officers from year 2010 to 2015? more than 3 officers refers to officer_count > 3; from year 2010 to 2015 refers to date between '2010-01-01' and '2015-12-31'; percentage = divide(count(case_number where officer_count > 3), count(case_number)) where date between '2010-01-01' and '2015-12-31' * 100%

    【Evidence】
    more than 3 officers refers to officer_count > 3; from year 2010 to 2015 refers to date between '2010-01-01' and '2015-12-31'; percentage = divide(count(case_number where officer_count > 3), count(case_number)) where date between '2010-01-01' and '2015-12-31' * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(officer_count > 3, 1, 0)) AS REAL) * 100 / COUNT(case_number) FROM incidents WHERE STRFTIME('%Y', date) BETWEEN '2010' AND '2015'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 shooting
【Schema】
# Table: officers
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, W, B]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Patino, Fillingim, Padilla]),
(first_name:TEXT, Examples: [Michael, Brian, Gilbert]),
(full_name:TEXT, Examples: [Patino, Michael, Fillingim, Brian, Padilla, Gilbert])
]
# Table: incidents
[
(case_number:TEXT, Primary Key, Examples: [031347-2015, 072458-2016, 089985-2016]),
(date:DATE),
(location:TEXT, Examples: [7400 Bonnie View Road]),
(subject_statuses:TEXT, Examples: [Deceased, Shoot and Miss, Injured]),
(subject_weapon:TEXT, Examples: [Vehicle, Shotgun, Handgun]),
(subjects:TEXT, Examples: [Luster, Desmond Dwayne B/M]),
(subject_count:INTEGER, Examples: [1, 2, 0]),
(officers:TEXT, Examples: [Tollerton, Aaron W/M, Cardenas, Steven L/M, Ruben, Fredirick W/M])
]
# Table: subjects
[
(case_number:TEXT, Examples: [44523A, 121982X, 605484T]),
(race:TEXT, Examples: [L, B, W]),
(gender:TEXT, Examples: [M, F]),
(last_name:TEXT, Examples: [Curry, Chavez, Salinas]),
(first_name:TEXT, Examples: [James, Gabriel, Nick]),
(full_name:TEXT, Examples: [Curry, James, Chavez, Gabriel, Salinas, Nick])
]
【Foreign keys】
officers.case_number=incidents.case_number
subjects.case_number=incidents.case_number

    【Question】
    In which year has the greatest number of cases where Handgun was used as weapon? year refers to year(date); the greatest number of cases refers to max(count(case_number)); OS Spray was used as weapon refers to subject_weapon = 'OS Spray'

    【Evidence】
    year refers to year(date); the greatest number of cases refers to max(count(case_number)); OS Spray was used as weapon refers to subject_weapon = 'OS Spray'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT STRFTIME('%Y', date) FROM incidents WHERE subject_weapon = 'Handgun' GROUP BY STRFTIME('%Y', date) ORDER BY COUNT(case_number) DESC LIMIT 1