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】 world
【Schema】
# Table: Country
[
(Code:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Name:TEXT, Examples: [Aruba, Afghanistan, Angola]),
(Continent:TEXT, Examples: [North America, Asia, Africa]),
(Region:TEXT, Examples: [Caribbean]),
(SurfaceArea:REAL, Examples: [193.0, 652090.0, 1246700.0]),
(IndepYear:INTEGER, Examples: [1919, 1975, 1912]),
(Population:INTEGER, Examples: [103000, 22720000, 12878000]),
(LifeExpectancy:REAL, Examples: [78.4, 45.9, 38.3]),
(GNP:REAL, Examples: [828.0, 5976.0, 6648.0]),
(GNPOld:REAL, Examples: [793.0, 7984.0, 2500.0]),
(LocalName:TEXT, Examples: [Aruba]),
(GovernmentForm:TEXT, Examples: [Nonmetropolitan Territory of The Netherlands]),
(HeadOfState:TEXT, Examples: [Beatrix]),
(Capital:INTEGER, Examples: [129, 1, 56]),
(Code2:TEXT, Examples: [AW, AF, AO])
]
# Table: CountryLanguage
[
(CountryCode:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Language:TEXT, Primary Key, Examples: [Dutch, English, Papiamento]),
(IsOfficial:TEXT, Examples: [T, F]),
(Percentage:REAL, Examples: [5.3, 9.5, 76.7])
]
# Table: City
[
(ID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Name:TEXT, Examples: [Kabul, Qandahar, Herat]),
(CountryCode:TEXT, Examples: [AFG, NLD, ANT]),
(District:TEXT, Examples: [Kabol, Qandahar, Herat]),
(Population:INTEGER, Examples: [1780000, 237500, 186800])
]
【Foreign keys】
CountryLanguage.CountryCode=Country.Code
City.CountryCode=Country.Code

    【Question】
    In countries with constitutional monarchy, what is the percentage of cities located in the district of England? constitutional monarchy refers to GovernmentForm = 'Constitutional Monarchy'; percentage = MULTIPLY(DIVIDE(SUM(GovernmentForm = 'Constitutional Monarchy' WHERE District = 'England'), COUNT(GovernmentForm = 'Constitutional Monarchy')), 100)

    【Evidence】
    constitutional monarchy refers to GovernmentForm = 'Constitutional Monarchy'; percentage = MULTIPLY(DIVIDE(SUM(GovernmentForm = 'Constitutional Monarchy' WHERE District = 'England'), COUNT(GovernmentForm = 'Constitutional Monarchy')), 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(CASE WHEN T1.District = 'England' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T2.GovernmentForm = 'Constitutional Monarchy'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 world
【Schema】
# Table: Country
[
(Code:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Name:TEXT, Examples: [Aruba, Afghanistan, Angola]),
(Continent:TEXT, Examples: [North America, Asia, Africa]),
(Region:TEXT, Examples: [Caribbean]),
(SurfaceArea:REAL, Examples: [193.0, 652090.0, 1246700.0]),
(IndepYear:INTEGER, Examples: [1919, 1975, 1912]),
(Population:INTEGER, Examples: [103000, 22720000, 12878000]),
(LifeExpectancy:REAL, Examples: [78.4, 45.9, 38.3]),
(GNP:REAL, Examples: [828.0, 5976.0, 6648.0]),
(GNPOld:REAL, Examples: [793.0, 7984.0, 2500.0]),
(LocalName:TEXT, Examples: [Aruba]),
(GovernmentForm:TEXT, Examples: [Nonmetropolitan Territory of The Netherlands]),
(HeadOfState:TEXT, Examples: [Beatrix]),
(Capital:INTEGER, Examples: [129, 1, 56]),
(Code2:TEXT, Examples: [AW, AF, AO])
]
# Table: CountryLanguage
[
(CountryCode:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Language:TEXT, Primary Key, Examples: [Dutch, English, Papiamento]),
(IsOfficial:TEXT, Examples: [T, F]),
(Percentage:REAL, Examples: [5.3, 9.5, 76.7])
]
# Table: City
[
(ID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Name:TEXT, Examples: [Kabul, Qandahar, Herat]),
(CountryCode:TEXT, Examples: [AFG, NLD, ANT]),
(District:TEXT, Examples: [Kabol, Qandahar, Herat]),
(Population:INTEGER, Examples: [1780000, 237500, 186800])
]
【Foreign keys】
CountryLanguage.CountryCode=Country.Code
City.CountryCode=Country.Code

    【Question】
    Among the cities with a population between 140000 and 150000, list the country that has life expectancy greater than 80% life expectancy of all countries. life expectancy greater than 80% life expectancy of all countries refers to LifeExpectancy < (MULTIPLY(AVG(LifeExpectancy), 0.8));

    【Evidence】
    life expectancy greater than 80% life expectancy of all countries refers to LifeExpectancy < (MULTIPLY(AVG(LifeExpectancy), 0.8));

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

SQL: SELECT T2.Name FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.Population BETWEEN 140000 AND 150000 GROUP BY T2.Name, LifeExpectancy HAVING LifeExpectancy < ( SELECT AVG(LifeExpectancy) FROM Country ) * 0.8
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 world
【Schema】
# Table: Country
[
(Code:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Name:TEXT, Examples: [Aruba, Afghanistan, Angola]),
(Continent:TEXT, Examples: [North America, Asia, Africa]),
(Region:TEXT, Examples: [Caribbean]),
(SurfaceArea:REAL, Examples: [193.0, 652090.0, 1246700.0]),
(IndepYear:INTEGER, Examples: [1919, 1975, 1912]),
(Population:INTEGER, Examples: [103000, 22720000, 12878000]),
(LifeExpectancy:REAL, Examples: [78.4, 45.9, 38.3]),
(GNP:REAL, Examples: [828.0, 5976.0, 6648.0]),
(GNPOld:REAL, Examples: [793.0, 7984.0, 2500.0]),
(LocalName:TEXT, Examples: [Aruba]),
(GovernmentForm:TEXT, Examples: [Nonmetropolitan Territory of The Netherlands]),
(HeadOfState:TEXT, Examples: [Beatrix]),
(Capital:INTEGER, Examples: [129, 1, 56]),
(Code2:TEXT, Examples: [AW, AF, AO])
]
# Table: CountryLanguage
[
(CountryCode:TEXT, Primary Key, Examples: [ABW, AFG, AGO]),
(Language:TEXT, Primary Key, Examples: [Dutch, English, Papiamento]),
(IsOfficial:TEXT, Examples: [T, F]),
(Percentage:REAL, Examples: [5.3, 9.5, 76.7])
]
# Table: City
[
(ID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Name:TEXT, Examples: [Kabul, Qandahar, Herat]),
(CountryCode:TEXT, Examples: [AFG, NLD, ANT]),
(District:TEXT, Examples: [Kabol, Qandahar, Herat]),
(Population:INTEGER, Examples: [1780000, 237500, 186800])
]
【Foreign keys】
CountryLanguage.CountryCode=Country.Code
City.CountryCode=Country.Code

    【Question】
    Among the countries that use Italian as their language, what is the percentage of republic countries? use Italian as their language refers to `Language` = 'Italian'; percentage = MULTIPLY(DIVIDE(SUM(`Language` = 'Italian' WHERE GovernmentForm = 'Republic'), COUNT(`Language` = 'Italian')), 100); use Italian as their language refers to `Language` = 'Italian'; republic countries refers to GovernmentForm = 'Republic';

    【Evidence】
    use Italian as their language refers to `Language` = 'Italian'; percentage = MULTIPLY(DIVIDE(SUM(`Language` = 'Italian' WHERE GovernmentForm = 'Republic'), COUNT(`Language` = 'Italian')), 100); use Italian as their language refers to `Language` = 'Italian'; republic countries refers to GovernmentForm = 'Republic';

    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(CASE WHEN T2.GovernmentForm = 'Republic' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM CountryLanguage AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.Language = 'Italian'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    How many podcasts are there in the category which has the most podcasts? category which has the most podcast refers to the category with Max(count(podcast_id))

    【Evidence】
    category which has the most podcast refers to the category with Max(count(podcast_id))

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

SQL: SELECT COUNT(podcast_id) FROM categories WHERE category = ( SELECT category FROM categories GROUP BY category ORDER BY COUNT(podcast_id) DESC LIMIT 1 )
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    What is the percentage of the podcast that are categorized in four or more categories? categorized in 4 or more refers to Count(category) > 4; percentage = Divide(Count(podcast_id(count(category) > 4)), Count(podcast_id)) * 100

    【Evidence】
    categorized in 4 or more refers to Count(category) > 4; percentage = Divide(Count(podcast_id(count(category) > 4)), Count(podcast_id)) * 100

    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.podcast_id) FROM ( SELECT podcast_id FROM categories GROUP BY podcast_id HAVING COUNT(category) >= 4 ) AS T1
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Provide the itunes id and url for podcast titled 'Brown Suga Diaries'. url refers to itunes_url; 'Brown Suga Diaries' is the title of podcast

    【Evidence】
    url refers to itunes_url; 'Brown Suga Diaries' is the title of podcast

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

SQL: SELECT itunes_id, itunes_url FROM podcasts WHERE title = 'Brown Suga Diaries'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    List all podcast with its itunes url for all title containing the word 'Dream'. containing the word 'Dream' refers to title LIKE '%Dream%'

    【Evidence】
    containing the word 'Dream' refers to title LIKE '%Dream%'

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

SQL: SELECT itunes_url FROM podcasts WHERE title LIKE '%Dream%' GROUP BY itunes_url
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Name all the categories for podcast titled 'I Heart My Life Show'. 'I Hearty My Life Show' is the title of podcast

    【Evidence】
    'I Hearty My Life Show' is the title of podcast

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

SQL: SELECT T1.category FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.title = 'I Heart My Life Show'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    List all the podcast title and its itunes url under the 'society-culture' category.

    【Evidence】
    

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

SQL: SELECT T2.title, T2.itunes_url FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'society-culture'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    How many people rated 5 for the podcast which title contains the word 'spoiler' under the 'art' category '? rated 5 refers to rating = 5; contain the word 'spoilers' refers to title like '%spoilers%'; 'art' is the category name;

    【Evidence】
    rated 5 refers to rating = 5; contain the word 'spoilers' refers to title like '%spoilers%'; 'art' is the category name;

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

SQL: SELECT COUNT(T3.podcast_id) FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id INNER JOIN reviews AS T3 ON T3.podcast_id = T2.podcast_id WHERE T2.title LIKE '%spoilers%' AND T1.category = 'arts' AND T3.rating = 5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    List the authors who created review for podcast titled 'Pop Rocket' in 2016 with rating less than 5. Pop Rocket' is the title of podcast; in 2016 refers to created_at like'2016%'; rating less than 5 refers to rating < 5; author refers to author_id

    【Evidence】
    Pop Rocket' is the title of podcast; in 2016 refers to created_at like'2016%'; rating less than 5 refers to rating < 5; author refers to author_id

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

SQL: SELECT T2.author_id FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'Pop Rocket' AND T2.created_at LIKE '2016-%' AND T2.rating < 5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Name all the podcast title and its category with average rating of more than 3.0. average rating of more than 3.0 refers to avg(rating) > 3.0

    【Evidence】
    average rating of more than 3.0 refers to avg(rating) > 3.0

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

SQL: SELECT T2.title, T1.category FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id INNER JOIN reviews AS T3 ON T3.podcast_id = T2.podcast_id GROUP BY T3.podcast_id HAVING AVG(T3.rating) > 3