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】 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】
    To which categories do the podcasts of the reviewer whose id is EFB34EAC8E9397C belong? reviewer whose id is EFB34EAC8E9397C refers to author_id = 'EFB34EAC8E9397C'

    【Evidence】
    reviewer whose id is EFB34EAC8E9397C refers to author_id = 'EFB34EAC8E9397C'

    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.category FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.author_id = 'EFB34EAC8E9397C'
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】
    Indicate the slug and the itunes url of the podcast whose review content was written Can't stop listening. review content was written Can't stop listening refers to content = 'Can't stop listening'

    【Evidence】
    review content was written Can't stop listening refers to content = 'Can't stop listening'

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

SQL: SELECT slug, itunes_url FROM podcasts WHERE podcast_id IN ( SELECT podcast_id FROM reviews WHERE content = 'Can''t stop listening' )
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 dates were the Don't Lie To Your Life Coach podcast reviews created? "Don't Lie To Your Life Coach" refers to title of podcast; date refers to created_at

    【Evidence】
    "Don't Lie To Your Life Coach" refers to title of podcast; date refers to created_at

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

SQL: SELECT created_at FROM reviews WHERE podcast_id = ( SELECT podcast_id FROM podcasts WHERE title = 'Don''t Lie To Your Life Coach' )
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】
    In how many categories were podcast reviews created in the last six months of 2016? List them. created in last six months of 2016 refers to created_at BETWEEN '2016-07-01T00:00:00-07:00' and '2016-12-31T23:59:59-07:00'

    【Evidence】
    created in last six months of 2016 refers to created_at BETWEEN '2016-07-01T00:00:00-07:00' and '2016-12-31T23:59:59-07:00'

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

SQL: SELECT COUNT(DISTINCT T1.category) FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.created_at BETWEEN '2016-07-01T00:00:00-07:00' AND '2016-12-31T23:59:59-07:00'
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】
    Calculate the average rating of the true crime category. average rating = Divide (Sum(rating(category = 'true-crime')), Count(podcast_id(category = 'true-crime')))

    【Evidence】
    average rating = Divide (Sum(rating(category = 'true-crime')), Count(podcast_id(category = 'true-crime')))

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

SQL: SELECT AVG(T2.rating) FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'true-crime'
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 titles of the art category. art category refers to category = 'arts'

    【Evidence】
    art category refers to category = 'arts'

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

SQL: SELECT DISTINCT T2.title FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'arts'
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 average rating of all the podcasts in category art? category art refers to category = 'arts'; average rating = Divide (Sum (rating), Count (podcast_id))

    【Evidence】
    category art refers to category = 'arts'; average rating = Divide (Sum (rating), 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 AVG(T2.rating) FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'arts'
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 names of podcasts in the art category in 2018. art category refers to category = 'arts'; in 2018 refers to created_at like '2018%'; name of podcast refers to title

    【Evidence】
    art category refers to category = 'arts'; in 2018 refers to created_at like '2018%'; name of podcast refers to title

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

SQL: SELECT DISTINCT T2.title FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'arts' AND T2.created_at LIKE '2018-%'
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】
    Write the names of the podcasts in the music category that have a rating greater than 3. music category refers to category = 'music'; rating greater than 3 refers to rating > 3; name of the podcast refers to title

    【Evidence】
    music category refers to category = 'music'; rating greater than 3 refers to rating > 3; name of the podcast refers to title

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

SQL: SELECT DISTINCT T2.title FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'music' AND T2.rating > 3
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】
    Which titles have the content "love" but the category is art produced between 2018 and 2019. content love refers to content = 'love'; 'arts' is the category; produced between 2018 and 2019 refers to year (created_at) BETWEEN 2018 and 2019

    【Evidence】
    content love refers to content = 'love'; 'arts' is the category; produced between 2018 and 2019 refers to year (created_at) BETWEEN 2018 and 2019

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

SQL: SELECT DISTINCT T2.title FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE (T2.created_at LIKE '2018-%' AND T1.category = 'arts' AND T2.content LIKE '%love%') OR (T2.created_at LIKE '2019-%' AND T1.category = 'arts' AND T2.content LIKE '%love%')
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 category and itune url of the title "Scaling Global"?

    【Evidence】
    

    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, T2.itunes_url FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.title = 'Scaling Global'
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 average rating of podcasts in comedy category? comedy category refers to category = 'comedy'; average rating = Divide (Sum(rating), Count(podcast_id))

    【Evidence】
    comedy category refers to category = 'comedy'; average rating = Divide (Sum(rating), 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 AVG(T2.rating) FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'comedy'
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 least common category? least common category refers to Min(Count(category))

    【Evidence】
    least common category refers to Min(Count(category))

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

SQL: SELECT category FROM categories GROUP BY category ORDER BY COUNT(podcast_id) ASC LIMIT 1