db_id stringclasses 69 values | question stringlengths 24 325 | evidence stringlengths 0 673 | SQL stringlengths 23 804 |
|---|---|---|---|
genes | If a pair of genes is positively correlated, what is the possibility of it being composed of two genes both with over 10 chromosomes? | Positively correlated means Expression_Corr > 0; Possibility = count(the pairs of genes with both chromosomes over 20) / count(pairs of genes that are positively correlated) | SELECT CAST(SUM(IIF(T1.Chromosome > 10 AND T3.Chromosome > 10, 1, 0)) AS REAL) * 100 / COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 INNER JOIN Genes AS T3 ON T3.GeneID = T2.GeneID2 WHERE T2.Expression_Corr > 0 |
genes | For the pairs of genes both from the class ATPases, what is the average expression correlation score? | SELECT AVG(T2.Expression_Corr) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Class = 'ATPases' | |
genes | Lists all genes by identifier number located in the cytoplasm and whose function is metabolism. | SELECT DISTINCT GeneID FROM Genes WHERE Localization = 'cytoplasm' AND Function = 'METABOLISM' | |
genes | How many different genes do we have if we add those located in the plasma and in the nucleus? | SELECT COUNT(GeneID) FROM Classification WHERE Localization IN ('plasma', 'nucleus') | |
genes | What kind of expression correlation occurs in physical type interacting gene pairs and what percentage of these are negatively correlated? | If the Expression_Corr value is negative then it's negatively correlated. Percentage of Negative Correlation = count(negative Expression_Corr physical type) / count(Expression_Corr physical type) * 100% | SELECT Expression_Corr FROM Interactions WHERE Type = 'Physical' UNION ALL SELECT CAST(SUM(Expression_Corr < 0) AS REAL) * 100 / COUNT(*) FROM Interactions WHERE Type = 'Physical' |
genes | What percentage of genes located in the cytoskeleton are of unknown class? And of these, how many are not conditional phenotypes? | Percentage = count(genes located in the cytoskeleton unknown class) / count(genes located in the cytoskeleton) * 100% | SELECT SUM(Localization = 'cytoskeleton' AND Phenotype = 'Conditional phenotypes') , CAST(SUM(Localization = 'cytoskeleton') AS REAL) * 100 / COUNT(GeneID) FROM Genes; |
genes | What type of interactions occurs in genes whose function is cellular transport and transport medicine and are classified as non-essential? | SELECT T2.Type FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Function = 'TRANSCRIPTION' AND T1.Essential = 'Non-Essential' | |
genes | List all genes whose interaction is with genes located in the nucleus in which it is positively correlated. | If the Expression_Corr value is positive then it's positively correlated | SELECT T1.GeneID FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr > 0 AND T1.Localization = 'nucleus' |
genes | Taking all the essential genes of the transcription factors class located in the nucleus as a reference, how many of them carry out a genetic-type interaction with another gene? List them. | SELECT T2.GeneID1 FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization = 'nucleus' AND T1.Class = 'Transcription factors' AND T1.Essential = 'Essential' AND T2.Expression_Corr != 0 | |
genes | Of all the nonessential genes that are not of the motorprotein class and whose phenotype is cell cycle defects, how many do not have a physical type of interaction? | SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Type != 'Physical' AND T1.Phenotype = 'Cell cycle defects' AND T1.Class != 'Motorproteins' AND T1.Essential = 'Non-Essential' | |
genes | Of the genes whose phenotype and motif are nucleic acid metabolism defects, PS00107, what percentage perform positive interaction with another gene? | Percentage of no interaction = [count(nucleic acid metabolism, PS00107, no interaction) / count(nucleic acid metabolism, PS00107) * 100% | SELECT CAST(SUM(IIF(T2.Expression_Corr > 0, 1, 0)) AS REAL) * 100 / COUNT(T2.GeneID1) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Phenotype = 'Nucleic acid metabolism defects' AND T1.Motif = 'PS00107' |
genes | Which negatively correlated, genetically interacting genes are non-essential? What percentage do they represent with respect to those that are essential? | If the Expression_Corr value is negative then it's negatively correlated; Percentage of Essensity = [count(negatively correlated, genetical interaction, non-essential) / count(negatively correlated, genetical interaction, non-essential+negatively correlated, genetical interaction, essential)] * 100% | SELECT CAST(COUNT(T1.GeneID) AS REAL) * 100 / ( SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 ) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Essential = 'Non-Essential' |
app_store | How many apps were last updated in January of 2018? Please write one translated review with positive sentiment for each app, if there's any. | updated in January of 2018 refers to Last Updated BETWEEN 'January 1, 2018' and 'January 31, 2018'; | SELECT DISTINCT Translated_Review FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE `Last Updated` BETWEEN 'January 1, 2018' AND 'January 31, 2018' ) AND Sentiment = 'Positive' |
app_store | How many users mildly likes the 7 Minute Workout app and when was it last updated? | mildly likes the app refers to Sentiment_Polarity> = 0 and Sentiment_Polarity<0.5; | SELECT COUNT(T2.Sentiment_Polarity), T1."Last Updated" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = '7 Minute Workout' AND T2.Sentiment_Polarity BETWEEN 0 AND 0.5 |
app_store | How many users holds neutral attitude towards the HTC Weather app? Indicate the app's rating on the Google Play Store. | user holds neutral attitude refers to Sentiment = 'Neutral'; | SELECT COUNT(T1.Rating), T1.Rating FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'HTC Weather' AND T2.Sentiment = 'Neutral' |
app_store | What is the name and category of the app with the highest amount of -1 sentiment polarity score? | highest amount of -1 sentiment polarity score refers to MAX(Count(Sentiment_Polarity = 1.0)) | SELECT DISTINCT T1.App, T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity = '-1.0' |
app_store | What is the average sentiment polarity score of the Cooking Fever app? Indicate the age group that the app is targeted at. | average sentiment polarity score = AVG(Sentiment_Polarity); age group the app is target at refers to Content Rating; | SELECT AVG(T2.Sentiment_Polarity), T1."Content Rating" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Cooking Fever' |
app_store | What is the lowest sentiment polarity score of the Basketball Stars app for people who dislikes the app pretty much and how many downloads does it have? | lowest sentiment polarity score refers to MIN(Sentiment_Polarity); user dislike the app pretty much refers to Sentiment_Polarity<-0.5; number of downloads it has refers to installs; | SELECT MIN(T2.Sentiment_Polarity), T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Basketball Stars' |
app_store | For the Akinator app, how many reviews have sentiment subjectivity of no more than 0.5 and what is its current version? | Sentiment_Subjectivity<0.5; current version refers to Current Ver; | SELECT COUNT(T2.Sentiment_Subjectivity), T1."Current Ver" FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Akinator' AND T2.Sentiment_Subjectivity < 0.5 |
app_store | How many apps have rating of 5? | FALSE; | SELECT COUNT(App) FROM playstore WHERE Rating = 5 |
app_store | What are the top 5 installed free apps? | free app refers to price = 0; most installed app refers to MAX(Installs); | SELECT App FROM playstore WHERE Price = 0 ORDER BY CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER) DESC LIMIT 5 |
app_store | Name the top 10 most reviewed apps. | most reviewed app refers to MAX(Reviews); | SELECT DISTINCT App FROM playstore ORDER BY Reviews DESC LIMIT 10 |
app_store | How many of the users hold neutral attitude on "10 Best Foods for You" app and what category is this app? | neutral attitude refers to Sentiment = 'Neutral'; | SELECT COUNT(T2.App), T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = '10 Best Foods for You' AND T2.Sentiment = 'Neutral' |
app_store | What are the apps that users pretty like this app and how many installs amount of these apps? | users pretty much likes the app refers to Sentiment_Polarity = 'Positive'; | SELECT DISTINCT T1.App, T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity > 0 |
app_store | List apps whose rating is 3.9 and state the translated review of each app. | lowest rating refers to Rating = 1; | SELECT T1.App, T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Rating = 3.9 |
app_store | How many apps that are only compatible with Android ver 8.0 and above? List down the users' sentiment of these apps. | compatible with android refers to Android Ver; Android Ver" = '8.0 and up'; | SELECT DISTINCT Sentiment FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE `Android Ver` = '8.0 and up' ) |
app_store | Which apps have multiple genres and what is the total sentiment subjectivity of these apps? | multiple genres refers to COUNT(Genres>1; total sentiment subjectivity = Sum(Sentiment_Subjectivity); | SELECT SUM(T2.Sentiment_Subjectivity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres > 1 |
app_store | Which apps have not been updated since year 2015 and what kind of sentiment users hold on it? | since year 2015 refers to "Last Updated"<'January 1, 2015'; | SELECT DISTINCT App, Sentiment FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE CAST(SUBSTR('Last Updated', -4, 4) AS INTEGER) < 2015 ) |
app_store | What is the total installs of apps with content rating of adults only 18+ and what are the translated reviews of it? | total installs = SUM(Installs); | SELECT SUM(T1.Installs), T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1."Content Rating" = 'Adults only 18+' |
app_store | Which of the app is the best selling app and what is the sentiments polarity of it? | best selling app = MAX(MULTIPLY(Price, Installs)); | SELECT T1.App, T2.Sentiment_Polarity FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App ORDER BY T1.Price * CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER) DESC LIMIT 1 |
app_store | What is the average rating of comic category apps? How many users hold positive attitude towards this app? | average rating = AVG(Rating where Category = 'COMICS'); number of users who hold a positive attitude towards the app refers to SUM(Sentiment = 'Positive'); | SELECT AVG(T1.Rating) , COUNT(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE NULL END) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Category = 'COMICS' |
app_store | What is the rating for "Draw A Stickman"? | Draw A Stickman refers to App = 'Draw A Stickman'; | SELECT Rating FROM playstore WHERE APP = 'Draw A Stickman' |
app_store | How many of the reviews for the app "Brit + Co" have a comment? | Brit + Co refers to App = 'Brit + Co'; comment refers to Translated Review NOT null; | SELECT COUNT(App) FROM user_reviews WHERE App = 'Brit + Co' AND Translated_Review IS NOT NULL |
app_store | List the top 5 shopping apps with the most reviews. | shopping apps refers to Genre = 'Shopping'; most reviews refers to MAX(Reviews); | SELECT DISTINCT App FROM playstore WHERE Genres = 'Shopping' GROUP BY App ORDER BY COUNT(App) DESC LIMIT 5 |
app_store | How many neutral reviews does the app "Dino War: Rise of Beasts" have? | neutral reviews refers to Sentiment = 'Neutral'; | SELECT COUNT(App) FROM user_reviews WHERE App = 'Dino War: Rise of Beasts' AND Sentiment = 'Neutral' |
app_store | What are the apps with only 5,000+ installs? | Installs = '5,000+'; | SELECT DISTINCT App FROM playstore WHERE Installs = '5,000+' |
app_store | List all the negative comments on the "Dog Run - Pet Dog Simulator" app. | negative comment refers to Sentiment = 'Negative'; | SELECT Translated_Review FROM user_reviews WHERE App = 'Dog Run - Pet Dog Simulator' AND Sentiment = 'Negative' |
app_store | Which free app has the most Negative comments? | paid app refers to Type = 'Paid'; negative comment refers to Sentiment = 'Negative'; paid app with most negative comments refers to MAX(COUNT(Sentiment = 'Negative')) where Type = 'Paid'; | SELECT T1.App FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Type = 'Free' AND T2.Sentiment = 'Negative' GROUP BY T1.App ORDER BY COUNT(T2.Sentiment) DESC LIMIT 1 |
app_store | How many negative comments are there in all the apps with 100,000,000+ installs? | negative comment refers to Sentiment = 'Negative'; Installs = '100,000,000+'; | SELECT COUNT(T2.Sentiment) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Installs = '100,000,000+' AND T2.Sentiment = 'Negative' |
app_store | What are the content ratings for the apps that have "gr8" in their comments? | app with gr8 in their comments refers to Translated_Review LIKE '%gr8%'; | SELECT DISTINCT T1.`Content Rating` FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Translated_Review LIKE '%gr8%' |
app_store | What is the total Sentiment polarity score of the most expensive app? | total sentiment polarity score = sum(Sentiment_Polarity); most expensive app refers to MAX(Price); | SELECT SUM(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Price = ( SELECT MAX(Price) FROM playstore ) |
app_store | What is the rating for "Garden Coloring Book"? List all of its reviews. | Golfshot Plus: Golf GPS refers to App = 'Golfshot Plus: Golf GPS'; review refers to Translated_Review; | SELECT T1.Rating, T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Garden Coloring Book' |
app_store | Which Photography app has the highest total Sentiment subjectivity score? | Photography app refers to Genre = 'Photography'; highest total sentiment subjectivity score = MAX(sum(Sentiment_Subjectivity)); | SELECT T1.App FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres = 'Photography' GROUP BY T1.App ORDER BY SUM(T2.Sentiment_Subjectivity) DESC LIMIT 1 |
app_store | List all the comments on the lowest rated Mature 17+ app. | comments refers to Translated_Review; lowest rated refers to Rating = 1; Mature 17+ refers to Content Rating = 'Mature 17+ '; | SELECT T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1."Content Rating" = 'Mature 17+' ORDER BY T1.Rating LIMIT 1 |
app_store | What is the number of installments of the app with the highest total Sentiment polarity score? | installments refers to Installs; highest total sentiment polarity score = MAX(SUM(Sentiment_Polarity)); | SELECT T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App GROUP BY T1.App ORDER BY SUM(T2.Sentiment_Polarity) DESC LIMIT 1 |
app_store | What is the number of neutral comments from all the weather apps? | neutral comments refers to Sentiment = 'Neutral'; weather app refers to Genre = 'Weather'; | SELECT COUNT(T2.Sentiment) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres = 'Weather' AND T2.Sentiment = 'Neutral' |
app_store | Which 1,000,000,000+ intalls apps has the most no comment reviews? | no comment refers to Translated_Review = 'nan'; most no comment reviews = (MAX(COUNT(Translated_Review = 'nan'))); | SELECT T1.App FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Installs = '1,000,000+' AND T2.Translated_Review = 'nan' GROUP BY T1.App ORDER BY COUNT(T2.Translated_Review) DESC LIMIT 1 |
app_store | What is the rating and the total Sentiment subjectivity score of "Onefootball - Soccer Scores"? | Onefootball - Soccer Scores refers to App = 'Onefootball - Soccer Scores'; | SELECT T1.Rating, SUM(T2.Sentiment_Subjectivity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Onefootball - Soccer Scores' |
app_store | What percentage of no comment reviews are from "Teen" content rating apps? | no comment refers to Translated_Review = 'nan'; percentage = DIVIDE((SUM(Content Rating = 'Teen')), COUNT(*)); | SELECT CAST(COUNT(CASE WHEN T1.`Content Rating` = 'Teen' THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T1.App) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Translated_Review = 'nan' |
app_store | Which apps have 5 rating? List out then application name. | application name refers to App; | SELECT DISTINCT App FROM playstore WHERE Rating = 5 |
app_store | Which apps have been reviewed more than 75 000 000 times and the content is suitable for teenagers? | Reviews>75000000; suitable for teenagers refers to Content Rating = 'Teen'; | SELECT DISTINCT App FROM playstore WHERE Reviews > 75000000 AND `Content Rating` = 'Teen' |
app_store | List out genre that have downloads more than 1000000000. | downloads and installs are synonyms; Installs = '1,000,000,000+'; | SELECT Genres FROM playstore WHERE Installs = '1,000,000,000+' GROUP BY Genres |
app_store | What is the average price for a dating application? | average price = AVG(Price where Genre = 'Dating'); dating application refers to Genre = 'Dating'; | SELECT AVG(Price) FROM playstore WHERE Genres = 'Dating' |
app_store | What is the average download for entertainment apps with size no more than 1.0 M? | downloads and installs are synonyms; entertainment apps refers to Category = 'ENTERTAINMENT'; | SELECT AVG(CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER)) FROM playstore WHERE Category = 'ENTERTAINMENT' AND Size < '1.0M' |
app_store | What is the average review number for application with 5 rating? | average review = AVG(Review); application refers to app; Rating = 5; | SELECT AVG(Reviews) FROM playstore WHERE Rating = 5 |
app_store | List out the top 3 genre for application with a sentiment review greater than 0.5. | sentiment review refers to Sentiment_Polarity; Sentiment_Polarity>0.5; | SELECT Genres FROM playstore WHERE App IN ( SELECT App FROM user_reviews WHERE Sentiment = 'Positive' AND Sentiment_Polarity > 0.5 ORDER BY Sentiment_Polarity DESC LIMIT 3 ) |
app_store | What is the percentage of application with 4.7 rating having more positives sentiment than negative sentiment? | percentage = DIVIDE(SUBTRACT(SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative')), SUM(Sentiment = 'Negative')) as percentage; having more positive sentiment than negative sentiment refers to Sentiment = 'Positive'>Sentiment = 'Negative'; | SELECT CAST(COUNT(CASE WHEN ( SELECT COUNT(CASE WHEN Sentiment = 'Positive' THEN 1 ELSE NULL END) - COUNT(CASE WHEN Sentiment = 'Negative' THEN 1 ELSE NULL END) FROM user_reviews GROUP BY App ) > 0 THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T2.Sentiment) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Rating = 4.7 |
app_store | List down app that does not have negative sentiment and give their average rating? | doest not have negative sentiment refers to Sentiment! = 'Negative'; average = AVG(Sentiment_Polarity); | SELECT T1.App, AVG(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment != 'Negative' GROUP BY T1.App |
app_store | List down application that have not been updated since 2015. What is the percentage of this application having more negative sentiment than positive sentiment? | percentage = DIVIDE(SUBTRACT(SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), (SUM(Sentiment = 'Negative')) as percent; Last Updated>'2015'; | SELECT CAST((( SELECT COUNT(*) Po FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' AND T2.Sentiment = 'Positive' ) - ( SELECT COUNT(*) Ne FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' AND T2.Sentiment = 'Negative' )) AS REAL) * 100 / ( SELECT COUNT(*) NUM FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' ) |
app_store | What is the percentage for free application with a rating 4.5 and above have not been updated since 2018? | paid refers to Type = 'Paid'; application refers to App; Rating>4.5; Last Updated>'2018; percentage = DIVIDE(SUM(Genres = 'Mature 17+' and Rating>4.5 and substr("Last Updated",-4,4)>'2018' )), (COUNT(App)) as percent; | SELECT CAST(SUM(CASE WHEN SUBSTR('Last Updated', -4) > '2018' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(App) PER FROM playstore WHERE Type = 'Free' AND Rating >= 4.5 |
app_store | What genre does Honkai Impact 3rd belong to? | Honkai Impact 3rd is the App; | SELECT DISTINCT Genres FROM playstore WHERE App = 'Honkai Impact 3rd' |
app_store | List down the rating for the App Learn C++. | FALSE; | SELECT DISTINCT Rating FROM playstore WHERE App = 'Learn C++' |
app_store | What is the average price of games belonging in the arcade genre which has a content rating of Everyone 10+? | average price = AVG(Price); | SELECT AVG(Price) FROM playstore WHERE 'Content Rating' = 'Everyone 10+' AND Genres = 'Arcade' |
app_store | How much is the size of Browser 4G and how many users have a pretty positive favorability on it? | Browser 4G is the App; pretty positive favorability refers to Sentiment_Polarity score = 0.5 | SELECT T1.Size, COUNT(T1.App) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Browser 4G' AND T2.Sentiment_Polarity >= 0.5 |
app_store | Name the Apps with a sentiment objectivity of 0.3 and include their number of installs. | FALSE; | SELECT DISTINCT T1.App, T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity = 0.3 |
app_store | How much is the average sentiment polarity score of Golf GPS Rangefinder: Golf Pad and what is it's rating in the Google Play Store? | average sentiment polarity score = AVG(Sentiment_Polarity); Golf GPS Rangefinder: Golf Pad is the App; | SELECT AVG(T2.Sentiment_Polarity), T1.Rating FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Golf GPS Rangefinder: Golf Pad' |
app_store | List the top 5 lowest rated puzzle games and count the number of negative sentiments the games received. | lowest rating refers to MIN(Rating); puzzle is the genre; | SELECT T1.App, COUNT(T1.App) COUNTNUMBER FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment = 'Negative' GROUP BY T1.App ORDER BY T1.Rating LIMIT 5 |
app_store | What is the percentage ratio between positive sentiments and negative sentiments that are in Fate/Grand Order? Also indicate the current version. | Fate/Grand Order is the App; percentage ratio = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), 100); | SELECT CAST(SUM(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T2.Sentiment = 'Negative' THEN 1 ELSE 0 END), T1.`Current Ver` FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Fate/Grand Order (English)' AND T1.`Current Ver` = '1.18.0' |
app_store | Indicate the number of installs and include the percentage of positive sentiments of FREEDOME VPN Unlimited anonymous Wifi Security. | FREEDOME VPN Unlimited anonymous Wifi Security is the App; percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*))), 100) | SELECT T1.Installs , CAST(SUM(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE 0 END) * 100 / SUM(CASE WHEN T2.Sentiment IS NOT NULL THEN 1.0 ELSE 0 END) AS REAL) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'FREEDOME VPN Unlimited anonymous Wifi Security' |
app_store | For the Honkai Impact 3rd App, what is the highest sentiment polarity score and what genre does it belong to? | highest sentiment polarity score refers to MAX(Sentiment_Polarity); | SELECT MAX(T2.Sentiment_Polarity), T1.Genres FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Honkai Impact 3rd' AND T2.Sentiment_Polarity > 0.5 GROUP BY T1.Genres |
app_store | What is the rating of Dragon Ball Legends and how many users dislike this App? | Dragon Ball Legends is the app; users who dislikes the app refers to Sentiment_Polarity<-0.5; | SELECT T1.Rating, COUNT(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Dragon Ball Legends' AND CAST(Sentiment_Polarity AS INTEGER) < -0.5 |
app_store | Which education App has the worst rating and state the translated review if available. | education App refers to Category = 'EDUCATION'; worst rated app refers to Rating = 1; | SELECT T1.App, T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Category = 'EDUCATION' GROUP BY T1.App, T2.Translated_Review ORDER BY T1.Rating ASC LIMIT 1 |
app_store | List all free sports Apps and their translated review. | paid sports Apps refers to type = 'Paid' and Category = 'SPORTS'; | SELECT T1.App, T2.Translated_Review FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Type = 'Free' AND T1.Category = 'SPORTS' |
app_store | Among the role playing game genre, how many are targeted to teens and what is their average sentiment polarity score? | targeted to teen refers to Content Rating = 'Teen'; average = AVG(Sentiment_Polarity); | SELECT COUNT(T1.App), AVG(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1."Content Rating" = 'Teen' AND T1.Genres = 'Role Playing' |
app_store | What is the average rating of Apps falling under the racing genre and what is the percentage ratio of positive sentiment reviews? | average rating = AVG(Rating); percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*)), 100)); | SELECT AVG(T1.Rating), CAST(COUNT(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T2.Sentiment) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres = 'Racing' |
regional_sales | Which region has the most number of sales team? | the most number of sales team refers to MAX(COUNT(Sales Team)); | SELECT Region FROM `Sales Team` GROUP BY Region ORDER BY COUNT(DISTINCT `Sales Team`) DESC LIMIT 1 |
regional_sales | List all the customers with name containing the word 'Group'. | name containing the word 'Group' refers to Customer Names LIKE '%Group%'; | SELECT T FROM ( SELECT IIF(`Customer Names` LIKE '%Group%', `Customer Names`, NULL) AS T FROM Customers ) WHERE T IS NOT NULL |
regional_sales | What is the average median income for all City type of stores? | AVG(Median Income) where Type = 'City'; | SELECT AVG(`Median Income`) FROM `Store Locations` WHERE Type = 'City' |
regional_sales | Name the sales team and the region of order number 'SO - 000137'. | SELECT T2.`Sales Team`, T2.Region FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T1.OrderNumber = 'SO - 000137' | |
regional_sales | List all the order numbers along with its product name for each order under the sales team of 'Douglas Tucker'. | SELECT DISTINCT T1.ProductID, T1.`Product Name` FROM Products AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._ProductID = T1.ProductID INNER JOIN `Sales Team` AS T3 ON T3.SalesTeamID = T2._SalesTeamID WHERE T3.`Sales Team` = 'Douglas Tucker' | |
regional_sales | Among orders in 2020, name the customers who had the greatest discount applied for 'Cocktail Glasses' | MAX(Discount Applied) where Product Name = 'Cocktail Glasses'; orders in 2020 refer to the OrderDate between 01-01-2020 and 31-12-2020; | SELECT DISTINCT T1.`Customer Names` FROM Customers AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products AS T3 ON T3.ProductID = T2._ProductID WHERE T3.`Product Name` = 'Cocktail Glasses' AND SUBSTR(T2.OrderDate, -2) = '20' AND T2.`Discount Applied` = ( SELECT T2.`Discount Applied` FROM Customers AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products AS T3 ON T3.ProductID = T2._ProductID WHERE T3.`Product Name` = 'Cocktail Glasses' AND T2.OrderDate LIKE '%/%/20' ORDER BY T2.`Discount Applied` DESC LIMIT 1 ) |
regional_sales | List all the order numbers for In-Store sales and find the city where the store is located. | In-Store sales refer to Sales Channel = 'In-Store'; city refers to City Name; | SELECT DISTINCT T1.OrderNumber, T2.`City Name` FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID WHERE T1.`Sales Channel` = 'In-Store' |
regional_sales | Name the most expensive ordered? Who, when was it ordered? | the most expensive refers to MAX(Unit Cost); who refers to Customer Names; when refers to OrderDate; | SELECT T2.OrderNumber, T1.`Customer Names`, T2.OrderDate FROM Customers AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products AS T3 ON T3.ProductID = T2._ProductID ORDER BY T2.`Unit Cost` DESC LIMIT 1 |
regional_sales | List all the numbers ordered by 'Rochester Ltd' in 2018. | Rochester Ltd is the name of the customer; all the numbers ordered refer to OrderNumber; 2018 refers to SUBSTR(OrderDate, -2) = '18'; | SELECT DISTINCT T FROM ( SELECT CASE WHEN T1.OrderDate LIKE '%/%/18' AND T2.`Customer Names` = 'Rochester Ltd' THEN T1.OrderNumber ELSE NULL END AS T FROM `Sales Orders` T1 INNER JOIN Customers T2 ON T2.CustomerID = T1._CustomerID ) WHERE T IS NOT NULL |
regional_sales | Provide all the orders from WARE-NMK1003. Name the product and sales team for each of these order. | all the orders from WARE-NMK1003 refer to OrderNumber where WarehouseCode = 'WARE-NMK1003'; product refers to Product Name; | SELECT DISTINCT T1.`Product Name`, T3.`Sales Team` FROM Products AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._ProductID = T1.ProductID INNER JOIN `Sales Team` AS T3 ON T3.SalesTeamID = T2._SalesTeamID WHERE T2.WarehouseCode = 'WARE-NMK1003' |
regional_sales | List the name of all customers who had made orders online. | orders online refer to Sales Channel = 'Online'; | SELECT T FROM ( SELECT CASE WHEN T2.`Sales Channel` = 'Online' THEN T1.`Customer Names` ELSE NULL END AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID ) WHERE T IS NOT NULL |
regional_sales | Calculate the average net profit for bakeware product. | net profit can be computed as SUBTRACT(Unit Price, Unit Cost); AVG(net profit) where Product Name = 'Bakeware'; | SELECT AVG(REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '')) FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T2.`Product Name` = 'Bakeware' |
regional_sales | Name the sales team name who had orders with the greatest net profit in 2020. | net profit can be computed as SUBTRACT(Unit Price, Unit Cost); the greatest net profit in 2020 refers to MAX(net profit) where OrderDate LIKE '%/20'; | SELECT T2.`Sales Team` FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T1.OrderDate LIKE '%/%/20' GROUP BY T2.`Sales Team` ORDER BY SUM(REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '')) DESC LIMIT 1 |
regional_sales | Sate the order number and calculate the net profit for each order under Joshua Bennett. | net profit can be computed as SUBTRACT(Unit Price, Unit Cost); Joshua Bennett is the name of Sales Team; | SELECT T1.OrderNumber , REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '') FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T2.`Sales Team` = 'Joshua Bennett' |
regional_sales | Among the sales order shipped in July 2018, calculate the percentage of orders for home fragrances. | shipped in July 2018 refers to ShipDate between 01-07-2018 and 31-07-2018; DIVIDE(COUNT(OrderNumber where Product Name = 'Home Fragrances' and SUBSTR(OrderDate, 1, 1) = '7'), COUNT(OrderNumber where SUBSTR(ShipDate, -2) = '18')) as percentage; | SELECT SUM(CASE WHEN T2.`Product Name` = 'Home Fragrances' THEN 1 ELSE 0 END) * 100 / COUNT(T1.OrderNumber) FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T1.ShipDate LIKE '7/%/18' |
regional_sales | List down the customer IDs and names that start with alphabet "W". | names that start with alphabet "W" refer to Customer Names LIKE 'W%'; | SELECT DISTINCT CustomerID, `Customer Names` FROM Customers WHERE `Customer Names` LIKE 'W%' ORDER BY `Customer Names` DESC |
regional_sales | List down the product IDs and names that include the word "Outdoor". | names that include the word "Outdoor" refer to Product Name LIKE '%Outdoor%'; | SELECT ProductID, T FROM ( SELECT ProductID , CASE WHEN `Product Name` LIKE '%Outdoor%' THEN `Product Name` ELSE NULL END AS T FROM Products ) WHERE T IS NOT NULL ORDER BY T DESC |
regional_sales | Among the sales with 40% discount via in-store channel, how many products were shipped from warehouse code of WARE-NMK1003? | 40% discount refers to Discount Applied = 0.4; in-store channel refers to Sales Channel = 'In-Store'; orders refer to OrderNumber; | SELECT COUNT(DISTINCT T) FROM ( SELECT CASE WHEN `Sales Channel` = 'In-Store' AND WarehouseCode = 'WARE-NMK1003' AND `Discount Applied` = '0.4' THEN OrderNumber ELSE NULL END AS T FROM `Sales Orders` ) WHERE T IS NOT NULL |
regional_sales | Mention the most populated city and median income of the store in Florida state. | most populated refers to Max(Population); | SELECT `City Name`, `Median Income` FROM `Store Locations` WHERE State = 'Florida' ORDER BY Population DESC LIMIT 1 |
regional_sales | Describe the ID, city and region of the stores which are in Allen country. | ID refers to StoreID; | SELECT DISTINCT T2.StoreID, T2.`City Name`, T1.Region FROM Regions AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StateCode = T1.StateCode WHERE T2.County = 'Allen County' |
regional_sales | List the ID, city, state and region for the store type which is fewer between borough and CDP. | COUNT(StoreID) < COUNT(StoreID where Type = 'Borough') < COUNT(StoreID where Type = 'CDP'); | SELECT DISTINCT T2.StoreID, T2.`City Name`, T1.State, T2.Type FROM Regions AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StateCode = T1.StateCode WHERE T2.Type = 'Borough' OR T2.Type = 'CDP' |
regional_sales | Write down the region and name of the sale team ID of 18 and compare their orders between in-store and online. | sale team ID of 18 refers to _SalesTeamID = 18; COUNT(OrderNumber where Sales Channel = 'In-Store') > COUNT(OrderNumber where Sales Channel = 'Online'); | SELECT T2.Region, T2.`Sales Team` FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T2.SalesTeamID = 18 AND T1.`Sales Channel` = 'In-Store' OR T1.`Sales Channel` = 'Online' |
regional_sales | Calculate the percentage of order via in-store channel of customer "Medline". | Medline is the name of the customer; DIVIDE(COUNT(OrderNumber where Sales Channel = 'In-Store' and Customer Names = 'Medline'), COUNT(OrderNumber where Customer Names = 'Medline')) as percentage; | SELECT CAST(SUM(CASE WHEN T1.`Sales Channel` = 'In-Store' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1._CustomerID) FROM `Sales Orders` AS T1 INNER JOIN Customers AS T2 ON T2.CustomerID = T1._CustomerID WHERE T2.`Customer Names` = 'Medline ' |
regional_sales | Describe the customer names and lasting delivery periods for the product of "Bedroom Furniture" by wholesale channel in 2019. | delivery period in 2019 means time between placing of an order and the receipt of product and refers to SUBTRACT(DeliveryDate, OrderDate) where SUBSTR(OrderDate, -2 ) = '19'; Sales Channel = 'Wholesale'; Product Name = 'Bedroom Furniture'; | SELECT T1.`Customer Names`, T2.DeliveryDate FROM Customers AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products AS T3 ON T3.ProductID = T2._ProductID WHERE T2.`Sales Channel` = 'Wholesale' AND T3.`Product Name` = 'Bedroom Furniture' AND T2.OrderDate LIKE '%/%/19' |
regional_sales | Describe the customer names and product names which had over 3800 USD in net profit. | over 3800 USD in net profit refers to SUBTRACT(Unit Price, Unit Cost) where Net Profit > 3800; | SELECT DISTINCT `Customer Names`, `Product Name` FROM ( SELECT T1.`Customer Names`, T3.`Product Name` , REPLACE(T2.`Unit Price`, ',', '') - REPLACE(T2.`Unit Cost`, ',', '') AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products T3 ON T3.ProductID = T2._ProductID ) WHERE T > 3800 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.