Chinastark/bird_dataset huggingface dataset:
Database: app_store
Question: What is the rating for "Draw A Stickman"?
Evidence: Draw A Stickman refers to App = 'Draw A Stickman';
SQL: SELECT Rating FROM playstore WHERE APP = 'Draw A Stickman'
Database: app_store
Question: How many of the reviews for the app "Brit + Co" have a comment?
Evidence: Brit + Co refers to App = 'Brit + Co'; comment refers to Translated Review NOT null;
SQL: SELECT COUNT(App) FROM user_reviews WHERE App = 'Brit + Co' AND Translated_Review IS NOT NULL
Database: app_store
Question: List the top 5 shopping apps with the most reviews.
Evidence: shopping apps refers to Genre = 'Shopping'; most reviews refers to MAX(Reviews);
SQL: SELECT DISTINCT App FROM playstore WHERE Genres = 'Shopping' GROUP BY App ORDER BY COUNT(App) DESC LIMIT 5
Database: app_store
Question: How many neutral reviews does the app "Dino War: Rise of Beasts" have?
Evidence: neutral reviews refers to Sentiment = 'Neutral';
SQL: SELECT COUNT(App) FROM user_reviews WHERE App = 'Dino War: Rise of Beasts' AND Sentiment = 'Neutral'
Database: app_store
Question: What are the apps with only 5,000+ installs?
Evidence: Installs = '5,000+';
SQL: SELECT DISTINCT App FROM playstore WHERE Installs = '5,000+'
Database: app_store
Question: List all the negative comments on the "Dog Run - Pet Dog Simulator" app.
Evidence: negative comment refers to Sentiment = 'Negative';
SQL: SELECT Translated_Review FROM user_reviews WHERE App = 'Dog Run - Pet Dog Simulator' AND Sentiment = 'Negative'
Database: app_store
Question: Which free app has the most Negative comments?
Evidence: 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';
SQL: 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
Database: app_store
Question: How many negative comments are there in all the apps with 100,000,000+ installs?
Evidence: negative comment refers to Sentiment = 'Negative'; Installs = '100,000,000+';
SQL: 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'
Database: app_store
Question: What are the content ratings for the apps that have "gr8" in their comments?
Evidence: app with gr8 in their comments refers to Translated_Review LIKE '%gr8%';
SQL: 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%'
Database: app_store
Question: What is the total Sentiment polarity score of the most expensive app?
Evidence: total sentiment polarity score = sum(Sentiment_Polarity); most expensive app refers to MAX(Price);
SQL: 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 )
Database: app_store
Question: What is the rating for "Garden Coloring Book"? List all of its reviews.
Evidence: Golfshot Plus: Golf GPS refers to App = 'Golfshot Plus: Golf GPS'; review refers to Translated_Review;
SQL: 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'
Database: app_store
Question: Which Photography app has the highest total Sentiment subjectivity score?
Evidence: Photography app refers to Genre = 'Photography'; highest total sentiment subjectivity score = MAX(sum(Sentiment_Subjectivity));
SQL: 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
Database: app_store
Question: List all the comments on the lowest rated Mature 17+ app.
Evidence: comments refers to Translated_Review; lowest rated refers to Rating = 1; Mature 17+ refers to Content Rating = 'Mature 17+ ';
SQL: 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
Database: app_store
Question: What is the number of installments of the app with the highest total Sentiment polarity score?
Evidence: installments refers to Installs; highest total sentiment polarity score = MAX(SUM(Sentiment_Polarity));
SQL: 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
Database: app_store
Question: What is the number of neutral comments from all the weather apps?
Evidence: neutral comments refers to Sentiment = 'Neutral'; weather app refers to Genre = 'Weather';
SQL: 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'
Database: app_store
Question: Which 1,000,000,000+ intalls apps has the most no comment reviews?
Evidence: no comment refers to Translated_Review = 'nan'; most no comment reviews = (MAX(COUNT(Translated_Review = 'nan')));
SQL: 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
Database: app_store
Question: What is the rating and the total Sentiment subjectivity score of "Onefootball - Soccer Scores"?
Evidence: Onefootball - Soccer Scores refers to App = 'Onefootball - Soccer Scores';
SQL: 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'
Database: app_store
Question: What percentage of no comment reviews are from "Teen" content rating apps?
Evidence: no comment refers to Translated_Review = 'nan'; percentage = DIVIDE((SUM(Content Rating = 'Teen')), COUNT(*));
SQL: 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'
Database: app_store
Question: Which apps have 5 rating? List out then application name.
Evidence: application name refers to App;
SQL: SELECT DISTINCT App FROM playstore WHERE Rating = 5
Database: app_store
Question: Which apps have been reviewed more than 75 000 000 times and the content is suitable for teenagers?
Evidence: Reviews>75000000; suitable for teenagers refers to Content Rating = 'Teen';
SQL: SELECT DISTINCT App FROM playstore WHERE Reviews > 75000000 AND `Content Rating` = 'Teen'
Database: app_store
Question: List out genre that have downloads more than 1000000000.
Evidence: downloads and installs are synonyms; Installs = '1,000,000,000+';
SQL: SELECT Genres FROM playstore WHERE Installs = '1,000,000,000+' GROUP BY Genres
Database: app_store
Question: What is the average price for a dating application?
Evidence: average price = AVG(Price where Genre = 'Dating'); dating application refers to Genre = 'Dating';
SQL: SELECT AVG(Price) FROM playstore WHERE Genres = 'Dating'
Database: app_store
Question: What is the average download for entertainment apps with size no more than 1.0 M?
Evidence: downloads and installs are synonyms; entertainment apps refers to Category = 'ENTERTAINMENT';
SQL: SELECT AVG(CAST(REPLACE(REPLACE(Installs, ',', ''), '+', '') AS INTEGER)) FROM playstore WHERE Category = 'ENTERTAINMENT' AND Size < '1.0M'
Database: app_store
Question: What is the average review number for application with 5 rating?
Evidence: average review = AVG(Review); application refers to app; Rating = 5;
SQL: SELECT AVG(Reviews) FROM playstore WHERE Rating = 5
Database: app_store
Question: List out the top 3 genre for application with a sentiment review greater than 0.5.
Evidence: sentiment review refers to Sentiment_Polarity; Sentiment_Polarity>0.5;
SQL: 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 )
Database: app_store
Question: What is the percentage of application with 4.7 rating having more positives sentiment than negative sentiment?
Evidence: 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';
SQL: 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
Database: app_store
Question: List down app that does not have negative sentiment and give their average rating?
Evidence: doest not have negative sentiment refers to Sentiment! = 'Negative'; average = AVG(Sentiment_Polarity);
SQL: 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
Database: app_store
Question: List down application that have not been updated since 2015. What is the percentage of this application having more negative sentiment than positive sentiment?
Evidence: percentage = DIVIDE(SUBTRACT(SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), (SUM(Sentiment = 'Negative')) as percent; Last Updated>'2015';
SQL: 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' )
Database: app_store
Question: What is the percentage for free application with a rating 4.5 and above have not been updated since 2018?
Evidence: 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;
SQL: 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
Database: app_store
Question: What genre does Honkai Impact 3rd belong to?
Evidence: Honkai Impact 3rd is the App;
SQL: SELECT DISTINCT Genres FROM playstore WHERE App = 'Honkai Impact 3rd'
Database: app_store
Question: List down the rating for the App Learn C++.
Evidence: FALSE;
SQL: SELECT DISTINCT Rating FROM playstore WHERE App = 'Learn C++'
Database: app_store
Question: What is the average price of games belonging in the arcade genre which has a content rating of Everyone 10+?
Evidence: average price = AVG(Price);
SQL: SELECT AVG(Price) FROM playstore WHERE 'Content Rating' = 'Everyone 10+' AND Genres = 'Arcade'
Database: app_store
Question: How much is the size of Browser 4G and how many users have a pretty positive favorability on it?
Evidence: Browser 4G is the App; pretty positive favorability refers to Sentiment_Polarity score = 0.5
SQL: 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
Database: app_store
Question: Name the Apps with a sentiment objectivity of 0.3 and include their number of installs.
Evidence: FALSE;
SQL: 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
Database: app_store
Question: 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?
Evidence: average sentiment polarity score = AVG(Sentiment_Polarity); Golf GPS Rangefinder: Golf Pad  is the App;
SQL: 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'
Database: app_store
Question: List the top 5 lowest rated puzzle games and count the number of negative sentiments the games received.
Evidence: lowest rating refers to MIN(Rating); puzzle is the genre;
SQL: 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
Database: app_store
Question: What is the percentage ratio between positive sentiments and negative sentiments that are in Fate/Grand Order? Also indicate the current version.
Evidence: Fate/Grand Order is the App; percentage ratio = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), 100);
SQL: 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'
Database: app_store
Question: Indicate the number of installs and include the percentage of positive sentiments of FREEDOME VPN Unlimited anonymous Wifi Security.
Evidence: FREEDOME VPN Unlimited anonymous Wifi Security is the App; percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*))), 100)
SQL: 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'
Database: app_store
Question: For the Honkai Impact 3rd App, what is the highest sentiment polarity score and what genre does it belong to?
Evidence: highest sentiment polarity score refers to MAX(Sentiment_Polarity);
SQL: 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
Database: app_store
Question: What is the rating of Dragon Ball Legends and how many users dislike this App?
Evidence: Dragon Ball Legends is the app; users who dislikes the app refers to Sentiment_Polarity<-0.5;
SQL: 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
Database: app_store
Question: Which education App has the worst rating and state the translated review if available.
Evidence: education App refers to Category = 'EDUCATION'; worst rated app refers to Rating = 1;
SQL: 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
Database: app_store
Question: List all free sports Apps and their translated review.
Evidence: paid sports Apps refers to type = 'Paid' and Category = 'SPORTS';
SQL: 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'
Database: app_store
Question: Among the role playing game genre, how many are targeted to teens and what is their average sentiment polarity score?
Evidence: targeted to teen refers to Content Rating = 'Teen'; average = AVG(Sentiment_Polarity);
SQL: 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'
Database: app_store
Question: What is the average rating of Apps falling under the racing genre and what is the percentage ratio of positive sentiment reviews?
Evidence: average rating = AVG(Rating); percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*)), 100));
SQL: 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'
Database: regional_sales
Question: Which region has the most number of sales team?
Evidence: the most number of sales team refers to MAX(COUNT(Sales Team));
SQL: SELECT Region FROM `Sales Team` GROUP BY Region ORDER BY COUNT(DISTINCT `Sales Team`) DESC LIMIT 1
Database: regional_sales
Question: List all the customers with name containing the word 'Group'.
Evidence: name containing the word 'Group' refers to Customer Names LIKE '%Group%';
SQL: SELECT T FROM ( SELECT IIF(`Customer Names` LIKE '%Group%', `Customer Names`, NULL) AS T FROM Customers ) WHERE T IS NOT NULL
Database: regional_sales
Question: What is the average median income for all City type of stores?
Evidence: AVG(Median Income) where Type = 'City';
SQL: SELECT AVG(`Median Income`) FROM `Store Locations` WHERE Type = 'City'
Database: regional_sales
Question: Name the sales team and the region of order number 'SO - 000137'.
SQL: 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'
Database: regional_sales
Question: List all the order numbers along with its product name for each order under the sales team of 'Douglas Tucker'.
SQL: 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'
Database: regional_sales
Question: Among orders in 2020, name the customers who had the greatest discount applied for 'Cocktail Glasses'
Evidence: MAX(Discount Applied) where Product Name = 'Cocktail Glasses'; orders in 2020 refer to the OrderDate between 01-01-2020 and 31-12-2020;
SQL: 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 )
Database: regional_sales
Question: List all the order numbers for In-Store sales and find the city where the store is located.
Evidence: In-Store sales refer to Sales Channel = 'In-Store'; city refers to City Name;
SQL: 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'
Database: regional_sales
Question: Name the most expensive ordered? Who, when was it ordered?
Evidence: the most expensive refers to MAX(Unit Cost); who refers to Customer Names; when refers to OrderDate;
SQL: 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
Database: regional_sales
Question: List all the numbers ordered by 'Rochester Ltd' in 2018.
Evidence: Rochester Ltd is the name of the customer; all the numbers ordered refer to OrderNumber; 2018 refers to SUBSTR(OrderDate, -2) = '18';
SQL: 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
Database: regional_sales
Question: Provide all the orders from WARE-NMK1003. Name the product and sales team for each of these order.
Evidence: all the orders from WARE-NMK1003 refer to OrderNumber where WarehouseCode = 'WARE-NMK1003'; product refers to Product Name;
SQL: 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'
Database: regional_sales
Question: List the name of all customers who had made orders online.
Evidence: orders online refer to Sales Channel = 'Online';
SQL: 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
Database: regional_sales
Question: Calculate the average net profit for bakeware product.
Evidence: net profit can be computed as SUBTRACT(Unit Price, Unit Cost); AVG(net profit) where Product Name = 'Bakeware';
SQL: 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'
Database: regional_sales
Question: Name the sales team name who had orders with the greatest net profit in 2020.
Evidence: 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';
SQL: 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
Database: regional_sales
Question: Sate the order number and calculate the net profit for each order under Joshua Bennett.
Evidence: net profit can be computed as SUBTRACT(Unit Price, Unit Cost); Joshua Bennett is the name of Sales Team;
SQL: 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'
Database: regional_sales
Question: Among the sales order shipped in July 2018, calculate the percentage of orders for home fragrances.
Evidence: 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;
SQL: 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'
Database: regional_sales
Question: List down the customer IDs and names that start with alphabet "W".
Evidence: names that start with alphabet "W" refer to Customer Names LIKE 'W%';
SQL: SELECT DISTINCT CustomerID, `Customer Names` FROM Customers WHERE `Customer Names` LIKE 'W%' ORDER BY `Customer Names` DESC
Database: regional_sales
Question: List down the product IDs and names that include the word "Outdoor".
Evidence: names that include the word "Outdoor" refer to Product Name LIKE '%Outdoor%';
SQL: 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