1sf/bird-sql-train-with-schema huggingface dataset:
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: Of the 4 root beers that Frank-Paul Santangelo purchased on 2014/7/7, how many of them were in cans?
Evidence: on 2014/7/7 refers to transactiondate = '2014-07-07'; in cans refers to containertype = 'Can';
SQL: SELECT COUNT(T1.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeer AS T3 ON T2.RootBeerID = T3.RootBeerID WHERE T1.First = 'Frank-Paul' AND T1.Last = 'Santangelo' AND T2.TransactionDate = '2014-07-07' AND T3.ContainerType = 'Can'
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: How many root beers of the Bulldog were purchased in August, 2014?
Evidence: Bulldog refers to BrandName = 'Bulldog'; purchased in August, 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '08';
SQL: SELECT COUNT(T1.BrandID) FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2014-08%' AND T3.BrandName = 'Bulldog'
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: Please list the full names of the customers who have purchased at least one root beer produced by AJ Stephans Beverages.
Evidence: full name = First, Last; customers who have purchased at least one root beer refers to CustomerID > = 1; produced by AJ Stephans Beverages refers to BreweryName = 'AJ Stephans Beverages';
SQL: SELECT T1.First, T1.Last FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeer AS T3 ON T2.RootBeerID = T3.RootBeerID INNER JOIN rootbeerbrand AS T4 ON T3.BrandID = T4.BrandID WHERE T4.BreweryName = 'AJ Stephans Beverages'
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: Among the root beer brands that do not advertise on Twitter, how many of them have root beers sold in August, 2014?
Evidence: do not advertise on Twitter refers to twitter IS NULL; in August, 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '08';
SQL: SELECT COUNT(T1.BrandID) FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2014-08%' AND T3.Twitter IS NULL
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: What is the number of the credit card that Frank-Paul Santangelo used to purchase root beers on 2014/7/7?
Evidence: number of the credit card refers to CreditCardNumber; on 2014/7/7 refers to TransactionDate = '2014-07-07';
SQL: SELECT DISTINCT T2.CreditCardNumber FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.First = 'Frank-Paul' AND T1.Last = 'Santangelo' AND T2.TransactionDate = '2014-07-07'
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: Among all the root beers purchased by Frank-Paul Santangelo, how many of them were non-sweetened?
Evidence: non-sweetened refers to honey = 'FALSE' AND artificialsweetener = 'FALSE';
SQL: SELECT COUNT(T1.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeer AS T3 ON T2.RootBeerID = T3.RootBeerID INNER JOIN rootbeerbrand AS T4 ON T3.BrandID = T4.BrandID WHERE T1.First = 'Frank-Paul' AND T1.Last = 'Santangelo' AND T4.ArtificialSweetener = 'FALSE' AND T4.Honey = 'FALSE'
Database: beer_factory
Tables: customers, geolocation, location, rootbeerbrand, rootbeer, rootbeerreview, transaction
Columns (table index, column name): -1, *, 0, CustomerID, 0, First, 0, Last, 0, StreetAddress, 0, City, 0, State, 0, ZipCode, 0, Email, 0, PhoneNumber, 0, FirstPurchaseDate, 0, SubscribedToEmailList, 0, Gender, 1, LocationID, 1, Latitude, 1, Longitude, 2, LocationID, 2, LocationName, 2, StreetAddress, 2, City, 2, State, 2, ZipCode, 3, BrandID, 3, BrandName, 3, FirstBrewedYear, 3, BreweryName, 3, City, 3, State, 3, Country, 3, Description, 3, CaneSugar, 3, CornSyrup, 3, Honey, 3, ArtificialSweetener, 3, Caffeinated, 3, Alcoholic, 3, AvailableInCans, 3, AvailableInBottles, 3, AvailableInKegs, 3, Website, 3, FacebookPage, 3, Twitter, 3, WholesaleCost, 3, CurrentRetailPrice, 4, RootBeerID, 4, BrandID, 4, ContainerType, 4, LocationID, 4, PurchaseDate, 5, CustomerID, 5, BrandID, 5, StarRating, 5, ReviewDate, 5, Review, 6, TransactionID, 6, CreditCardNumber, 6, CustomerID, 6, TransactionDate, 6, CreditCardType, 6, LocationID, 6, RootBeerID, 6, PurchasePrice
Question: Please list the dates on which a male customer has purchased more than 3 root beers.
Evidence: dates of purchase refers to TransactionDate; male customer refers to Gender = 'M'; purchased more than 3 root beers refers to COUNT(CustomerID) > 3;
SQL: SELECT T2.TransactionDate FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.Gender = 'M' GROUP BY T2.TransactionDate HAVING COUNT(T2.CustomerID) > 3
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: Among the cities with an area code 939, which city has the highest Asian population?
Evidence: highest asian population refers to Max(asian_population)
SQL: SELECT T2.city FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 939 ORDER BY T2.asian_population DESC LIMIT 1
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: Give the name of the country and state of the city with elevation of 1039.
Evidence: elevation of 1039 refers to elevation = 1039
SQL: SELECT DISTINCT T1.name, T2.state FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state INNER JOIN zip_data AS T3 ON T2.zip_code = T3.zip_code WHERE T3.elevation = 1039
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: Provide the alias and elevation of the city with zip code 1028.
SQL: SELECT T1.alias, T2.elevation FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.zip_code = 1028
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: What is the area code of the city with the largest land area?
Evidence: largest land area refers to Max(land_area)
SQL: SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.land_area = ( SELECT MAX(land_area) FROM zip_data )
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: Give the area code of the city with the white population ranging between 1700 to 2000.
SQL: SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.white_population BETWEEN 1700 AND 2000
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: What is the Asian population in the city with the alias Leeds?
SQL: SELECT SUM(T2.asian_population) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Leeds'
Database: address
Tables: CBSA, state, congress, zip_data, alias, area_code, avoid, country, zip_congress
Columns (table index, column name): -1, *, 0, CBSA, 0, CBSA_name, 0, CBSA_type, 1, abbreviation, 1, name, 2, cognress_rep_id, 2, first_name, 2, last_name, 2, CID, 2, party, 2, state, 2, abbreviation, 2, House, 2, District, 2, land_area, 3, zip_code, 3, city, 3, state, 3, multi_county, 3, type, 3, organization, 3, time_zone, 3, daylight_savings, 3, latitude, 3, longitude, 3, elevation, 3, state_fips, 3, county_fips, 3, region, 3, division, 3, population_2020, 3, population_2010, 3, households, 3, avg_house_value, 3, avg_income_per_household, 3, persons_per_household, 3, white_population, 3, black_population, 3, hispanic_population, 3, asian_population, 3, american_indian_population, 3, hawaiian_population, 3, other_population, 3, male_population, 3, female_population, 3, median_age, 3, male_median_age, 3, female_median_age, 3, residential_mailboxes, 3, business_mailboxes, 3, total_delivery_receptacles, 3, businesses, 3, 1st_quarter_payroll, 3, annual_payroll, 3, employees, 3, water_area, 3, land_area, 3, single_family_delivery_units, 3, multi_family_delivery_units, 3, total_beneficiaries, 3, retired_workers, 3, disabled_workers, 3, parents_and_widowed, 3, spouses, 3, children, 3, over_65, 3, monthly_benefits_all, 3, monthly_benefits_retired_workers, 3, monthly_benefits_widowed, 3, CBSA, 4, zip_code, 4, alias, 5, zip_code, 5, area_code, 6, zip_code, 6, bad_alias, 7, zip_code, 7, county, 7, state, 8, zip_code, 8, district
Question: List down the area code and country of the city named Savoy.
Evidence: Savoy is the city;
SQL: SELECT T1.area_code, T2.county FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN zip_data AS T3 ON T1.zip_code = T3.zip_code WHERE T3.city = 'Savoy'