1sf/bird-sql-train-with-schema huggingface dataset:
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: In what year and season did Sohail Abbas compete?
Evidence: year refers to games_year;
SQL: SELECT T1.games_year, T1.season FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.full_name = 'Sohail Abbas'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: What is the average weight of the competitors who won a silver medal?
Evidence: AVG(weight) where medal_name = 'Silver';
SQL: SELECT AVG(T1.weight) FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T4.medal_name = 'Silver'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: In which city the 2004 Summer was held?
Evidence: in which city refers to city_name; the 2004 Summer refers to games_name = '2004 Summer';
SQL: SELECT T2.city_name FROM games_city AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.id INNER JOIN games AS T3 ON T1.games_id = T3.id WHERE T3.games_name = '2004 Summer'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: What is the season of the game where a competitor who weighted 73 kg and 180 cm tall, participated?
Evidence: competitor who weighted 73 kg and 180 cm tall refers to person_id where height = 180 and weight = 73;
SQL: SELECT DISTINCT T1.season FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.height = 180 AND T3.weight = 73
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: Provide the names of competitors who received a gold medal.
Evidence: names of competitors refer to full_name; gold medal refers to medal_name = 'Gold';
SQL: SELECT DISTINCT T1.full_name FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T4.medal_name = 'Gold'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: Compute the average height of competitors whose age ranges from 22 to 28.
Evidence: AVG(height) where age BETWEEN 22 AND 28;
SQL: SELECT AVG(T1.height) FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id WHERE T2.age BETWEEN 22 AND 28
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: How many female competitors were from Iran?
Evidence: female competitors refer to person_id where gender = 'F'; from Iran refers to region_name = 'Iran';
SQL: SELECT COUNT(T2.person_id) FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Iran' AND T3.gender = 'F'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: Provide the age of the tallest competitor.
Evidence: the tallest refers to MAX(height);
SQL: SELECT T2.age FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id ORDER BY T1.height DESC LIMIT 1
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: Among the competitors with age ranges 24 and below, calculate the difference between the number of competitors who weighed greater than 70 kg and competitors who weighted less than 70 kg.
Evidence: SUBTRACT(COUNT(weight > 70), COUNT(weight < 70)) where age < 24;
SQL: SELECT COUNT(CASE WHEN T1.weight > 70 THEN 1 ELSE NULL END) - COUNT(CASE WHEN T1.weight < 70 THEN 1 ELSE NULL END) FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id WHERE T2.age < 24
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: In the 2014 Winter game, what is the percentage of competitors who age 28 years old?
Evidence: DIVIDE(COUNT(age = 28), COUNT(id)) as percentage where games_name = '2014 Winter';
SQL: SELECT CAST(COUNT(CASE WHEN T2.age = 28 THEN 1 END) AS REAL) * 100 / COUNT(T2.person_id) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.games_name = '2014 Winter'
Database: olympics
Tables: city, games, games_city, medal, noc_region, person, games_competitor, person_region, sport, event, competitor_event
Columns (table index, column name): -1, *, 0, id, 0, city_name, 1, id, 1, games_year, 1, games_name, 1, season, 2, games_id, 2, city_id, 3, id, 3, medal_name, 4, id, 4, noc, 4, region_name, 5, id, 5, full_name, 5, gender, 5, height, 5, weight, 6, id, 6, games_id, 6, person_id, 6, age, 7, person_id, 7, region_id, 8, id, 8, sport_name, 9, id, 9, sport_id, 9, event_name, 10, event_id, 10, competitor_id, 10, medal_id
Question: Among the males, list the region name of people with height greater than 87% of the average height of all people listed.
Evidence: males refer to gender = 'M'; height greater than 87% of the average height refers to height > MULTIPLY(AVG(height), 0.87);
SQL: SELECT DISTINCT T1.region_name FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.gender = 'M' AND T3.height * 100 > ( SELECT AVG(height) FROM person WHERE gender = 'M' ) * 87
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 total number of households in Arecibo county?
Evidence: "ARECIBO" is the county; total number of households refer to sum(households)
SQL: SELECT SUM(T1.households) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'
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: Which residential area in Arecibo county has the highest average house value? Please give its zip_code.
Evidence: "ARECIBO" is the county; highest average house value refers to Max(avg_house_value)
SQL: SELECT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' ORDER BY T1.avg_house_value 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: Please list the numbers of males in all the residential areas in Arecibo county.
Evidence: "ARECIBO" is the county; number of males refers to Sum(male_population)
SQL: SELECT SUM(T1.male_population) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'
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 all the residential areas in Delaware, how many of them implement daylight saving?
Evidence: "Delaware" is a county; implement daylight savings refers to daylight_saving = 'Yes'
SQL: SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'DELAWARE' AND T1.daylight_savings = 'Yes'
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 all the residential areas in Arecibo county, what is the zip_code of the one with the highest white population?
Evidence: "ARECIBO" is the county; highest white population refers to Max(white_population)
SQL: SELECT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' ORDER BY T1.white_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: In which county is the residential area with the highest average income per household located?
Evidence: highest average income per household refers to Max(avg_income_per_household)
SQL: SELECT T2.county FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' GROUP BY T2.county ORDER BY T1.avg_income_per_household 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: Please list the names of all the counties with at least one residential area that implements daylight saving.
Evidence: implements daylight savings refers to daylight_savings = 'Yes'
SQL: SELECT DISTINCT T2.county FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T1.daylight_savings = 'Yes'