1sf/bird-sql-train-with-schema huggingface dataset:
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 daylight savings areas in the Midwest region, how many postal points are there in Illinois?
Evidence: postal points refer to zip_code; the daylight savings areas refer to daylight_savings = 'Yes'; Illinois is the name of the state, in which name = 'Illinois';
SQL: SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Illinois' AND T2.daylight_savings = 'Yes' AND T2.region = 'Midwest'
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 countries and the zip codes in the Virgin Islands.
Evidence: the Virgin Islands refer to state where name = 'Virgin Islands';
SQL: SELECT T2.county, T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virgin Islands'
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 zip codes and the alias of Greeneville.
Evidence: Greeneville is the city;
SQL: SELECT T2.zip_code, T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Greeneville'
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: Compare the numbers of postal points under Smith Adrian and Heck Joe.
Evidence: COUNT(zip_code where first_name = 'Smith' and last_name = 'Adrian') > COUNT(zip_code where first_name = 'Heck' and last_name = 'Joe');
SQL: SELECT CASE WHEN COUNT(CASE WHEN T1.first_name = 'Smith' AND T1.last_name = 'Adrian' THEN T2.zip_code ELSE NULL END) > COUNT(CASE WHEN T1.first_name = 'Heck' AND T1.last_name = 'Joe' THEN T2.zip_code ELSE NULL END) THEN 'Smith Adrian>Heck Joe' ELSE 'Smith Adrian<=Heck Joe' END AS COMPARE FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district
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 zip codes and CBSA officers of the postal point in Oxford.
Evidence: CBSA officers refer to CBSA_name; postal point refers to zip_code; Oxford is the city;
SQL: SELECT T2.zip_code, T1.CBSA_name FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Oxford'
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 zip codes and their affiliated organization for the postal point under Kingsport-Bristol, TN-VA.
Evidence: postal point under Kingsport-Bristol, TN-VA refers to zip_code where CBSA_name = 'Kingsport-Bristol, TN-VA'; affiliated organization refers to organization from CBSA;
SQL: SELECT T2.zip_code, T2.organization FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Kingsport-Bristol, TN-VA'
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 zip codes and the congress representatives' names of the postal points which are affiliated with Readers Digest.
Evidence: representative's full names refer to first_name, last_name; postal points affiliated with Readers Digest refer to zip_code where organization = 'Readers Digest';
SQL: SELECT T1.zip_code, T3.first_name, T3.last_name FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code INNER JOIN congress AS T3 ON T2.district = T3.cognress_rep_id WHERE T1.organization = 'Readers Digest'
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 postal points in California, calculate the percentage of them in post office types.
Evidence: DIVIDE(COUNT(zip_code where type = 'Post Office'), COUNT(zip_code)) as percentage where name = 'California';
SQL: SELECT CAST(COUNT(CASE WHEN T2.type = 'Post Office' THEN T2.zip_code ELSE NULL END) AS REAL) * 100 / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'California'
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 are the zip code for the Senate house?
Evidence: House of Repsentatives can stand for senate house
SQL: SELECT T2.zip_code FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.House = 'House of Repsentatives' GROUP BY T2.zip_code
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 city has the most bad aliases?
Evidence: the most bad aliases refer to MAX(COUNT(bad_alias));
SQL: SELECT T2.city FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T1.bad_alias ORDER BY COUNT(T1.zip_code) 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: List all the counties in Georgia.
Evidence: Georgia is the name of the state, in which name = 'Georgia';
SQL: SELECT T2.county FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Georgia' GROUP BY T2.county
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 all the locations of postal points with the area code "410".
Evidence: latitude and longitude coordinates can be used to identify the location; postal points refer to zip_code;
SQL: SELECT T2.latitude, T2.longitude FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 410