set
dict |
|---|
{
"query": "How many zip codes are under Barre, VT?",
"pos": [
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'"
],
"neg": [
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"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",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"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'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"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'",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1"
]
} |
{
"query": "What is the country and state of the city named Dalton?",
"pos": [
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county"
],
"neg": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'"
]
} |
{
"query": "Which state has the most bad aliases?",
"pos": [
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1"
],
"neg": [
"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 = 'NEW HAVEN'",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000"
]
} |
{
"query": "What is the CBSA name and type in York, ME?",
"pos": [
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'"
],
"neg": [
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"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",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1"
]
} |
{
"query": "Among the residential areas with the bad alias \"Internal Revenue Service\", how many of them are in the Eastern time zone?",
"pos": [
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'"
],
"neg": [
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"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 = 636",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT 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.city = 'Guanica'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'"
]
} |
{
"query": "Please list the Asian populations of all the residential areas with the bad alias \"URB San Joaquin\".",
"pos": [
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'"
],
"neg": [
"SELECT 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.city = 'Guanica'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"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 = 636",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1"
]
} |
{
"query": "State the male population for all zip code which were under the Berlin, NH CBSA.",
"pos": [
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population"
],
"neg": [
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"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'",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"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 = 636"
]
} |
{
"query": "List 10 cities with a median age over 40. Include their zip codes and area codes.",
"pos": [
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10"
],
"neg": [
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"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"
]
} |
{
"query": "Among all the residential areas in Delaware, how many of them implement daylight saving?",
"pos": [
"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'"
],
"neg": [
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"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'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"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 = '787' GROUP BY T2.latitude, T2.longitude"
]
} |
{
"query": "What is the average household income in the city known as \"Danzig\"?",
"pos": [
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'"
],
"neg": [
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county",
"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'",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"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'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000"
]
} |
{
"query": "Among the cities belonging to the country named Arroyo, calculate the percentage of increase in the population in these cities from 2010 to 2020.",
"pos": [
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'"
],
"neg": [
"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'",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population"
]
} |
{
"query": "What are the top 3 states with the highest Asian population? List the full names of all the representatives in the said states.",
"pos": [
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3"
],
"neg": [
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"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'",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30"
]
} |
{
"query": "Provide the zip code, city, and congress representative's full names of the area which has highest population in 2020.",
"pos": [
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1"
],
"neg": [
"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'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'"
]
} |
{
"query": "Compare the numbers of postal points under Smith Adrian and Heck Joe.",
"pos": [
"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"
],
"neg": [
"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",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"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'",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'"
]
} |
{
"query": "Calculate the percentage of congress representatives from the Democrat party. Among them, how many postal points are in the Hawaii state?",
"pos": [
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district"
],
"neg": [
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
]
} |
{
"query": "Among the cities with area code 608, how many cities implement daylight savings?",
"pos": [
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'"
],
"neg": [
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"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'",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"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 = 'NEW HAVEN'"
]
} |
{
"query": "List down the area code and country of the city named Savoy.",
"pos": [
"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'"
],
"neg": [
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"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'",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
]
} |
{
"query": "Provide the zip codes and coordinates of the postal points under Allentown-Bethlehem-Easton, PA-NJ.",
"pos": [
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'"
],
"neg": [
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT 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.city = 'Guanica'",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"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'",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"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",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'"
]
} |
{
"query": "In the state where Lisa Murkowski is the representative, how many cities have zero employees?",
"pos": [
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
],
"neg": [
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"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 = 'NEW HAVEN'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10"
]
} |
{
"query": "Please list the zip_codes of all the residential areas in Huntingdon county with over 30 employees.",
"pos": [
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30"
],
"neg": [
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000"
]
} |
{
"query": "Name 10 cities with their states that are under the Lexington-Fayette, KY office of the Canada Border Services Agency.",
"pos": [
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10"
],
"neg": [
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"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 = 'NEW HAVEN'",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"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",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1"
]
} |
{
"query": "Tell the name of the county which is represented by Hartzler Vicky.",
"pos": [
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county"
],
"neg": [
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"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",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1"
]
} |
{
"query": "Which city and state has the bad alias of Lawrenceville?",
"pos": [
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state"
],
"neg": [
"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",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district"
]
} |
{
"query": "What is the area code of Bishopville, SC?",
"pos": [
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'"
],
"neg": [
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county",
"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'",
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type"
]
} |
{
"query": "Which city has the most bad aliases?",
"pos": [
"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"
],
"neg": [
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'"
]
} |
{
"query": "Give at least five alias of cities with a postal point of post office.",
"pos": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5"
],
"neg": [
"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'",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"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'",
"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"
]
} |
{
"query": "What percentage of households are in \"Coroyell\" out of its state?",
"pos": [
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code"
],
"neg": [
"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",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
]
} |
{
"query": "Provide the congress representatives' IDs of the postal points in East Springfield.",
"pos": [
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'"
],
"neg": [
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"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'",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"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'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'"
]
} |
{
"query": "Provide the zip codes and area codes of the postal points with the community post office type at the elevation above 6000.",
"pos": [
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000"
],
"neg": [
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"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'",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"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",
"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 = 636"
]
} |
{
"query": "In which county is the residential area with the highest average income per household located?",
"pos": [
"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"
],
"neg": [
"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'",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"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",
"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'",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation"
]
} |
{
"query": "Please list the bad alias of all the residential areas with a median female age of over 32.",
"pos": [
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32"
],
"neg": [
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"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'",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'"
]
} |
{
"query": "Which district has the largest land area in Wisconsin? Write the full name of the congress representative and include the postal codes.",
"pos": [
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1"
],
"neg": [
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"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'"
]
} |
{
"query": "How many counties are there in Alabama?",
"pos": [
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'"
],
"neg": [
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"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'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1"
]
} |
{
"query": "Calculate the average number of beneficiaries per postal point in Guam.",
"pos": [
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'"
],
"neg": [
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"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'",
"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'",
"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",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"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",
"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 = 'NEW HAVEN'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'"
]
} |
{
"query": "Which zip code in Massachusetts that have more than 1 area code?",
"pos": [
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1"
],
"neg": [
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"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'",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'"
]
} |
{
"query": "Provide the zip codes and the alias of Greeneville.",
"pos": [
"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'"
],
"neg": [
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT 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.city = 'Guanica'",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1"
]
} |
{
"query": "Calculate the average male median age of all the residential areas in Windham county.",
"pos": [
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'"
],
"neg": [
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"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'",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000"
]
} |
{
"query": "What is the average median female age of all the residential areas in the Arecibo county?",
"pos": [
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'"
],
"neg": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"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'"
]
} |
{
"query": "Indicate the name of the country with a population greater than 10000 in 2010.",
"pos": [
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000"
],
"neg": [
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"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",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"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'",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3"
]
} |
{
"query": "How many states are in the central time zone? Write their full names.",
"pos": [
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'"
],
"neg": [
"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'",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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 = 'NEW HAVEN'",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'"
]
} |
{
"query": "Among the zip code under Saint Croix county, which zip code has the biggest land area?",
"pos": [
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1"
],
"neg": [
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"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",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"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'",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'"
]
} |
{
"query": "List all the bad alias for zip codes in Puerto Rico.",
"pos": [
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'"
],
"neg": [
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'"
]
} |
{
"query": "Provide the average elevation of the cities with alias Amherst.",
"pos": [
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'"
],
"neg": [
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"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"
]
} |
{
"query": "Provide the zip codes and the congress representatives' names of the postal points which are affiliated with Readers Digest.",
"pos": [
"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'"
],
"neg": [
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"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'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'"
]
} |
{
"query": "Calculate the ratio between the number of representatives in Alabama and the number of representatives in Illinois.",
"pos": [
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress"
],
"neg": [
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"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'",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"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'",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'"
]
} |
{
"query": "How many males are there in New Haven County's residential areas?",
"pos": [
"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 = 'NEW HAVEN'"
],
"neg": [
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30"
]
} |
{
"query": "What is the area code of Phillips county in Montana?",
"pos": [
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'"
],
"neg": [
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT 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.city = 'Guanica'",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10"
]
} |
{
"query": "Which state is Outagamie County in? Give the full name of the state.",
"pos": [
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'"
],
"neg": [
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"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'",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"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",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'"
]
} |
{
"query": "What is the elevation of the city belonging to Hampden, Massachusetts?",
"pos": [
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation"
],
"neg": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"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",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"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'",
"SELECT 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.city = 'Guanica'",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3"
]
} |
{
"query": "Show the alias for the county at coordinate (18.090875, -66.867756).",
"pos": [
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756"
],
"neg": [
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"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'",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation"
]
} |
{
"query": "What are the states with an above-average female population?",
"pos": [
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )"
],
"neg": [
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"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",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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'",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT T2.zip_code, T1.first_name, T1.last_name FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.state = 'Wisconsin' ORDER BY T1.land_area DESC LIMIT 1",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1"
]
} |
{
"query": "What is the difference in the number of cities with P.O. box only and cities with Post Office among the cities with area code 787?",
"pos": [
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787"
],
"neg": [
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"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 = 'NEW HAVEN'",
"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'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'"
]
} |
{
"query": "Give the location coordinates of the city with area code 636.",
"pos": [
"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 = 636"
],
"neg": [
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"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'",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"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",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"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'"
]
} |
{
"query": "How many postal points with unique post office types are there in Ohio?",
"pos": [
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'"
],
"neg": [
"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",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"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 = 'NEW HAVEN'",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"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'",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )"
]
} |
{
"query": "Give the alias of the cities with an Asian population of 7.",
"pos": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7"
],
"neg": [
"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 = 'NEW HAVEN'",
"SELECT 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.city = 'Guanica'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )"
]
} |
{
"query": "Among all the residential areas in Arecibo county, what is the zip_code of the one with the highest white population?",
"pos": [
"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"
],
"neg": [
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"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",
"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 = 636",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1"
]
} |
{
"query": "Indicate the name of the congressman represent in Guanica.",
"pos": [
"SELECT 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.city = 'Guanica'"
],
"neg": [
"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",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"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'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'"
]
} |
{
"query": "List down the country of the cities with a population greater than 97% of the average population of all countries in 2020.",
"pos": [
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )"
],
"neg": [
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"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 = 'NEW HAVEN'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"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",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"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",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'York' AND T2.state = 'ME'",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'"
]
} |
{
"query": "Count the number of postal points under New York-Newark-Jersey City, NY-NJ-PA.",
"pos": [
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'"
],
"neg": [
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT 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.city = 'Guanica'",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
]
} |
{
"query": "Provide the zip codes and CBSA officers of the postal point in Oxford.",
"pos": [
"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'"
],
"neg": [
"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'",
"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",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT 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.city = 'Guanica'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'"
]
} |
{
"query": "Among the postal points in California, calculate the percentage of them in post office types.",
"pos": [
"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'"
],
"neg": [
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"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'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'",
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"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",
"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'"
]
} |
{
"query": "Provide the population of Arecibo in 2020.",
"pos": [
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'"
],
"neg": [
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type",
"SELECT T2.county 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.city = 'Dalton' GROUP BY T2.county"
]
} |
{
"query": "Among the listed cities, provide the area code of the city with the largest water area.",
"pos": [
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )"
],
"neg": [
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"SELECT SUM(CASE WHEN T1.time_zone = 'Central' THEN 1 ELSE 0 END) AS count FROM zip_data AS T1 INNER JOIN state AS T2 ON T2.abbreviation = T1.state WHERE T1.time_zone = 'Central'",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT CAST((SUM(T2.population_2020) - SUM(T2.population_2010)) AS REAL) * 100 / SUM(T2.population_2010) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Arroyo'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'"
]
} |
{
"query": "What is the highest gender ratio of the residential areas in Arecibo county?",
"pos": [
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1"
],
"neg": [
"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 = '787' GROUP BY T2.latitude, T2.longitude",
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'",
"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'",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"SELECT CAST(SUM(T2.total_beneficiaries) AS REAL) / COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Guam'",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"SELECT 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.city = 'Guanica'",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress"
]
} |
{
"query": "Among the postal points in Texas, provide the zip codes and cities of postal points which have total beneficiaries of above 10000.",
"pos": [
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000"
],
"neg": [
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000"
]
} |
{
"query": "What is the total number of households in Arecibo county?",
"pos": [
"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'"
],
"neg": [
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"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",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"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'",
"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'",
"SELECT T2.zip_code, T2.city FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Texas' AND T2.total_beneficiaries > 10000"
]
} |
{
"query": "What are the precise locations of the cities with an area code of 787?",
"pos": [
"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 = '787' GROUP BY T2.latitude, T2.longitude"
],
"neg": [
"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 = 636",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'"
]
} |
{
"query": "How many counties are there in Virginia State?",
"pos": [
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Virginia'"
],
"neg": [
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 > 0.97 * ( SELECT AVG(population_2020) FROM zip_data )",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"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'"
]
} |
{
"query": "In which county can you find the city with the highest number of females?",
"pos": [
"SELECT T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1"
],
"neg": [
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress",
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.asian_population = 7",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'",
"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 = 'NEW HAVEN'",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000"
]
} |
{
"query": "List all the zip codes in the county of New Castle in Delaware.",
"pos": [
"SELECT DISTINCT T2.zip_code FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T2.county = 'NEW CASTLE' AND T1.name = 'Delaware'"
],
"neg": [
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"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",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT T1.bad_alias FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'PR'",
"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 = 636",
"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'",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT SUM(T1.asian_population) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'URB San Joaquin'",
"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T2.bad_alias = 'Internal Revenue Service' AND T1.time_zone = 'Eastern'",
"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'"
]
} |
{
"query": "What is the name and the position of the CBSA officer in the city of Cabo Rojo?",
"pos": [
"SELECT T1.CBSA_name, T1.CBSA_type FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.city = 'Cabo Rojo' GROUP BY T1.CBSA_name, T1.CBSA_type"
],
"neg": [
"SELECT T2.zip_code, T2.latitude, T2.longitude FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ'",
"SELECT T1.county FROM country 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 T3.first_name = 'Hartzler' AND T3.last_name = 'Vicky' GROUP BY T1.county",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"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'",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population",
"SELECT COUNT(CASE WHEN T2.type = 'P.O. Box Only' THEN 1 ELSE NULL END) - COUNT(CASE WHEN T2.type = 'Post Office' THEN 1 ELSE NULL END) AS DIFFERENCE FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 787",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'"
]
} |
{
"query": "What is the bad alias of the residential area with the highest average house value?",
"pos": [
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1"
],
"neg": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )",
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'",
"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'",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district",
"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",
"SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'"
]
} |
{
"query": "In California, how many delivery receptacles are there in the community post office that has the highest number of delivery receptacles?",
"pos": [
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'"
],
"neg": [
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"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'",
"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'",
"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 = 636",
"SELECT COUNT(T2.city) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 608 AND T2.daylight_savings = 'Yes'",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"SELECT t.state, T1.first_name, T1.last_name FROM zip_data AS T INNER JOIN congress AS T1 ON t.state = T1.abbreviation GROUP BY t.state ORDER BY SUM(t.asian_population) DESC LIMIT 3",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10"
]
} |
{
"query": "Describe the number of postal points and the countries in West Virginia.",
"pos": [
"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'"
],
"neg": [
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1",
"SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10",
"SELECT T1.zip_code, T1.city, 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 GROUP BY T2.district ORDER BY T1.population_2020 DESC LIMIT 1",
"SELECT CAST(T1.male_population AS REAL) / T1.female_population FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' AND T1.female_population <> 0 ORDER BY 1 DESC LIMIT 1",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC'",
"SELECT T2.city, T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Lawrenceville' GROUP BY T2.city, T2.state",
"SELECT T2.avg_income_per_household FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Danzig'",
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT COUNT(T3.city) FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T1.first_name = 'Murkowski' AND T1.last_name = 'Lisa' AND T3.employees = 0"
]
} |
{
"query": "Provide the alias of the city with the highest population in year 2020.",
"pos": [
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )"
],
"neg": [
"SELECT DISTINCT T1.county FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2010 > 10000",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Barre, VT'",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT DISTINCT T2.name FROM country AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation WHERE T1.county = 'OUTAGAMIE'",
"SELECT COUNT(T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Alabama'",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT T2.state FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T2.state ORDER BY COUNT(T1.bad_alias) DESC LIMIT 1",
"SELECT T2.district FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code WHERE T1.city = 'East Springfield'"
]
} |
{
"query": "List all the counties in Georgia.",
"pos": [
"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"
],
"neg": [
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'",
"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",
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Post Office' LIMIT 5",
"SELECT DISTINCT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'HUNTINGDON' AND T1.employees > 30",
"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'",
"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 = 'NEW HAVEN'",
"SELECT T1.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.type = 'Community Post Office ' AND T2.elevation > 6000",
"SELECT T2.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population"
]
} |
{
"query": "How many post offices are there in New York?",
"pos": [
"SELECT COUNT(DISTINCT T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'NY' AND T2.type = 'Post Office'"
],
"neg": [
"SELECT T3.elevation 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 T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation",
"SELECT COUNT(*) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.abbreviation = 'CA' AND T2.type LIKE '%Community Post Office%' AND T1.name = 'California' AND T2.state = 'CA'",
"SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Ohio' AND T2.type = 'Unique Post Office'",
"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",
"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",
"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1",
"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 = 'NEW HAVEN'",
"SELECT CAST(SUM(CASE WHEN T1.county = 'CORYELL' THEN T2.households ELSE 0 END) AS REAL) * 100 / SUM(T2.households) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code",
"SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.water_area = ( SELECT MAX(water_area) FROM zip_data )",
"SELECT T1.zip_code FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'SAINT CROIX' ORDER BY T2.land_area DESC LIMIT 1"
]
} |
{
"query": "For the county where DeSantis Ron is from, what is the average female median age?",
"pos": [
"SELECT SUM(T4.female_median_age) / COUNT(T1.county) FROM country 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 INNER JOIN zip_data AS T4 ON T1.zip_code = T4.zip_code WHERE T3.first_name = 'DeSantis' AND T3.last_name = 'Ron'"
],
"neg": [
"SELECT SUM(T2.male_median_age) / COUNT(T2.median_age) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'WINDHAM'",
"SELECT SUM(T2.population_2020) FROM country AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.county = 'ARECIBO'",
"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )",
"SELECT T2.alias FROM zip_data AS T1 INNER JOIN alias AS T2 ON T1.zip_code = T2.zip_code WHERE T1.latitude = 18.090875 AND T1.longitude = -66.867756",
"SELECT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.avg_house_value = ( SELECT MAX(avg_house_value) FROM zip_data ) LIMIT 1",
"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10",
"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'",
"SELECT DISTINCT T1.area_code FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN state AS T3 ON T2.state = T3.abbreviation WHERE T2.county = 'PHILLIPS' AND T3.name = 'Montana'",
"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",
"SELECT SUM(T1.female_median_age) / COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'"
]
} |
{
"query": "For the flight on 2018/8/1 that was delayed for the longest time, which was the destination airport of this flight?",
"pos": [
"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/1' ORDER BY T2.DEP_DELAY DESC LIMIT 1"
],
"neg": [
"SELECT CAST(SUM(CASE WHEN T2.DEP_DELAY < 0 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%American Airlines%'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEP_DELAY = 0 GROUP BY T1.Description",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1",
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Chicago, IL: Chicago O''Hare International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Atlanta, GA: Hartsfield-Jackson Atlanta International' )",
"SELECT COUNT(*) FROM Airlines WHERE FL_DATE = '2018/8/1' AND DEP_DELAY <= 0",
"SELECT T1.DEP_TIME FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code INNER JOIN Airports AS T3 ON T1.DEST = T3.Code WHERE T1.FL_DATE = '2018/8/20' AND T1.TAIL_NUM = 'N903JB' AND T2.Description LIKE '%JetBlue Airways%' AND T3.Description LIKE '%Fort Lauderdale-Hollywood%'",
"SELECT COUNT(T3.TAIL_NUM) FROM ( SELECT T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Southwest Airlines Co.: WN' GROUP BY T1.TAIL_NUM ) T3",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.TAIL_NUM = 'N702SK' GROUP BY T2.Description"
]
} |
{
"query": "Which airline operated more flights on 2018/8/1, American Airlines Inc. or Endeavor Air Inc.?",
"pos": [
"SELECT CASE WHEN COUNT(CASE WHEN T3.Description = 'American Airlines Inc.: AA' THEN 1 ELSE NULL END) > COUNT(CASE WHEN T3.Description = 'Endeavor Air Inc.: 9E' THEN 1 ELSE NULL END) THEN 'American Airlines Inc.: AA' ELSE 'Endeavor Air Inc.: 9E' END AS RESULT FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1'"
],
"neg": [
"SELECT T1.Description, T1.Code FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID ORDER BY T2.ARR_TIME ASC LIMIT 1",
"SELECT CAST(SUM(CASE WHEN T2.CANCELLATION_CODE = 'C' THEN 1.0 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018/8/15' AND T2.CANCELLATION_CODE IS NOT NULL AND T1.Description = 'Los Angeles, CA: Los Angeles International'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT COUNT(T3.TAIL_NUM) FROM ( SELECT T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Southwest Airlines Co.: WN' GROUP BY T1.TAIL_NUM ) T3",
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1",
"SELECT T2.TAIL_NUM FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE LIKE '2018/8%' AND T1.Description = 'Bakersfield, CA: Meadows Field' AND T2.DEST = 'BFL' AND T2.ARR_DELAY <= 0 GROUP BY T2.TAIL_NUM",
"SELECT SUM(CASE WHEN T2.ARR_DELAY < 0 THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'New York, NY: John F. Kennedy International'",
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'",
"SELECT T2.FL_DATE FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE LIKE '2018/8%' AND T1.Description = 'Dallas/Fort Worth, TX: Dallas/Fort Worth International' AND T2.ORIGIN = 'DFW' AND T2.CANCELLED = 1 AND T2.CANCELLATION_CODE = 'A' GROUP BY T2.FL_DATE ORDER BY COUNT(T2.FL_DATE) DESC LIMIT 1",
"SELECT T1.TAIL_NUM, SUM(CAST(T1.LATE_AIRCRAFT_DELAY AS REAL) / 60) AS delay FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.FL_DATE LIKE '2018/8/%' AND T2.Description = 'Delta Air Lines Inc.: DL' ORDER BY delay DESC LIMIT 1"
]
} |
{
"query": "For the flight with the tail number 'N702SK', which air carrier does it belong to?",
"pos": [
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.TAIL_NUM = 'N702SK' GROUP BY T2.Description"
],
"neg": [
"SELECT CAST(SUM(CASE WHEN T2.DEP_DELAY < 0 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%American Airlines%'",
"SELECT T2.FL_DATE FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ACTUAL_ELAPSED_TIME < 100 AND T1.Description = 'Profit Airlines Inc.: XBH'",
"SELECT COUNT(T3.TAIL_NUM) FROM ( SELECT T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Southwest Airlines Co.: WN' GROUP BY T1.TAIL_NUM ) T3",
"SELECT DEST FROM Airlines WHERE ORIGIN = 'ABY' GROUP BY DEST",
"SELECT T1.Description, T1.Code FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID ORDER BY T2.ARR_TIME ASC LIMIT 1",
"SELECT Code FROM Airports WHERE Description LIKE '%Ankara, Turkey%'",
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT SUM(CASE WHEN T2.ARR_DELAY < 0 THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'New York, NY: John F. Kennedy International'",
"SELECT T1.TAIL_NUM, SUM(CAST(T1.LATE_AIRCRAFT_DELAY AS REAL) / 60) AS delay FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.FL_DATE LIKE '2018/8/%' AND T2.Description = 'Delta Air Lines Inc.: DL' ORDER BY delay DESC LIMIT 1",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'"
]
} |
{
"query": "What is the actual departure time of JetBlue Airways with the plane's tail number N903JB to Fort Lauderdale-Hollywood International Airport on the 20th of August 2018?",
"pos": [
"SELECT T1.DEP_TIME FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code INNER JOIN Airports AS T3 ON T1.DEST = T3.Code WHERE T1.FL_DATE = '2018/8/20' AND T1.TAIL_NUM = 'N903JB' AND T2.Description LIKE '%JetBlue Airways%' AND T3.Description LIKE '%Fort Lauderdale-Hollywood%'"
],
"neg": [
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'",
"SELECT Code FROM Airports WHERE Description LIKE '%Ankara, Turkey%'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.CANCELLED = 1 GROUP BY T1.Description",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Southwest Airlines Co.: WN' AND T2.ACTUAL_ELAPSED_TIME < ( SELECT AVG(ACTUAL_ELAPSED_TIME) * 0.8 FROM Airlines )",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT SUM(CASE WHEN T2.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT SUM(CASE WHEN T2.FL_DATE = '2018/8/27' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Los Angeles, CA: Los Angeles International'",
"SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'",
"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/1' ORDER BY T2.DEP_DELAY DESC LIMIT 1",
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/2' AND T2.Description = 'Alaska Airlines Inc.: AS' AND T1.DEP_DELAY > 0"
]
} |
{
"query": "List the air carrier description and code of the flight with the shortest arrival time.",
"pos": [
"SELECT T1.Description, T1.Code FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID ORDER BY T2.ARR_TIME ASC LIMIT 1"
],
"neg": [
"SELECT T3.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T1.Description = 'Chicago, IL: Chicago Midway International' AND T2.DEST = 'MDW' GROUP BY T3.Description ORDER BY COUNT(T3.Description) DESC LIMIT 1",
"SELECT CAST( SUM(CASE WHEN T2.FL_DATE LIKE '2018/8%' THEN 1 ELSE 0 END) AS REAL) / 31 FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1",
"SELECT DEST FROM Airlines WHERE ORIGIN = 'ABY' GROUP BY DEST",
"SELECT CAST(SUM(CASE WHEN T2.CANCELLATION_CODE = 'C' THEN 1.0 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018/8/15' AND T2.CANCELLATION_CODE IS NOT NULL AND T1.Description = 'Los Angeles, CA: Los Angeles International'",
"SELECT T1.DEP_TIME FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code INNER JOIN Airports AS T3 ON T1.DEST = T3.Code WHERE T1.FL_DATE = '2018/8/20' AND T1.TAIL_NUM = 'N903JB' AND T2.Description LIKE '%JetBlue Airways%' AND T3.Description LIKE '%Fort Lauderdale-Hollywood%'",
"SELECT COUNT(*) FROM Airports WHERE Code LIKE 'C%'",
"SELECT SUM(CASE WHEN T1.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/31' AND T2.Description = 'Endeavor Air Inc.: 9E'",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'"
]
} |
{
"query": "What is the only flight destination for flights from Albany?",
"pos": [
"SELECT DEST FROM Airlines WHERE ORIGIN = 'ABY' GROUP BY DEST"
],
"neg": [
"SELECT CAST( SUM(CASE WHEN T2.FL_DATE LIKE '2018/8%' THEN 1 ELSE 0 END) AS REAL) / 31 FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT Code FROM Airports WHERE Description LIKE '%Ankara, Turkey%'",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Asap Air Inc.: ASP' ORDER BY T2.DEP_DELAY DESC LIMIT 1",
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'",
"SELECT T1.OP_CARRIER_AIRLINE_ID FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ACTUAL_ELAPSED_TIME IS NOT NULL AND T1.CRS_ELAPSED_TIME IS NOT NULL ORDER BY T1.ACTUAL_ELAPSED_TIME - T1.CRS_ELAPSED_TIME ASC LIMIT 1",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEP_DELAY = 0 GROUP BY T1.Description",
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'"
]
} |
{
"query": "Provide the number of airplanes that landed on Oakland Airport on 2018/8/7.",
"pos": [
"SELECT SUM(CASE WHEN T1.Description LIKE '%Oakland%' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/7'"
],
"neg": [
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1",
"SELECT T3.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T1.Description = 'Chicago, IL: Chicago Midway International' AND T2.DEST = 'MDW' GROUP BY T3.Description ORDER BY COUNT(T3.Description) DESC LIMIT 1",
"SELECT CAST( SUM(CASE WHEN T2.FL_DATE LIKE '2018/8%' THEN 1 ELSE 0 END) AS REAL) / 31 FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT TAIL_NUM FROM Airlines WHERE FL_DATE = '2018/8/17' GROUP BY TAIL_NUM",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'",
"SELECT T1.OP_CARRIER_AIRLINE_ID FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ACTUAL_ELAPSED_TIME IS NOT NULL AND T1.CRS_ELAPSED_TIME IS NOT NULL ORDER BY T1.ACTUAL_ELAPSED_TIME - T1.CRS_ELAPSED_TIME ASC LIMIT 1",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ARR_TIME <= 1000 AND T1.Description = 'Iscargo Hf: ICQ'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEST = 'MIA'",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1"
]
} |
{
"query": "What is the name of the airline with the highest number of non-cancelled flights?",
"pos": [
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1"
],
"neg": [
"SELECT T3.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T1.Description = 'Chicago, IL: Chicago Midway International' AND T2.DEST = 'MDW' GROUP BY T3.Description ORDER BY COUNT(T3.Description) DESC LIMIT 1",
"SELECT COUNT(*) FROM Airlines WHERE FL_DATE = '2018/8/1' AND DEP_DELAY <= 0",
"SELECT SUM(CASE WHEN T1.Description LIKE '%Oakland%' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/7'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT T2.TAIL_NUM FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE LIKE '2018/8%' AND T1.Description = 'Bakersfield, CA: Meadows Field' AND T2.DEST = 'BFL' AND T2.ARR_DELAY <= 0 GROUP BY T2.TAIL_NUM",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Southwest Airlines Co.: WN' AND T2.ACTUAL_ELAPSED_TIME < ( SELECT AVG(ACTUAL_ELAPSED_TIME) * 0.8 FROM Airlines )",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'",
"SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'",
"SELECT T2.OP_CARRIER_FL_NUM FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA' AND T1.Description = 'New York, NY: John F. Kennedy International' AND T2.FL_DATE = '2018/8/1'"
]
} |
{
"query": "How many flights of air carrier called JetBlue Airways: B6 have 0 new arrival delay?",
"pos": [
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%JetBlue Airways: B6%' AND T2.ARR_DELAY_NEW = 0"
],
"neg": [
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEP_DELAY = 0 GROUP BY T1.Description",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'",
"SELECT DEST FROM Airlines WHERE ORIGIN = 'ABY' GROUP BY DEST",
"SELECT COUNT(*) FROM Airports WHERE Code LIKE 'C%'",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.TAIL_NUM = 'N922US' AND T1.ORIGIN = 'PHX' GROUP BY T2.Description",
"SELECT CAST( SUM(CASE WHEN T2.FL_DATE LIKE '2018/8%' THEN 1 ELSE 0 END) AS REAL) / 31 FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ARR_TIME <= 1000 AND T1.Description = 'Iscargo Hf: ICQ'",
"SELECT COUNT(*) FROM Airlines WHERE FL_DATE = '2018/8/1' AND DEP_DELAY <= 0",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT T2.OP_CARRIER_FL_NUM FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA' AND T1.Description = 'New York, NY: John F. Kennedy International' AND T2.FL_DATE = '2018/8/1'"
]
} |
{
"query": "Provide the origin of the flight that has the shortest actual elapsed time.",
"pos": [
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1"
],
"neg": [
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Republic Airline%' AND T2.DEP_DELAY > 30",
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.CANCELLED = 1 GROUP BY T1.Description",
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.TAIL_NUM = 'N922US' AND T1.ORIGIN = 'PHX' GROUP BY T2.Description",
"SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1"
]
} |
{
"query": "Which airport did Republic Airline fly the most from?",
"pos": [
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1"
],
"neg": [
"SELECT T1.DEP_TIME FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code INNER JOIN Airports AS T3 ON T1.DEST = T3.Code WHERE T1.FL_DATE = '2018/8/20' AND T1.TAIL_NUM = 'N903JB' AND T2.Description LIKE '%JetBlue Airways%' AND T3.Description LIKE '%Fort Lauderdale-Hollywood%'",
"SELECT Code FROM Airports WHERE Description LIKE '%Ankara, Turkey%'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEST = 'MIA'",
"SELECT COUNT(T3.TAIL_NUM) FROM ( SELECT T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Southwest Airlines Co.: WN' GROUP BY T1.TAIL_NUM ) T3",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'",
"SELECT T2.ACTUAL_ELAPSED_TIME FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Semo Aviation Inc.: SEM'",
"SELECT COUNT(*) FROM Airports WHERE Code LIKE 'C%'",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEST = 'PHX' AND T2.ARR_TIME < ( SELECT AVG(ARR_TIME) * 0.4 FROM Airlines ) GROUP BY T1.Description",
"SELECT COUNT(*) AS num FROM Airlines WHERE Origin = 'OKC'",
"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/1' ORDER BY T2.DEP_DELAY DESC LIMIT 1"
]
} |
{
"query": "What is the average departure delay time of flights operated by American Airlines Inc.?",
"pos": [
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'"
],
"neg": [
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.CANCELLED = 1 GROUP BY T1.Description",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Southwest Airlines Co.: WN' AND T2.ACTUAL_ELAPSED_TIME < ( SELECT AVG(ACTUAL_ELAPSED_TIME) * 0.8 FROM Airlines )",
"SELECT T2.FL_DATE FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ACTUAL_ELAPSED_TIME < 100 AND T1.Description = 'Profit Airlines Inc.: XBH'",
"SELECT TAIL_NUM FROM Airlines WHERE FL_DATE = '2018/8/17' GROUP BY TAIL_NUM",
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Chicago, IL: Chicago O''Hare International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Atlanta, GA: Hartsfield-Jackson Atlanta International' )",
"SELECT Code FROM Airports WHERE Description = 'Driftwood Bay, AK: Driftwood Bay Airport'",
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'"
]
} |
{
"query": "What is the total number of flights that flew on August 2, 2018 with air carrier described as Horizon Air?",
"pos": [
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%Horizon Air%' AND T2.FL_DATE = '2018/8/2'"
],
"neg": [
"SELECT T2.TAIL_NUM FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE LIKE '2018/8%' AND T1.Description = 'Bakersfield, CA: Meadows Field' AND T2.DEST = 'BFL' AND T2.ARR_DELAY <= 0 GROUP BY T2.TAIL_NUM",
"SELECT COUNT(*) AS num FROM Airlines WHERE Origin = 'OKC'",
"SELECT SUM(CASE WHEN T2.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT T2.FL_DATE FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE LIKE '2018/8%' AND T1.Description = 'Dallas/Fort Worth, TX: Dallas/Fort Worth International' AND T2.ORIGIN = 'DFW' AND T2.CANCELLED = 1 AND T2.CANCELLATION_CODE = 'A' GROUP BY T2.FL_DATE ORDER BY COUNT(T2.FL_DATE) DESC LIMIT 1",
"SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'",
"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/1' ORDER BY T2.DEP_DELAY DESC LIMIT 1",
"SELECT TAIL_NUM FROM Airlines WHERE FL_DATE = '2018/8/17' GROUP BY TAIL_NUM",
"SELECT T2.ACTUAL_ELAPSED_TIME FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Semo Aviation Inc.: SEM'",
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1"
]
} |
{
"query": "How many planes of Spirit Air Lines took off on 2018/8/7?",
"pos": [
"SELECT COUNT(T2.Code) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/7' AND T2.Description = 'Spirit Air Lines: NK'"
],
"neg": [
"SELECT SUM(CASE WHEN T1.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018/8/31' AND T2.Description = 'Endeavor Air Inc.: 9E'",
"SELECT T1.TAIL_NUM, SUM(CAST(T1.LATE_AIRCRAFT_DELAY AS REAL) / 60) AS delay FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.FL_DATE LIKE '2018/8/%' AND T2.Description = 'Delta Air Lines Inc.: DL' ORDER BY delay DESC LIMIT 1",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.FL_DATE = '2018/8/25' GROUP BY T1.Description",
"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/1' ORDER BY T2.DEP_DELAY DESC LIMIT 1",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT SUM(CASE WHEN T1.Description LIKE '%Oakland%' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/7'",
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'",
"SELECT SUM(CASE WHEN T2.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE FL_DATE LIKE '2018/8%' AND ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'San Diego, CA: San Diego International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Los Angeles, CA: Los Angeles International' )",
"SELECT COUNT(*) AS num FROM Airlines WHERE Origin = 'OKC'"
]
} |
{
"query": "How many flights from Charlotte Douglas International Airport to Austin - Bergstrom International Airport experienced serious reasons that cause flight cancellation?",
"pos": [
"SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A'"
],
"neg": [
"SELECT Code FROM Airports WHERE Description = 'Driftwood Bay, AK: Driftwood Bay Airport'",
"SELECT COUNT(*) AS num FROM Airlines WHERE Origin = 'OKC'",
"SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'",
"SELECT T2.ACTUAL_ELAPSED_TIME FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Semo Aviation Inc.: SEM'",
"SELECT COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT COUNT(T3.TAIL_NUM) FROM ( SELECT T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Southwest Airlines Co.: WN' GROUP BY T1.TAIL_NUM ) T3",
"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ARR_TIME <= 1000 AND T1.Description = 'Iscargo Hf: ICQ'",
"SELECT AVG(T1.DEP_DELAY) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T1.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T3.Description = 'American Airlines Inc.: AA'",
"SELECT T3.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T1.Description = 'Chicago, IL: Chicago Midway International' AND T2.DEST = 'MDW' GROUP BY T3.Description ORDER BY COUNT(T3.Description) DESC LIMIT 1",
"SELECT CAST(SUM(CASE WHEN T2.DEP_DELAY < 0 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%American Airlines%'"
]
} |
{
"query": "What is the percentage of flights from Los Angeles International airport that were cancelled due to a type C cancellation code?",
"pos": [
"SELECT CAST(SUM(CASE WHEN T2.CANCELLATION_CODE = 'C' THEN 1.0 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018/8/15' AND T2.CANCELLATION_CODE IS NOT NULL AND T1.Description = 'Los Angeles, CA: Los Angeles International'"
],
"neg": [
"SELECT T2.FL_DATE FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ACTUAL_ELAPSED_TIME < 100 AND T1.Description = 'Profit Airlines Inc.: XBH'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Chicago, IL: Chicago O''Hare International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Atlanta, GA: Hartsfield-Jackson Atlanta International' )",
"SELECT CASE WHEN COUNT(CASE WHEN T3.Description = 'American Airlines Inc.: AA' THEN 1 ELSE NULL END) > COUNT(CASE WHEN T3.Description = 'Endeavor Air Inc.: 9E' THEN 1 ELSE NULL END) THEN 'American Airlines Inc.: AA' ELSE 'Endeavor Air Inc.: 9E' END AS RESULT FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1'",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%JetBlue Airways: B6%' AND T2.ARR_DELAY_NEW = 0",
"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.DEST = 'PHX' AND T2.ARR_TIME < ( SELECT AVG(ARR_TIME) * 0.4 FROM Airlines ) GROUP BY T1.Description",
"SELECT T1.Description, T1.Code FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID ORDER BY T2.ARR_TIME ASC LIMIT 1",
"SELECT SUM(CASE WHEN T2.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'",
"SELECT COUNT(*) AS num FROM Airlines WHERE Origin = 'OKC'",
"SELECT ORIGIN_AIRPORT_ID FROM Airlines ORDER BY LATE_AIRCRAFT_DELAY DESC LIMIT 1"
]
} |
{
"query": "How many flights operated by American Airlines Inc. on 2018/8/1 were faster than scheduled?",
"pos": [
"SELECT SUM(CASE WHEN T2.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018/8/1' AND T3.Description = 'American Airlines Inc.: AA'"
],
"neg": [
"SELECT T2.ACTUAL_ELAPSED_TIME FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Semo Aviation Inc.: SEM'",
"SELECT ORIGIN_AIRPORT_ID FROM Airlines ORDER BY LATE_AIRCRAFT_DELAY DESC LIMIT 1",
"SELECT ORIGIN FROM Airlines ORDER BY ACTUAL_ELAPSED_TIME ASC LIMIT 1",
"SELECT T2.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Republic Airline: YX' GROUP BY T2.DEST ORDER BY COUNT(T2.DEST) DESC LIMIT 1",
"SELECT CAST(SUM(CASE WHEN T2.DEP_DELAY < 0 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description LIKE '%American Airlines%'",
"SELECT T1.TAIL_NUM, SUM(CAST(T1.LATE_AIRCRAFT_DELAY AS REAL) / 60) AS delay FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.FL_DATE LIKE '2018/8/%' AND T2.Description = 'Delta Air Lines Inc.: DL' ORDER BY delay DESC LIMIT 1",
"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.TAIL_NUM = 'N702SK' GROUP BY T2.Description",
"SELECT CAST(SUM(CASE WHEN T2.CANCELLATION_CODE = 'C' THEN 1.0 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018/8/15' AND T2.CANCELLATION_CODE IS NOT NULL AND T1.Description = 'Los Angeles, CA: Los Angeles International'",
"SELECT COUNT(FL_DATE) FROM Airlines WHERE ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Chicago, IL: Chicago O''Hare International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Atlanta, GA: Hartsfield-Jackson Atlanta International' )",
"SELECT SUM(CASE WHEN T1.Description LIKE '%Oakland%' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/7'"
]
} |
End of preview. Expand
in Dataset Viewer.
README.md exists but content is empty.
Use the Edit dataset card button to edit it.
- Downloads last month
- 31