Free U.S. ZIP Code Database with 7 essential data fields for personal use. Includes all 42,000+ ZIP codes with city, state, latitude, longitude, classification, and 2020 Census population. Updated monthly with lifetime access. Download in CSV, Excel, Access, and SQL formats at no cost. Perfect for educational projects, address validation, basic mapping, and personal applications. No credit card required.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

This is the list of census zip codes according to census.gov for 2013.This dataset gets refreshed as new data is published by the U.S. Census Bureau.

Includes Maryland Zip Codes and their corresponding cities/towns, and counties.

US Geographic Codes Dataset

This code is used to generate a combined data set of US ZIP, FIPS, and County data for most ZIP Codes in the U.S. (41,867 to be exact).

Code to generate the data set from the government files listed below can be found here.

The Data

The dataset is organized as follows:

• Zip Code: USPS ZIP code from here
• State Name: Full state name (E.g. Michigan)
• State Abrv: USPS abbreviated state name (E.g: MI)
• State Code: FIPS State Code from here
• County Name: County in which ZIP is located
• County Code: FIPS County Code
• FIPS Code: FIPS State Code + FIPS County Code from here
• ANSI Code: American National Standards Institute Code
• Centroid Lat: Latitude value of the county center
• Centroid Long: Longitude value of the county center

Sources

The data used to create this data set was taken from several recent government data sets.

These are:

• 2017 US Gazetteer FIPS Dataset
• 2017 US Census County Polygons Dataset
• 2015 Department of Labor GPCI Dataset
• General Census State Code Reference Document

Disclaimers

The final csv is in 'latin1' encoding to preserve the Spanish county names in Puerto Rico.

This data is from, and shall remain in the public domain, and the onus of responsibility lies with the user of this data.

March 2024

A dataset listing New York zip codes by population for 2024.

Dataset

This dataset was created by haakakak

Contents

A dataset listing Florida zip codes by population for 2024.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

This dataset is used in Mito a Excel-like pivot tables in Python. It contains all city and states name and postal code. Columns - 8 (zip, city, state, division, parking, pets, food, lounge) Shape - 854 * 8

Comprehensive Canadian Postal Code Database with complete PCCF-equivalent Statistics Canada census geography linkage. Includes 900,000+ postal codes with latitude/longitude coordinates, census demographic data, Federal Electoral Districts, and 17 supplemental reference tables. Available in Standard (8 fields), Deluxe (15 fields), and Business (43 fields) editions. Business edition includes pre-integrated Census Metropolitan Areas (CMA), Census Divisions (CD), Census Subdivisions (CSD), Dissemination Areas (DA), Census Tracts (CT), Economic Regions (ER), Population Centres, and Federal Electoral Districts-eliminating the need for separate PCCF file management. All editions include monthly updates from Canada Post, bilingual municipality names, accent supplement tables, and geocoding coordinates with ~99% coverage. Multiple formats: Microsoft Access, Excel, and CSV. Includes free FTP access, U.S.-based phone and email support, and 30-day money-back guarantee.

This dataset contains annual Excel pivot tables that identify where a facility’s patients come from (Patient Origin) and where patients from a specific area go to (Market Share) for hospital inpatient, emergency department, and ambulatory surgery treatment. The Patient Origin Report shows the ZIP codes of origin (based on a selected facility or facility county) and the Market Share Report lists the destination facilities (based on a selected patient ZIP code or patient county). Note: Physician-owned ambulatory surgery clinics do not report their data to HCAI and, therefore, are not included here.

This dataset, named "state_trends.csv," contains information about different U.S. states. Let's break down the attributes and understand what each column represents:

1. state: The name of the U.S. state.
2. state_code: The two-letter postal code abbreviation for the state.
3. population: The population of the state.
4. sq_miles: The total land area of the state in square miles.
5. pop_density: Population density, which is the number of people per square mile.
6. region: The geographical region of the United States to which the state belongs (e.g., South, West).
7. psych_region: A description of the psychological region based on personality traits.
8. psy_reg: A shortened version of the psychological region.
9. extraversion: A measure of the state's population tendency toward extraversion.
10. agreeableness: A measure of the state's population tendency toward agreeableness.
11. conscientiousness: A measure of the state's population tendency toward conscientiousness.
12. neuroticism: A measure of the state's population tendency toward neuroticism.
13. openness: A measure of the state's population tendency toward openness.
14. data_science: A score related to the state's interest or proficiency in the field of data science.
15. artificial_intelligence: A score related to the state's interest or proficiency in artificial intelligence.
16. machine_learning: A score related to the state's interest or proficiency in machine learning.
17. data_analysis: A score related to the state's interest or proficiency in data analysis.
18. business_intelligence: A score related to the state's interest or proficiency in business intelligence.
19. spreadsheet: A score related to the state's interest or proficiency in spreadsheet usage.
20. statistics: A score related to the state's interest or proficiency in statistics.
21. art: A score related to the state's interest or involvement in the field of art.
22. dance: A score related to the state's interest or involvement in dance.
23. museum: A score related to the state's interest or presence of museums.
24. basketball: A score related to the state's interest or involvement in basketball.
25. football: A score related to the state's interest or involvement in football.
26. baseball: A score related to the state's interest or involvement in baseball.
27. soccer: A score related to the state's interest or involvement in soccer.
28. hockey: A score related to the state's interest or involvement in hockey.
29. has_nba: Indicates whether the state has a National Basketball Association (NBA) team (Yes/No).
30. has_nfl: Indicates whether the state has a National Football League (NFL) team (Yes/No).
31. has_mlb: Indicates whether the state has a Major League Baseball (MLB) team (Yes/No).
32. has_mls: Indicates whether the state has a Major League Soccer (MLS) team (Yes/No).
33. has_nhl: Indicates whether the state has a National Hockey League (NHL) team (Yes/No).
34. has_any: Indicates whether the state has any of the mentioned professional sports teams (Yes/No).

In summary, this dataset provides a variety of information about U.S. states, including demographic data, geographical region, psychological region, personality traits, and scores related to interests or proficiencies in various fields such as data science, art, and sports.

The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census. The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences. The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences. An update of the Rural-Urban Commuting Area Codes is planned for late 2013.

This tool--a simple csv or Stata file for merging--gives you a fast way to assign Census county FIPS codes to variously presented county names. This is useful for dealing with county names collected from official sources, such as election returns, which inconsistently present county names and often have misspellings. It will likely take less than ten minutes the first time, and about one minute thereafter--assuming all versions of your county names are in this file. There are about 3,142 counties in the U.S., and there are 77,613 different permutations of county names in this file (ave=25 per county, max=382). Counties with more likely permutations have more versions. Misspellings were added as I came across them over time. I DON'T expect people to cite the use of this tool. DO feel free to suggest the addition of other county name permutations.