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Welcome to uszipcode Documentation

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uszipcode is the most powerful and easy to use programmable zipcode database in Python. It comes with a rich feature and easy-to-use zipcode search engine. And it is easy to customize the search behavior as you wish.

About the Data


I started from a academic research project for personal use. I don’t promise for data accuracy, please use with your own risk.

Where the data comes from?

The data is crawled from There’s data tool allows you to explore 1300+ data points of a zipcode. You can play it yourself with this link

Is this data set Up-to-Date?

Even the use different source for different data fields. For example, the latest general population / income / education data by zipcode are still from Census2010. But population over time data are based from IRS until FY 2018.

In general, static statistic data are from Census 2010. Demographic statistics over time has data utill 2020.

How many Zipcode in this Database

There are 42,724 zipcodes in this database. There are four different type zipcode:

  • STANDARD: most common zipcode

  • PO Box: for post office

  • UNIQUE: special location, usually a single building

  • MILITARY: military location

Number of zipcodes for each type:

| zipcode_type | count | percentage |
|   STANDARD   | 30001 |   70.22    |
|    PO BOX    |  9397 |   21.99    |
|    UNIQUE    |  2539 |    5.94    |
|   MILITARY   |  787  |    1.84    |

I found a Great data source, how to contribute?

You can open an Issue and leave the URL of the data source, brief description about the dataset.

The Data point

Address, Postal

  • zipcode

  • zipcode_type

  • major_city

  • post_office_city

  • common_city_list

  • county

  • state

  • area_code_list


  • lat

  • lng

  • timezone

  • radius_in_miles

  • land_area_in_sqmi

  • water_area_in_sqmi

  • bounds_west

  • bounds_east

  • bounds_north

  • bounds_south

  • border polygon

Stats and Demographics

  • population

  • population_density

  • population_by_year

  • population_by_age

  • population_by_gender

  • population_by_race

  • head_of_household_by_age

  • families_vs_singles

  • households_with_kids

  • children_by_age

Real Estate and Housing

  • housing_units

  • occupied_housing_units

  • median_home_value

  • median_household_income

  • housing_type

  • year_housing_was_built

  • housing_occupancy

  • vacancy_reason

  • owner_occupied_home_values

  • rental_properties_by_number_of_rooms

  • monthly_rent_including_utilities_studio_apt

  • monthly_rent_including_utilities_1_b

  • monthly_rent_including_utilities_2_b

  • monthly_rent_including_utilities_3plus_b

Employment, Income, Earnings, and Work

  • employment_status

  • average_household_income_over_time

  • household_income

  • annual_individual_earnings

  • sources_of_household_income____percent_of_households_receiving_income

  • sources_of_household_income____average_income_per_household_by_income_source

  • household_investment_income____percent_of_households_receiving_investment_income

  • household_investment_income____average_income_per_household_by_income_source

  • household_retirement_income____percent_of_households_receiving_retirement_incom

  • household_retirement_income____average_income_per_household_by_income_source

  • source_of_earnings

  • means_of_transportation_to_work_for_workers_16_and_over

  • travel_time_to_work_in_minutes


  • educational_attainment_for_population_25_and_over

  • school_enrollment_age_3_to_17


uszipcode is released on PyPI, so all you need is:

$ pip install uszipcode

To upgrade to latest version:

$ pip install --upgrade uszipcode

Table of Content

About the Author

(\ (\
( -.-)o    I am a lovely Rabbit!

Sanhe Hu is a very active Python Developer Since 2010. Research area includes Machine Learning, Big Data Infrastructure, Block Chain, Business Intelligent, AWS, Distributive System. Love photography, outdoor, arts, game, and also the best Python.

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