US Census Geocoder

(Unofficial) Python Binding for the US Census Geocoder API


Unit Tests


Build Status (Travis CI) Code Coverage Status (Codecov) Documentation Status (ReadTheDocs)


Build Status (Travis CI) Code Coverage Status (Codecov) Documentation Status (ReadTheDocs)


Build Status (Travis CI) Code Coverage Status (Codecov) Documentation Status (ReadTheDocs)

The US Census Geocoder is a Python library that provides Python bindings for the U.S. Census Geocoder API. It enables you to use simple Python function calls to retrieve Python object representations of geographic meta-data for the addresses or coordinates that you are searching for.


The US Census Geocoder is completely unofficial, and is in no way affiliated with the US Government or the US Census Bureau. We strongly recommend that you do business with them directly as needed, and simply provide this Python library as a facilitator for your programmatic interactions with the excellent services provided by the US Census Bureau.


To install the US Census Geocoder, just execute:

$ pip install census-geocoder

Why the Census Geocoder?

In fulfilling its constitutional and statutory obligations, the US Census Bureau provides extensive data about the United States. They make this data available publicly through their website, through their raw data files, and through their APIs. However, while their public APIs provide great data, they are limited in both tooling and documentation. So to help with that, we’ve created the US Census Geocoder library.

The Census Geocoder library is designed to provide a Pythonic interface for interacting with the Census Bureau’s Geocoder API. It is specifically designed to eliminate the scaffolding needed to query the API directly, and provides for simpler and cleaner function calls to return forward geocoding and reverse geocoding information. Furthermore, it exposes Python object representations of the outputs returned by the API making it easy to work with the API’s data in your applications.

Key Census Geocoder Features

  • Easy to adopt. Just install and import the library, and you can be forward geocoding and reverse geocoding with just two lines of code.

  • Extensive documentation. One of the main limitations of the Geocoder API is that its documentation is scattered across the different datasets released by the Census Bureau, making it hard to navigate and understand. We’ve tried to fix that.

  • Location Search

    • Using Geographic Coordinates (reverse geocoding)

    • Using a One-line Address

    • Using a Parametrized Address

    • Using Batched Addresses

  • Geography Search

    • Using Geographic Coordinates (reverse geocoding)

    • Using a One-line Address

    • Using a Parametrized Address

    • Using Batched Addresses

  • Supports all available benchmarks, vintages, and layers.

  • Simplified syntax for indicating benchmarks, vintages, and layers.

  • No more hard to interpret field names. The library uses simplified (read: human understandable) names for location and geography properties.

The US Census Geocoder vs Alternatives

While we’re partial to the US Census Geocoder as our primary means of interacting with the Census Geocoder API, there are obviously alternatives for you to consider. Some might be better for your use specific use cases, so here’s how we think about them:

The Census Geocoder API is a straightforward RESTful API. Which means that you can just execute your own HTTP requests against it, retrieve the JSON results, and work with the resulting data entirely yourself. This is what I did for years, until I got tired of repeating the same patterns over and over again, and decided to build the Census Geocoder instead.

For a super-simple use case, probably the most expedient way to do it. But of course, more robust use cases would require your own scaffolding with built-in retry-logic, object representation, error handling, etc. which becomes non-trivial.

Why not use a library with batteries included?


When to use it?

In practice, I find that rolling my own solution is great when it’s an extremely simple use case, or a one-time operation (e.g. in a Jupyter Notebook) with no business logic to speak of. It’s a “quick-and-dirty” solution, where I’m trading rapid implementation (yay!) for less flexibility/functionality (boo!).

Considering how easy the Census Geocoder is to use, however, I find that I never really roll my own scaffolding when working with the Census Geocoder API.

Hello World and Basic Usage

1. Import the Census Geocoder

import census_geocoder as geocoder

2. Execute a Coding Request

Using a One-line Address

location = geocoder.location.from_address('4600 Silver Hill Rd, Washington, DC 20233')

geography = geocoder.geography.from_address('4600 Silver Hill Rd, Washington, DC 20233')

Using a Parametrized Address

location = geocoder.location.from_address(street_1 = '4600 Silver Hill Rd',
                                          city = 'Washington',
                                          state = 'DC',
                                          zip_code = '20233')

geography = geocoder.geography.from_address(street_1 = '4600 Silver Hill Rd',
                                            city = 'Washington',
                                            state = 'DC',
                                            zip_code = '20233')

Using Batched Addresses

# Via a CSV File
location = geocoder.location.from_batch('my-batched-address-file.csv')

geography = geocoder.geography.from_batch('my-batched-address-file.csv')

Using Coordinates

location = geocoder.location.from_coordinates(latitude = 38.845985,
                                              longitude = -76.92744)

geography = geocoder.geography.from_coordinates(latitude = 38.845985,
                                                longitude = -76.92744)

3. Work with the Results

Work with Python Objects



Questions and Issues

You can ask questions and report issues on the project’s Github Issues Page


We welcome contributions and pull requests! For more information, please see the Contributor Guide. And thanks to all those who’ve already contributed:


We use TravisCI for our build automation and ReadTheDocs for our documentation.

Detailed information about our test suite and how to run tests locally can be found in our Testing Reference.


The Census Geocoder is made available under an MIT License.