Skip to main content

Census data for arbitrary geographies

Project description

This Python library extends the Sunlight Foundation’s Census API Wrapper to allow querying Census tracts, block groups, and blocks by Census place, as well as by arbitrary geographies.


Get the library and its dependencies using pip:

pip install census_area


from census_area import Census

c = Census("MY_API_KEY")
old_homes = c.acs5.state_place_tract(('NAME', 'B25034_010E'), 17, 14000)

The call above will return the name of the census tract and the number of homes that were built before 1939 for every tract in the City of Chicago. 17 is the FIPS code for Illinois and 14000 is the FIPS code for Chicago.

By default, this method will return a list of dictionaries, where each dictionary represents the data for one tract.

With the return_geometry argument, you can have the method return a geojson-like dictionary. Each tract is a feature, and the census variables about the tract appear in the feature’s property attributes.

old_homes_geojson = c.acs5.state_place_tract(('NAME', 'B25034_010E'), 17, 14000), return_geometry=True)

There are similar methods for block groups

old_home_block_groups = c.acs5.state_place_blockgroup(('NAME', 'B25034_010E'), 17, 14000))

And blocks. Note that block level geographies are only available for the short-form data from the Decennial Census

owner_occupied = c.sf1.state_place_block(('NAME', 'H016F0002'), 17, 14000)

The tract and blockgroup methods are also available for the Decennial Census.

owner_occupied_blockgroup = c.sf1.state_place_tract(('NAME', 'H016F0002'), 17, 14000)
owner_occupied_tract = c.sf1.state_place_blockgroup(('NAME', 'H016F0002'), 17, 14000)

old_homes = c.sf1.state_place_tract('NAME', 'H034010'), 17, 14000)
old_homes = c.sf1.state_place_blockgroup('NAME', 'H034010'), 17, 14000)

In addition to these convenient methods, there are three lower level ways to get census tracts, blocks, and groups for arbitrary geometries.

import json

with open('my_shape.geojson') as infile:
    my_shape_geojson = json.load(infile)
features = []
old_homes = c.acs5.geo_tract(('NAME', 'B25034_010E'), my_shape_geojson['geometry'])
for tract_geojson, tract_data, tract_proportion in old_homes:

my_shape_with_new_data_geojson = {'type': "FeatureCollection", 'features': features}

The method takes in the census variables you want and a geojson geometry, and returns a generator of the tract shapes, as geojson features, and the variables for that tract. Additionally, the generator returns a “tract proportion”; this is the proportion of the area of the tract that falls within your target shape.

Similar methods are provided for block groups and blocks, for the ACS 5-year and Decennial Census.

c.acs5.geo_blockgroup(('NAME', 'B25034_010E'), my_shape_geojson['geometry'])

c.sf1.geo_block(('NAME', 'H016F0002'), my_shape_geojson['geometry'])
c.sf1.geo_blockgroup(('NAME', 'H016F0002'), my_shape_geojson['geometry'])
c.sf1.geo_tract(('NAME', 'H016F0002'), my_shape_geojson['geometry'])

c.sf1.state_place_tract('NAME', 'H034010'), my_shape_geojson['geometry'])
c.sf1.state_place_blockgroup('NAME', 'H034010'), my_shape_geojson['geometry'])


  • Jean Cochrane, DataMade

  • Forest Gregg, DataMade

Errors and bugs

If something is not behaving intuitively, it is a bug and should be reported. Report it here by creating an issue:

Help us fix the problem as quickly as possible by following Mozilla’s guidelines for reporting bugs.

Patches and pull requests

Your patches are welcome. Here’s our suggested workflow:

  • Fork the project.

  • Make your feature addition or bug fix.

  • Send us a pull request with a description of your work. Bonus points for topic branches!

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

census_area-0.4.3.tar.gz (10.9 kB view hashes)

Uploaded source

Built Distribution

census_area-0.4.3-py2.py3-none-any.whl (10.1 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page