autocensus 0.2.0

A tool for collecting ACS and geospatial data from the Census API

autocensus

Python package for collecting American Community Survey (ACS) data from the Census API, along with associated geospatial points and boundaries, in a pandas dataframe.

Uses aiohttp to call Census endpoints with a series of concurrent requests, which saves a bit of time.

This package is under active development and breaking changes to its API are expected.

Installation

autocensus requires Python 3.7 or higher. Install from this repository as follows:

pip install git+git://github.com/socrata/autocensus@master

To run autocensus, you must specify a Census API key via either the census_api_key keyword argument (as shown in the example below) or by setting the environment variable CENSUS_API_KEY.

Example

from autocensus import Query

# Configure query
query = Query(
    estimate=5,
    years=[2014, 2015, 2016, 2017],
    variables=['B01002_001E', 'B03001_001E'],
    for_geo='tract:*',
    in_geo=['state:08', 'county:005'],
    # Fill in the following with your actual Census API key
    census_api_key='Your Census API key'
)

# Run query and collect output in dataframe
dataframe = query.run()

Output:

name	geo_id	year	date	variable_code	variable_label	variable_concept	annotation	value	percent_change	difference	centroid	internal_point	geometry
Census Tract 151, Arapahoe County, Colorado	1400000US08005015100	2014	2014-12-31	B01002_001E	Median age - Total	Median Age by Sex		45.7			POINT (…)	POINT (…)	MULTIPOLYGON (…)
Census Tract 151, Arapahoe County, Colorado	1400000US08005015100	2015	2015-12-31	B01002_001E	Median age - Total	Median Age by Sex		45.2	-1.1	-0.5	POINT (…)	POINT (…)	MULTIPOLYGON (…)
Census Tract 151, Arapahoe County, Colorado	1400000US08005015100	2016	2016-12-31	B01002_001E	Median age - Total	Median Age by Sex		45.9	1.6	0.7	POINT (…)	POINT (…)	MULTIPOLYGON (…)
Census Tract 151, Arapahoe County, Colorado	1400000US08005015100	2017	2017-12-31	B01002_001E	Median age - Total	Median Age by Sex		45.7	-0.4	-0.2	POINT (…)	POINT (…)	MULTIPOLYGON (…)
Census Tract 49.51, Arapahoe County, Colorado	1400000US08005004951	2014	2018-12-31	B01002_001E	Median age - Total	Median Age by Sex		26.4			POINT (…)	POINT (…)	MULTIPOLYGON (…)

Other tables

By default, autocensus queries the detailed tables of the ACS. If your variables are located in other tables, use the table keyword argument:

query = Query(
    estimate=5,
    years=[2016, 2017],
    variables=['DP03_0025E'],
    for_geo='tract:*',
    in_geo=['state:17', 'county:031'],
    table='profile'
)

autocensus will map the following table codes to their associated Census API endpoints:

• Detailed tables: detail
• Data profiles: profile
• Subject tables: subject
• Comparison profiles: cprofile

Joining geospatial data

autocensus will automatically join geospatial data (centroids, representative points, and geometry) for the geography types state, county, zip code tabulation area, tract, and place for years 2013 and on. For queries spanning earlier years, these geometry fields will be populated with null values. (Census boundary shapefiles are not available for years prior to 2013.)

If you don't need geospatial data, set the keyword arg join_geography to False when initializing your query:

query = Query(
    estimate=5,
    years=[2011, 2012, 2013, 2014, 2015, 2016, 2017],
    variables=['B01002_001E', 'B03001_001E'],
    for_geo='tract:*',
    in_geo=['state:08', 'county:005'],
    join_geography=False
)

If join_geography is False, the centroid, internal_point, and geometry columns will not be included in your results.

Publishing to Socrata

If socrata-py is installed, you can publish query results directly to Socrata via the method Query.to_socrata.

Credentials

You must have a Socrata account with appropriate permissions on the domain to which you are publishing. By default, autocensus will look up your Socrata account credentials under the following pairs of common environment variables:

• SOCRATA_KEY_ID, SOCRATA_KEY_SECRET
• SOCRATA_USERNAME, SOCRATA_PASSWORD
• MY_SOCRATA_USERNAME, MY_SOCRATA_PASSWORD
• SODA_USERNAME, SODA_PASSWORD

Alternatively, you can supply credentials explicitly by way of the auth keyword argument:

auth = (os.environ['MY_SOCRATA_KEY'], os.environ['MY_SOCRATA_KEY_SECRET'])
query.to_socrata(
    'some-domain.data.socrata.com',
    auth=auth
)

Example: Create a new dataset

from autocensus import Query

# Configure query
query = Query(
    estimate=5,
    years=range(2013, 2018),
    variables=['DP03_0025E'],
    for_geo='county:*',
    in_geo=['state:08'],
    table='profile'
)

# Run query and publish results as a new dataset on Socrata domain
query.to_socrata(
    'some-domain.data.socrata.com',
    name='Average Commute Time by Colorado County, 2013–2017'  # Optional
)

Example: Replace rows in an existing dataset

# Run query and publish results to an existing dataset on Socrata domain
query.to_socrata(
    'some-domain.data.socrata.com',
    dataset_id='xxxx-xxxx'
)

Topics

autocensus is packaged with some pre-built lists of pertinent ACS variables around topics like race, education, and housing. These live within the autocensus.topics module:

import autocensus
from autocensus import Query

query = Query(
    estimate=5,
    years=[2013, 2014, 2015, 2016, 2017],
    # Housing variables: B25035_001E, B25064_001E, B25077_001E
    variables=autocensus.topics.housing,
    for_geo='tract:*',
    in_geo=['state:08', 'county:005']
)

Topics currently included with autocensus are population, race, education, income, and housing.

Tests

Use pytest to run the test suite:

pytest