autocensus

A Python package for collecting American Community Survey (ACS) data and associated geometry from the Census API in a pandas dataframe.

Contents

• Installation
• Quickstart
• Geometry
  • Points
  • Polygons
    • Shapefile resolution
    • Shapefile caching

Installation

autocensus requires Python 3.9 or higher. Install as follows:

pip install autocensus

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.

Quickstart

from autocensus import Query

# Configure query
query = Query(
    estimate=1,
    years=[2017, 2018],
    variables=['DP03_0025E', 'S0103_C01_104E'],
    for_geo='county:033',
    in_geo=['state:53'],
    # Optional arg to add geometry: 'points', 'polygons', or None (default)
    geometry='points',
    # 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:

Geometry

autocensus supports point- and polygon-based geometry data for many years and geographies by way of the Census Bureau's Gazetteer Files and Cartographic Boundary Files.

Here's how to add geometry to your data:

Points

Point data from the Census Bureau's Gazetteer Files is generally available for years from 2012 on in the following geographies:

• Nation-level
  • urban area
  • zip code tabulation area
  • county
  • congressional district
  • metropolitan statistical area/micropolitan statistical area
  • american indian area/alaska native area/hawaiian home land
• State-level
  • county subdivision
  • tract
  • place
  • state legislative district (upper chamber)
  • state legislative district (lower chamber)

Example:

from autocensus import Query

query = Query(
    estimate=5,
    years=[2018],
    variables=['DP03_0025E'],
    for_geo=['county:033'],
    in_geo=['state:53'],
    geometry='points'
)
dataframe = query.run()

Polygons

Polygon data from the Census Bureau's Cartographic Boundary Shapefiles is generally available for years from 2013 on in the following geographies:

• Nation-level
  • nation
  • region
  • division
  • state
  • urban area
  • zip code tabulation area
  • county
  • congressional district
  • metropolitan statistical area/micropolitan statistical area
  • combined statistical area
  • american indian area/alaska native area/hawaiian home land
  • new england city and town area
• State-level
  • alaska native regional corporation
  • block group
  • county subdivision
  • tract
  • place
  • public use microdata area
  • state legislative district (upper chamber)
  • state legislative district (lower chamber)

Example:

from autocensus import Query

query = Query(
    estimate=5,
    years=[2018],
    variables=['DP03_0025E'],
    for_geo=['county:033'],
    in_geo=['state:53'],
    geometry='polygons'
)
dataframe = query.run()

Shapefile resolution

By default, autocensus will attempt to fetch almost all shapefiles at a resolution of 1 : 500,000 (500k). Some sources among the Cartographic Boundary Shapefiles are also available at the lower resolutions of 1 : 5,000,000 (5m) or 1 : 20,000,000 (20m). To attempt to download a shapefile at a specific resolution, pass a value to Query's optional resolution parameter:

from autocensus import Query

query = Query(
    estimate=5,
    years=[2018],
    variables=['DP03_0025E'],
    for_geo=['county:*'],
    in_geo=['state:53'],
    geometry='polygons',
    # Optional arg to set a specific resolution: '500k', '5m', or '20m'
    resolution='20m'
)

Setting a specific resolution is only supported for polygon-based geometry.

Shapefile caching

To improve performance across queries that include polygon-based geometry data, autocensus will cache Census shapefiles on disk by default. The cache directory location depends on your OS; you can look it up from autocensus.constants.CACHE_DIRECTORY_PATH like so:

python -c "import autocensus; print(autocensus.constants.CACHE_DIRECTORY_PATH)"

Sometimes it is useful to clear this cache directory, especially if you're running into persistent shapefile-related problems. You can clear the cache by manually deleting the cache directory or by executing the autocensus.clear_cache function:

python -c "import autocensus; autocensus.clear_cache()"