autocensus 1.0.7

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 asyncio/aiohttp to request data concurrently.

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

Contents

• Installation
• Example
• Joining geospatial data
  • Caching
• Publishing to Socrata
  • Credentials
  • Example: Create a new dataset
  • Example: Replace rows in an existing dataset
  • Example: Create a new dataset from multiple queries
• Troubleshooting
  • Clearing the cache
  • SSL errors

Installation

autocensus requires Python 3.7 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.

Example

from autocensus import Query

# Configure query
query = Query(
    estimate=5,
    years=[2014, 2015, 2016, 2017],
    variables=['B01002_001E', 'B03001_001E', 'DP03_0025E', 'S0503_C02_077E'],
    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	geo_type	year	date	variable_code	variable_label	variable_concept	annotation	value	percent_change	difference	centroid	internal_point	geometry
Census Tract 151, Arapahoe County, Colorado	1400000US08005015100	tract	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	tract	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	tract	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	tract	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	tract	2014	2018-12-31	B01002_001E	Median age - Total	Median Age by Sex		26.4			POINT (…)	POINT (…)	MULTIPOLYGON (…)

Joining geospatial data

autocensus will automatically join geospatial data (centroids, representative points, and geometry) for the following geography types for years 2013 and on:

• 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)

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=[2014, 2015, 2016, 2017],
    variables=['B01002_001E', 'B03001_001E', 'DP03_0025E', 'S0503_C02_077E'],
    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.

Caching

To improve performance across queries, autocensus caches shapefiles on disk by default. The cache location varies by platform:

• Linux: /home/{username}/.cache/autocensus
• Mac: /Users/{username}/Library/Application Support/Caches/autocensus
• Windows: C:\\Users\\{username}\\AppData\\Local\\socrata\\autocensus

You can clear the cache by manually deleting the cache directory or by executing the autocensus.clear_cache function. See the section Troubleshooting: Clearing the cache for more details.

Publishing to Socrata

If socrata-py is installed, you can publish query results (or dataframes containing the results of multiple queries) 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

# 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
    description='5-year estimates from the American Community Survey'  # 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'
)

Example: Create a new dataset from multiple queries

from autocensus import Query
from autocensus.socrata import to_socrata
import pandas as pd

# County-level query
county_query = Query(
    estimate=5,
    years=range(2013, 2018),
    variables=['DP03_0025E'],
    for_geo='county:*',
    in_geo='state:08'
)
county_dataframe = county_query.run()

# State-level query
state_query = Query(
    estimate=5,
    years=range(2013, 2018),
    variables=['DP03_0025E'],
    for_geo='state:08'
)
state_dataframe = state_query.run()

# Concatenate dataframes and upload to Socrata
combined_dataframe = pd.concat([
    county_dataframe,
    state_dataframe
])
to_socrata(
    'some-domain.data.socrata.com',
    dataframe=combined_dataframe,
    name='Average Commute Time by Colorado County with Statewide Averages, 2013–2017',  # Optional
    description='5-year estimates from the American Community Survey'  # Optional
)

Troubleshooting

Clearing the cache

Sometimes it is useful to clear the cache directory that autocensus uses to store downloaded shapefiles for future queries, especially if you're running into BadZipFile: File is not a zip file errors or other shapefile-related problems. Clear your cache like so:

import autocensus

autocensus.clear_cache()

SSL errors

To disable SSL verification, specify verify_ssl=False when initializing your Query:

query = Query(
    estimate=5,
    years=[2014, 2015, 2016, 2017],
    variables=['B01002_001E', 'B03001_001E', 'DP03_0025E', 'S0503_C02_077E'],
    for_geo='tract:*',
    in_geo=['state:08', 'county:005'],
    verify_ssl=False
)