A tool for collecting ACS and geospatial data from the Census API
Project description
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 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'],
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.
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
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
.
Known issues
SSL errors
Some users report errors like the following when querying the Census API:
SSL handshake failed on verifying the certificate
protocol: <asyncio.sslproto.SSLProtocol object at 0x11f805ac8>
transport: <_SelectorSocketTransport fd=11 read=polling write=<idle, bufsize=0>>
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'],
for_geo='tract:*',
in_geo=['state:08', 'county:005'],
verify_ssl=False
)
Tests
Use pytest to run the test suite:
pytest
Project details
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