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 asyncio/aiohttp to request data concurrently.
Contents
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=1,
years=[2017, 2018],
variables=['DP03_0025E', 'S0103_C01_104E'],
for_geo='county:033',
in_geo=['state:53'],
# 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
King County, Washington | 0500000US53033 | county | 2017 | 2017-12-31 | DP03_0025E | COMMUTING TO WORK - Mean travel time to work (minutes) | Selected Economic Characteristics | 30.0 | POINT (…) | POINT (…) | MULTIPOLYGON (…) | |||
King County, Washington | 0500000US53033 | county | 2018 | 2018-12-31 | DP03_0025E | COMMUTING TO WORK - Workers 16 years and over - Mean travel time to work (minutes) | Selected Economic Characteristics | 30.2 | 0.01 | 0.2 | POINT (…) | POINT (…) | MULTIPOLYGON (…) | |
King County, Washington | 0500000US53033 | county | 2017 | 2017-12-31 | S0103_C01_104E | Total - Estimate - GROSS RENT - Median gross rent (dollars) | Population 65 Years and Over in the United States | 1555.0 | POINT (…) | POINT (…) | MULTIPOLYGON (…) | |||
King County, Washington | 0500000US53033 | county | 2018 | 2018-12-31 | S0103_C01_104E | Total - Renter-occupied housing units - GROSS RENT - Median gross rent (dollars) | Population 65 Years and Over in the United States | 1674.0 | 0.08 | 119.0 | 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=1,
years=[2017, 2018],
variables=['DP03_0025E', 'S0103_C01_104E'],
for_geo='county:033',
in_geo=['state:53'],
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='Median Commute Time by Colorado County, 2013–2017', # Optional
description='1-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=1,
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=1,
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='Median Commute Time for Colorado State and Counties, 2013–2017', # Optional
description='1-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=1,
years=[2017, 2018],
variables=['DP03_0025E', 'S0103_C01_104E'],
for_geo='county:033',
in_geo=['state:53'],
verify_ssl=False
)
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