An interface to explore and query the US Census API and return Pandas
Dataframes. Ideally, this package is intended for exploratory data
analysis and draws inspiration from sqlalchemy-like interfaces and
acs.R.
An intro notebook is available.
Also, a great example on how to grab work with cenpy, moving from nothing to data to map, is here, by @dfolch. Installation ------------
This package depends on Pandas and
requests. You can
install cenpy and other dependencies using pip:
pip install cenpy
If you do not have pip, simply copy the module somewhere in your
python path.
Once done, importing cenpy will provide the explorer and
base modules. To create a connection:
cxn = cenpy.base.Connection('2010sf1')
Check the variables required and geographies supported:
cxn.variables #is a pandas dataframe containing query-able vbls cxn.geographies #is a pandas dataframe containing query-able geographies
Note that some geographies (like tract) have higher-level requirements that you'll have to specify for the query to work.
The structure of the query function maps to the Census API's use of
get, for, and in. The main arguments for the query function
are cols, geo_unit and geo_filter, and map backwards to
those predicates, respectively. If more predicates are required for the
search, they can be added as keyword arguments at the end of the query.
The cols argument must be a list of columns to retrieve from the
dataset. Then, you must specify the geo_unit and geo_filter,
which provide what the unit of aggregation should be and where the
units should be. geo_unit must be a string containing the unit of
analysis and an identifier. For instance, if you want all counties in
Arizona, you specify geo_unit = 'county:*' and geo_filter =
{'state','04'}.
- [ ] Recursively search for incompletely-specified hierarchies
- [ ] Build out commonly-used data resources (like specific ACS products or
- Decennials)