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.

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
• [ ] Document commonly-used data resources (like tract_to_block in cenpy.tools).