cenpy 1.0.0rc3

Explore and download data from Census APIs

A new interface for cenpy is under construction for the 1.0 release. Please consider reading this, and using the prerelease/beta version of cenpy 1.0 using: pip install --pre cenpy

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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.

A few examples are available in the notebooks repository.

• getting data quickly using Cenpy.

• analyzing segregation over time & across space

• a road to frictionless urban data science

• developer building blocks.

Also, a great example on how to grab work with cenpy, moving from nothing to data to map, is here, by @dfolch.

Installation

Cenpy is easiest to install using conda, a commonly-used package manager for scientific python. First, install Anaconda.

Then, cenpy is available on the conda-forge channel:

conda install -c conda-forge cenpy

Alternatively, you can install cenpy via pip, the python package manager, if you have installed geopandas and rtree:

pip install cenpy

For Users

Most of the time, users want a simple and direct interface to the US Census Bureau’s main products: the 2010 Census and the American Community Survey. Fortunately, cenpy provides a direct interface to these products. For instance, the American Community Survey’s most recent 5-year estimates can be accessed using:

import cenpy
acs = cenpy.products.ACS()
acs.from_place('Chicago, IL')

Likewise, the decennial census can be accessed using:

import cenpy
decennial = cenpy.products.Decennial2010()
decennial.from_place('Seattle, WA')

For more information on how the product API works, consult the notebook on the topic <https://nbviewer.jupyter.org/github/cenpy-devs/cenpy/blob/product/notebooks/product-api.ipynb>.

For Developers

The API reference is available at cenpy-devs.github.io/cenpy. The products are typically what most end-users will want to interact with. If you want more fine-grained access to the USCB APIs, you will likely want to build on top of APIConnection and TigerConnection.

To create a connection:

cxn = cenpy.remote.APIConnection('DECENNIALSF12010')

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 back 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'}.

ToDo:

• A product in cenpy.products for County Business Statistics