{ "info": { "author": "Levi John Wolf", "author_email": "levi.john.wolf@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "CenPy\n=====\n.. image:: https://img.shields.io/static/v1.svg?label=documentation&message=latest&color=blueviolet\n :target: https://cenpy-devs.github.io/cenpy\n.. image:: https://travis-ci.org/cenpy-devs/cenpy.svg?branch=master\n :target: https://travis-ci.org/cenpy-devs/cenpy\n.. image:: https://img.shields.io/pypi/dm/cenpy.svg\n :target: https://pypi.org/project/cenpy/\n.. image:: https://zenodo.org/badge/36956226.svg\n :target: https://zenodo.org/badge/latestdoi/36956226\n\nAn interface to explore and query the US Census API and return Pandas\nDataframes. This package is intended for exploratory data\nanalysis and draws inspiration from sqlalchemy-like interfaces and\n``acs.R``. With separate APIs for application developers and folks who \nonly want to get their data quickly & painlessly, ``cenpy`` should meet\nthe needs of most who aim to get US Census Data from Python. \n\nA few examples are available from `our website `__:\n\n- `getting data quickly using Cenpy `__.\n- `analyzing segregation over time & across space using cenpy and segregation `__\n- `a road to frictionless urban data science using cenpy and osmnx `__\n- `developer building blocks `__.\n- `piecing together the developer building blocks (by @dfolch) `__\n\nInstallation\n------------\n\nCenpy is easiest to install using ``conda``, a commonly-used package manager for scientific python. First, `install Anaconda `__.\n\nThen, ``cenpy`` is available on the ``conda-forge`` channel:\n::\n\n conda install -c conda-forge cenpy\n\nAlternatively, you can install cenpy *via* ``pip``, the python package manager, if you have installed ``geopandas`` and ``rtree``:\n:: \n\n pip install cenpy\n\n\nFor Users\n----------\nMost 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:\n\n::\n\n import cenpy\n acs = cenpy.products.ACS()\n acs.from_place('Chicago, IL')\n\nLikewise, the decennial census can be accessed using:\n\n::\n\n import cenpy\n decennial = cenpy.products.Decennial2010()\n decennial.from_place('Seattle, WA')\n\nFor more information on how the product API works, consult the `notebook on the topic `__.\n\n\nFor Developers\n----------------\nThe 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``. \n\nTo create a connection:\n\n::\n\n cxn = cenpy.remote.APIConnection('DECENNIALSF12010')\n\nCheck the variables required and geographies supported:\n\n::\n\n cxn.variables #is a pandas dataframe containing query-able vbls\n cxn.geographies #is a pandas dataframe containing query-able geographies\n\nNote that some geographies (like tract) have higher-level requirements\nthat you'll have to specify for the query to work.\n\nThe structure of the query function maps to the Census API's use of\n``get``, ``for``, and ``in``. The main arguments for the query function\nare ``cols``, ``geo_unit`` and ``geo_filter``, and map back to those predicates, respectively. If more predicates are required for the\nsearch, they can be added as keyword arguments at the end of the query.\n\nThe ``cols`` argument must be a list of columns to retrieve from the\ndataset. 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