Skip to main content

Multiple correspondence analysis with pandas

Project description

https://badge.fury.io/py/mca.png https://travis-ci.org/esafak/mca.png?branch=master

mca is a Multiple Correspondence Analysis (MCA) package for python, intended to be used with pandas. MCA is a feature extraction method; essentially PCA for categorical variables. You can use it, for example, to address multicollinearity or the curse of dimensionality with big categorical variables.

Installation

pip install --user mca

Usage

Please refer to the usage notes and this illustrated ipython notebook.

Reference

Michael Greenacre, Jörg Blasius (2006). Multiple Correspondence Analysis and Related Methods, CRC Press. ISBN 1584886285.

History

  • 1.0 (2014-06-24)

    First release. I’m sure it’s an auspicious date somewhere in the world.

  • 1.01 (2015-03-23)

    More documentation, in the form of an ipython notebook. Fixed bug #2 affecting python 2.x

  • 1.02 (2017-07-29)

    Fixed division-by-zero bug (issue #14)

  • 1.03 (2018-01-10)

    Added sparse matrix support

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mca-1.0.3.tar.gz (17.7 kB view details)

Uploaded Source

File details

Details for the file mca-1.0.3.tar.gz.

File metadata

  • Download URL: mca-1.0.3.tar.gz
  • Upload date:
  • Size: 17.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for mca-1.0.3.tar.gz
Algorithm Hash digest
SHA256 f8403283f39122eee221112b3f20206720cda94d8c3fbdba61cb84e70e94b120
MD5 432f684d1267f86fa0a250f9a0a9aec7
BLAKE2b-256 7d2a6e07182d578514f25877872c2b320f5d6d9eee81d9d397d575c9dc2ae827

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page