Metadata-Version: 2.4
Name: mca
Version: 1.0.4
Summary: Multiple correspondence analysis with pandas
Home-page: https://github.com/esafak/mca
Download-URL: https://github.com/esafak/mca/tarball/master
Author: Emre Safak
Author-email: misteremre@yahoo.com
License: MIT AND (Apache-2.0 OR BSD-2-Clause)
Keywords: mca,statistics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/x-rst
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: pandas
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===============================
mca
===============================

.. image:: https://badge.fury.io/py/mca.png
    :target: https://pypi.org/project/mca/
    
.. image:: https://img.shields.io/github/actions/workflow/status/esafak/mca/test_mca.yaml
    :target: https://github.com/esafak/mca/actions/workflows/test_mca.yaml

mca is a `Multiple Correspondence Analysis <http://en.wikipedia.org/wiki/Multiple_correspondence_analysis>`_ (MCA) package for python, intended to be used with `pandas <http://pandas.pydata.org/>`_. MCA is a `feature extraction <http://en.wikipedia.org/wiki/Feature_extraction>`_ method; essentially `PCA <http://en.wikipedia.org/wiki/Principal_component_analysis>`_ for `categorical variables <http://en.wikipedia.org/wiki/Categorical_variable>`_. You can use it, for example, to address `multicollinearity <http://en.wikipedia.org/wiki/Multicollinearity>`_ or the `curse of dimensionality <http://en.wikipedia.org/wiki/Curse_of_dimensionality>`_ with big categorical variables.

Installation
------------

.. code :: bash

    pip install --user mca

Usage
-----

Please refer to the `usage notes <https://github.com/esafak/mca/blob/master/docs/usage.rst>`_ and `this illustrated ipython notebook <http://nbviewer.ipython.org/github/esafak/mca/blob/master/docs/mca-BurgundiesExample.ipynb>`_.

References
----------

* Michael Greenacre, Jörg Blasius (2006). `Multiple Correspondence Analysis and Related Methods <http://www.crcpress.com/product/isbn/9781584886280>`_, CRC Press. ISBN 1584886285.
* François Husson, `Multiple Correspondence Analysis Youtube Playlist <https://www.youtube.com/playlist?list=PLnZgp6epRBbTVjKd_-KPhaGWLE7K7InL6>`_, Youtube



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
* **1.04** (2025-05-15)
	Improved SVD efficiency (issue #23)
