{ "info": { "author": "Emre Safak", "author_email": "misteremre@yahoo.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "===============================\nmca\n===============================\n\n.. image:: https://badge.fury.io/py/mca.png\n :target: http://badge.fury.io/py/mca\n \n.. image:: https://travis-ci.org/esafak/mca.png?branch=master\n :target: https://travis-ci.org/esafak/mca\n\nmca 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.\n\nInstallation\n------------\n\n.. code :: bash\n\n\tpip install --user mca\n\nUsage\n------------------\n\nPlease refer to the `usage notes `_ and `this illustrated ipython notebook `_.\n\nReference\n---------\n\nMichael Greenacre, J\u00f6rg Blasius (2006). `Multiple Correspondence Analysis and Related Methods `_, CRC Press. ISBN 1584886285.\n\n\n\n\nHistory\n-------\n\n* **1.0** (2014-06-24)\n\tFirst release. I'm sure it's an auspicious date somewhere in the world.\n* **1.01** (2015-03-23)\n\tMore documentation, in the form of an ipython notebook. Fixed bug #2 affecting python 2.x\n* **1.02** (2017-07-29)\n\tFixed division-by-zero bug (issue #14)\n* **1.03** (2018-01-10)\t\n\tAdded sparse matrix support", "description_content_type": null, "docs_url": null, "download_url": "https://github.com/esafak/mca/tarball/master", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/esafak/mca", "keywords": "mca,statistics", "license": "BSD", "maintainer": "", "maintainer_email": "", "name": "mca", "package_url": "https://pypi.org/project/mca/", "platform": "", "project_url": "https://pypi.org/project/mca/", "project_urls": { "Download": "https://github.com/esafak/mca/tarball/master", "Homepage": "https://github.com/esafak/mca" }, "release_url": "https://pypi.org/project/mca/1.0.3/", "requires_dist": null, "requires_python": "", "summary": "Multiple correspondence analysis with pandas", "version": "1.0.3" }, "last_serial": 3479825, "releases": { "1.0": [ { "comment_text": "", "digests": { "md5": "0968bb8002faad8e04c886b0154bb2ed", "sha256": "b8580dfe776467c528412f4fcbe02215ce6e5c4f18c52eece49a7ce4af362c94" }, "downloads": -1, "filename": "mca-1.0.tar.gz", "has_sig": false, "md5_digest": "0968bb8002faad8e04c886b0154bb2ed", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 16852, "upload_time": "2014-06-26T01:50:43", "url": "https://files.pythonhosted.org/packages/1a/0b/0d58bceb3d25f066f7538c9ae8fa2abd6785b64c883f1c3c79cc044dc8da/mca-1.0.tar.gz" } ], "1.0.2": [ { "comment_text": "", "digests": { "md5": "67c3bc7caa462f1ce20d996b0340cbf8", "sha256": "058b318b4a9e59e3359befb09037a9d8944390e072e9cf0b07981e34807a8910" }, "downloads": -1, "filename": "mca-1.0.2.tar.gz", "has_sig": false, "md5_digest": "67c3bc7caa462f1ce20d996b0340cbf8", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 18501, "upload_time": "2017-07-26T19:30:42", "url": "https://files.pythonhosted.org/packages/35/62/5c8e8287434938285455d308f0090b4c619becab4da41f52bcd7f3c518ff/mca-1.0.2.tar.gz" } ], "1.0.3": [ { "comment_text": "", "digests": { "md5": "432f684d1267f86fa0a250f9a0a9aec7", "sha256": "f8403283f39122eee221112b3f20206720cda94d8c3fbdba61cb84e70e94b120" }, "downloads": -1, "filename": "mca-1.0.3.tar.gz", "has_sig": false, "md5_digest": "432f684d1267f86fa0a250f9a0a9aec7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 17703, "upload_time": "2018-01-11T06:14:05", "url": "https://files.pythonhosted.org/packages/7d/2a/6e07182d578514f25877872c2b320f5d6d9eee81d9d397d575c9dc2ae827/mca-1.0.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "432f684d1267f86fa0a250f9a0a9aec7", "sha256": "f8403283f39122eee221112b3f20206720cda94d8c3fbdba61cb84e70e94b120" }, "downloads": -1, "filename": "mca-1.0.3.tar.gz", "has_sig": false, "md5_digest": "432f684d1267f86fa0a250f9a0a9aec7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 17703, "upload_time": "2018-01-11T06:14:05", "url": "https://files.pythonhosted.org/packages/7d/2a/6e07182d578514f25877872c2b320f5d6d9eee81d9d397d575c9dc2ae827/mca-1.0.3.tar.gz" } ] }