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        "author": "Elton Law",
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        "bugtrack_url": null,
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            "Development Status :: 3 - Alpha",
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            "Intended Audience :: Science/Research",
            "Programming Language :: Python",
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        "description": ".. image:: https://travis-ci.org/eltonlaw/impyute.svg?branch=master\n    :target: https://travis-ci.org/eltonlaw/impyute\n\n.. image:: https://img.shields.io/pypi/v/impyute.svg\n    :target: https://pypi.python.org/pypi/impyute\n\nImpyute\n========\n\nImpyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. \n\n.. code-block:: python\n\n    >>> n = 5\n    >>> arr = np.random.uniform(high=6, size=(n, n))\n    >>> for _ in range(3):\n    >>>    arr[np.random.randint(n), np.random.randint(n)] = np.nan\n    >>> print(arr)\n    array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan],\n           [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],\n           [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172],\n           [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392],\n           [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])\n    >>> import impyute as impy\n    >>> print(impy.mean(arr))\n    array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365],\n           [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],\n           [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172],\n           [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392],\n           [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])\n\nFeature Support\n---------------\n\n* Imputation of Cross Sectional Data\n    * K-Nearest Neighbours\n    * Multivariate Imputation by Chained Equations\n    * Expectation Maximization\n    * Mean Imputation\n    * Mode Imputation\n    * Median Imputation\n    * Random Imputation\n* Imputation of Time Series Data\n    * Last Observation Carried Forward\n    * Moving Window\n    * Autoregressive Integrated Moving Average (WIP)\n* Diagnostic Tools\n    * Loggers\n    * Distribution of Null Values\n    * Comparison of imputations\n    * Little's MCAR Test (WIP)\n\nVersions\n--------\n\nCurrently tested on 2.7, 3.4, 3.5, 3.6 and 3.7\n\nInstallation\n------------\n\nTo install impyute, run the following:\n\n.. code-block:: bash\n\n    $ pip install impyute\n\nOr to get the most current version:\n\n.. code-block:: bash\n\n    $ git clone https://github.com/eltonlaw/impyute\n    $ cd impyute\n    $ python setup.py install\n\nDocumentation\n-------------\n\nDocumentation is available here: http://impyute.readthedocs.io/\n\n\nHow to Contribute\n-----------------\n\nCheck out CONTRIBUTING_\n\n.. _CONTRIBUTING: https://github.com/eltonlaw/impyute/blob/master/CONTRIBUTING.md\n\n\n\n",
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