Metadata-Version: 2.1
Name: impyute
Version: 0.0.7
Summary: Cross-sectional and time-series data imputation algorithms
Home-page: http://impyute.readthedocs.io/en/latest/
Author: Elton Law
Author-email: eltonlaw296@gmail.com
License: GPL-3.0
Download-URL: https://github.com/eltonlaw/impyute
Description: .. image:: https://travis-ci.org/eltonlaw/impyute.svg?branch=master
            :target: https://travis-ci.org/eltonlaw/impyute
        
        .. image:: https://img.shields.io/pypi/v/impyute.svg
            :target: https://pypi.python.org/pypi/impyute
        
        Impyute
        ========
        
        Impyute 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. 
        
        .. code-block:: python
        
            >>> n = 5
            >>> arr = np.random.uniform(high=6, size=(n, n))
            >>> for _ in range(3):
            >>>    arr[np.random.randint(n), np.random.randint(n)] = np.nan
            >>> print(arr)
            array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan],
                   [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],
                   [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172],
                   [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392],
                   [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])
            >>> import impyute as impy
            >>> print(impy.mean(arr))
            array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365],
                   [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],
                   [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172],
                   [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392],
                   [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])
        
        Feature Support
        ---------------
        
        * Imputation of Cross Sectional Data
            * K-Nearest Neighbours
            * Multivariate Imputation by Chained Equations
            * Expectation Maximization
            * Mean Imputation
            * Mode Imputation
            * Median Imputation
            * Random Imputation
        * Imputation of Time Series Data
            * Last Observation Carried Forward
            * Moving Window
            * Autoregressive Integrated Moving Average (WIP)
        * Diagnostic Tools
            * Loggers
            * Distribution of Null Values
            * Comparison of imputations
            * Little's MCAR Test (WIP)
        
        Versions
        --------
        
        Currently tested on 2.7, 3.4, 3.5, 3.6 and 3.7
        
        Installation
        ------------
        
        To install impyute, run the following:
        
        .. code-block:: bash
        
            $ pip install impyute
        
        Or to get the most current version:
        
        .. code-block:: bash
            
            $ git clone https://github.com/eltonlaw/impyute
            $ cd impyute
            $ python setup.py install
        
        Documentation
        -------------
        
        Documentation is available here: http://impyute.readthedocs.io/
        
        
        How to Contribute
        -----------------
        
        Check out CONTRIBUTING_
        
        .. _CONTRIBUTING: https://github.com/eltonlaw/impyute/blob/master/CONTRIBUTING.md
        
        
Keywords: imputation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Provides-Extra: test
Provides-Extra: dev
