https://github.com/eltonlaw/impyute

Data imputations library to preprocess datasets with missing data
https://github.com/eltonlaw/impyute

imputation missing-data python scientific-computing

Last synced: 4 days ago
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Data imputations library to preprocess datasets with missing data

• Host: GitHub
• URL: https://github.com/eltonlaw/impyute
• Owner: eltonlaw
• License: mit
• Created: 2017-01-21T09:16:27.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2021-11-06T21:15:04.000Z (about 3 years ago)
• Last Synced: 2024-10-02T07:21:13.010Z (about 1 month ago)
• Topics: imputation, missing-data, python, scientific-computing
• Language: Python
• Homepage: http://impyute.readthedocs.io/
• Size: 2.43 MB
• Stars: 352
• Watchers: 11
• Forks: 49
• Open Issues: 30
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-meteo - Impyute
• awesome-python-machine-learning - impyute - Impyute is a library of missing data imputation algorithms. (Uncategorized / Uncategorized)
• awesome-python-machine-learning-resources - GitHub - 42% open · ⏱️ 06.11.2021): (Others)

README

        
.. 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