impyute 0.0.6

Cross-sectional and time-series data imputation algorithms

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.

>>> 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
  • 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 and 3.6

Installation

To install impyute, run the following:

$ pip install impyute

Or to get the most current version:

$ 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