Cross-sectional and time-series data imputation algorithms
Project description
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
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:
$ pip install impyuteOr to get the most current version:
$ git clone https://github.com/eltonlaw/impyute
$ cd impyute
$ python setup.py installDocumentation
Documentation is available here: http://impyute.readthedocs.io/
How to Contribute
Check out CONTRIBUTING
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Source Distribution
Built Distribution
Hashes for impyute-0.0.8-py2.py3-none-any.whl
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