impyute 0.0.1

[](https://travis-ci.org/eltonlaw/impyute)


# impyute

impyute is a library of missing data imputation algorithms written in Python 3. This library was designed to be super lightweight, here's a sneak peak at what impyute can do.

``` python3
>>> from impyute.datasets import random_uniform
>>> raw_data = random_uniform(shape=(5, 5), missingness="mcar", th=0.2)
>>> print(raw_data)
[[ 1. 0. 4. 0. 1.]
[ 1. nan 6. 4. nan]
[ 5. 0. nan 1. 3.]
[ 2. 1. 5. 4. 6.]
[ 2. 1. 0. 0. 6.]]
>>> from impyute.imputations.cs import mean_imputation
>>> complete_data = random_imputation(raw_data)
>>> print(complete_data)
[[ 1. 0. 4. 0. 1. ]
[ 1. 0.5 6. 4. 4. ]
[ 5. 0. 3.75 1. 3. ]
[ 2. 1. 5. 4. 6. ]
[ 2. 1. 0. 0. 6. ]]
```

## Features

* Imputation of Cross Sectional Data
* Multivariate Imputation by Chained Equations
* Expectation Maximization
* Mean Imputation
* Mode Imputation
* Median Imputation
* Random Imputation
* Imputation of Time Series Data
* Autoregressive Integrated Moving Average
* Expectation Maximization with the Kalman Filter
* Last Observation Carried Forward
* Raw and Complete Dataset Generation
* Diagnostic Tools
* Loggers
* Dataset Properties


## Install

To install impyute, run the following:

``` shell
$ pip install impyute
```

## Documentation

Documentation is available here: http://impyute.readthedocs.io/

## Contributing

Check out https://github.com/eltonlaw/impyute/blob/master/CONTRIBUTING.md