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 impyute
```

Or to get the most current version:

```\$ git clone https://github.com/eltonlaw/impyute
\$ cd impyute
\$ python setup.py install
```

### How to Contribute

Check out CONTRIBUTING

## Project details

This version 0.0.8 0.0.7 0.0.6 0.0.5 0.0.4 0.0.3 0.0.2 0.0.1