Library of the different imputation algorithms; methods for dealing with ambiguity and handling missing data.
Project description
[![travis-CI](https://travis-ci.org/eltonlaw/impyute.svg?branch=master)](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
# 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
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
impyute-0.0.1.tar.gz
(10.2 kB
view hashes)
Built Distribution
Close
Hashes for impyute-0.0.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2e953fd6bdee2d8c04f6a29ed5d93112d5e72095be29506f1bdb63f84885b208 |
|
MD5 | d4f5bcafc737422b9de0e40b145a9053 |
|
BLAKE2b-256 | 55c9ef76ebd9bee4fbf7e57398e9cdd767df01fa8576eeac3fb5cc281f995944 |