Metadata-Version: 2.0
Name: optimuspyspark
Version: 0.3.0
Summary: Optimus is the missing library for cleaning and preprocessing data in a distributed fashion with pyspark.
Home-page: https://github.com/ironmussa/Optimus/
Author: Favio Vazquez
Author-email: favio.vazquez@ironmussa.com
License: APACHE
Download-URL: https://github.com/ironmussa/Optimus/archive/0.3.0.tar.gz
Keywords: datacleaner,apachespark,spark,pyspark,data-wrangling
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: findspark
Requires-Dist: pytest
Requires-Dist: pytest-spark
Provides-Extra: all
Requires-Dist: findspark; extra == 'all'
Requires-Dist: mock; extra == 'all'
Requires-Dist: nose; extra == 'all'
Requires-Dist: pytest; extra == 'all'
Requires-Dist: pytest; extra == 'all'
Requires-Dist: pytest-spark; extra == 'all'
Provides-Extra: docs
Requires-Dist: mock; extra == 'docs'
Requires-Dist: nose; extra == 'docs'
Requires-Dist: pytest; extra == 'docs'
Requires-Dist: sphinx; extra == 'docs'
Provides-Extra: lint
Requires-Dist: pep8; extra == 'lint'
Requires-Dist: pyflakes; extra == 'lint'
Provides-Extra: test
Requires-Dist: mock; extra == 'test'
Requires-Dist: nose; extra == 'test'
Requires-Dist: pytest; extra == 'test'

Optimus is the missing library for cleaning and pre-processing data in a distributed fashion. 
It uses all the power of Apache Spark (optimized via Catalyst) to do it. It implements several handy tools for data wrangling and munging that will make your life much easier. The first obvious advantage over any other public data cleaning library is that it will work on your laptop or your big cluster, and second, it is amazingly easy to install, use and understand.

Requirements
* Apache Spark 1.6
* Python 3.5

## Installation:

In your terminal just type:

pip install optimuspyspark


