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A data analysis and visualization helper module.

Project Description

[![Build Status](](

# Data Utilities

Data utilities library focused on machine learning and data analysis.

The library relies upon python's scientific/numeric stack to expand their
capabilities. The dependencies are:
* numpy
* scipy
* pandas
* matplotlib
* seaborn
* scikit-learn

Optional dependencies are:
* XGBoost
* deap

Highlights are:
* matplotlib_utilities: out-of-the-shelf data description with
* pandas_utilities: easier dataframe preparation with
`rename_columns_to_lower`, `categorical_serie_to_binary_dataframe`,
`balance_ndframe` and `get_numeric_columns`.
* sklearn_utilities: multiprocessing and persistence support for hyper
parameter grid search, both exhaustive and using a genetic algorithmic
approach; convenience functions to the XGBoost module.

And much more.

# Organization and files

├── sklearn_utilities
│   ├──
│   ├──
│   └──
└── tests

Each of python's significant data modules has its own set of functions.
Optional dependencies functions are interspersed throughout the code.

This module does not intend to create its own API or standards. Instead each of
the utilities module should follow the guidelines and APIs provided by the
parent module.

Note: This is a primitive project. Expect backwards incompatible changes as I
figure out the best way to to develop the utilities.

# What's new

Check our [changelog](

# Development guidelines

* Coding style: [PEP 8]( compliant.
* Docstrings: [google docstrings](
* Before commiting new versions do a test for different versions of python3:
* python3.4
* python3.5
* python3.6
* (newer versions)
* Rationale: even though stability is expected between python versions some
changes occur. See for instance that on commit v1.2.8 (60573d7) there was
as unexpected import error on python34 but not on python36.

Release History

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