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scikit-data

version 0.1.2

The propose of this library is to allow the data analysis process more easy and automatic.

Download: https://github.com/OpenDataScienceLab/skdata/archive/master.tar.gz
Homepage: https://github.com/OpenDataScienceLab/skdata
PyPI: http://pypi.python.org/pypi/scikit-data
People: Ivan Ogasawara

Description

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SciKit Data
===============================


.. image:: https://img.shields.io/pypi/v/scikit-data.svg
:target: https://pypi.python.org/pypi/scikit-data

.. image:: https://img.shields.io/travis/OpenDataScienceLab/skdata.svg
:target: https://travis-ci.org/OpenDataScienceLab/skdata

.. image:: https://readthedocs.org/projects/skdata/badge/?version=latest
:target: https://skdata.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status


Conda package current release info
====================

.. image:: https://anaconda.org/conda-forge/scikit-data/badges/version.svg
:target: https://anaconda.org/conda-forge/scikit-data
:alt: Anaconda-Server Badge

.. image:: https://anaconda.org/conda-forge/scikit-data/badges/downloads.svg
:target: https://anaconda.org/conda-forge/scikit-data
:alt: Anaconda-Server Badge


About SciKit Data
=================

The propose of this library is to allow the data analysis process more easy and automatic.

This library is based on some important libraries as:

- pandas;
- jupyter;
- matplotlib;
- scikit-learn;


* Free software: MIT license
* Documentation: https://skdata.readthedocs.io.

Features
--------

Books used as reference to guide this project:

- https://www.packtpub.com/big-data-and-business-intelligence/clean-data
- https://www.packtpub.com/big-data-and-business-intelligence/python-data-analysis
- https://www.packtpub.com/big-data-and-business-intelligence/mastering-machine-learning-scikit-learn

Some other materials used as reference:

- https://github.com/rsouza/MMD/blob/master/notebooks/3.1_Kaggle_Titanic.ipynb
- https://github.com/agconti/kaggle-titanic/blob/master/Titanic.ipynb
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb


This project contemplates the follow features:

- Data conversions:

- soon ...
- Data collection:

- soon ...
- Data cleaning:

- ...
- Data storage:

- soon ...
- Data integration:

- soon ...
- Data manipulation:

- ...
- Outliers removal:

- ...


Installing scikit-data
======================

Using conda
-----------

Installing `scikit-data` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:

.. code-block:: console

$ conda config --add channels conda-forge


Once the `conda-forge` channel has been enabled, `scikit-data` can be installed with:

.. code-block:: console

$ conda install scikit-data


It is possible to list all of the versions of `scikit-data` available on your platform with:

.. code-block:: console

$ conda search scikit-data --channel conda-forge


Using pip
---------

To install scikit-data, run this command in your terminal:

.. code-block:: console

$ pip install skdata

If you don't have `pip`_ installed, this `Python installation guide`_ can guide
you through the process.

.. _pip: https://pip.pypa.io
.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/




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History
=======

0.1.0 (2016-08-14)
------------------

* First release on PyPI.

Installation

PyPI

You can download the latest distribution from PyPI here: http://pypi.python.org/pypi/scikit-data

Using pip

You can install scikit-data for yourself from the terminal by running:

pip install --user scikit-data

If you want to install it for all users on your machine, do:

pip install scikit-data
On Linux, do sudo pip install scikit-data.

If you don't yet have the pip tool, you can get it following these instructions.

This package was discovered in PyPI.