Inequality Coefficients:

This is small library with some implemented coefficients (or indices) intended to measure inequality or concentration of the values in a population.

Implemented coefficients

• Gini Coefficient:

  • Ordinary. Follows this formula:

  

  • Corrected. Uses a correction for small datasets based on Deltas, 2003.

• Ratio top / rest. Follows this formula:

  

Where k is is the ceil value for 100 - percentage you define. For instance, if you take k = 10, you are getting the ratio of inequality between the top 10% percentage and the rest 90% percentage. In particular, this specific value of k is given to you directly by the ratio_top10_rest() function.

Installation

This library is hosted on PyPI, so installation is straightforward. The easiest way to install type this at the command line (Linux, Mac, or Windows):

pip install inequality_coefficients

This library also depends on numpy, but pip should take of that for you already.

Basic Usage

For the simplest, typical use cases, this tells you everything you need to know.:

import inequality_coefficients as ineq
data = array([1.7, 3.2 ...]) # data can be list of nums or numpy array
gini_coeff = ineq.gini(data)
ratio_top_rest = ineq.ratio_top10_rest(data)

Development

To setup the development environment install all the dev dependiencies with pip install -r requirements.txt and install the latest version in your sites-packages with python setup.py develop.

Run tests

I use pytest. Install it with pip install -U pytest and run the test with the development setup with pytest.

Acknowledgements

Firstly, I was based on Felipe Ortega's wikixray code for implementing the gini coefficient, however, my code has changed so much (I have even fixed a bug in his code) and also now I'm using numpy as backend.

Anyway, I want to thank him for open sourcing that project.