Metadata-Version: 2.1
Name: inequality_coefficients
Version: 1.2.1
Summary: Coefficients to measure inequality.
Home-page: https://github.com/Grasia/inequality_coefficients
Author: Abel 'Akronix' Serrano Juste
Author-email: akronix5@gmail.com
License: MIT
Description: 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:
            
            ![Gini formula](assets/gini_formula.png)
            
            * Corrected. Uses a correction for small datasets based on [Deltas,
        2003](https://doi.org/10.1162/rest.2003.85.1.226).
        * Ratio top / rest. Follows this formula:
        
            ![Ratio top formula](assets/ratio_10_90_formula.png)
        
        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.
        
Keywords: inequality coefficient index gini ratio top
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3
Description-Content-Type: text/markdown
