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        "author": "Abel 'Akronix' Serrano Juste",
        "author_email": "akronix5@gmail.com",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "Inequality Coefficients:\n========================\n\nThis is small library with some implemented coefficients (or indices)\nintended to measure inequality or concentration of the values in a\npopulation.\n\nImplemented coefficients\n-------------\n* Gini Coefficient:\n    * Ordinary. Follows this formula:\n    \n    ![Gini formula](https://raw.githubusercontent.com/Grasia/inequality_coefficients/master/assets/gini_formula.png)\n    \n    * Corrected. Uses a correction for small datasets based on [Deltas,\n2003](https://doi.org/10.1162/rest.2003.85.1.226).\n* Ratio top / rest. Follows this formula:\n\n    ![Ratio top formula](https://raw.githubusercontent.com/Grasia/inequality_coefficients/master/assets/ratio_10_90_formula.png)\n\nWhere k is is the ceil value for 100 - percentage you define.\nFor 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.\n\nInstallation\n------------\n\nThis library is hosted on PyPI, so installation is straightforward. The\neasiest way to install type this at the command line (Linux, Mac, or\nWindows):\n\n    pip install inequality_coefficients\n\nThis library also depends on numpy, but `pip` should take of that for\nyou already.\n\nBasic Usage\n-----------\n\nFor the simplest, typical use cases, this tells you everything you need\nto know.:\n\n    import inequality_coefficients as ineq\n    data = array([1.7, 3.2 ...]) # data can be list of nums or numpy array\n    gini_coeff = ineq.gini(data)\n    ratio_top_rest = ineq.ratio_top10_rest(data)\n\n# Development\n\nTo 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`.\n\n## Run tests\n\nI use pytest. Install it with `pip install -U pytest` and run the test with the development setup with `pytest`.\n\n\nAcknowledgements\n----------------\n\nFirstly, 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.\n\nAnyway, I want to thank him for open sourcing that project.",
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        "keywords": "inequality coefficient index gini ratio top",
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