{
    "info": {
        "author": "Maximiliano Greco",
        "author_email": "mmngreco@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
        ],
        "description": "IneqPy Package\n==============\n\nThis package provides statistics to do a properly quantitative analysis of inequality. Among the estimators provided by this package you can find:\n\n- Atkinson Index\n- Gini Index\n- Kakwani Index\n- Lorenz curve\n- Variance\n- Mean\n- Kurtosis\n- Skewness\n\n-----------\nFirst-steps\n-----------\n\n- Installation\n- Examples\n\n\nInstall\n-------\n\n.. code-block:: bash\n\n  git clone https://github.com/mmngreco/IneqPy.git\n  cd IneqPy\n  pip install .\n\n--------\nExamples\n--------\n\nSome examples of how use this package:\nData of example:\n\n.. code-block:: python\n\n    import pandas as pd\n    import numpy as np\n    import ineqpy\n    d\n                 renta   factor\n    0        -13004.12   1.0031\n    89900    141656.97   1.4145\n    179800     1400.38   4.4122\n    269700   415080.96   1.3295\n    359600    69165.22   1.3282\n    449500     9673.83  19.4605\n    539400    55057.72   1.2923\n    629300     -466.73   1.0050\n    719200     3431.86   2.2861\n    809100      423.24   1.1552\n    899000        0.00   1.0048\n    988900     -344.41   1.0028\n    1078800   56254.09   1.2752\n    1168700   60543.33   2.0159\n    1258600    2041.70   2.7381\n    1348500     581.38   7.9426\n    1438400   55646.05   1.2818\n    1528300       0.00   1.0281\n    1618200   69650.24   1.2315\n    1708100   -2770.88   1.0035\n    1798000    4088.63   1.1256\n    1887900       0.00   1.0251\n    1977800   10662.63  28.0409\n    2067700    3281.95   1.1670\n\n----------------------\nDescriptive statistics\n----------------------\n\n.. code-block:: python\n\n    ineqpy.xbar(x=d.renta, weights=d.factor)\n    20444.700666031338\n    ineqpy.var(x=d.renta, weights=d.factor)\n    2982220948.7413292\n    x, w = d.renta.values, d.factor.values\n\nNote that the standardized moment for order `n`, retrieve the value in that\ncolumn:\n\n==== =========\n `n`  value\n==== =========\n 1    0\n---- ---------\n 2    1\n---- ---------\n 3    Skew\n---- ---------\n 4    Kurtosis\n==== =========\n\nA helpful table of interpretation of the moments\n\n.. code-block:: python\n\n    ineqpy.stdmoment(x, w, 1)  # = 0\n    2.4624948200717338e-17\n    ineqpy.stdmoment(x, w, 2)  # = 1\n    1.0\n    ineqpy.stdmoment(x, w, 3)  # = skew\n    5.9965055750379426\n    ineqpy.skew(x, w)\n    5.9965055750379426\n    ineqpy.stdmoment(x, w, 4)  # = kurtosis\n    42.319928851703004\n    ineqpy.kurt(x, w)\n    42.319928851703004\n\n---------------------\nInequality estimators\n---------------------\n\n.. code-block:: python\n\n    # pass a pandas.DataFrame and inputs as strings\n    ineqpy.gini(df=d, income='renta', weights='factor')\n    0.76739136365917116\n    # you can pass arrays too\n    ineqpy.gini(income=d.renta.values, weights=d.factor.values)\n    0.76739136365917116",
        "description_content_type": null,
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        "license": "UNKNOWN",
        "maintainer": null,
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        "name": "IneqPy",
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        "summary": "A Python Package To Quantitative Analysis Of Inequality",
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