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        "author": "Jack Peterson",
        "author_email": "<jack@tinybike.net>",
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        "description": "WeightedStats\n=============\n\n.. image:: https://travis-ci.org/tinybike/weightedstats.svg?branch=master\n    :target: https://travis-ci.org/tinybike/weightedstats\n\n.. image:: https://coveralls.io/repos/tinybike/weightedstats/badge.svg?branch=master :target: https://coveralls.io/r/tinybike/weightedstats?branch=master\n\n.. image:: https://badge.fury.io/py/weightedstats.svg\n    :target: http://badge.fury.io/py/weightedstats\n\nPython functions to calculate the mean, weighted mean, median, and weighted median.\n\nInstallation\n^^^^^^^^^^^^\n\nThe easiest way to install WeightedStats is to use pip::\n\n    $ pip install weightedstats\n\nUsage\n^^^^^\n\nWeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays.\n\nExample::\n\n    import weightedstats as ws\n\n    my_data = [1, 2, 3, 4, 5]\n    my_weights = [10, 1, 1, 1, 9]\n\n    # Ordinary (unweighted) mean and median\n    ws.mean(my_data)    # equivalent to ws.weighted_mean(my_data)\n    ws.median(my_data)  # equivalent to ws.weighted_median(my_data)\n    \n    # Weighted mean and median\n    ws.weighted_mean(my_data, weights=my_weights)\n    ws.weighted_median(my_data, weights=my_weights)\n\n    # Special weighted mean and median functions for use with numpy arrays\n    ws.numpy_weighted_mean(my_data, weights=my_weights)\n    ws.numpy_weighted_median(my_data, weights=my_weights)\n\nTests\n^^^^^\n\nUnit tests are in the test/ directory.\n",
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