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        "description": "StatsCounter: A statistics-enabled Python container\r\n---------------------------------------------------\r\n\r\n::\r\n\r\n         _        _                             _\r\n     ___| |_ __ _| |_ ___  ___ ___  _   _ _ __ | |_ ___ _ __\r\n    / __| __/ _` | __/ __|/ __/ _ \\| | | | '_ \\| __/ _ \\ '__|\r\n    \\__ \\ || (_| | |_\\__ \\ (_| (_) | |_| | | | | ||  __/ |\r\n    |___/\\__\\__,_|\\__|___/\\___\\___/ \\__,_|_| |_|\\__\\___|_|\r\n\r\n\r\nStatsCounter is a GNU Licensed, statistics powered version\r\nof Python's standard library ``Counter`` class. It attaches\r\nseveral helpful methods that can be used to make your\r\ndata-driven uses a breeze.\r\n\r\nUsage\r\n-----\r\n\r\nAs a histogram\r\n~~~~~~~~~~~~~~\r\n\r\n.. code-block:: python\r\n\r\n    >>> import statscounter as stats\r\n    >>> letter_freq = stats.StatsCounter(a=1, b=2, c=3, d=4, e=4, f=6)\r\n    >>> letter_freq.mean()      # average frequency\r\n    3.3333333333333335\r\n    >>> letter_freq.mode()      # most frequent element\r\n    4\r\n    >>> letter_freq.median()    # the median number (avg if even # of items)\r\n    3.5\r\n    >>> letter_freq.variance()  # sample variance\r\n    3.066666666666667\r\n    >>> letter_freq.stdev()     # sample standard deviation\r\n    1.7511900715418263\r\n    >>> letter_freq.pvariance() # population variance\r\n    2.555555555555556\r\n    >>> letter_freq.pstdev()    # population std. dev.\r\n    1.5986105077709065\r\n    >>> letter_freq.max()       # the maximum value\r\n    6\r\n    >>> letter_freq.argmax()    # the argument yielding the maximum value\r\n    \"f\"\r\n\r\nAs a utility\r\n~~~~~~~~~~~~\r\n\r\n.. code-block:: python\r\n\r\n    >>> import statscounter as stats\r\n    >>> stats.mean([1, 2, 3, 4, 4, 6])      # average frequency\r\n    3.3333333333333335\r\n    >>> stats.mode([1, 2, 3, 4, 4, 6])      # most frequent element\r\n    4\r\n    >>> stats.median([1, 2, 3, 4, 4, 6])    # the median number (avg if even # of items)\r\n    3.5\r\n    >>> stats.variance([1, 2, 3, 4, 4, 6])  # sample variance\r\n    3.066666666666667\r\n    >>> stats.stdev([1, 2, 3, 4, 4, 6])     # sample standard deviation\r\n    1.7511900715418263\r\n    >>> stats.pvariance([1, 2, 3, 4, 4, 6]) # population variance\r\n    2.555555555555556\r\n    >>> stats.pstdev([1, 2, 3, 4, 4, 6])    # population std. dev.\r\n    1.5986105077709065\r\n",
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