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        "author": "Benjamin Weber",
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            "Programming Language :: Python :: 3",
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            "Programming Language :: Python :: 3.6"
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        "description": "effectus - A cause-effect library\n=================================\n\n.. image:: https://img.shields.io/pypi/v/effectus.svg\n  :target: https://pypi.python.org/pypi/effectus\n  :alt: Pypi version\n\n.. image:: https://img.shields.io/pypi/pyversions/effectus.svg\n  :target: https://pypi.python.org/pypi/effectus\n  :alt: Python versions\n\n.. image:: https://img.shields.io/appveyor/ci/hyllos/effectus-python/default.svg\n  :target: https://ci.appveyor.com/project/hyllos/effectus-python\n  :alt: Windows Built Status\n\n.. image:: https://img.shields.io/codecov/c/bitbucket/hyllos/effectus-python/default.svg\n  :target: https://codecov.io/bb/hyllos/effectus-python/branch/default\n  :alt: Code coverage \n\nWhat?\n-----\n\nYou provide it with a series of numbers.\nIt tells you whether a pareto distribution is present.\n\nWhy?\n----\n\n``Mean``, ``Median`` and ``Most likely value`` regularly hide that a\nminority of causes provokes a majority of effects.\n\n`The Fallacy of the Arithmetic Mean <http://docs.unterschied.cc/effectus-python/fallacy.html>`_ explains the situation in-depth.\n\nHow?\n----\n\nFirst, install it:\n\n.. code-block:: bash\n\n    $ pip3 install effectus\n\nThen, in your Python shell do:\n\n.. code-block:: python\n\n   from effectus import Effects\n   Effects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n   <pareto present [0.707]: 1/5 causes => 4/5 effects [total \u2206: 2.3 %]>\n\nIf you want 80% of results, you need only 20% of causes.\n\n`\u00bb Documentation <http://docs.unterschied.cc/effectus-python>`_\n---------------------------------------------------------------",
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        "keywords": "pareto cause-effect power-law entropy",
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        "name": "effectus",
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