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    "info": {
        "author": "Benjamin Weber",
        "author_email": "mail@bwe.im",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Customer Service",
            "Intended Audience :: Developers",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Healthcare Industry",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Manufacturing",
            "Intended Audience :: Science/Research",
            "Intended Audience :: Telecommunications Industry",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
        ],
        "description": ".. image:: http://ci.appveyor.com/api/projects/status/m0f9fw5b670whkw8?svg=true\n    :target: https://ci.appveyor.com/project/hyllos/cause-effect\n\nInstall it\n-----------\n\nYou can install ``cause_effect`` via:\n\n.. code-block:: bash\n\n  $ pip install cause_effect\n\nAlternatively, you can install from the code repository directly:\n\n.. code-block:: bash\n\n  $ pip install hg+http://bitbucket.org/hyllos/cause_effect\n\nCore Functions\n--------------\n\n``pareto(values)``\n    Is a pareto distribution present for a list of numbers (``ratio`` <= 1)?\n\n``mccauses(values)``\n    Which causes have the highest concentration (rank * value)?\n\n``mceffects(values)``\n    Which effects have the highest concentration?\n\n``separator(values)```\n    From which value (including) does the highest concentration begin?\n\n``causes(values, effects=0.8)``\n    Determine causes for specified share of effects.\n\n``effects(values, causes=0.2)``\n    Determine effects for specified share of causes.\n\nSecondary Functions\n-------------------\n\n``ratio(values)``\n    ``entropy`` divided by ``control_limit``.\n\n``entropy(values)``\n    Calculate entropy for values.\n\n``control_limit(count)``\n    Calculate control entropy for ``count`` number of elements (length of ``values``).\n\nTertiary Functions\n-------------------\n\n``make_causes(count)``\n    Return list of causes that is cumulative percent of ``count`` number of elements.\n\n``make_effects(values)``\n    Return list of effects that is cumulative percent of values.\n\n``make_concentration(values)``\n    Return list of concentration for list of ``values`` that is rank * value.\n\n``sort_list(values)``\n    Return sorted list of numbers.\n\nParameters\n-----------\n\n``values`` is a list of numbers.\n``effects`` and ``causes`` must be a number between 0 and 1 (including).\n``count`` is the length of the list of ``values``.\n\nUse it\n------\n\nThe function ``pareto`` tells you whether a pareto distribution is present for a list of numbers:\n\n.. code-block:: python\n\n  from pareto import pareto, mccauses, mceffects\n  pareto([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n  True\n\nHere, we have a pareto distribution present.\nThat is a minority causes a majority of effects.\n\nBut which minority causes which majority?\n\n.. code-block:: python\n\n  mccauses([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n  0.2\n  mceffects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n  0.818815331010453\n\n20% of causes effect 82% of results.\n\nBut which values are that 20%?\n\n.. code-block:: python\n\n  separator([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n  621\n\nAll values greater or equal than 621 are those 20% causing 82% of results.\n\n**That's it.**\n\nDig Deeper\n-----------\n\nHow many causes are required for only 90% of effects?\n\n.. code-block:: python\n\n  from pareto import causes, effects\n  causes([789, 621, 109, 65, 45, 30, 27, 15, 12, 9], 0.9)\n  0.4\n\n40%.\n\nHow many effects are behind only 10% of causes?\n\n.. code-block:: python\n\n  effects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9], 0.1)\n  0.458\n\n45.8%.\n\nHow does it work?\n-----------------\n\n``pareto`` calculates the `entropy`_ for a list of effects:\n\n.. code-block:: python\n\n  from pareto import entropy, control_limit, ratio\n  entropy([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])\n  1.9593816735406657\n\nIt calculates the entropy for a control group of ten elements. That is the length of our list.\n\n.. code-block:: python\n\n  control_limit(10)\n  2.7709505944546686\n\nIt then checks ``entropy`` is less or equal than ``control_limit``.\n\nThis can be simplified to:\n\n.. code-block:: python\n\n  values = [789, 621, 109, 65, 45, 30, 27, 15, 12, 9]\n  entropy(values) / control_limit(len(values)) <= 1\n\nThe left side of the comparison is done by ``ratio``.\nSo, if you want to find out how nearby or far off you are from a pareto distribution, do:\n\n.. code-block:: python\n\n  ratio([109, 65, 45, 30, 27, 15, 12, 9])\n  1.051\n\nIf we remove the first two effects, the ``control_limit`` will be exceeded by the values.\nSo, we learn here that the pareto distribution disappears with the first two effects.\n\n.. _entropy: http://www.boazronen.org/PDF/The%20Pareto%20managerial%20principle%20-%20when%20does%20it%20apply.pdf\n\n``mccauses`` and ``mceffects`` return the respective share of the causes and effects where concentration (rank * value) is highest.\n\n\n=======\nHistory\n=======\n\n0.2.0 (2016-10-21)\n------------------\n\n* Add function separator().\n* Streamline tests.\n\n0.1.0 (2016-10-20)\n------------------\n\n* First release on PyPI.",
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        "keywords": "pareto cause-effect power-law entropy",
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