Metadata-Version: 1.1
Name: effectus
Version: 1.0.0.dev4
Summary: effectus tells you which minority of causes provokes which majority of effects.
Home-page: http://bitbucket.com/hyllos/effectus-python
Author: Benjamin Weber
Author-email: mail@bwe.im
License: MIT license
Description: effectus - A cause-effect library
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          :alt: Code coverage 
        
        What?
        -----
        
        You provide it with a series of numbers.
        It tells you whether a pareto distribution is present.
        
        Why?
        ----
        
        ``Mean``, ``Median`` and ``Most likely value`` regularly hide that a
        minority of causes provokes a majority of effects.
        
        `The Fallacy of the Arithmetic Mean <http://docs.unterschied.cc/effectus-python/fallacy.html>`_ explains the situation in-depth.
        
        How?
        ----
        
        First, install it:
        
        .. code-block:: bash
        
            $ pip3 install effectus
        
        Then, in your Python shell do:
        
        .. code-block:: python
        
           from effectus import Effects
           Effects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
           <pareto present [0.707]: 1/5 causes => 4/5 effects [total ∆: 2.3 %]>
        
        If you want 80% of results, you need only 20% of causes.
        
        `» Documentation <http://docs.unterschied.cc/effectus-python>`_
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Keywords: pareto cause-effect power-law entropy
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
