{ "info": { "author": "Taurus Olson", "author_email": "taurusolson@gmail.com", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 2.7", "Topic :: Utilities" ], "description": "fntools\n=======\n\n\n.. image:: https://readthedocs.org/projects/fntools/badge/?version=master\n\n:target: https://readthedocs.org/projects/fntools/?badge=master\n:alt: Documentation Status\n\n**fntools** provides functional programming tools for data processing. This\nmodule is a set of functions that I needed in my work and found useful.\n\n\nInstallation\n------------\n\n::\n\n pip install fntools\n\n\nExamples\n--------\n\n* Split a list of elements with factors with `split`::\n\n songs = ('Black', 'Even Flow', 'Amongst the waves', 'Sirens')\n albums = ('Ten', 'Ten', 'Backspacer', 'Lightning Bolt')\n print split(songs, albums)\n {'Lightning Bolt': ['Sirens'], 'Ten': ['Black', 'Even Flow'], 'Backspacer': ['Amongst the waves']}\n\n\n* Determine whether any element of a list is included in another list with `any_in`::\n\n print any_in(['Oceans', 'Big Wave'], ['Once', 'Alive', 'Oceans', 'Release'])\n True\n\n print any_in(['Better Man'], ['Man of the Hour', 'Thumbing my way'])\n False\n\n\n* Apply many functions on the data with `dispatch`::\n\n # Suppose we want to know the mean, the standard deviation and the median of\n # a distribution (here we use the standard normal distribution)\n\n import numpy as np\n np.random.seed(10)\n x = np.random.randn(10000)\n\n print dispatch(x, (np.mean, np.std, np.median))\n [0.0051020560019149385, 0.98966401277169491, 0.013111308495186252]\n\n\nMany more useful functions are available. 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