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https://github.com/yougov/fuzzy


https://github.com/yougov/fuzzy

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README

        

.. image:: https://img.shields.io/pypi/v/Fuzzy.svg
:target: https://pypi.org/project/Fuzzy

.. image:: https://img.shields.io/pypi/pyversions/Fuzzy.svg

.. image:: https://img.shields.io/travis/yougov/fuzzy/master.svg
:target: http://travis-ci.org/yougov/fuzzy

Fuzzy is a python library implementing common phonetic algorithms quickly.
Typically this is in string similarity exercises, but they're pretty versatile.

It uses C Extensions (via Cython) for speed.

The algorithms are:

* `Soundex `_
* `NYSIIS `_
* `Double Metaphone `_ Based on Maurice
Aubrey's C code from his perl implementation.

Usage
=====

The functions are quite easy to use!

>>> import fuzzy
>>> soundex = fuzzy.Soundex(4)
>>> soundex('fuzzy')
'F200'
>>> dmeta = fuzzy.DMetaphone()
>>> dmeta('fuzzy')
['FS', None]
>>> fuzzy.nysiis('fuzzy')
'FASY'

Performance
===========

Fuzzy's Double Metaphone was ~10 times faster than the pure python
implementation by `Andrew Collins `_
in some recent `testing `_.
Soundex and NYSIIS should be similarly faster. Using iPython's timeit::

In [3]: timeit soundex('fuzzy')
1000000 loops, best of 3: 326 ns per loop

In [4]: timeit dmeta('fuzzy')
100000 loops, best of 3: 2.18 us per loop

In [5]: timeit fuzzy.nysiis('fuzzy')
100000 loops, best of 3: 13.7 us per loop

Distance Metrics
================

We recommend the `Python-Levenshtein `_
module for fast, C based string distance/similarity metrics. Among others
functions it includes:

* `Levenshtein `_ edit distance
* `Jaro `_ distance
* `Jaro-Winkler `_ distance
* `Hamming distance `_

In testing it's been several times faster than comparable pure python
implementations of those algorithms.