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

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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 <http://en.wikipedia.org/wiki/Soundex>_
• NYSIIS <http://en.wikipedia.org/wiki/NYSIIS>_
• Double Metaphone <http://en.wikipedia.org/wiki/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 <http://www.atomodo.com/code/double-metaphone>_ in some recent testing <http://chmullig.com/2011/03/pypy-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 <http://code.google.com/p/pylevenshtein/>_ module for fast, C based string distance/similarity metrics. Among others functions it includes:

• Levenshtein <http://en.wikipedia.org/wiki/Levenshtein_distance>_ edit distance
• Jaro <http://en.wikipedia.org/wiki/Jaro_distance>_ distance
• Jaro-Winkler <http://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance>_ distance
• Hamming distance <http://en.wikipedia.org/wiki/Hamming_distance>_

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