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.