• osx-64 v0.16.0

conda install

To install this package run one of the following:
conda install jkroes::fuzzywuzzy

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.. image:: https://travis-ci.org/seatgeek/fuzzywuzzy.svg?branch=master :target: https://travis-ci.org/seatgeek/fuzzywuzzy

FuzzyWuzzy

Fuzzy string matching like a boss. It uses Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance>_ to calculate the differences between sequences in a simple-to-use package.

Requirements

• Python 2.4 or higher
• difflib
• python-Levenshtein <https://github.com/ztane/python-Levenshtein/>_ (optional, provides a 4-10x speedup in String Matching, though may result in differing results for certain cases <https://github.com/seatgeek/fuzzywuzzy/issues/128>_)

Installation

Using PIP via PyPI

.. code:: bash

pip install fuzzywuzzy

or the following to install python-Levenshtein too

.. code:: bash

pip install fuzzywuzzy[speedup]

Using PIP via Github

.. code:: bash

pip install git+git://github.com/seatgeek/[email protected]#egg=fuzzywuzzy

Adding to your requirements.txt file (run pip install -r requirements.txt afterwards)

.. code:: bash

git+ssh://[email protected]/seatgeek/[email protected]#egg=fuzzywuzzy

Manually via GIT

.. code:: bash

git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
cd fuzzywuzzy
python setup.py install

Usage

.. code:: python

>>> from fuzzywuzzy import fuzz
>>> from fuzzywuzzy import process

Simple Ratio ~~~~~~~~~~~~

.. code:: python

>>> fuzz.ratio("this is a test", "this is a test!")
    97

Partial Ratio ~~~~~~~~~~~~~

.. code:: python

>>> fuzz.partial_ratio("this is a test", "this is a test!")
    100

Token Sort Ratio ~~~~~~~~~~~~~~~~

.. code:: python

>>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    91
>>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    100

Token Set Ratio ~~~~~~~~~~~~~~~

.. code:: python

>>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
    84
>>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
    100

Process ~~~~~~~

.. code:: python

>>> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
>>> process.extract("new york jets", choices, limit=2)
    [('New York Jets', 100), ('New York Giants', 78)]
>>> process.extractOne("cowboys", choices)
    ("Dallas Cowboys", 90)

You can also pass additional parameters to extractOne method to make it use a specific scorer. A typical use case is to match file paths:

.. code:: python

>>> process.extractOne("System of a down - Hypnotize - Heroin", songs)
    ('/music/library/good/System of a Down/2005 - Hypnotize/01 - Attack.mp3', 86)
>>> process.extractOne("System of a down - Hypnotize - Heroin", songs, scorer=fuzz.token_sort_ratio)
    ("/music/library/good/System of a Down/2005 - Hypnotize/10 - She's Like Heroin.mp3", 61)

.. |Build Status| image:: https://api.travis-ci.org/seatgeek/fuzzywuzzy.png?branch=master :target: https:travis-ci.org/seatgeek/fuzzywuzzy

Known Ports

FuzzyWuzzy is being ported to other languages too! Here are a few ports we know about:

• Java: xpresso's fuzzywuzzy implementation <https://github.com/WantedTechnologies/xpresso/wiki/Approximate-string-comparison-and-pattern-matching-in-Java>_
• Java: fuzzywuzzy (java port) <https://github.com/xdrop/fuzzywuzzy>_
• Rust: fuzzyrusty (Rust port) <https://github.com/logannc/fuzzyrusty>_
• JavaScript: fuzzball.js (JavaScript port) <https://github.com/nol13/fuzzball.js>_
• C++: Tmplt/fuzzywuzzy <https://github.com/Tmplt/fuzzywuzzy>_