FuzzyWuzzy

Fuzzy string matching like a boss. It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.

Always Case Sensitive

>>> fuzz.ratio("this is a test", "this is a test!")
97
>>> fuzz.ratio("this is a test", "this is a TEST!")
69
>>> fuzz.ratio("this is a test".lower(), "this is a TEST!".lower())
97

>>> fuzz.partial_ratio("this is a test", "this is a test!")
100
>>> fuzz.partial_ratio("this is a test", "this is a TEST!")
71
>>> fuzz.partial_ratio("this is a test".lower(), "this is a TEST!".lower())
100

Requirements

• Python 2.4 or higher
• difflib
• python-Levenshtein (optional, provides a 4-10x speedup in String Matching, though may result in differing results for certain cases)

Installation

Using PIP via PyPI

pip install fuzzywuzzy

or the following to install python-Levenshtein too

pip install fuzzywuzzy[speedup]

Using PIP via Github

pip install git+git://github.com/seatgeek/fuzzywuzzy.git@0.15.1#egg=fuzzywuzzy

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

git+ssh://git@github.com/seatgeek/fuzzywuzzy.git@0.15.1#egg=fuzzywuzzy

Manually via GIT

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

Usage

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

Simple Ratio

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

Partial Ratio

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

Token Sort Ratio

>>> 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

>>> 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

>>> 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:

>>> 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)

Known Ports

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

• Java: xpresso's fuzzywuzzy implementation
• Java: fuzzywuzzy (java port)
• Rust: fuzzyrusty (Rust port)
• JavaScript: fuzzball.js (JavaScript port)