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 ========= .. code:: bash >>> 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
- 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)
Using PIP via PyPI
pip install fuzzywuzzyor the following to install python-Levenshtein too
pip install fuzzywuzzy[speedup]Using PIP via Github
pip install git+git://github.com/seatgeek/[email protected]#egg=fuzzywuzzyAdding to your requirements.txt file (run pip install -r requirements.txt afterwards)
git+ssh://[email protected]/seatgeek/[email protected]#egg=fuzzywuzzyManually via GIT
git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
cd fuzzywuzzy
python setup.py install>>> from fuzzywuzzy import fuzz
>>> from fuzzywuzzy import process>>> fuzz.ratio("this is a test", "this is a test!")
97>>> fuzz.partial_ratio("this is a test", "this is a test!")
100>>> 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>>> 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>>> 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)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)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent