Fuzzy string matching like a boss. It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.
- Python 3.7 or higher
- rapidfuzz
- pycodestyle
- hypothesis
- pytest
Using PIP via PyPI
pip install thefuzzUsing PIP via Github
pip install git+git://github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzzAdding to your requirements.txt file (run pip install -r requirements.txt afterwards)
git+ssh://git@github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzzManually via GIT
git clone git://github.com/seatgeek/thefuzz.git thefuzz
cd thefuzz
python setup.py install>>> from thefuzz import fuzz
>>> from thefuzz 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>>> fuzz.token_sort_ratio("fuzzy was a bear", "wuzzy fuzzy was a bear")
84
>>> fuzz.partial_token_sort_ratio("fuzzy was a bear", "wuzzy 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)