thefuzz: A Python repository from kandyjam

TheFuzz

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

Requirements

Python 3.7 or higher
difflib
python-Levenshtein (optional, provides a 4-10x speedup in String Matching, though may result in differing results for certain cases)

For testing

pycodestyle
hypothesis
pytest

Installation

Using PIP via PyPI

pip install thefuzz

or the following to install python-Levenshtein too

pip install thefuzz[speedup]

Using PIP via Github

pip install git+git://github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzz

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

git+ssh://git@github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzz

Manually via GIT

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

Usage

>>> from thefuzz import fuzz
>>> from thefuzz 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

Partial Token Sort Ratio

>>> 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

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)

kandyjam/thefuzz