.. image:: https://github.com/seatgeek/thefuzz/actions/workflows/ci.yml/badge.svg
:target: https://github.com/seatgeek/thefuzz
TheFuzz
=======
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 3.8 or higher
- `rapidfuzz <https://github.com/maxbachmann/RapidFuzz/>`_
For testing
~~~~~~~~~~~
- pycodestyle
- hypothesis
- pytest
Installation
============
Using pip via PyPI
.. code:: bash
pip install thefuzz
Using pip via GitHub
.. code:: bash
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)
.. code:: bash
git+ssh://git@github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzz
Manually via GIT
.. code:: bash
git clone git://github.com/seatgeek/thefuzz.git thefuzz
cd thefuzz
python setup.py install
Usage
=====
.. code:: python
>>> from thefuzz import fuzz
>>> from thefuzz 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
Partial Token Sort Ratio
~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> 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
~~~~~~~
.. 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://github.com/seatgeek/thefuzz/actions/workflows/ci.yml/badge.svg
:target: https://github.com/seatgeek/thefuzz
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