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:target: https://travis-ci.org/seatgeek/fuzzywuzzy
FuzzyWuzzy
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 2.7 or higher
- difflib
python-Levenshtein <https://github.com/ztane/python-Levenshtein/>_ (optional, provides a 4-10x speedup in String
Matching, though may result indiffering results for certain cases <https://github.com/seatgeek/fuzzywuzzy/issues/128>_)
For testing
- pycodestyle
- hypothesis
- pytest
Installation
============
Using PIP via PyPI
.. code:: bash
pip install fuzzywuzzy
or the following to install `python-Levenshtein` too
.. code:: bash
pip install fuzzywuzzy[speedup]
Using PIP via Github
.. code:: bash
pip install git+git://github.com/seatgeek/fuzzywuzzy.git@0.18.0#egg=fuzzywuzzy
Adding to your ``requirements.txt`` file (run ``pip install -r requirements.txt`` afterwards)
.. code:: bash
git+ssh://git@github.com/seatgeek/fuzzywuzzy.git@0.18.0#egg=fuzzywuzzy
Manually via GIT
.. code:: bash
git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
cd fuzzywuzzy
python setup.py install
Usage
=====
.. code:: python
>>> from fuzzywuzzy import fuzz
>>> from fuzzywuzzy 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
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)
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Known Ports
============
FuzzyWuzzy is being ported to other languages too! Here are a few ports we know about:
- Java: `xpresso's fuzzywuzzy implementation <https://github.com/WantedTechnologies/xpresso/wiki/Approximate-string-comparison-and-pattern-matching-in-Java>`_
- Java: `fuzzywuzzy (java port) <https://github.com/xdrop/fuzzywuzzy>`_
- Rust: `fuzzyrusty (Rust port) <https://github.com/logannc/fuzzyrusty>`_
- JavaScript: `fuzzball.js (JavaScript port) <https://github.com/nol13/fuzzball.js>`_
- C++: `Tmplt/fuzzywuzzy <https://github.com/Tmplt/fuzzywuzzy>`_
- C#: `fuzzysharp (.Net port) <https://github.com/BoomTownRoi/BoomTown.FuzzySharp>`_
- Go: `go-fuzzywuzz (Go port) <https://github.com/paul-mannino/go-fuzzywuzzy>`_
- Free Pascal: `FuzzyWuzzy.pas (Free Pascal port) <https://github.com/DavidMoraisFerreira/FuzzyWuzzy.pas>`_
- Kotlin multiplatform: `FuzzyWuzzy-Kotlin <https://github.com/willowtreeapps/fuzzywuzzy-kotlin>`_
- R: `fuzzywuzzyR (R port) <https://github.com/mlampros/fuzzywuzzyR>`_