fuzzywuzzymit 0.0.0

Fuzzy string matching in python

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fuzzywuzzymit
==========

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 2.4 or higher
- difflib

For testing
-----------
- pycodestyle
- hypothesis
- pytest

Installation
============

Using PIP via PyPI

.. code:: bash

pip install fuzzywuzzymit

Using PIP via Github

.. code:: bash

pip install git+git://github.com/graingert/fuzzywuzzymit.git@0.16.0#egg=fuzzywuzzymit

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

.. code:: bash

git+ssh://[e-mail]/graingert/fuzzywuzzymit.git@0.16.0#egg=fuzzywuzzymit

Manually via GIT

.. code:: bash

git clone git://github.com/graingert/fuzzywuzzymit.git fuzzywuzzymit
cd fuzzywuzzymit
python setup.py install


Usage
=====

.. code:: python

>>> from fuzzywuzzymit import fuzz
>>> from fuzzywuzzymit 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
============

fuzzywuzzymit is being ported to other languages too! Here are a few ports we know about:

- Java: `xpresso's fuzzywuzzymit implementation <https://github.com/WantedTechnologies/xpresso/wiki/Approximate-string-comparison-and-pattern-matching-in-Java>`_
- Java: `fuzzywuzzymit (java port) <https://github.com/xdrop/fuzzywuzzymit>`_
- Rust: `fuzzyrusty (Rust port) <https://github.com/logannc/fuzzyrusty>`_
- JavaScript: `fuzzball.js (JavaScript port) <https://github.com/nol13/fuzzball.js>`_
- C++: `Tmplt/fuzzywuzzymit <https://github.com/Tmplt/fuzzywuzzymit>`_