<commit_before>===============================
fuzzysearch
===============================

.. image:: https://pypip.in/version/fuzzysearch/badge.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Latest Version

.. image:: https://travis-ci.org/taleinat/fuzzysearch.svg?branch=master
    :target: https://travis-ci.org/taleinat/fuzzysearch/branches
    :alt: Build & Tests Status

.. image:: https://coveralls.io/repos/taleinat/fuzzysearch/badge.svg?branch=master
    :target: https://coveralls.io/r/taleinat/fuzzysearch?branch=master
    :alt: Test Coverage

.. image:: https://pypip.in/download/fuzzysearch/badge.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Downloads

.. image:: https://pypip.in/py_versions/fuzzysearch/badge.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python versions

.. image:: https://pypip.in/implementation/fuzzysearch/badge.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python implementations

.. image:: https://pypip.in/license/fuzzysearch/badge.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch/
    :alt: License

fuzzysearch is useful for finding approximate subsequence matches

* Free software: `MIT license <LICENSE>`_
* Documentation: http://fuzzysearch.rtfd.org.

Installation
------------
Just install using pip::

    $ pip install fuzzysearch

Features
--------

* Fuzzy sub-sequence search: Find parts of a sequence which match a given
  sub-sequence up to a given maximum Levenshtein distance.
* Set individual limits for the number of substitutions, insertions and/or
  deletions allowed for a near-match.
* Includes optimized implementations for specific use-cases, e.g. only allowing<commit_msg>updated all README badges to use shields.io<commit_after>===============================
fuzzysearch
===============================

.. image:: https://img.shields.io/pypi/v/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Latest Version

.. image:: https://img.shields.io/travis/taleinat/fuzzysearch.svg?branch=master
    :target: https://travis-ci.org/taleinat/fuzzysearch/branches
    :alt: Build & Tests Status

.. image:: https://img.shields.io/coveralls/taleinat/fuzzysearch.svg?branch=master
    :target: https://coveralls.io/r/taleinat/fuzzysearch?branch=master
    :alt: Test Coverage

.. image:: https://img.shields.io/pypi/dm/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Downloads

.. image:: https://img.shields.io/pypi/wheel/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Wheels

.. image:: https://img.shields.io/pypi/pyversions/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python versions

.. image:: https://img.shields.io/pypi/implementation/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python implementations

.. image:: https://img.shields.io/pypi/l/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch/
    :alt: License

fuzzysearch is useful for finding approximate subsequence matches

* Free software: `MIT license <LICENSE>`_
* Documentation: http://fuzzysearch.rtfd.org.

Installation
------------
Just install using pip::

    $ pip install fuzzysearch

Features
--------

* Fuzzy sub-sequence search: Find parts of a sequence which match a given
  sub-sequence up to a given maximum Levenshtein distance.
* Set individual limits for the number of substitutions, insertions and/or
  deletions allowed for a near-match.
* Includes optimized implementations for specific use-cases, e.g. only allowing
{'fraction_non_alphanumeric': 0.16517857142857142, 'fraction_numerical': 0.0, 'mean_word_length': 4.038359788359788, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 30, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '40099', 'n_tokens_mistral': 1309, 'n_tokens_neox': 1210, 'n_words': 251}