gituser/docker_multiarch/: pylev-1.4.0 metadata and description

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A pure Python Levenshtein implementation that's not freaking GPL'd.

author Daniel Lindsley
author_email daniel@toastdriven.com
classifiers
  • Development Status :: 5 - Production/Stable
  • Intended Audience :: Developers
  • License :: OSI Approved :: BSD License
  • Operating System :: OS Independent
  • Programming Language :: Python
  • Programming Language :: Python :: 3
File Tox results History
pylev-1.4.0-py2.py3-none-any.whl
Size
6 KB
Type
Python Wheel
Python
2.7

A pure Python Levenshtein implementation that’s not freaking GPL’d.

Based off the Wikipedia code samples at http://en.wikipedia.org/wiki/Levenshtein_distance.

Requirements

  • Python 2.7.X, Python 3.3+ or PyPy 1.6.0+

Usage

Usage is fairly straightforward:

import pylev
distance = pylev.levenshtein('kitten', 'sitting')
assert distance == 3

License

New BSD.

Tests

Setup:

$ git clone https://github.com/toastdriven/pylev.git
$ cd pylev

Running:

$ python -m unittest tests
https://travis-ci.com/toastdriven/pylev.svg?branch=main

Version History

  • v1.4.0

    • Updated for current versions of Python

    • Integrated a better Travis matrix. Thanks to @grainert!

    • Fixed mistaken docs about the assert. Thanks to @adamchainz!

    • Reorganized the package.

    • Blacked all the source code.

  • v1.3.0

    • Implemented a considerably faster variants (orders of magnitude).

    • Tested & working on Python 2.7.4, Python 3.3.1 & PyPy 1.9.0.

  • v1.2.0

    • Fixed all incorrect spellings of “Levenshtein” (there’s no “c” in it).

    • Old methods are aliased for backward-compatibility.

  • v1.1.0

    • Implemented a much faster variant (several orders of magnitude).

    • The older variant was renamed to classic_levenschtein.

    • Tested & working on Python 3.3 & PyPy 1.6.0 as well.

  • v1.0.2

    • Python packaging is REALLY hard. Including the README this time.

  • v1.0.1

    • Python packaging is hard. Including the README this time.

  • v1.0.0

    • Initial release, just the naive implementation of Levenshtein.