35 lines
1.6 KiB
Markdown
35 lines
1.6 KiB
Markdown
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---
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template: overrides/main.html
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---
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<h1 align="center">
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<img src="img/RapidFuzz.svg" alt="RapidFuzz" width="400">
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</h1>
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<h4 align="center">Rapid fuzzy string matching using the Levenshtein Distance</h4>
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<p align="center">
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<a href="https://pypi.org/project/rapidfuzz/">
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<img src="https://img.shields.io/pypi/v/rapidfuzz"
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alt="PyPI package version">
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</a>
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<a href="https://anaconda.org/conda-forge/rapidfuzz">
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<img src="https://img.shields.io/conda/vn/conda-forge/rapidfuzz.svg"
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alt="Conda Version">
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</a>
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<a href="https://gitter.im/rapidfuzz/community">
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<img src="https://badges.gitter.im/rapidfuzz/community.svg"
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alt="Gitter chat">
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</a>
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<a href="https://github.com/maxbachmann/rapidfuzz/blob/dev/LICENSE">
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<img src="https://img.shields.io/github/license/maxbachmann/rapidfuzz"
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alt="GitHub license">
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</a>
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</p>
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---
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RapidFuzz is a fast string matching library for Python and C++, which is using the string similarity calculations from [FuzzyWuzzy](https://github.com/seatgeek/fuzzywuzzy). However there are two aspects that set RapidFuzz apart from FuzzyWuzzy:
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1) It is MIT licensed so it can be used whichever License you might want to choose for your project, while you're forced to adopt the GPL license when using FuzzyWuzzy
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2) It is mostly written in C++ and on top of this comes with a lot of Algorithmic improvements to make string matching even faster, while still providing the same results. More details on these performance improvements in form of benchmarks can be found [here](https://github.com/maxbachmann/rapidfuzz/blob/master/Benchmarks.md)
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