RapidFuzz

rapid fuzzy string matching

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Project links

Homepage

Project description

Rapid fuzzy string matching in Python and C++ using the Levenshtein Distance

Description • Installation • Usage • License

Description

RapidFuzz is a fast string matching library for Python and C++, which is using the string similarity calculations from FuzzyWuzzy. However there are two aspects that set RapidFuzz apart from FuzzyWuzzy:

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
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

Requirements

Python 2.7 or later
On Windows the Visual C++ 2019 redistributable is required

Installation

There are several ways to install RapidFuzz, the recommended methods are to either use pip(the Python package manager) or conda (an open-source, cross-platform, package manager)

with pip

RapidFuzz can be installed with pip the following way:

pip install rapidfuzz

There are pre-built binaries (wheels) of RapidFuzz for MacOS (10.9 and later), Linux x86_64 and Windows. Wheels for armv6l (Raspberry Pi Zero) and armv7l (Raspberry Pi) are available on piwheels.

:heavy_multiplication_x: failure "ImportError: DLL load failed"

If you run into this error on Windows the reason is most likely, that the Visual C++ 2019 redistributable is not installed, which is required to find C++ Libraries (The C++ 2019 version includes the 2015, 2017 and 2019 version).

with conda

RapidFuzz can be installed with conda:

conda install -c conda-forge rapidfuzz

from git

RapidFuzz can be installed directly from the source distribution by cloning the repository. This requires a C++14 capable compiler.

git clone https://github.com/maxbachmann/rapidfuzz.git
cd rapidfuzz
pip install .

Usage

> from rapidfuzz import fuzz
> from rapidfuzz import process

Simple Ratio

> fuzz.ratio("this is a test", "this is a test!")
96.55171966552734

Partial Ratio

> fuzz.partial_ratio("this is a test", "this is a test!")
100.0

Token Sort Ratio

> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
90.90908813476562
> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
100.0

Token Set Ratio

> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
83.8709716796875
> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
100.0

Process

> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
> process.extract("new york jets", choices, limit=2)
[('New York Jets', 100, 1), ('New York Giants', 78.57142639160156, 2)]
> process.extractOne("cowboys", choices)
("Dallas Cowboys", 90, 3)

License

RapidFuzz is licensed under the MIT license since I believe that everyone should be able to use it without being forced to adopt the GPL license. Thats why the library is based on an older version of fuzzywuzzy that was MIT licensed as well. This old version of fuzzywuzzy can be found here.

Project details

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Project links

Homepage

Release history Release notifications | RSS feed

3.10.1

Oct 24, 2024

3.10.0

Sep 23, 2024

3.9.7

Sep 2, 2024

3.9.6

Aug 6, 2024

3.9.5

Jul 29, 2024

3.9.4

Jul 2, 2024

3.9.3

May 31, 2024

3.9.2

May 29, 2024

3.9.1

May 19, 2024

3.9.0

May 2, 2024

3.8.1

Apr 7, 2024

3.8.0

Apr 6, 2024

3.7.0

Mar 24, 2024

3.6.2

Mar 6, 2024

3.6.1

Dec 29, 2023

3.6.0

Dec 26, 2023

3.5.2

Nov 2, 2023

3.5.1

Nov 1, 2023

3.4.0

Oct 9, 2023

3.3.1

Sep 25, 2023

3.3.0

Sep 13, 2023

3.2.0

Aug 2, 2023

3.1.2

Jul 20, 2023

3.1.1

Jun 6, 2023

3.1.0

Jun 2, 2023

3.0.0

Apr 17, 2023

2.15.2

Oct 7, 2023

2.15.1

Apr 11, 2023

2.15.0

Apr 1, 2023

2.14.0

Mar 31, 2023

2.13.7

Dec 20, 2022

2.13.6

Dec 11, 2022

2.13.5

Dec 11, 2022

2.13.4

Dec 8, 2022

2.13.3

Dec 3, 2022

2.13.2

Nov 6, 2022

2.13.1

Nov 3, 2022

2.13.0

Oct 30, 2022

2.12.0

Oct 24, 2022

2.11.1

Oct 4, 2022

2.11.0

Oct 2, 2022

2.10.3

Sep 30, 2022

2.10.2

Sep 27, 2022

2.10.1

Sep 25, 2022

2.10.0

Sep 18, 2022

2.9.0

Sep 16, 2022

2.8.0

Sep 12, 2022

2.7.0

Sep 11, 2022

2.6.1

Sep 4, 2022

2.6.0

Aug 20, 2022

2.5.0

Aug 14, 2022

2.4.3

Aug 8, 2022

2.4.2

Jul 30, 2022

2.4.1

Jul 30, 2022

2.3.0

Jul 23, 2022

2.2.0

Jul 19, 2022

2.1.4

Jul 17, 2022

2.1.3

Jul 9, 2022

2.1.2

Jul 4, 2022

2.1.1

Jul 1, 2022

2.1.0

Jun 29, 2022

2.0.15

Jun 24, 2022

2.0.14

Jun 23, 2022

2.0.13

Jun 22, 2022

2.0.12

Jun 22, 2022

2.0.11

Apr 23, 2022

2.0.10

Apr 18, 2022

2.0.9

Apr 15, 2022

2.0.8

Apr 8, 2022

2.0.7

Mar 13, 2022

2.0.6

Mar 6, 2022

2.0.5

Feb 25, 2022

2.0.4

Feb 24, 2022

2.0.3

Feb 19, 2022

2.0.2

Feb 12, 2022

2.0.1

Feb 11, 2022

2.0.0

Feb 9, 2022

1.9.1

Dec 13, 2021

1.9.0

Dec 11, 2021

1.8.3

Nov 19, 2021

1.8.2

Oct 27, 2021

1.8.1

Oct 22, 2021

1.8.0

Oct 20, 2021

1.7.1

Oct 2, 2021

1.7.0

Sep 27, 2021

1.6.2

Sep 15, 2021

1.6.1

Sep 11, 2021

1.6.0

Sep 10, 2021

1.5.1

Sep 1, 2021

1.5.0

Aug 21, 2021

1.4.1

Mar 29, 2021

1.4.0

Mar 29, 2021

1.3.3

Mar 20, 2021

1.3.2

Mar 20, 2021

1.3.1

Mar 20, 2021

1.3.0

Mar 16, 2021

1.2.1

Mar 8, 2021

1.2.0

Mar 7, 2021

1.1.2

Mar 3, 2021

1.1.1

Feb 23, 2021

1.1.0

Feb 21, 2021

1.0.2

Feb 19, 2021

1.0.1

Feb 17, 2021

1.0.0

Feb 12, 2021

This version

0.14.2

Dec 31, 2020

0.14.1

Dec 14, 2020

0.14.0

Dec 9, 2020

0.13.4

Nov 30, 2020

0.13.3

Nov 21, 2020

0.13.2

Nov 21, 2020

0.13.1

Nov 17, 2020

0.13.0

Nov 16, 2020

0.12.5

Oct 26, 2020

0.12.4

Oct 22, 2020

0.12.3

Oct 9, 2020

0.12.2

Oct 1, 2020

0.12.1

Sep 30, 2020

0.12.0

Sep 30, 2020

0.11.3

Sep 22, 2020

0.11.2

Sep 12, 2020

0.11.1

Sep 1, 2020

0.11.0

Aug 22, 2020

0.10.0

Aug 14, 2020

0.9.1

May 24, 2020

0.9.0

May 22, 2020

0.8.2

May 21, 2020

0.8.1

May 20, 2020

0.8.0

May 16, 2020

0.7.12

May 7, 2020

0.7.11

May 7, 2020

0.7.10

Apr 29, 2020

0.7.9

Apr 28, 2020

0.7.8

Apr 24, 2020

0.7.7

Apr 22, 2020

0.7.6

Apr 16, 2020

0.7.5

Apr 15, 2020

0.7.4

Apr 15, 2020

0.7.3

Apr 9, 2020

0.7.2

Apr 9, 2020

0.7.1

Apr 8, 2020

0.7.0

Apr 8, 2020

0.6.8

Apr 7, 2020

0.6.7

Apr 5, 2020

0.6.6

Apr 5, 2020

0.6.5

Apr 5, 2020

0.6.4

Apr 5, 2020

0.6.3

Apr 4, 2020

0.6.2

Apr 4, 2020

0.6.1

Apr 4, 2020

0.6.0

Apr 4, 2020

0.5.3

Apr 3, 2020

0.5.2

Apr 3, 2020

0.5.1

Apr 1, 2020

0.5.0

Apr 1, 2020

0.4.0

Mar 29, 2020

0.3.0

Mar 26, 2020

0.2.2

Mar 23, 2020

0.2.1

Mar 22, 2020

0.2.0

Mar 22, 2020

0.1.1

Mar 20, 2020

0.1.0

Mar 20, 2020

0.0.8

Mar 19, 2020

0.0.7

Mar 18, 2020

0.0.6

Mar 18, 2020

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SHA256	`4de55a2910758969c6dbe18a7887f7fae332eb4c2accf7c71584f742aeb9b533`
MD5	`04c407ce1118b563c8c3abc99bf32314`
BLAKE2b-256	`94ac414850b8ff68906a4719a1a6f56737a51ed2d0cf62074ff458d12e21e4d9`

Algorithm	Hash digest
SHA256	`594919edd0f9312ad4a7a1aeeae0ce26fc9c61d6607566ca8a13e8a827f6e257`
MD5	`8b2c8af38b9af5094d54702e707c3efe`
BLAKE2b-256	`f40ebff04087999458cbf3858aadf47520eadf9bc7d1cac21ac852671e678311`

Algorithm	Hash digest
SHA256	`2250248fd95976fa3fda5b9c4eb496ddfeb85970d0f4dbb5b5958c92b364ec15`
MD5	`a9669bd3ed10f469366ebd315a5da10d`
BLAKE2b-256	`76998e3d15e99928f59b50d098e52d3c374bc0331c730b2607a6b402ac3122c8`

Algorithm	Hash digest
SHA256	`2a8b46f43d47c5c47797b15f0f37f5087260215c0bd6925facd5f9f32d6c0a08`
MD5	`f0aedb14e0581f7c2f0dd4ddec7f2b35`
BLAKE2b-256	`60e9869ce9e99ddcae32529b024279e9d0f265cf9fea9370c830fe7a8ee3cd22`

Algorithm	Hash digest
SHA256	`fe54baf0507a707c42767cdc518dac75dd8af5f3cfdc5bf7b4758e0e6c107d05`
MD5	`a8406f3b2e87913bc9c7a80ce11627d5`
BLAKE2b-256	`579b861665ce06f9770db93211f45a44575453b3b9ecec8477670b38637da2eb`

Algorithm	Hash digest
SHA256	`3af12500c160b8a3813cd13f249a8d8c8d5b169469858d5e6c26860d3bdb1b8d`
MD5	`127ff10912dcc9a79205d80048260c34`
BLAKE2b-256	`277f44948a43f50c7dc3fa0b0aeb09bf637bb8eae9c2b8d37a283955c46eb96d`

Algorithm	Hash digest
SHA256	`e15b68c3e448f39da5fa1871a9dcadaec7f84b81f6e1383c70a16d1f5a745594`
MD5	`db0f41e37084a269dbdf79feed7de205`
BLAKE2b-256	`e3740f06b4842ea85f240cfa4069a13a46a3f1842cf9395f104b9125a544a69c`

Algorithm	Hash digest
SHA256	`d0ed8661b46adf47eabaf64b559b569f942d117a1fd042d7d6cad09bc82269ac`
MD5	`51cd42b3f0735926c94fccbb524744b1`
BLAKE2b-256	`eceb605d7a109756718ecbe81911de3bff01446483be8ff36333b8721efcb1ac`

Algorithm	Hash digest
SHA256	`22da56d187f041b9cc4f762ef3677a37518f4dbed5da4197b4efeee49373ae1f`
MD5	`0ee500b12f47d94cc3dc8e3f4b2ddd21`
BLAKE2b-256	`c00b3b172937bde6fd0f71a3f2492cda37e745df7d179f2d884d9721730a1af0`

Algorithm	Hash digest
SHA256	`6b77d7fc42819f4d05fdd0ef7bdabdfe76852ed8cb30dc9df8d3cce41a7b9072`
MD5	`53d0d927fb1d129e8bbb8066879b1043`
BLAKE2b-256	`456ebd6d48a83ea49d72c2bb8a7e60b9cc7a2804e1f36c69539f805a598425da`

RapidFuzz 0.14.2

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Rapid fuzzy string matching in Python and C++ using the Levenshtein Distance

Description

Requirements

Installation

with pip

with conda

from git

Usage

Simple Ratio

Partial Ratio

Token Sort Ratio

Token Set Ratio

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Algorithm	Hash digest
SHA256	`1cd7fdb1d90435fd06330e59e30f09f7d646f7962f055ddd44de0b510a75bc32`
MD5	`bd7922349dc46a0e7e74d8dfb38030c9`
BLAKE2b-256	`298bc3bd1b52cf7ea6a0d4d4b7036a97bff68e956d196d08284bcdbe52e9b2c8`

Algorithm	Hash digest
SHA256	`040ee5c57a3f1e0ce32819881d4769c67dcc557d0c07d5d41a3f28f173ca1a0d`
MD5	`278d0ac3e5a4c5246f4c7607a7aae9b7`
BLAKE2b-256	`f59456bba388bf55840f4dde8f468d745b21fbf61ba8e806166d87f70df849ba`

Algorithm	Hash digest
SHA256	`4e55da0f148cb687563e58c841864febcbad497cddf30835bd6165ec1426aad9`
MD5	`8bf5c14c29c7ace81046dd6e68b446db`
BLAKE2b-256	`2fcc0eef70f89244e67321447432eabdb5b9b2fb080d9b76593601c1570ba3fc`

Algorithm	Hash digest
SHA256	`5949013dc4e431b356ecda9aabcb80da758d8effabecbae846e8770521be85a1`
MD5	`a29e0e51fcfab0d644d8ce545a46dbb9`
BLAKE2b-256	`e70a5cd38489fa029f27142717fc865c87c9feb7c3eb6c8c316e393810b4903b`

Algorithm	Hash digest
SHA256	`120ab0f475c0f11f7425e5dce6403b17fc671794bad0ba71a13c8251a4aa007f`
MD5	`f4a24538ea05d37d4e8ddaae9a62930f`
BLAKE2b-256	`c1b47dba527e6c516b0ee1a406e4b54af59ea6a952b692ddf8cfd6496c230f91`

Algorithm	Hash digest
SHA256	`34981441a08efac835b610aba2ff4e3b391a6b7294d64b0bfc7bac91c211df34`
MD5	`5eaf55051e04d24eb5f058fc6f53e008`
BLAKE2b-256	`31d493c34b681bb267dcc2655276db2f07ef3b491c129f037f280c68d9be7226`

Algorithm	Hash digest
SHA256	`cdc0e1c4d3a9dd3b687d88e8121cf91e13497a14508b00fbea093cac07f7d495`
MD5	`a9d3933df7e25fdd0fc59222b04d3857`
BLAKE2b-256	`c5bc7f0cc69b4f91c280afe308a61528ad8e09463990777a0873ad4d1668a1db`

Algorithm	Hash digest
SHA256	`c68f48c25c34de3a8ace130f3088dc19493c7f3508d1cf8d1604abcbbb719927`
MD5	`a91a94dadc565dac16d878ef4508465e`
BLAKE2b-256	`c16e07d384d324535bcb1cc678f30716782ab91d6269f8cf2fb2235786144051`

Algorithm	Hash digest
SHA256	`46cc5c2dc095a77a2578d6936157b5d783f9a0714198abb44b6ce0c863843746`
MD5	`d500de5711569e6c807892626410acc6`
BLAKE2b-256	`60502217b3eb1195e8492551a1d5feae24ce2f7c9f6c1e6f3e7b51e3df631a9b`

Algorithm	Hash digest
SHA256	`e85c28068cf22257050ffc8663f9b03f594479f0a7f11018cbb32099f1251da4`
MD5	`85faad0d110f5ecfccef5d8645276ac0`
BLAKE2b-256	`3fbaf992cfaec3f69bad51bac895b494c63e754cb5cce3c6876f7c838be219e9`