rapid fuzzy string matching

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for maxbachmann from gravatar.com maxbachmann

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Max Bachmann
  • Requires: Python >=3.6
Classifiers

Project description

RapidFuzz

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

Continous Integration PyPI package version Conda Version Python versions
Documentation GitHub license

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 a couple of aspects that set RapidFuzz apart from FuzzyWuzzy:

  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
  2. It provides many string_metrics like hamming or jaro_winkler, which are not included in FuzzyWuzzy
  3. 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. For detailed benchmarks check the documentation
  4. Fixes multiple bugs in the partial_ratio implementation

Requirements

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

Usage

Some simple functions are shown below. A complete documentation of all functions can be found here.

Scorers

Scorers in RapidFuzz can be found in the modules fuzz and string_metric.

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

The process module makes it compare strings to lists of strings. This is generally more performant than using the scorers directly from Python. Here are some examples on the usage of processors in RapidFuzz:

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

The full documentation of processors can be found here

Benchmark

The following benchmark gives a quick performance comparision between RapidFuzz and FuzzyWuzzy. More detailed benchmarks for the string metrics can be found in the documentation. For this simple comparision I generated a list of 10.000 strings with length 10, that is compared to a sample of 100 elements from this list:

words = [
  ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(10))
  for _ in range(10_000)
]
samples = words[::len(words) // 100]

The first benchmark compares the performance of the scorers in FuzzyWuzzy and RapidFuzz when they are used directly from Python in the following way:

for sample in samples:
  for word in words:
    scorer(sample, word)

The following graph shows how many elements are processed per second with each of the scorers. There are big performance differences between the different scorers. However each of the scorers is faster in RapidFuzz

Benchmark Scorer

The second benchmark compares the performance when the scorers are used in combination with extractOne in the following way:

for sample in samples:
  extractOne(sample, word, scorer=scorer)

The following graph shows how many elements are processed per second with each of the scorers. In RapidFuzz the usage of scorers through processors like extractOne is a lot faster than directly using it. Thats why they should be used whenever possible.

Benchmark extractOne

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for maxbachmann from gravatar.com maxbachmann

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Max Bachmann
  • Requires: Python >=3.6
Classifiers

Release history Release notifications | RSS feed

3.10.1

3.10.0

3.9.7

3.9.6

3.9.5

3.9.4

3.9.3

3.9.2

3.9.1

3.9.0

3.8.1

3.8.0

3.7.0

3.6.2

3.6.1

3.6.0

3.5.2

3.5.1

3.4.0

3.3.1

3.3.0

3.2.0

3.1.2

3.1.1

3.1.0

3.0.0

2.15.2

2.15.1

2.15.0

2.14.0

2.13.7

2.13.6

2.13.5

2.13.4

2.13.3

2.13.2

2.13.1

2.13.0

2.12.0

2.11.1

2.11.0

2.10.3

2.10.2

2.10.1

2.10.0

2.9.0

2.8.0

2.7.0

2.6.1

2.6.0

2.5.0

2.4.3

2.4.2

2.4.1

2.3.0

2.2.0

2.1.4

2.1.3

2.1.2

2.1.1

This version

2.1.0

2.0.15

2.0.14

2.0.13

2.0.12

2.0.11

2.0.10

2.0.9

2.0.8

2.0.7

2.0.6

2.0.5

2.0.4

2.0.3

2.0.2

2.0.1

2.0.0

1.9.1

1.9.0

1.8.3

1.8.2

1.8.1

1.8.0

1.7.1

1.7.0

1.6.2

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.3

1.3.2

1.3.1

1.3.0

1.2.1

1.2.0

1.1.2

1.1.1

1.1.0

1.0.2

1.0.1

1.0.0

0.14.2

0.14.1

0.14.0

0.13.4

0.13.3

0.13.2

0.13.1

0.13.0

0.12.5

0.12.4

0.12.3

0.12.2

0.12.1

0.12.0

0.11.3

0.11.2

0.11.1

0.11.0

0.10.0

0.9.1

0.9.0

0.8.2

0.8.1

0.8.0

0.7.12

0.7.11

0.7.10

0.7.9

0.7.8

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.8

0.6.7

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.3

0.5.2

0.5.1

0.5.0

0.4.0

0.3.0

0.2.2

0.2.1

0.2.0

0.1.1

0.1.0

0.0.8

0.0.7

0.0.6

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rapidfuzz-2.1.0.tar.gz (1.1 MB view details)

Uploaded Source

Built Distributions

rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp310-cp310-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.10 Windows x86-64

rapidfuzz-2.1.0-cp310-cp310-win32.whl (1.1 MB view details)

Uploaded CPython 3.10 Windows x86

rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (2.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp310-cp310-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_universal2.whl (3.5 MB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

rapidfuzz-2.1.0-cp39-cp39-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.9 Windows x86-64

rapidfuzz-2.1.0-cp39-cp39-win32.whl (1.1 MB view details)

Uploaded CPython 3.9 Windows x86

rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (2.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp39-cp39-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_universal2.whl (3.5 MB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

rapidfuzz-2.1.0-cp38-cp38-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.8 Windows x86-64

rapidfuzz-2.1.0-cp38-cp38-win32.whl (1.1 MB view details)

Uploaded CPython 3.8 Windows x86

rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (2.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp38-cp38-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_universal2.whl (3.5 MB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

rapidfuzz-2.1.0-cp37-cp37m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.7m Windows x86-64

rapidfuzz-2.1.0-cp37-cp37m-win32.whl (1.1 MB view details)

Uploaded CPython 3.7m Windows x86

rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (2.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp37-cp37m-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

rapidfuzz-2.1.0-cp36-cp36m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.6m Windows x86-64

rapidfuzz-2.1.0-cp36-cp36m-win32.whl (1.1 MB view details)

Uploaded CPython 3.6m Windows x86

rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (2.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.1.0-cp36-cp36m-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file rapidfuzz-2.1.0.tar.gz.

File metadata

  • Download URL: rapidfuzz-2.1.0.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0.tar.gz
Algorithm Hash digest
SHA256 faef97d2b096b43abdcc4e903d2264f753ccd9384c825a9092bf79e8992cbf7b
MD5 7a66848a504149b86039459607071344
BLAKE2b-256 7d9b327be325c2780baadade66d39d0997576a7530e91e661d0b0ae738e8b90f

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4f3cfea41e9505b3d8a08f369a28db526bd11995d63a36f9130114f67ec101e4
MD5 ca765d2934d37c5c6775ea90fd755d9b
BLAKE2b-256 dd5190cd6863b9da9f6b4f74d10a72f3f709a436613f070ac61c36c98404cc1e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 8a1e9baa28d0f261bfe89aba8b6e495027bafd470c422165841f866fa5a86e23
MD5 66a0b87565437a20b5c076157d34a969
BLAKE2b-256 10effb863c7267914f6b802cda8a2d11ca72be0402388c8c19deea9094e67077

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bf3a3f3a750a3dbeee1bba65176ae61ede7cea1763277ba5983cd0f28b94360a
MD5 07c18db78ab13245cd53bced49be3cb4
BLAKE2b-256 49f6a958def972384668e76e715dab046cf364b236306b52c9fb548c48a31da9

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1e92295fb9ef86cd2bc801a30827ff72d17197b897d84ce7ec5a367e9579c21e
MD5 211453daa6fc789bf31e065033901299
BLAKE2b-256 459c8823b442733e38470efbb435370e4485f059b05338dcf3a8a79d70e45db7

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 5d5973df90ccedf09d82ef609d617eb16cfabf1621d5f275cb4985c523f10a15
MD5 62ba22a86b1a6d0c42829f42d95520db
BLAKE2b-256 62444eb764ef3d8c8871ba263353d1a84c106076cfaceed4bd514eb4d84c0936

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0ac26ae6bd6df4e327a2069a7d3d125c32ca98fd6866fd7aa1e7fb590ce22789
MD5 3ed21674c9cdce5e7103bad581d90179
BLAKE2b-256 4f930559b1651f10d2f061c47703b917491c7c69478f5d4197907781b6d887d0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 82e5d56e40d0ba1f83bd0f41f3d1192c62d09a117c6bb0d5a67ba92525f22121
MD5 6454e762f5aa912a7907d7ac7bf52819
BLAKE2b-256 36958da9df9d9033df2f32f1614dd182a751ca3ed1f22fe9edf59b0235a7b276

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 00149bdea04f882d9ce2ac788d5c32ebbd0cd1bb9c4a03da68497fbe43589747
MD5 6989e6a33f163628ab94b3ada234131b
BLAKE2b-256 b758befcbf0f3ac6afdf65f2e85dfcd16b2eab46c3cbc67c9c42099c21016d3b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 215caaab6b8dcf08fe570eb774b3ea8ad0433f86a629ca8580dc8565140c3c6a
MD5 68040e8701c81671e6ba419739afa2b1
BLAKE2b-256 1cec48733097362ec65fb1460119145d9ba708bf68a6b29f8475babfd188ad0a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 11756b52c60853e42dd2c2c119bdc3f2dc0f5770cd66ce7eec5186ab0a0afc1c
MD5 f497da49a1340b38e70843d2607aacef
BLAKE2b-256 f98065a8ee7da92c8b50463db2246d1729fc1f970f919b12c618a3b0d5a1d763

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 24848feb377a78425a897e969102e8bf00fe214c6c8557702e9efb87f2dc486b
MD5 795482d23ad33a3577c8d9a212ad8470
BLAKE2b-256 911e217ed0ba6b88785cdfbb679cde4ed26a20478852c1a735f6770bfeaeba57

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 deee99c6365ec08460c6205ecf338a77c01385309cfb29e7f5a7971adf03c686
MD5 846f43e275aa136fe405b709dc7beaa6
BLAKE2b-256 f3dd57b7872134271fb95aa66eb4f8df013af3a3b4484791193e182c3adbed37

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 7753b4ea4e9e41cbff7aeaac6f9b32eac103ce51e0bc5963e278886b52e6a9f7
MD5 53acb923c23d3e58ef890908ba3a19ff
BLAKE2b-256 d6d6c055ddc81b0f6efb0669126bd5dc7728cfa31cda7a18929da06011e244e9

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d36083bb6e207c97fa65b6139db7287e63e9683e9844719f599ccf8f885984d6
MD5 61c3f20e6fa22becf4f753cea308d3a5
BLAKE2b-256 2ac9079489bdec7e017b403562035d92c98cb613f029a969956fe1878e3b6329

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 44f4c20a80b1a9ebdf9291c6c5f4a857bc5ca6dffd2de5b3f98f8c967ae39ee6
MD5 4da01a681f2f666bcf163ba1f49332ff
BLAKE2b-256 d9c176fe3dfe7de02cd96bf27ed630a4c853b6c01129fb2a9f2b0dbbcd101c47

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8b597a8b9e860d29972f34acfbf3fdf7e583f3739f9938df16885a1f5da1f0e4
MD5 cf0dade934d302fcc8710f1c176883ae
BLAKE2b-256 1e11c2309466af2d2e73a77e70b9d5bb96e95c2ce0d95efc5f02a44570e6030a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 b625707322428683cd60c7ffd869c1781a1a646ce61d4b0f2fef202717d11490
MD5 54a769bbfb42f1433f418d1a1e4a3a55
BLAKE2b-256 76052c74e6103fc2a6dfc768343bcc253eccc4d2573ce1a628371fb51917a361

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 87c4427448c561d79ccef94a516f0736128f4df932d00b63bdf05505009ab37f
MD5 94f48008a064b86cb4720867619ff822
BLAKE2b-256 5705b5f72a30d504311e44b6ae58d76e803c4f70e1d221c5cbe0412d268eb30a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 9ba7e8498923da21c79c3c4aa65b8aeb53f1e12c14b511b6bd0b0e2bf275b561
MD5 8da588a15504bb77a22b9fef4d552cc1
BLAKE2b-256 254b401da4fdb49baa1298b63968f161435ac5e0f0a6f4c55f55d39648a2259b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0615e6fd3ce4d748f93c320f78dfd1247923aaca0bf05fdb50e8705ef0cba9f4
MD5 c10ad941eeb807746fc577fb3ca1c781
BLAKE2b-256 66c5d83600fc9c69c9b88356678834ebb44e564c1c59ec8c22e61ff0cd447fe0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 a406503c3a41d8cb076b8382e73c805e936efd35ddd7895af77a54b25c6ead23
MD5 0e5e68f646e14e1da7c393df94e5e77f
BLAKE2b-256 41d6824c5769d91f1e27e8c1ce63457985e57fe05bff9c51251bfac2936902d7

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 63d79467546399c9207b2ad6c88df78492ce0a6aa970ba8fd30e8be38faaa7ef
MD5 47a87bbcde6811fea02d7365e920580c
BLAKE2b-256 4b50dcd93a34a42ff477ff482e0f8736ff7837adfc71ca5503cfb6fb7b7a52bc

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3473ca97cd654cc885b6a8971e4bbc2b223cc02327a2e08b5e7eea54950f19f9
MD5 7ed66663845e047619e63d12da1d99b1
BLAKE2b-256 e0cfca123ecc05367f524504554f106d1d5a27b40d1d3aed94063ba3359b8382

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0d14c4abf1e88d0c35f15190577d466a24ac775c2500efd4389b625fab7e99e4
MD5 486dd3fee59d2cbfb9c3da8201ac601e
BLAKE2b-256 392c89e3b532f724f493293dba7400a971d4d3b2163638725f454eb89366c7dd

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 342cc6d9b3cbaf8e889ae5da3da988b0ffe226f1793c24a649befd19d783e7ec
MD5 63c6830ec9e010b93b85128732b71f2f
BLAKE2b-256 d4a3fd4815e65f585b4997258b54fa5b7f5b01a2ff373b52cc84b91eb9babb69

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 693a1e2fc15be254b9d2f6da61881a16ca536536c1be66562ac99a2318db13d6
MD5 85ff0457e16b8b536247c031f856a455
BLAKE2b-256 84b76846fe839685ac2cbde0d1afdab86df92cb7a0c971a5e35904584253f8f2

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 567ccd50419bd6d78ea6aa42a80a4b161609711c43c0efa3a03ff6ec54a08800
MD5 814fdcbf88e4887380fea3150129c77a
BLAKE2b-256 91f852b38d7b57de14b2015106b8afb1368c34beef75e4ec3b736156513b5519

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 00af9dcaa26b6a8742053c7cc518833c4e55f1d23cbb8ae6cbdb9b46cb045a33
MD5 1347a11348e34a204c5df2e06e09c21f
BLAKE2b-256 41c0c98cb02b8420fbfa31da9fdc937aa0888a9d1d60c814302697659249f635

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 2c53629372aa4efc6125d861d5e3a6a79a9d89138f9b5000af82a98f4651acee
MD5 476dd8dca89fd120038c1d6dab7f4e7e
BLAKE2b-256 a58dffd99582fe38759090d280353fea65fd947a55ebceb46ca2c86c4d9d34e3

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5528375324b8ace9f705e18350b14c62d1a8cbadbbf20f66d7bff0810a6d4313
MD5 10a9272fe4400c92e4de060855176ef5
BLAKE2b-256 fba0cb412af02f53ab070c60660517fb51b2df3b4b59253301d658e48a4051a0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 db551cb97e6fd911fcf11bfa642b4441d28f42d20f9a171bc5eb1fe12feffa2f
MD5 31104ea9aeefe9eb7181168d578c2f7c
BLAKE2b-256 df79dcf36ede8b729c562266d6bfdf0ff30281fd8abac397520cce8a4920f113

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d5ce6de84b76741de84fc1602a7fa58649fcda114c22bc91f910225df177b0b5
MD5 12e202feb13d74007fb24b6e6dbd9a19
BLAKE2b-256 5092c6c4f3d64a6640130222723d579ffe88e20fd0bcb042c8ba61f3bbca5e83

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 2c6a204718aff42b45a03cddab32fd5524def9a052b573a00861edc48148cd0d
MD5 f4c6514c3543c43b7dd5927eb4a8627e
BLAKE2b-256 3b76fbc1436d8645413072fdad741fa4ac86024cd4b36ea85c0ac4619c665249

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 3a62ec231c7aa701bec5d7a5a9253228a8fe0484f5e38add1fa3e3b93be0fb3f
MD5 35964435892f95741505c2ac419187e9
BLAKE2b-256 d4e50786d7ae59ac164c0b84e5d9fb584cfa08435959c2ffe66c7f9dfca2ea4a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 ccbcc073a3e015190d0b477234a2e66aa234be3bc000afaf19f757a1f5b33484
MD5 4a71358bae099c71e045b882e9c10a1f
BLAKE2b-256 f6969a74b76f5709363578e7f88f102c575b53db7c4e290070f560dc364da564

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 95e91179dbb3ba0d05bad8913471dfe0d86bf09dbd1af952254135e318f3858a
MD5 ef7c6d496c28cf63f583c3f85675e8a9
BLAKE2b-256 e95a75aea40a5dd56dd53f1d2b7f2abbd55ea31764ca0afb0c9319011a96cbaa

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 97bcf8bafcbe9564764fc00fe0aa80429ee546fb8ac1766da5bfac8e837afc0d
MD5 fdd2c531ccf9fef840f0e00fe6a7dd0c
BLAKE2b-256 e4f28e8e4a3a9df14c74f3f43b157ca7f474cb67fabc58973a9587abe42b096e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bb24a13380db103613cd287c7464823a31f350a3c5ca06c6acb47f9d670c55e3
MD5 12a585892af4d5b56e6973d902968c03
BLAKE2b-256 697450d5b9f766629a37aef2bd3da867b0ae595791412aca5c44097ac5135eef

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4fd28a0c85ae87228976dffe18b5728a4e8652a752b6d795966d8d869f8aa7b9
MD5 03e2275f8ca091a6c12d144b2be8e1de
BLAKE2b-256 3ce980f7162b78a3f384c8b8d66804bd5b29b858cdc259bc95058e3001266f62

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 3e88fa024758c66a906a3a72288b1e48003b57172d4b1bcab90b09dabc342a37
MD5 80ec5b9f371ba8a1ceb4c08252742168
BLAKE2b-256 8635b041815b3484c2205c72b0284c7cc7523bc144a2c0812d6669426603c37a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.1.0-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 5a720ce5d17b17096e0b3d7a8b32fea191f22dcc2bf42fb7332dff63d47b1f3f
MD5 336f951ab06d589e8e4af06be4e654b9
BLAKE2b-256 505e9fee8c6100aa3f44fa996497242780eff81751d29692ab6abd2cf4816084

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 66c371ac0c0841e33240c6c901d3cecaae29fb34f4590c9431e4fc0e8e912db9
MD5 f4e465f026c334aae103c32774c9f844
BLAKE2b-256 9336a021182260d696c50ed7f61934fea66a9eaf4a96b8b06599600fe4e23a0c

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 a49356ba8ee5159456ec414fd9eb429630c2ee81861c1fd3caadcc8f19e73215
MD5 8a3f449c6be21a0c8b6cd418cd5bb7f6
BLAKE2b-256 59af8ed4f4f2987fddf98488d287cf5339b9267768897fc8b29b28c531a0833c

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a907f21485afb7de2330207bea19022f79e0f8dae0523c9189179f3fcdc1c2d2
MD5 291754b556b5f2d32287b69a7ff38786
BLAKE2b-256 3a7de758f88f55f974d467dacd7d0c062211e7c048855f3a9d596666d351550b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.1.0-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.1.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bf740cadbc60a07b2032bf899ed98c0491b33b45dfe2db8c411bb7fd3c39e7b8
MD5 4b26df8a86872c510237d90562320072
BLAKE2b-256 eaf778c5d55818c0c2e42545fb143bd77f1dbfa9a796a98603cbd0fde54c95ae

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page