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

2.1.0

2.0.15

2.0.14

2.0.13

2.0.12

2.0.11

2.0.10

This version

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-2.0.9.tar.gz (1.0 MB view details)

Uploaded Source

Built Distributions

rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp310-cp310-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

rapidfuzz-2.0.9-cp310-cp310-win32.whl (1.0 MB view details)

Uploaded CPython 3.10 Windows x86

rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp310-cp310-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.9-cp39-cp39-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

rapidfuzz-2.0.9-cp39-cp39-win32.whl (1.0 MB view details)

Uploaded CPython 3.9 Windows x86

rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp39-cp39-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.9-cp38-cp38-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

rapidfuzz-2.0.9-cp38-cp38-win32.whl (1.0 MB view details)

Uploaded CPython 3.8 Windows x86

rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp38-cp38-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.9-cp37-cp37m-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

rapidfuzz-2.0.9-cp37-cp37m-win32.whl (1.0 MB view details)

Uploaded CPython 3.7m Windows x86

rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

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

rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp37-cp37m-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

rapidfuzz-2.0.9-cp36-cp36m-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

rapidfuzz-2.0.9-cp36-cp36m-win32.whl (1.0 MB view details)

Uploaded CPython 3.6m Windows x86

rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

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

rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.9-cp36-cp36m-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file rapidfuzz-2.0.9.tar.gz.

File metadata

  • Download URL: rapidfuzz-2.0.9.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9.tar.gz
Algorithm Hash digest
SHA256 c5ac9477bc7479799ef81d5bb02e657503eda5241490fd6f632a8db5e4a29639
MD5 1a16d86356f5963fe100af2c9c1d495b
BLAKE2b-256 f06d9ce931610ff4e599fc59c2238186f512437ad5a8cd00a7c25dbce1d9d2c3

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 88b39749b28d575b5473191fd7a1c696ecd076b92abb90f64621da9f10407843
MD5 7f276709b934a57787ed85f86598a234
BLAKE2b-256 358f12b0f65a2fac060039f5a0863166f2a827e7360b60d71473ad86dd0e6938

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 a288c5b2fd296264056089f699377cae7515982245403e2c1479b680edb88111
MD5 f4da58d1b89aae2d9967263631e1a8f9
BLAKE2b-256 e4e1f02088d159ab0b0f1553238af195cbe66da5ea2e2edf910c12dd7b86a470

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 218d5072c43bb6c3ab75ccf820cf44cf56716ddd071f681323a28bf82e805518
MD5 7b197da4f587346098a252d0d8dfaf46
BLAKE2b-256 14e84632ad2eb8fcdd45beeb058bd9649ae3465f52b7cb2f45a9c0a4186c0cec

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c6a5daa48cea6fec678dce73ccc35402bf395eaa29f3ffc34b221b5b238173db
MD5 033c48910b61dec9cefb68ecd210c2cc
BLAKE2b-256 a5de0a0528e92f936acdef8464c72ccef30022e519514a8a1bdf49ae39db7b9d

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 6afac0989ca049732065be21feba460f3416ff98e0fd5aeaba30550c892558ca
MD5 05a0e03f75ca02830117a1899b903e2a
BLAKE2b-256 29e09e0c8e5df0661f72908622901819e377d49c827a464577c317b968bfb5f9

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e1e4f736faca4ff3333a0ddfd7eefb0652dbda5aaab75ddb150208b8170b411f
MD5 f8865ef317899b9c116078d9027f21ea
BLAKE2b-256 b146b86c01a645079b5fc0f44438d5297271bad337185ff6b9400224ccafac6e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 e31522ab7a288a9a52bb48f5f9627832055af2b0abf8d334c316a4133536fd9c
MD5 8538fe9c915f03c60cf3e20282c7689d
BLAKE2b-256 f287c98cf85407d12a3174dd28e0568e5735c6c14ac1cfdfe64e653370e90b89

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 34a73c69da7148351a5c9d130f826c69bb1e0fd09a5b5f84364c67ff302219d8
MD5 ec360554fea0fd750b61ead37844caea
BLAKE2b-256 b374e189620acf1563cb567e666cfbc89bb8c814883b15fdf794083239ffb2c2

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bbc7ab39586ce474f9d647a98018b30047c92a167e6a4228257763dbc3ddf580
MD5 443c67686a46077d3771ab996b878afb
BLAKE2b-256 23423628ac23b675f1c9f2c6e6f15c99d9371f9bda7a6a37cb19022bdb7f201a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 036f6e1329ae3428ace5ce25c15cff05aac63277d524e4bb25e3cf85a771bde0
MD5 98af393215a746f7d6bd18d3f317ba51
BLAKE2b-256 c7cab836043bc5d652462b00a0362e534efa401ffb24c8bfa1bb1fe48ce505fc

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3c424ba2d381ceb7f3b1b4e87892e218cf295f240dbae435ac5c183db63b8a9e
MD5 a8a9ee6cab9fdda2e9e5443e1e5f42c2
BLAKE2b-256 40ef76504ec2265796b5231a2bbe2eed3a8827c619aa1db754005558649907c4

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1273d7d63761169f93ddc7a72adae50df7f6113f4f14b8af790be97f43619b94
MD5 953a0f5a2d1ef0f6dd4c9e51104e5233
BLAKE2b-256 3e1e6c55bef58dc7313827948b136dd2129ddec76e0a41ccd4653572d8f370f2

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9b0107b4a1cafae123cbd1349f0e9b793b57d8a32c2e5c26ac2ff5b83aa458f2
MD5 e4da5cc73a6fd548b77a3e95eae03277
BLAKE2b-256 10936d3a54aba6fd1151a0166146e313fea3f3bd1a22e75a3eb9b70cef513e04

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 c13cfb8c89695bbca49bd62187f41aaae2b6e49783fdd544a8c0435d3d18fc9a
MD5 0d8aba6bcfbc923324a23de677432381
BLAKE2b-256 c76749d58b6c42cd009bdf2c3a606aae0b9870602f1f0f380573a62095aff141

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 7c0ea91f942aaa63f1f1f54df2d9a03c174d381b26cd73b35e2825ff286ae26e
MD5 8c039190656b1fdecbef49a796113abc
BLAKE2b-256 105698a61d48b617b760efa424a2e884ae321f238ddbd947b3b8fb0070769a72

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 4dfc40879ba5f07b810e140bbeb9da984829b3041e3b6b51abdba05d39340e76
MD5 1d05f56d37578b0549d7ed7adb6bce87
BLAKE2b-256 9628d64e20a79d7478d54ea0af04ab35c5c378be8117d71ca3f894c975d5cb89

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4c54359b2e9079f770a88b653ac999f6f0ee26d868eef3374b2e03f9ea4e4253
MD5 557f5141be4022f6a9e592754caa9198
BLAKE2b-256 f143bb0da5326c03d887395a7978ec7e56abb441de5270b22d9cd9a7168d7cf7

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 e358761b2092857d07a53580505b010e0c5f74f8e9da7cb9561739f8ae9cda75
MD5 9dca8da267f2a4f0ea13e12d1477a2e4
BLAKE2b-256 5657d7cb10d8f9e1e6de1b781c86b2743bd8b71b8e2a414e93b8bebf6ad0d050

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5c35f9efa8d0c58e6d0681b356456a85d6d08dc945aa5643a1353c1e81c31fd4
MD5 6c21ca57be70ce140b401248639cf3c3
BLAKE2b-256 3ed7b132d1839ce8e5bde51055f3918401ed7f8726a1c367f84333e4a270ddc8

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 dda47dcee07d193cf57fbb7b7f3464016d47ea73444a655879f5f9c5c528a6d7
MD5 92706b1004e889235cbafd41d53ef15f
BLAKE2b-256 5963acfd1945fa295f94e9f2bed626245a3b8a1873ed87c059e426c45d338964

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8c57ac4b1c77841234226d5256f84bb58b70ef78313b65e31b34cf5d35514cd6
MD5 45084a741e6627f717229b8f1aaddd33
BLAKE2b-256 629f2ea20995727145f889e0c269f4cde28a1197f57d9831deff1a0ab49422e0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 195b60904013bcd84de0a0c63d9cbf6fc860269f37146c89df2fd439083ba8b8
MD5 2d23fb6ebbaa9e7dfa0d37c2ae2a3f16
BLAKE2b-256 9b5fad59269d9ab7956e5a3b4c3840458dfcdcc161c0885a6cd5b2c84b8e9366

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 58ceb1be2c4582e5852dcc783c60f3c8fb619bddec757b788b94104b44ba1ed8
MD5 8f1d059df345c3b267d9a5748b8ffd26
BLAKE2b-256 ffbfa73f86172b7889567b1effad68cbbd7bd16e2ecc945e63065571561f654b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 72f6c050f1207099f7d09a2fdf48cbccd2a3e1df09d3cf85865c80c1b9c6a8c0
MD5 0e6a1a593fe34535e7fc7e97d9798a43
BLAKE2b-256 87b714934024b519a4ad32a0ea1170ebbfd2a05d7ec4296a4466dac2e4d03eec

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 20de72aed1fbb4725ba7a0665ca483c8ccca16923191345e9b1802257895c4d0
MD5 ccd0749dc1ce13cfe3337ca3055bb6e1
BLAKE2b-256 17eb4e98caedc024526f317e4f9798253597ab4519e5ab2fbe628bba2a1dde7a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 80d6856f7c3311dbdd8bf626f5e5cebbc22989b9bd2cf1008486d99b88f4b2d9
MD5 ce834b79df13b45a392e04043b1bfdac
BLAKE2b-256 dda94d44668692527f05a60858fe92c8914320a4de091444f58738da1b1f6f81

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 247012b91abfc97fc32b6c3dc3e305c79f953f60136c5ce3c0ad6ded22d22b9a
MD5 8386556da478a138a0ccbf11be67cb2f
BLAKE2b-256 eeb5c7480d4784f2584f18d3f96444f142caa3a651de422ab7475ecf2cb5e8ea

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c71854f84bfed7ae00d45d07f2677cc04ebcd1428d73d179f19cd8237eecb337
MD5 a2c978461e4e99c431a2fa007e82f897
BLAKE2b-256 a63ea3f8a26c4ed15ce613832284c082da4ee9133489c986cc302636fd8ff36d

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 27f876a7c2311a0f333243db1ff7f9b67bcc575783af93fe62e771afc7a55f92
MD5 ef84755365e6401dc3bada80009d62c0
BLAKE2b-256 08e6368b3d4107cf4f30a3b65b41a40767fc18cdd1c27866ed4d67797b813096

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 539f12cd3f15955fa4b1a1a1a40d6fe386bd5cba4e37a9549a9aaeacf22346bf
MD5 3e010308801fc6bc0940a8f2e12df8d8
BLAKE2b-256 b4c9f88bc9d7cb5cca1b5fce7c62972c2b6edc2a53ccab62c1b259b25820733a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 aaa40d8fc3e4db6077f147316d14108b0f47318319ae1dd088d6c40fe24bf1bf
MD5 1fab71818b54e5a30b93b37188778f68
BLAKE2b-256 6c7dfe81efdf5faecae2fb2a8183410692fc1caab8464308684302be83d7e2ba

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 b851dca7c16bd8854acdadebbcbce067796fce947a80cd5eccb4e15364b8367e
MD5 9fb36b111abfcb478f37ec5154259cd6
BLAKE2b-256 7f2dd85a45653d276f2ed1c0e382ebaefcc9e294de544d062abf23c80fd56760

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7ed225c1f1eacc9a8793eb208c8dcd2c438c60152457c972c1904a2cc95a9c63
MD5 4539cda140a9a9ba59d554296d532439
BLAKE2b-256 8b4cc36c36516acc5f52d45a1f74e7683e768e6b181c638a746e53a764835d77

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 d74a3b14eee29c224acb17ab8c6ebd0fb5de5f6ba17a94e6951b316c95df2067
MD5 85062c8481b6bed86268dcfd9cfdf9a7
BLAKE2b-256 96a29b1e1d97bf51e789a114477976a063787c43df59a29e83b67f36b18bd886

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 87424f5ee2d666d8182d16a7d2360a74a6bca344349f4741a9e23b8fd17afe35
MD5 72f1a0af1ce6b9160134ded9da6b2919
BLAKE2b-256 b7863fda2125859ec50ae499992cc1ae83a0419edecad0daa02da7731321f1cb

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 31112676466fd7d587f4fdb2beb2ea49d08adcd8bef4e0312b741e110d032d9e
MD5 b36e0d5b59e360419df2568e6e6b1edf
BLAKE2b-256 7dd6759bcd106ae5f8363af288b7e2c50b47dd0e1917a289d28c1360735c3411

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 63d21c48e820f826b16eee909ed2e092bd82a467a3477fb0b9b901a523600f53
MD5 b316714459d1f791b512b0f62f0e3535
BLAKE2b-256 257565865d99111c9083edc8bbc53e5a694b19778a7183e27a74cae23e43bf19

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.9-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 9b4036d9f5df17bb12ecbae51b96a00e02ebf61de6ffd1133c2a2d051f9031a4
MD5 0a616fcde257dd62981e7e9d328a45d4
BLAKE2b-256 96f6a0d7612fc688c70513006adc1196ecfc02b1ed38c1e241364d98fe5e3c8b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c71514ab8a262825e5372c72ea9a6ebfd87d72a51d818cef64f8cb90f4dc1c13
MD5 6fcfce5d57ede222f71e376194da7318
BLAKE2b-256 4d8c5d1f68774b9d3deed25daad49e4402c05ba0e3e51b91c5f14ea31d14549f

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 1312b7e3986afc79d94d0e92df390da6d2106c6defbc1a5534151dcd85e7be6e
MD5 ca8e89d9d5287298d5ad1f70b83bf57d
BLAKE2b-256 d1c6e1fe442dafc9a5d3173ce8a78f12191c64e67b90671ec2dee99ed0a15a84

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 168e83708cde9957a63becee372172b3c3f453b11af5f19b39dba014c465410b
MD5 bc0b939add028aebaa1f90d6236566a4
BLAKE2b-256 6d7ad2cf89acdfc3fb91942898525e92b4384fafc22bf7c333217284ce2518df

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.9-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.9-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f27b72d9ca13e678ce07fed2a51aecc0a5ad7b54d15d4ec22b9f1e7581e4db60
MD5 9b032baa93e69ae5b535e842da9af659
BLAKE2b-256 a5a29c60b185f4340e8aca67f5c3be9801d06a9ea959d4505bbc65f34db6e514

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