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

This version

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-2.0.14.tar.gz (1.0 MB view details)

Uploaded Source

Built Distributions

rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-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-2.0.14-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-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-2.0.14-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp310-cp310-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

rapidfuzz-2.0.14-cp310-cp310-win32.whl (967.2 kB view details)

Uploaded CPython 3.10 Windows x86

rapidfuzz-2.0.14-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-2.0.14-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp310-cp310-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_universal2.whl (3.1 MB view details)

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

rapidfuzz-2.0.14-cp39-cp39-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

rapidfuzz-2.0.14-cp39-cp39-win32.whl (969.3 kB view details)

Uploaded CPython 3.9 Windows x86

rapidfuzz-2.0.14-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-2.0.14-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp39-cp39-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_universal2.whl (3.1 MB view details)

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

rapidfuzz-2.0.14-cp38-cp38-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.8 Windows x86-64

rapidfuzz-2.0.14-cp38-cp38-win32.whl (969.3 kB view details)

Uploaded CPython 3.8 Windows x86

rapidfuzz-2.0.14-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-2.0.14-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp38-cp38-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_universal2.whl (3.1 MB view details)

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

rapidfuzz-2.0.14-cp37-cp37m-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.7m Windows x86-64

rapidfuzz-2.0.14-cp37-cp37m-win32.whl (969.9 kB view details)

Uploaded CPython 3.7m Windows x86

rapidfuzz-2.0.14-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-2.0.14-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp37-cp37m-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

rapidfuzz-2.0.14-cp36-cp36m-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.6m Windows x86-64

rapidfuzz-2.0.14-cp36-cp36m-win32.whl (957.1 kB view details)

Uploaded CPython 3.6m Windows x86

rapidfuzz-2.0.14-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-2.0.14-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.14-cp36-cp36m-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file rapidfuzz-2.0.14.tar.gz.

File metadata

  • Download URL: rapidfuzz-2.0.14.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.14.tar.gz
Algorithm Hash digest
SHA256 165b6d4a341a207b7a15bd10befef231d8fa581c720571a137d23f2f6a1db5a7
MD5 8c3aab7e8dacfa8e2c9c5131c55bc9b3
BLAKE2b-256 3a0e42462f5d4f34937023c1e51308a85601a68c3886dab81d1f2aa376134b58

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 377bcdfc0930fdc541a2ecd0d2d0a93c026be3ec029f949fdc50d2563cb506ef
MD5 4f2b62c2006e2a30a06e50861c40dbe3
BLAKE2b-256 b1252d6697da54f43b367e46741ae62f02f1b473346c0de6bf4df8b9488dfe72

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 50facc6fca6cb0812e029fd879c5a5a52ef48e57bb8ebd1655ca8821f325396a
MD5 f169933a6c894da15e4c81f1decc92b3
BLAKE2b-256 40372421f8137bd1e315657855195abf499fc8674d2e04fdca36d86b42540388

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e57edd7be7ae7244939dd789e5014202e9c7b5570e690277d484a89f3922c209
MD5 12be745f660304f1109d15be869a7ecc
BLAKE2b-256 05fa23a59f94c1d9df767b6f46954c4433f76467ef0fa957a64626729dbf78b8

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 06638653416851a9d2f3b408f8198030d71d71860da5eb641d826ef51c6d5754
MD5 f8082fef35e8a72f709e8ec937e2764b
BLAKE2b-256 627ddd29de3b3dc1eb7384025059bcaee4b3da9bbcd387b8f90db7b5d1645b5d

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 89dd59f749669f9ba7f56ca7f42940e7d33b3b62f4d96ed2c9b306d48cbf866b
MD5 fd59f57721a6ab1799a9bfb7b47c2d67
BLAKE2b-256 bf78269864b1c5d6e69970b00598c3d30d565da826430e5a7f1297ea9c0ea578

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2cf3cf73b8c79c645bd2163023097c80381e81fc97b37e6b997936471ca4a1d2
MD5 ef47bb3d99492457a6f6f31b0566a9dc
BLAKE2b-256 32289ac5373824a6be19b2047caffa86279f50c27cf65c896b0503f486b2aaae

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2c55eaa00499eaaa3b851c9f6408ff41fe430bb508bc216480dff9fc28ef25ae
MD5 f517018beebfa738ef6c882cf753abe5
BLAKE2b-256 1c41370412de125bb87faa15c1fced1ebd549f9657c16186f5f977c0ca4cb7e0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 6c1ba6a67dd9cdb467358c920f28f623a626f171122e0312003ba8ae3170a741
MD5 a3f4ce98168be609321461bfdc81ae0c
BLAKE2b-256 478a2edd7bce552526b3fdaf2c49840755cec8da6370edebc31daafa78ff9166

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b4d1551a37cdb841dcd1de72e3bc51962b2b6d40113dab14c29ee9182ed2f1f8
MD5 33c6423a0fa91835fe9d037274d8cba6
BLAKE2b-256 c4a51093cc5d04b7d239e2d56004fc60fbee5e916f5860822a4f655a6acf06b2

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 9c578289cad28bf374ca64db6708c3d3cb037bca5b253786c15e7c815ce4cd98
MD5 58ef79a986323b5b19d16f22f36ef150
BLAKE2b-256 7acbedcf73f84a1c579f7881ca62ed1d0439fa550e4e7b1bc964f0cf75aec80e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.14-cp310-cp310-win32.whl
  • Upload date:
  • Size: 967.2 kB
  • 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-2.0.14-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 2b33b834f3280c7fa682e25d6025d551aa95d4fda12598159ad2ea4c30f690b6
MD5 0a4bb6d774ac518c389b8eccbb88a46e
BLAKE2b-256 2ef54b67e5f4ba842da356d6a95c1b72116f193f080a37ebb9016b6edfd976a6

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6935b60c9ccd427b6bf2b247d7d3c169028735f15133d64c874053f1e317d6c4
MD5 cba472e1a4d330ed055dc4cc582b6ec3
BLAKE2b-256 5fc38b8624bb09a6906e589c8214ba4aa24c79cb91461478b90890ddaaf4cbeb

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 f1a580e88bdef68f0de79e52f53fed01473672d59f86d87539b7efdf2ddc5d81
MD5 37fdcee408a0864eec1861f2e604bffe
BLAKE2b-256 8ee0c9f8a8328b35c28ce88683f8b3b957987a522dc7aafbec4be81b726a5515

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 84dfe5fe304d946fd2b91e3372afb3553cf55752a7460bc2d4b9500ac8e31a8f
MD5 cfe482557f7195f91f3a621f99b2e3da
BLAKE2b-256 fd8a5eda5bd7b821331e8d246b84bddb1064c404bcd27ae1b4955520879a54c1

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1048d8ef50bbef6cc2f117ba7cfef7603e462284684ac7e7fb2121383bb4a6b7
MD5 9a3f8c4a8a81613fbb8b260b0421f5b6
BLAKE2b-256 e846746f386213d56a3c11a324d8f1a7a1a9aff07b9b41bee4747b457f2e6765

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 10037473355070a0be34dc2032646d2c5e7d6906eb74718dc40c3b19bd382bde
MD5 8edf297e28d4ffb940e7f242d38ae7d1
BLAKE2b-256 2165f5f66b41e11e544da3f177040c77e6fefa0c30e9daea13d7e560aaed05ea

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 9a8627ed1700ac0ab9993af5f7b810e1ffa9fe91bb18e06952190b0c431e0e7a
MD5 8734b8d32061c84e91d42a41afeaf228
BLAKE2b-256 d00958f677ac8a4455a711b3dfe3e320019b3fe2ee7c31298ae510dc8b111f4e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 1e34dcd912bfbcb13aaf0ee8814c3ce7f9812539901649c5a4ddf43102099364
MD5 61bfaca846dc8335c7f102b45352a954
BLAKE2b-256 4777a079290e9a4888fec5a18b89975777ac9c2a0561e072a3e2c7a224f61f78

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.14-cp39-cp39-win32.whl
  • Upload date:
  • Size: 969.3 kB
  • 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-2.0.14-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 bcec22bcae884d725e0c46fab58c6f8c4d0f9f99bce81f4bd9530ce2e78dc71b
MD5 e4e51afa977727ade1f0a535036aa3b9
BLAKE2b-256 57f020c26506b46d7206347574cc38eb574d514c27dfae5ce7d1d01c0a6d9211

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1b184c5c6fdb7fa771f5252d2dd58eefe38bdbdb8da3e95a63bf3b727c2e2dba
MD5 17c310e1ca0cd77a11a9c8be8a2f5f59
BLAKE2b-256 6a8079dc9cf7644f20017dcf6be1477b089274984a20df1ba848512b5d8cbd1d

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 e43c59dde9ffc7df7445bf0f29129f224ad585f046823b675ce7ceda52e0e6a8
MD5 f2c40d55daff6d893d7302a82a2ccccf
BLAKE2b-256 3d31c32c4838007f3e67e58f3d5040d656db4813082aeb0c511dece2a8552a53

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a6fc1fa3263773d733b00a8f47c247e740f27c3dba2312068a2ff0aa71c76c28
MD5 d13e240b15cc400f5df38fe922e54a61
BLAKE2b-256 3a932985ca4f96f56c308c72d542ec01f8a1612f38d60ad277c56019f7ed645a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5479694a8185c6715e02470c0412d9a6b2d3fa88aa8200391148821086e4d13a
MD5 7aece916b965b68ed1c90869a236d6b2
BLAKE2b-256 7fce13e207cfcaf2d494c0400959ad44cd090a699c654f43de7426879ebb41cd

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 de531b95b6e6e2431dcce817888cac3e6d83b457395991028f33fe15292e8fd2
MD5 099baa22170e5448ea0cb558b38f488b
BLAKE2b-256 5fa40b0518423f470e6909c655dbc35a75d10a766c2458cd499d4017f4f1d9a4

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 0010c18009544130c40cbdb5ad4a717f13b34780cabf5bb2666f97ee9a2f2a4f
MD5 53ad82dede59be92288140a8eadcabd3
BLAKE2b-256 89ab3d5a4dd57823887cf7c932b2c97c462dcc5d6d53a6b70f135b3f711138aa

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b39db4574389dfa7623c32b0b37f98a067b440731209d3fe7162e4c3064ba8a0
MD5 26718b3bca5db71b1385c0ba74ab6b71
BLAKE2b-256 92fa744dbcfbcd31d855bb62c998569fcd7d77e6618d0c2dde5a2949d020a741

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.14-cp38-cp38-win32.whl
  • Upload date:
  • Size: 969.3 kB
  • 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-2.0.14-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 7ead37cf00cd9739d84c437c1813e0650260d6d26e92950a9e141a6f32caed08
MD5 7ae1cfbc2d3d386f63a3312e15e2e4de
BLAKE2b-256 ed9616e8c849917ee28dfcbe8b809fa88fc7d2cb254a707d9912253810c64cdd

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cf2d0b2ec9269924156f896bf790e788bdf03592314fd0a78c0a620ff7d98622
MD5 67e3ba52c67029de51b078b5f8e240c4
BLAKE2b-256 5860035a2cef17b6b81114626f7fd5181c44699e8066d17378e9c01bd21581b0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 7ca016e7e546676797e0e248e498ce596c0f4ef0ebddd596b1a0ff9ef016eac2
MD5 b2dcddda74357d68b6eb448e02724ad3
BLAKE2b-256 fe1b4808a387043329cd08bea23df4040b0cf1c4d45c13cf278a74ed475b5299

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cbd31a5bc37b4c95b9ade35cb939b7370cbdd62f36ab37d63dd9945546786680
MD5 3e2d03ad7974427928bbaf1fc7d6599c
BLAKE2b-256 991ec37a963379e6bbb70056a42fbe4e9594e394070624ab3c42b0f1889865c1

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e3f988420b76997c690dd369a86dd7093344ac61b7c6bb8b097cf2cb13d78e42
MD5 6bbe2a22beda65ba0cba33a4d5a63cc4
BLAKE2b-256 87078d0a69fc20d4808c3e817424501edf85831e1f08f0c0e7cd97c75d58359e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 deb4c4fe2e08370971f12385b7d9839de039d0ad20b8f8eba63ae41a1ded3e16
MD5 237ecc678ef9756edcd96d9b3d911b3c
BLAKE2b-256 efe0cbaa5f1f786b692a561e442f28271bafe19411b007ef933a4df210294ffe

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 c30bed06070a5eea4f08e64e785dfc5dad30a576671f6d3d788b1d97bdeb04ef
MD5 0e310da0c83fc8a94b12d866ff4382d1
BLAKE2b-256 f26039231a296bbe2001db1a70ae2ed77dcc9f7da7ee0197d503ee81a475de73

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 7595b6757946df25207999e35eee92a2f105ffa4c4750beb42928f11a70461b0
MD5 f8948b3f63b717d5d036d535179101ca
BLAKE2b-256 283b974c1ed6f502ace8e7a80638ace0ad9f10eb9a7457d8d6240564a2d717fa

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.14-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 969.9 kB
  • 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-2.0.14-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 8368f6b39fa27fbaadfa5b7ee5a065363432bd90f8a9b50a4b409fb463ce2e64
MD5 c29c49e5a7b5cbf361af9b2ca8ed8186
BLAKE2b-256 4e8043e59ae9204de086d372c8afccc55161c16a093fd42bd660652ed35eee08

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 df68ba5b4e36ec250b582e88eeee86c926bf54cd60a71ad33cc4caffc10ea7c5
MD5 990ce958bfb6384561c253018bbebd1e
BLAKE2b-256 0bfaafc4ebb429ea91815d3239925ec1354d3492c98ecabe15ecd19dc5ab7b28

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 928c0fa546ecdbd95d58c802d949a73e7fe1a6ab8a9ee4ba3118e7c7dadcbf93
MD5 9ab6f646ef7e08699352d58da0a9f535
BLAKE2b-256 9201a7c5c4694164cefa50eee1181630de2757080ad96142072022eedda68461

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0b3dc1f1c214ff132f167084045fb8d034b177b5e424ad0dca9305a2922db9f3
MD5 5957139bb8ee89d668e3d3d04b0737ac
BLAKE2b-256 e4aaed2e9448c8260a2998a37c8b46c49192001c0cf8ff5199cb61ff5d972ba3

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5a295ef34b439b1beb79e6ca7203150e2ad172df64914a2f31a81068d5158a51
MD5 223f9f655323b869ac452738d1025999
BLAKE2b-256 a23b74400d15f9dfab8361a79d30d0d54287f1287dedc317f1dc45fe4893eae3

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 f0773f22f7559cf25ff8730fdc25337d231a5c38093bef907f1ceddd664a8036
MD5 4779bb308afae76fbf61bbae463689bb
BLAKE2b-256 d6a6606117092913f7f17900e30eb7bc4b15902b03d843b311eba44bcab51cf0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.14-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 957.1 kB
  • 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-2.0.14-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 8e6c30f841a23b473452716341e0fecf021571ff68710ccd9683ec5882fce87d
MD5 47b3b0da2d39bec8bfcb13354078f113
BLAKE2b-256 1d5280d83b5b2caff8898fa5f36511f9cf90590ee5222d6bd941e72303821603

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ef4ca195a7bbfe4cf044bf0ccd680694ff4777204d303a9eab5fe489de27200c
MD5 7c3b4cff22f7fb2bbc55332b91efe7cd
BLAKE2b-256 90e3833bef89369c7a074361a3564c284f6dc0904bb0ef568d7646d24b77a44b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 f9ec08b9a2c7d8349b2b25b25f18866b18edfd737d764250d17400bfbdb71e97
MD5 010c93f4d1b21ccf17c376e1519cae25
BLAKE2b-256 5255a65f7f9c7545aeb022e21eacc02f1c9731850d4a1b4f511450b3cd5b0e3c

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 17b1812dfc20840c8f7316b8c36e9cec587598b20057f2de87107df0f36764e1
MD5 227fc25b49dae1cc9bdf8abde4f8f796
BLAKE2b-256 6e24f5ee2f899f2134128f0e15616bc2317fa57dfb4f43956e4d66546631903a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.14-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.14-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 45014b1b7ec5999d0147ef6a74ddcebe78394c6bc8003597bb51dcc89358f51a
MD5 2a3197c960e4fb35fb6e952a1729c321
BLAKE2b-256 8daa113446cd006aed1a059c6c590e252e232b42cc3595b8f633ecb929414902

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