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

This version

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-2.0.13.tar.gz (912.6 kB view details)

Uploaded Source

Built Distributions

rapidfuzz-2.0.13-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-2.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-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-2.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-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-2.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (2.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp310-cp310-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

rapidfuzz-2.0.13-cp310-cp310-win32.whl (1.0 MB view details)

Uploaded CPython 3.10 Windows x86

rapidfuzz-2.0.13-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-2.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp310-cp310-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.13-cp39-cp39-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

rapidfuzz-2.0.13-cp39-cp39-win32.whl (1.0 MB view details)

Uploaded CPython 3.9 Windows x86

rapidfuzz-2.0.13-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-2.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp39-cp39-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.13-cp38-cp38-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

rapidfuzz-2.0.13-cp38-cp38-win32.whl (1.0 MB view details)

Uploaded CPython 3.8 Windows x86

rapidfuzz-2.0.13-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-2.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp38-cp38-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_universal2.whl (2.7 MB view details)

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

rapidfuzz-2.0.13-cp37-cp37m-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

rapidfuzz-2.0.13-cp37-cp37m-win32.whl (1.0 MB view details)

Uploaded CPython 3.7m Windows x86

rapidfuzz-2.0.13-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-2.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (2.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp37-cp37m-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

rapidfuzz-2.0.13-cp36-cp36m-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

rapidfuzz-2.0.13-cp36-cp36m-win32.whl (991.9 kB view details)

Uploaded CPython 3.6m Windows x86

rapidfuzz-2.0.13-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-2.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (2.4 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

rapidfuzz-2.0.13-cp36-cp36m-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file rapidfuzz-2.0.13.tar.gz.

File metadata

  • Download URL: rapidfuzz-2.0.13.tar.gz
  • Upload date:
  • Size: 912.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.13.tar.gz
Algorithm Hash digest
SHA256 6293d26d593e1d71b9c64657c1b470c9856030c8f88eb5e2bdf42bfb18979c27
MD5 b51c126bc0ec906d4c916734d7a2d9d8
BLAKE2b-256 fe65e1dfbb259de983add1d580ea7ba71157cda4cf5f79744059a0bc1084dd34

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a783c79a3c74f232b70cbc111a78e5ded923b1b8e5800dad9e87b841d64b9b23
MD5 329345c4cdd043077365ff2a743507a7
BLAKE2b-256 2e5092a7197571e002cf8a215078ef241855262d18eac3623d95b58f35bc6912

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 175e7cbd361387f8d055cac84d5160b75c717a0a18e96f8acc63267436402921
MD5 21458b9858ede4a6fcdc95f823fa9f59
BLAKE2b-256 2eedc6afaa4a8b6ea59aaf201abae01019ec91fdcdea792f3b7bb5cf4ee8c3bd

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 26afc412ce9b351e32451208ef38f74a157dcbe59c10ae3fb1499a2cb41ebee9
MD5 dd89e756d6b640fc3912be3b8c7b95a6
BLAKE2b-256 b47ee08d0ca7454d4fa4e6ca7b5ecbd9b42e790af2248906b7f087e0ca7b46cf

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 51945e6a9b7c3bf9bb7d9efa8284b10c82b99f359711b63aa3fa876b77f9b61c
MD5 5d8c2c4e912a8115ffd8615048eadc93
BLAKE2b-256 fd1b774a8aad0998f72ebf813ad2379908c000eef5c1aa3da3791e13e179ebe0

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 0be4d1e2208e76d7e5625a60bca28e384073b2e8c60fddeeed44557623f1b5c7
MD5 671f104180b15bda20988e84aea330ff
BLAKE2b-256 d7826285dfb3609f0be76c89e6fb3a8d098419c988e7a5a969700564e5e4f3cb

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3e7e3349b386fb66ff6920f7deb99c6433d8246d7a16abe072f7963efddf88a8
MD5 716acc7b686ad50ae5efb6504d1e763f
BLAKE2b-256 59c33ea3233b1b1d2b675f538a8413675b59df6eb19eba7ab8038b51a935069a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1eaa7da7d2db9ac280f5eae19e2831a5c3278fe416ceaa3388e10aacc219a3da
MD5 1f58c17bb443173549d61f133b62ef41
BLAKE2b-256 6e845894b89bf396b2b761d482457c4561899130611b9ef91e737537e13d43cf

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 89b3fd0c2c90ed83b91dcf2ecb136141a8bd607b816e5b8a184e954e2e912a8d
MD5 d35845c6f3c492ab8a892a8c9e5d0d7f
BLAKE2b-256 ab0d9145cd90bd934d4c064a1a7f5f859cdaabbbc02af5087992f0f6b4d3c7ec

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e7ebfa9f6968cc5ee09a3943b03d91498898f823aeb5cfe1b8d1c5a9029f5fa8
MD5 39b1fc82be8cb72f439bb848d46b0d4c
BLAKE2b-256 a02379941d9e242e551725e7777fd996498843dc5a28c32d6c2c66a2f532a922

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 9bee6a574e504fb507391a12972a07d531e7ccf1c5c296ec26098b3458ac8df9
MD5 74de7375995e404dbc021a162775e420
BLAKE2b-256 cb776ef5efb1423fabd1bfa787eb9701619359db77609cbc116519279f51c8e7

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.13-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 26f5773e1232068f2b2cbed31b0930f5902b39f57c537c4d8af2402d22fe807d
MD5 78a3546acf9c27c04c80df3268d47423
BLAKE2b-256 004c5c834066bdfd6a2239f0b5f8b58acae8c69be8eaad78b41e972fb47daf5d

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 319267e7d02f23d29776321d3102fe464fae9207f27c10484892634a8a956991
MD5 b809d36bc871de5ce335b6f3761039a4
BLAKE2b-256 1839b791e29b769344fc0f9856ccea43f89a110d7f60a1eea62ab5c258145c06

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 41aced2fdaa0ac5907d91220c2ba2f0c097db32abe047e837b80be23898310e7
MD5 b21d0dc9556072ecd595e6a19702cf76
BLAKE2b-256 0d055279602b41f12a1dc70122b0849969164fb0060515564ab29d0b2b9f313e

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bea2dabe3f822de0ef609a782ce480b599a4536c01ec4d6cefdb786bf82ef316
MD5 772962630165b15ea13b2f9d90e39794
BLAKE2b-256 f23f9d19d73ea79a8cf5472280fbd8582b1f2679397cea1398b222a015de2490

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3377abd1d8f934d5a3da5ae21744d600ff8dd2ea98e4a8d6eab9692a5d6a1b4b
MD5 f00a8738673f44b85f14a873598b50d4
BLAKE2b-256 480795e49ce95263ca829c3652b4f8359b86f3218a3afbec6926f4bb210e1127

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2e59a52edf2f147d53152b5e4de1c4dfbcd0260056fadab5f47923d3bfe2fc1c
MD5 f03ea8bc29c2547b8fb71fd50810b7a1
BLAKE2b-256 fa3737395f9863c8811ba7ba6325bf819d124e44602c9c6dbc0c96ffab70ab17

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 1a9b6266455647f67d919577d8d1ac869a96757c48732a0e05f2639c14bbd3fa
MD5 93dd8c1dd186578f8db711a349c9438e
BLAKE2b-256 294c4fa0b65e69194e5659d19917535c18432dcfe71c40386dd777f555f6c13c

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 b0690f669ab2150f4d3c6be867aec4b75c3f2740ea6f11030c12d03a46876131
MD5 335d412d4623109a669108bc63a086ad
BLAKE2b-256 afd01523976d7d5e5e4cadf86e8ff14a2601b59142c6859e36521861da6dd428

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.13-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 145a47b8ec23fa15a3c7db363ce240f504be391e49804c4aab6b10f5377a460e
MD5 57fa1ae2419bffcec16caaf2e181c790
BLAKE2b-256 6c342e0161ccc615f17085e673629ee1b0075779f22b388748b261c14ec949ca

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6ba44ed75689e3ad33b028a07f38c9e13d4603310c0faa09fb4caaedd27c14e7
MD5 40d70447416a9df1e1e1be831e4ca21b
BLAKE2b-256 f6412fd8ad9ad137391fbcfd5c34e71a4309697f5ba3fc48ed240fab3a9b7186

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 1c20938421cc67b9905a58ed6c0aec4f6717aa08d16335b5a0a94f7ad4d84d6c
MD5 c3349cce1cb3be64cb995ca7c098c1ac
BLAKE2b-256 780ee0fa7561450014846349b04897afdf1f62d5964f49f0fb75cbf1157cac67

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7143be271d566a99e721d6a3bb793bded43f7103075bdbf80ae853523f9efe8c
MD5 0a3705ce337607b18544d3016e6a5f0a
BLAKE2b-256 2469d2ba5e0dcfaccad2c237f68ce02be4dd9bf80e7b6514815f821eb1a10ab9

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 266f59919910414ddf06cfebe1001142ec877aecedcdaa04ef7b792c6049a895
MD5 bfe38e365ee1d1a0c4dd78a085b7a121
BLAKE2b-256 fa5a49b2a4f575296d7f566a385f679057ed7d0f45a02cd5043db8159ea293ad

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1872a004e0f7e75cf2c8e0ddfdd56c9b95c5984b9c2833166e7fc49ba012caa4
MD5 26f7345354f61d75a7608a0c6bf4506e
BLAKE2b-256 33592e2ac421b3dce3ec5759f7421e9598fe74040fb2bc8261768e4dff5511d6

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 f8592f7bbe34a4c8cf847f1933f073980284a0b1f13110494d34c68a5a071488
MD5 8e1af2ecf3216f5d6baf0b9987efb14d
BLAKE2b-256 8a2d572153e3cdfaaec564cfc65500f1cd5c411a4a68260f1309f91caf5cef14

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b11e9f738ca5686ad7462ddf1a4aad36c0b4ee6ad2ec3a0eb057ebe7e122a92c
MD5 f6b991f6b22afe5bd42048134951c365
BLAKE2b-256 e6eadad68ab43f81d33f251224627485557b2e5dcd4ab9eb414de18c11d3cd3f

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.13-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 e79d8ae94dd6c604f46d939a41eca02a3f57e00ca8e75a376bba5d3541adc707
MD5 becd118f050cd83919e7c3cab37b5f62
BLAKE2b-256 d9e3020a4f53b177454b0cb8b3fc73ae5c8b13760c97ac1568ab6cb117adcb39

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c7f219d98f9235a27fc2e2b299eaaee486db0263c7d482f9b7daee95716a6baa
MD5 c09470d9aacc93e74208094b066e8844
BLAKE2b-256 f8e83a7b739dfa4da5c07e421f626c6dbe14fb6736190a9ae714222bb2de2efc

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 11cd99516dda8da2b22db41be61c6c91674992bc5212ebb3ac7e968ca81e0db9
MD5 8fe3c43e0b4ba09ef0ca6f41755038b2
BLAKE2b-256 936cd230018587a336f37335983e5d95a7560644964b5e94135b900e9cef1cdb

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fd77ca44abe8bc63c60dd0a8c6f397434c15dea5a063037eb30cb922c8491faf
MD5 98715ea3e4dde0f45d696792ec26013a
BLAKE2b-256 7c30d06e7e7ad47f18b66dd6a1709b704507b22a09842262ab6457417c78048b

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 133111702238c1ea05aca2eebb225b5d620bd9c1f279343549501d0299708c42
MD5 c9c616c03d0513c37b52f2e00ead2717
BLAKE2b-256 74f8d0494dcabf6eeee928e98d3e52108599c3c5968df20c4175610bdae59c63

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b49a4abad2cdbc119d8a9769ac5b562e10dcd44c8d863fd8baa5b0c619c5aac5
MD5 75e66ac22cacd9d11d3c937c67531faa
BLAKE2b-256 ce4ffd7671d13f19859cce521f6f13e78d4955a5c0c965f1a268003f1b1666a1

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 7522b5937fcc6d54a7f4977358ed4ae4b1dad5a8b3182edc4e8a0cd517e93b86
MD5 c33e39bd0b0660ccb1ba1cd1609eee3b
BLAKE2b-256 b4562e8040ed5b849da45c5ff59e1fdf2e0e2d531392bdcc2bbe0913bf41ffbd

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 270b574f951fbc2846c901644b589ad744f2ca4395d7491fe9e92a09f7a60592
MD5 5ae6f8e905803e9ac1172db1e0aff522
BLAKE2b-256 05d639867418f85068386f39babcb51db335f73b0c19ef5527cc795f558330fe

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.13-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 3f0969880a16e476cdb04a874c803c5a724b9b9cd149427649ca19fe7d704a6e
MD5 57c70043d137dd52602b6ac2ad123d09
BLAKE2b-256 a30c06881e80fa176c2804be9f5fd5922198e715f7b5ce09c4d24af904763767

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a0aeeefd1f40282c42461cc38f61b175f1c349e6a6627cff377df96cff46e143
MD5 5430e26dbf907c5305d9353772e29f56
BLAKE2b-256 4682bb18185e9769784ad4333563d7bd90e52c842d18ad87b4e4c2b6de6a4844

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 9318ce6b4e866fbcbb4fbb6a49dd07f17c4322edddf73f02870dc77bacc6ae4d
MD5 81a0f3000f1dce22da74d18fb6a4b3d9
BLAKE2b-256 dd74e638c3cfbbb70aab25599e35e11527d3dc943f9fa68f6a4f1e5e0a050f7a

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c65a10397bf3f8c8162e817e24b58b2a3a6a512e94f7356e16a3d3abe4c7e7d3
MD5 9917ad163cdce31c01a15c53bf4f1e82
BLAKE2b-256 ce1d951748db67a40486de2457ce0b646baf15ec78f0d6fb304140500707ed01

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 330e28bd61cd983f7e4496e0b2ee1a85709d6eac89fd75f7a5e1fd31ee9cb7cf
MD5 7a53112957d5ec77213a35e1161b135c
BLAKE2b-256 42c13f35c75b409e8cd9516d28f76fef0e54836ec702f8a1fdc6275ea071fd56

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 334f27153d75f89fc269a4cf725a0abde70d10fca5cc639e976b0a715a631cef
MD5 597a90cf83bf338dc062980653e159e5
BLAKE2b-256 896e38b2c714f02d07630d2cba3eca749ac8bb8c61ec5457f543c2507d82f8cc

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-win32.whl.

File metadata

  • Download URL: rapidfuzz-2.0.13-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 991.9 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-2.0.13-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 44d1c497413ca6aaeeab8536c526b9fc5e95b3e5e056b6df638df1455145efdc
MD5 829d946e3280d9b8a2dc1371c718d332
BLAKE2b-256 dc4f973fb94fd3b90a6645dedae5174bc0cd310e1e93a1611f774b8205feb662

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e6e08ddae58a0c07d6e8d59bb22470ef7a665aa4d3c7334b3eb1976ccad17677
MD5 187c27c1d321a2973d9ba1be2ff65268
BLAKE2b-256 7a04b23e020f09b171719720affe1caf95123e614fd6e643bb60bff83174e9bf

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 cf445e31807696a956d7fcf50e1766afa4c4fce83c175dce5542dcf04ebf0860
MD5 d32214ad8c1e0983cfeb808344773b08
BLAKE2b-256 2df6903be5c775c83673de6d81052dae316ccc053b57a5f6237a9eb5a4ebed75

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 60a2e38f990f7960555c29671ef7132d73b41335be08a771a132ce1ef8e51b8b
MD5 3770f04fb22bbfd3647acba7051e7816
BLAKE2b-256 ea4a5fca31132d6cb4b1857c4aca3f875adee5b3f8eccd53be17de2aa7d24cbe

See more details on using hashes here.

File details

Details for the file rapidfuzz-2.0.13-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for rapidfuzz-2.0.13-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 08bab6ccdf6757d56ad04230b793c9898514e8a421a5b4ec9f93e872d1d0e603
MD5 87592e3016e750acc3cf9e37626d9a2b
BLAKE2b-256 248d17d40be6df775bf1bc4222f8ecc53722c66afa1adbf864ca7312de03dc0c

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