library for fast approximate string matching using Jaro and Jaro-Winkler similarity

These details have not been verified by PyPI

Project links

Homepage

Project description

JaroWinkler

JaroWinkler is a library to calculate the Jaro and Jaro-Winkler similarity. It is easy to use, is far more performant than all alternatives and is designed to integrate seemingless with RapidFuzz.

:zap: Quickstart

>>> from jarowinkler import *

>>> jaro_similarity("Johnathan", "Jonathan")
0.8796296296296297

>>> jarowinkler_similarity("Johnathan", "Jonathan")
0.9037037037037037

🚀 Benchmarks

The implementation is based on a novel approach to calculate the Jaro-Winkler similarity using bitparallelism. This is significantly faster than the original approach used in other libraries. The following benchmark shows the performance difference to jellyfish and python-Levenshtein.

Benchmark JaroWinkler

⚙️ Installation

You can install this library from PyPI with pip:

pip install jarowinkler

JaroWinkler provides binary wheels for all common platforms.

Source builds

For a source build (for example from a SDist packaged) you only require a C++14 compatible compiler. You can install directly from GitHub if you would like.

pip install git+https://github.com/maxbachmann/JaroWinkler.git@main

📖 Usage

Any algorithms in JaroWinkler can not only be used with strings, but with any arbitary sequences of hashable objects:

from jarowinkler import jarowinkler_similarity


jarowinkler_similarity("this is an example".split(), ["this", "is", "a", "example"])
# 0.8666666666666667

So as long as two objects have the same hash they are treated as similar. You can provide a __hash__ method for your own object instances.

class MyObject:
    def __init__(self, hash):
        self.hash = hash

    def __hash__(self):
        return self.hash

jarowinkler_similarity([MyObject(1), MyObject(2)], [MyObject(1), MyObject(2), MyObject(3)])
# 0.9111111111111111

All algorithms provide a score_cutoff parameter. This parameter can be used to filter out bad matches. Internally this allows JaroWinkler to select faster implementations in some places:

jaro_similarity("Johnathan", "Jonathan", score_cutoff=0.9)
# 0.0

jaro_similarity("Johnathan", "Jonathan", score_cutoff=0.85)
# 0.8796296296296297

JaroWinkler can be used with RapidFuzz, which provides multiple methods to compute string metrics on collections of inputs. JaroWinkler implements the RapidFuzz C-API which allows RapidFuzz to call the functions without any of the usual overhead of python, which makes this even faster.

from rapidfuzz import process

process.cdist(["Johnathan", "Jonathan"], ["Johnathan", "Jonathan"], scorer=jarowinkler_similarity)
array([[1.       , 0.9037037],
       [0.9037037, 1.       ]], dtype=float32)

👍 Contributing

PRs are welcome!

Found a bug? Report it in form of an issue or even better fix it!
Can make something faster? Great! Just avoid external dependencies and remember that existing functionality should still work.
Something else that do you think is good? Do it! Just make sure that CI passes and everything from the README is still applicable (interface, features, and so on).
Have no time to code? Tell your friends and subscribers about JaroWinkler. More users, more contributions, more amazing features.

Thank you :heart:

⚠️ License

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

2.0.1

Nov 3, 2023

2.0.0

Nov 1, 2023

1.2.3

Sep 27, 2022

1.2.2

Sep 25, 2022

1.2.1

Aug 12, 2022

1.2.0

Jul 19, 2022

This version

1.1.2

Jul 11, 2022

1.1.1

Jul 9, 2022

1.1.0

Jul 4, 2022

1.0.5

Jun 29, 2022

1.0.4

Jun 23, 2022

1.0.3

Jun 22, 2022

1.0.2

Mar 13, 2022

1.0.1

Mar 6, 2022

1.0.0

Mar 6, 2022

Download files

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

Source Distribution

jarowinkler-1.1.2.tar.gz (83.4 kB view details)

Uploaded Jul 11, 2022 Source

Built Distributions

jarowinkler-1.1.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (92.6 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (89.2 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (118.5 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (63.6 kB view details)

Uploaded Jul 11, 2022 PyPy macOS 10.9+ x86-64

jarowinkler-1.1.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (92.6 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (89.1 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (118.5 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (63.8 kB view details)

Uploaded Jul 11, 2022 PyPy macOS 10.9+ x86-64

jarowinkler-1.1.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (92.6 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (89.1 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (118.5 kB view details)

Uploaded Jul 11, 2022 PyPy manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (63.8 kB view details)

Uploaded Jul 11, 2022 PyPy macOS 10.9+ x86-64

jarowinkler-1.1.2-cp310-cp310-win_amd64.whl (61.4 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 Windows x86-64

jarowinkler-1.1.2-cp310-cp310-win32.whl (67.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 Windows x86

jarowinkler-1.1.2-cp310-cp310-musllinux_1_1_x86_64.whl (630.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 musllinux: musl 1.1+ x86-64

jarowinkler-1.1.2-cp310-cp310-musllinux_1_1_s390x.whl (686.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 musllinux: musl 1.1+ s390x

jarowinkler-1.1.2-cp310-cp310-musllinux_1_1_ppc64le.whl (669.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 musllinux: musl 1.1+ ppc64le

jarowinkler-1.1.2-cp310-cp310-musllinux_1_1_i686.whl (717.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 musllinux: musl 1.1+ i686

jarowinkler-1.1.2-cp310-cp310-musllinux_1_1_aarch64.whl (613.4 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 musllinux: musl 1.1+ ARM64

jarowinkler-1.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (105.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl (103.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 manylinux: glibc 2.17+ s390x

jarowinkler-1.1.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (114.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 manylinux: glibc 2.17+ ppc64le

jarowinkler-1.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (100.0 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (134.2 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-cp310-cp310-macosx_11_0_arm64.whl (57.4 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 macOS 11.0+ ARM64

jarowinkler-1.1.2-cp310-cp310-macosx_10_9_x86_64.whl (70.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 macOS 10.9+ x86-64

jarowinkler-1.1.2-cp310-cp310-macosx_10_9_universal2.whl (121.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

jarowinkler-1.1.2-cp39-cp39-win_amd64.whl (61.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 Windows x86-64

jarowinkler-1.1.2-cp39-cp39-win32.whl (67.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 Windows x86

jarowinkler-1.1.2-cp39-cp39-musllinux_1_1_x86_64.whl (630.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 musllinux: musl 1.1+ x86-64

jarowinkler-1.1.2-cp39-cp39-musllinux_1_1_s390x.whl (686.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 musllinux: musl 1.1+ s390x

jarowinkler-1.1.2-cp39-cp39-musllinux_1_1_ppc64le.whl (670.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 musllinux: musl 1.1+ ppc64le

jarowinkler-1.1.2-cp39-cp39-musllinux_1_1_i686.whl (717.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 musllinux: musl 1.1+ i686

jarowinkler-1.1.2-cp39-cp39-musllinux_1_1_aarch64.whl (613.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 musllinux: musl 1.1+ ARM64

jarowinkler-1.1.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (105.0 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl (104.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 manylinux: glibc 2.17+ s390x

jarowinkler-1.1.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (114.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 manylinux: glibc 2.17+ ppc64le

jarowinkler-1.1.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (100.2 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (134.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-cp39-cp39-macosx_11_0_arm64.whl (58.0 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 macOS 11.0+ ARM64

jarowinkler-1.1.2-cp39-cp39-macosx_10_9_x86_64.whl (71.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 macOS 10.9+ x86-64

jarowinkler-1.1.2-cp39-cp39-macosx_10_9_universal2.whl (123.0 kB view details)

Uploaded Jul 11, 2022 CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

jarowinkler-1.1.2-cp38-cp38-win_amd64.whl (61.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 Windows x86-64

jarowinkler-1.1.2-cp38-cp38-win32.whl (67.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 Windows x86

jarowinkler-1.1.2-cp38-cp38-musllinux_1_1_x86_64.whl (630.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 musllinux: musl 1.1+ x86-64

jarowinkler-1.1.2-cp38-cp38-musllinux_1_1_s390x.whl (686.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 musllinux: musl 1.1+ s390x

jarowinkler-1.1.2-cp38-cp38-musllinux_1_1_ppc64le.whl (669.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 musllinux: musl 1.1+ ppc64le

jarowinkler-1.1.2-cp38-cp38-musllinux_1_1_i686.whl (718.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 musllinux: musl 1.1+ i686

jarowinkler-1.1.2-cp38-cp38-musllinux_1_1_aarch64.whl (613.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 musllinux: musl 1.1+ ARM64

jarowinkler-1.1.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (105.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl (102.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 manylinux: glibc 2.17+ s390x

jarowinkler-1.1.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (113.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 manylinux: glibc 2.17+ ppc64le

jarowinkler-1.1.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (100.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (135.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-cp38-cp38-macosx_11_0_arm64.whl (57.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 macOS 11.0+ ARM64

jarowinkler-1.1.2-cp38-cp38-macosx_10_9_x86_64.whl (71.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 macOS 10.9+ x86-64

jarowinkler-1.1.2-cp38-cp38-macosx_10_9_universal2.whl (122.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

jarowinkler-1.1.2-cp37-cp37m-win_amd64.whl (62.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m Windows x86-64

jarowinkler-1.1.2-cp37-cp37m-win32.whl (67.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m Windows x86

jarowinkler-1.1.2-cp37-cp37m-musllinux_1_1_x86_64.whl (630.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m musllinux: musl 1.1+ x86-64

jarowinkler-1.1.2-cp37-cp37m-musllinux_1_1_s390x.whl (686.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m musllinux: musl 1.1+ s390x

jarowinkler-1.1.2-cp37-cp37m-musllinux_1_1_ppc64le.whl (668.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m musllinux: musl 1.1+ ppc64le

jarowinkler-1.1.2-cp37-cp37m-musllinux_1_1_i686.whl (717.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m musllinux: musl 1.1+ i686

jarowinkler-1.1.2-cp37-cp37m-musllinux_1_1_aarch64.whl (613.5 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m musllinux: musl 1.1+ ARM64

jarowinkler-1.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (104.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl (103.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m manylinux: glibc 2.17+ s390x

jarowinkler-1.1.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (112.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m manylinux: glibc 2.17+ ppc64le

jarowinkler-1.1.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (100.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (135.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-cp37-cp37m-macosx_10_9_x86_64.whl (71.7 kB view details)

Uploaded Jul 11, 2022 CPython 3.7m macOS 10.9+ x86-64

jarowinkler-1.1.2-cp36-cp36m-win_amd64.whl (60.4 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m Windows x86-64

jarowinkler-1.1.2-cp36-cp36m-win32.whl (66.4 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m Windows x86

jarowinkler-1.1.2-cp36-cp36m-musllinux_1_1_x86_64.whl (626.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m musllinux: musl 1.1+ x86-64

jarowinkler-1.1.2-cp36-cp36m-musllinux_1_1_s390x.whl (685.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m musllinux: musl 1.1+ s390x

jarowinkler-1.1.2-cp36-cp36m-musllinux_1_1_ppc64le.whl (665.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m musllinux: musl 1.1+ ppc64le

jarowinkler-1.1.2-cp36-cp36m-musllinux_1_1_i686.whl (716.9 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m musllinux: musl 1.1+ i686

jarowinkler-1.1.2-cp36-cp36m-musllinux_1_1_aarch64.whl (610.2 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m musllinux: musl 1.1+ ARM64

jarowinkler-1.1.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (101.1 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m manylinux: glibc 2.17+ x86-64

jarowinkler-1.1.2-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl (100.6 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m manylinux: glibc 2.17+ s390x

jarowinkler-1.1.2-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl (109.8 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m manylinux: glibc 2.17+ ppc64le

jarowinkler-1.1.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (96.0 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m manylinux: glibc 2.17+ ARM64

jarowinkler-1.1.2-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl (133.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m manylinux: glibc 2.17+ i686 manylinux: glibc 2.5+ i686

jarowinkler-1.1.2-cp36-cp36m-macosx_10_9_x86_64.whl (69.3 kB view details)

Uploaded Jul 11, 2022 CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file jarowinkler-1.1.2.tar.gz.

File metadata

Download URL: jarowinkler-1.1.2.tar.gz
Upload date: Jul 11, 2022
Size: 83.4 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for jarowinkler-1.1.2.tar.gz
Algorithm	Hash digest
SHA256	`1899cdd09fb1bfa6f32dad30ff8bc9c64b4ad2cb4fe8875306192752ff0cf8f6`
MD5	`6a3654b91fcac3fdb5d7f354a15d97ae`
BLAKE2b-256	`ce8f5efa1249a5aeca64fa0e92a3788609121404a1b6887a239ddd632b53686f`

Algorithm	Hash digest
SHA256	`c41658512ab5290f8e9fa5339480556c873518df21b54a778b876f1d3fb3d6d5`
MD5	`ecd6ddfd106fbe8b48107f64d548dd1d`
BLAKE2b-256	`5227dbe1b305372fb83bc7bccc2dfab1464b52fcb8bbb665d34307fe8bbe4125`

Algorithm	Hash digest
SHA256	`4c1c5acf7c5c487f2e560108048052918030492a2740f3212561a0d09e8240e5`
MD5	`452f8f365795285336ba08d072c997be`
BLAKE2b-256	`2843f8e3589cde2f5e4c663c19e8c6a6092f457c585846cfdb5b9ed2ea29cc1f`

Algorithm	Hash digest
SHA256	`6ef3ea074d6e9f0c0307b2fe89c7489c0136e38d570cf63c3921778fcd52949b`
MD5	`535245ea50b7fd30f8332373187b3b52`
BLAKE2b-256	`25f5ce18e7276c86edc31132ec06539ae2467830b432100f58f09177fa08aec8`

Algorithm	Hash digest
SHA256	`a2a08a8f15891cfcc97f2317b3b2e875cf4ed7c9800854bc8317adf16a514476`
MD5	`54d4e663891d3d6e248f950a8fc75de7`
BLAKE2b-256	`44e7ffc0eabf79c238dcbe283c39ff6c1f1541897239b97bbc73ae384c8ce8c0`

Algorithm	Hash digest
SHA256	`e76b6be8445691cadc89838a7f3c59e54a764ef315479de5869e4bb189007110`
MD5	`77c174ffe871773f1cac6dfc9d0be524`
BLAKE2b-256	`fa0723d345db9e6ce3d2d0b3576227217e300186862378bb60610ca5e4da92f4`

Algorithm	Hash digest
SHA256	`77ab38950d87064fd4cc0e596b223c1bc3f702ac49ed76a9f0c91a45d2c5f714`
MD5	`a41de496eba60081bffab91426612001`
BLAKE2b-256	`e4dbdb7d38782c6369114556d4ca831995854cc4ce60ca8c8a6ec1f0118f9cc2`

Algorithm	Hash digest
SHA256	`2cfa4d3ade71766ca769271495afe68a7f1923cd8dc101b1a8f0a391ad677469`
MD5	`5cc58e54071a4d153d87ed77ddccb5d1`
BLAKE2b-256	`b4825955deb441d6cea6dacb3d53ee96eab06b4bce5332972994931e7667095f`

Algorithm	Hash digest
SHA256	`da9f26c48d259d59d3442ea6f40c6b60c1da2a1e0a88115e6ec06babd72a6268`
MD5	`1fe5bb3b2e0f044cdf8d648a65c8f936`
BLAKE2b-256	`bf7e1e66c92d36b104a9f69202448f26f97ef4ecc9adb0e53c44aaa0ca24d221`

Algorithm	Hash digest
SHA256	`1ee38954d881a2c48d8072110be6ca23c60abc419c06c2bf88f6055ec7239b1a`
MD5	`db7cba309b55ee69c02178520731849b`
BLAKE2b-256	`49f6e7bfb8fb4c4f69641645092aa43f42cfb80dec8653bed9c6724d10398210`

Algorithm	Hash digest
SHA256	`9d86172efe4fa62c1ecd8d0af0be75ec1f50d203c3b7daccd83df7661301c654`
MD5	`20605058b962627d947728e20105731a`
BLAKE2b-256	`40d3f5f5f77549205a01e7913564e55d69a7efcc5d200c76d079c82ab3e721fa`

Algorithm	Hash digest
SHA256	`483ffc652a32a33d9c2392447c779c573f963a7ba1f8680a65dbe5890a95910e`
MD5	`c39b43a17855c2836010dd8f3f3d03cf`
BLAKE2b-256	`b72e2fb10758eedf5edf4955fa3966d89e5cc35a6af48130c242825a6c3065a6`

Algorithm	Hash digest
SHA256	`97f3c8cd0787da27bda7b579aa0a55f6d462e029b0784cd175605a8c7c354a87`
MD5	`87d7ff4e81ef304db083cb518b7a9b18`
BLAKE2b-256	`84eba8709c7878ec699bdc559cdea65ca14e4a0d8d0a31ef8435622c37cfcb1e`

Algorithm	Hash digest
SHA256	`01dae4461cc06d62852ed9deca31683bb76f13f186627a58789733daa1501075`
MD5	`10ea2218271504c27853377667bc80b2`
BLAKE2b-256	`4e0a45196241061213d17f59514c4af7fdf004cc5cea43740977b2b4cdff1c89`

Algorithm	Hash digest
SHA256	`247073e6e04bb08629931b84dccc885575247d4dd60bb184c686ea821c57f301`
MD5	`3f0193e97c51297555506e778b9539a5`
BLAKE2b-256	`575e5b3f1e2950fc6892761cfe29cdf6d8331311372c34f2d49c2d5789e80d98`

Algorithm	Hash digest
SHA256	`23cefca8fc2da67f6b50c2485b90ca2a0c7bd9fe618e240b652975a968765982`
MD5	`922ae19ee5f20295f81c5ea0d4ba80af`
BLAKE2b-256	`ba9f62d0a148a0470216aac549b27039a8a4fd381b7cf686debc4fedc868100c`

Algorithm	Hash digest
SHA256	`6591cb61c18d2490e65754b8023acb0c22c2df1b16e30b4ce65d700649fe17f6`
MD5	`bf6fe4362cc84fd50a225c4bc4229c9a`
BLAKE2b-256	`68ce7db501b2dfd595b755d58c993b851977fb59f088e76908eef5eea9a6b0b2`

Algorithm	Hash digest
SHA256	`3a7988b03fc30da334bb0795ae0bc209519b5dc0b2e103b55121ccd3032c07ea`
MD5	`1898f820216a392594004c6ec8d4321f`
BLAKE2b-256	`ea8d2f6e59796e661e5c28b659b892d9fd7a0f017385293b4c38603fbb72949a`

Algorithm	Hash digest
SHA256	`d4f178a436624ccf9632a7156c9d5e5e985f01495c69dd9c078c6bb341392d94`
MD5	`f48a043b02bd0ccc11dccdfc5c10fd0b`
BLAKE2b-256	`befa51168468ff4e8d63a059d30b762ad9e573bd2c443c1a4371565e643afdff`

Algorithm	Hash digest
SHA256	`42a168bba88f03b5f2656fe97d63b9b4562cf4790ed9971c43070aa2a207d640`
MD5	`df4b6dbc2f491df7f932d5de33e7a0e0`
BLAKE2b-256	`3c4c8a59373387d76eef5ec1550f696f9fe756af806666e57d70ff417b322797`

Algorithm	Hash digest
SHA256	`1e98cd8186f4912543c25229dfbcb2f4a311281b0b468833863d157f4a5f7724`
MD5	`4c5f80b5bad4875a0cc25fc4a6e49003`
BLAKE2b-256	`229a1c63ff5a31e1728418934e4effbdaff7807fb39b5927923108295789d808`

jarowinkler 1.1.2

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

JaroWinkler

JaroWinkler is a library to calculate the Jaro and Jaro-Winkler similarity. It is easy to use, is far more performant than all alternatives and is designed to integrate seemingless with RapidFuzz.

:zap: Quickstart

🚀 Benchmarks

⚙️ Installation

Source builds

📖 Usage

👍 Contributing

⚠️ License

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Algorithm	Hash digest
SHA256	`62a0a2959f269cdb00003cc1a804df95c196ac1e673e07736b00a95456c86c48`
MD5	`efb0d725062305cfca28413e21d08354`
BLAKE2b-256	`890cbd1566eea32e216ed1d6c12a07db782210ef56488f265efa766d07cc2853`

Algorithm	Hash digest
SHA256	`653044f8bb1ebf3e58bb2bad1fbe8c4a4222d2b91ff66ffa6c3a11d584bf93f0`
MD5	`0548c18840ba6c8b9f83f88428a2af42`
BLAKE2b-256	`a122ac8d162ea9283176ea6cf9cace4fe262283f84446caca0bfa490250469e3`

Algorithm	Hash digest
SHA256	`e0991dacc7f9f872b919f1dc0c5c567322978ce618fc2db807a38696e345bdf6`
MD5	`d48824ee1ff8afb2b9b8a66e96a2e555`
BLAKE2b-256	`69024262e9a493861f420e77eb4c498ca66f616810e19bf1579c3d2c7f83d5f0`

Algorithm	Hash digest
SHA256	`fcc6aac9fed6b5171f4e85f0d3e672aaa49bb12fc1bcc21fb9add09886b1c2f3`
MD5	`d0a4fb92306215be2161824522ccce83`
BLAKE2b-256	`67e571b88391261db092f2a61c0373d3cf41566792f35880e98f07705d0a81e6`

Algorithm	Hash digest
SHA256	`849391bb325f312575d31730316e0c27de3d2c7ac8a7a45d517b8ed417d04d57`
MD5	`359cad12b1a8f0e018e4d91c19ce7d7a`
BLAKE2b-256	`ec5f9a92ab14592c17af70002437cb1db7f619f3338191ff694f47f19df91e50`

Algorithm	Hash digest
SHA256	`82b0362527a287d5a8b1cd7fdfee88772382d012f32d783f9ef1c2e869c58034`
MD5	`3c9217f26a13721fd09dd275ce46fa99`
BLAKE2b-256	`933272b476c005d627ea91f340521ea801535982a4a2a78415d9c665927576c9`

Algorithm	Hash digest
SHA256	`9c23494ade7903a413c618d6bd6f780fc7551f212768985dc8182b7ef4b1dddd`
MD5	`ec543f95ff2b7f1190c91024e0930ced`
BLAKE2b-256	`693cc5bbb9e1e11e5c73e3dbb0950f3396f94d7dfda65a507e335c8c5f4626cf`

Algorithm	Hash digest
SHA256	`f189a7306693c57b946c9cd9d5b0b17e486246b8ff4dbce4dfc68f216c35699e`
MD5	`880271e9e1ac319bcd468ef18cc1db97`
BLAKE2b-256	`117efbaf1351adbdb75a94839b7e5da13bb01d538762e0f602eb8b26be581a56`

Algorithm	Hash digest
SHA256	`1e258123981d86faf63f4bf686413a26c7416802bb803083adb77872b1440e85`
MD5	`9d98b9c5ea9391060834d89a9fc567b9`
BLAKE2b-256	`fba41e544f6115edb9c76d3ce1312c7f41a3fde646099840c3c048c95b333790`

Algorithm	Hash digest
SHA256	`841e7fdbdb9b0af5b49fe4594117fc543517f5ea65942db9c960b609adebd67f`
MD5	`1a1d75d074181922d4eb5a0fe9a345f1`
BLAKE2b-256	`610c4b77718e7ed17cff9f8e740e392aeab8408f56f44fa98b8ba88a1dc8f0b8`