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Description:

pystrsim 0.1.0

pystrsim
Python wrapper for Rust's strsim library
Usage:
import pystrsim

print(f"hamming: {pystrsim.hamming('hamming', 'hammers')} should be 3")
print(f"levenshtein: {pystrsim.levenshtein('kitten', 'sitting')} should be 3")
print(
f"normalized_levenshtein: {pystrsim.normalized_levenshtein('kitten', 'sitting')} should be ~0.571"
)
print(f"osa_distance: {pystrsim.osa_distance('ac', 'cba')} should be 3")
print(f"damerau_levenshtein: {pystrsim.damerau_levenshtein('ac', 'cba')} should be 2")
print(
f"normalized_damerau_levenshtein: {pystrsim.normalized_damerau_levenshtein('levenshtein', 'löwenbräu')} should be ~0.272"
)
print(
f"jaro: {pystrsim.jaro('Friedrich Nietzsche', 'Jean-Paul Sartre')} should be ~0.392"
)
print(
f"jaro_winkler: {pystrsim.jaro_winkler('cheeseburger', 'cheese fries')} should be ~0.911"
)
print(
f"sorensen_dice: {pystrsim.sorensen_dice('web applications', 'applications of the web')} should be ~0.7878787878787878"
)

Is it blazingly fast?
Well, no : )
Jellyfish and Levenshtein are faster.
See the benchmark/benchmark.py file.

algorithm
library
function
time

DamerauLevenshtein
jellyfish
damerau_levenshtein_distance
0.00593378

Hamming
Levenshtein
hamming
0.000683438

Hamming
jellyfish
hamming_distance
0.00112426

Jaro
jellyfish
jaro_similarity
0.00206124

JaroWinkler
jellyfish
jaro_winkler_similarity
0.00221943

Levenshtein
Levenshtein
distance
0.00115115

Levenshtein
jellyfish
levenshtein_distance
0.00257007

damerau_levenshtein
pystrsim
damerau_levenshtein
0.380067

hamming
pystrsim
hamming
0.0116847

jaro
pystrsim
jaro
0.0547281

jaro_winkler
pystrsim
jaro_winkler
0.057244

levenshtein
pystrsim
levenshtein
0.102525

normalized_damerau_levenshtein
pystrsim
normalized_damerau_levenshtein
0.389092

normalized_levenshtein
pystrsim
normalized_levenshtein
0.107314

osa_distance
pystrsim
osa_distance
0.15746

sorensen_dice
pystrsim
sorensen_dice
0.0973786