xdistances

Python wrapper on strsim a Rust implementations of string similarity metrics:

• Hamming
• Levenshtein - distance & normalized
• Optimal string alignment
• Damerau-Levenshtein - distance & normalized
• Jaro and Jaro-Winkler - this implementation of Jaro-Winkler does not limit the common prefix length

The normalized versions return values between 0.0 and 1.0, where 1.0 means an exact match.

Installation

pip install xdistances

Usage

Go to https://xdistances.readthedocs.io for the full documentation.

Examples

>>> import xdistances
>>> xdistances.hamming("hamming", "hammers")
3
>>> xdistances.hamming("hamming", "hammer")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: Lenght mismatch
>>> xdistances.levenshtein("kitten", "sitting")
3
>>> xdistances.normalized_levenshtein("kitten", "sitting")
0.5714285714285714
>>> xdistances.osa_distance("ac", "cba")
3
>>> xdistances.damerau_levenshtein("ac", "cba")
2
>>> xdistances.normalized_damerau_levenshtein("levenshtein", "löwenbräu")
0.2727272727272727
>>> xdistances.jaro("Friedrich Nietzsche", "Jean-Paul Sartre")
0.39188596491228067
>>> xdistances.jaro_winkler("cheeseburger", "cheese fries")
0.9111111111111111

Contributing

If you don't want to install Rust itself, you can run $ ./dev for a development CLI if you have Docker installed.

Benchmarks require a Nightly toolchain. Run $ cargo +nightly bench.

Credits

strsim: crates - Github

License

MIT