API Reference¶

lev ¶

distance(s1: str, s2: str) -> int

Levenshtein edit distance between two strings.

The distance is the minimum number of single-character insertions, deletions, or substitutions required to transform s1 into s2.

Lengths are measured in Unicode scalar values (chars), so multi-byte characters count as a single edit regardless of their UTF-8 encoded length.

Parameters:

Name	Type	Description	Default
`s1`	`str`	First input string.	required
`s2`	`str`	Second input string.	required

Returns:

Type	Description
`int`	Non-negative integer edit distance.

Examples:

>>> import lev
>>> lev.distance("kitten", "sitting")
3
>>> lev.distance("flaw", "lawn")
2
>>> lev.distance("résumé", "resume")
2

ratio(s1: str, s2: str) -> float

Calculate normalized Levenshtein similarity ratio in [0.0, 1.0].

Defined as 1 - distance(s1, s2) / (len(s1) + len(s2)), where lengths are measured in Unicode scalar values.

Two empty strings return 1.0 by convention.

Parameters:

Name	Type	Description	Default
`s1`	`str`	First input string.	required
`s2`	`str`	Second input string.	required

Returns:

Type	Description
`float`	Similarity score between `0.0` (completely different) and `1.0` (identical).

Examples:

>>> import lev
>>> lev.ratio("kitten", "sitting")
0.7692307692307693
>>> lev.ratio("", "")
1.0