Damerau Levenshtein¶

Functions¶

distance¶

rapidfuzz.distance.DamerauLevenshtein.distance(s1, s2, *, processor=None, score_cutoff=None)¶

Calculates the Damerau-Levenshtein distance.

Parameters:

s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (int, optional) – Maximum distance between s1 and s2, that is considered as a result. If the distance is bigger than score_cutoff, score_cutoff + 1 is returned instead. Default is None, which deactivates this behaviour.

Returns:

distance – distance between s1 and s2

Return type:

int

Examples

Find the Damerau-Levenshtein distance between two strings:

>>> from rapidfuzz.distance import DamerauLevenshtein
>>> DamerauLevenshtein.distance("CA", "ABC")
2

normalized_distance¶

rapidfuzz.distance.DamerauLevenshtein.normalized_distance(s1, s2, *, processor=None, score_cutoff=None)¶

Calculates a normalized Damerau-Levenshtein distance in the range [1, 0].

This is calculated as distance / max(len1, len2).

Parameters:

s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour.

Returns:

norm_dist – normalized distance between s1 and s2 as a float between 0 and 1.0

Return type:

float

similarity¶

rapidfuzz.distance.DamerauLevenshtein.similarity(s1, s2, *, processor=None, score_cutoff=None)¶

Calculates the Damerau-Levenshtein similarity in the range [max, 0].

This is calculated as max(len1, len2) - distance.

Parameters:

s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (int, optional) – Maximum distance between s1 and s2, that is considered as a result. If the similarity is smaller than score_cutoff, 0 is returned instead. Default is None, which deactivates this behaviour.

Returns:

similarity – similarity between s1 and s2

Return type:

int

normalized_similarity¶

rapidfuzz.distance.DamerauLevenshtein.normalized_similarity(s1, s2, *, processor=None, score_cutoff=None)¶

Calculates a normalized Damerau-Levenshtein similarity in the range [0, 1].

This is calculated as 1 - normalized_distance

Parameters:

s1 (Sequence[Hashable]) – First string to compare.
s2 (Sequence[Hashable]) – Second string to compare.
processor (callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.
score_cutoff (float, optional) – Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour.

Returns:

norm_sim – normalized similarity between s1 and s2 as a float between 0 and 1.0

Return type:

float

Performance¶

The following image shows a benchmark of the Damerau Levenshtein distance in RapidFuzz and jellyfish. Both have a time complexity of O(NM). However RapidFuzz only requires O(N + M) while the implementation in jellyfish requires has a memory usage of O(NM).