The Levenshtein distance between two strings is the minimum number of single-character edits required to turn one word into the other.
The word “edits” includes substitutions, insertions, and deletions.
For example, suppose we have the following two words:
The Levenshtein distance between the two words (i.e. the number of edits we have to make to turn one word into the other) would be 2:
In practice, the Levenshtein distance is used in many different applications including approximate string matching, spell-checking, and natural language processing.
This tutorial explains how to calculate the Levenshtein distance between strings in Python by using the python-Levenshtein module.
You can use the following syntax to install this module:
pip install python-Levenshtein
You can then load the function to calculate the Levenshtein distance:
from Levenshtein import distance as lev
The following examples show how to use this function in practice.
Example 1: Levenshtein Distance Between Two Strings
The following code shows how to calculate the Levenshtein distance between the two strings “party” and “park”:
#calculate Levenshtein distance lev('party', 'park') 2
The Levenshtein distance turns out to be 2.
Example 2: Levenshtein Distance Between Two Arrays
The following code shows how to calculate the Levenshtein distance between every pairwise combination of strings in two different arrays:
#define arrays a = ['Mavs', 'Spurs', 'Lakers', 'Cavs'] b <- ['Rockets', 'Pacers', 'Warriors', 'Celtics'] #calculate Levenshtein distance between two arrays for i,k in zip(a, b): print(lev(i, k)) 6 4 5 5
The way to interpret the output is as follows:
- The Levenshtein distance between ‘Mavs’ and ‘Rockets’ is 6.
- The Levenshtein distance between ‘Spurs’ and ‘Pacers’ is 4.
- The Levenshtein distance between ‘Lakers’ and ‘Warriors’ is 5.
- The Levenshtein distance between ‘Cavs’ and ‘Celtics’ is 5.
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