# How to Calculate Levenshtein Distance in Python

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:

• PARTY
• PARK

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.