How To Use Python Levenshtein

In this tutorial, we will be discussing a powerful Python library called Levenshtein. This library allows us to calculate the Levenshtein distance between two given strings, which is the number of single-character edits required to transform one string into the other.

The Levenshtein distance has various applications, such as spell-checking, DNA sequence alignment, and natural language processing.

Step 1: Install the Python Levenshtein Library

The Python Levenshtein library can be installed using the pip command. Simply run the following command to install the library:

Step 2: Import the Library in Your Python Script

Once the library is installed, you can import it into your Python script by adding the following line:

Step 3: Calculating the Levenshtein Distance

With the Python Levenshtein library imported, we can now calculate the Levenshtein distance between two given strings. To do this, we will use the distance() the function provided by the Levenshtein Library.

Here’s an example of how to calculate the Levenshtein distance between the strings “kitten” and “sitting”:

This script will output the Levenshtein distance between the two strings:


The output 3 denotes that it takes three single-character edits to transform the string “kitten” into the string “sitting”.

Step 4: Other Functions in the Levenshtein Library

In addition to calculating the Levenshtein distance, the Levenshtein library provides several other useful functions. Some of these functions are:

  1. ratio(): This function returns the similarity between two strings as a float value between 0 and 1, where 1 means that the strings are completely equal.



  1. hamming(): This function calculates the Hamming distance between two strings of equal length. The Hamming distance is the number of positions at which the corresponding characters are different.




Full Code

Here’s the full code showing the various functions discussed in this tutorial:


Levenshtein distance: 3
Levenshtein ratio: 0.6153846153846154
Hamming distance: 3


In this tutorial, we explored how to use the Python Levenshtein library to calculate the Levenshtein distance, similarity ratio, and Hamming distance between two given strings.

Having a deeper understanding of these metrics and how they can be utilized in various applications, such as spell-checking and natural language processing, can lead to more efficient and accurate solutions in the future.