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:

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pip install python-Levenshtein |

### 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:

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import Levenshtein |

### 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”:

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import Levenshtein string1 = "kitten" string2 = "sitting" lev_distance = Levenshtein.distance(string1, string2) print(lev_distance) |

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

3

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:

**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.

**Example:**

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import Levenshtein string1 = "kitten" string2 = "sitting" lev_ratio = Levenshtein.ratio(string1, string2) print(lev_ratio) |

**Output:**

0.5714285714285714

**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.

Example:

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import Levenshtein string1 = "kitten" string2 = "kitkat" ham_distance = Levenshtein.hamming(string1, string2) print(ham_distance) |

**Output:**

2

## Full Code

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

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import Levenshtein string1 = "kitten" string2 = "sitting" # Calculate Levenshtein distance lev_distance = Levenshtein.distance(string1, string2) print("Levenshtein distance:", lev_distance) # Calculate Levenshtein ratio lev_ratio = Levenshtein.ratio(string1, string2) print("Levenshtein ratio:", lev_ratio) # Calculate Hamming distance string3 = "kitkat" ham_distance = Levenshtein.hamming(string1, string3) print("Hamming distance:", ham_distance) |

**Output:**

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

## Conclusion

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.