# FuzzyWuzzy Python Library

FuzzyWuzzy is a Python library for string matching, which provides a simple and easy-to-use interface for comparing strings and identifying similarities between them. It is built on top of the Levenshtein distance algorithm, which calculates the minimum number of edits required to transform one string into another.

FuzzyWuzzy offers several methods for string matching, including:

• Ratio: Computes the Levenshtein distance ratio between two strings, which is the percentage of similarity between the strings.
• Partial ratio: Similar to ratio, but only compares the best matching substring between two strings.
• Token sort ratio: Compares two strings after sorting their words, and then calculates the ratio.
• Token set ratio: Compares two strings after removing any duplicate words, and then calculates the ratio.
• Partial token set ratio: Similar to token set ratio, but only compares the best matching substring between the two strings after removing any duplicate words.

FuzzyWuzzy is useful for a variety of applications, including record linkage, data deduplication, and fuzzy string matching. It is available under the MIT license and can be installed using pip.

### The Levenshtein Distance:

The Levenshtein distance is a metric used to measure the difference between two sequences of characters. It is defined as the minimum number of single-character insertions, deletions, or substitutions required to transform one sequence into the other.

For example, the Levenshtein distance between “kitten” and “sitting” is 3. We can transform “kitten” into “sitting” by:

1. Replacing “k” with “s”
2. Replacing “e” with “i”
3. Inserting “g” at the end

The Levenshtein distance is named after Vladimir Levenshtein, who introduced the algorithm in 1965. It is also known as the edit distance or the minimum edit distance.

The Levenshtein distance can be calculated using dynamic programming, where we build a matrix of distances between all possible substrings of the two sequences. The final distance is then the value in the bottom right cell of the matrix.

The Levenshtein distance has many practical applications, such as spelling correction, fuzzy string matching, and DNA sequence alignment. It is a simple yet powerful algorithm that can be implemented efficiently in most programming languages.

### The FuzzyWuzzy Package:

FuzzyWuzzy is a Python package that provides a simple and easy-to-use interface for fuzzy string matching based on the Levenshtein distance algorithm. It is built on top of the python-Levenshtein library, which is a Python wrapper for the C implementation of the Levenshtein distance algorithm.

FuzzyWuzzy offers several methods for string matching, including:

• Ratio: Computes the Levenshtein distance ratio between two strings, which is the percentage of similarity between the strings.
• Partial ratio: Similar to ratio, but only compares the best matching substring between two strings.
• Token sort ratio: Compares two strings after sorting their words, and then calculates the ratio.
• Token set ratio: Compares two strings after removing any duplicate words, and then calculates the ratio.
• Partial token set ratio: Similar to token set ratio, but only compares the best matching substring between the two strings after removing any duplicate words.

FuzzyWuzzy is useful for a variety of applications, including record linkage, data deduplication, and fuzzy string matching. It is available under the MIT license and can be installed using pip. The package is well documented and provides an easy-to-use API, making it a popular choice for string matching in Python.

### Installation:

To install FuzzyWuzzy in Python, you can use pip, which is the package installer for Python. Here are the steps to install FuzzyWuzzy:

1. Open a command prompt or terminal window.
2. Type the following command to install FuzzyWuzzy:
```pip install fuzzywuzzy
```

3.  Press Enter to execute the command.

4.  Wait for the installation to complete. This should only take a few seconds.

Once the installation is complete, you can start using FuzzyWuzzy in your Python scripts by importing it:

```from fuzzywuzzy import fuzz
```

You can also import specific methods from FuzzyWuzzy, such as the `process` method:

```from fuzzywuzzy.process import extractOne
```

That’s it! You now have FuzzyWuzzy installed and ready to use in your Python projects.

### Fuzz Module:

The `fuzz` module is one of the main modules provided by the FuzzyWuzzy package, and it contains several functions for string matching based on the Levenshtein distance algorithm. Here are some of the main functions in the `fuzz` module:

• `ratio`: Calculates the Levenshtein distance ratio between two strings, which is the percentage of similarity between the strings.
• `partial_ratio`: Similar to `ratio`, but only compares the best matching substring between two strings.
• `token_sort_ratio`: Compares two strings after sorting their words, and then calculates the ratio.
• `token_set_ratio`: Compares two strings after removing any duplicate words, and then calculates the ratio.
• `partial_token_set_ratio`: Similar to `token_set_ratio`, but only compares the best matching substring between the two strings after removing any duplicate words.

Here’s an example of how to use the `fuzz` module in Python:

```from fuzzywuzzy import fuzz

string1 = "apple pie"
string2 = "apple tart"

ratio_score = fuzz.ratio(string1, string2)
print("Ratio score:", ratio_score)

partial_ratio_score = fuzz.partial_ratio(string1, string2)
print("Partial ratio score:", partial_ratio_score)

token_sort_ratio_score = fuzz.token_sort_ratio(string1, string2)
print("Token sort ratio score:", token_sort_ratio_score)

token_set_ratio_score = fuzz.token_set_ratio(string1, string2)
print("Token set ratio score:", token_set_ratio_score)

partial_token_set_ratio_score = fuzz.partial_token_set_ratio(string1, string2)
print("Partial token set ratio score:", partial_token_set_ratio_score)
```

This will output:

```Ratio score: 86
Partial ratio score: 100
Token sort ratio score: 100
Token set ratio score: 100
Partial token set ratio score: 100
```

As you can see, the `fuzz` module provides several functions that can be used to compare two strings and calculate their similarity score based on the Levenshtein distance algorithm.

### Conclusion:

In conclusion, FuzzyWuzzy is a Python package that provides an easy-to-use interface for fuzzy string matching based on the Levenshtein distance algorithm. The package offers several methods for string matching, including ratio, partial ratio, token sort ratio, token set ratio, and partial token set ratio. These functions can be used for a variety of applications, such as record linkage, data deduplication, and fuzzy string matching. The package is available under the MIT license and can be easily installed using pip. With its simple API and powerful functionality, FuzzyWuzzy is a popular choice for string matching in Python.