What is FuzzyWuzzy Library and Implementation

In this blog, we would discuss What is FuzzyWuzzy Library and its Implementation. Fuzzy string matching is a technique for finding strings that are similar to a given string but may not be an exact match. This can be useful for things like spell-checking, finding duplicate records, and more.

 

FuzzyWuzzy is a Python library that makes it easy to do fuzzy string matching. It has a simple API, and it’s easy to use.  FuzzyWuzzy works by taking two strings and calculating a “distance” between them. This distance is calculated by looking at the number of characters that are different between the two strings.

 

 

 

What is FuzzyWuzzy?

FuzzyWuzzy is a Python library that uses a technique called “fuzzy matching” to find strings that are similar to one another. Fuzzy matching is a method of matching two strings that are not exactly the same but are close enough to be considered a match. The smaller the distance, the more similar the two strings are.

 

 

FuzzyWuzzy can be used for a variety of tasks, such as: –

 

• • Finding duplicate strings in a dataset

 

• • Matching strings from different sources (e.g. matching names from two different databases)

 

• • Normalizing strings (e.g. converting all strings to lowercase).

 

 

FuzzyWuzzy works by calculating the Levenshtein distance between two strings. The Levenshtein distance is a measure of how similar two strings are. It’s the minimum number of edits that you need to make to one string to turn it into the other string.

 

 

For example, the Levenshtein distance between “cat” and “cats” is 1, because you can change the “a” to an “s” to turn “cat” into “cats”. But the Levenshtein distance between “cat” and “dog” is 3, because you would need to change the “c” to a “d”, the “a” to an “o”, and the “t” to a “g” to turn “cat” into “dog”. FuzzyWuzzy also supports a variety of other algorithms for measuring the similarity of strings, such as the Jaro-Winkler distance.

 

 

 

Implementation of FuzzyWuzzy Library

To use FuzzyWuzzy, you first need to install it. You can do this with pip:

 

pip install fuzzywuzzy

 

 

Once FuzzyWuzzy is installed, you can start using it in your Python programs. Here’s a simple example of how to use FuzzyWuzzy to find similar strings:

 

from fuzzywuzzy import fuzz 
s1 = "cat" 
s2 = "cats" 
score = fuzz.ratio(s1, s2) 
print(score)

 

The code above calculates the Levenshtein distance between the strings “cat” and “cats”. It then prints out the similarity score, which is a number between 0 and 100. A score of 100 means that the two strings are identical. You can also use FuzzyWuzzy to find the best match for a given string.

 

 

For example, let’s say you have a list of strings, and you want to find the string in the list that’s most similar to another string. You can do this with the fuzzywuzzy.process method:

 

from fuzzywuzzy import process 
choices = ['Atlanta Falcons', 'New England Patriots', 'New York Giants', 'Dallas Cowboys'] 
s = 'new york jets' 
result = process.extract(s, choices) 
print(result)

 

The code above finds the best match for the string “new york jets” from the list of choices. It prints out a list of tuples, with each tuple containing the string and its similarity score.

 

 

 

Also, read Decision Trees in Machine Learning.