FuzzyWuzzy Python library

There are many ways to compare strings in Python. Some of the main methods are:

1. Using regular expressions
2. Simple comparison
3. Using difflib

But one of very simple methods — use the library fuzzywuzzy, where we can get a score of 100, which means the two strings are equal, giving a similarity index. This article explains how we got started using the fuzzywuzzy library.

FuzzyWuzzy — it is a Python library that is used for string matching. Fuzzy string matching — it is the process of finding strings that match a given pattern. It mainly uses Levenshtein distance to calculate differences between sequences. 
FuzzyWuzzy was developed and launched by SeatGeek, a ticket finder for sports and concert events. Their original use case is as described in their blog.

Fuzzy requirements
• Python 2.4 or higher
• Python-Levenshtein

Install via pip:

  pip install fuzzywuzzy pip install python-Levenshtein  

How to use this library?

First import these modules,

from fuzzywuzzy import fuzz from fuzzywuzzy import process

Simple ratio usage :

fuzz.ratio ( ’pythonengineering’ , ’geeksgeeks’ ) 87   # Exact match fuzz.ratio ( ’GeeksforGeeks’ , ’GeeksforGeeks’ )     100 fuzz.ratio ( ’ geeks for geeks’ , ’Geeks For Geeks’ )  80

< table border = "0" cellpadding = "0" cellspacing = "0">

fuzz.partial_ratio ( "geeks for geeks" , "geeks for geeks!" )

100
# Exclamation point in the second line,

but still partially words are same so score comes 100

 

fuzz.partial_ratio ( "geeks for geeks " , " geeks geeks " )

64
# less rating because there is additional

token in the middle middle of the string. 

The token now sets the token’s sort ratio:

# Token Sort Ratio fuzz.token_sort_ratio ( " geeks for geeks " , " for geeks geeks " ) 100   # This gives 100 since every word is the same regardless of position   # Token Set Ratio fuzz.token_sort_ratio ( "geeks for geeks" , "geeks for for geeks" ) 8 8 fuzz.token_set_ratio ( " geeks for geeks " , " geeks for geeks " ) 100 # The score comes 100 in the second case, because token_set_ratio considers duplicate words as a single word.

Now suppose that if we have a list of parameters, and we want to find the closest matches, we can use the process module

query = ’geeks for geeks’ choices = [ ’geek for geek’ , ’geek geek’ , ’ g. for geeks’ ]    # Get a list of matches ordered by score, the default limit is 5 process.extract (query, choices) [( ’geeks geeks’ , 95 ), ( ’ g. For geeks’ , 95 ), ( ’geek for geek’ , 93 )]    # If we only want the top one process.extractOne (query, choices) ( ’geeks geeks’ , 95 )

There is also another relationship, which is often called WRatio , sometimes it is better to use WRatio instead of a simple relationship, as WRatio handles lowercase and uppercase and some other parameters.

fuzz.WRatio ( ’geeks for geeks’ , ’Geeks For Geeks’ ) 100 fuzz.WRatio ( ’geeks for geeks !!!’ , ’geeks for geeks’ ) 100 # whereas a simple ratio will give for the above case fuzz.ratio ( ’geeks for geeks !!!’ , ’ geeks for geeks’ ) 91

Full code

# Python code showing all relationships together # make sure you have fuzzywuzzy module installed   from fuzzywuzzy import fuzz from fuzzywuzzy import process   s1 = "I love GeeksforGeeks" s2 = "I am loving GeeksforGeeks" print "FuzzyWuzzy Ratio:" , fuzz.ratio (s1, s2) print "FuzzyWuzzy PartialRatio:" , fuzz.partial_ratio ( s1, s2) print "FuzzyWuzzy TokenSortRatio:" , fuzz.token_sort_ratio (s1, s2) print " FuzzyWuzzy TokenSetRatio: " , fuzz.token_set_ratio (s1, s2) print " FuzzyWuzzy WRatio: " , fuzz.WRatio (s1, s2), ’ ’    # for process library query = ’geeks for geeks’ choices = [ ’ geek for geek’ , ’geek geek’ , ’g. for geeks’ ]  print "List of ratios: " print process.extract (query, choices), ’’ print "Best among the above list:" , process.extractOne (query, choices)

Output:

 FuzzyWuzzy Ratio: 84 FuzzyWuzzy PartialRatio: 85 FuzzyWuzzy TokenSortRatio: 84 FuzzyWuzzy TokenSetRatio: 86 FuzzyWuzzy WRatio: 84 List of ratios: [’g. 95), (’geek for geek’, 93), (’ geek geek’, 86)] Best among the above list: (’g. For geeks’, 95) 

FuzzyWuzzy is built on top of the library difflib, python-Levenshtein is used for speed. So this is one of the best ways to match strings in Python.