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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.

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