Python Fuzzy Matching (FuzzyWuzzy) - Keep only Best Match

Several pieces of your code can be greatly simplified by using process.extractOne() from FuzzyWuzzy. Not only does it just return the top match, you can set a score threshold for it within the function call, rather than needing to perform a separate logical step, e.g.:

process.extractOne(row, data, score_cutoff = 60)

This function will return a tuple of the highest match plus the accompanying score if it finds a match satisfying the condition. It will return None otherwise.

fuzzywuzzy's process.extract() returns the list in reverse sorted order , with the best match coming first.

so to find just the best match, you can set the limit argument as 1 , so that it only returns the best match, and if that is greater than 60 , you can write it to the csv, like you are doing now.

Example -

from fuzzywuzzy import process
## For each row in the lookup compute the partial ratio
for row in parse_csv("names_2.csv"):

    for found, score, matchrow in process.extract(row, data, limit=1):
        if score >= 60:
            print('%d%% partial match: "%s" with "%s" ' % (score, row, found))
            Digi_Results = [row, score, found]
            writer.writerow(Digi_Results)

I just wrote the same thing for myself but in pandas....

import pandas as pd
import numpy as np
from fuzzywuzzy import fuzz
from fuzzywuzzy import process

d1={1:'Tim','2':'Ted',3:'Sally',4:'Dick',5:'Ethel'}
d2={1:'Tam','2':'Tid',3:'Sally',4:'Dicky',5:'Aardvark'}

df1=pd.DataFrame.from_dict(d1,orient='index')
df2=pd.DataFrame.from_dict(d2,orient='index')

df1.columns=['Name']
df2.columns=['Name']

def match(Col1,Col2):
    overall=[]
    for n in Col1:
        result=[(fuzz.partial_ratio(n, n2),n2) 
                for n2 in Col2 if fuzz.partial_ratio(n, n2)>50
               ]
        if len(result):
            result.sort()    
            print('result {}'.format(result))
            print("Best M={}".format(result[-1][1]))
            overall.append(result[-1][1])
        else:
            overall.append(" ")
    return overall

print(match(df1.Name,df2.Name))

I have used a threshold of 50 in this - but it is configurable.

Dataframe1 looks like

    Name
1   Tim
2   Ted
3   Sally
4   Dick
5   Ethel

And Dataframe2 looks like

Name
1   Tam
2   Tid
3   Sally
4   Dicky
5   Aardvark

So running it produces the matches of

['Tid', 'Tid', 'Sally', 'Dicky', ' ']

Hope this helps.