Python Fuzzy Matching (FuzzyWuzzy) - Keep only Best Match

Question

I\'m trying to fuzzy match two csv files, each containing one column of names, that are similar but not the same.

My code so far is as follows:

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Answer 1

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.