fuzzylookup in python

This post will help you to get output using fuzzy lookup in python.

In this example I have used 2 datasets, input dataset and master dataset.

import pandas as pd
import fuzzywuzzy as fuzz
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import difflib

dfinput = pd.read_excel("E:/006 Projects/005 Dataset/fuzzygroup.xlsx", sheet="input")

dfinput.describe()
dfinput.head()

dfiCorrect = pd.read_excel("E:/006 Projects/005 Dataset/fuzzygroupcorrect.xlsx", 

sheet="Correct")

dfiCorrect.describe()
dfiCorrect.head()
dfiCorrect.columns
dfinput.columns

compare = pd.MultiIndex.from_product([dfinput['name'], dfiCorrect['TextMat']]).to_series()

def metrics(tup):
    return pd.Series([fuzz.ratio(*tup),                      

    fuzz.token_sort_ratio(*tup)],                     

    ['ratio', 'token'])

result = compare.apply(metrics).unstack().idxmax().unstack(0)
result.to_csv("E:/006 Projects/005 Dataset/output.csv")

using fuzzywuzzy python library we can generate the correct output with few lines of code.

Another way to write the same code which provide % of match which is additional information.

# second option
wrong_names=dfinput['name'].dropna().values
#Correct country names dataset
correct_names=dfiCorrect['TextMat'].values

names_array=[]
ratio_array=[]
def match_names(wrong_names,correct_names):
    for row in wrong_names:
        x=process.extractOne(row, correct_names)
        names_array.append(x[0])
        ratio_array.append(x[1])
    return names_array,ratio_array

name_match,ratio_match=match_names(wrong_names,correct_names)

dfinput['correct_country_name']=pd.Series(name_match)
dfinput['country_names_ratio']=pd.Series(ratio_match)
dfinput.columns

dfinput.to_csv("E:/006 Projects/005 Dataset/string_matched_country_names.csv")
result.to_csv("E:/006 Projects/005 Dataset/output.csv")

print(dfinput[['name','correct_country_name','country_names_ratio']].head(10))

Refer this screenshot