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:
import pandas as pd
from pandas import DataFrame
from fuzzywuzzy import process
import csv
save_file = open('fuzzy_match_results.csv', 'w')
writer = csv.writer(save_file, lineterminator = '\n')
def parse_csv(path):
with open(path,'r') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
yield row
if __name__ == "__main__":
## Create lookup dictionary by parsing the products csv
data = {}
for row in parse_csv('names_1.csv'):
data[row[0]] = row[0]
## For each row in the lookup compute the partial ratio
for row in parse_csv("names_2.csv"):
#print(process.extract(row,data, limit = 100))
for found, score, matchrow in process.extract(row, data, limit=100):
if score >= 60:
print('%d%% partial match: "%s" with "%s" ' % (score, row, found))
Digi_Results = [row, score, found]
writer.writerow(Digi_Results)
save_file.close()
The output is as follows:
Name11 , 90 , Name25
Name11 , 85 , Name24
Name11 , 65 , Name29
The script works fine. The output is as expected. But what I am looking for is only the best match.
Name11 , 90 , Name25
Name12 , 95 , Name21
Name13 , 98 , Name22
So I need to somehow drop the duplicated names in column 1, based on the highest value in column 2. It should be fairly straightforward, but I can't seem to figure it out. Any help would be appreciated.
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)