Match dataframe column with values in list with fuzzy matching and add matched values to new column

I have two dataframes:

df = pd.DataFrame({'company name':['apple inc', 'google llc', 'netflix, inc', 'facebook', 'shopify incorporated', 'amazon.com'],
                   'employees':[25000, 50000, 25000, 45000, 15000, 50000]})

ticker_lookup = pd.DataFrame({'standard company name':['EBay', 'Etsy', 'Apple', 'Google', 'Netflix', 'Facebook', 'Shopify', 'Amazon'],
                              'ticker':['ebay', 'etsy', 'aapl', 'googl', 'nflx', 'fb', 'shop', 'amzn']})

I want to perform a fuzzy match on df['company name'] to match the values in ticker_lookup['standard company name'] so that I can bring in the ticker symbols into df. I would also like the standard company names too.

I have chosen RapidFuzz for this task, but I suppose FuzzyWuzzy would work too. Using RapidFuzz, I wrote the following code:

import pandas as pd
from rapidfuzz.process import extractOne
from rapidfuzz.fuzz import ratio

# Get list of lookup values from ticker_lookup, and create empty list to append results
lookup_list = list(ticker_lookup['standard company name'])
matched_values = []

# For each value in 'company name', run rapidfuzz's extractOne module to perform the fuzzy matching
# This results in a list of tuples each containing: matched value, similarity score, and position of match in list
for i in list(df['company name']):
    matched_values.append(extractOne(i, lookup_list))

# Store results in a DataFrame
matched_df = pd.DataFrame(matched_values, columns=['standard company name','similarity score', 'index in list'])

# Concat results with original DataFrame
result = pd.concat([df, matched_df], axis=1)

While this method works, my actual dataframe contains more than 100K records, and the lookup list is around 3K so this process takes a very long time. I am wondering if there is another approach that is more condensed, such that I can perform the fuzzy matching and append the results directly to df within a for loop.

Any suggestions would be greatly appreciated!

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