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)

The other answer is wrong in a key respect - the inference that the result of process.extract was the same as fuzz.partial_ratio in one case, therefore they are doing the same thing by default.

process.extract actually uses WRatio() by default, which is a weighted combination of the four fuzz ratios. This is actually a cool functionality that empirically works pretty well across fuzzy matching scenarios.

Still, you can manually specify the string comparison function via the scorer argument to extract

Source for process.extract:https://github.com/seatgeek/fuzzywuzzy/blob/master/fuzzywuzzy/process.py

See the difference:

Your code:

test_var = print(process.extract(str_user_question, df_chat_bot["Question"], limit = 1))

Test code:

temp_var =       process.extract(test_user_question,df_chat_bot["Question"], limit = 1)

In your code, the process.extract() is wrapped in print(). The function print() always returns None and assigns that to test_var, which is why you're getting an error. Of course type(None) is NoneType.

If you want to both assign and print the return value of process.extract(...), you have to do them in separate statements, as is done in the second example.

its

process.extractOne(key, choices=alertname,scorer=fuzz.token_sort_ratio,limit=10)

you missed to captalize O