Python - Fuzzy matching result in new column for category based on ratio over 80

Question

I would like to scan a folder to pick up all the files end with '.txt' and then create a data frame by creating a new column for categorization with similar file names (partial score of ratio >=80)

import os
path = '../../../files'
text_files = [f for f in os.listdir(path) if f.endswith('.txt')]
text_files 

from fuzzywuzzy import fuzz
from fuzzywuzzy import process

s1 = "programmi.txt"
s2 = "programmi-2.txt"
fuzz.ratio(s1, s2)

The result I expect to see is like below:

Answer 1

Here's a solution which uses two for loops to compare each text to all the others to obtain the fuzz ratio needed for the categorisations.

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

txt_list = [
    "programmi.txt",
    "readl-001.txt",
    "dict_class124.txt",
    "readl-002.txt",
    "programmi-2.txt",
    "programmi-re.txt",
    "readl-003.txt",
    "dict_class125.txt",
    "dict_class1264.txt",
    "hello world"

]

list_categorised_texts = []
txt_category = []
category_index = 0
threshold = 80

# two for loops since we need to compare each text to all the others
for txt_1 in txt_list:

    if txt_1 not in list_categorised_texts:  # if the first text of the current pair is not yet categorised, add as new category
        category_index += 1
        list_categorised_texts.append(txt_1)
        txt_category.append(category_index)
        
    for txt_2 in txt_list:
        
        if txt_1 == txt_2:  # we don't want to compare the same texts
            continue
        
        elif txt_2 in list_categorised_texts:  # skip already classified texts
            continue

        else:  # if the txt_2 is similar, add to list of classified texts with corresponding category
            similarity = fuzz.ratio(txt_1, txt_2)
            if similarity >= threshold:
                list_categorised_texts.append(txt_2)
                txt_category.append(category_index)
        
            
data = {
    'texts': list_categorised_texts,
    'category': txt_category
}

df = pd.DataFrame(data)
print(df.to_markdown())

Result:

|    | texts              |   category |
|---:|:-------------------|-----------:|
|  0 | programmi.txt      |          1 |
|  1 | programmi-2.txt    |          1 |
|  2 | programmi-re.txt   |          1 |
|  3 | readl-001.txt      |          2 |
|  4 | readl-002.txt      |          2 |
|  5 | readl-003.txt      |          2 |
|  6 | dict_class124.txt  |          3 |
|  7 | dict_class125.txt  |          3 |
|  8 | dict_class1264.txt |          3 |
|  9 | hello world        |          4 |

Warning:

Please note that this approach has an order-dependency: In the example below, comparing dict_cl.txt to the other names only leads to one match, while comparing dict_class12.txt to all other names leads to 3 matches. For your use case, where we assume that each group is very distinct from each other, this should not be a problem. However, this example shows that pairwise comparisons are a bit tricky in more sophisticated situations.

print(fuzz.ratio('dict_cl.txt', 'dict_class125.txt'))  # 79 -> not same category
print(fuzz.ratio('dict_cl.txt', 'dict_class1264.txt'))  # 76 -> not same category
print(fuzz.ratio('dict_cl.txt', 'dict_class12.txt'))  # 81 -> same category
print("###")
print(fuzz.ratio('dict_class12.txt', 'dict_cl.txt'))  # 81 -> same category
print(fuzz.ratio('dict_class12.txt', 'dict_class125.txt'))  # 97 -> same category
print(fuzz.ratio('dict_class12.txt', 'dict_class1264.txt'))  # 94 -> same category