how can I infer the candidate key from csv files

I have a folder with csv files with the following files: car.csv, person.csv, student.csv.... every files have columns.

I am trying to read the column values and put them inside a list.

this is my function:

import pandas
from itertools import chain, combinations
def key_options(items):
        print(7,items)
        return chain.from_iterable(combinations(items, r) for r in range(1, len(items)+1) )

def primary_key_discovery(data_path):
    csv_files = glob.glob(os.path.join(data_path, "*.csv"))
    for f in csv_files:
        dataframes = pd.read_csv(f)
        for candidate in key_options(list(dataframes)[1:]):
            deduped = dataframes.drop_duplicates(candidate)
            if len(deduped.index) == len(dataframes.index):
                print(f,','.join(candidate))
print(primary_key_discovery('Data1/'))

this function gives me the output like this Data1\vehicle.csv model,price,engine-size I want to print the column values for example model:camery,altima,aclass,....

Assuming you are storing your candidate keys in a list and not just printing the keys out, you can print out the unique values by flattening your list of list of keys, selecting the unique keys, and then selecting the unique values from your pandas array for that column.

Here is an example of how to flatten lists in python