Traceback (most recent call last)

import pandas as pd
import os
import time
from mlxtend.frequent_patterns import apriori, association_rules
from mlxtend.preprocessing import TransactionEncoder
 
def main():
    start_time=time.time()
    file_name='dataset.csv'
    df = pd.read_csv(file_name, names=['products'], sep=',', header=None)
    rows_count=len(df)
    data=list(df["products"].apply(lambda x: x.split(",")))
    a=TransactionEncoder()
    a_data=a.fit(data).transform(data)
    df=pd.DataFrame(a_data, columns=a.columns_)
    df=df.replace(False, 0)
    df=df.replace(True, 1)
    min_support=0.3
    df=apriori(df, min_support=min_support, use_colnames=True, verbose=1)
    print('\nItems')
    print(f'Min Support: {min_support}')
    print(df.sort_values('support', ascending=False)[df.columns[::-1]].to_string(index=False))
    min_confidence=0.6
    df_ar=association_rules(df, metric='confidence', min_threshold=min_confidence)
    sort_args=['confidence','support']
    print('\nRules')
    print(f'Min Confidence: {min_confidence}')
    print(f'Order By: {sort_args[0]} & {sort_args[1]}')
    df_ar=df_ar.sort_values(sort_args, ascending=[False, False])
    print(df_ar.to_string(index=False))
    print('\nReport:')
    print(f'Time (sec): {(time.time()-start_time)}')
    print(f'File: {file_name}')
    print(f'File size (bytes): {os.path.getsize(file_name)}')
    print(f'Rows in file:{rows_count}')
    print(f'Totally rules:{len(df_ar)}')
 
if __name__=='__main__':
    main()

...
    df = pd.read_csv(file_name, header=None, sep='\n')
    df = df[0].str.split(',', expand=True)
...

...
def main():
    start_time=time.time()
    file_name='dataset.csv'
    df = pd.read_csv(file_name, header=None, sep='\r')
    #df = df[0].str.split(',', expand=True)
    
    print(df)
...

import pandas as pd
#import os
import time
#from mlxtend.frequent_patterns import apriori, association_rules
#from mlxtend.preprocessing import TransactionEncoder
 
pd.set_option('display.max_rows',None)
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.max_columns',None)
def main():
    start_time=time.time()
    file_name='dataset.csv'
    df = pd.read_csv(file_name, header=None, sep='\r')
    #df = df[0].str.split(',', expand=True)
    data = df[0].map(lambda x: x.split(",")).to_list()
    print(data)