Below, a script is provided that reads a CSV file and translate the outcome into a dataframe:
import csv import pandas as pd with open(r"C:\Users\tomva\SynologyDrive\python\pandas\Incomplete\banking.csv","r") as csv_file: csv_reader = csv.reader(csv_file, delimiter = ',') print(type(csv_reader)) df = pd.DataFrame() i = 0 for row in csv_reader: if i == 0: cols = row if i > 0: for j in range(len(row)): df.loc[i, j] = row[j] if i%1000 == 0: print(i) i = i + 1 print(cols) df = df.rename(columns={0: 'age', 1: 'job', 2: 'marital', 3: 'education', 4: 'default', 5: 'housing', 6: 'loan', 7: 'contact', 8: 'month', 9: 'day_of_week', 10: 'duration', 11: 'campaign', 12: 'pdays', 13: 'previous', 14: 'poutcome', 15: 'emp_var_rate', 16: 'cons_price_idx', 17: 'cons_conf_idx', 18: 'euribor3m', 19: 'nr_employed', 20: 'y'}) df.info() print('********* Klaar ********************')