Parsing Nested CSV with Varying Column Counts in Python - working with IndexErrors
Can someone help me understand I'm trying to parse a nested CSV file where each row contains a variable number of columns, and some rows have nested lists as their last column. I'm using Python 3.10 with the built-in `csv` module for this. My CSV looks something like this: ```csv name,age,hobbies John,30,"['reading', 'gaming']" Jane,25,"['swimming']" Doe,40," "``` The last column is a string representation of a list, which poses a scenario in parsing. When I attempt to convert this column into an actual list, I run into an `IndexError` because not every row has the same number of elements. Here’s a snippet of what I’ve attempted: ```python import csv import ast with open('data.csv', newline='') as csvfile: reader = csv.reader(csvfile) for row in reader: try: name = row[0] age = int(row[1]) # Attempting to convert the last column string to list hobbies = ast.literal_eval(row[2]) if len(row) > 2 else [] print(name, age, hobbies) except IndexError: print('IndexError: row has fewer columns than expected') except (ValueError, SyntaxError) as e: print(f'behavior parsing hobbies: {e}') ``` I receive an `IndexError` sometimes when there are fewer columns than anticipated. I also get `SyntaxErrors` if the list in the last column is improperly formatted. I understand that this might be due to the CSV structure, but I need to predict how many columns each row will have. How can I robustly handle this situation to avoid errors and ensure I can still parse the data correctly? Any suggestions or examples would be greatly appreciated! Any pointers in the right direction?