Parsing CSV with Variable Number of Columns in Python - working with IndexError While Accessing Data
I'm refactoring my project and I'm working through a tutorial and I've tried everything I can think of but I'm trying to parse a CSV file in Python using the `csv` module, but I'm working with an `IndexError` when trying to access certain columns. The CSV I'm working with has a variable number of columns for different rows, as it's generated from a reporting tool that sometimes omits certain data points. Here's a snippet of the CSV: ``` Name, Age, Location Alice, 30, New York Bob, 25 Charlie, 35, San Francisco, Developer ``` As you can see, the second row is missing the 'Location' column, which is causing issues when I try to access it directly. I've implemented the parsing like this: ```python import csv with open('data.csv', newline='') as csvfile: reader = csv.reader(csvfile) for row in reader: print(row[2]) # Trying to access the Location column ``` When I run this code, I get an `IndexError: list index out of range` for the second row. I've tried adding a conditional check to see if the length of the row is sufficient: ```python if len(row) > 2: print(row[2]) ``` This prevents the behavior, but now I’m wondering if there’s a more robust way to handle varying columns without hardcoding indices. Ideally, I’d like to extract each column by name for better readability and maintainability. What is the best practice for dynamically handling CSV files with variable column counts in Python? Should I consider using a library like `pandas`, or is there a way to improve my current approach with the `csv` module? This is part of a larger web app I'm building. For context: I'm using Python on Debian. For context: I'm using Python on macOS. How would you solve this?