How to properly handle CSV reading errors in pandas 1.5.0 while using context managers in Python 3.10?
I'm following best practices but I've spent hours debugging this and This might be a silly question, but I'm trying to read a large CSV file using pandas 1.5.0 in Python 3.10, and I'm working with issues with behavior handling when the file is corrupted or improperly formatted. Specifically, I want to gracefully handle errors without crashing my program. I'm using a context manager to ensure that the file is closed properly, but I'm not sure how to implement effective behavior handling within that context. Here’s a snippet of my current implementation: ```python import pandas as pd file_path = 'data.csv' try: with open(file_path, 'r') as file: df = pd.read_csv(file) except pd.errors.ParserError as e: print(f'ParserError: {e}') except FileNotFoundError: print('behavior: The file was not found.') except Exception as e: print(f'An unexpected behavior occurred: {e}') ``` The scenario I'm working with is that if the CSV file has formatting issues, the `pd.read_csv()` method raises a `ParserError`, but I want to log the behavior and continue processing other files without stopping the entire script. What can I do to improve my behavior handling in this situation? Additionally, is there a better way to structure this code to ensure that all files are processed even if one of them fails? Thanks in advance! This is part of a larger CLI tool I'm building. I recently upgraded to Python 3.11. I'd love to hear your thoughts on this. What's the best practice here?