👀 Views: 1478 💬 Answers: 1 📅 Created: 2025-06-14

pandas csv unicode data-cleaning error-handling Python

I'm experimenting with I'm relatively new to this, so bear with me. This might be a silly question, but I'm trying to read a CSV file using Pandas' `read_csv` function, but I'm working with a `UnicodeDecodeError`. The CSV file is expected to be in UTF-8 encoding, but when I attempt to read it, I get the following behavior: ``` UnicodeDecodeError: 'utf-8' codec need to decode byte 0x80 in position 24: invalid start byte ``` I've ensured that I specify the correct encoding like this: ```python import pandas as pd df = pd.read_csv('myfile.csv', encoding='utf-8') ``` However, this doesn't resolve the scenario. I also tried using `encoding='latin1'`, but that led to unexpected characters appearing in the DataFrame. The CSV file seems to have been generated by an external system, and I suspect that there may be some malformed bytes that are causing the decoding issues. I've also experimented with `errors='replace'` and `errors='ignore'`, but those approaches either replaced important data or skipped crucial rows altogether. Here's the function that I've been using for reading the file: ```python def load_data(file_path): try: df = pd.read_csv(file_path, encoding='utf-8') return df except UnicodeDecodeError as e: print(f'Decoding behavior: {e}') return None ``` Is there a reliable way to handle this situation without losing data integrity? Should I inspect the file for specific problematic bytes, or is there a better way to read a potentially corrupted CSV file with mixed encodings? I'm working on a application that needs to handle this. This is my first time working with Python LTS. Is there a better approach? What's the best practice here? Any examples would be super helpful. I'm working in a Debian environment. Any examples would be super helpful.