👀 Views: 94 💬 Answers: 1 📅 Created: 2025-06-16

pandas csv data-cleaning Python

I tried several approaches but none seem to work... Hey everyone, I'm running into an issue that's driving me crazy. I'm trying to read a large CSV file using the Pandas library (version 1.3.3) in Python 3.9, but I keep working with a `ParserError: behavior tokenizing data`... The file is about 500MB and has a mix of numeric and string data. I suspect there might be some malformed rows or inconsistent delimiters, but I'm not sure how to handle this without losing data. I've tried using the `error_bad_lines=False` argument in `pd.read_csv()`, but it doesn't seem to resolve the scenario completely. Here’s the code I’m currently using: ```python import pandas as pd file_path = 'path/to/large_file.csv' data = pd.read_csv(file_path, error_bad_lines=False) ``` When I run this, I still see a lot of warning messages indicating that some lines are problematic, but the reading process still fails. Additionally, I tried setting `delimiter=';'`, as I thought maybe the file has inconsistent delimiters, but that resulted in another behavior: `ParserError: Expected 10 fields in line 15, saw 11`. I've also considered using `chunksize` to read the file in smaller parts, but I'm not sure how to effectively combine them back together while ensuring data integrity. Any recommendations on how to efficiently read this large CSV file while handling potential parsing issues would be greatly appreciated! This is part of a larger CLI tool I'm building. Thanks for your help in advance! The project is a microservice built with Python. Has anyone dealt with something similar? I appreciate any insights!