Pandas read_csv: implementing inconsistent number of columns across rows in a large CSV file
I'm building a feature where I just started working with I'm wondering if anyone has experience with I'm trying to read a large CSV file using `pandas.read_csv()` but I'm working with issues due to inconsistent numbers of columns across different rows. The CSV file has a header, but several rows contain missing data which leads to a `ParserError`. I've already set `error_bad_lines=False` to skip problematic lines, but I'm still working with issues with data alignment in my DataFrame. Here’s the code I'm currently using: ```python import pandas as pd file_path = 'path/to/your/file.csv' data = pd.read_csv(file_path, error_bad_lines=False) print(data.head()) ``` The CSV file is quite large (over 10 million rows), and I noticed that some rows have extra commas which might be causing additional confusion. When I run the code, I see warnings like: ``` Skipping line 12345: expected 5 fields, saw 6 ``` I need to ensure that my DataFrame retains as much relevant data as possible without losing critical information. What strategies can I employ to handle this situation, especially for rows that have missing data or extra delimiters? Is there a way to preprocess the CSV file to standardize the row lengths before loading it into a DataFrame? I'm working on a web app that needs to handle this. I appreciate any insights! Hoping someone can shed some light on this.