How to Solve 'pandas.parser.CParserError: Error tokenizing data' When Reading CSV Files?

Handling "pandas.parser.CParserError: Error tokenizing data" When Reading CSV Files

The "pandas.parser.CParserError: Error tokenizing data" error occurs when pandas encounters an inconsistency in the number of fields in a CSV line. To resolve this error and ensure smooth data manipulation, consider the following:

1. Check for Coding Errors

Review your CSV file for any coding errors, such as missing field delimiters or incorrectly formatted values. Additionally, check if the file has the correct file extension (e.g., .csv).

2. Adjust CSV Delimiter

By default, pandas uses a comma as the delimiter for CSV files. However, if your CSV file uses a different delimiter (such as a semicolon), specify it using the delimiter parameter in read_csv().

3. Ignore Bad Lines

If you encounter a small number of problematic lines, you can instruct pandas to skip them while reading the CSV file. You can do this using the on_bad_lines='skip' parameter in read_csv().

4. Use the CSV Module

As an alternative to pandas, you can use the Python csv module to read and parse CSV files. This module provides more control over the parsing process, allowing you to handle errors or inconsistencies more flexibly.

Example:

To use the csv module, you can try the following code:

with open(path, 'r') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    data = list(csv_reader)

Additional Tips:

• For Pandas versions less than 1.3.0, use error_bad_lines=False to suppress the error.
• If you expect to encounter a significant number of bad lines, use on_bad_lines='warn' or a custom callable to handle them appropriately.
• Consider validating the CSV data before importing it into pandas to ensure its integrity.

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