Parsing Bad Lines in CSV Files: A Practical Guide with Python

Parsing CSV Files with Bad Lines and Log Line Numbers in Python

As a technical blogger, I often come across questions from developers who are struggling to parse CSV files that contain bad data. In this article, we will explore how to use the pandas library to read CSV files with bad lines and extract the line numbers of the bad lines.

Introduction to Bad Lines in CSV Files

A “bad line” in a CSV file refers to a line that does not conform to the expected format. This can be due to various reasons such as missing or extra fields, incorrect field types, etc. When we read a CSV file using pandas, it throws an error for each bad line.

Using `error_bad_lines` and `warn_bad_lines` Parameters

The error_bad_lines and warn_bad_lines parameters in the read_csv function are used to control how pandas handles bad lines. By default, if error_bad_lines is set to True, pandas will raise an error for each bad line. If warn_bad_lines is set to True, it will print a warning message instead.

In the provided Stack Overflow question, the developer has set both parameters to False and used the redirect_stderr function from the contextlib module to redirect the output of the warning message to a string buffer. This allows us to extract the warning messages and process them further.

Extracting Warning Messages

The warning messages are stored in the f object, which is an instance of the StringIO class. We can extract the contents of this object using the getvalue method.

f = io.StringIO()
with redirect_stderr(f):
    df = pd.read_csv(fname, sep=',', error_bad_lines=False, warn_bad_lines=True)

if f.getvalue():
    msg = f.getvalue()

Processing Warning Messages

The warning message is a string that contains the line numbers of the bad lines. We can use regular expressions to extract these line numbers.

import re
regex = re.compile('line ([0-9]*)')
print(regex.findall(msg))

This will output ['Skipping line 4: expected 85 fields, saw 86', 'Skipping line 6: expected 85 fields, saw 101'].

Custom Write Log Method

The developer wants to write individual entries in the log with string replacement and splitting. We can use a custom method to achieve this.

def write_log(msg_list):
    for i, msg in enumerate(msg_list):
        print(f"Warning {i+1}: {msg}")

msg_list = msg.replace('b\'', '').replace('\'', '').split('\n')
write_log(msg_list)

This will output:

Warning 1: Skipping line 4: expected 85 fields, saw 86
Warning 2: Skipping line 6: expected 85 fields, saw 101

Getting Line Numbers

Finally, the developer wants to get the line numbers of the bad lines without including the warning messages. We can use a regular expression to extract only the line numbers.

import re
regex = re.compile('line ([0-9]*)')
print(regex.findall(msg))

This will output ['4', '6'].

Conclusion

In this article, we explored how to parse CSV files with bad lines and extract the line numbers of the bad lines. We used the pandas library to read the CSV file, the redirect_stderr function to redirect the output of the warning message, and regular expressions to extract the line numbers.

Example Use Cases

Reading a CSV file that contains bad data
Extracting line numbers from a log file
Processing warning messages in a custom way

Last modified on 2024-11-24