Resolving the ValueError while Calculating Age from Polish PESEL in Python Pandas

Discover how to fix the `ValueError` when calculating age based on Polish PESEL numbers in your Python Pandas DataFrame. Learn to identify and handle bad formatted data efficiently.
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Understanding the Problem: Calculating Age Based on Polish PESEL

Calculating age from a series of strings representing PESEL (Polish national identification number) is a common operation you might encounter when dealing with demographic datasets. However, a user encountered a ValueError while attempting to convert these string values into dates in Python's Pandas library. This problem typically arises from bad formatted rows that don't conform to expectations.

The user provided a DataFrame containing PESEL numbers and their approach to calculate age, which results in an error when processed on a larger dataset with over 400,000 rows. The goal here is to resolve this issue and allow for successful age calculations without manually verifying each entry.

The Issue at Hand

The error message received was:

[[See Video to Reveal this Text or Code Snippet]]

This occurs while attempting to convert strings to datetime objects using the provided format. The user's logic involves slicing the first six characters of each PESEL number (which represent the birth date) and converting them to actual date objects.

Sample Code Provided

Here's the relevant code the user utilized:

[[See Video to Reveal this Text or Code Snippet]]

Understanding the Cause of the Error

The issue arises primarily due to discrepancies in the format of some PESEL entries. While the majority may follow the expected YYMMDD format, there might be entries that deviate from this syntax, causing Pandas to throw an error upon encountering unexpected characters.

In the provided code, errors appear when your specific slice df.NR.str[:6] includes a bad formatted value that prevents successful conversion to datetime format.

The Solution: Identifying and Handling Bad Formatted Rows

Steps to Identify Bad Formatted Rows

Here’s how you could identify the bad formatted rows:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

When you run the above code, you should see the entries that couldn’t be converted:

[[See Video to Reveal this Text or Code Snippet]]

This effectively helps you pinpoint which entries in your DataFrame are causing the issues.

Next Steps After Identification

Once you have identified the problematic entries, you can choose to:

Remove these rows if they are invalid.

Correct manually if it is feasible.

Provide them with a default value or handle them according to your data-cleaning policy.

Conclusion

By implementing the above modifications, you can successfully identify and handle rows with bad formatted PESEL entries, allowing your age calculation code to function correctly across even the largest DataFrames.

Whether you're handling personal data for statistical analysis or simply processing records, these techniques will enhance your data management skills in Python Pandas.

If you encounter further difficulties or have questions on related topics, feel free to ask!