Understanding How to Set Columns with Substrings in Pandas DataFrames

Learn how to extract and assign substring values from one column to another in a Pandas DataFrame using specific boolean masks.
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Understanding How to Set Columns with Substrings in Pandas DataFrames

When working with Pandas, it’s common to extract substrings from one column and assign them to another column based on certain conditions. However, as users sometimes discover, there can be nuances that lead to unexpected results, such as NaN values appearing where valid data should be. In this article, we'll dive into a common pitfall when trying to set a new column in a DataFrame and how to resolve it.

The Problem

A user recently faced the issue while trying to create a new column based on the extraction of substrings from another column, using a boolean mask for filtering. Here's a summary of the scenario:

The user wants to create a new column called derived_col based on the contents of base_col, specifically extracting values associated with 'key=' from rows where type is 'A'.

Despite having correct boolean masking and substring extraction, the resulting derived_col was filled with NaN values instead of the expected substring values.

Code Illustrating the Issue

Here's the initial code provided by the user that demonstrates the problem:

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

Output Problem

The output of the code resulted in all derived_col being NaN:

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

This was unexpected since the derived_col should have contained the string 'val' for rows where type was 'A'.

The Solution

The root cause of this issue lies in how the Pandas DataFrame handles the assignment of values when extracting substrings. Specifically, the result of the substring extraction needs to be indexed correctly.

Correcting the Code

The adjustment needed is to explicitly select the first column of the extracted result, as shown below:

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

Conclusion

With this simple change—the addition of [0] to the extraction step—the derived_col is populated correctly. Now, when we run the corrected code, the output is as expected:

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

Key Takeaways

Using boolean masks effectively allows for precise filtering of data, making it easier to manipulate and derive new columns as needed.

By understanding these concepts, you can avoid common pitfalls and work more efficiently with Pandas DataFrames. Happy coding!