Скачать или смотреть How to Use apply and transform with Pandas GroupBy for Multi-Index DataFrames

Learn how to correctly use `apply` and `transform` methods in Pandas when working with multi-index DataFrames to prevent unexpected NaN values.
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This video is based on the question https://stackoverflow.com/q/64580509/ asked by the user 'TFC' ( https://stackoverflow.com/u/12757690/ ) and on the answer https://stackoverflow.com/a/64580568/ provided by the user 'Scott Boston' ( https://stackoverflow.com/u/6361531/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Handling NaN Values when Adding New Columns in Multi-Index Pandas DataFrames

When working with multi-index DataFrames in Pandas, we might often find ourselves needing to apply certain operations to group data. One common problem that comes up is trying to create a new column by applying a function to a grouped DataFrame, but instead of getting the desired results, we end up with unexpected NaN values. In this post, we will break down how to resolve this issue effectively.

The Problem: Unexpected NaN Column Values

Consider the following scenario: you have a DataFrame with a multi-index based on some criteria (in this case, class), and you want to concatenate the values of the height column for each group into a new column. The operation involves using a custom function that concatenates those height values as strings.

Here’s a sample DataFrame to illustrate the issue:

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

With a custom function defined:

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

When trying to apply this operation using apply, the resultant DataFrame throws unexpected NaN values in the new column:

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

The Solution: Use transform Instead of apply

The key to solving the issue of NaN values is to utilize the transform method rather than apply. The transform method returns a DataFrame that is the same size as the original, enabling you to keep your DataFrame's structure while applying your function.

Here’s how to do it correctly:

Use transform for Group Operations:

By replacing apply with transform, you would write:

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

Expected Output:

Using this method, the output of your DataFrame will align correctly:

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

Alternative: Dropping Duplicates

If you want to ensure you don’t fill rows with duplicate values (specifically if you want NaN for non-header rows), you can also follow this approach:

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

Output After Dropping Duplicates:

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

Conclusion

In summary, if you're working with multi-index DataFrames in Pandas and you encounter NaN values when trying to add new columns, remember to use the transform method instead of apply. This will keep the original DataFrame structure intact and provide you with the results you desire without unexpected NaNs. Happy coding!