BUG: Fix groupby.apply() dropping _metadata from subclassed DataFrame #62135
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When extending pandas.DataFrame by subclassing, most operations preserve the _metadata attributes. This fix ensures that groupby.apply() also preserves these fields, making it consistent with other groupby operations like groupby.sum(). Fixes pandas-dev#62134
rhshadrach
reviewed
Aug 18, 2025
- Update metadata preservation logic for DataFrames and Series in groupby operations - Fix DataFrame.__setitem__ with MultiIndex columns and scalar indexer - Adjust formatting and naming conventions in the code
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… scalar value - Address issue where name attribute was lost when setting new column with scalar value and tuple key in MultiIndex DataFrame - Implement check for tuple key and scalar value in MultiIndex DataFrame - Create Series with proper name to ensure name attribute matches the key - Update test case to assert Series name in scalar assignment with MultiIndex
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@rhshadrach Why this failed? Unit Tests / Linux-32-bit (pull_request) and Unit Tests / Pyodide build (pull_request) |
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Seems related to the change you're making here in |
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This pull request is stale because it has been open for thirty days with no activity. Please update and respond to this comment if you're still interested in working on this. |
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Thanks for the PR, closing as stale for now. If you'd like to continue, fixup the failing tests and happy to reopen! |
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When extending pandas.DataFrame by subclassing, most operations preserve the _metadata attributes. This fix ensures that groupby.apply() also preserves these fields, making it consistent with other groupby operations like groupby.sum().
Fixes #62134