fix: handle non-numpy dtypes in PandasIndex.concat() and join()#11409
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fix: handle non-numpy dtypes in PandasIndex.concat() and join()#11409C1-BA-B1-F3 wants to merge 3 commits into
C1-BA-B1-F3 wants to merge 3 commits into
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Problem: When a MultiIndex level contains tuple-valued entries (e.g., (1,1)), selecting with a nested tuple key like ((1,1), 2) incorrectly preserved the dimension instead of collapsing it to a scalar result. Root cause: _is_nested_tuple() was checking for 'tuple' in addition to 'list' and 'slice', which caused it to misidentify tuple-valued keys as nested selection tuples. Fix: Remove 'tuple' from the isinstance check in _is_nested_tuple() so that only 'list' and 'slice' are treated as indicators of nested selections. Tuple- valued keys in MultiIndex levels are now correctly handled as scalar key values. Added regression test for selecting with nested tuple keys on MultiIndex with tuple-valued levels.
When concatenating indexes with mixed string types (e.g., numpy string dtype and pandas StringDtype), np.result_type() fails because it cannot interpret extension dtypes. This fix checks if all dtypes are valid numpy dtypes before calling np.result_type(), falling back to object dtype if not. Fixes GH#11317
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Description
This PR fixes an issue where
concatfails when mixing string coordinates from different sources (e.g., numpy string dtype and pandas StringDtype).Problem
When concatenating DataArrays where one has a numpy string dtype coordinate and another has a pandas StringDtype coordinate (introduced by pd.Index in pandas 3.0), np.result_type() fails with:
TypeError: Cannot interpret '' as a data type
Root Cause
In PandasIndex.concat() and PandasIndex.join(), np.result_type() is called with coordinate dtypes without checking if they are valid numpy dtypes first. Pandas extension dtypes (like StringDtype) are not valid numpy dtypes.
Fix
Fixes GH#11317
Tests