whatsnew

Author: rhshadrach

doc/source/whatsnew/v3.0.0.rst

@@ -432,6 +432,7 @@ Performance improvements
 - Performance improvement in :meth:`RangeIndex.reindex` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57647`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.take` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57445`, :issue:`57752`)
 - Performance improvement in :func:`merge` if hash-join can be used (:issue:`57970`)
+- Performance improvement in :meth:`DataFrame.stack` when using ``future_stack=True`` and the DataFrame does not have a :class:`MultiIndex` (:issue:`58391`)
 - Performance improvement in :meth:`to_hdf` avoid unnecessary reopenings of the HDF5 file to speedup data addition to files with a very large number of groups . (:issue:`58248`)
 - Performance improvement in ``DataFrameGroupBy.__len__`` and ``SeriesGroupBy.__len__`` (:issue:`57595`)
 - Performance improvement in indexing operations for string dtypes (:issue:`56997`)

pandas/core/reshape/reshape.py

@@ -938,7 +938,7 @@ def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
 
     result: Series | DataFrame
     if not isinstance(frame.columns, MultiIndex):
-        # Fast path when we're stacking the columns of a non-MultiIndex.
+        # GH#58817 Fast path when we're stacking the columns of a non-MultiIndex.
         # When columns are homogeneous EAs, we pass through object
         # dtype but this is still slightly faster than the normal path.
         if len(frame.columns) > 0 and frame._is_homogeneous_type:

pandas/tests/extension/base/reshaping.py

@@ -269,7 +269,7 @@ def test_stack(self, data, columns, future_stack):
 
         if isinstance(expected, pd.Series):
             if future_stack and isinstance(data.dtype, NumpyEADtype):
-                # future_stack=True constructs the result specifying the dtype
+                # GH#58817 future_stack=True constructs the result specifying the dtype
                 # using the dtype of the input; we thus get the underlying
                 # NumPy dtype as the result instead of the NumpyExtensionArray
                 assert result.dtype == df.iloc[:, 0].to_numpy().dtype