PERF: future_stack=True with non-MulitIndex columns

doc/source/whatsnew/v3.0.0.rst

@@ -622,6 +622,7 @@ Performance improvements
 - Performance improvement in :meth:`CategoricalDtype.update_dtype` when ``dtype`` is a :class:`CategoricalDtype` with non ``None`` categories and ordered (:issue:`59647`)
 - Performance improvement in :meth:`DataFrame.__getitem__` when ``key`` is a :class:`DataFrame` with many columns (:issue:`61010`)
 - Performance improvement in :meth:`DataFrame.astype` when converting to extension floating dtypes, e.g. "Float64" (:issue:`60066`)
+- 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:`DataFrame.where` when ``cond`` is a :class:`DataFrame` with many columns (:issue:`61010`)
 - 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`)

pandas/core/reshape/reshape.py

@@ -936,7 +936,20 @@ def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
         [k for k in range(frame.columns.nlevels - 1, -1, -1) if k not in set_levels]
     )
 
-    result = stack_reshape(frame, level, set_levels, stack_cols)
+    result: Series | DataFrame
+    if not isinstance(frame.columns, 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:
+            dtype = frame._mgr.blocks[0].dtype
+        else:
+            dtype = None
+        result = frame._constructor_sliced(
+            frame._values.reshape(-1, order="F"), dtype=dtype
+        )
+    else:
+        result = stack_reshape(frame, level, set_levels, stack_cols)
 
     # Construct the correct MultiIndex by combining the frame's index and
     # stacked columns.
@@ -1018,6 +1031,8 @@ def stack_reshape(
     -------
     The data of behind the stacked DataFrame.
     """
+    # non-MultIndex takes a fast path.
+    assert isinstance(frame.columns, MultiIndex)
     # If we need to drop `level` from columns, it needs to be in descending order
     drop_levnums = sorted(level, reverse=True)
 
@@ -1027,18 +1042,14 @@ def stack_reshape(
         if len(frame.columns) == 1:
             data = frame.copy(deep=False)
         else:
-            if not isinstance(frame.columns, MultiIndex) and not isinstance(idx, tuple):
-                # GH#57750 - if the frame is an Index with tuples, .loc below will fail
-                column_indexer = idx
-            else:
-                # Take the data from frame corresponding to this idx value
-                if len(level) == 1:
-                    idx = (idx,)
-                gen = iter(idx)
-                column_indexer = tuple(
-                    next(gen) if k in set_levels else slice(None)
-                    for k in range(frame.columns.nlevels)
-                )
+            # Take the data from frame corresponding to this idx value
+            if len(level) == 1:
+                idx = (idx,)
+            gen = iter(idx)
+            column_indexer = tuple(
+                next(gen) if k in set_levels else slice(None)
+                for k in range(frame.columns.nlevels)
+            )
             data = frame.loc[:, column_indexer]
 
         if len(level) < frame.columns.nlevels:

pandas/tests/extension/base/reshaping.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.dtypes import NumpyEADtype
+
 import pandas as pd
 import pandas._testing as tm
 from pandas.api.extensions import ExtensionArray
@@ -266,7 +268,13 @@ def test_stack(self, data, columns, future_stack):
         expected = expected.astype(object)
 
         if isinstance(expected, pd.Series):
-            assert result.dtype == df.iloc[:, 0].dtype
+            if future_stack and isinstance(data.dtype, NumpyEADtype):
+                # 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
+            else:
+                assert result.dtype == df.iloc[:, 0].dtype
         else:
             assert all(result.dtypes == df.iloc[:, 0].dtype)