REGR: DataFrame.transpose resulting in not contiguous data on nullable EAs

Author: rhshadrach

doc/source/whatsnew/v2.2.1.rst

@@ -30,6 +30,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.sort_index` not producing a stable sort for a index with duplicates (:issue:`57151`)
 - Fixed regression in :meth:`DataFrame.to_dict` with ``orient='list'`` and datetime or timedelta types returning integers (:issue:`54824`)
 - Fixed regression in :meth:`DataFrame.to_json` converting nullable integers to floats (:issue:`57224`)
+- Fixed regression in :meth:`DataFrame.transpose` with nullable extension dtypes not having F-contiguous data potentially causing exceptions when used (:issue:`57315`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` ignoring the ``skipna`` argument (:issue:`57040`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` where values containing the minimum or maximum value for the dtype could produce incorrect results (:issue:`57040`)
 - Fixed regression in :meth:`ExtensionArray.to_numpy` raising for non-numeric masked dtypes (:issue:`56991`)

pandas/core/arrays/masked.py

@@ -1661,7 +1661,9 @@ def transpose_homogeneous_masked_arrays(
     arr_type = dtype.construct_array_type()
     transposed_arrays: list[BaseMaskedArray] = []
     for i in range(transposed_values.shape[1]):
-        transposed_arr = arr_type(transposed_values[:, i], mask=transposed_masks[:, i])
+        transposed_arr = arr_type(
+            transposed_values[:, i].copy(), mask=transposed_masks[:, i].copy()
+        )
         transposed_arrays.append(transposed_arr)
 
     return transposed_arrays

pandas/tests/frame/methods/test_transpose.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pytest
 
+import pandas as pd
 from pandas import (
     DataFrame,
     DatetimeIndex,
@@ -179,3 +180,19 @@ def test_transpose_not_inferring_dt_mixed_blocks(self):
             dtype=object,
         )
         tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype1", ["Int64", "Float64"])
+    @pytest.mark.parametrize("dtype2", ["Int64", "Float64"])
+    def test_transpose(self, dtype1, dtype2):
+        # GH#57315 - transpose should have C/F contiguous blocks
+        df = DataFrame(
+            {
+                "a": pd.array([1, 1, 2], dtype=dtype1),
+                "b": pd.array([3, 4, 5], dtype=dtype2),
+            }
+        )
+        result = df.T
+        for blk in result._mgr.blocks:
+            # When dtypes are unequal, we get NumPy object array
+            data = blk.values._data if dtype1 == dtype2 else blk.values
+            assert data.flags["F_CONTIGUOUS"]