Backport PR #42475: PERF/REGR: astype changing order of some 2d data

doc/source/whatsnew/v1.3.1.rst

@@ -18,6 +18,8 @@ Fixed regressions
 - :class:`DataFrame` constructed with an older version of pandas could not be unpickled (:issue:`42345`)
 - Performance regression in constructing a :class:`DataFrame` from a dictionary of dictionaries (:issue:`42248`)
 - Fixed regression in :meth:`DataFrame.agg` dropping values when the DataFrame had an Extension Array dtype, a duplicate index, and ``axis=1`` (:issue:`42380`)
+- Fixed regression in :meth:`DataFrame.astype` changing the order of noncontiguous data (:issue:`42396`)
+- Performance regression in :class:`DataFrame` in reduction operations requiring casting such as :meth:`DataFrame.mean` on integer data (:issue:`38592`)
 - Performance regression in :meth:`DataFrame.to_dict` and :meth:`Series.to_dict` when ``orient`` argument one of "records", "dict", or "split" (:issue:`42352`)
 - Fixed regression in indexing with a ``list`` subclass incorrectly raising ``TypeError`` (:issue:`42433`, :issue:`42461`)
 -

pandas/core/dtypes/cast.py

@@ -92,8 +92,6 @@
 )
 
 if TYPE_CHECKING:
-    from typing import Literal
-
     from pandas.core.arrays import (
         DatetimeArray,
         ExtensionArray,
@@ -1093,14 +1091,11 @@ def astype_nansafe(
         The dtype was a datetime64/timedelta64 dtype, but it had no unit.
     """
     if arr.ndim > 1:
-        # Make sure we are doing non-copy ravel and reshape.
-        flags = arr.flags
-        flat = arr.ravel("K")
+        flat = arr.ravel()
         result = astype_nansafe(flat, dtype, copy=copy, skipna=skipna)
-        order: Literal["C", "F"] = "F" if flags.f_contiguous else "C"
         # error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
         # attribute "reshape"
-        return result.reshape(arr.shape, order=order)  # type: ignore[union-attr]
+        return result.reshape(arr.shape)  # type: ignore[union-attr]
 
     # We get here with 0-dim from sparse
     arr = np.atleast_1d(arr)

pandas/tests/frame/methods/test_astype.py

@@ -670,6 +670,26 @@ def test_astype_bytes(self):
         result = DataFrame(["foo", "bar", "baz"]).astype(bytes)
         assert result.dtypes[0] == np.dtype("S3")
 
+    @pytest.mark.parametrize(
+        "index_slice",
+        [
+            np.s_[:2, :2],
+            np.s_[:1, :2],
+            np.s_[:2, :1],
+            np.s_[::2, ::2],
+            np.s_[::1, ::2],
+            np.s_[::2, ::1],
+        ],
+    )
+    def test_astype_noncontiguous(self, index_slice):
+        # GH#42396
+        data = np.arange(16).reshape(4, 4)
+        df = DataFrame(data)
+
+        result = df.iloc[index_slice].astype("int16")
+        expected = df.iloc[index_slice]
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
 
 class TestAstypeCategorical:
     def test_astype_from_categorical3(self):