BUG: DataFrame constructor reordering elements with ndarray from datetime dtype not datetime64[ns] (pandas-dev#39442)

Author: phofl

doc/source/whatsnew/v1.3.0.rst

@@ -274,6 +274,7 @@ Datetimelike
 - Bug in constructing a :class:`DataFrame` or :class:`Series` with mismatched ``datetime64`` data and ``timedelta64`` dtype, or vice-versa, failing to raise ``TypeError`` (:issue:`38575`, :issue:`38764`, :issue:`38792`)
 - Bug in constructing a :class:`Series` or :class:`DataFrame` with a ``datetime`` object out of bounds for ``datetime64[ns]`` dtype or a ``timedelta`` object out of bounds for ``timedelta64[ns]`` dtype (:issue:`38792`, :issue:`38965`)
 - Bug in :meth:`DatetimeIndex.intersection`, :meth:`DatetimeIndex.symmetric_difference`, :meth:`PeriodIndex.intersection`, :meth:`PeriodIndex.symmetric_difference` always returning object-dtype when operating with :class:`CategoricalIndex` (:issue:`38741`)
+- Bug in :class:`DataFrame` constructor reordering element when construction from datetime ndarray with dtype not ``"datetime64[ns]"`` (:issue:`39422`)
 - Bug in :meth:`Series.where` incorrectly casting ``datetime64`` values to ``int64`` (:issue:`37682`)
 - Bug in :class:`Categorical` incorrectly typecasting ``datetime`` object to ``Timestamp`` (:issue:`38878`)
 - Bug in comparisons between :class:`Timestamp` object and ``datetime64`` objects just outside the implementation bounds for nanosecond ``datetime64`` (:issue:`39221`)

pandas/_libs/tslibs/conversion.pyx

@@ -224,7 +224,7 @@ def ensure_datetime64ns(arr: ndarray, copy: bool=True):
 
     ivalues = arr.view(np.int64).ravel("K")
 
-    result = np.empty(shape, dtype=DT64NS_DTYPE)
+    result = np.empty_like(arr, dtype=DT64NS_DTYPE)
     iresult = result.ravel("K").view(np.int64)
 
     if len(iresult) == 0:

pandas/tests/frame/test_constructors.py

@@ -1762,6 +1762,70 @@ def test_constructor_datetimes_with_nulls(self, arr):
         expected = Series([np.dtype("datetime64[ns]")])
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize("order", ["K", "A", "C", "F"])
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "datetime64[M]",
+            "datetime64[D]",
+            "datetime64[h]",
+            "datetime64[m]",
+            "datetime64[s]",
+            "datetime64[ms]",
+            "datetime64[us]",
+            "datetime64[ns]",
+        ],
+    )
+    def test_constructor_datetimes_non_ns(self, order, dtype):
+        na = np.array(
+            [
+                ["2015-01-01", "2015-01-02", "2015-01-03"],
+                ["2017-01-01", "2017-01-02", "2017-02-03"],
+            ],
+            dtype=dtype,
+            order=order,
+        )
+        df = DataFrame(na)
+        expected = DataFrame(
+            [
+                ["2015-01-01", "2015-01-02", "2015-01-03"],
+                ["2017-01-01", "2017-01-02", "2017-02-03"],
+            ]
+        )
+        expected = expected.astype(dtype=dtype)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("order", ["K", "A", "C", "F"])
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "timedelta64[D]",
+            "timedelta64[h]",
+            "timedelta64[m]",
+            "timedelta64[s]",
+            "timedelta64[ms]",
+            "timedelta64[us]",
+            "timedelta64[ns]",
+        ],
+    )
+    def test_constructor_timedelta_non_ns(self, order, dtype):
+        na = np.array(
+            [
+                [np.timedelta64(1, "D"), np.timedelta64(2, "D")],
+                [np.timedelta64(4, "D"), np.timedelta64(5, "D")],
+            ],
+            dtype=dtype,
+            order=order,
+        )
+        df = DataFrame(na).astype("timedelta64[ns]")
+        expected = DataFrame(
+            [
+                [Timedelta(1, "D"), Timedelta(2, "D")],
+                [Timedelta(4, "D"), Timedelta(5, "D")],
+            ],
+        )
+        tm.assert_frame_equal(df, expected)
+
     def test_constructor_for_list_with_dtypes(self):
         # test list of lists/ndarrays
         df = DataFrame([np.arange(5) for x in range(5)])