[ArrayManager] DataFrame constructor from ndarray

pandas/core/internals/construction.py

@@ -30,9 +30,11 @@
     maybe_convert_platform,
     maybe_infer_to_datetimelike,
     maybe_upcast,
+    sanitize_to_nanoseconds,
 )
 from pandas.core.dtypes.common import (
     is_datetime64tz_dtype,
+    is_datetime_or_timedelta_dtype,
     is_dtype_equal,
     is_extension_array_dtype,
     is_integer_dtype,
@@ -58,6 +60,7 @@
     DatetimeArray,
 )
 from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
     extract_array,
     sanitize_array,
 )
@@ -304,10 +307,33 @@ def ndarray_to_mgr(
     index, columns = _get_axes(
         values.shape[0], values.shape[1], index=index, columns=columns
     )
-    values = values.T
 
     _check_values_indices_shape_match(values, index, columns)
 
+    if typ == "array":
+
+        values = sanitize_to_nanoseconds(values)
+        if issubclass(values.dtype.type, str):
+            values = np.array(values, dtype=object)
+
+        if dtype is None and is_object_dtype(values.dtype):
+            arrays = [
+                ensure_wrapped_if_datetimelike(
+                    maybe_infer_to_datetimelike(values[:, i].copy())
+                )
+                for i in range(values.shape[1])
+            ]
+        elif is_datetime_or_timedelta_dtype(values.dtype):
+            arrays = [
+                ensure_wrapped_if_datetimelike(values[:, i].copy())
+                for i in range(values.shape[1])
+            ]
+        else:
+            arrays = [values[:, i].copy() for i in range(values.shape[1])]
+        return ArrayManager(arrays, [index, columns], verify_integrity=False)
+
+    values = values.T
+
     # if we don't have a dtype specified, then try to convert objects
     # on the entire block; this is to convert if we have datetimelike's
     # embedded in an object type
@@ -349,13 +375,13 @@ def _check_values_indices_shape_match(
     Check that the shape implied by our axes matches the actual shape of the
     data.
     """
-    if values.shape[0] != len(columns):
+    if values.shape[1] != len(columns) or values.shape[0] != len(index):
         # Could let this raise in Block constructor, but we get a more
         #  helpful exception message this way.
-        if values.shape[1] == 0:
+        if values.shape[0] == 0:
             raise ValueError("Empty data passed with indices specified.")
 
-        passed = values.T.shape
+        passed = values.shape
         implied = (len(index), len(columns))
         raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
 

pandas/tests/frame/methods/test_astype.py

@@ -428,11 +428,23 @@ def test_astype_to_incorrect_datetimelike(self, unit):
         other = f"m8[{unit}]"
 
         df = DataFrame(np.array([[1, 2, 3]], dtype=dtype))
-        msg = fr"Cannot cast DatetimeArray to dtype timedelta64\[{unit}\]"
+        msg = "|".join(
+            [
+                fr"Cannot cast DatetimeArray to dtype timedelta64\[{unit}\]",
+                "cannot astype a datetimelike from "
+                fr"\[datetime64\[ns\]\] to \[timedelta64\[{unit}\]\]",
+            ]
+        )
         with pytest.raises(TypeError, match=msg):
             df.astype(other)
 
-        msg = fr"Cannot cast TimedeltaArray to dtype datetime64\[{unit}\]"
+        msg = "|".join(
+            [
+                fr"Cannot cast TimedeltaArray to dtype datetime64\[{unit}\]",
+                "cannot astype a timedelta from "
+                fr"\[timedelta64\[ns\]\] to \[datetime64\[{unit}\]\]",
+            ]
+        )
         df = DataFrame(np.array([[1, 2, 3]], dtype=other))
         with pytest.raises(TypeError, match=msg):
             df.astype(dtype)

pandas/tests/frame/test_constructors.py

@@ -46,6 +46,7 @@
 )
 import pandas._testing as tm
 from pandas.arrays import (
+    DatetimeArray,
     IntervalArray,
     PeriodArray,
     SparseArray,
@@ -2569,6 +2570,13 @@ def test_construction_from_set_raises(self, typ):
         with pytest.raises(TypeError, match=msg):
             Series(values)
 
+    def test_construction_from_ndarray_datetimelike(self):
+        # ensure the underlying arrays are properly wrapped as EA when
+        # constructed from 2D ndarray
+        arr = np.arange(0, 12, dtype="datetime64[ns]").reshape(4, 3)
+        df = DataFrame(arr)
+        assert all(isinstance(arr, DatetimeArray) for arr in df._mgr.arrays)
+
 
 def get1(obj):
     if isinstance(obj, Series):