API: Datetime/TimedeltaArray from to_datetime

Closes #24656

Author: TomAugspurger

pandas/core/arrays/array_.py

@@ -1,6 +1,7 @@
 from pandas._libs import lib, tslibs
 
-from pandas.core.dtypes.common import is_extension_array_dtype
+from pandas.core.dtypes.common import (
+    is_datetime64_ns_dtype, is_extension_array_dtype, is_timedelta64_ns_dtype)
 from pandas.core.dtypes.dtypes import registry
 
 from pandas import compat
@@ -75,9 +76,10 @@ def array(data,         # type: Sequence[object]
     See Also
     --------
     numpy.array : Construct a NumPy array.
-    arrays.PandasArray : ExtensionArray wrapping a NumPy array.
     Series : Construct a pandas Series.
     Index : Construct a pandas Index.
+    arrays.PandasArray : ExtensionArray wrapping a NumPy array.
+    Series.array : Extract the array stored within a Series.
 
     Notes
     -----
@@ -120,6 +122,16 @@ def array(data,         # type: Sequence[object]
     ['a', 'b']
     Length: 2, dtype: str32
 
+    Finally, Pandas has arrays that mostly overlap with NumPy
+
+      * :class:`arrays.DatetimeArray`
+      * :class:`arrays.TimedeltaArray`
+
+    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
+    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
+    rather than a ``PandasArray``. This is for symmetry with the case of
+    timezone-aware data, which NumPy does not natively support.
+
     Examples
     --------
     If a dtype is not specified, `data` is passed through to
@@ -239,5 +251,14 @@ def array(data,         # type: Sequence[object]
 
         # TODO(BooleanArray): handle this type
 
+    # Pandas overrides NumPy for
+    #   1. datetime64[ns]
+    #   2. timedelta64[ns]
+    # so that a DatetimeArray is returned.
+    if is_datetime64_ns_dtype(dtype):
+        return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
+    if is_timedelta64_ns_dtype(dtype):
+        return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
+
     result = PandasArray._from_sequence(data, dtype=dtype, copy=copy)
     return result

pandas/tests/arrays/test_array.py

@@ -36,8 +36,30 @@
 
     # Datetime (naive)
     ([1, 2], np.dtype('datetime64[ns]'),
-     PandasArray(np.array([1, 2], dtype='datetime64[ns]'))),
-    # TODO(DatetimeArray): add here
+     pd.arrays.DatetimeArray._from_sequence(
+         np.array([1, 2], dtype='datetime64[ns]'))),
+
+    (np.array([1, 2], dtype='datetime64[ns]'), None,
+     pd.arrays.DatetimeArray._from_sequence(
+         np.array([1, 2], dtype='datetime64[ns]'))),
+
+    (pd.DatetimeIndex(['2000', '2001']), np.dtype('datetime64[ns]'),
+     pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
+
+    (['2000', '2001'], np.dtype('datetime64[ns]'),
+     pd.arrays.DatetimeArray._from_sequence(['2000', '2001'])),
+
+    # Datetime (tz-aware)
+    (['2000', '2001'], pd.DatetimeTZDtype(tz="CET"),
+     pd.arrays.DatetimeArray._from_sequence(
+         ['2000', '2001'], dtype=pd.DatetimeTZDtype(tz="CET"))),
+
+    # Timedelta
+    (['1H', '2H'], np.dtype('timedelta64[ns]'),
+     pd.arrays.TimedeltaArray._from_sequence(['1H', '2H'])),
+
+    (pd.TimedeltaIndex(['1H', '2H']), np.dtype('timedelta64[ns]'),
+     pd.arrays.TimedeltaArray._from_sequence(['1H', '2H'])),
 
     # Category
     (['a', 'b'], 'category', pd.Categorical(['a', 'b'])),