Reflect reviews

- update doc
- update dtype matching fucntion
- update test

Author: inmoonlight

doc/source/whatsnew/v0.25.1.rst

@@ -32,7 +32,6 @@ Categorical
 Datetimelike
 ^^^^^^^^^^^^
 - Bug in :func:`to_datetime` where passing a timezone-naive :class:`DatetimeArray` or :class:`DatetimeIndex` and ``utc=True`` would incorrectly return a timezone-naive result (:issue:`27733`)
-- Bug in :func:`maybe_cast_to_datetime` where converting a `np.datetime64` to `datetime64[D]` raise `TypeError` (:issue: `27395`)
 -
 -
 
@@ -85,6 +84,7 @@ Indexing
 - Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
 - Break reference cycle involving :class:`Index` and other index classes to allow garbage collection of index objects without running the GC. (:issue:`27585`, :issue:`27840`)
 - Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
+- Fix assignment of column via `.loc` with numpy `non-ns datetime` type (:issue: `27395`)
 -
 
 Missing

pandas/core/dtypes/cast.py

@@ -1026,7 +1026,7 @@ def maybe_cast_to_datetime(value, dtype, errors="raise"):
             )
 
             if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
-                if dtype.name in ("datetime64", "datetime64[ns]", "datetime64[D]"):
+                if dtype.kind == "M":
                     if dtype.name == "datetime64":
                         raise ValueError(msg.format(dtype=dtype.name))
                     dtype = _NS_DTYPE
@@ -1044,7 +1044,7 @@ def maybe_cast_to_datetime(value, dtype, errors="raise"):
                     value = [value]
 
             elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
-                if dtype.name in ("timedelta64", "timedelta64[ns]", "timedelta64[D]"):
+                if dtype.kind == "m":
                     if dtype.name == "timedelta64":
                         raise ValueError(msg.format(dtype=dtype.name))
                     dtype = _TD_DTYPE

pandas/tests/indexing/test_loc.py

@@ -9,6 +9,7 @@
 import pandas as pd
 from pandas import DataFrame, Series, Timestamp, date_range
 from pandas.api.types import is_scalar
+from pandas.core.dtypes.cast import maybe_cast_to_datetime
 from pandas.tests.indexing.common import Base
 from pandas.util import testing as tm
 
@@ -690,6 +691,24 @@ def test_loc_setitem_consistency_slice_column_len(self):
         )
         tm.assert_series_equal(df[("Respondent", "Duration")], expected)
 
+    @pytest.mark.parametrize(
+        "obj,dtype",
+        [
+            (np.datetime64("2017-01-01 01:00:00"), "datetime64"),
+            (np.datetime64("2017-01-01 02:00:00"), "datetime64[ns]"),
+            (np.datetime64("2017-01-02 01:00:00"), "datetime64[Y]"),
+            (np.datetime64("2017-01-03 02:00:00"), "datetime64[M]"),
+            (np.datetime64("2017-01-04 02:00:00"), "datetime64[D]"),
+            (np.datetime64("2017-01-05 02:00:00"), "datetime64[h]"),
+            (np.datetime64("2017-01-06 02:10:00"), "datetime64[m]"),
+            (np.datetime64("2017-01-07 02:20:10"), "datetime64[s]"),
+            (np.datetime64("2017-01-08 02:40:20"), "datetime64[ms]"),
+            (np.datetime64("2017-01-09 02:50:30"), "datetime64[ns]"),
+        ],
+    )
+    def test_maybe_cast_to_datetime(obj, dtype):
+        maybe_cast_to_datetime(obj, dtype)
+
     def test_loc_setitem_frame(self):
         df = self.frame_labels