BUG: Series[object].__setitem__(ndarray[M8ns]_with_matching_size) (#43868)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -446,6 +446,7 @@ Indexing
 - Bug in :meth:`DataFrame.query` where method calls in query strings led to errors when the ``numexpr`` package was installed. (:issue:`22435`)
 - Bug in :meth:`DataFrame.nlargest` and :meth:`Series.nlargest` where sorted result did not count indexes containing ``np.nan`` (:issue:`28984`)
 - Bug in indexing on a non-unique object-dtype :class:`Index` with an NA scalar (e.g. ``np.nan``) (:issue:`43711`)
+- Bug in :meth:`Series.__setitem__` with object dtype when setting an array with matching size and dtype='datetime64[ns]' or dtype='timedelta64[ns]' incorrectly converting the datetime/timedeltas to integers (:issue:`43868`)
 -
 
 Missing

pandas/core/indexers/utils.py

@@ -174,7 +174,7 @@ def check_setitem_lengths(indexer, value, values) -> bool:
                 if not (
                     isinstance(indexer, np.ndarray)
                     and indexer.dtype == np.bool_
-                    and len(indexer[indexer]) == len(value)
+                    and indexer.sum() == len(value)
                 ):
                     raise ValueError(
                         "cannot set using a list-like indexer "

pandas/core/internals/blocks.py

@@ -46,7 +46,6 @@
 from pandas.core.dtypes.common import (
     is_1d_only_ea_dtype,
     is_1d_only_ea_obj,
-    is_categorical_dtype,
     is_dtype_equal,
     is_extension_array_dtype,
     is_list_like,
@@ -91,8 +90,6 @@
     Categorical,
     DatetimeArray,
     ExtensionArray,
-    FloatingArray,
-    IntegerArray,
     IntervalArray,
     PandasArray,
     PeriodArray,
@@ -110,7 +107,6 @@
 from pandas.core.indexers import (
     check_setitem_lengths,
     is_empty_indexer,
-    is_exact_shape_match,
     is_scalar_indexer,
 )
 import pandas.core.missing as missing
@@ -928,18 +924,15 @@ def setitem(self, indexer, value):
         if is_extension_array_dtype(getattr(value, "dtype", None)):
             # We need to be careful not to allow through strings that
             #  can be parsed to EADtypes
-            is_ea_value = True
             arr_value = value
         else:
-            is_ea_value = False
             arr_value = np.asarray(value)
 
         if transpose:
             values = values.T
 
         # length checking
         check_setitem_lengths(indexer, value, values)
-        exact_match = is_exact_shape_match(values, arr_value)
 
         if is_empty_indexer(indexer, arr_value):
             # GH#8669 empty indexers
@@ -950,30 +943,6 @@ def setitem(self, indexer, value):
             #  be e.g. a list; see GH#6043
             values[indexer] = value
 
-        elif exact_match and is_categorical_dtype(arr_value.dtype):
-            # GH25495 - If the current dtype is not categorical,
-            # we need to create a new categorical block
-            values[indexer] = value
-
-        elif exact_match and is_ea_value:
-            # GH#32395 if we're going to replace the values entirely, just
-            #  substitute in the new array
-            if not self.is_object and isinstance(value, (IntegerArray, FloatingArray)):
-                # _can_hold_element will only allow us to get here if value
-                #  has no NA entries.
-                values[indexer] = value.to_numpy(value.dtype.numpy_dtype)
-            else:
-                values[indexer] = np.asarray(value)
-
-        # if we are an exact match (ex-broadcasting),
-        # then use the resultant dtype
-        elif exact_match:
-            # We are setting _all_ of the array's values, so can cast to new dtype
-            values[indexer] = value
-
-        elif is_ea_value:
-            values[indexer] = value
-
         else:
             # error: Argument 1 to "setitem_datetimelike_compat" has incompatible type
             # "Union[ndarray, ExtensionArray]"; expected "ndarray"

pandas/tests/series/indexing/test_setitem.py

@@ -141,6 +141,24 @@ def test_setitem_with_tz_dst(self, indexer_sli):
         indexer_sli(ser)[[1, 2]] = vals
         tm.assert_series_equal(ser, exp)
 
+    def test_object_series_setitem_dt64array_exact_match(self):
+        # make sure the dt64 isn't cast by numpy to integers
+        # https://github.com/numpy/numpy/issues/12550
+
+        ser = Series({"X": np.nan}, dtype=object)
+
+        indexer = [True]
+
+        # "exact_match" -> size of array being set matches size of ser
+        value = np.array([4], dtype="M8[ns]")
+
+        ser.iloc[indexer] = value
+
+        expected = Series([value[0]], index=["X"], dtype=object)
+        assert all(isinstance(x, np.datetime64) for x in expected.values)
+
+        tm.assert_series_equal(ser, expected)
+
 
 class TestSetitemScalarIndexer:
     def test_setitem_negative_out_of_bounds(self):