BUG: Series[dt64].__setitem__ with all-false mask incorrectly upcasting (pandas-dev#45967)

Author: jbrockmendel

doc/source/whatsnew/v1.5.0.rst

@@ -327,6 +327,7 @@ Indexing
 - Bug in :meth:`loc.__setitem__` treating ``range`` keys as positional instead of label-based (:issue:`45479`)
 - Bug in :meth:`Series.__setitem__` when setting ``boolean`` dtype values containing ``NA`` incorrectly raising instead of casting to ``boolean`` dtype (:issue:`45462`)
 - Bug in :meth:`Series.__setitem__` where setting :attr:`NA` into a numeric-dtpye :class:`Series` would incorrectly upcast to object-dtype rather than treating the value as ``np.nan`` (:issue:`44199`)
+- Bug in :meth:`Series.__setitem__` with ``datetime64[ns]`` dtype, an all-``False`` boolean mask, and an incompatible value incorrectly casting to ``object`` instead of retaining ``datetime64[ns]`` dtype (:issue:`45967`)
 - Bug in :meth:`Index.__getitem__`  raising ``ValueError`` when indexer is from boolean dtype with ``NA`` (:issue:`45806`)
 - Bug in :meth:`Series.mask` with ``inplace=True`` or setting values with a boolean mask with small integer dtypes incorrectly raising (:issue:`45750`)
 - Bug in :meth:`DataFrame.mask` with ``inplace=True`` and ``ExtensionDtype`` columns incorrectly raising (:issue:`45577`)

pandas/core/array_algos/putmask.py

@@ -81,7 +81,11 @@ def putmask_without_repeat(
     # TODO: this prob needs some better checking for 2D cases
     nlocs = mask.sum()
     if nlocs > 0 and is_list_like(new) and getattr(new, "ndim", 1) == 1:
-        if nlocs == len(new):
+        shape = np.shape(new)
+        # np.shape compat for if setitem_datetimelike_compat
+        #  changed arraylike to list e.g. test_where_dt64_2d
+
+        if nlocs == shape[-1]:
             # GH#30567
             # If length of ``new`` is less than the length of ``values``,
             # `np.putmask` would first repeat the ``new`` array and then
@@ -90,7 +94,7 @@ def putmask_without_repeat(
             # to place in the masked locations of ``values``
             np.place(values, mask, new)
             # i.e. values[mask] = new
-        elif mask.shape[-1] == len(new) or len(new) == 1:
+        elif mask.shape[-1] == shape[-1] or shape[-1] == 1:
             np.putmask(values, mask, new)
         else:
             raise ValueError("cannot assign mismatch length to masked array")

pandas/core/internals/blocks.py

@@ -1478,26 +1478,48 @@ def putmask(self, mask, new) -> list[Block]:
         new = self._maybe_squeeze_arg(new)
         mask = self._maybe_squeeze_arg(mask)
 
+        if not mask.any():
+            return [self]
+
         try:
             # Caller is responsible for ensuring matching lengths
             values._putmask(mask, new)
         except (TypeError, ValueError) as err:
             _catch_deprecated_value_error(err)
 
-            if is_interval_dtype(self.dtype):
-                # Discussion about what we want to support in the general
-                #  case GH#39584
-                blk = self.coerce_to_target_dtype(orig_new)
-                return blk.putmask(orig_mask, orig_new)
+            if self.ndim == 1 or self.shape[0] == 1:
 
-            elif isinstance(self, NDArrayBackedExtensionBlock):
-                # NB: not (yet) the same as
-                #  isinstance(values, NDArrayBackedExtensionArray)
-                blk = self.coerce_to_target_dtype(orig_new)
-                return blk.putmask(orig_mask, orig_new)
+                if is_interval_dtype(self.dtype):
+                    # Discussion about what we want to support in the general
+                    #  case GH#39584
+                    blk = self.coerce_to_target_dtype(orig_new)
+                    return blk.putmask(orig_mask, orig_new)
+
+                elif isinstance(self, NDArrayBackedExtensionBlock):
+                    # NB: not (yet) the same as
+                    #  isinstance(values, NDArrayBackedExtensionArray)
+                    blk = self.coerce_to_target_dtype(orig_new)
+                    return blk.putmask(orig_mask, orig_new)
+
+                else:
+                    raise
 
             else:
-                raise
+                # Same pattern we use in Block.putmask
+                is_array = isinstance(orig_new, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = orig_new
+                    if is_array:
+                        # we have a different value per-column
+                        n = orig_new[:, i : i + 1]
+
+                    submask = orig_mask[:, i : i + 1]
+                    rbs = nb.putmask(submask, n)
+                    res_blocks.extend(rbs)
+                return res_blocks
 
         return [self]
 

pandas/tests/frame/indexing/test_where.py

@@ -988,8 +988,16 @@ def _check_where_equivalences(df, mask, other, expected):
     res = df.mask(~mask, other)
     tm.assert_frame_equal(res, expected)
 
-    # Note: we cannot do the same with frame.mask(~mask, other, inplace=True)
-    #  bc that goes through Block.putmask which does *not* downcast.
+    # Note: frame.mask(~mask, other, inplace=True) takes some more work bc
+    #  Block.putmask does *not* downcast.  The change to 'expected' here
+    #  is specific to the cases in test_where_dt64_2d.
+    df = df.copy()
+    df.mask(~mask, other, inplace=True)
+    if not mask.all():
+        # with mask.all(), Block.putmask is a no-op, so does not downcast
+        expected = expected.copy()
+        expected["A"] = expected["A"].astype(object)
+    tm.assert_frame_equal(df, expected)
 
 
 def test_where_dt64_2d():

pandas/tests/series/indexing/test_setitem.py

@@ -1625,3 +1625,18 @@ def test_setitem_bool_indexer_dont_broadcast_length1_values(size, mask, item, bo
         expected = Series(np.arange(size, dtype=float))
         expected[selection] = item
         tm.assert_series_equal(ser, expected)
+
+
+def test_setitem_empty_mask_dont_upcast_dt64():
+    dti = date_range("2016-01-01", periods=3)
+    ser = Series(dti)
+    orig = ser.copy()
+    mask = np.zeros(3, dtype=bool)
+
+    ser[mask] = "foo"
+    assert ser.dtype == dti.dtype  # no-op -> dont upcast
+    tm.assert_series_equal(ser, orig)
+
+    ser.mask(mask, "foo", inplace=True)
+    assert ser.dtype == dti.dtype  # no-op -> dont upcast
+    tm.assert_series_equal(ser, orig)