BUG: Series __setitem__ gives wrong result with bool indexer

doc/source/whatsnew/v1.0.0.rst

@@ -858,6 +858,7 @@ Indexing
 - Bug when indexing with ``.loc`` where the index was a :class:`CategoricalIndex` with non-string categories didn't work (:issue:`17569`, :issue:`30225`)
 - :meth:`Index.get_indexer_non_unique` could fail with `TypeError` in some cases, such as when searching for ints in a string index (:issue:`28257`)
 - Bug in :meth:`Float64Index.get_loc` incorrectly raising ``TypeError`` instead of ``KeyError`` (:issue:`29189`)
+- Bug in :meth:`Series.__setitem__` incorrectly assigning values with boolean indexer when the length of new data matches the number of ``True`` values and new data is not a ``Series`` or an ``np.array`` (:issue:`30567`)
 
 Missing
 ^^^^^^^

pandas/core/internals/blocks.py

@@ -944,15 +944,14 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False)
                 and np.any(mask[mask])
                 and getattr(new, "ndim", 1) == 1
             ):
-
-                if not (
-                    mask.shape[-1] == len(new)
-                    or mask[mask].shape[-1] == len(new)
-                    or len(new) == 1
-                ):
+                if mask[mask].shape[-1] == len(new):  # GH 30567
+                    np.place(new_values, mask, new)
+                elif mask.shape[-1] == len(new) or len(new) == 1:
+                    np.putmask(new_values, mask, new)
+                else:
                     raise ValueError("cannot assign mismatch length to masked array")
-
-            np.putmask(new_values, mask, new)
+            else:
+                np.putmask(new_values, mask, new)
 
         # maybe upcast me
         elif mask.any():

pandas/tests/indexing/test_indexing.py

@@ -1190,3 +1190,13 @@ def test_duplicate_index_mistyped_key_raises_keyerror():
 
     with pytest.raises(KeyError):
         ser.index._engine.get_loc(None)
+
+
+def test_setitem_with_bool_mask_and_values_matching_n_trues_in_length():
+    # GH 30567
+    ser = pd.Series([None] * 10)
+    mask = [False] * 3 + [True] * 5 + [False] * 2
+    ser[mask] = range(5)
+    result = ser
+    expected = pd.Series([None] * 3 + list(range(5)) + [None] * 2).astype("object")
+    tm.assert_series_equal(result, expected)