BUG: setting td64 value into Series[numeric] (#39086)

Author: jbrockmendel

doc/source/whatsnew/v1.3.0.rst

@@ -251,7 +251,7 @@ Indexing
 - Bug in :meth:`DataFrame.__setitem__` raising ``ValueError`` with empty :class:`DataFrame` and specified columns for string indexer and non empty :class:`DataFrame` to set (:issue:`38831`)
 - Bug in :meth:`DataFrame.iloc.__setitem__` and :meth:`DataFrame.loc.__setitem__` with mixed dtypes when setting with a dictionary value (:issue:`38335`)
 - Bug in :meth:`DataFrame.loc` dropping levels of :class:`MultiIndex` when :class:`DataFrame` used as input has only one row (:issue:`10521`)
--
+- Bug in setting ``timedelta64`` values into numeric :class:`Series` failing to cast to object dtype (:issue:`39086`)
 
 Missing
 ^^^^^^^

pandas/core/dtypes/cast.py

@@ -1874,9 +1874,9 @@ def validate_numeric_casting(dtype: np.dtype, value: Scalar) -> None:
     ):
         raise ValueError("Cannot assign nan to integer series")
 
-    if (
-        issubclass(dtype.type, (np.integer, np.floating, complex))
-        and not issubclass(dtype.type, np.bool_)
-        and is_bool(value)
-    ):
-        raise ValueError("Cannot assign bool to float/integer series")
+    if dtype.kind in ["i", "u", "f", "c"]:
+        if is_bool(value) or isinstance(value, np.timedelta64):
+            # numpy will cast td64 to integer if we're not careful
+            raise ValueError(
+                f"Cannot assign {type(value).__name__} to float/integer series"
+            )

pandas/core/internals/blocks.py

@@ -678,11 +678,7 @@ def convert(
 
     def _can_hold_element(self, element: Any) -> bool:
         """ require the same dtype as ourselves """
-        dtype = self.values.dtype.type
-        tipo = maybe_infer_dtype_type(element)
-        if tipo is not None:
-            return issubclass(tipo.type, dtype)
-        return isinstance(element, dtype)
+        raise NotImplementedError("Implemented on subclasses")
 
     def should_store(self, value: ArrayLike) -> bool:
         """
@@ -1969,10 +1965,10 @@ class ComplexBlock(NumericBlock):
     def _can_hold_element(self, element: Any) -> bool:
         tipo = maybe_infer_dtype_type(element)
         if tipo is not None:
-            return issubclass(tipo.type, (np.floating, np.integer, np.complexfloating))
-        return isinstance(
-            element, (float, int, complex, np.float_, np.int_)
-        ) and not isinstance(element, (bool, np.bool_))
+            return tipo.kind in ["c", "f", "i", "u"]
+        return (
+            lib.is_integer(element) or lib.is_complex(element) or lib.is_float(element)
+        )
 
 
 class IntBlock(NumericBlock):

pandas/tests/series/indexing/test_setitem.py

@@ -395,3 +395,27 @@ def test_setitem_slice_into_readonly_backing_data():
         series[1:3] = 1
 
     assert not array.any()
+
+
+@pytest.mark.parametrize(
+    "key", [0, slice(0, 1), [0], np.array([0]), range(1)], ids=type
+)
+@pytest.mark.parametrize("dtype", [complex, int, float])
+def test_setitem_td64_into_complex(key, dtype, indexer_sli):
+    # timedelta64 should not be treated as integers
+    arr = np.arange(5).astype(dtype)
+    ser = Series(arr)
+    td = np.timedelta64(4, "ns")
+
+    indexer_sli(ser)[key] = td
+    assert ser.dtype == object
+    assert arr[0] == 0  # original array is unchanged
+
+    if not isinstance(key, int) and not (
+        indexer_sli is tm.loc and isinstance(key, slice)
+    ):
+        # skip key/indexer_sli combinations that will have mismatched lengths
+        ser = Series(arr)
+        indexer_sli(ser)[key] = np.full((1,), td)
+        assert ser.dtype == object
+        assert arr[0] == 0  # original array is unchanged