Fix type casting in Series.__setitem__

To mimic pandas, we must upcast a column to the numpy result_type of
the column itself and the input value dtype. This previously occurred
in all relevant cases except when the index provided to __setitem__
was a single integer (originally introduced in rapidsai#2442). Closes rapidsai#11901.

python/cudf/cudf/core/series.py

@@ -222,11 +222,12 @@ def __setitem__(self, key, value):
             and _is_non_decimal_numeric_dtype(value.dtype)
         ):
             # normalize types if necessary:
-            if not is_integer(key):
-                to_dtype = np.result_type(
-                    value.dtype, self._frame._column.dtype
-                )
-                value = value.astype(to_dtype)
+            # In contrast to Column.__setitem__ (which downcasts the value to
+            # the dtype of the column) here we upcast the series to the
+            # larger data type mimicing pandas
+            to_dtype = np.result_type(value.dtype, self._frame._column.dtype)
+            value = value.astype(to_dtype)
+            if to_dtype != self._frame._column.dtype:
                 self._frame._column._mimic_inplace(
                     self._frame._column.astype(to_dtype), inplace=True
                 )

python/cudf/cudf/tests/test_setitem.py

@@ -297,3 +297,19 @@ def test_series_slice_setitem_struct():
     actual[0:3] = cudf.Scalar({"a": {"b": 5050}, "b": 101})
 
     assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
+@pytest.mark.parametrize("indices", [0, [1, 2]])
+def test_series_setitem_upcasting(dtype, indices):
+    sr = pd.Series([0, 0, 0], dtype=dtype)
+    cr = cudf.from_pandas(sr)
+    assert_eq(sr.values, cr.values)
+    new_value = np.float64(10.5)
+    col_ref = cr._column
+    sr[indices] = new_value
+    cr[indices] = new_value
+    assert_eq(sr.values, cr.values)
+    if dtype == np.float64:
+        # no-op type cast should not modify backing column
+        assert col_ref == cr._column