Enable scattering integers into decimal columns (#8225)

Closes #8157

Authors:
  - https://github.com/brandon-b-miller

Approvers:
  - Christopher Harris (https://github.com/cwharris)
  - Ashwin Srinath (https://github.com/shwina)
  - Keith Kraus (https://github.com/kkraus14)

URL: #8225

python/cudf/cudf/_lib/scalar.pyx

@@ -405,7 +405,7 @@ cdef _get_np_scalar_from_timedelta64(unique_ptr[scalar]& s):
 
 def as_device_scalar(val, dtype=None):
     if dtype:
-        if isinstance(val, (cudf.Scalar, DeviceScalar)):
+        if isinstance(val, (cudf.Scalar, DeviceScalar)) and dtype != val.dtype:
             raise TypeError("Can't update dtype of existing GPU scalar")
         else:
             return cudf.Scalar(value=val, dtype=dtype).device_value

python/cudf/cudf/core/column/column.py

@@ -681,7 +681,7 @@ def __setitem__(self, key: Any, value: Any):
             nelem = len(key)
 
         if is_scalar(value):
-            value = self.dtype.type(value) if value is not None else value
+            value = cudf.Scalar(value, dtype=self.dtype)
         else:
             if len(value) != nelem:
                 msg = (

python/cudf/cudf/core/column/decimal.py

@@ -29,6 +29,11 @@ class DecimalColumn(ColumnBase):
     def __truediv__(self, other):
         return self.binary_operator("div", other)
 
+    def __setitem__(self, key, value):
+        if isinstance(value, np.integer):
+            value = int(value)
+        super().__setitem__(key, value)
+
     @classmethod
     def from_arrow(cls, data: pa.Array):
         dtype = Decimal64Dtype.from_arrow(data.type)

python/cudf/cudf/core/indexing.py

@@ -101,9 +101,11 @@ def __setitem__(self, key, value):
             value = to_cudf_compatible_scalar(value)
         else:
             value = column.as_column(value)
-
         if (
-            not is_categorical_dtype(self._sr._column.dtype)
+            not isinstance(
+                self._sr._column.dtype,
+                (cudf.Decimal64Dtype, cudf.CategoricalDtype),
+            )
             and hasattr(value, "dtype")
             and pd.api.types.is_numeric_dtype(value.dtype)
         ):

python/cudf/cudf/tests/test_decimal.py

@@ -268,6 +268,10 @@ def test_typecast_from_decimal(data, from_dtype, to_dtype):
             1,
             pa.lib.ArrowInvalid,
         ),
+        # We will allow for setting scalars into decimal columns
+        (["1", "2", "3"], Decimal64Dtype(1, 0), 5, 1, ["1", "5", "3"]),
+        # But not if it has too many digits to fit the precision
+        (["1", "2", "3"], Decimal64Dtype(1, 0), 50, 1, pa.lib.ArrowInvalid),
     ],
 )
 def test_series_setitem_decimal(args):