Make NumericalBaseColumn Scannable.

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

@@ -12,9 +12,10 @@
 from cudf import _lib as libcudf
 from cudf._typing import ScalarLike
 from cudf.core.column import ColumnBase
+from cudf.core.mixins import Scannable
 
 
-class NumericalBaseColumn(ColumnBase):
+class NumericalBaseColumn(ColumnBase, Scannable):
     """A column composed of numerical data.
 
     This class encodes a standard interface for different types of columns
@@ -32,6 +33,13 @@ class NumericalBaseColumn(ColumnBase):
         "std",
     }
 
+    _VALID_SCANS = {
+        "cumsum",
+        "cumprod",
+        "cummin",
+        "cummax",
+    }
+
     def _can_return_nan(self, skipna: bool = None) -> bool:
         return not skipna and self.has_nulls()
 
@@ -174,7 +182,7 @@ def round(
         """Round the values in the Column to the given number of decimals."""
         return libcudf.round.round(self, decimal_places=decimals, how=how)
 
-    def _apply_scan_op(self, op: str) -> ColumnBase:
-        return libcudf.reduce.scan(op, self, True)._with_type_metadata(
-            self.dtype
-        )
+    def _scan(self, op: str) -> ColumnBase:
+        return libcudf.reduce.scan(
+            op.replace("cum", ""), self, True
+        )._with_type_metadata(self.dtype)

python/cudf/cudf/core/frame.py

@@ -125,6 +125,7 @@ class Frame(Scannable):
     # Necessary because the function names don't directly map to the docs.
     _SCAN_DOCSTRINGS = {
         "cumsum": {"op_name": "cumulative sum"},
+        "cumprod": {"op_name": "cumulative product"},
         "cummin": {"op_name": "cumulative min"},
         "cummax": {"op_name": "cumulative max"},
     }
@@ -4701,7 +4702,6 @@ def _scan(self, op, axis=None, skipna=True):
         3  10  34
         """
         cast_to_int = op in ("cumsum", "cumprod")
-        op = op.replace("cum", "")
         skipna = True if skipna is None else skipna
 
         results = {}
@@ -4734,7 +4734,7 @@ def _scan(self, op, axis=None, skipna=True):
                 # For reductions that accumulate a value (e.g. sum, not max)
                 # pandas returns an int64 dtype for all int or bool dtypes.
                 result_col = result_col.astype(np.int64)
-            results[name] = result_col._apply_scan_op(op)
+            results[name] = getattr(result_col, op)()
         # TODO: This will work for Index because it's passing self._index
         # (which is None), but eventually we may want to remove that parameter
         # for Index._from_data and simplify.