Annotate ColumnAccessor._data labels as Hashable

python/cudf/cudf/_lib/column.pyi

@@ -54,7 +54,7 @@ class Column:
     @property
     def mask_ptr(self) -> int: ...
     def set_base_mask(self, value: Buffer | None) -> None: ...
-    def set_mask(self, value: Buffer | None) -> Self: ...
+    def set_mask(self, value: ColumnBase | Buffer | None) -> Self: ...
     @property
     def null_count(self) -> int: ...
     @property

python/cudf/cudf/core/_internals/timezones.py

@@ -120,7 +120,7 @@ def _read_tzfile_as_columns(
 
         # this happens for UTC-like zones
         min_date = np.int64(np.iinfo("int64").min + 1).astype("M8[s]")
-        return (as_column([min_date]), as_column([np.timedelta64(0, "s")]))
+        return (as_column([min_date]), as_column([np.timedelta64(0, "s")]))  # type: ignore[return-value]
     return tuple(transition_times_and_offsets)  # type: ignore[return-value]
 
 

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

@@ -984,9 +984,9 @@ def find_and_replace(
         )
         replacement_col = catmap._data["index"].astype(replaced.codes.dtype)
 
-        replaced = column.as_column(replaced.codes)
+        replaced_codes = column.as_column(replaced.codes)
         output = libcudf.replace.replace(
-            replaced, to_replace_col, replacement_col
+            replaced_codes, to_replace_col, replacement_col
         )
 
         result = column.build_categorical_column(
@@ -1064,7 +1064,7 @@ def _validate_fillna_value(
                 raise TypeError(
                     "Cannot set a categorical with non-categorical data"
                 )
-            fill_value = fill_value._set_categories(
+            fill_value = cast(CategoricalColumn, fill_value)._set_categories(
                 self.categories,
             )
             return fill_value.codes.astype(self.codes.dtype)

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

@@ -553,7 +553,7 @@ def __setitem__(self, key: Any, value: Any):
         """
 
         # Normalize value to scalar/column
-        value_normalized = (
+        value_normalized: cudf.Scalar | ColumnBase = (
             cudf.Scalar(value, dtype=self.dtype)
             if is_scalar(value)
             else as_column(value, dtype=self.dtype)
@@ -609,9 +609,12 @@ def _scatter_by_slice(
                 )
 
         # step != 1, create a scatter map with arange
-        scatter_map = as_column(
-            rng,
-            dtype=cudf.dtype(np.int32),
+        scatter_map = cast(
+            cudf.core.column.NumericalColumn,
+            as_column(
+                rng,
+                dtype=cudf.dtype(np.int32),
+            ),
         )
 
         return self._scatter_by_column(scatter_map, value)
@@ -1111,11 +1114,16 @@ def argsort(
         if (ascending and self.is_monotonic_increasing) or (
             not ascending and self.is_monotonic_decreasing
         ):
-            return as_column(range(len(self)))
+            return cast(
+                cudf.core.column.NumericalColumn, as_column(range(len(self)))
+            )
         elif (ascending and self.is_monotonic_decreasing) or (
             not ascending and self.is_monotonic_increasing
         ):
-            return as_column(range(len(self) - 1, -1, -1))
+            return cast(
+                cudf.core.column.NumericalColumn,
+                as_column(range(len(self) - 1, -1, -1)),
+            )
         else:
             return libcudf.sort.order_by(
                 [self], [ascending], na_position, stable=True
@@ -1752,7 +1760,7 @@ def as_column(
     nan_as_null: bool | None = None,
     dtype: Dtype | None = None,
     length: int | None = None,
-):
+) -> ColumnBase:
     """Create a Column from an arbitrary object
 
     Parameters

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

@@ -256,7 +256,10 @@ def from_sequences(
                 offset += len(data)
                 offset_vals.append(offset)
 
-        offset_col = column.as_column(offset_vals, dtype=size_type_dtype)
+        offset_col = cast(
+            NumericalColumn,
+            column.as_column(offset_vals, dtype=size_type_dtype),
+        )
 
         # Build ListColumn
         res = cls(
@@ -338,7 +341,7 @@ def __init__(self, parent: ParentType):
 
     def get(
         self,
-        index: int,
+        index: int | ColumnLike,
         default: ScalarLike | ColumnLike | None = None,
     ) -> ParentType:
         """

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

@@ -142,7 +142,7 @@ def __setitem__(self, key: Any, value: Any):
         """
 
         # Normalize value to scalar/column
-        device_value = (
+        device_value: cudf.Scalar | ColumnBase = (
             cudf.Scalar(
                 value,
                 dtype=self.dtype
@@ -552,7 +552,7 @@ def _validate_fillna_value(
     ) -> cudf.Scalar | ColumnBase:
         """Align fill_value for .fillna based on column type."""
         if is_scalar(fill_value):
-            cudf_obj = cudf.Scalar(fill_value)
+            cudf_obj: cudf.Scalar | ColumnBase = cudf.Scalar(fill_value)
             if not as_column(cudf_obj).can_cast_safely(self.dtype):
                 raise TypeError(
                     f"Cannot safely cast non-equivalent "

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

@@ -776,11 +776,13 @@ def contains(
             # TODO: we silently ignore the `regex=` flag here
             if case is False:
                 input_column = libstrings.to_lower(self._column)
-                pat = libstrings.to_lower(column.as_column(pat, dtype="str"))
+                col_pat = libstrings.to_lower(
+                    column.as_column(pat, dtype="str")
+                )
             else:
                 input_column = self._column
-                pat = column.as_column(pat, dtype="str")
-            result_col = libstrings.contains_multiple(input_column, pat)
+                col_pat = column.as_column(pat, dtype="str")
+            result_col = libstrings.contains_multiple(input_column, col_pat)
         return self._return_or_inplace(result_col)
 
     def like(self, pat: str, esc: str | None = None) -> SeriesOrIndex: