Move some misc Frame methods to appropriate locations

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

@@ -1124,6 +1124,11 @@ def __cuda_array_interface__(self) -> abc.Mapping[str, Any]:
     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
         return _array_ufunc(self, ufunc, method, inputs, kwargs)
 
+    def __invert__(self):
+        raise TypeError(
+            f"Operation `~` not supported on {self.dtype.type.__name__}"
+        )
+
     def searchsorted(
         self,
         value,

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

@@ -194,6 +194,14 @@ def unary_operator(self, unaryop: Union[str, Callable]) -> ColumnBase:
         unaryop = pylibcudf.unary.UnaryOperator[unaryop]
         return libcudf.unary.unary_operation(self, unaryop)
 
+    def __invert__(self):
+        if self.dtype.kind in "ui":
+            return self.unary_operator("invert")
+        elif self.dtype.kind == "b":
+            return self.unary_operator("not")
+        else:
+            return super().__invert__()
+
     def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
         int_float_dtype_mapping = {
             np.int8: np.float32,

python/cudf/cudf/core/frame.py

@@ -32,7 +32,7 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._typing import Dtype
-from cudf.api.types import is_bool_dtype, is_dtype_equal, is_scalar
+from cudf.api.types import is_dtype_equal, is_scalar
 from cudf.core.buffer import acquire_spill_lock
 from cudf.core.column import (
     ColumnBase,
@@ -1455,51 +1455,6 @@ def _get_sorted_inds(
             stable=True,
         )
 
-    @_cudf_nvtx_annotate
-    def _is_sorted(self, ascending=None, null_position=None):
-        """
-        Returns a boolean indicating whether the data of the Frame are sorted
-        based on the parameters given. Does not account for the index.
-
-        Parameters
-        ----------
-        self : Frame
-            Frame whose columns are to be checked for sort order
-        ascending : None or list-like of booleans
-            None or list-like of boolean values indicating expected sort order
-            of each column. If list-like, size of list-like must be
-            len(columns). If None, all columns expected sort order is set to
-            ascending. False (0) - ascending, True (1) - descending.
-        null_position : None or list-like of booleans
-            None or list-like of boolean values indicating desired order of
-            nulls compared to other elements. If list-like, size of list-like
-            must be len(columns). If None, null order is set to before. False
-            (0) - before, True (1) - after.
-
-        Returns
-        -------
-        returns : boolean
-            Returns True, if sorted as expected by ``ascending`` and
-            ``null_position``, False otherwise.
-        """
-        if ascending is not None and not cudf.api.types.is_list_like(
-            ascending
-        ):
-            raise TypeError(
-                f"Expected a list-like or None for `ascending`, got "
-                f"{type(ascending)}"
-            )
-        if null_position is not None and not cudf.api.types.is_list_like(
-            null_position
-        ):
-            raise TypeError(
-                f"Expected a list-like or None for `null_position`, got "
-                f"{type(null_position)}"
-            )
-        return libcudf.sort.is_sorted(
-            [*self._columns], ascending=ascending, null_position=null_position
-        )
-
     @_cudf_nvtx_annotate
     def _split(self, splits):
         """Split a frame with split points in ``splits``. Returns a list of
@@ -1920,7 +1875,7 @@ def __invert__(self):
         """Bitwise invert (~) for integral dtypes, logical NOT for bools."""
         return self._from_data_like_self(
             self._data._from_columns_like_self(
-                (_apply_inverse_column(col) for col in self._data.columns)
+                (~col for col in self._data.columns)
             )
         )
 
@@ -1970,15 +1925,3 @@ def __dask_tokenize__(self):
             str(dict(self._dtypes)),
             normalize_token(self.to_pandas()),
         ]
-
-
-def _apply_inverse_column(col: ColumnBase) -> ColumnBase:
-    """Bitwise invert (~) for integral dtypes, logical NOT for bools."""
-    if np.issubdtype(col.dtype, np.integer):
-        return col.unary_operator("invert")
-    elif is_bool_dtype(col.dtype):
-        return col.unary_operator("not")
-    else:
-        raise TypeError(
-            f"Operation `~` not supported on {col.dtype.type.__name__}"
-        )

python/cudf/cudf/core/multiindex.py

@@ -1636,9 +1636,54 @@ def is_unique(self):
     def dtype(self):
         return np.dtype("O")
 
+    @_cudf_nvtx_annotate
+    def _is_sorted(self, ascending=None, null_position=None) -> bool:
+        """
+        Returns a boolean indicating whether the data of the MultiIndex are sorted
+        based on the parameters given. Does not account for the index.
+
+        Parameters
+        ----------
+        self : MultiIndex
+            MultiIndex whose columns are to be checked for sort order
+        ascending : None or list-like of booleans
+            None or list-like of boolean values indicating expected sort order
+            of each column. If list-like, size of list-like must be
+            len(columns). If None, all columns expected sort order is set to
+            ascending. False (0) - ascending, True (1) - descending.
+        null_position : None or list-like of booleans
+            None or list-like of boolean values indicating desired order of
+            nulls compared to other elements. If list-like, size of list-like
+            must be len(columns). If None, null order is set to before. False
+            (0) - before, True (1) - after.
+
+        Returns
+        -------
+        returns : boolean
+            Returns True, if sorted as expected by ``ascending`` and
+            ``null_position``, False otherwise.
+        """
+        if ascending is not None and not cudf.api.types.is_list_like(
+            ascending
+        ):
+            raise TypeError(
+                f"Expected a list-like or None for `ascending`, got "
+                f"{type(ascending)}"
+            )
+        if null_position is not None and not cudf.api.types.is_list_like(
+            null_position
+        ):
+            raise TypeError(
+                f"Expected a list-like or None for `null_position`, got "
+                f"{type(null_position)}"
+            )
+        return libcudf.sort.is_sorted(
+            [*self._columns], ascending=ascending, null_position=null_position
+        )
+
     @cached_property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_increasing(self):
+    def is_monotonic_increasing(self) -> bool:
         """
         Return if the index is monotonic increasing
         (only equal or increasing) values.
@@ -1647,7 +1692,7 @@ def is_monotonic_increasing(self):
 
     @cached_property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_decreasing(self):
+    def is_monotonic_decreasing(self) -> bool:
         """
         Return if the index is monotonic decreasing
         (only equal or decreasing) values.