Merge pull request rapidsai#18834 from rapidsai/branch-25.06

Forward-merge branch-25.06 into branch-25.08

Author: GPUtester

python/cudf/cudf/core/_base_index.py

@@ -6,7 +6,6 @@
 from functools import cached_property
 from typing import TYPE_CHECKING, Any, Literal
 
-import numpy as np
 import pandas as pd
 from typing_extensions import Self
 
@@ -2171,9 +2170,3 @@ def _split(self, splits):
 
 def _get_result_name(left_name, right_name):
     return left_name if _is_same_name(left_name, right_name) else None
-
-
-def _return_get_indexer_result(result: cupy.ndarray) -> cupy.ndarray:
-    if cudf.get_option("mode.pandas_compatible"):
-        return result.astype(np.dtype(np.int64))
-    return result

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

@@ -2662,7 +2662,7 @@ def as_column(
         if isinstance(arbitrary, cudf.Series):
             arbitrary = arbitrary._column
         elif isinstance(arbitrary, cudf.BaseIndex):
-            arbitrary = arbitrary._values
+            arbitrary = arbitrary._column
         if dtype is not None:
             return arbitrary.astype(dtype)
         return arbitrary

python/cudf/cudf/core/frame.py

@@ -300,6 +300,69 @@ def size(self) -> int:
         """
         return self._num_columns * self._num_rows
 
+    @property
+    @_performance_tracking
+    def empty(self):
+        """
+        Indicator whether DataFrame or Series is empty.
+
+        True if DataFrame/Series is entirely empty (no items),
+        meaning any of the axes are of length 0.
+
+        Returns
+        -------
+        out : bool
+            If DataFrame/Series is empty, return True, if not return False.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'A' : []})
+        >>> df
+        Empty DataFrame
+        Columns: [A]
+        Index: []
+        >>> df.empty
+        True
+
+        If we only have `null` values in our DataFrame, it is
+        not considered empty! We will need to drop
+        the `null`'s to make the DataFrame empty:
+
+        >>> df = cudf.DataFrame({'A' : [None, None]})
+        >>> df
+              A
+        0  <NA>
+        1  <NA>
+        >>> df.empty
+        False
+        >>> df.dropna().empty
+        True
+
+        Non-empty and empty Series example:
+
+        >>> s = cudf.Series([1, 2, None])
+        >>> s
+        0       1
+        1       2
+        2    <NA>
+        dtype: int64
+        >>> s.empty
+        False
+        >>> s = cudf.Series([])
+        >>> s
+        Series([], dtype: float64)
+        >>> s.empty
+        True
+
+        .. pandas-compat::
+            :attr:`pandas.DataFrame.empty`, :attr:`pandas.Series.empty`
+
+            If DataFrame/Series contains only `null` values, it is still not
+            considered empty. See the example above.
+        """
+        return self.size == 0
+
     def memory_usage(self, deep: bool = False) -> int:
         """Return the memory usage of an object.
 

python/cudf/cudf/core/groupby/groupby.py

@@ -3625,7 +3625,7 @@ def _handle_frequency_grouper(self, by):
 
     def _handle_level(self, by):
         level_values = self._obj.index.get_level_values(by)
-        self._key_columns.append(level_values._values)
+        self._key_columns.append(level_values._column)
         self.names.append(level_values.name)
 
     def _handle_misc(self, by):