Align Index APIs with pandas 2.x

docs/cudf/source/conf.py

@@ -561,6 +561,10 @@ def on_missing_reference(app, env, node, contnode):
     ("py:class", "ScalarLike"),
     ("py:class", "ParentType"),
     ("py:class", "ColumnLike"),
+    ("py:class", "ColumnLike"),
+    ("py:obj", "cudf.Index.transpose"),
+    ("py:obj", "cudf.Index.to_flat_index"),
+    ("py:obj", "cudf.MultiIndex.to_flat_index"),
     # TODO: Remove this when we figure out why typing_extensions doesn't seem
     # to map types correctly for intersphinx
     ("py:class", "typing_extensions.Self"),

python/cudf/cudf/core/_base_index.py

@@ -868,6 +868,24 @@ def to_numpy(self):
         """Convert to a numpy array."""
         raise NotImplementedError
 
+    def to_flat_index(self) -> Self:
+        """
+        Identity method.
+
+        This is implemented for compatibility with subclass implementations
+        when chaining.
+
+        Returns
+        -------
+        pd.Index
+            Caller.
+
+        See Also
+        --------
+        MultiIndex.to_flat_index : Subclass implementation.
+        """
+        return self
+
     def any(self):
         """
         Return whether any elements is True in Index.
@@ -945,7 +963,7 @@ def to_pandas(self, *, nullable: bool = False, arrow_type: bool = False):
         """
         raise NotImplementedError
 
-    def isin(self, values):
+    def isin(self, values, level=None):
         """Return a boolean array where the index values are in values.
 
         Compute boolean array of whether each index value is found in
@@ -956,6 +974,9 @@ def isin(self, values):
         ----------
         values : set, list-like, Index
             Sought values.
+        level : str or int, optional
+            Name or position of the index level to use (if the index is a
+            `MultiIndex`).
 
         Returns
         -------
@@ -979,7 +1000,7 @@ def isin(self, values):
         # ColumnBase.isin).
         raise NotImplementedError
 
-    def unique(self):
+    def unique(self, level: int | None = None):
         """
         Return unique values in the index.
 

python/cudf/cudf/core/index.py

@@ -540,8 +540,12 @@ def memory_usage(self, deep: bool = False) -> int:
             )
         return 0
 
-    def unique(self) -> Self:
+    def unique(self, level: int | None = None) -> Self:
         # RangeIndex always has unique values
+        if level is not None and level > 0:
+            raise IndexError(
+                f"Too many levels: Index has only 1 level, not {level + 1}"
+            )
         return self.copy()
 
     @_performance_tracking
@@ -964,7 +968,11 @@ def _indices_of(self, value) -> cudf.core.column.NumericalColumn:
             i = []
         return as_column(i, dtype=size_type_dtype)
 
-    def isin(self, values):
+    def isin(self, values, level=None):
+        if level is not None and level > 0:
+            raise IndexError(
+                f"Too many levels: Index has only 1 level, not {level + 1}"
+            )
         if is_scalar(values):
             raise TypeError(
                 "only list-like objects are allowed to be passed "
@@ -1616,12 +1624,20 @@ def append(self, other):
 
         return self._concat(to_concat)
 
-    def unique(self):
+    def unique(self, level: int | None = None) -> Self:
+        if level is not None and level > 0:
+            raise IndexError(
+                f"Too many levels: Index has only 1 level, not {level + 1}"
+            )
         return cudf.core.index._index_from_data(
             {self.name: self._values.unique()}, name=self.name
         )
 
-    def isin(self, values):
+    def isin(self, values, level=None):
+        if level is not None and level > 0:
+            raise IndexError(
+                f"Too many levels: Index has only 1 level, not {level + 1}"
+            )
         if is_scalar(values):
             raise TypeError(
                 "only list-like objects are allowed to be passed "

python/cudf/cudf/core/multiindex.py

@@ -1149,6 +1149,15 @@ def from_tuples(cls, tuples, names=None):
     def to_numpy(self):
         return self.values_host
 
+    def to_flat_index(self):
+        """
+        Convert a MultiIndex to an Index of Tuples containing the level values.
+
+        This is not currently implemented
+        """
+        # TODO: Could implement as Index of ListDtype?
+        raise NotImplementedError("to_flat_index is not currently supported.")
+
     @property  # type: ignore
     @_performance_tracking
     def values_host(self):
@@ -1712,8 +1721,11 @@ def fillna(self, value):
         return super().fillna(value=value)
 
     @_performance_tracking
-    def unique(self):
-        return self.drop_duplicates(keep="first")
+    def unique(self, level: int | None = None) -> Self | cudf.Index:
+        if level is None:
+            return self.drop_duplicates(keep="first")
+        else:
+            return self.get_level_values(level).unique()
 
     @_performance_tracking
     def nunique(self, dropna: bool = True) -> int:

python/cudf/cudf/core/series.py

@@ -2775,14 +2775,6 @@ def cov(self, other, min_periods=None, ddof: int | None = None):
                 f"{other.dtype}"
             )
 
-    @_performance_tracking
-    def transpose(self):
-        """Return the transpose, which is by definition self."""
-
-        return self
-
-    T = property(transpose, doc=transpose.__doc__)
-
     @_performance_tracking
     def duplicated(self, keep="first"):
         """

python/cudf/cudf/core/single_column_frame.py

@@ -389,3 +389,10 @@ def where(self, cond, other=None, inplace=False):
         result = cudf._lib.copying.copy_if_else(input_col, other, cond)
 
         return _make_categorical_like(result, self_column)
+
+    @_performance_tracking
+    def transpose(self):
+        """Return the transpose, which is by definition self."""
+        return self
+
+    T = property(transpose, doc=transpose.__doc__)

python/cudf/cudf/tests/test_multiindex.py

@@ -2170,3 +2170,12 @@ def test_bool_raises():
         lfunc_args_and_kwargs=[[cudf.MultiIndex.from_arrays([range(1)])]],
         rfunc_args_and_kwargs=[[pd.MultiIndex.from_arrays([range(1)])]],
     )
+
+
+def test_unique_level():
+    pd_mi = pd.MultiIndex.from_arrays([[1, 1, 2], [3, 3, 2]])
+    cudf_mi = cudf.MultiIndex.from_pandas(pd_mi)
+
+    result = pd_mi.unique(level=1)
+    expected = cudf_mi.unique(level=1)
+    assert_eq(result, expected)