Add ability to modify and propagate names of columns object

python/cudf/cudf/core/_base_index.py

@@ -1446,7 +1446,7 @@ def _union(self, other, sort=None):
         other_df["order"] = other_df.index
         res = self_df.merge(other_df, on=[0], how="outer")
         res = res.sort_values(
-            by=res._data.to_pandas_index()[1:], ignore_index=True
+            by=res._data.to_pandas_index[1:], ignore_index=True
         )
         union_result = cudf.core.index._index_from_data({0: res._data[0]})
 

python/cudf/cudf/core/column_accessor.py

@@ -207,11 +207,16 @@ def _from_columns_like_self(
 
     @property
     def level_names(self) -> tuple[abc.Hashable, ...]:
+        if self.is_cached("to_pandas_index"):
+            return self.to_pandas_index.names
         if self._level_names is None or len(self._level_names) == 0:
             return tuple((None,) * max(1, self.nlevels))
         else:
             return self._level_names
 
+    def is_cached(self, attr_name: str) -> bool:
+        return attr_name in self.__dict__
+
     @property
     def nlevels(self) -> int:
         if len(self) == 0:
@@ -262,7 +267,12 @@ def _clear_cache(self, old_ncols: int, new_ncols: int) -> None:
         new_ncols: int
             len(self) after self._data was modified
         """
-        cached_properties = ("columns", "names", "_grouped_data")
+        cached_properties = (
+            "columns",
+            "names",
+            "_grouped_data",
+            "to_pandas_index",
+        )
         for attr in cached_properties:
             try:
                 self.__delattr__(attr)
@@ -276,6 +286,7 @@ def _clear_cache(self, old_ncols: int, new_ncols: int) -> None:
             except AttributeError:
                 pass
 
+    @cached_property
     def to_pandas_index(self) -> pd.Index:
         """Convert the keys of the ColumnAccessor to a Pandas Index object."""
         if self.multiindex and len(self.level_names) > 0:
@@ -726,10 +737,10 @@ def droplevel(self, level: int) -> None:
         }
         new_ncols = len(self)
         self._level_names = (
-            self._level_names[:level] + self._level_names[level + 1 :]
+            self.level_names[:level] + self.level_names[level + 1 :]
         )
 
-        if len(self._level_names) == 1:
+        if len(self.level_names) == 1:
             # can't use nlevels, as it depends on multiindex
             self.multiindex = False
         self._clear_cache(old_ncols, new_ncols)

python/cudf/cudf/core/dataframe.py

@@ -961,7 +961,7 @@ def _init_from_series_list(self, data, columns, index):
                 warnings.simplefilter("ignore", FutureWarning)
                 concat_df = cudf.concat(data, axis=1)
 
-            cols = concat_df._data.to_pandas_index()
+            cols = concat_df._data.to_pandas_index
             if cols.dtype == "object":
                 concat_df.columns = cols.astype("str")
 
@@ -2068,7 +2068,7 @@ def _make_operands_and_index_for_binop(
             equal_columns = True
         elif isinstance(other, Series):
             if (
-                not (self_pd_columns := self._data.to_pandas_index()).equals(
+                not (self_pd_columns := self._data.to_pandas_index).equals(
                     other_pd_index := other.index.to_pandas()
                 )
                 and not can_reindex
@@ -2093,8 +2093,8 @@ def _make_operands_and_index_for_binop(
                 and fn in cudf.utils.utils._EQUALITY_OPS
                 and (
                     not self.index.equals(other.index)
-                    or not self._data.to_pandas_index().equals(
-                        other._data.to_pandas_index()
+                    or not self._data.to_pandas_index.equals(
+                        other._data.to_pandas_index
                     )
                 )
             ):
@@ -2138,11 +2138,11 @@ def _make_operands_and_index_for_binop(
 
         if not equal_columns:
             if isinstance(other, DataFrame):
-                column_names_list = self._data.to_pandas_index().join(
-                    other._data.to_pandas_index(), how="outer"
+                column_names_list = self._data.to_pandas_index.join(
+                    other._data.to_pandas_index, how="outer"
                 )
             elif isinstance(other, Series):
-                column_names_list = self._data.to_pandas_index().join(
+                column_names_list = self._data.to_pandas_index.join(
                     other.index.to_pandas(), how="outer"
                 )
             else:
@@ -2602,8 +2602,8 @@ def update(
         if not isinstance(other, DataFrame):
             other = DataFrame(other)
 
-        self_cols = self._data.to_pandas_index()
-        if not self_cols.equals(other._data.to_pandas_index()):
+        self_cols = self._data.to_pandas_index
+        if not self_cols.equals(other._data.to_pandas_index):
             other = other.reindex(self_cols, axis=1)
         if not self.index.equals(other.index):
             other = other.reindex(self.index, axis=0)
@@ -2639,7 +2639,7 @@ def __iter__(self):
     def __contains__(self, item):
         # This must check against containment in the pandas Index and not
         # self._column_names to handle NA, None, nan, etc. correctly.
-        return item in self._data.to_pandas_index()
+        return item in self._data.to_pandas_index
 
     @_performance_tracking
     def items(self):
@@ -2676,14 +2676,14 @@ def at(self):
 
     @property  # type: ignore
     @_external_only_api(
-        "Use _column_names instead, or _data.to_pandas_index() if a pandas "
+        "Use _column_names instead, or _data.to_pandas_index if a pandas "
         "index is absolutely necessary. For checking if the columns are a "
         "MultiIndex, use _data.multiindex."
     )
     @_performance_tracking
     def columns(self):
         """Returns a tuple of columns"""
-        return self._data.to_pandas_index()
+        return self._data.to_pandas_index
 
     @columns.setter  # type: ignore
     @_performance_tracking
@@ -2892,7 +2892,7 @@ def reindex(
             df = self
         else:
             columns = cudf.Index(columns)
-            intersection = self._data.to_pandas_index().intersection(
+            intersection = self._data.to_pandas_index.intersection(
                 columns.to_pandas()
             )
             df = self.loc[:, intersection]
@@ -3409,7 +3409,7 @@ def axes(self):
             Index(['key', 'k2', 'val', 'temp'], dtype='object')]
 
         """
-        return [self.index, self._data.to_pandas_index()]
+        return [self.index, self._data.to_pandas_index]
 
     def diff(self, periods=1, axis=0):
         """
@@ -4108,7 +4108,7 @@ def transpose(self):
             Not supporting *copy* because default and only behavior is
             copy=True
         """
-        index = self._data.to_pandas_index()
+        index = self._data.to_pandas_index
         columns = self.index.copy(deep=False)
         if self._num_columns == 0 or self._num_rows == 0:
             return DataFrame(index=index, columns=columns)
@@ -5514,7 +5514,7 @@ def to_pandas(
         }
 
         out_df = pd.DataFrame(out_data, index=out_index)
-        out_df.columns = self._data.to_pandas_index()
+        out_df.columns = self._data.to_pandas_index
 
         return out_df
 
@@ -6466,7 +6466,7 @@ def _reduce(
             source = self._get_columns_by_label(numeric_cols)
             if source.empty:
                 return Series(
-                    index=self._data.to_pandas_index()[:0]
+                    index=self._data.to_pandas_index[:0]
                     if axis == 0
                     else source.index,
                     dtype="float64",
@@ -6519,7 +6519,7 @@ def _reduce(
                         "Columns must all have the same dtype to "
                         f"perform {op=} with {axis=}"
                     )
-                pd_index = source._data.to_pandas_index()
+                pd_index = source._data.to_pandas_index
                 if source._data.multiindex:
                     idx = MultiIndex.from_pandas(pd_index)
                 else:
@@ -7221,7 +7221,7 @@ def stack(
         ]
         has_unnamed_levels = len(unnamed_levels_indices) > 0
 
-        column_name_idx = self._data.to_pandas_index()
+        column_name_idx = self._data.to_pandas_index
         # Construct new index from the levels specified by `level`
         named_levels = pd.MultiIndex.from_arrays(
             [column_name_idx.get_level_values(lv) for lv in level_indices]
@@ -7411,7 +7411,7 @@ def cov(self, min_periods=None, ddof: int = 1, numeric_only: bool = False):
             )
 
         cov = cupy.cov(self.values, ddof=ddof, rowvar=False)
-        cols = self._data.to_pandas_index()
+        cols = self._data.to_pandas_index
         df = DataFrame(cupy.asfortranarray(cov), index=cols)
         df._set_columns_like(self._data)
         return df
@@ -7454,7 +7454,7 @@ def corr(
             )
 
         corr = cupy.corrcoef(values, rowvar=False)
-        cols = self._data.to_pandas_index()
+        cols = self._data.to_pandas_index
         df = DataFrame(cupy.asfortranarray(corr), index=cols)
         df._set_columns_like(self._data)
         return df
@@ -7523,7 +7523,7 @@ def keys(self):
         >>> df.keys()
         Index([0, 1, 2, 3], dtype='int64')
         """
-        return self._data.to_pandas_index()
+        return self._data.to_pandas_index
 
     def itertuples(self, index=True, name="Pandas"):
         """
@@ -7757,7 +7757,7 @@ def nunique(self, axis=0, dropna: bool = True) -> Series:
             raise NotImplementedError("axis parameter is not supported yet.")
         counts = [col.distinct_count(dropna=dropna) for col in self._columns]
         return self._constructor_sliced(
-            counts, index=self._data.to_pandas_index()
+            counts, index=self._data.to_pandas_index
         )
 
     def _sample_axis_1(

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

@@ -3084,7 +3084,7 @@ def agg(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
 
         # drop the first level if we have a multiindex
         if result._data.nlevels > 1:
-            result.columns = result._data.to_pandas_index().droplevel(0)
+            result.columns = result._data.to_pandas_index.droplevel(0)
 
         return result
 

python/cudf/cudf/core/indexed_frame.py

@@ -1104,13 +1104,11 @@ def dot(self, other, reflect=False):
             lhs = self.reindex(index=common, copy=False).values
             rhs = other.reindex(index=common, copy=False).values
             if isinstance(other, cudf.DataFrame):
-                result_index = other._data.to_pandas_index()
+                result_index = other._data.to_pandas_index
         elif isinstance(self, cudf.DataFrame) and isinstance(
             other, (cudf.Series, cudf.DataFrame)
         ):
-            common = self._data.to_pandas_index().union(
-                other.index.to_pandas()
-            )
+            common = self._data.to_pandas_index.union(other.index.to_pandas())
             if len(common) > self._num_columns or len(common) > len(
                 other.index
             ):
@@ -1122,7 +1120,7 @@ def dot(self, other, reflect=False):
             rhs = other.reindex(index=common, copy=False).values
             lhs = lhs.values
             if isinstance(other, cudf.DataFrame):
-                result_cols = other._data.to_pandas_index()
+                result_cols = other._data.to_pandas_index
 
         elif isinstance(
             other, (cp.ndarray, np.ndarray)
@@ -2242,7 +2240,7 @@ def truncate(self, before=None, after=None, axis=0, copy=True):
         if not copy:
             raise ValueError("Truncating with copy=False is not supported.")
         axis = self._get_axis_from_axis_arg(axis)
-        ax = self.index if axis == 0 else self._data.to_pandas_index()
+        ax = self.index if axis == 0 else self._data.to_pandas_index
 
         if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
             raise ValueError("truncate requires a sorted index")
@@ -6735,7 +6733,7 @@ def _drop_rows_by_labels(
             return obj.__class__._from_data(
                 join_res.iloc[:, idx_nlv:]._data,
                 index=midx,
-                columns=obj._data.to_pandas_index(),
+                columns=obj._data.to_pandas_index,
             )
 
     else:

python/cudf/cudf/core/multiindex.py

@@ -1125,7 +1125,7 @@ def _concat(cls, objs) -> Self:
         # TODO: Verify if this is really necessary or if we can rely on
         # DataFrame._concat.
         if len(source_data) > 1:
-            colnames = source_data[0]._data.to_pandas_index()
+            colnames = source_data[0]._data.to_pandas_index
             for obj in source_data[1:]:
                 obj.columns = colnames
 
@@ -2070,7 +2070,7 @@ def _union(self, other, sort=None) -> Self:
 
         result_df = self_df.merge(other_df, on=col_names, how="outer")
         result_df = result_df.sort_values(
-            by=result_df._data.to_pandas_index()[self.nlevels :],
+            by=result_df._data.to_pandas_index[self.nlevels :],
             ignore_index=True,
         )
 

python/cudf/cudf/core/reshape.py

@@ -431,8 +431,9 @@ def concat(
 
             result_columns = (
                 objs[0]
-                ._data.to_pandas_index()
-                .append([obj._data.to_pandas_index() for obj in objs[1:]])
+                ._data.to_pandas_index.append(
+                    [obj._data.to_pandas_index for obj in objs[1:]]
+                )
                 .unique()
             )
 
@@ -689,7 +690,7 @@ def _tile(A, reps):
     if not value_vars:
         # TODO: Use frame._data.label_dtype when it's more consistently set
         var_data = cudf.Series(
-            value_vars, dtype=frame._data.to_pandas_index().dtype
+            value_vars, dtype=frame._data.to_pandas_index.dtype
         )
     else:
         var_data = (
@@ -1096,6 +1097,7 @@ def pivot(
         2  <NA>  <NA>  three
 
     """
+    # import pdb;pdb.set_trace()
     values_is_list = True
     if values is no_default:
         already_selected = set(
@@ -1272,7 +1274,7 @@ def unstack(df, level, fill_value=None, sort: bool = True):
         res = df.T.stack(future_stack=False)
         # Result's index is a multiindex
         res.index.names = (
-            tuple(df._data.to_pandas_index().names) + df.index.names
+            tuple(df._data.to_pandas_index.names) + df.index.names
         )
         return res
     else:

python/cudf/cudf/testing/testing.py

@@ -692,8 +692,8 @@ def assert_frame_equal(
     )
 
     pd.testing.assert_index_equal(
-        left._data.to_pandas_index(),
-        right._data.to_pandas_index(),
+        left._data.to_pandas_index,
+        right._data.to_pandas_index,
         exact=check_column_type,
         check_names=check_names,
         check_exact=check_exact,

python/cudf/cudf/tests/test_column_accessor.py

@@ -64,7 +64,7 @@ def test_to_pandas_simple(simple_data):
     # Index([], dtype='object'), and `integer` for RangeIndex()
     # to ignore this `inferred_type` comparison, we pass exact=False.
     assert_eq(
-        ca.to_pandas_index(),
+        ca.to_pandas_index,
         pd.DataFrame(
             {key: value.values_host for key, value in simple_data.items()}
         ).columns,
@@ -75,7 +75,7 @@ def test_to_pandas_simple(simple_data):
 def test_to_pandas_multiindex(mi_data):
     ca = ColumnAccessor(mi_data, multiindex=True)
     assert_eq(
-        ca.to_pandas_index(),
+        ca.to_pandas_index,
         pd.DataFrame(
             {key: value.values_host for key, value in mi_data.items()}
         ).columns,
@@ -89,7 +89,7 @@ def test_to_pandas_multiindex_names():
         level_names=("foo", "bar"),
     )
     assert_eq(
-        ca.to_pandas_index(),
+        ca.to_pandas_index,
         pd.MultiIndex.from_tuples(
             (("a", "b"), ("c", "d")), names=("foo", "bar")
         ),