Generate correct metadata in to_arrow with preserve_index=True

When preserving the index and we have a RangeIndex, we must
materialize it, and write that information in the metadata correctly.

- Closes rapidsai#14159

python/cudf/cudf/core/dataframe.py

@@ -5485,14 +5485,18 @@ def from_arrow(cls, table):
         return out
 
     @_cudf_nvtx_annotate
-    def to_arrow(self, preserve_index=True):
+    def to_arrow(self, preserve_index=None):
         """
         Convert to a PyArrow Table.
 
         Parameters
         ----------
-        preserve_index : bool, default True
-            whether index column and its meta data needs to be saved or not
+        preserve_index : bool, optional
+            whether index column and its meta data needs to be saved
+            or not. The default of None will store the index as a
+            column, except for a RangeIndex which is stored as
+            metadata only. Setting preserve_index to True will force
+            a RangeIndex to be materialized.
 
         Returns
         -------
@@ -5523,34 +5527,35 @@ def to_arrow(self, preserve_index=True):
 
         data = self.copy(deep=False)
         index_descr = []
-        if preserve_index:
-            if isinstance(self.index, cudf.RangeIndex):
+        write_index = preserve_index is not False
+        keep_range_index = write_index and preserve_index is None
+        index = self.index
+        if write_index:
+            if isinstance(index, cudf.RangeIndex) and keep_range_index:
                 descr = {
                     "kind": "range",
-                    "name": self.index.name,
-                    "start": self.index._start,
-                    "stop": self.index._stop,
+                    "name": index.name,
+                    "start": index._start,
+                    "stop": index._stop,
                     "step": 1,
                 }
             else:
-                if isinstance(self.index, MultiIndex):
+                if isinstance(index, cudf.RangeIndex):
+                    index = index._as_int_index()
+                    index.name = "__index_level_0__"
+                if isinstance(index, MultiIndex):
                     gen_names = tuple(
-                        f"level_{i}"
-                        for i, _ in enumerate(self.index._data.names)
+                        f"level_{i}" for i, _ in enumerate(index._data.names)
                     )
                 else:
                     gen_names = (
-                        self.index.names
-                        if self.index.name is not None
-                        else ("index",)
+                        index.names if index.name is not None else ("index",)
                     )
-                for gen_name, col_name in zip(
-                    gen_names, self.index._data.names
-                ):
+                for gen_name, col_name in zip(gen_names, index._data.names):
                     data._insert(
                         data.shape[1],
                         gen_name,
-                        self.index._data[col_name],
+                        index._data[col_name],
                     )
                 descr = gen_names[0]
             index_descr.append(descr)
@@ -5560,7 +5565,7 @@ def to_arrow(self, preserve_index=True):
             columns_to_convert=[self[col] for col in self._data.names],
             df=self,
             column_names=out.schema.names,
-            index_levels=[self.index],
+            index_levels=[index],
             index_descriptors=index_descr,
             preserve_index=preserve_index,
             types=out.schema.types,