Cleanup.

python-package/xgboost/core.py

@@ -3201,11 +3201,7 @@ def trees_to_dataframe(self, fmap: PathLike = "") -> DataFrame:
             }
         )
 
-        if callable(getattr(df, "sort_values", None)):
-            # pylint: disable=no-member
-            return df.sort_values(["Tree", "Node"]).reset_index(drop=True)
-        # pylint: disable=no-member
-        return df.sort(["Tree", "Node"]).reset_index(drop=True)
+        return df.sort_values(["Tree", "Node"]).reset_index(drop=True)
 
     def _assign_dmatrix_features(self, data: DMatrix) -> None:
         if data.num_row() == 0:

python-package/xgboost/dask/data.py

@@ -192,26 +192,25 @@ def sort_data_by_qid(**kwargs: List[Any]) -> Dict[str, List[Any]]:
     else:
         from pandas import DataFrame
 
-    def get_dict(i: int) -> dict:
-        def _get(attr: Optional[List[Any]]) -> Optional[Any]:
+    def get_dict(i: int) -> Dict[str, list]:
+        """Return a dictionary containing all the meta info and all partitions."""
+
+        def _get(attr: Optional[List[Any]]) -> Optional[list]:
             if attr is not None:
                 return attr[i]
             return None
 
-        data = {k: _get(kwargs.get(k, None)) for k in meta}
-        data = {k: v for k, v in data.items() if v is not None}
+        data_opt = {name: _get(kwargs.get(name, None)) for name in meta}
+        # Filter out None values.
+        data = {k: v for k, v in data_opt.items() if v is not None}
         return data
 
-    # This function was created for the `dd.from_mapq constructor for sorting with a
-    # Dask DF. We did not proceed with that route but kept some of the utilities. It
-    # might be necessary to try again in the future since concatenating and sorting is
-    # extremely expensive in terms of memory usage.
     def map_fn(i: int) -> pd.DataFrame:
         data = get_dict(i)
         return DataFrame(data)
 
-    qid_parts = [map_fn(i) for i in range(n_parts)]
-    dfq = concat(qid_parts)
+    meta_parts = [map_fn(i) for i in range(n_parts)]
+    dfq = concat(meta_parts)
     if dfq.qid.is_monotonic_increasing:
         return kwargs