enh: pandas-like group-by complex-agg

narwhals/_pandas_like/group_by.py

@@ -45,6 +45,7 @@ def agg(
             **named_aggs,
         )
         implementation: Implementation = self._df._implementation
+        backend = self._df.__native_namespace__()
         output_names: list[str] = copy(self._keys)
         for expr in exprs:
             if expr._output_names is None:
@@ -64,7 +65,7 @@ def agg(
             self._from_native_dataframe,
             dataframe_is_empty=self._df._native_dataframe.empty,
             implementation=implementation,
-            backend_version=self._df._backend_version,
+            backend=backend,
         )
 
     def _from_native_dataframe(self, df: PandasLikeDataFrame) -> PandasLikeDataFrame:
@@ -99,8 +100,8 @@ def agg_pandas(
     from_dataframe: Callable[[Any], PandasLikeDataFrame],
     *,
     implementation: Any,
-    backend_version: tuple[int, ...],
     dataframe_is_empty: bool,
+    backend: Any,
 ) -> PandasLikeDataFrame:
     """
     This should be the fastpath, but cuDF is too far behind to use it.
@@ -189,11 +190,8 @@ def func(df: Any) -> Any:
             implementation=implementation,
         )
 
-    if implementation is Implementation.PANDAS and backend_version >= (2, 2):
-        result_complex = grouped.apply(func, include_groups=False)
-    else:  # pragma: no cover
-        result_complex = grouped.apply(func)
-
-    result = result_complex.reset_index()
-
-    return from_dataframe(result.loc[:, output_names])
+    output_values = [
+        dict(zip(output_names, (*grp_name, *func(grp_values))))
+        for grp_name, grp_values in grouped
+    ]
+    return from_dataframe(backend.DataFrame(output_values))