diff --git a/narwhals/pandas_like/group_by.py b/narwhals/pandas_like/group_by.py
index b2bbe37bc..0170f5cde 100644
--- a/narwhals/pandas_like/group_by.py
+++ b/narwhals/pandas_like/group_by.py
@@ -71,7 +71,7 @@ def _from_dataframe(self, df: PandasDataFrame) -> PandasDataFrame:
         )
 
 
-def agg_pandas(  # noqa: PLR0913,PLR0915
+def agg_pandas(  # noqa: PLR0913
     grouped: Any,
     exprs: list[PandasExpr],
     keys: list[str],
@@ -89,32 +89,31 @@ def agg_pandas(  # noqa: PLR0913,PLR0915
 
     from narwhals.pandas_like.namespace import PandasNamespace
 
-    simple_aggs = []
-    complex_aggs = []
+    all_simple_aggs = True
     for expr in exprs:
-        if is_simple_aggregation(expr):
-            simple_aggs.append(expr)
-        else:
-            complex_aggs.append(expr)
-    simple_aggregations = {}
-    for expr in simple_aggs:
-        if expr._depth == 0:
-            # e.g. agg(pl.len())
-            assert expr._output_names is not None
-            for output_name in expr._output_names:
-                simple_aggregations[output_name] = (
-                    keys[0],
-                    expr._function_name.replace("len", "size"),
-                )
-            continue
+        if not is_simple_aggregation(expr):
+            all_simple_aggs = False
+            break
 
-        assert expr._root_names is not None
-        assert expr._output_names is not None
-        for root_name, output_name in zip(expr._root_names, expr._output_names):
-            name = remove_prefix(expr._function_name, "col->")
-            simple_aggregations[output_name] = (root_name, name)
+    if all_simple_aggs:
+        simple_aggregations: dict[str, tuple[str, str]] = {}
+        for expr in exprs:
+            if expr._depth == 0:
+                # e.g. agg(pl.len())
+                assert expr._output_names is not None
+                for output_name in expr._output_names:
+                    simple_aggregations[output_name] = (
+                        keys[0],
+                        expr._function_name.replace("len", "size"),
+                    )
+                continue
+
+            assert expr._root_names is not None
+            assert expr._output_names is not None
+            for root_name, output_name in zip(expr._root_names, expr._output_names):
+                name = remove_prefix(expr._function_name, "col->")
+                simple_aggregations[output_name] = (root_name, name)
 
-    if simple_aggregations:
         aggs = collections.defaultdict(list)
         name_mapping = {}
         for output_name, named_agg in simple_aggregations.items():
@@ -128,49 +127,37 @@ def agg_pandas(  # noqa: PLR0913,PLR0915
             ) from exc
         result_simple.columns = [f"{a}_{b}" for a, b in result_simple.columns]
         result_simple = result_simple.rename(columns=name_mapping).reset_index()
-    else:
-        result_simple = None
+        return from_dataframe(result_simple.loc[:, output_names])
+
+    warnings.warn(
+        "Found complex group-by expression, which can't be expressed efficiently with the "
+        "pandas API. If you can, please rewrite your query such that group-by aggregations "
+        "are simple (e.g. mean, std, min, max, ...).",
+        UserWarning,
+        stacklevel=2,
+    )
 
     plx = PandasNamespace(implementation=implementation)
 
     def func(df: Any) -> Any:
         out_group = []
         out_names = []
-        for expr in complex_aggs:
+        for expr in exprs:
             results_keys = expr._call(from_dataframe(df))
             for result_keys in results_keys:
                 out_group.append(item(result_keys._series))
                 out_names.append(result_keys.name)
         return plx.make_native_series(name="", data=out_group, index=out_names)
 
-    if complex_aggs:
-        warnings.warn(
-            "Found complex group-by expression, which can't be expressed efficiently with the "
-            "pandas API. If you can, please rewrite your query such that group-by aggregations "
-            "are simple (e.g. mean, std, min, max, ...).",
-            UserWarning,
-            stacklevel=2,
-        )
-        if implementation == "pandas":
-            import pandas as pd
-
-            if parse_version(pd.__version__) < parse_version("2.2.0"):  # pragma: no cover
-                result_complex = grouped.apply(func)
-            else:
-                result_complex = grouped.apply(func, include_groups=False)
-        else:  # pragma: no cover
+    if implementation == "pandas":
+        import pandas as pd
+
+        if parse_version(pd.__version__) < parse_version("2.2.0"):  # pragma: no cover
             result_complex = grouped.apply(func)
+        else:
+            result_complex = grouped.apply(func, include_groups=False)
+    else:  # pragma: no cover
+        result_complex = grouped.apply(func)
 
-    if result_simple is not None and not complex_aggs:
-        result = result_simple
-    elif result_simple is not None and complex_aggs:
-        result = pd.concat(
-            [result_simple, result_complex.reset_index(drop=True)],
-            axis=1,
-            copy=False,
-        )
-    elif complex_aggs:
-        result = result_complex.reset_index()
-    else:
-        raise AssertionError("At least one aggregation should have been passed")
+    result = result_complex.reset_index()
     return from_dataframe(result.loc[:, output_names])