Updating _resolve_numeric_only function of GroupBy

pandas/core/groupby/groupby.py

@@ -10,6 +10,7 @@ class providing the base-class of operations.
 
 from contextlib import contextmanager
 import datetime
+import numpy as np
 from functools import (
     partial,
     wraps,
@@ -1119,7 +1120,15 @@ def _resolve_numeric_only(self, numeric_only: bool | lib.NoDefault) -> bool:
             # i.e. not explicitly passed by user
             if self.obj.ndim == 2:
                 # i.e. DataFrameGroupBy
-                numeric_only = True
+                # Checking if the dataframe has non-numeric features
+
+                non_num_cols = self.obj.select_dtypes(
+                    exclude=[np.number, np.datetime64, np.timedelta64]).columns
+
+                if len(set(non_num_cols) - set(self.keys)) > 0:
+                    numeric_only = False
+                else:
+                    numeric_only = True
             else:
                 numeric_only = False
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -113,7 +113,19 @@ def test_groupby_aggregation_mixed_dtype():
     g = df.groupby(["by1", "by2"])
     result = g[["v1", "v2"]].mean()
     tm.assert_frame_equal(result, expected)
+    expected2 = DataFrame(
+        {
+            "v1": [15, 7, 9, 3, 3, 5],
+            "v2": [165, 77, 99, 33, 33, 55],
+            "by2": [293, 194, 0, 'damp', 'dry', 'wetred']
+        },
+        index=Index([1, 2, 12, 'big', 'blue', 'red'],
+                      dtype='object', name='by1'),
+    )
 
+    g = df.groupby(["by1"])
+    result = g.sum()
+    tm.assert_frame_equal(result, expected2)
 
 def test_groupby_aggregation_multi_level_column():
     # GH 29772