Updating _resolve_numeric_only function of GroupBy

pandas/core/groupby/groupby.py

@@ -1102,14 +1102,11 @@ def _wrap_applied_output(self, data, keys, values, not_indexed_same: bool = Fals
     def _resolve_numeric_only(self, numeric_only: bool | lib.NoDefault) -> bool:
         """
         Determine subclass-specific default value for 'numeric_only'.
-
         For SeriesGroupBy we want the default to be False (to match Series behavior).
         For DataFrameGroupBy we want it to be True (for backwards-compat).
-
         Parameters
         ----------
         numeric_only : bool or lib.no_default
-
         Returns
         -------
         bool
@@ -1120,12 +1117,15 @@ def _resolve_numeric_only(self, numeric_only: bool | lib.NoDefault) -> bool:
             if self.obj.ndim == 2:
                 # i.e. DataFrameGroupBy
                 numeric_only = True
+                obj = self._obj_with_exclusions
+                check = obj._get_numeric_data()
+                if len(obj.columns) and not len(check.columns) and not obj.empty:
+                    warnings.warn("... Explicitly pass numeric_only ...")
+                    numeric_only = False
+
             else:
                 numeric_only = False
-
-        # error: Incompatible return value type (got "Union[bool, NoDefault]",
-        # expected "bool")
-        return numeric_only  # type: ignore[return-value]
+        return numeric_only
 
     # -----------------------------------------------------------------
     # numba

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -17,8 +17,10 @@
 from pandas import (
     DataFrame,
     Index,
+    Int64Index,
     MultiIndex,
     Series,
+    Timedelta,
     concat,
 )
 import pandas._testing as tm
@@ -115,6 +117,69 @@ def test_groupby_aggregation_mixed_dtype():
     tm.assert_frame_equal(result, expected)
 
 
+def test_groupby_aggregation_non_numeric_dtype():
+    # GH #43108
+    df = DataFrame(
+        [["M", [1]], ["M", [1]], ["W", [10]], ["W", [20]]], columns=["MW", "v"]
+    )
+
+    expected = DataFrame(
+        {
+            "v": [[1, 1], [10, 20]],
+        },
+        index=Index(["M", "W"], dtype="object", name="MW"),
+    )
+
+    with tm.assert_produces_warning(UserWarning):
+        g = df.groupby(by=["MW"])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_multi_non_numeric_dtype():
+    # GH #42395
+    df = DataFrame(
+        {
+            "x": [1, 0, 1, 1, 0],
+            "y": [Timedelta(i, "days") for i in range(1, 6)],
+            "z": [Timedelta(i * 10, "days") for i in range(1, 6)],
+        }
+    )
+
+    expected = DataFrame(
+        {
+            "y": [Timedelta(i, "days") for i in range(7, 9)],
+            "z": [Timedelta(i * 10, "days") for i in range(7, 9)],
+        },
+        index=Int64Index([0, 1], dtype="int64", name="x"),
+    )
+
+    with tm.assert_produces_warning(UserWarning):
+        g = df.groupby(by=["x"])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_numeric_with_non_numeric_dtype():
+    # GH #43108
+    df = DataFrame(
+        {
+            "x": [1, 0, 1, 1, 0],
+            "y": [Timedelta(i, "days") for i in range(1, 6)],
+            "z": [i for i in range(1, 6)],
+        }
+    )
+
+    expected = DataFrame(
+        {"z": [7, 8]},
+        index=Int64Index([0, 1], dtype="int64", name="x"),
+    )
+
+    g = df.groupby(by=["x"])
+    result = g.sum()
+    tm.assert_frame_equal(result, expected)
+
+
 def test_groupby_aggregation_multi_level_column():
     # GH 29772
     lst = [