Backport PR #38094 on branch 1.1.x (REGR: fix regression in groupby aggregation with out-of-bounds datetimes)

doc/source/whatsnew/v1.1.5.rst

@@ -20,6 +20,7 @@ Fixed regressions
 - Fixed regression in inplace operations on :class:`Series` with ``ExtensionDtype`` with NumPy dtyped operand (:issue:`37910`)
 - Fixed regression in metadata propagation for ``groupby`` iterator (:issue:`37343`)
 - Fixed regression in indexing on a :class:`Series` with ``CategoricalDtype`` after unpickling (:issue:`37631`)
+- Fixed regression in :meth:`DataFrame.groupby` aggregation with out-of-bounds datetime objects in an object-dtype column (:issue:`36003`)
 - Fixed regression in ``df.groupby(..).rolling(..)`` with the resulting :class:`MultiIndex` when grouping by a label that is in the index (:issue:`37641`)
 - Fixed regression in :meth:`DataFrame.fillna` not filling ``NaN`` after other operations such as :meth:`DataFrame.pivot` (:issue:`36495`).
 

pandas/_libs/reduction.pyx

@@ -46,7 +46,9 @@ cdef class _BaseGrouper:
                                     Slider islider, Slider vslider):
         if cached_typ is None:
             cached_ityp = self.ityp(islider.buf)
-            cached_typ = self.typ(vslider.buf, index=cached_ityp, name=self.name)
+            cached_typ = self.typ(
+                vslider.buf, dtype=vslider.buf.dtype, index=cached_ityp, name=self.name
+            )
         else:
             # See the comment in indexes/base.py about _index_data.
             # We need this for EA-backed indexes that have a reference

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -1,6 +1,7 @@
 """
 test .agg behavior / note that .apply is tested generally in test_groupby.py
 """
+import datetime
 import functools
 from functools import partial
 
@@ -1089,3 +1090,21 @@ def test_agg_no_suffix_index():
     result = df["A"].agg(["sum", lambda x: x.sum(), lambda x: x.sum()])
     expected = pd.Series([12, 12, 12], index=["sum", "<lambda>", "<lambda>"], name="A")
     tm.assert_series_equal(result, expected)
+
+
+def test_aggregate_datetime_objects():
+    # https://github.com/pandas-dev/pandas/issues/36003
+    # ensure we don't raise an error but keep object dtype for out-of-bounds
+    # datetimes
+    df = DataFrame(
+        {
+            "A": ["X", "Y"],
+            "B": [
+                datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
+                datetime.datetime(3005, 1, 1, 10, 30, 23, 540000),
+            ],
+        }
+    )
+    result = df.groupby("A").B.max()
+    expected = df.set_index("A")["B"]
+    tm.assert_series_equal(result, expected)