BUG/TST: fix and test for timezone drop in GroupBy.shift/bfill/ffill

doc/source/whatsnew/v0.25.1.rst

@@ -120,6 +120,7 @@ Groupby/resample/rolling
 - Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` where applying a timezone conversion lambda function would drop timezone information (:issue:`27496`)
 - Bug in windowing over read-only arrays (:issue:`27766`)
 - Fixed segfault in `pandas.core.groupby.DataFrameGroupBy.quantile` when an invalid quantile was passed (:issue:`27470`)
+- Bug in :meth:`pandas.core.groupby.GroupBy.shift`, :meth:`pandas.core.groupby.GroupBy.bfill` and :meth:`pandas.core.groupby.GroupBy.ffill` where timezone information would be dropped (:issue:`27992`)
 -
 
 Reshaping

pandas/core/groupby/groupby.py

@@ -2220,26 +2220,28 @@ def _get_cythonized_result(
         base_func = getattr(libgroupby, how)
 
         for name, obj in self._iterate_slices():
+            values = obj._data._values
+
             if aggregate:
                 result_sz = ngroups
             else:
-                result_sz = len(obj.values)
+                result_sz = len(values)
 
             if not cython_dtype:
-                cython_dtype = obj.values.dtype
+                cython_dtype = values.dtype
 
             result = np.zeros(result_sz, dtype=cython_dtype)
             func = partial(base_func, result, labels)
             inferences = None
 
             if needs_values:
-                vals = obj.values
+                vals = values
                 if pre_processing:
                     vals, inferences = pre_processing(vals)
                 func = partial(func, vals)
 
             if needs_mask:
-                mask = isna(obj.values).view(np.uint8)
+                mask = isna(values).view(np.uint8)
                 func = partial(func, mask)
 
             if needs_ngroups:
@@ -2248,7 +2250,7 @@ def _get_cythonized_result(
             func(**kwargs)  # Call func to modify indexer values in place
 
             if result_is_index:
-                result = algorithms.take_nd(obj.values, result)
+                result = algorithms.take_nd(values, result)
 
             if post_processing:
                 result = post_processing(result, inferences)

pandas/tests/groupby/test_groupby.py

@@ -1882,3 +1882,69 @@ def test_groupby_axis_1(group_name):
     results = df.groupby(group_name, axis=1).sum()
     expected = df.T.groupby(group_name).sum().T
     assert_frame_equal(results, expected)
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        (
+            "shift",
+            {
+                "time": [
+                    None,
+                    None,
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    None,
+                    None,
+                ]
+            },
+        ),
+        (
+            "bfill",
+            {
+                "time": [
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                ]
+            },
+        ),
+        (
+            "ffill",
+            {
+                "time": [
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                ]
+            },
+        ),
+    ],
+)
+def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
+    # GH27992: Check that timezone does not drop in shift, bfill, and ffill
+    tz = tz_naive_fixture
+    data = {
+        "id": ["A", "B", "A", "B", "A", "B"],
+        "time": [
+            Timestamp("2019-01-01 12:00:00"),
+            Timestamp("2019-01-01 12:30:00"),
+            None,
+            None,
+            Timestamp("2019-01-01 14:00:00"),
+            Timestamp("2019-01-01 14:30:00"),
+        ],
+    }
+    df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))
+
+    grouped = df.groupby("id")
+    result = getattr(grouped, op)()
+    expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
+    assert_frame_equal(result, expected)