🐛 don't remove timezone-awareness when using the method from DataFrame

Author: MarcoGorelli

doc/source/whatsnew/v1.0.0.rst

@@ -701,6 +701,7 @@ Datetimelike
 - Bug in :meth:`DatetimeIndex.strftime` and :meth:`Series.dt.strftime` where ``NaT`` was converted to the string ``'NaT'`` instead of ``np.nan`` (:issue:`29578`)
 - Bug in :attr:`Timestamp.resolution` being a property instead of a class attribute (:issue:`29910`)
 - Bug in :func:`pandas.to_datetime` when called with ``None`` raising ``TypeError`` instead of returning ``NaT`` (:issue:`30011`)
+- Bug in :meth:`DataFrame.append` would remove the timezone-awareness of new data (:issue:`30238`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/frame.py

@@ -6762,7 +6762,7 @@ def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
                 combined_columns = self.columns.astype(object).append(idx_diff)
             other = other.reindex(combined_columns, copy=False)
             other = DataFrame(
-                other.values.reshape((1, len(other))),
+                other.transpose(),
                 index=index,
                 columns=combined_columns,
             )

pandas/tests/frame/test_combine_concat.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import pytest
+import pytz
 
 import pandas as pd
 from pandas import DataFrame, Index, Series, Timestamp, date_range
@@ -288,6 +289,20 @@ def test_append_dtypes(self):
         expected = DataFrame({"bar": Series([Timestamp("20130101"), 1])})
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "timestamp, timezone",
+        [
+            ("2019-07-19 07:04:57+0100", pytz.FixedOffset(60)),
+            ("2019-07-19 07:04:57", None),
+        ],
+    )
+    def test_append_timestamps_aware_or_naive(self, timestamp, timezone):
+        # GH 30238
+        df = pd.DataFrame([pd.Timestamp(timestamp, tz=timezone)])
+        result = df.append(df.iloc[0]).iloc[-1]
+        expected = pd.Series(pd.Timestamp(timestamp, tz=timezone), name=0)
+        pd.testing.assert_series_equal(result, expected)
+
     def test_update(self):
         df = DataFrame(
             [[1.5, np.nan, 3.0], [1.5, np.nan, 3.0], [1.5, np.nan, 3], [1.5, np.nan, 3]]