BUG: Fix apply on series backed by datetime aware values (#25959)

Author: ThomasKluiters

doc/source/whatsnew/v0.25.0.rst

@@ -387,6 +387,7 @@ Reshaping
 - Bug in :func:`concat` where order of ``OrderedDict`` (and ``dict`` in Python 3.6+) is not respected, when passed in as  ``objs`` argument (:issue:`21510`)
 - Bug in :func:`concat` where the resulting ``freq`` of two :class:`DatetimeIndex` with the same ``freq`` would be dropped (:issue:`3232`).
 - Bug in :func:`merge` where merging with equivalent Categorical dtypes was raising an error (:issue:`22501`)
+- Bug in :func:`Series.apply` failed when the series is a timezone aware :class:`DatetimeIndex` (:issue:`25959`)
 
 Sparse
 ^^^^^^

pandas/core/series.py

@@ -3687,7 +3687,11 @@ def f(x):
 
         if len(mapped) and isinstance(mapped[0], Series):
             from pandas.core.frame import DataFrame
-            return DataFrame(mapped.tolist(), index=self.index)
+            # GH 25959 we don't need to call tolist
+            # if we've been given an ExtensionArray
+            if not is_extension_array_dtype(mapped.dtype):
+                mapped = mapped.tolist()
+            return DataFrame(mapped, index=self.index)
         else:
             return self._constructor(mapped,
                                      index=self.index).__finalize__(self)

pandas/tests/series/test_apply.py

@@ -677,3 +677,16 @@ def test_map_missing_mixed(self, vals, mapping, exp):
         result = s.map(mapping)
 
         tm.assert_series_equal(result, pd.Series(exp))
+
+    @pytest.mark.parametrize("dti,exp", [
+        (Series([1, 2], index=pd.DatetimeIndex([0, 31536000000])),
+            DataFrame(np.repeat([[1, 2]], 2, axis=0), dtype='int64')),
+        (tm.makeTimeSeries(nper=30),
+            DataFrame(np.repeat([[1, 2]], 30, axis=0), dtype='int64'))
+    ])
+    def test_apply_on_date_time_index_aware_series(self, dti, exp):
+        # GH 25959
+        # Calling apply on a localized time series should not cause an error
+        index = dti.tz_localize('UTC').index
+        result = pd.Series(index).apply(lambda x: pd.Series([1, 2]))
+        assert_frame_equal(result, exp)