BUG: DataFrame.resample raised AmbiguousTimeError at a midnight DST transition (#33137)

* BUG: DataFrame.resample raised AmbiguousTimeError at a midnight DST transition

* whatsnew grammar

Co-authored-by: Matt Roeschke <[email protected]>

Author: mroeschke

doc/source/whatsnew/v1.1.0.rst

@@ -423,7 +423,7 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.apply` raises ``ValueError`` when the ``by`` axis is not sorted and has duplicates and the applied ``func`` does not mutate passed in objects (:issue:`30667`)
 - Bug in :meth:`DataFrameGroupby.transform` produces incorrect result with transformation functions (:issue:`30918`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` produces inconsistent type when aggregating Boolean series (:issue:`32894`)
-
+- Bug in :meth:`DataFrame.resample` where an ``AmbiguousTimeError`` would be raised when the resulting timezone aware :class:`DatetimeIndex` had a DST transition at midnight (:issue:`25758`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/resample.py

@@ -1422,13 +1422,15 @@ def _get_time_bins(self, ax):
         # because replace() will swallow the nanosecond part
         # thus last bin maybe slightly before the end if the end contains
         # nanosecond part and lead to `Values falls after last bin` error
+        # GH 25758: If DST lands at midnight (e.g. 'America/Havana'), user feedback
+        # has noted that ambiguous=True provides the most sensible result
         binner = labels = date_range(
             freq=self.freq,
             start=first,
             end=last,
             tz=ax.tz,
             name=ax.name,
-            ambiguous="infer",
+            ambiguous=True,
             nonexistent="shift_forward",
         )
 

pandas/tests/resample/test_datetime_index.py

@@ -1440,6 +1440,24 @@ def test_downsample_across_dst_weekly():
     tm.assert_series_equal(result, expected)
 
 
+def test_downsample_dst_at_midnight():
+    # GH 25758
+    start = datetime(2018, 11, 3, 12)
+    end = datetime(2018, 11, 5, 12)
+    index = pd.date_range(start, end, freq="1H")
+    index = index.tz_localize("UTC").tz_convert("America/Havana")
+    data = list(range(len(index)))
+    dataframe = pd.DataFrame(data, index=index)
+    result = dataframe.groupby(pd.Grouper(freq="1D")).mean()
+    expected = DataFrame(
+        [7.5, 28.0, 44.5],
+        index=date_range("2018-11-03", periods=3).tz_localize(
+            "America/Havana", ambiguous=True
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 def test_resample_with_nat():
     # GH 13020
     index = DatetimeIndex(