BUG: DataFrame with scalar tzaware Timestamp

doc/source/whatsnew/v1.4.0.rst

@@ -573,6 +573,7 @@ Conversion
 - Bug in :class:`IntegerDtype` not allowing coercion from string dtype (:issue:`25472`)
 - Bug in :func:`to_datetime` with ``arg:xr.DataArray`` and ``unit="ns"`` specified raises TypeError (:issue:`44053`)
 - Bug in :meth:`DataFrame.convert_dtypes` not returning the correct type when a subclass does not overload :meth:`_constructor_sliced` (:issue:`43201`)
+- Bug in creating a :class:`DataFrame` from a timezone-aware :class:`Timestamp` scalar near a Daylight Savings Time transition (:issue:`42505`)
 -
 
 Strings

pandas/core/arrays/datetimes.py

@@ -2084,8 +2084,13 @@ def _sequence_to_dt64ns(
             )
             if tz and inferred_tz:
                 #  two timezones: convert to intended from base UTC repr
-                data = tzconversion.tz_convert_from_utc(data.view("i8"), tz)
-                data = data.view(DT64NS_DTYPE)
+                if data.dtype == "i8":
+                    # GH#42505
+                    # by convention, these are _already_ UTC, e.g
+                    return data.view(DT64NS_DTYPE), tz, None
+
+                utc_vals = tzconversion.tz_convert_from_utc(data.view("i8"), tz)
+                data = utc_vals.view(DT64NS_DTYPE)
             elif inferred_tz:
                 tz = inferred_tz
 

pandas/tests/frame/test_constructors.py

@@ -70,6 +70,19 @@
 
 
 class TestDataFrameConstructors:
+    def test_constructor_dict_with_tzaware_scalar(self):
+        # GH#42505
+        dt = Timestamp("2019-11-03 01:00:00-0700").tz_convert("America/Los_Angeles")
+
+        df = DataFrame({"dt": dt}, index=[0])
+        expected = DataFrame({"dt": [dt]})
+        tm.assert_frame_equal(df, expected)
+
+        # Non-homogeneous
+        df = DataFrame({"dt": dt, "value": [1]})
+        expected = DataFrame({"dt": [dt], "value": [1]})
+        tm.assert_frame_equal(df, expected)
+
     def test_construct_ndarray_with_nas_and_int_dtype(self):
         # GH#26919 match Series by not casting np.nan to meaningless int
         arr = np.array([[1, np.nan], [2, 3]])