fix: scalar timestamp assignment (pandas-dev#19843)

Author: DylanDmitri

pandas/core/frame.py

@@ -2868,9 +2868,15 @@ def reindexer(value):
                 value = maybe_infer_to_datetimelike(value)
 
         else:
-            # upcast the scalar
+            # cast ignores pandas dtypes. so save the dtype first
+            from pandas.core.dtypes.cast import infer_dtype_from_scalar
+            pd_dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
+
+            # upcast
             value = cast_scalar_to_array(len(self.index), value)
-            value = maybe_cast_to_datetime(value, value.dtype)
+
+            # then add dtype back in
+            value = maybe_cast_to_datetime(value, pd_dtype)
 
         # return internal types directly
         if is_extension_type(value) or is_extension_array_dtype(value):

pandas/tests/scalar/timestamp/test_assignment_dtype.py

@@ -0,0 +1,23 @@
+import pandas as pd
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+
+# referencing #19843: scalar assignment of a tz-aware is object dtype
+
+class TestTimestampProperties(object):
+
+    def test_scalar_assignment(self):
+        df = pd.DataFrame(index=(0, 1, 2))
+
+        df['now'] = pd.Timestamp('20130101', tz='UTC')
+
+        assert isinstance(df.dtypes[0], DatetimeTZDtype)
+
+    def test_datetime_index_assignment(self):
+        df = pd.DataFrame(index=(0, 1, 2))
+
+        di = pd.DatetimeIndex(
+            [pd.Timestamp('20130101', tz='UTC')]).repeat(len(df))
+        df['now'] = di
+
+        assert isinstance(df.dtypes[0], DatetimeTZDtype)