BUG: Interval with Timestamp with tz shows tz

doc/source/whatsnew/v2.2.0.rst

@@ -207,7 +207,7 @@ Strings
 
 Interval
 ^^^^^^^^
--
+- Bug in :class:`Interval` ``__repr__`` not displaying UTC offsets for :class:`Timestamp` bounds. Additionally the hour, minute and second components will now be shown. (:issue:`55015`)
 -
 
 Indexing

pandas/_libs/interval.pyx

@@ -478,31 +478,16 @@ cdef class Interval(IntervalMixin):
         args = (self.left, self.right, self.closed)
         return (type(self), args)
 
-    def _repr_base(self):
-        left = self.left
-        right = self.right
-
-        # TODO: need more general formatting methodology here
-        if isinstance(left, _Timestamp) and isinstance(right, _Timestamp):
-            left = left._short_repr
-            right = right._short_repr
-
-        return left, right
-
     def __repr__(self) -> str:
-
-        left, right = self._repr_base()
-        disp = str if isinstance(left, np.generic) else repr
+        disp = str if isinstance(self.left, (np.generic, _Timestamp)) else repr
         name = type(self).__name__
-        repr_str = f"{name}({disp(left)}, {disp(right)}, closed={repr(self.closed)})"
+        repr_str = f"{name}({disp(self.left)}, {disp(self.right)}, closed={repr(self.closed)})"  # noqa: E501
         return repr_str
 
     def __str__(self) -> str:
-
-        left, right = self._repr_base()
         start_symbol = "[" if self.closed_left else "("
         end_symbol = "]" if self.closed_right else ")"
-        return f"{start_symbol}{left}, {right}{end_symbol}"
+        return f"{start_symbol}{self.left}, {self.right}{end_symbol}"
 
     def __add__(self, y):
         if (

pandas/_libs/tslibs/timestamps.pyx

@@ -1078,18 +1078,6 @@ cdef class _Timestamp(ABCTimestamp):
 
         return result
 
-    @property
-    def _short_repr(self) -> str:
-        # format a Timestamp with only _date_repr if possible
-        # otherwise _repr_base
-        if (self.hour == 0 and
-                self.minute == 0 and
-                self.second == 0 and
-                self.microsecond == 0 and
-                self.nanosecond == 0):
-            return self._date_repr
-        return self._repr_base
-
     # -----------------------------------------------------------------
     # Conversion Methods
 

pandas/core/indexes/interval.py

@@ -1039,8 +1039,9 @@ def interval_range(
 
     >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
     ...                   end=pd.Timestamp('2017-01-04'))
-    IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
-                   (2017-01-03, 2017-01-04]],
+    IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
+                   (2017-01-02 00:00:00, 2017-01-03 00:00:00],
+                   (2017-01-03 00:00:00, 2017-01-04 00:00:00]],
                   dtype='interval[datetime64[ns], right]')
 
     The ``freq`` parameter specifies the frequency between the left and right.
@@ -1056,8 +1057,9 @@ def interval_range(
 
     >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
     ...                   periods=3, freq='MS')
-    IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
-                   (2017-03-01, 2017-04-01]],
+    IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],
+                   (2017-02-01 00:00:00, 2017-03-01 00:00:00],
+                   (2017-03-01 00:00:00, 2017-04-01 00:00:00]],
                   dtype='interval[datetime64[ns], right]')
 
     Specify ``start``, ``end``, and ``periods``; the frequency is generated

pandas/tests/frame/methods/test_to_csv.py

@@ -1326,6 +1326,6 @@ def test_to_csv_categorical_and_interval(self):
         )
         df["a"] = df["a"].astype("category")
         result = df.to_csv()
-        expected_rows = [",a", '0,"[2020-01-01, 2020-01-02]"']
+        expected_rows = [",a", '0,"[2020-01-01 00:00:00, 2020-01-02 00:00:00]"']
         expected = tm.convert_rows_list_to_csv_str(expected_rows)
         assert result == expected

pandas/tests/indexes/interval/test_formats.py

@@ -80,7 +80,11 @@ def test_repr_floats(self):
                     ((Timestamp("20180102"), Timestamp("20180103"))),
                 ],
                 "both",
-                ["[2018-01-01, 2018-01-02]", "NaN", "[2018-01-02, 2018-01-03]"],
+                [
+                    "[2018-01-01 00:00:00, 2018-01-02 00:00:00]",
+                    "NaN",
+                    "[2018-01-02 00:00:00, 2018-01-03 00:00:00]",
+                ],
             ),
             (
                 [
@@ -103,3 +107,19 @@ def test_to_native_types(self, tuples, closed, expected_data):
         result = index._format_native_types()
         expected = np.array(expected_data)
         tm.assert_numpy_array_equal(result, expected)
+
+    def test_timestamp_with_timezone(self):
+        # GH 55035
+        index = IntervalIndex(
+            [
+                Interval(
+                    Timestamp("2020-01-01", tz="UTC"), Timestamp("2020-01-02", tz="UTC")
+                )
+            ]
+        )
+        result = repr(index)
+        expected = (
+            "IntervalIndex([(2020-01-01 00:00:00+00:00, 2020-01-02 00:00:00+00:00]], "
+            "dtype='interval[datetime64[ns, UTC], right]')"
+        )
+        assert result == expected

pandas/tests/io/excel/test_writers.py

@@ -307,10 +307,10 @@ def test_multiindex_interval_datetimes(self, ext):
                 [
                     range(4),
                     [
-                        "(2020-01-31, 2020-07-31]",
-                        "(2020-07-31, 2021-01-31]",
-                        "(2021-01-31, 2021-07-31]",
-                        "(2021-07-31, 2022-01-31]",
+                        "(2020-01-31 00:00:00, 2020-07-31 00:00:00]",
+                        "(2020-07-31 00:00:00, 2021-01-31 00:00:00]",
+                        "(2021-01-31 00:00:00, 2021-07-31 00:00:00]",
+                        "(2021-07-31 00:00:00, 2022-01-31 00:00:00]",
                     ],
                 ]
             ),