This fix enables you to preserve the datetime precision when using the melt method. It fixes the issues raised in #55254.

Author: paulreece

doc/source/whatsnew/v2.2.0.rst

@@ -250,8 +250,8 @@ Bug fixes
 - Bug in :class:`AbstractHolidayCalendar` where timezone data was not propagated when computing holiday observances (:issue:`54580`)
 - Bug in :class:`pandas.core.window.Rolling` where duplicate datetimelike indexes are treated as consecutive rather than equal with ``closed='left'`` and ``closed='neither'`` (:issue:`20712`)
 - Bug in :meth:`DataFrame.apply` where passing ``raw=True`` ignored ``args`` passed to the applied function (:issue:`55009`)
+- Bug in :meth:`pandas.DataFrame.melt` where it would not preserve the datetime (:issue:`55254`)
 - Bug in :meth:`pandas.read_excel` with a ODS file without cached formatted cell for float values (:issue:`55219`)
-
 Categorical
 ^^^^^^^^^^^
 - :meth:`Categorical.isin` raising ``InvalidIndexError`` for categorical containing overlapping :class:`Interval` values (:issue:`34974`)

pandas/core/reshape/melt.py

@@ -9,6 +9,14 @@
 
 from pandas.core.dtypes.common import is_list_like
 from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+    SparseDtype,
+)
 from pandas.core.dtypes.missing import notna
 
 import pandas.core.algorithms as algos
@@ -120,6 +128,14 @@ def melt(
     K -= len(id_vars)
 
     mdata: dict[Hashable, AnyArrayLike] = {}
+    pd_dtypes = (
+        CategoricalDtype,
+        DatetimeTZDtype,
+        ExtensionDtype,
+        IntervalDtype,
+        PeriodDtype,
+        SparseDtype,
+    )
     for col in id_vars:
         id_data = frame.pop(col)
         if not isinstance(id_data.dtype, np.dtype):
@@ -133,8 +149,9 @@ def melt(
             mdata[col] = np.tile(id_data._values, K)
 
     mcolumns = id_vars + var_name + [value_name]
-
-    if frame.shape[1] > 0:
+    if frame.shape[1] > 0 and not any(
+        isinstance(dt, pd_dtypes) for dt in frame.dtypes.values
+    ):
         mdata[value_name] = concat(
             [frame.iloc[:, i] for i in range(frame.shape[1])]
         ).values

pandas/tests/reshape/test_melt.py

@@ -459,6 +459,47 @@ def test_melt_ea_columns(self):
         )
         tm.assert_frame_equal(result, expected)
 
+    def test_melt_preserves_datetime(self):
+        df = DataFrame(
+            data=[
+                {
+                    "type": "A0",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/10", tz="Asia/Tokyo"),
+                },
+                {
+                    "type": "A1",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/11", tz="Asia/Tokyo"),
+                },
+            ],
+            index=["aaaa", "bbbb"],
+        )
+        result = df.melt(
+            id_vars=["type"],
+            value_vars=["start_date", "end_date"],
+            var_name="start/end",
+            value_name="date",
+        )
+        expected = DataFrame(
+            {
+                "type": {0: "A0", 1: "A1", 2: "A0", 3: "A1"},
+                "start/end": {
+                    0: "start_date",
+                    1: "start_date",
+                    2: "end_date",
+                    3: "end_date",
+                },
+                "date": {
+                    0: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    1: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    2: pd.Timestamp("2023-03-10 00:00:00+0900", tz="Asia/Tokyo"),
+                    3: pd.Timestamp("2023-03-11 00:00:00+0900", tz="Asia/Tokyo"),
+                },
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
 
 class TestLreshape:
     def test_pairs(self):