From f6fd475680761f12dbc4abe3db4a26e55b68fd90 Mon Sep 17 00:00:00 2001
From: "Lumberbot (aka Jack)"
 <39504233+meeseeksmachine@users.noreply.github.com>
Date: Wed, 31 Jan 2024 02:52:25 +0100
Subject: [PATCH] Backport PR #57157 on branch 2.2.x (BUG: Fix to_dict with
 datelike types and orient=list) (#57160)

Backport PR #57157: BUG: Fix to_dict with datelike types and orient=list

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>
---
 doc/source/whatsnew/v2.2.1.rst             |  1 +
 pandas/core/methods/to_dict.py             |  8 ++------
 pandas/tests/frame/methods/test_to_dict.py | 14 ++++++++++++++
 3 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/doc/source/whatsnew/v2.2.1.rst b/doc/source/whatsnew/v2.2.1.rst
index 1302648c3fc9a..19b7e3493f964 100644
--- a/doc/source/whatsnew/v2.2.1.rst
+++ b/doc/source/whatsnew/v2.2.1.rst
@@ -18,6 +18,7 @@ Fixed regressions
 - Fixed regression in :func:`merge_ordered` raising ``TypeError`` for ``fill_method="ffill"`` and ``how="left"`` (:issue:`57010`)
 - Fixed regression in :func:`wide_to_long` raising an ``AttributeError`` for string columns (:issue:`57066`)
 - Fixed regression in :meth:`DataFrame.loc` raising ``IndexError`` for non-unique, masked dtype indexes where result has more than 10,000 rows (:issue:`57027`)
+- Fixed regression in :meth:`DataFrame.to_dict` with ``orient='list'`` and datetime or timedelta types returning integers (:issue:`54824`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` ignoring the ``skipna`` argument (:issue:`57040`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` where values containing the minimum or maximum value for the dtype could produce incorrect results (:issue:`57040`)
 - Fixed regression in :meth:`Index.join` raising ``TypeError`` when joining an empty index to a non-empty index containing mixed dtype values (:issue:`57048`)
diff --git a/pandas/core/methods/to_dict.py b/pandas/core/methods/to_dict.py
index 7bd4851425c3b..accbd92a91ed6 100644
--- a/pandas/core/methods/to_dict.py
+++ b/pandas/core/methods/to_dict.py
@@ -171,13 +171,9 @@ def to_dict(
         return into_c(
             (
                 k,
-                list(
-                    map(
-                        maybe_box_native, v.to_numpy(na_value=box_na_values[i]).tolist()
-                    )
-                )
+                list(map(maybe_box_native, v.to_numpy(na_value=box_na_values[i])))
                 if i in object_dtype_indices_as_set
-                else v.to_numpy().tolist(),
+                else list(map(maybe_box_native, v.to_numpy())),
             )
             for i, (k, v) in enumerate(df.items())
         )
diff --git a/pandas/tests/frame/methods/test_to_dict.py b/pandas/tests/frame/methods/test_to_dict.py
index 61f0ad30b4519..570f85a4a31ee 100644
--- a/pandas/tests/frame/methods/test_to_dict.py
+++ b/pandas/tests/frame/methods/test_to_dict.py
@@ -12,8 +12,11 @@
     NA,
     DataFrame,
     Index,
+    Interval,
     MultiIndex,
+    Period,
     Series,
+    Timedelta,
     Timestamp,
 )
 import pandas._testing as tm
@@ -519,3 +522,14 @@ def test_to_dict_pos_args_deprecation(self):
         )
         with tm.assert_produces_warning(FutureWarning, match=msg):
             df.to_dict("records", {})
+
+
+@pytest.mark.parametrize(
+    "val", [Timestamp(2020, 1, 1), Timedelta(1), Period("2020"), Interval(1, 2)]
+)
+def test_to_dict_list_pd_scalars(val):
+    # GH 54824
+    df = DataFrame({"a": [val]})
+    result = df.to_dict(orient="list")
+    expected = {"a": [val]}
+    assert result == expected