From f0afc77f42f9fca7a423fa0eeb205b220ad3dd94 Mon Sep 17 00:00:00 2001
From: Joris Van den Bossche <jorisvandenbossche@gmail.com>
Date: Wed, 3 Dec 2025 14:10:28 +0100
Subject: [PATCH] [Python] Compat with pandas 3.0 changed default datetime unit

---
 python/pyarrow/pandas_compat.py     |  4 ++--
 python/pyarrow/tests/test_pandas.py | 12 +++++++-----
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/python/pyarrow/pandas_compat.py b/python/pyarrow/pandas_compat.py
index dfed76d3711..dfca59cbf5f 100644
--- a/python/pyarrow/pandas_compat.py
+++ b/python/pyarrow/pandas_compat.py
@@ -140,7 +140,7 @@ def get_extension_dtype_info(column):
         physical_dtype = str(cats.codes.dtype)
     elif hasattr(dtype, 'tz'):
         metadata = {'timezone': pa.lib.tzinfo_to_string(dtype.tz)}
-        physical_dtype = 'datetime64[ns]'
+        physical_dtype = f'datetime64[{dtype.unit}]'
     else:
         metadata = None
         physical_dtype = str(dtype)
@@ -1188,7 +1188,7 @@ def _reconstruct_columns_from_metadata(columns, column_indexes):
             if _pandas_api.is_ge_v3():
                 # with pandas 3+, to_datetime returns a unit depending on the string
                 # data, so we restore it to the original unit from the metadata
-                level = level.as_unit(np.datetime_data(dtype)[0])
+                level = level.as_unit(np.datetime_data(numpy_dtype)[0])
         # GH-41503: if the column index was decimal, restore to decimal
         elif pandas_dtype == "decimal":
             level = _pandas_api.pd.Index([decimal.Decimal(i) for i in level])
diff --git a/python/pyarrow/tests/test_pandas.py b/python/pyarrow/tests/test_pandas.py
index 7f9b04eaabd..e41eb3d3801 100644
--- a/python/pyarrow/tests/test_pandas.py
+++ b/python/pyarrow/tests/test_pandas.py
@@ -212,13 +212,14 @@ def test_column_index_names_are_preserved(self):
         df.columns.names = ['a']
         _check_pandas_roundtrip(df, preserve_index=True)
 
-    def test_column_index_names_with_tz(self):
+    @pytest.mark.parametrize("tz", [None, "Europe/Brussels"])
+    def test_column_index_names_datetime(self, tz):
         # ARROW-13756
         # Bug if index is timezone aware DataTimeIndex
 
         df = pd.DataFrame(
             np.random.randn(5, 3),
-            columns=pd.date_range("2021-01-01", periods=3, freq="50D", tz="CET")
+            columns=pd.date_range("2021-01-01", periods=3, freq="50D", tz=tz)
         )
         _check_pandas_roundtrip(df, preserve_index=True)
 
@@ -451,7 +452,7 @@ def test_datetimetz_column_index(self):
         df = pd.DataFrame(
             [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
             columns=pd.date_range(
-                start='2017-01-01', periods=3, tz='America/New_York'
+                start='2017-01-01', periods=3, tz='America/New_York', unit='us'
             )
         )
         t = pa.Table.from_pandas(df, preserve_index=True)
@@ -460,7 +461,7 @@ def test_datetimetz_column_index(self):
         column_indexes, = js['column_indexes']
         assert column_indexes['name'] is None
         assert column_indexes['pandas_type'] == 'datetimetz'
-        assert column_indexes['numpy_type'] == 'datetime64[ns]'
+        assert column_indexes['numpy_type'] == 'datetime64[us]'
 
         md = column_indexes['metadata']
         assert md['timezone'] == 'America/New_York'
@@ -709,7 +710,8 @@ def test_mismatch_metadata_schema(self):
         # It is possible that the metadata and actual schema is not fully
         # matching (eg no timezone information for tz-aware column)
         # -> to_pandas() conversion should not fail on that
-        df = pd.DataFrame({"datetime": pd.date_range("2020-01-01", periods=3)})
+        df = pd.DataFrame({"datetime": pd.date_range(
+            "2020-01-01", periods=3, unit='ns')})
 
         # OPTION 1: casting after conversion
         table = pa.Table.from_pandas(df)