diff --git a/python/pyarrow/tests/parquet/common.py b/python/pyarrow/tests/parquet/common.py
index cbff41c7b10..09bc1ed7b3b 100644
--- a/python/pyarrow/tests/parquet/common.py
+++ b/python/pyarrow/tests/parquet/common.py
@@ -172,8 +172,8 @@ def alltypes_sample(size=10000, seed=0, categorical=False):
         # TODO(wesm): Test other timestamp resolutions now that arrow supports
         # them
         'datetime': np.arange("2016-01-01T00:00:00.001", size,
-                              dtype='datetime64[ms]'),
-        'timedelta': np.arange(0, size, dtype="timedelta64[s]"),
+                              dtype='datetime64[ms]').astype('datetime64[ns]'),
+        'timedelta': np.arange(0, size, dtype="timedelta64[ns]"),
         'str': pd.Series([str(x) for x in range(size)]),
         'empty_str': [''] * size,
         'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
diff --git a/python/pyarrow/tests/parquet/test_dataset.py b/python/pyarrow/tests/parquet/test_dataset.py
index 6fdc7435418..ef75c3efac7 100644
--- a/python/pyarrow/tests/parquet/test_dataset.py
+++ b/python/pyarrow/tests/parquet/test_dataset.py
@@ -1252,6 +1252,7 @@ def _test_write_to_dataset_with_partitions(base_path,
                               'nan': [np.nan] * 10,
                               'date': np.arange('2017-01-01', '2017-01-11',
                                                 dtype='datetime64[D]')})
+    output_df["date"] = output_df["date"].astype('datetime64[ns]')
     cols = output_df.columns.tolist()
     partition_by = ['group1', 'group2']
     output_table = pa.Table.from_pandas(output_df, schema=schema, safe=False,
@@ -1312,6 +1313,7 @@ def _test_write_to_dataset_no_partitions(base_path,
                               'num': list(range(10)),
                               'date': np.arange('2017-01-01', '2017-01-11',
                                                 dtype='datetime64[D]')})
+    output_df["date"] = output_df["date"].astype('datetime64[ns]')
     cols = output_df.columns.tolist()
     output_table = pa.Table.from_pandas(output_df)
 
@@ -1440,6 +1442,7 @@ def test_write_to_dataset_with_partitions_and_custom_filenames(
                               'nan': [np.nan] * 10,
                               'date': np.arange('2017-01-01', '2017-01-11',
                                                 dtype='datetime64[D]')})
+    output_df["date"] = output_df["date"].astype('datetime64[ns]')
     partition_by = ['group1', 'group2']
     output_table = pa.Table.from_pandas(output_df)
     path = str(tempdir)
diff --git a/python/pyarrow/tests/test_pandas.py b/python/pyarrow/tests/test_pandas.py
index 7022441396e..f843904f126 100644
--- a/python/pyarrow/tests/test_pandas.py
+++ b/python/pyarrow/tests/test_pandas.py
@@ -69,7 +69,7 @@ def _alltypes_example(size=100):
         # TODO(wesm): Pandas only support ns resolution, Arrow supports s, ms,
         # us, ns
         'datetime': np.arange("2016-01-01T00:00:00.001", size,
-                              dtype='datetime64[ms]'),
+                              dtype='datetime64[ms]').astype("datetime64[ns]"),
         'str': [str(x) for x in range(size)],
         'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
         'empty_str': [''] * size
@@ -1016,7 +1016,7 @@ def test_timestamps_with_timezone(self):
                 '2007-07-13T01:23:34.123',
                 '2006-01-13T12:34:56.432',
                 '2010-08-13T05:46:57.437'],
-                dtype='datetime64[ms]')
+                dtype='datetime64[ms]').astype("datetime64[ns]")
         })
         df['datetime64'] = df['datetime64'].dt.tz_localize('US/Eastern')
         _check_pandas_roundtrip(df)
@@ -2718,7 +2718,7 @@ def test_strided_data_import(self):
         cases.append(boolean_objects)
 
         cases.append(np.arange("2016-01-01T00:00:00.001", N * K,
-                               dtype='datetime64[ms]')
+                               dtype='datetime64[ms]').astype("datetime64[ns]")
                      .reshape(N, K).copy())
 
         strided_mask = (random_numbers > 0).astype(bool)[:, 0]