diff --git a/bigframes/operations/datetimes.py b/bigframes/operations/datetimes.py
index 1d79afd97f..c259dd018e 100644
--- a/bigframes/operations/datetimes.py
+++ b/bigframes/operations/datetimes.py
@@ -25,7 +25,7 @@
 from bigframes.core import log_adapter
 import bigframes.operations as ops
 
-_ONE_DAY = pandas.Timedelta("1d")
+_ONE_DAY = pandas.Timedelta("1D")
 _ONE_SECOND = pandas.Timedelta("1s")
 _ONE_MICRO = pandas.Timedelta("1us")
 _SUPPORTED_FREQS = ("Y", "Q", "M", "W", "D", "h", "min", "s", "ms", "us")
diff --git a/tests/unit/test_local_data.py b/tests/unit/test_local_data.py
index 6f23036efb..1537c896fb 100644
--- a/tests/unit/test_local_data.py
+++ b/tests/unit/test_local_data.py
@@ -44,6 +44,12 @@
 def test_local_data_well_formed_round_trip():
     local_entry = local_data.ManagedArrowTable.from_pandas(pd_data)
     result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns)
+    result = result.assign(
+        **{
+            col: result[col].astype(pd_data_normalized[col].dtype)
+            for col in pd_data_normalized.columns
+        }
+    )
     pandas.testing.assert_frame_equal(pd_data_normalized, result, check_dtype=False)
 
 
@@ -118,6 +124,12 @@ def test_local_data_well_formed_round_trip_chunked():
     as_rechunked_pyarrow = pa.Table.from_batches(pa_table.to_batches(max_chunksize=2))
     local_entry = local_data.ManagedArrowTable.from_pyarrow(as_rechunked_pyarrow)
     result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns)
+    result = result.assign(
+        **{
+            col: result[col].astype(pd_data_normalized[col].dtype)
+            for col in pd_data_normalized.columns
+        }
+    )
     pandas.testing.assert_frame_equal(pd_data_normalized, result, check_dtype=False)
 
 
@@ -126,6 +138,12 @@ def test_local_data_well_formed_round_trip_sliced():
     as_rechunked_pyarrow = pa.Table.from_batches(pa_table.slice(0, 4).to_batches())
     local_entry = local_data.ManagedArrowTable.from_pyarrow(as_rechunked_pyarrow)
     result = pd.DataFrame(local_entry.itertuples(), columns=pd_data.columns)
+    result = result.assign(
+        **{
+            col: result[col].astype(pd_data_normalized[col].dtype)
+            for col in pd_data_normalized.columns
+        }
+    )
     pandas.testing.assert_frame_equal(
         pd_data_normalized[0:4].reset_index(drop=True),
         result.reset_index(drop=True),