diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
index f803b66ab6..d5cac76e9b 100644
--- a/bigframes/dataframe.py
+++ b/bigframes/dataframe.py
@@ -734,7 +734,7 @@ def _apply_binop(
         how: str = "outer",
         reverse: bool = False,
     ):
-        if isinstance(other, (float, int, bool)):
+        if isinstance(other, bigframes.dtypes.LOCAL_SCALAR_TYPES):
             return self._apply_scalar_binop(other, op, reverse=reverse)
         elif isinstance(other, DataFrame):
             return self._apply_dataframe_binop(other, op, how=how, reverse=reverse)
@@ -752,7 +752,10 @@ def _apply_binop(
         )
 
     def _apply_scalar_binop(
-        self, other: float | int, op: ops.BinaryOp, reverse: bool = False
+        self,
+        other: bigframes.dtypes.LOCAL_SCALAR_TYPE,
+        op: ops.BinaryOp,
+        reverse: bool = False,
     ) -> DataFrame:
         if reverse:
             expr = op.as_expr(
diff --git a/bigframes/dtypes.py b/bigframes/dtypes.py
index bc5b89b779..c71531f9f3 100644
--- a/bigframes/dtypes.py
+++ b/bigframes/dtypes.py
@@ -59,6 +59,25 @@
 # Used when storing Null expressions
 DEFAULT_DTYPE = FLOAT_DTYPE
 
+LOCAL_SCALAR_TYPE = Union[
+    bool,
+    np.bool_,
+    int,
+    np.integer,
+    float,
+    np.floating,
+    decimal.Decimal,
+    str,
+    np.str_,
+    bytes,
+    np.bytes_,
+    datetime.datetime,
+    pd.Timestamp,
+    datetime.date,
+    datetime.time,
+]
+LOCAL_SCALAR_TYPES = typing.get_args(LOCAL_SCALAR_TYPE)
+
 
 # Will have a few dtype variants: simple(eg. int, string, bool), complex (eg. list, struct), and virtual (eg. micro intervals, categorical)
 @dataclass(frozen=True)
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index 1fb12d3f82..02b771a1bd 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -1020,13 +1020,20 @@ def test_df_interpolate(scalars_dfs):
     )
 
 
-def test_df_fillna(scalars_dfs):
+@pytest.mark.parametrize(
+    "col, fill_value",
+    [
+        (["int64_col", "float64_col"], 3),
+        (["string_col"], "A"),
+        (["datetime_col"], pd.Timestamp("2023-01-01")),
+    ],
+)
+def test_df_fillna(scalars_dfs, col, fill_value):
     scalars_df, scalars_pandas_df = scalars_dfs
-    df = scalars_df[["int64_col", "float64_col"]].fillna(3)
-    bf_result = df.to_pandas()
-    pd_result = scalars_pandas_df[["int64_col", "float64_col"]].fillna(3)
+    bf_result = scalars_df[col].fillna(fill_value).to_pandas()
+    pd_result = scalars_pandas_df[col].fillna(fill_value)
 
-    pandas.testing.assert_frame_equal(bf_result, pd_result)
+    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
 
 def test_df_replace_scalar_scalar(scalars_dfs):