googleapis · sycai · Nov 13, 2025 · Nov 11, 2025 · Nov 12, 2025
@@ -71,12 +71,11 @@ def crosstab(
        columns=tmp_col_names,
        aggfunc=aggfunc or "count",
        sort=False,
+        fill_value=0 if (aggfunc is None) else None,
    )
+    # Undo temporary unique level labels
    pivot_table.index.names = rownames or [i.name for i in index]
    pivot_table.columns.names = colnames or [c.name for c in columns]
-    if aggfunc is None:
-        # TODO: Push this into pivot_table itself
-        pivot_table = pivot_table.fillna(0)
    return pivot_table



@@ -3486,10 +3486,6 @@ def pivot_table(
        observed: bool = False,
        sort: bool = True,
    ) -> DataFrame:
-        if fill_value is not None:
-            raise NotImplementedError(
-                "DataFrame.pivot_table fill_value arg not supported. {constants.FEEDBACK_LINK}"
-            )
        if margins:
            raise NotImplementedError(
                "DataFrame.pivot_table margins arg not supported. {constants.FEEDBACK_LINK}"
@@ -3549,14 +3545,16 @@ def pivot_table(
            index=index,
            values=values if len(values) > 1 else None,
        )
+        if fill_value is not None:
+            pivoted = pivoted.fillna(fill_value)
        if sort:
            pivoted = pivoted.sort_index()

        # TODO: Remove the reordering step once the issue is resolved.
        # The pivot_table method results in multi-index columns that are always ordered.
        # However, the order of the pivoted result columns is not guaranteed to be sorted.
        # Sort and reorder.
-        return pivoted[pivoted.columns.sort_values()]
+        return pivoted.sort_index(axis=1)  # type: ignore

    def stack(self, level: LevelsType = -1):
        if not isinstance(self.columns, pandas.MultiIndex):

@@ -3784,12 +3784,18 @@ def test_df_pivot_hockey(hockey_df, hockey_pandas_df, values, index, columns):


 @pytest.mark.parametrize(
-    ("values", "index", "columns", "aggfunc"),
+    ("values", "index", "columns", "aggfunc", "fill_value"),
    [
-        (("culmen_length_mm", "body_mass_g"), "species", "sex", "std"),
-        (["body_mass_g", "culmen_length_mm"], ("species", "island"), "sex", "sum"),
-        ("body_mass_g", "sex", ["island", "species"], "mean"),
-        ("culmen_depth_mm", "island", "species", "max"),
+        (("culmen_length_mm", "body_mass_g"), "species", "sex", "std", 1.0),
+        (
+            ["body_mass_g", "culmen_length_mm"],
+            ("species", "island"),
+            "sex",
+            "sum",
+            None,
+        ),
+        ("body_mass_g", "sex", ["island", "species"], "mean", None),
+        ("culmen_depth_mm", "island", "species", "max", -1),
    ],
 )
 def test_df_pivot_table(
@@ -3799,12 +3805,21 @@ def test_df_pivot_table(
    index,
    columns,
    aggfunc,
+    fill_value,
 ):
    bf_result = penguins_df_default_index.pivot_table(
-        values=values, index=index, columns=columns, aggfunc=aggfunc
+        values=values,
+        index=index,
+        columns=columns,
+        aggfunc=aggfunc,
+        fill_value=fill_value,
    ).to_pandas()
    pd_result = penguins_pandas_df_default_index.pivot_table(
-        values=values, index=index, columns=columns, aggfunc=aggfunc
+        values=values,
+        index=index,
+        columns=columns,
+        aggfunc=aggfunc,
+        fill_value=fill_value,
    )
    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_column_type=False

@@ -6414,6 +6414,10 @@ def pivot_table(self, values=None, index=None, columns=None, aggfunc="mean"):
            aggfunc (str, default "mean"):
                Aggregation function name to compute summary statistics (e.g., 'sum', 'mean').

+            fill_value (scalar, default None):
+                Value to replace missing values with (in the resulting pivot table, after
+                aggregation).
+
        Returns:
            bigframes.pandas.DataFrame: An Excel style pivot table.
        """