diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
index 7b282783bd..32f5a36f79 100644
--- a/bigframes/dataframe.py
+++ b/bigframes/dataframe.py
@@ -2132,6 +2132,66 @@ def pivot(
     ) -> DataFrame:
         return self._pivot(columns=columns, index=index, values=values)
 
+    def pivot_table(
+        self,
+        values: typing.Optional[
+            typing.Union[blocks.Label, Sequence[blocks.Label]]
+        ] = None,
+        index: typing.Optional[
+            typing.Union[blocks.Label, Sequence[blocks.Label]]
+        ] = None,
+        columns: typing.Union[blocks.Label, Sequence[blocks.Label]] = None,
+        aggfunc: str = "mean",
+    ) -> DataFrame:
+        if isinstance(index, Iterable) and not (
+            isinstance(index, blocks.Label) and index in self.columns
+        ):
+            index = list(index)
+        else:
+            index = [index]
+
+        if isinstance(columns, Iterable) and not (
+            isinstance(columns, blocks.Label) and columns in self.columns
+        ):
+            columns = list(columns)
+        else:
+            columns = [columns]
+
+        if isinstance(values, Iterable) and not (
+            isinstance(values, blocks.Label) and values in self.columns
+        ):
+            values = list(values)
+        else:
+            values = [values]
+
+        # Unlike pivot, pivot_table has values always ordered.
+        values.sort()
+
+        keys = index + columns
+        agged = self.groupby(keys, dropna=True)[values].agg(aggfunc)
+
+        if isinstance(agged, bigframes.series.Series):
+            agged = agged.to_frame()
+
+        agged = agged.dropna(how="all")
+
+        if len(values) == 1:
+            agged = agged.rename(columns={agged.columns[0]: values[0]})
+
+        agged = agged.reset_index()
+
+        pivoted = agged.pivot(
+            columns=columns,
+            index=index,
+            values=values if len(values) > 1 else None,
+        ).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()]
+
     def stack(self, level: LevelsType = -1):
         if not isinstance(self.columns, pandas.MultiIndex):
             if level not in [0, -1, self.columns.name]:
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index 0811defbc1..ba205078ed 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -2606,6 +2606,34 @@ def test_df_pivot_hockey(hockey_df, hockey_pandas_df, values, index, columns):
     pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
 
+@pytest.mark.parametrize(
+    ("values", "index", "columns", "aggfunc"),
+    [
+        (("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"),
+    ],
+)
+def test_df_pivot_table(
+    penguins_df_default_index,
+    penguins_pandas_df_default_index,
+    values,
+    index,
+    columns,
+    aggfunc,
+):
+    bf_result = penguins_df_default_index.pivot_table(
+        values=values, index=index, columns=columns, aggfunc=aggfunc
+    ).to_pandas()
+    pd_result = penguins_pandas_df_default_index.pivot_table(
+        values=values, index=index, columns=columns, aggfunc=aggfunc
+    )
+    pd.testing.assert_frame_equal(
+        bf_result, pd_result, check_dtype=False, check_column_type=False
+    )
+
+
 def test_ipython_key_completions_with_drop(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     col_names = "string_col"
diff --git a/third_party/bigframes_vendored/pandas/core/frame.py b/third_party/bigframes_vendored/pandas/core/frame.py
index ed615000c1..1fc80449d1 100644
--- a/third_party/bigframes_vendored/pandas/core/frame.py
+++ b/third_party/bigframes_vendored/pandas/core/frame.py
@@ -4711,6 +4711,88 @@ def pivot(self, *, columns, index=None, values=None):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def pivot_table(self, values=None, index=None, columns=None, aggfunc="mean"):
+        """
+        Create a spreadsheet-style pivot table as a DataFrame.
+
+        The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes)
+        on the index and columns of the result DataFrame.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame({
+            ...     'Product': ['Product A', 'Product B', 'Product A', 'Product B', 'Product A', 'Product B'],
+            ...     'Region': ['East', 'West', 'East', 'West', 'West', 'East'],
+            ...     'Sales': [100, 200, 150, 100, 200, 150],
+            ...     'Rating': [3, 5, 4, 3, 3, 5]
+            ... })
+            >>> df
+                 Product Region  Sales  Rating
+            0  Product A   East    100       3
+            1  Product B   West    200       5
+            2  Product A   East    150       4
+            3  Product B   West    100       3
+            4  Product A   West    200       3
+            5  Product B   East    150       5
+            <BLANKLINE>
+            [6 rows x 4 columns]
+
+        Using `pivot_table` with default aggfunc "mean":
+
+            >>> pivot_table = df.pivot_table(
+            ...     values=['Sales', 'Rating'],
+            ...     index='Product',
+            ...     columns='Region'
+            ... )
+            >>> pivot_table
+                      Rating       Sales
+            Region      East West   East   West
+            Product
+            Product A    3.5  3.0  125.0  200.0
+            Product B    5.0  4.0  150.0  150.0
+            <BLANKLINE>
+            [2 rows x 4 columns]
+
+        Using `pivot_table` with specified aggfunc "max":
+
+            >>> pivot_table = df.pivot_table(
+            ...     values=['Sales', 'Rating'],
+            ...     index='Product',
+            ...     columns='Region',
+            ...     aggfunc="max"
+            ... )
+            >>> pivot_table
+                      Rating      Sales
+            Region      East West  East West
+            Product
+            Product A      4    3   150  200
+            Product B      5    5   150  200
+            <BLANKLINE>
+            [2 rows x 4 columns]
+
+        Args:
+            values (str, object or a list of the previous, optional):
+                Column(s) to use for populating new frame's values. If not
+                specified, all remaining columns will be used and the result will
+                have hierarchically indexed columns.
+
+            index (str or object or a list of str, optional):
+                Column to use to make new frame's index. If not given, uses existing index.
+
+            columns (str or object or a list of str):
+                Column to use to make new frame's columns.
+
+            aggfunc (str, default "mean"):
+                Aggregation function name to compute summary statistics (e.g., 'sum', 'mean').
+
+        Returns:
+            DataFrame: An Excel style pivot table.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def stack(self, level=-1):
         """
         Stack the prescribed level(s) from columns to index.