diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
index e1bb885558..647d174f44 100644
--- a/bigframes/dataframe.py
+++ b/bigframes/dataframe.py
@@ -2359,6 +2359,9 @@ def where(self, cond, other=None):
         result.columns.names = self.columns.names
         return result
 
+    def mask(self, cond, other=None):
+        return self.where(~cond, other=other)
+
     def dropna(
         self,
         *,
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index 5b94df2446..a097f8c64d 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -331,6 +331,17 @@ def test_where_series_cond(scalars_df_index, scalars_pandas_df_index):
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+def test_mask_series_cond(scalars_df_index, scalars_pandas_df_index):
+    cond_bf = scalars_df_index["int64_col"] > 0
+    cond_pd = scalars_pandas_df_index["int64_col"] > 0
+
+    bf_df = scalars_df_index[["int64_too", "int64_col", "float64_col"]]
+    pd_df = scalars_pandas_df_index[["int64_too", "int64_col", "float64_col"]]
+    bf_result = bf_df.mask(cond_bf, bf_df + 1).to_pandas()
+    pd_result = pd_df.mask(cond_pd, pd_df + 1)
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
 def test_where_series_multi_index(scalars_df_index, scalars_pandas_df_index):
     # Test when a dataframe has multi-index or multi-columns.
     columns = ["int64_col", "float64_col"]
diff --git a/third_party/bigframes_vendored/pandas/core/frame.py b/third_party/bigframes_vendored/pandas/core/frame.py
index a44d6b629f..66dbff1b6a 100644
--- a/third_party/bigframes_vendored/pandas/core/frame.py
+++ b/third_party/bigframes_vendored/pandas/core/frame.py
@@ -2048,6 +2048,98 @@ def where(self, cond, other):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def mask(self, cond, other):
+        """Replace values where the condition is False.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame({'a': [20, 10, 0], 'b': [0, 10, 20]})
+            >>> df
+                a   b
+            0  20   0
+            1  10  10
+            2   0  20
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+        You can filter the values in the dataframe based on a condition. The
+        values matching the condition would be kept, and not matching would be
+        replaced. The default replacement value is ``NA``. For example, when the
+        condition is a dataframe:
+
+            >>> df.mask(df > 0)
+                  a     b
+            0  <NA>     0
+            1  <NA>  <NA>
+            2     0  <NA>
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+        You can specify a custom replacement value for non-matching values.
+
+            >>> df.mask(df > 0, -1)
+                a   b
+            0  -1   0
+            1  -1  -1
+            2   0  -1
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+        Besides dataframe, the condition can be a series too. For example:
+
+            >>> df.mask(df['a'] > 10, -1)
+                a   b
+            0  -1  -1
+            1  10  10
+            2   0  20
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+        As for the replacement, it can be a dataframe too. For example:
+
+            >>> df.mask(df > 10, -df)
+                  a     b
+            0   -20     0
+            1    10    10
+            2     0   -20
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+            >>> df.mask(df['a'] > 10, -df)
+                  a     b
+            0   -20     0
+            1    10    10
+            2     0    20
+            <BLANKLINE>
+            [3 rows x 2 columns]
+
+        Please note, replacement doesn't support Series for now. In pandas, when
+        specifying a Series as replacement, the axis value should be specified
+        at the same time, which is not supported in bigframes DataFrame.
+
+        Args:
+            cond (bool Series/DataFrame, array-like, or callable):
+                Where cond is False, keep the original value. Where True, replace
+                with corresponding value from other. If cond is callable, it is
+                computed on the Series/DataFrame and returns boolean
+                Series/DataFrame or array. The callable must not change input
+                Series/DataFrame (though pandas doesn’t check it).
+            other (scalar, DataFrame, or callable):
+                Entries where cond is True are replaced with corresponding value
+                from other. If other is callable, it is computed on the
+                DataFrame and returns scalar or DataFrame. The callable must not
+                change input DataFrame (though pandas doesn’t check it). If not
+                specified, entries will be filled with the corresponding NULL
+                value (np.nan for numpy dtypes, pd.NA for extension dtypes).
+
+        Returns:
+            DataFrame: DataFrame after the replacement.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     # ----------------------------------------------------------------------
     # Sorting