feat: Support callable for dataframe mask method

Author: jialuoo

bigframes/dataframe.py

@@ -2787,6 +2787,19 @@ def itertuples(
             for item in df.itertuples(index=index, name=name):
                 yield item
 
+    def _apply_callable(self, condition):
+        """Executes the possible callable condition as needed."""
+        if callable(condition):
+            # When it's a bigframes function.
+            if hasattr(condition, "bigframes_bigquery_function"):
+                return self.apply(condition, axis=1)
+
+            # When it's a plain Python function.
+            return condition(self)
+
+        # When it's not a callable.
+        return condition
+
     def where(self, cond, other=None):
         if isinstance(other, bigframes.series.Series):
             raise ValueError("Seires is not a supported replacement type!")
@@ -2798,16 +2811,8 @@ def where(self, cond, other=None):
 
         # Execute it with the DataFrame when cond or/and other is callable.
         # It can be either a plain python function or remote/managed function.
-        if callable(cond):
-            if hasattr(cond, "bigframes_bigquery_function"):
-                cond = self.apply(cond, axis=1)
-            else:
-                cond = cond(self)
-        if callable(other):
-            if hasattr(other, "bigframes_bigquery_function"):
-                other = self.apply(other, axis=1)
-            else:
-                other = other(self)
+        cond = self._apply_callable(cond)
+        other = self._apply_callable(other)
 
         aligned_block, (_, _) = self._block.join(cond._block, how="left")
         # No left join is needed when 'other' is None or constant.
@@ -2858,7 +2863,7 @@ def where(self, cond, other=None):
         return result
 
     def mask(self, cond, other=None):
-        return self.where(~cond, other=other)
+        return self.where(~self._apply_callable(cond), other=other)
 
     def dropna(
         self,

tests/system/large/functions/test_managed_function.py

@@ -965,7 +965,7 @@ def float_parser(row):
         )
 
 
-def test_managed_function_df_where(session, dataset_id, scalars_dfs):
+def test_managed_function_df_where_mask(session, dataset_id, scalars_dfs):
     try:
 
         # The return type has to be bool type for callable where condition.
@@ -987,15 +987,15 @@ def is_sum_positive(a, b):
         pd_int64_df = scalars_pandas_df[int64_cols]
         pd_int64_df_filtered = pd_int64_df.dropna()
 
-        # Use callable condition in dataframe.where method.
+        # Test callable condition in dataframe.where method.
         bf_result = bf_int64_df_filtered.where(is_sum_positive_mf).to_pandas()
         # Pandas doesn't support such case, use following as workaround.
         pd_result = pd_int64_df_filtered.where(pd_int64_df_filtered.sum(axis=1) > 0)
 
         # Ignore any dtype difference.
         pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
-        # Make sure the read_gbq_function path works for this function.
+        # Make sure the read_gbq_function path works for dataframe.where method.
         is_sum_positive_ref = session.read_gbq_function(
             function_name=is_sum_positive_mf.bigframes_bigquery_function
         )
@@ -1012,14 +1012,27 @@ def is_sum_positive(a, b):
             bf_result_gbq, pd_result_gbq, check_dtype=False
         )
 
+        # Test callable condition in dataframe.mask method.
+        bf_result_gbq = bf_int64_df_filtered.mask(
+            is_sum_positive_ref, -bf_int64_df_filtered
+        ).to_pandas()
+        pd_result_gbq = pd_int64_df_filtered.mask(
+            pd_int64_df_filtered.sum(axis=1) > 0, -pd_int64_df_filtered
+        )
+
+        # Ignore any dtype difference.
+        pandas.testing.assert_frame_equal(
+            bf_result_gbq, pd_result_gbq, check_dtype=False
+        )
+
     finally:
         # Clean up the gcp assets created for the managed function.
         cleanup_function_assets(
             is_sum_positive_mf, session.bqclient, ignore_failures=False
         )
 
 
-def test_managed_function_df_where_series(session, dataset_id, scalars_dfs):
+def test_managed_function_df_where_mask_series(session, dataset_id, scalars_dfs):
     try:
 
         # The return type has to be bool type for callable where condition.
@@ -1041,14 +1054,14 @@ def is_sum_positive_series(s):
         pd_int64_df = scalars_pandas_df[int64_cols]
         pd_int64_df_filtered = pd_int64_df.dropna()
 
-        # Use callable condition in dataframe.where method.
+        # Test callable condition in dataframe.where method.
         bf_result = bf_int64_df_filtered.where(is_sum_positive_series).to_pandas()
         pd_result = pd_int64_df_filtered.where(is_sum_positive_series)
 
         # Ignore any dtype difference.
         pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
-        # Make sure the read_gbq_function path works for this function.
+        # Make sure the read_gbq_function path works for dataframe.where method.
         is_sum_positive_series_ref = session.read_gbq_function(
             function_name=is_sum_positive_series_mf.bigframes_bigquery_function,
             is_row_processor=True,
@@ -1070,6 +1083,19 @@ def func_for_other(x):
             bf_result_gbq, pd_result_gbq, check_dtype=False
         )
 
+        # Test callable condition in dataframe.mask method.
+        bf_result_gbq = bf_int64_df_filtered.mask(
+            is_sum_positive_series_ref, func_for_other
+        ).to_pandas()
+        pd_result_gbq = pd_int64_df_filtered.mask(
+            is_sum_positive_series, func_for_other
+        )
+
+        # Ignore any dtype difference.
+        pandas.testing.assert_frame_equal(
+            bf_result_gbq, pd_result_gbq, check_dtype=False
+        )
+
     finally:
         # Clean up the gcp assets created for the managed function.
         cleanup_function_assets(

tests/system/large/functions/test_remote_function.py

@@ -2850,7 +2850,7 @@ def foo(x: int) -> int:
 
 
 @pytest.mark.flaky(retries=2, delay=120)
-def test_remote_function_df_where(session, dataset_id, scalars_dfs):
+def test_remote_function_df_where_mask(session, dataset_id, scalars_dfs):
     try:
 
         # The return type has to be bool type for callable where condition.
@@ -2873,14 +2873,22 @@ def is_sum_positive(a, b):
         pd_int64_df = scalars_pandas_df[int64_cols]
         pd_int64_df_filtered = pd_int64_df.dropna()
 
-        # Use callable condition in dataframe.where method.
+        # Test callable condition in dataframe.where method.
         bf_result = bf_int64_df_filtered.where(is_sum_positive_mf, 0).to_pandas()
         # Pandas doesn't support such case, use following as workaround.
         pd_result = pd_int64_df_filtered.where(pd_int64_df_filtered.sum(axis=1) > 0, 0)
 
         # Ignore any dtype difference.
         pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
+        # Test callable condition in dataframe.mask method.
+        bf_result = bf_int64_df_filtered.mask(is_sum_positive_mf, 0).to_pandas()
+        # Pandas doesn't support such case, use following as workaround.
+        pd_result = pd_int64_df_filtered.mask(pd_int64_df_filtered.sum(axis=1) > 0, 0)
+
+        # Ignore any dtype difference.
+        pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
+
     finally:
         # Clean up the gcp assets created for the remote function.
         cleanup_function_assets(
@@ -2889,7 +2897,7 @@ def is_sum_positive(a, b):
 
 
 @pytest.mark.flaky(retries=2, delay=120)
-def test_remote_function_df_where_series(session, dataset_id, scalars_dfs):
+def test_remote_function_df_where_mask_series(session, dataset_id, scalars_dfs):
     try:
 
         # The return type has to be bool type for callable where condition.
@@ -2916,7 +2924,7 @@ def is_sum_positive_series(s):
         def func_for_other(x):
             return -x
 
-        # Use callable condition in dataframe.where method.
+        # Test callable condition in dataframe.where method.
         bf_result = bf_int64_df_filtered.where(
             is_sum_positive_series, func_for_other
         ).to_pandas()
@@ -2925,6 +2933,15 @@ def func_for_other(x):
         # Ignore any dtype difference.
         pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
 
+        # Test callable condition in dataframe.mask method.
+        bf_result = bf_int64_df_filtered.mask(
+            is_sum_positive_series_mf, func_for_other
+        ).to_pandas()
+        pd_result = pd_int64_df_filtered.mask(is_sum_positive_series, func_for_other)
+
+        # Ignore any dtype difference.
+        pandas.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
+
     finally:
         # Clean up the gcp assets created for the remote function.
         cleanup_function_assets(

tests/system/small/test_dataframe.py

@@ -406,6 +406,18 @@ def test_mask_series_cond(scalars_df_index, scalars_pandas_df_index):
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+def test_mask_callable(scalars_df_index, scalars_pandas_df_index):
+    def is_positive(x):
+        return x > 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=is_positive, other=lambda x: x + 1).to_pandas()
+    pd_result = pd_df.mask(cond=is_positive, other=lambda x: x + 1)
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
 def test_where_multi_column(scalars_df_index, scalars_pandas_df_index):
     # Test when a dataframe has multi-columns.
     columns = ["int64_col", "float64_col"]