feat: support Series.dot on a DataFrame input

bigframes/series.py

@@ -680,6 +680,17 @@ def rdivmod(self, other) -> Tuple[Series, Series]:  # type: ignore
         return (self.rfloordiv(other), self.rmod(other))
 
     def __matmul__(self, other):
+        if isinstance(other, bigframes.dataframe.DataFrame):
+            return Series(
+                [
+                    pandas.NA if other[col].isna().any() else (self * other[col]).sum()
+                    for col in other.columns
+                ],
+                index=other.columns,
+                name=self.name,
+            )
+
+        # At this point other must be a Series
         return (self * other).sum()
 
     dot = __matmul__

tests/system/small/test_multiindex.py

@@ -1045,3 +1045,21 @@ def test_column_multi_index_dot_not_supported():
         NotImplementedError, match="Multi-level column input is not supported"
     ):
         bf1 @ bf2
+
+
+def test_series_dot_df_column_multi_index():
+    left = [10, 11, 12, 13]  # series data
+    right = [[0, 1, 2], [-2, 3, -4], [4, -5, 6], [6, 7, -8]]  # dataframe data
+
+    multi_level_columns = pandas.MultiIndex.from_arrays(
+        [["col0", "col0", "col1"], ["col00", "col01", "col11"]]
+    )
+
+    bf_result = bpd.Series(left) @ bpd.DataFrame(right, columns=multi_level_columns)
+    pd_result = pandas.Series(left) @ pandas.DataFrame(
+        right, columns=multi_level_columns
+    )
+
+    pandas.testing.assert_series_equal(
+        bf_result.to_pandas(), pd_result, check_index_type=False, check_dtype=False
+    )

tests/system/small/test_series.py

@@ -2266,6 +2266,27 @@ def test_dot(scalars_dfs):
     assert bf_result == pd_result
 
 
+def test_dot_df(matrix_3by4_df, matrix_3by4_pandas_df):
+    bf_result = matrix_3by4_df["w"] @ matrix_3by4_df
+    pd_result = matrix_3by4_pandas_df["w"] @ matrix_3by4_pandas_df
+
+    pd.testing.assert_series_equal(
+        bf_result.to_pandas(), pd_result, check_index_type=False, check_dtype=False
+    )
+
+
+def test_dot_df_with_na(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df["int64_too"] @ scalars_df[["int64_col", "int64_too"]]
+    pd_result = (
+        scalars_pandas_df["int64_too"] @ scalars_pandas_df[["int64_col", "int64_too"]]
+    )
+
+    pd.testing.assert_series_equal(
+        bf_result.to_pandas(), pd_result, check_index_type=False, check_dtype=False
+    )
+
+
 @pytest.mark.parametrize(
     ("left", "right", "inclusive"),
     [

third_party/bigframes_vendored/pandas/core/series.py

@@ -620,8 +620,7 @@ def dot(self, other) -> Series | np.ndarray:
         Compute the dot product between the Series and the columns of other.
 
         This method computes the dot product between the Series and another
-        one, or the Series and each columns of a DataFrame, or the Series and
-        each columns of an array.
+        one, or the Series and each columns of a DataFrame.
 
         It can also be called using `self @ other` in Python >= 3.5.
 
@@ -646,15 +645,25 @@ def dot(self, other) -> Series | np.ndarray:
             >>> s @ other
             8
 
+        The other operand can be a DataFrame:
+
+            >>> other = bpd.DataFrame({"a" : [-1, 2, -3, 4],
+            ...                        "b" : [-10, 20, -30, 40],
+            ...                        "c" : [-1, 2, -3, bpd.NA]})
+            >>> s @ other
+            a     8.0
+            b    80.0
+            c    <NA>
+            dtype: Float64
+
         Args:
-            other (Series):
+            other (Series, or DataFrame):
                 The other object to compute the dot product with its columns.
 
         Returns:
-            scalar, Series or numpy.ndarray: Return the dot product of the Series
-                and other if other is a Series, the Series of the dot product of
-                Series and each rows of other if other is a DataFrame or a
-                numpy.ndarray between the Series and each columns of the numpy array.
+            scalar, Series: Return the dot product of the Series
+                and other if other is a Series, or the Series of the dot product
+                of Series and each column of other if other is a DataFrame.
 
 
         """