feat: support multi-column assignment for DataFrame

bigframes/dataframe.py

@@ -26,6 +26,7 @@
 import traceback
 import typing
 from typing import (
+    Any,
     Callable,
     Dict,
     Hashable,
@@ -91,6 +92,7 @@
     import bigframes.session
 
     SingleItemValue = Union[bigframes.series.Series, int, float, str, Callable]
+    MultiItemValue = Union["DataFrame", Sequence[int | float | str | Callable]]
 
 LevelType = typing.Hashable
 LevelsType = typing.Union[LevelType, typing.Sequence[LevelType]]
@@ -884,8 +886,13 @@ def __delitem__(self, key: str):
         df = self.drop(columns=[key])
         self._set_block(df._get_block())
 
-    def __setitem__(self, key: str, value: SingleItemValue):
-        df = self._assign_single_item(key, value)
+    def __setitem__(
+        self, key: str | list[str], value: SingleItemValue | MultiItemValue
+    ):
+        if isinstance(key, list):
+            df = self._assign_multi_items(key, value)
+        else:
+            df = self._assign_single_item(key, value)
         self._set_block(df._get_block())
 
     __setitem__.__doc__ = inspect.getdoc(vendored_pandas_frame.DataFrame.__setitem__)
@@ -2212,7 +2219,7 @@ def assign(self, **kwargs) -> DataFrame:
     def _assign_single_item(
         self,
         k: str,
-        v: SingleItemValue,
+        v: SingleItemValue | MultiItemValue,
     ) -> DataFrame:
         if isinstance(v, bigframes.series.Series):
             return self._assign_series_join_on_index(k, v)
@@ -2230,7 +2237,33 @@ def _assign_single_item(
         elif utils.is_list_like(v):
             return self._assign_single_item_listlike(k, v)
         else:
-            return self._assign_scalar(k, v)
+            return self._assign_scalar(k, v)  # type: ignore
+
+    def _assign_multi_items(
+        self,
+        k: list[str],
+        v: SingleItemValue | MultiItemValue,
+    ) -> DataFrame:
+        value_sources: Sequence[Any] = []
+        if isinstance(v, DataFrame):
+            value_sources = [v[col] for col in v.columns]
+        elif isinstance(v, bigframes.series.Series):
+            # For behavior consistency with Pandas.
+            raise ValueError("Columns must be same length as key")
+        elif isinstance(v, Sequence):
+            value_sources = v
+        else:
+            # We assign the same scalar value to all target columns.
+            value_sources = [v] * len(k)
+
+        if len(value_sources) != len(k):
+            raise ValueError("Columns must be same length as key")
+
+        # Repeatedly assign columns in order.
+        result = self._assign_single_item(k[0], value_sources[0])
+        for target, source in zip(k[1:], value_sources[1:]):
+            result = result._assign_single_item(target, source)
+        return result
 
     def _assign_single_item_listlike(self, k: str, v: Sequence) -> DataFrame:
         given_rows = len(v)

tests/system/small/test_dataframe.py

@@ -1138,6 +1138,67 @@ def test_assign_new_column_w_setitem_list_error(scalars_dfs):
         bf_df["new_col"] = [1, 2, 3]
 
 
+@pytest.mark.parametrize(
+    ("key", "value"),
+    [
+        pytest.param(["int64_col", "int64_too"], 1, id="scalar_to_existing_column"),
+        pytest.param(
+            ["int64_col", "int64_too"], [1, 2], id="sequence_to_existing_column"
+        ),
+        pytest.param(
+            ["int64_col", "new_col"], [1, 2], id="sequence_to_partial_new_column"
+        ),
+        pytest.param(
+            ["new_col", "new_col_too"], [1, 2], id="sequence_to_full_new_column"
+        ),
+    ],
+)
+def test_setitem_multicolumn_with_literals(scalars_dfs, key, value):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df.copy()
+    pd_result = scalars_pandas_df.copy()
+
+    bf_result[key] = value
+    pd_result[key] = value
+
+    pd.testing.assert_frame_equal(pd_result, bf_result.to_pandas(), check_dtype=False)
+
+
+def test_setitem_multicolumn_with_literals_different_lengths_raise_error(scalars_dfs):
+    scalars_df, _ = scalars_dfs
+    bf_result = scalars_df.copy()
+
+    with pytest.raises(ValueError):
+        bf_result[["int64_col", "int64_too"]] = [1]
+
+
+def test_setitem_multicolumn_with_dataframes(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df.copy()
+    pd_result = scalars_pandas_df.copy()
+
+    bf_result[["int64_col", "int64_too"]] = bf_result[["int64_too", "int64_col"]] / 2
+    pd_result[["int64_col", "int64_too"]] = pd_result[["int64_too", "int64_col"]] / 2
+
+    pd.testing.assert_frame_equal(pd_result, bf_result.to_pandas(), check_dtype=False)
+
+
+def test_setitem_multicolumn_with_dataframes_series_on_rhs_raise_error(scalars_dfs):
+    scalars_df, _ = scalars_dfs
+    bf_result = scalars_df.copy()
+
+    with pytest.raises(ValueError):
+        bf_result[["int64_col", "int64_too"]] = bf_result["int64_col"] / 2
+
+
+def test_setitem_multicolumn_with_dataframes_different_lengths_raise_error(scalars_dfs):
+    scalars_df, _ = scalars_dfs
+    bf_result = scalars_df.copy()
+
+    with pytest.raises(ValueError):
+        bf_result[["int64_col"]] = bf_result[["int64_col", "int64_too"]] / 2
+
+
 def test_assign_existing_column(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     kwargs = {"int64_col": 2}

third_party/bigframes_vendored/pandas/core/frame.py

@@ -7626,11 +7626,43 @@ def __setitem__(self, key, value):
             <BLANKLINE>
             [3 rows x 5 columns]
 
+        You can assign a scalar to multiple columns.
+
+            >>> df[["age", "new_age"]] = 25
+            >>> df
+                name  age location country  new_age
+            0  alpha   25       WA     USA       25
+            1   beta   25       NY     USA       25
+            2  gamma   25       CA     USA       25
+            <BLANKLINE>
+            [3 rows x 5 columns]
+
+        You can use a sequence of scalars for assignment of multiple columns:
+
+            >>> df[["age", "is_happy"]] = [20, True]
+            >>> df
+                name  age location country  new_age  is_happy
+            0  alpha   20       WA     USA       25      True
+            1   beta   20       NY     USA       25      True
+            2  gamma   20       CA     USA       25      True
+            <BLANKLINE>
+            [3 rows x 6 columns]
+
+        You can use a dataframe for assignment of multiple columns:
+            >>> df[["age", "new_age"]] = df[["new_age", "age"]]
+            >>> df
+            name  age location country  new_age  is_happy
+            0  alpha   25       WA     USA       20      True
+            1   beta   25       NY     USA       20      True
+            2  gamma   25       CA     USA       20      True
+            <BLANKLINE>
+            [3 rows x 6 columns]
+
         Args:
             key (column index):
                 It can be a new column to be inserted, or an existing column to
                 be modified.
-            value (scalar or Series):
+            value (scalar, Sequence, DataFrame, or Series):
                 Value to be assigned to the column
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)