feat: Allow DataFrame binary ops to align on either axis and with loc…

bigframes/core/normalize.py

@@ -0,0 +1,49 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import pandas as pd
+
+import bigframes.core.indexes.index as index
+import bigframes.series as series
+
+
+def normalize_to_bf_series(obj, default_index: index.Index) -> series.Series:
+    if isinstance(obj, series.Series):
+        return obj
+    if isinstance(obj, pd.Series):
+        return series.Series(obj)
+    if isinstance(obj, index.Index):
+        return series.Series(obj, default_index)
+    if isinstance(obj, pd.Index):
+        return series.Series(obj, default_index)
+    if pd.api.types.is_list_like(obj):
+        return series.Series(obj, default_index)
+    else:
+        raise TypeError(f"Cannot interpret {obj} as series.")
+
+
+def normalize_to_pd_series(obj, default_index: pd.Index) -> pd.Series:
+    if isinstance(obj, series.Series):
+        return obj.to_pandas()
+    if isinstance(obj, pd.Series):
+        return obj
+    if isinstance(obj, index.Index):
+        return pd.Series(obj.to_pandas(), default_index)
+    if isinstance(obj, pd.Index):
+        return pd.Series(obj, default_index)
+    if pd.api.types.is_list_like(obj):
+        return pd.Series(obj, default_index)
+    else:
+        raise TypeError(f"Cannot interpret {obj} as series.")

bigframes/dataframe.py

@@ -55,6 +55,7 @@
 import bigframes.core.guid
 import bigframes.core.indexers as indexers
 import bigframes.core.indexes as indexes
+import bigframes.core.normalize
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
 import bigframes.core.window
@@ -663,22 +664,24 @@ def _apply_binop(
         how: str = "outer",
         reverse: bool = False,
     ):
-        if isinstance(other, (float, int)):
+        if isinstance(other, (float, int, bool)):
             return self._apply_scalar_binop(other, op, reverse=reverse)
-        elif isinstance(other, indexes.Index):
-            return self._apply_series_binop(
-                other.to_series(index=self.index),
-                op,
-                axis=axis,
-                how=how,
-                reverse=reverse,
-            )
-        elif isinstance(other, bigframes.series.Series):
-            return self._apply_series_binop(
-                other, op, axis=axis, how=how, reverse=reverse
-            )
         elif isinstance(other, DataFrame):
             return self._apply_dataframe_binop(other, op, how=how, reverse=reverse)
+        elif isinstance(other, pandas.DataFrame):
+            return self._apply_dataframe_binop(
+                DataFrame(other), op, how=how, reverse=reverse
+            )
+        elif utils.get_axis_number(axis) == 0:
+            bf_series = bigframes.core.normalize.normalize_to_bf_series(
+                other, self.index
+            )
+            return self._apply_series_binop_axis_0(bf_series, op, how, reverse)
+        elif utils.get_axis_number(axis) == 1:
+            pd_series = bigframes.core.normalize.normalize_to_pd_series(
+                other, self.columns
+            )
+            return self._apply_series_binop_axis_1(pd_series, op, how, reverse)
         raise NotImplementedError(
             f"binary operation is not implemented on the second operand of type {type(other).__name__}."
             f"{constants.FEEDBACK_LINK}"
@@ -700,22 +703,13 @@ def _apply_scalar_binop(
             block = block.drop_columns([column_id])
         return DataFrame(block)
 
-    def _apply_series_binop(
+    def _apply_series_binop_axis_0(
         self,
         other: bigframes.series.Series,
         op: ops.BinaryOp,
-        axis: str | int = "columns",
         how: str = "outer",
         reverse: bool = False,
     ) -> DataFrame:
-        if axis not in ("columns", "index", 0, 1):
-            raise ValueError(f"Invalid input: axis {axis}.")
-
-        if axis in ("columns", 1):
-            raise NotImplementedError(
-                f"Row Series operations haven't been supported. {constants.FEEDBACK_LINK}"
-            )
-
         block, (get_column_left, get_column_right) = self._block.join(
             other._block, how=how
         )
@@ -738,6 +732,63 @@ def _apply_series_binop(
         block = block.with_index_labels(self.index.names)
         return DataFrame(block)
 
+    def _apply_series_binop_axis_1(
+        self,
+        other: pandas.Series,
+        op: ops.BinaryOp,
+        how: str = "outer",
+        reverse: bool = False,
+    ) -> DataFrame:
+        # Somewhat different alignment than df-df so separate codepath for now.
+        if self.columns.equals(other.index):
+            columns, lcol_indexer, rcol_indexer = self.columns, None, None
+        else:
+            if not (self.columns.is_unique and other.index.is_unique):
+                raise ValueError("Cannot align non-unique indices")
+            columns, lcol_indexer, rcol_indexer = self.columns.join(
+                other.index, how=how, return_indexers=True
+            )
+
+        binop_result_ids = []
+
+        column_indices = zip(
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns)),
+            rcol_indexer if (rcol_indexer is not None) else range(len(columns)),
+        )
+
+        block = self._block
+        for left_index, right_index in column_indices:
+            if left_index >= 0 and right_index >= 0:  # -1 indices indicate missing
+                self_col_id = self._block.value_columns[left_index]
+                other_scalar = other.iloc[right_index]
+                expr = (
+                    op.as_expr(ex.const(other_scalar), self_col_id)
+                    if reverse
+                    else op.as_expr(self_col_id, ex.const(other_scalar))
+                )
+            elif left_index >= 0:
+                self_col_id = self._block.value_columns[left_index]
+                expr = (
+                    op.as_expr(ex.const(None), self_col_id)
+                    if reverse
+                    else op.as_expr(self_col_id, ex.const(None))
+                )
+            elif right_index >= 0:
+                other_scalar = other.iloc[right_index]
+                expr = (
+                    op.as_expr(ex.const(other_scalar), ex.const(None))
+                    if reverse
+                    else op.as_expr(ex.const(None), ex.const(other_scalar))
+                )
+            else:
+                # Should not be possible
+                raise ValueError("No right or left index.")
+            block, result_col_id = block.project_expr(expr)
+            binop_result_ids.append(result_col_id)
+
+        block = block.select_columns(binop_result_ids)
+        return DataFrame(block.with_column_labels(columns))
+
     def _apply_dataframe_binop(
         self,
         other: DataFrame,

tests/system/small/test_dataframe.py

@@ -27,6 +27,7 @@
 
 import bigframes
 import bigframes._config.display_options as display_options
+import bigframes.core.indexes.index as bf_indexes
 import bigframes.dataframe as dataframe
 import bigframes.series as series
 from tests.system.utils import (
@@ -2074,6 +2075,37 @@ def test_series_binop_axis_index(
     assert_pandas_df_equal(bf_result, pd_result)
 
 
+@skip_legacy_pandas
+@pytest.mark.parametrize(
+    ("input"),
+    [
+        ((1000, 2000, 3000)),
+        (pd.Index([1000, 2000, 3000])),
+        (bf_indexes.Index([1000, 2000, 3000])),
+        (pd.Series((1000, 2000), index=["int64_too", "float64_col"])),
+        (series.Series((1000, 2000), index=["int64_too", "float64_col"])),
+    ],
+    ids=[
+        "tuple",
+        "pd_index",
+        "bf_index",
+        "pd_series",
+        "bf_series",
+    ],
+)
+def test_listlike_binop_axis_1(scalars_dfs, input):
+    scalars_df, scalars_pandas_df = scalars_dfs
+
+    df_columns = ["int64_col", "float64_col", "int64_too"]
+
+    bf_result = scalars_df[df_columns].add(input, axis=1).to_pandas()
+    if hasattr(input, "to_pandas"):
+        input = input.to_pandas()
+    pd_result = scalars_pandas_df[df_columns].add(input, axis=1)
+
+    assert_pandas_df_equal(bf_result, pd_result, check_dtype=False)
+
+
 @pytest.mark.parametrize(
     ("left_labels", "right_labels"),
     [