googleapis · tswast · May 21, 2026 · May 21, 2026 · gemini-code-assist · May 21, 2026
@@ -16,14 +16,41 @@
 
 from __future__ import annotations
 
-from typing import Any, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import pandas as pd
 
 import bigframes.core.col
 import bigframes.core.expression as ex
+import bigframes.core.global_session as global_session
 import bigframes.core.sentinels as sentinels
 import bigframes.series as series
 from bigframes.operations import googlesql
 
+if TYPE_CHECKING:
+    import bigframes.session
+
+
+def _is_pandas_series(arg: Any) -> bool:
+    return isinstance(arg, pd.Series)
+
+
+def _find_session(*args: Any) -> Optional[bigframes.session.Session]:
+    import bigframes.core.indexes as indexes
+    import bigframes.dataframe as dataframe
+
+    for arg in args:
+        if isinstance(arg, (series.Series, dataframe.DataFrame, indexes.Index)):
+            return arg._session
+    return None
+
+
+def _get_session(*args: Any) -> bigframes.session.Session:
+    session = _find_session(*args)
+    if session is not None:
+        return session
+    return global_session.get_global_session()
+
 
 def apply_googlesql_scalar_op(
     op: googlesql.GoogleSqlScalarOp,
@@ -44,6 +71,14 @@ def apply_googlesql_scalar_op(
             The result of the operation. If any of ``args`` is a Series, returns
             a Series. Otherwise, returns an Expression.
     """
+    has_pandas_series = any(_is_pandas_series(arg) for arg in args)
+
+    if has_pandas_series:
+        session = _get_session(*args)
+        args = tuple(
+            session.read_pandas(arg) if _is_pandas_series(arg) else arg for arg in args
+        )
+
     # Find the first Series to use for alignment
     first_series = None
     for arg in args:

@@ -9,6 +9,7 @@ packages.
 
     bigframes._config
     bigframes.bigquery
+    bigframes.bigquery.aead
     bigframes.bigquery.ai
     bigframes.bigquery.ml
     bigframes.bigquery.obj

@@ -0,0 +1,268 @@
+# Copyright 2026 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.
+
+import unittest.mock as mock
+
+import pandas as pd
+
+import bigframes.core.col as col
+import bigframes.core.expression as ex
+import bigframes.core.global_session
+import bigframes.core.googlesql as core_googlesql
+import bigframes.series as series
+from bigframes.operations import googlesql
+from bigframes.testing import mocks
+
+# Define a test op
+_TEST_OP = googlesql.GoogleSqlScalarOp(
+    "TEST_OP",
+    args=(googlesql.ArgSpec(), googlesql.ArgSpec()),
+    signature=lambda *args: None,
+)
+
+
+def test_apply_googlesql_scalar_op_expressions():
+    # Only expressions
+    result = core_googlesql.apply_googlesql_scalar_op(
+        _TEST_OP,
+        col.col("a"),
+        col.col("b"),
+    )
+    assert isinstance(result, col.Expression)
+
+
+def test_apply_googlesql_scalar_op_pandas_series_global_session(monkeypatch):
+    # Setup mock session
+    session = mocks.create_bigquery_session()
+    monkeypatch.setattr(bigframes.core.global_session, "_global_session", session)
+    bigframes.options.bigquery._session_started = True
+
+    # Create a real-ish Series to return from read_pandas
+    df = mocks.create_dataframe(monkeypatch, session=session, data={"col": [1, 2, 3]})
+    bf_series = df["col"]
+
+    # Mock read_pandas on the session
+    mock_read_pandas = mock.MagicMock(return_value=bf_series)
+    session.read_pandas = mock_read_pandas
+
+    # Mock _apply_nary_op on Series class to avoid real compilation/execution
+    mock_apply_nary_op = mock.MagicMock(return_value=bf_series)
+    monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op)
+
+    pd_series = pd.Series([1, 2, 3])
+
+    # Call the function with a pandas Series and a literal
+    result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, 42)
+
+    # Verify read_pandas was called on the global session
+    mock_read_pandas.assert_called_once_with(pd_series)
+
+    # Verify _apply_nary_op was called on the converted series
+    mock_apply_nary_op.assert_called_once()
+    # First arg to _apply_nary_op is the op, second is the processed_args
+    assert mock_apply_nary_op.call_args[0][0] == _TEST_OP
+    # processed_args should contain the converted bf_series and the literal 42
+    processed_args = mock_apply_nary_op.call_args[0][1]
+    assert processed_args[0] is bf_series
+    assert processed_args[1] == 42
+
+    # Verify result is a Series
+    assert isinstance(result, series.Series)
+
+
+def test_apply_googlesql_scalar_op_pandas_series_with_bf_series(monkeypatch):
+    # Setup mock session 1 (global) and session 2 (associated with bf_series)
+    global_session = mocks.create_bigquery_session(session_id="global")
+    monkeypatch.setattr(
+        bigframes.core.global_session, "_global_session", global_session
+    )
+    bigframes.options.bigquery._session_started = True
+
+    bf_session = mocks.create_bigquery_session(session_id="bf_session")
+
+    # Create a bf_series associated with bf_session
+    df = mocks.create_dataframe(
+        monkeypatch, session=bf_session, data={"col": [1, 2, 3]}
+    )
+    bf_series = df["col"]
+
+    assert bf_series._session == bf_session
+
+    # Mock read_pandas on both sessions
+    mock_global_read_pandas = mock.MagicMock()
+    global_session.read_pandas = mock_global_read_pandas
+
+    mock_bf_read_pandas = mock.MagicMock(return_value=bf_series)
+    bf_session.read_pandas = mock_bf_read_pandas
+
+    # Mock _apply_nary_op
+    mock_apply_nary_op = mock.MagicMock(return_value=bf_series)
+    monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op)
+
+    pd_series = pd.Series([1, 2, 3])
+
+    # Call with both pandas Series and BigFrames Series
+    result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_series)
+
+    # Verify read_pandas was called on bf_session, NOT global_session
+    mock_bf_read_pandas.assert_called_once_with(pd_series)
+    mock_global_read_pandas.assert_not_called()
+
+    # Verify _apply_nary_op was called
+    mock_apply_nary_op.assert_called_once()
+    processed_args = mock_apply_nary_op.call_args[0][1]
+    # Both arguments to the op should now be BigFrames Series
+    assert processed_args[0] is bf_series
+    assert processed_args[1] is bf_series
+
+    assert isinstance(result, series.Series)
+
+
+def test_apply_googlesql_scalar_op_mixed_args(monkeypatch):
+    session = mocks.create_bigquery_session()
+    monkeypatch.setattr(bigframes.core.global_session, "_global_session", session)
+    bigframes.options.bigquery._session_started = True
+
+    df = mocks.create_dataframe(monkeypatch, session=session, data={"col": [1, 2, 3]})
+    bf_series = df["col"]
+
+    mock_read_pandas = mock.MagicMock(return_value=bf_series)
+    session.read_pandas = mock_read_pandas
+
+    mock_apply_nary_op = mock.MagicMock(return_value=bf_series)
+    monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op)
+
+    pd_series = pd.Series([1, 2, 3])
+    expr = col.Expression(ex.const(10))
+
+    # Call with pandas Series, Expression, and Literal
+    result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, expr, 42)
+
+    # Verify pandas Series was converted
+    mock_read_pandas.assert_called_once_with(pd_series)
+
+    # Verify _apply_nary_op was called
+    mock_apply_nary_op.assert_called_once()
+    processed_args = mock_apply_nary_op.call_args[0][1]
+
+    # Processed args should be:
+    # 1. bf_series (converted from pd_series)
+    # 2. A new Series (projected from the expression onto bf_series' block)
+    # 3. Literal 42
+    assert isinstance(processed_args[0], series.Series)
+    assert processed_args[0] is bf_series
+
+    assert isinstance(processed_args[1], series.Series)
+    assert processed_args[1] is not bf_series
+
+    assert processed_args[2] == 42
+
+    assert isinstance(result, series.Series)
+
+
+def test_apply_googlesql_scalar_op_pandas_series_with_bf_dataframe(monkeypatch):
+    # Setup mock session 2 (associated with bf_dataframe)
+    bf_session = mocks.create_bigquery_session(session_id="bf_session")
+
+    # Create a bf_dataframe associated with bf_session
+    bf_dataframe = mocks.create_dataframe(
+        monkeypatch, session=bf_session, data={"col": [1, 2, 3]}
+    )
+    bf_series = bf_dataframe["col"]
+
+    # Setup mock session 1 (global) AFTER creating the dataframe
+    global_session = mocks.create_bigquery_session(session_id="global")
+    monkeypatch.setattr(
+        bigframes.core.global_session, "_global_session", global_session
+    )
+    bigframes.options.bigquery._session_started = True
+
+    assert bf_dataframe._session == bf_session
+
+    # Mock read_pandas on both sessions
+    mock_global_read_pandas = mock.MagicMock()
+    global_session.read_pandas = mock_global_read_pandas
+
+    mock_bf_read_pandas = mock.MagicMock(return_value=bf_series)
+    bf_session.read_pandas = mock_bf_read_pandas
+
+    # Mock _apply_nary_op
+    mock_apply_nary_op = mock.MagicMock(return_value=bf_series)
+    monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op)
+
+    pd_series = pd.Series([1, 2, 3])
+
+    # Call with pandas Series and BigFrames DataFrame
+    result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_dataframe)
+
+    # Verify read_pandas was called on bf_session, NOT global_session
+    mock_bf_read_pandas.assert_called_once_with(pd_series)
+    mock_global_read_pandas.assert_not_called()
+
+    # Verify _apply_nary_op was called
+    mock_apply_nary_op.assert_called_once()
+    processed_args = mock_apply_nary_op.call_args[0][1]
+    assert processed_args[0] is bf_series
+    assert processed_args[1] is bf_dataframe
+
+    assert isinstance(result, series.Series)
+
+
+def test_apply_googlesql_scalar_op_pandas_series_with_bf_index(monkeypatch):
+    # Setup mock session 2 (associated with bf_index)
+    bf_session = mocks.create_bigquery_session(session_id="bf_session")
+
+    # Create a bf_dataframe associated with bf_session to get an index
+    bf_dataframe = mocks.create_dataframe(
+        monkeypatch, session=bf_session, data={"col": [1, 2, 3]}
+    )
+    bf_index = bf_dataframe.index
+    bf_series = bf_dataframe["col"]
+
+    # Setup mock session 1 (global) AFTER creating the dataframe
+    global_session = mocks.create_bigquery_session(session_id="global")
+    monkeypatch.setattr(
+        bigframes.core.global_session, "_global_session", global_session
+    )
+    bigframes.options.bigquery._session_started = True
+
+    assert bf_index._session == bf_session
+
+    # Mock read_pandas on both sessions
+    mock_global_read_pandas = mock.MagicMock()
+    global_session.read_pandas = mock_global_read_pandas
+
+    mock_bf_read_pandas = mock.MagicMock(return_value=bf_series)
+    bf_session.read_pandas = mock_bf_read_pandas
+
+    # Mock _apply_nary_op
+    mock_apply_nary_op = mock.MagicMock(return_value=bf_series)
+    monkeypatch.setattr(series.Series, "_apply_nary_op", mock_apply_nary_op)
+
+    pd_series = pd.Series([1, 2, 3])
+
+    # Call with pandas Series and BigFrames Index
+    result = core_googlesql.apply_googlesql_scalar_op(_TEST_OP, pd_series, bf_index)
+
+    # Verify read_pandas was called on bf_session, NOT global_session
+    mock_bf_read_pandas.assert_called_once_with(pd_series)
+    mock_global_read_pandas.assert_not_called()
+
+    # Verify _apply_nary_op was called
+    mock_apply_nary_op.assert_called_once()
+    processed_args = mock_apply_nary_op.call_args[0][1]
+    assert processed_args[0] is bf_series
+    assert processed_args[1] is bf_index
+
+    assert isinstance(result, series.Series)