googleapis · sycai · Mar 26, 2025 · Mar 25, 2025 · Mar 25, 2025 · Mar 25, 2025
@@ -15,18 +15,18 @@
 from __future__ import annotations
 
 import typing
-from typing import Sequence, Tuple, Union
+from typing import Literal, Sequence, Tuple, Union
 
 import bigframes_vendored.constants as constants
 import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby
 import jellyfish
 import pandas as pd
 
 from bigframes import session
+from bigframes.core import expression as ex
 from bigframes.core import log_adapter
 import bigframes.core.block_transforms as block_ops
 import bigframes.core.blocks as blocks
-import bigframes.core.expression
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
 import bigframes.core.validations as validations
@@ -305,13 +305,16 @@ def diff(self, periods=1) -> series.Series:
         return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
 
     @validations.requires_ordering()
-    def rolling(self, window: int, min_periods=None) -> windows.Window:
-        # To get n size window, need current row and n-1 preceding rows.
-        window_spec = window_specs.rows(
-            grouping_keys=tuple(self._by_col_ids),
-            start=-(window - 1),
-            end=0,
-            min_periods=min_periods or window,
+    def rolling(
+        self,
+        window: int,
+        min_periods=None,
+        closed: Literal["right", "left", "both", "neither"] = "right",
+    ) -> windows.Window:
+        window_spec = window_specs.WindowSpec(
+            bounds=window_specs.RowsWindowBounds.from_window_size(window, closed),
+            min_periods=min_periods if min_periods is not None else window,
+            grouping_keys=tuple(ex.deref(col) for col in self._by_col_ids),
         )
         block = self._block.order_by(
             [order.ascending_over(col) for col in self._by_col_ids],
@@ -361,7 +364,7 @@ def _agg_string(self, func: str) -> df.DataFrame:
         return dataframe if self._as_index else self._convert_index(dataframe)
 
     def _agg_dict(self, func: typing.Mapping) -> df.DataFrame:
-        aggregations: typing.List[bigframes.core.expression.Aggregation] = []
+        aggregations: typing.List[ex.Aggregation] = []
         column_labels = []
 
         want_aggfunc_level = any(utils.is_list_like(aggs) for aggs in func.values())
@@ -738,13 +741,16 @@ def diff(self, periods=1) -> series.Series:
         return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
 
     @validations.requires_ordering()
-    def rolling(self, window: int, min_periods=None) -> windows.Window:
-        # To get n size window, need current row and n-1 preceding rows.
-        window_spec = window_specs.rows(
-            grouping_keys=tuple(self._by_col_ids),
-            start=-(window - 1),
-            end=0,
-            min_periods=min_periods or window,
+    def rolling(
+        self,
+        window: int,
+        min_periods=None,
+        closed: Literal["right", "left", "both", "neither"] = "right",
+    ) -> windows.Window:
+        window_spec = window_specs.WindowSpec(
+            bounds=window_specs.RowsWindowBounds.from_window_size(window, closed),
+            min_periods=min_periods if min_periods is not None else window,
+            grouping_keys=tuple(ex.deref(col) for col in self._by_col_ids),
         )
         block = self._block.order_by(
             [order.ascending_over(col) for col in self._by_col_ids],
@@ -806,11 +812,9 @@ def _apply_window_op(
         return series.Series(block.select_column(result_id))
 
 
-def agg(input: str, op: agg_ops.AggregateOp) -> bigframes.core.expression.Aggregation:
+def agg(input: str, op: agg_ops.AggregateOp) -> ex.Aggregation:
     if isinstance(op, agg_ops.UnaryAggregateOp):
-        return bigframes.core.expression.UnaryAggregation(
-            o
10BC0
p, bigframes.core.expression.deref(input)
-        )
+        return ex.UnaryAggregation(op, ex.deref(input))
     else:
         assert isinstance(op, agg_ops.NullaryAggregateOp)
-        return bigframes.core.expression.NullaryAggregation(op)
+        return ex.NullaryAggregation(op)
@@ -15,7 +15,7 @@
 
 from dataclasses import dataclass, replace
 import itertools
-from typing import Mapping, Optional, Set, Tuple, Union
+from typing import Literal, Mapping, Optional, Set, Tuple, Union
 
 import bigframes.core.expression as ex
 import bigframes.core.identifiers as ids
@@ -140,6 +140,21 @@ class RowsWindowBounds:
     start: Optional[int] = None
     end: Optional[int] = None
 
+    @classmethod
+    def from_window_size(
+        cls, window: int, closed: Literal["right", "left", "both", "neither"]
+    ) -> RowsWindowBounds:
+        if closed == "right":
+            return cls(-(window - 1), 0)
+        elif closed == "left":
+            return cls(-window, -1)
+        elif closed == "both":
+            return cls(-window, 0)
+        elif closed == "neither":
+            return cls(-(window - 1), -1)
+        else:
+            raise ValueError(f"Unsupported value for 'closed' parameter: {closed}")
+
     def __post_init__(self):
         if self.start is None:
             return

@@ -3308,10 +3308,15 @@ def _perform_join_by_index(
         return DataFrame(block)
 
     @validations.requires_ordering()
-    def rolling(self, window: int, min_periods=None) -> bigframes.core.window.Window:
-        # To get n size window, need current row and n-1 preceding rows.
-        window_def = windows.rows(
-            start=-(window - 1), end=0, min_periods=min_periods or window
+    def rolling(
+        self,
+        window: int,
+        min_periods=None,
+        closed: Literal["right", "left", "both", "neither"] = "right",
+    ) -> bigframes.core.window.Window:
+        window_def = windows.WindowSpec(
+            bounds=windows.RowsWindowBounds.from_window_size(window, closed),
+            min_periods=min_periods if min_periods is not None else window,
         )
         return bigframes.core.window.Window(
             self._block, window_def, self._block.value_columns

@@ -35,11 +35,10 @@
 import typing_extensions
 
 import bigframes.core
-from bigframes.core import log_adapter
+from bigframes.core import groupby, log_adapter
 import bigframes.core.block_transforms as block_ops
 import bigframes.core.blocks as blocks
 import bigframes.core.expression as ex
-import bigframes.core.groupby as groupby
 import bigframes.core.indexers
 import bigframes.core.indexes as indexes
 import bigframes.core.ordering as order
@@ -1438,10 +1437,15 @@ def sort_index(self, *, axis=0, ascending=True, na_position="last") -> Series:
         return Series(block)
 
     @validations.requires_ordering()
-    def rolling(self, window: int, min_periods=None) -> bigframes.core.window.Window:
-        # To get n size window, need current row and n-1 preceding rows.
-        window_spec = windows.rows(
-            start=-(window - 1), end=0, min_periods=min_periods or window
+    def rolling(
+        self,
+        window: int,
+        min_periods=None,
+        closed: Literal["right", "left", "both", "neither"] = "right",
+    ) -> bigframes.core.window.Window:
+        window_spec = windows.WindowSpec(
+            bounds=windows.RowsWindowBounds.from_window_size(window, closed),
+            min_periods=min_periods if min_periods is not None else window,
         )
         return bigframes.core.window.Window(
             self._block, window_spec, self._block.value_columns, is_series=True

diff --git a/tests/system/small/test_window.py b/tests/system/small/test_window.py
@@ -16,6 +16,111 @@
 import pytest
 
 
+@pytest.fixture(scope="module")
+def rolling_dfs(scalars_dfs):
+    bf_df, pd_df = scalars_dfs
+
+    target_cols = ["int64_too", "float64_col", "bool_col"]
+
+    bf_df = bf_df[target_cols].set_index("bool_col")
+    pd_df = pd_df[target_cols].set_index("bool_col")
+
+    return bf_df, pd_df
+
+
+@pytest.fixture(scope="module")
+def rolling_series(scalars_dfs):
+    bf_df, pd_df = scalars_dfs
+    target_col = "int64_too"
+
+    return bf_df[target_col], pd_df[target_col]
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_dataframe_rolling_closed_param(rolling_dfs, closed):
+    bf_df, pd_df = rolling_dfs
+
+    actual_result = bf_df.rolling(window=3, closed=closed).sum().to_pandas()
+
+    expected_result = pd_df.rolling(window=3, closed=closed).sum()
+    pd.testing.assert_frame_equal(actual_result, expected_result, check_dtype=False)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_dataframe_groupby_rolling_closed_param(rolling_dfs, closed):
+    bf_df, pd_df = rolling_dfs
+
+    actual_result = (
+        bf_df.groupby(level=0).rolling(window=3, closed=closed).sum().to_pandas()
+    )
+
+    expected_result = pd_df.groupby(level=0).rolling(window=3, closed=closed).sum()
+    pd.testing.assert_frame_equal(actual_result, expected_result, check_dtype=False)
+
+
+def test_dataframe_rolling_default_closed_param(rolling_dfs):
+    bf_df, pd_df = rolling_dfs
+
+    actual_result = bf_df.rolling(window=3).sum().to_pandas()
+
+    expected_result = pd_df.rolling(window=3).sum()
+    pd.testing.assert_frame_equal(actual_result, expected_result, check_dtype=False)
+
+
+def test_dataframe_groupby_rolling_default_closed_param(rolling_dfs):
+    bf_df, pd_df = rolling_dfs
+
+    actual_result = bf_df.groupby(level=0).rolling(window=3).sum().to_pandas()
+
+    expected_result = pd_df.groupby(level=0).rolling(window=3).sum()
+    pd.testing.assert_frame_equal(actual_result, expected_result, check_dtype=False)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_series_rolling_closed_param(rolling_series, closed):
+    bf_series, df_series = rolling_series
+
+    actual_result = bf_series.rolling(window=3, closed=closed).sum().to_pandas()
+
+    expected_result = df_series.rolling(window=3, closed=closed).sum()
+    pd.testing.assert_series_equal(actual_result, expected_result, check_dtype=False)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_series_groupby_rolling_closed_param(rolling_series, closed):
+    bf_series, df_series = rolling_series
+
+    actual_result = (
+        bf_series.groupby(bf_series % 2)
+        .rolling(window=3, closed=closed)
+        .sum()
+        .to_pandas()
+    )
+
+    expected_result = (
+        df_series.groupby(df_series % 2).rolling(window=3, closed=closed).sum()
+    )
+    pd.testing.assert_series_equal(actual_result, expected_result, check_dtype=False)
+
+
+def test_series_rolling_default_closed_param(rolling_series):
+    bf_series, df_series = rolling_series
+
+    actual_result = bf_series.rolling(window=3).sum().to_pandas()
+
+    expected_result = df_series.rolling(window=3).sum()
+    pd.testing.assert_series_equal(actual_result, expected_result, check_dtype=False)
+
+
+def test_series_groupby_rolling_default_closed_param(rolling_series):
+    bf_series, df_series = rolling_series
+
+    actual_result = bf_series.groupby(bf_series % 2).rolling(window=3).sum().to_pandas()
+
+    expected_result = df_series.groupby(df_series % 2).rolling(window=3).sum()
+    pd.testing.assert_series_equal(actual_result, expected_result, check_dtype=False)
+
+
 @pytest.mark.parametrize(
     ("windowing"),
     [
@@ -41,20 +146,13 @@
         pytest.param(lambda x: x.var(), id="var"),
     ],
 )
-def test_series_window_agg_ops(
-    scalars_df_index, scalars_pandas_df_index, windowing, agg_op
-):
-    col_name = "int64_too"
-    bf_series = agg_op(windowing(scalars_df_index[col_name])).to_pandas()
-    pd_series = agg_op(windowing(scalars_pandas_df_index[col_name]))
-
-    # Pandas always converts to float64, even for min/max/count, which is not desired
-    pd_series = pd_series.astype(bf_series.dtype)
-
-    pd.testing.assert_series_equal(
-        pd_series,
-        bf_series,
-    )
+def test_series_window_agg_ops(rolling_series, windowing, agg_op):
+    bf_series, pd_series = rolling_series
+
+    actual_result = agg_op(windowing(bf_series)).to_pandas()
+
+    expected_result = agg_op(windowing(pd_series))
+    pd.testing.assert_series_equal(expected_result, actual_result, check_dtype=False)
 
 
 @pytest.mark.parametrize(
@@ -83,13 +181,10 @@ def test_series_window_agg_ops(
         pytest.param(lambda x: x.var(), id="var"),
     ],
 )
-def test_dataframe_window_agg_ops(
-    scalars_df_index, scalars_pandas_df_index, windowing, agg_op
-):
-    scalars_df_index = scalars_df_index.set_index("bool_col")
-    scalars_pandas_df_index = scalars_pandas_df_index.set_index("bool_col")
-    col_names = ["int64_too", "float64_col"]
-    bf_result = agg_op(windowing(scalars_df_index[col_names])).to_pandas()
-    pd_result = agg_op(windowing(scalars_pandas_df_index[col_names]))
+def test_dataframe_window_agg_ops(rolling_dfs, windowing, agg_op):
+    bf_df, pd_df = rolling_dfs
+
+    bf_result = agg_op(windowing(bf_df)).to_pandas()
 
+    pd_result = agg_op(windowing(pd_df))
     pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False)
@@ -27,3 +27,24 @@ def test_invalid_rows_window_boundary_raise_error(start, end):
 def test_invalid_range_window_boundary_raise_error(start, end):
     with pytest.raises(ValueError):
         window_spec.RangeWindowBounds(start, end)
+
+
+@pytest.mark.parametrize(
+    ("window", "closed", "start", "end"),
+    [
+        pytest.param(3, "left", -3, -1, id="left"),
+        pytest.param(3, "right", -2, 0, id="right"),
+        pytest.param(3, "neither", -2, -1, id="neither"),
+        pytest.param(3, "both", -3, 0, id="both"),
+    ],
+)
+def test_rows_window_bounds_from_window_size(window, closed, start, end):
+    actual_result = window_spec.RowsWindowBounds.from_window_size(window, closed)
+
+    expected_result = window_spec.RowsWindowBounds(start, end)
+    assert actual_result == expected_result
+
+
+def test_rows_window_bounds_from_window_size_invalid_closed_raise_error():
+    with pytest.raises(ValueError):
+        window_spec.RowsWindowBounds.from_window_size(3, "whatever")  # type:ignore