ENH: Add step to rolling

pandas-stubs/_typing.pyi

@@ -302,4 +302,6 @@ class StyleExportDict(TypedDict, total=False):
     hide_column_names: bool
     css: dict[str, str | int]
 
+CalculationMethod = Literal["single", "table"]
+
 __all__ = ["npt", "type_t"]

pandas-stubs/core/frame.pyi

@@ -27,6 +27,7 @@ from pandas.core.groupby.generic import (
     _DataFrameGroupByScalar,
 )
 from pandas.core.groupby.grouper import Grouper
+from pandas.core.indexers import BaseIndexer
 from pandas.core.indexes.base import Index
 from pandas.core.indexing import (
     _iLocIndexer,
@@ -46,6 +47,7 @@ from pandas.core.window.rolling import (
 import xarray as xr
 
 from pandas._libs.missing import NAType
+from pandas._libs.tslibs import BaseOffset
 from pandas._typing import (
     S1,
     AggFuncTypeBase,
@@ -56,6 +58,7 @@ from pandas._typing import (
     Axes,
     Axis,
     AxisType,
+    CalculationMethod,
     ColspaceArgType,
     CompressionOptions,
     Dtype,
@@ -1429,7 +1432,7 @@ class DataFrame(NDFrame, OpsMixin):
         self,
         min_periods: int = ...,
         axis: AxisType = ...,
-        method: Literal["single", "table"] = ...,
+        method: CalculationMethod = ...,
     ) -> Expanding[DataFrame]: ...
     @overload
     def ffill(
@@ -1763,26 +1766,30 @@ class DataFrame(NDFrame, OpsMixin):
     @overload
     def rolling(
         self,
-        window,
+        window: int | BaseOffset | BaseIndexer,
         min_periods: int | None = ...,
         center: _bool = ...,
         *,
         win_type: _str,
         on: Hashable | None = ...,
         axis: AxisType = ...,
         closed: IntervalClosedType | None = ...,
+        step: int | None = ...,
+        method: CalculationMethod = ...,
     ) -> Window[DataFrame]: ...
     @overload
     def rolling(
         self,
-        window,
+        window: int | BaseOffset | BaseIndexer,
         min_periods: int | None = ...,
         center: _bool = ...,
         *,
         win_type: None = ...,
         on: Hashable | None = ...,
         axis: AxisType = ...,
         closed: IntervalClosedType | None = ...,
+        step: int | None = ...,
+        method: CalculationMethod = ...,
     ) -> Rolling[DataFrame]: ...
     def rpow(
         self,

pandas-stubs/core/series.pyi

@@ -34,6 +34,7 @@ from pandas.core.groupby.generic import (
     _SeriesGroupByNonScalar,
     _SeriesGroupByScalar,
 )
+from pandas.core.indexers import BaseIndexer
 from pandas.core.indexes.accessors import (
     CombinedDatetimelikeProperties,
     PeriodProperties,
@@ -62,6 +63,7 @@ from pandas.core.window.rolling import (
 import xarray as xr
 
 from pandas._libs.missing import NAType
+from pandas._libs.tslibs import BaseOffset
 from pandas._typing import (
     S1,
     AggFuncTypeBase,
@@ -71,6 +73,7 @@ from pandas._typing import (
     Axes,
     Axis,
     AxisType,
+    CalculationMethod,
     CompressionOptions,
     DtypeObj,
     FilePathOrBuffer,
@@ -1318,7 +1321,7 @@ class Series(IndexOpsMixin, NDFrame, Generic[S1]):
         self,
         min_periods: int = ...,
         axis: SeriesAxisType = ...,
-        method: Literal["single", "table"] = ...,
+        method: CalculationMethod = ...,
     ) -> Expanding[Series]: ...
     def floordiv(
         self,
@@ -1496,26 +1499,30 @@ class Series(IndexOpsMixin, NDFrame, Generic[S1]):
     @overload
     def rolling(
         self,
-        window,
+        window: int | BaseOffset | BaseIndexer,
         min_periods: int | None = ...,
         center: _bool = ...,
         *,
         win_type: _str,
         on: _str | None = ...,
         axis: SeriesAxisType = ...,
         closed: _str | None = ...,
+        step: int | None = ...,
+        method: CalculationMethod = ...,
     ) -> Window[Series]: ...
     @overload
     def rolling(
         self,
-        window,
+        window: int | BaseOffset | BaseIndexer,
         min_periods: int | None = ...,
         center: _bool = ...,
         *,
         win_type: None = ...,
         on: _str | None = ...,
         axis: SeriesAxisType = ...,
         closed: _str | None = ...,
+        step: int | None = ...,
+        method: CalculationMethod = ...,
     ) -> Rolling[Series]: ...
     def rpow(
         self,

pandas-stubs/core/window/ewm.pyi

@@ -1,7 +1,6 @@
 from typing import (
     Any,
     Generic,
-    Literal,
     overload,
 )
 
@@ -17,6 +16,7 @@ from pandas._typing import (
     AggFuncTypeFrame,
     AggFuncTypeSeriesToFrame,
     Axis,
+    CalculationMethod,
     NDFrameT,
     TimedeltaConvertibleTypes,
     WindowingEngine,
@@ -36,7 +36,7 @@ class ExponentialMovingWindow(BaseWindow[NDFrameT], Generic[NDFrameT]):
         ignore_na: bool = ...,
         axis: Axis = ...,
         times: str | np.ndarray | Series | None = ...,
-        method: Literal["single", "table"] = ...,
+        method: CalculationMethod = ...,
     ) -> None: ...
     @overload
     def aggregate(

tests/test_windowing.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pandas as pd
 from pandas import (
     DataFrame,
     Series,
@@ -304,3 +305,35 @@ def test_ewm_aggregate_series() -> None:
         DataFrame,
     )
     check(assert_type(S.ewm(span=10).agg("sum"), Series), Series)
+
+
+def test_rolling_step_method() -> None:
+    check(
+        assert_type(DF.rolling(10, step=5, method="single"), "Rolling[DataFrame]"),
+        Rolling,
+    )
+    check(assert_type(DF.rolling(10, method="table"), "Rolling[DataFrame]"), Rolling)
+
+
+def test_rolling_window() -> None:
+    df_time = pd.DataFrame(
+        {"B": [0, 1, 2, np.nan, 4]},
+        index=[
+            pd.Timestamp("20130101 09:00:00"),
+            pd.Timestamp("20130101 09:00:02"),
+            pd.Timestamp("20130101 09:00:03"),
+            pd.Timestamp("20130101 09:00:05"),
+            pd.Timestamp("20130101 09:00:06"),
+        ],
+    )
+
+    indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    check(
+        assert_type(df_time.rolling(window=indexer, min_periods=1).sum(), DataFrame),
+        DataFrame,
+    )
+    s = df_time.iloc[:, 0]
+    check(
+        assert_type(s.rolling(window=indexer, min_periods=1).sum(), Series),
+        Series,
+    )