done

lib/python3.11/site-packages/pandas-stubs/core/window/__init__.pyi

@@ -0,0 +1,13 @@
+from pandas.core.window.ewm import (
+    ExponentialMovingWindow as ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby as ExponentialMovingWindowGroupby,
+)
+from pandas.core.window.expanding import (
+    Expanding as Expanding,
+    ExpandingGroupby as ExpandingGroupby,
+)
+from pandas.core.window.rolling import (
+    Rolling as Rolling,
+    RollingGroupby as RollingGroupby,
+    Window as Window,
+)

lib/python3.11/site-packages/pandas-stubs/core/window/ewm.pyi

@@ -0,0 +1,69 @@
+from pandas import (
+    DataFrame,
+    Series,
+)
+from pandas.core.window.rolling import (
+    BaseWindow,
+    BaseWindowGroupby,
+)
+
+from pandas._typing import (
+    NDFrameT,
+    WindowingEngine,
+    WindowingEngineKwargs,
+)
+
+class ExponentialMovingWindow(BaseWindow[NDFrameT]):
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: WindowingEngine = None,
+        engine_kwargs: WindowingEngineKwargs = None,
+    ) -> NDFrameT: ...
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: WindowingEngine = None,
+        engine_kwargs: WindowingEngineKwargs = None,
+    ) -> NDFrameT: ...
+    def std(self, bias: bool = False, numeric_only: bool = False) -> NDFrameT: ...
+    def var(self, bias: bool = False, numeric_only: bool = False) -> NDFrameT: ...
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ) -> NDFrameT: ...
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ) -> NDFrameT: ...
+
+class ExponentialMovingWindowGroupby(
+    BaseWindowGroupby[NDFrameT], ExponentialMovingWindow[NDFrameT]
+): ...
+
+class OnlineExponentialMovingWindow(ExponentialMovingWindow[NDFrameT]):
+    def reset(self) -> None: ...
+    def aggregate(self, func, *args, **kwargs): ...
+    def std(self, bias: bool = False, *args, **kwargs): ...  # pyrefly: ignore
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ): ...
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ): ...
+    def var(self, bias: bool = False, numeric_only: bool = False): ...
+    def mean(  # pyrefly: ignore
+        self, *args, update: NDFrameT | None = ..., update_times: None = ..., **kwargs
+    ) -> NDFrameT: ...

lib/python3.11/site-packages/pandas-stubs/core/window/expanding.pyi

@@ -0,0 +1,9 @@
+from pandas.core.window.rolling import (
+    BaseWindowGroupby,
+    RollingAndExpandingMixin,
+)
+
+from pandas._typing import NDFrameT
+
+class Expanding(RollingAndExpandingMixin[NDFrameT]): ...
+class ExpandingGroupby(BaseWindowGroupby[NDFrameT], Expanding[NDFrameT]): ...

lib/python3.11/site-packages/pandas-stubs/core/window/rolling.pyi

@@ -0,0 +1,168 @@
+from collections.abc import (
+    Callable,
+    Iterator,
+)
+import datetime as dt
+from typing import (
+    Any,
+    overload,
+)
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+from pandas.core.base import SelectionMixin
+from pandas.core.indexers import BaseIndexer
+from typing_extensions import Self
+
+from pandas._libs.tslibs import BaseOffset
+from pandas._typing import (
+    AggFuncTypeBase,
+    AggFuncTypeFrame,
+    AggFuncTypeSeriesToFrame,
+    AxisInt,
+    CalculationMethod,
+    IntervalClosedType,
+    NDFrameT,
+    QuantileInterpolation,
+    WindowingEngine,
+    WindowingEngineKwargs,
+    WindowingRankType,
+)
+
+class BaseWindow(SelectionMixin[NDFrameT]):
+    on: str | Index | None
+    closed: IntervalClosedType | None
+    step: int | None
+    window: int | dt.timedelta | str | BaseOffset | BaseIndexer | None
+    min_periods: int | None
+    center: bool | None
+    win_type: str | None
+    axis: AxisInt
+    method: CalculationMethod
+    def __getitem__(self, key) -> Self: ...
+    def __getattr__(self, attr: str) -> Self: ...
+    def __iter__(self) -> Iterator[NDFrameT]: ...
+    @overload
+    def aggregate(
+        self: BaseWindow[Series], func: AggFuncTypeBase, *args: Any, **kwargs: Any
+    ) -> Series: ...
+    @overload
+    def aggregate(
+        self: BaseWindow[Series],
+        func: AggFuncTypeSeriesToFrame,
+        *args: Any,
+        **kwargs: Any,
+    ) -> DataFrame: ...
+    @overload
+    def aggregate(
+        self: BaseWindow[DataFrame],
+        func: AggFuncTypeFrame,
+        *args: Any,
+        **kwargs: Any,
+    ) -> DataFrame: ...
+    agg = aggregate
+
+class BaseWindowGroupby(BaseWindow[NDFrameT]): ...
+
+class Window(BaseWindow[NDFrameT]):
+    def sum(self, numeric_only: bool = False, **kwargs: Any) -> NDFrameT: ...
+    def mean(self, numeric_only: bool = False, **kwargs: Any) -> NDFrameT: ...
+    def var(
+        self, ddof: int = ..., numeric_only: bool = False, **kwargs: Any
+    ) -> NDFrameT: ...
+    def std(
+        self, ddof: int = ..., numeric_only: bool = False, **kwargs: Any
+    ) -> NDFrameT: ...
+
+class RollingAndExpandingMixin(BaseWindow[NDFrameT]):
+    def count(self, numeric_only: bool = ...) -> NDFrameT: ...
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+        args: tuple[Any, ...] | None = ...,
+        kwargs: dict[str, Any] | None = ...,
+    ) -> NDFrameT: ...
+    def sum(
+        self,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def max(
+        self,
+        numeric_only: bool = ...,
+        *args,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def min(
+        self,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def mean(
+        self,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def median(
+        self,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def std(
+        self,
+        ddof: int = ...,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def var(
+        self,
+        ddof: int = ...,
+        numeric_only: bool = ...,
+        engine: WindowingEngine = ...,
+        engine_kwargs: WindowingEngineKwargs = ...,
+    ) -> NDFrameT: ...
+    def skew(self, numeric_only: bool = ...) -> NDFrameT: ...
+    def sem(self, ddof: int = ..., numeric_only: bool = ...) -> NDFrameT: ...
+    def kurt(self, numeric_only: bool = ...) -> NDFrameT: ...
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = ...,
+        numeric_only: bool = ...,
+    ) -> NDFrameT: ...
+    def rank(
+        self,
+        method: WindowingRankType = ...,
+        ascending: bool = ...,
+        pct: bool = ...,
+        numeric_only: bool = ...,
+    ) -> NDFrameT: ...
+    def cov(
+        self,
+        other: DataFrame | Series | None = ...,
+        pairwise: bool | None = ...,
+        ddof: int = ...,
+        numeric_only: bool = ...,
+    ) -> NDFrameT: ...
+    def corr(
+        self,
+        other: DataFrame | Series | None = ...,
+        pairwise: bool | None = ...,
+        ddof: int = ...,
+        numeric_only: bool = ...,
+    ) -> NDFrameT: ...
+
+class Rolling(RollingAndExpandingMixin[NDFrameT]): ...
+class RollingGroupby(BaseWindowGroupby[NDFrameT], Rolling[NDFrameT]): ...