rolling.py

"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from datetime import timedelta
from functools import partial
import inspect
from textwrap import dedent
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Optional,
    Set,
    Tuple,
    Type,
    Union,
)
import warnings

import numpy as np

from pandas._libs.tslibs import BaseOffset, to_offset
import pandas._libs.window.aggregations as window_aggregations
from pandas._typing import ArrayLike, Axis, FrameOrSeries, FrameOrSeriesUnion
from pandas.compat._optional import import_optional_dependency
from pandas.compat.numpy import function as nv
from pandas.util._decorators import Appender, Substitution, doc

from pandas.core.dtypes.common import (
    ensure_float64,
    is_bool,
    is_integer,
    is_list_like,
    is_scalar,
    needs_i8_conversion,
)
from pandas.core.dtypes.generic import (
    ABCDataFrame,
    ABCDatetimeIndex,
    ABCPeriodIndex,
    ABCSeries,
    ABCTimedeltaIndex,
)
from pandas.core.dtypes.missing import notna

from pandas.core.aggregation import aggregate
from pandas.core.base import DataError, SelectionMixin
from pandas.core.construction import extract_array
from pandas.core.groupby.base import GotItemMixin, ShallowMixin
from pandas.core.indexes.api import Index, MultiIndex
from pandas.core.util.numba_ import NUMBA_FUNC_CACHE, maybe_use_numba
from pandas.core.window.common import (
    _doc_template,
    _shared_docs,
    flex_binary_moment,
    zsqrt,
)
from pandas.core.window.indexers import (
    BaseIndexer,
    FixedWindowIndexer,
    GroupbyIndexer,
    VariableWindowIndexer,
)
from pandas.core.window.numba_ import (
    generate_numba_apply_func,
    generate_numba_table_func,
)

if TYPE_CHECKING:
    from pandas import DataFrame, Series
    from pandas.core.internals import Block  # noqa:F401


class BaseWindow(ShallowMixin, SelectionMixin):
    """Provides utilities for performing windowing operations."""

    _attributes: List[str] = [
        "window",
        "min_periods",
        "center",
        "win_type",
        "axis",
        "on",
        "closed",
        "method",
    ]
    exclusions: Set[str] = set()

    def __init__(
        self,
        obj: FrameOrSeries,
        window=None,
        min_periods: Optional[int] = None,
        center: bool = False,
        win_type: Optional[str] = None,
        axis: Axis = 0,
        on: Optional[Union[str, Index]] = None,
        closed: Optional[str] = None,
        method: str = "single",
        **kwargs,
    ):

        self.__dict__.update(kwargs)
        self.obj = obj
        self.on = on
        self.closed = closed
        self.window = window
        self.min_periods = min_periods
        self.center = center
        self.win_type = win_type
        self.axis = obj._get_axis_number(axis) if axis is not None else None
        self.method = method
        self._win_freq_i8 = None
        if self.on is None:
            if self.axis == 0:
                self._on = self.obj.index
            else:
                # i.e. self.axis == 1
                self._on = self.obj.columns
        elif isinstance(self.on, Index):
            self._on = self.on
        elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns:
            self._on = Index(self.obj[self.on])
        else:
            raise ValueError(
                f"invalid on specified as {self.on}, "
                "must be a column (of DataFrame), an Index or None"
            )
        self.validate()

    def validate(self) -> None:
        if self.center is not None and not is_bool(self.center):
            raise ValueError("center must be a boolean")
        if self.min_periods is not None:
            if not is_integer(self.min_periods):
                raise ValueError("min_periods must be an integer")
            elif self.min_periods < 0:
                raise ValueError("min_periods must be >= 0")
            elif is_integer(self.window) and self.min_periods > self.window:
                raise ValueError(
                    f"min_periods {self.min_periods} must be <= window {self.window}"
                )
        if self.closed is not None and self.closed not in [
            "right",
            "both",
            "left",
            "neither",
        ]:
            raise ValueError("closed must be 'right', 'left', 'both' or 'neither'")
        if not isinstance(self.obj, (ABCSeries, ABCDataFrame)):
            raise TypeError(f"invalid type: {type(self)}")
        if isinstance(self.window, BaseIndexer):
            # Validate that the passed BaseIndexer subclass has
            # a get_window_bounds with the correct signature.
            get_window_bounds_signature = inspect.signature(
                self.window.get_window_bounds
            ).parameters.keys()
            expected_signature = inspect.signature(
                BaseIndexer().get_window_bounds
            ).parameters.keys()
            if get_window_bounds_signature != expected_signature:
                raise ValueError(
                    f"{type(self.window).__name__} does not implement "
                    f"the correct signature for get_window_bounds"
                )
        if self.method not in ["table", "single"]:
            raise ValueError("method must be 'table' or 'single")

    def _create_data(self, obj: FrameOrSeries) -> FrameOrSeries:
        """
        Split data into blocks & return conformed data.
        """
        # filter out the on from the object
        if self.on is not None and not isinstance(self.on, Index):
            if obj.ndim == 2:
                obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False)
        if self.axis == 1:
            # GH: 20649 in case of mixed dtype and axis=1 we have to convert everything
            # to float to calculate the complete row at once. We exclude all non-numeric
            # dtypes.
            obj = obj.select_dtypes(include=["integer", "float"], exclude=["timedelta"])
            obj = obj.astype("float64", copy=False)
            obj._mgr = obj._mgr.consolidate()
        return obj

    def _gotitem(self, key, ndim, subset=None):
        """
        Sub-classes to define. Return a sliced object.

        Parameters
        ----------
        key : str / list of selections
        ndim : 1,2
            requested ndim of result
        subset : object, default None
            subset to act on
        """
        # create a new object to prevent aliasing
        if subset is None:
            subset = self.obj
        self = self._shallow_copy(subset)
        self._reset_cache()
        if subset.ndim == 2:
            if is_scalar(key) and key in subset or is_list_like(key):
                self._selection = key
        return self

    def __getattr__(self, attr: str):
        if attr in self._internal_names_set:
            return object.__getattribute__(self, attr)
        if attr in self.obj:
            return self[attr]

        raise AttributeError(
            f"'{type(self).__name__}' object has no attribute '{attr}'"
        )

    def _dir_additions(self):
        return self.obj._dir_additions()

    def _get_cov_corr_window(
        self, other: Optional[Union[np.ndarray, FrameOrSeries]] = None
    ) -> Optional[Union[int, timedelta, BaseOffset, BaseIndexer]]:
        """
        Return window length.

        Parameters
        ----------
        other :
            Used in Expanding

        Returns
        -------
        window : int
        """
        return self.window

    @property
    def _window_type(self) -> str:
        return type(self).__name__

    def __repr__(self) -> str:
        """
        Provide a nice str repr of our rolling object.
        """
        attrs_list = (
            f"{attr_name}={getattr(self, attr_name)}"
            for attr_name in self._attributes
            if getattr(self, attr_name, None) is not None
        )
        attrs = ",".join(attrs_list)
        return f"{self._window_type} [{attrs}]"

    def __iter__(self):
        obj = self._create_data(self._selected_obj)
        indexer = self._get_window_indexer()

        start, end = indexer.get_window_bounds(
            num_values=len(obj),
            min_periods=self.min_periods,
            center=self.center,
            closed=self.closed,
        )
        # From get_window_bounds, those two should be equal in length of array
        assert len(start) == len(end)

        for s, e in zip(start, end):
            result = obj.iloc[slice(s, e)]
            yield result

    def _prep_values(self, values: Optional[np.ndarray] = None) -> np.ndarray:
        """Convert input to numpy arrays for Cython routines"""
        if values is None:
            values = extract_array(self._selected_obj, extract_numpy=True)

        if needs_i8_conversion(values.dtype):
            raise NotImplementedError(
                f"ops for {self._window_type} for this "
                f"dtype {values.dtype} are not implemented"
            )
        else:
            # GH #12373 : rolling functions error on float32 data
            # make sure the data is coerced to float64
            try:
                values = ensure_float64(values)
            except (ValueError, TypeError) as err:
                raise TypeError(f"cannot handle this type -> {values.dtype}") from err

        # Convert inf to nan for C funcs
        inf = np.isinf(values)
        if inf.any():
            values = np.where(inf, np.nan, values)

        return values

    def _insert_on_column(self, result: "DataFrame", obj: "DataFrame"):
        # if we have an 'on' column we want to put it back into
        # the results in the same location
        from pandas import Series

        if self.on is not None and not self._on.equals(obj.index):
            name = self._on.name
            extra_col = Series(self._on, index=self.obj.index, name=name)
            if name in result.columns:
                # TODO: sure we want to overwrite results?
                result[name] = extra_col
            elif name in result.index.names:
                pass
            elif name in self._selected_obj.columns:
                # insert in the same location as we had in _selected_obj
                old_cols = self._selected_obj.columns
                new_cols = result.columns
                old_loc = old_cols.get_loc(name)
                overlap = new_cols.intersection(old_cols[:old_loc])
                new_loc = len(overlap)
                result.insert(new_loc, name, extra_col)
            else:
                # insert at the end
                result[name] = extra_col

    @property
    def _index_array(self):
        # TODO: why do we get here with e.g. MultiIndex?
        if needs_i8_conversion(self._on.dtype):
            return self._on.asi8
        return None

    def _get_window_indexer(self) -> BaseIndexer:
        """
        Return an indexer class that will compute the window start and end bounds
        """
        if isinstance(self.window, BaseIndexer):
            return self.window
        if self._win_freq_i8 is not None:
            return VariableWindowIndexer(
                index_array=self._index_array, window_size=self._win_freq_i8
            )
        return FixedWindowIndexer(window_size=self.window)

    def _apply_series(
        self, homogeneous_func: Callable[..., ArrayLike], name: Optional[str] = None
    ) -> "Series":
        """
        Series version of _apply_blockwise
        """
        obj = self._create_data(self._selected_obj)

        try:
            # GH 12541: Special case for count where we support date-like types
            input = obj.values if name != "count" else notna(obj.values).astype(int)
            values = self._prep_values(input)
        except (TypeError, NotImplementedError) as err:
            raise DataError("No numeric types to aggregate") from err

        result = homogeneous_func(values)
        return obj._constructor(result, index=obj.index, name=obj.name)

    def _apply_blockwise(
        self, homogeneous_func: Callable[..., ArrayLike], name: Optional[str] = None
    ) -> FrameOrSeriesUnion:
        """
        Apply the given function to the DataFrame broken down into homogeneous
        sub-frames.
        """
        if self._selected_obj.ndim == 1:
            return self._apply_series(homogeneous_func, name)

        obj = self._create_data(self._selected_obj)
        if name == "count":
            # GH 12541: Special case for count where we support date-like types
            obj = notna(obj).astype(int)
            obj._mgr = obj._mgr.consolidate()
        mgr = obj._mgr

        def hfunc(bvalues: ArrayLike) -> ArrayLike:
            # TODO(EA2D): getattr unnecessary with 2D EAs
            values = self._prep_values(getattr(bvalues, "T", bvalues))
            res_values = homogeneous_func(values)
            return getattr(res_values, "T", res_values)

        new_mgr = mgr.apply(hfunc, ignore_failures=True)
        out = obj._constructor(new_mgr)

        if out.shape[1] == 0 and obj.shape[1] > 0:
            raise DataError("No numeric types to aggregate")
        elif out.shape[1] == 0:
            return obj.astype("float64")

        self._insert_on_column(out, obj)
        return out

    def _apply_tablewise(
        self, homogeneous_func: Callable[..., ArrayLike], name: Optional[str] = None
    ) -> FrameOrSeriesUnion:
        if self._selected_obj.ndim == 1:
            raise ValueError("method='table' not applicable for Series objects.")
        obj = self._create_data(self._selected_obj)
        values = self._prep_values(obj.to_numpy())
        values = values.T if self.axis == 1 else values
        result = homogeneous_func(values)
        result = result.T if self.axis == 1 else result
        out = obj._constructor(result, index=obj.index, columns=obj.columns)

        if out.shape[1] == 0 and obj.shape[1] > 0:
            raise DataError("No numeric types to aggregate")
        elif out.shape[1] == 0:
            return obj.astype("float64")

        self._insert_on_column(out, obj)
        return out

    def _apply(
        self,
        func: Callable[..., Any],
        name: Optional[str] = None,
        numba_cache_key: Optional[Tuple[Callable, str]] = None,
        **kwargs,
    ):
        """
        Rolling statistical measure using supplied function.

        Designed to be used with passed-in Cython array-based functions.

        Parameters
        ----------
        func : callable function to apply
        name : str,
        numba_cache_key : tuple
            caching key to be used to store a compiled numba func
        **kwargs
            additional arguments for rolling function and window function

        Returns
        -------
        y : type of input
        """
        window_indexer = self._get_window_indexer()
        min_periods = (
            self.min_periods
            if self.min_periods is not None
            else window_indexer.window_size
        )

        def homogeneous_func(values: np.ndarray):
            # calculation function

            if values.size == 0:
                return values.copy()

            def calc(x):
                start, end = window_indexer.get_window_bounds(
                    num_values=len(x),
                    min_periods=min_periods,
                    center=self.center,
                    closed=self.closed,
                )
                return func(x, start, end, min_periods)

            with np.errstate(all="ignore"):
                if values.ndim > 1 and self.method == "single":
                    result = np.apply_along_axis(calc, self.axis, values)
                else:
                    result = calc(values)
                    result = np.asarray(result)

            if numba_cache_key is not None:
                NUMBA_FUNC_CACHE[numba_cache_key] = func

            return result

        if self.method == "single":
            return self._apply_blockwise(homogeneous_func, name)
        else:
            return self._apply_tablewise(homogeneous_func, name)

    def aggregate(self, func, *args, **kwargs):
        result, how = aggregate(self, func, *args, **kwargs)
        if result is None:
            return self.apply(func, raw=False, args=args, kwargs=kwargs)
        return result

    agg = aggregate

    _shared_docs["sum"] = dedent(
        """
    Calculate %(name)s sum of given DataFrame or Series.

    Parameters
    ----------
    *args, **kwargs
        For compatibility with other %(name)s methods. Has no effect
        on the computed value.

    Returns
    -------
    Series or DataFrame
        Same type as the input, with the same index, containing the
        %(name)s sum.

    See Also
    --------
    pandas.Series.sum : Reducing sum for Series.
    pandas.DataFrame.sum : Reducing sum for DataFrame.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4, 5])
    >>> s
    0    1
    1    2
    2    3
    3    4
    4    5
    dtype: int64

    >>> s.rolling(3).sum()
    0     NaN
    1     NaN
    2     6.0
    3     9.0
    4    12.0
    dtype: float64

    >>> s.expanding(3).sum()
    0     NaN
    1     NaN
    2     6.0
    3    10.0
    4    15.0
    dtype: float64

    >>> s.rolling(3, center=True).sum()
    0     NaN
    1     6.0
    2     9.0
    3    12.0
    4     NaN
    dtype: float64

    For DataFrame, each %(name)s sum is computed column-wise.

    >>> df = pd.DataFrame({"A": s, "B": s ** 2})
    >>> df
       A   B
    0  1   1
    1  2   4
    2  3   9
    3  4  16
    4  5  25

    >>> df.rolling(3).sum()
          A     B
    0   NaN   NaN
    1   NaN   NaN
    2   6.0  14.0
    3   9.0  29.0
    4  12.0  50.0
    """
    )

    _shared_docs["mean"] = dedent(
        """
    Calculate the %(name)s mean of the values.

    Parameters
    ----------
    *args
        Under Review.
    **kwargs
        Under Review.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.mean : Equivalent method for Series.
    pandas.DataFrame.mean : Equivalent method for DataFrame.

    Examples
    --------
    The below examples will show rolling mean calculations with window sizes of
    two and three, respectively.

    >>> s = pd.Series([1, 2, 3, 4])
    >>> s.rolling(2).mean()
    0    NaN
    1    1.5
    2    2.5
    3    3.5
    dtype: float64

    >>> s.rolling(3).mean()
    0    NaN
    1    NaN
    2    2.0
    3    3.0
    dtype: float64
    """
    )

    _shared_docs["var"] = dedent(
        """
    Calculate unbiased %(name)s variance.
    %(versionadded)s
    Normalized by N-1 by default. This can be changed using the `ddof`
    argument.

    Parameters
    ----------
    ddof : int, default 1
        Delta Degrees of Freedom.  The divisor used in calculations
        is ``N - ddof``, where ``N`` represents the number of elements.
    *args, **kwargs
        For NumPy compatibility. No additional arguments are used.

    Returns
    -------
    Series or DataFrame
        Returns the same object type as the caller of the %(name)s calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.var : Equivalent method for Series.
    pandas.DataFrame.var : Equivalent method for DataFrame.
    numpy.var : Equivalent method for Numpy array.

    Notes
    -----
    The default `ddof` of 1 used in :meth:`Series.var` is different than the
    default `ddof` of 0 in :func:`numpy.var`.

    A minimum of 1 period is required for the rolling calculation.

    Examples
    --------
    >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
    >>> s.rolling(3).var()
    0         NaN
    1         NaN
    2    0.333333
    3    1.000000
    4    1.000000
    5    1.333333
    6    0.000000
    dtype: float64

    >>> s.expanding(3).var()
    0         NaN
    1         NaN
    2    0.333333
    3    0.916667
    4    0.800000
    5    0.700000
    6    0.619048
    dtype: float64
    """
    )

    _shared_docs["std"] = dedent(
        """
    Calculate %(name)s standard deviation.
    %(versionadded)s
    Normalized by N-1 by default. This can be changed using the `ddof`
    argument.

    Parameters
    ----------
    ddof : int, default 1
        Delta Degrees of Freedom.  The divisor used in calculations
        is ``N - ddof``, where ``N`` represents the number of elements.
    *args, **kwargs
        For NumPy compatibility. No additional arguments are used.

    Returns
    -------
    Series or DataFrame
        Returns the same object type as the caller of the %(name)s calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.std : Equivalent method for Series.
    pandas.DataFrame.std : Equivalent method for DataFrame.
    numpy.std : Equivalent method for Numpy array.

    Notes
    -----
    The default `ddof` of 1 used in Series.std is different than the default
    `ddof` of 0 in numpy.std.

    A minimum of one period is required for the rolling calculation.

    Examples
    --------
    >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
    >>> s.rolling(3).std()
    0         NaN
    1         NaN
    2    0.577350
    3    1.000000
    4    1.000000
    5    1.154701
    6    0.000000
    dtype: float64

    >>> s.expanding(3).std()
    0         NaN
    1         NaN
    2    0.577350
    3    0.957427
    4    0.894427
    5    0.836660
    6    0.786796
    dtype: float64
    """
    )


def dispatch(name: str, *args, **kwargs):
    """
    Dispatch to groupby apply.
    """

    def outer(self, *args, **kwargs):
        def f(x):
            x = self._shallow_copy(x, groupby=self._groupby)
            return getattr(x, name)(*args, **kwargs)

        return self._groupby.apply(f)

    outer.__name__ = name
    return outer


class BaseWindowGroupby(GotItemMixin, BaseWindow):
    """
    Provide the groupby windowing facilities.
    """

    def __init__(self, obj, *args, **kwargs):
        kwargs.pop("parent", None)
        groupby = kwargs.pop("groupby", None)
        if groupby is None:
            groupby, obj = obj, obj._selected_obj
        self._groupby = groupby
        self._groupby.mutated = True
        self._groupby.grouper.mutated = True
        super().__init__(obj, *args, **kwargs)

    corr = dispatch("corr", other=None, pairwise=None)
    cov = dispatch("cov", other=None, pairwise=None)

    def _apply(
        self,
        func: Callable[..., Any],
        name: Optional[str] = None,
        numba_cache_key: Optional[Tuple[Callable, str]] = None,
        **kwargs,
    ) -> FrameOrSeries:
        result = super()._apply(
            func,
            name,
            numba_cache_key,
            **kwargs,
        )
        # Reconstruct the resulting MultiIndex
        # 1st set of levels = group by labels
        # 2nd set of levels = original DataFrame/Series index
        grouped_object_index = self.obj.index
        grouped_index_name = [*grouped_object_index.names]
        groupby_keys = [grouping.name for grouping in self._groupby.grouper._groupings]
        result_index_names = groupby_keys + grouped_index_name

        drop_columns = [
            key
            for key in groupby_keys
            if key not in self.obj.index.names or key is None
        ]
        if len(drop_columns) != len(groupby_keys):
            # Our result will have kept groupby columns which should be dropped
            result = result.drop(columns=drop_columns, errors="ignore")

        codes = self._groupby.grouper.codes
        levels = self._groupby.grouper.levels

        group_indices = self._groupby.grouper.indices.values()
        if group_indices:
            indexer = np.concatenate(list(group_indices))
        else:
            indexer = np.array([], dtype=np.intp)
        codes = [c.take(indexer) for c in codes]

        # if the index of the original dataframe needs to be preserved, append
        # this index (but reordered) to the codes/levels from the groupby
        if grouped_object_index is not None:
            idx = grouped_object_index.take(indexer)
            if not isinstance(idx, MultiIndex):
                idx = MultiIndex.from_arrays([idx])
            codes.extend(list(idx.codes))
            levels.extend(list(idx.levels))

        result_index = MultiIndex(
            levels, codes, names=result_index_names, verify_integrity=False
        )

        result.index = result_index
        return result

    def _create_data(self, obj: FrameOrSeries) -> FrameOrSeries:
        """
        Split data into blocks & return conformed data.
        """
        # Ensure the object we're rolling over is monotonically sorted relative
        # to the groups
        # GH 36197
        if not obj.empty:
            groupby_order = np.concatenate(
                list(self._groupby.grouper.indices.values())
            ).astype(np.int64)
            obj = obj.take(groupby_order)
        return super()._create_data(obj)

    def _gotitem(self, key, ndim, subset=None):
        # we are setting the index on the actual object
        # here so our index is carried through to the selected obj
        # when we do the splitting for the groupby
        if self.on is not None:
            self.obj = self.obj.set_index(self._on)
        return super()._gotitem(key, ndim, subset=subset)

    def _validate_monotonic(self):
        """
        Validate that "on" is monotonic; already validated at a higher level.
        """
        pass


class Window(BaseWindow):
    """
    Provide rolling window calculations.

    Parameters
    ----------
    window : int, offset, or BaseIndexer subclass
        Size of the moving window. This is the number of observations used for
        calculating the statistic. Each window will be a fixed size.

        If its an offset then this will be the time period of each window. Each
        window will be a variable sized based on the observations included in
        the time-period. This is only valid for datetimelike indexes.

        If a BaseIndexer subclass is passed, calculates the window boundaries
        based on the defined ``get_window_bounds`` method. Additional rolling
        keyword arguments, namely `min_periods`, `center`, and
        `closed` will be passed to `get_window_bounds`.
    min_periods : int, default None
        Minimum number of observations in window required to have a value
        (otherwise result is NA). For a window that is specified by an offset,
        `min_periods` will default to 1. Otherwise, `min_periods` will default
        to the size of the window.
    center : bool, default False
        Set the labels at the center of the window.
    win_type : str, default None
        Provide a window type. If ``None``, all points are evenly weighted.
        See the notes below for further information.
    on : str, optional
        For a DataFrame, a datetime-like column or MultiIndex level on which
        to calculate the rolling window, rather than the DataFrame's index.
        Provided integer column is ignored and excluded from result since
        an integer index is not used to calculate the rolling window.
    axis : int or str, default 0
    closed : str, default None
        Make the interval closed on the 'right', 'left', 'both' or
        'neither' endpoints. Defaults to 'right'.

        .. versionchanged:: 1.2.0

            The closed parameter with fixed windows is now supported.
    method : str {'single', 'table'}, default 'single'
        Execute the rolling operation per single column or row (``'single'``)
        or over the entire object (``'table'``).

        This argument is only implemented when specifying ``engine='numba'``
        in the method call.

        .. versionadded:: 1.3.0

    Returns
    -------
    a Window or Rolling sub-classed for the particular operation

    See Also
    --------
    expanding : Provides expanding transformations.
    ewm : Provides exponential weighted functions.

    Notes
    -----
    By default, the result is set to the right edge of the window. This can be
    changed to the center of the window by setting ``center=True``.

    To learn more about the offsets & frequency strings, please see `this link
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.

    If ``win_type=None``, all points are evenly weighted; otherwise, ``win_type``
    can accept a string of any `scipy.signal window function
    <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.

    Certain Scipy window types require additional parameters to be passed
    in the aggregation function. The additional parameters must match
    the keywords specified in the Scipy window type method signature.
    Please see the third example below on how to add the additional parameters.

    Examples
    --------
    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
    >>> df
         B
    0  0.0
    1  1.0
    2  2.0
    3  NaN
    4  4.0

    Rolling sum with a window length of 2, using the 'triang'
    window type.

    >>> df.rolling(2, win_type='triang').sum()
         B
    0  NaN
    1  0.5
    2  1.5
    3  NaN
    4  NaN

    Rolling sum with a window length of 2, using the 'gaussian'
    window type (note how we need to specify std).

    >>> df.rolling(2, win_type='gaussian').sum(std=3)
              B
    0       NaN
    1  0.986207
    2  2.958621
    3       NaN
    4       NaN

    Rolling sum with a window length of 2, min_periods defaults
    to the window length.

    >>> df.rolling(2).sum()
         B
    0  NaN
    1  1.0
    2  3.0
    3  NaN
    4  NaN

    Same as above, but explicitly set the min_periods

    >>> df.rolling(2, min_periods=1).sum()
         B
    0  0.0
    1  1.0
    2  3.0
    3  2.0
    4  4.0

    Same as above, but with forward-looking windows

    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
    >>> df.rolling(window=indexer, min_periods=1).sum()
         B
    0  1.0
    1  3.0
    2  2.0
    3  4.0
    4  4.0

    A ragged (meaning not-a-regular frequency), time-indexed DataFrame

    >>> df = 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')])

    >>> df
                           B
    2013-01-01 09:00:00  0.0
    2013-01-01 09:00:02  1.0
    2013-01-01 09:00:03  2.0
    2013-01-01 09:00:05  NaN
    2013-01-01 09:00:06  4.0

    Contrasting to an integer rolling window, this will roll a variable
    length window corresponding to the time period.
    The default for min_periods is 1.

    >>> df.rolling('2s').sum()
                           B
    2013-01-01 09:00:00  0.0
    2013-01-01 09:00:02  1.0
    2013-01-01 09:00:03  3.0
    2013-01-01 09:00:05  NaN
    2013-01-01 09:00:06  4.0
    """

    def validate(self):
        super().validate()

        if not isinstance(self.win_type, str):
            raise ValueError(f"Invalid win_type {self.win_type}")
        signal = import_optional_dependency(
            "scipy.signal", extra="Scipy is required to generate window weight."
        )
        self._scipy_weight_generator = getattr(signal, self.win_type, None)
        if self._scipy_weight_generator is None:
            raise ValueError(f"Invalid win_type {self.win_type}")

        if isinstance(self.window, BaseIndexer):
            raise NotImplementedError(
                "BaseIndexer subclasses not implemented with win_types."
            )
        elif not is_integer(self.window) or self.window < 0:
            raise ValueError("window must be an integer 0 or greater")

        if self.method != "single":
            raise NotImplementedError("'single' is the only supported method type.")

    def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
        """
        Center the result in the window for weighted rolling aggregations.
        """
        if self.axis > result.ndim - 1:
            raise ValueError("Requested axis is larger then no. of argument dimensions")

        if offset > 0:
            lead_indexer = [slice(None)] * result.ndim
            lead_indexer[self.axis] = slice(offset, None)
            result = np.copy(result[tuple(lead_indexer)])
        return result

    def _apply(
        self,
        func: Callable[[np.ndarray, int, int], np.ndarray],
        name: Optional[str] = None,
        numba_cache_key: Optional[Tuple[Callable, str]] = None,
        **kwargs,
    ):
        """
        Rolling with weights statistical measure using supplied function.

        Designed to be used with passed-in Cython array-based functions.

        Parameters
        ----------
        func : callable function to apply
        name : str,
        use_numba_cache : tuple
            unused
        **kwargs
            additional arguments for scipy windows if necessary

        Returns
        -------
        y : type of input
        """
        window = self._scipy_weight_generator(self.window, **kwargs)
        offset = (len(window) - 1) // 2 if self.center else 0

        def homogeneous_func(values: np.ndarray):
            # calculation function

            if values.size == 0:
                return values.copy()

            def calc(x):
                additional_nans = np.array([np.nan] * offset)
                x = np.concatenate((x, additional_nans))
                return func(x, window, self.min_periods or len(window))

            with np.errstate(all="ignore"):
                if values.ndim > 1:
                    result = np.apply_along_axis(calc, self.axis, values)
                else:
                    result = calc(values)
                    result = np.asarray(result)

            if self.center:
                result = self._center_window(result, offset)

            return result

        return self._apply_blockwise(homogeneous_func, name)

    _agg_see_also_doc = dedent(
        """
    See Also
    --------
    pandas.DataFrame.aggregate : Similar DataFrame method.
    pandas.Series.aggregate : Similar Series method.
    """
    )

    _agg_examples_doc = dedent(
        """
    Examples
    --------
    >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    >>> df
       A  B  C
    0  1  4  7
    1  2  5  8
    2  3  6  9

    >>> df.rolling(2, win_type="boxcar").agg("mean")
         A    B    C
    0  NaN  NaN  NaN
    1  1.5  4.5  7.5
    2  2.5  5.5  8.5
    """
    )

    @doc(
        _shared_docs["aggregate"],
        see_also=_agg_see_also_doc,
        examples=_agg_examples_doc,
        klass="Series/DataFrame",
        axis="",
    )
    def aggregate(self, func, *args, **kwargs):
        result, how = aggregate(self, func, *args, **kwargs)
        if result is None:

            # these must apply directly
            result = func(self)

        return result

    agg = aggregate

    @Substitution(name="window")
    @Appender(_shared_docs["sum"])
    def sum(self, *args, **kwargs):
        nv.validate_window_func("sum", args, kwargs)
        window_func = window_aggregations.roll_weighted_sum
        return self._apply(window_func, name="sum", **kwargs)

    @Substitution(name="window")
    @Appender(_shared_docs["mean"])
    def mean(self, *args, **kwargs):
        nv.validate_window_func("mean", args, kwargs)
        window_func = window_aggregations.roll_weighted_mean
        return self._apply(window_func, name="mean", **kwargs)

    @Substitution(name="window", versionadded="\n.. versionadded:: 1.0.0\n")
    @Appender(_shared_docs["var"])
    def var(self, ddof: int = 1, *args, **kwargs):
        nv.validate_window_func("var", args, kwargs)
        window_func = partial(window_aggregations.roll_weighted_var, ddof=ddof)
        kwargs.pop("name", None)
        return self._apply(window_func, name="var", **kwargs)

    @Substitution(name="window", versionadded="\n.. versionadded:: 1.0.0\n")
    @Appender(_shared_docs["std"])
    def std(self, ddof: int = 1, *args, **kwargs):
        nv.validate_window_func("std", args, kwargs)
        return zsqrt(self.var(ddof=ddof, name="std", **kwargs))


class RollingAndExpandingMixin(BaseWindow):

    _shared_docs["count"] = dedent(
        r"""
    The %(name)s count of any non-NaN observations inside the window.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.DataFrame.count : Count of the full DataFrame.

    Examples
    --------
    >>> s = pd.Series([2, 3, np.nan, 10])
    >>> s.rolling(2).count()
    0    1.0
    1    2.0
    2    1.0
    3    1.0
    dtype: float64
    >>> s.rolling(3).count()
    0    1.0
    1    2.0
    2    2.0
    3    2.0
    dtype: float64
    >>> s.rolling(4).count()
    0    1.0
    1    2.0
    2    2.0
    3    3.0
    dtype: float64
    """
    )

    def count(self):
        window_func = window_aggregations.roll_sum
        return self._apply(window_func, name="count")

    _shared_docs["apply"] = dedent(
        r"""
    Apply an arbitrary function to each %(name)s window.

    Parameters
    ----------
    func : function
        Must produce a single value from an ndarray input if ``raw=True``
        or a single value from a Series if ``raw=False``. Can also accept a
        Numba JIT function with ``engine='numba'`` specified.

        .. versionchanged:: 1.0.0

    raw : bool, default None
        * ``False`` : passes each row or column as a Series to the
          function.
        * ``True`` : the passed function will receive ndarray
          objects instead.
          If you are just applying a NumPy reduction function this will
          achieve much better performance.
    engine : str, default None
        * ``'cython'`` : Runs rolling apply through C-extensions from cython.
        * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
          Only available when ``raw`` is set to ``True``.
        * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``

          .. versionadded:: 1.0.0

    engine_kwargs : dict, default None
        * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
        * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
          and ``parallel`` dictionary keys. The values must either be ``True`` or
          ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
          ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be
          applied to both the ``func`` and the ``apply`` rolling aggregation.

          .. versionadded:: 1.0.0

    args : tuple, default None
        Positional arguments to be passed into func.
    kwargs : dict, default None
        Keyword arguments to be passed into func.

    Returns
    -------
    Series or DataFrame
        Return type is determined by the caller.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrame data.
    pandas.Series.apply : Similar method for Series.
    pandas.DataFrame.apply : Similar method for DataFrame.

    Notes
    -----
    See :ref:`window.numba_engine` for extended documentation and performance
    considerations for the Numba engine.
    """
    )

    def apply(
        self,
        func: Callable[..., Any],
        raw: bool = False,
        engine: Optional[str] = None,
        engine_kwargs: Optional[Dict[str, bool]] = None,
        args: Optional[Tuple[Any, ...]] = None,
        kwargs: Optional[Dict[str, Any]] = None,
    ):
        if args is None:
            args = ()
        if kwargs is None:
            kwargs = {}

        if not is_bool(raw):
            raise ValueError("raw parameter must be `True` or `False`")

        numba_cache_key = None
        if maybe_use_numba(engine):
            if raw is False:
                raise ValueError("raw must be `True` when using the numba engine")
            caller_name = type(self).__name__
            if self.method == "single":
                apply_func = generate_numba_apply_func(
                    args, kwargs, func, engine_kwargs, caller_name
                )
                numba_cache_key = (func, f"{caller_name}_apply_single")
            else:
                apply_func = generate_numba_table_func(
                    args, kwargs, func, engine_kwargs, f"{caller_name}_apply"
                )
                numba_cache_key = (func, f"{caller_name}_apply_table")
        elif engine in ("cython", None):
            if engine_kwargs is not None:
                raise ValueError("cython engine does not accept engine_kwargs")
            apply_func = self._generate_cython_apply_func(args, kwargs, raw, func)
        else:
            raise ValueError("engine must be either 'numba' or 'cython'")

        return self._apply(
            apply_func,
            numba_cache_key=numba_cache_key,
        )

    def _generate_cython_apply_func(
        self,
        args: Tuple[Any, ...],
        kwargs: Dict[str, Any],
        raw: bool,
        function: Callable[..., Any],
    ) -> Callable[[np.ndarray, np.ndarray, np.ndarray, int], np.ndarray]:
        from pandas import Series

        window_func = partial(
            window_aggregations.roll_apply,
            args=args,
            kwargs=kwargs,
            raw=raw,
            function=function,
        )

        def apply_func(values, begin, end, min_periods, raw=raw):
            if not raw:
                values = Series(values, index=self.obj.index)
            return window_func(values, begin, end, min_periods)

        return apply_func

    def sum(self, *args, **kwargs):
        nv.validate_window_func("sum", args, kwargs)
        window_func = window_aggregations.roll_sum
        return self._apply(window_func, name="sum", **kwargs)

    _shared_docs["max"] = dedent(
        """
    Calculate the %(name)s maximum.

    Parameters
    ----------
    *args, **kwargs
        Arguments and keyword arguments to be passed into func.
    """
    )

    def max(self, *args, **kwargs):
        nv.validate_window_func("max", args, kwargs)
        window_func = window_aggregations.roll_max
        return self._apply(window_func, name="max", **kwargs)

    _shared_docs["min"] = dedent(
        """
    Calculate the %(name)s minimum.

    Parameters
    ----------
    **kwargs
        Under Review.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with a Series.
    pandas.DataFrame.%(name)s : Calling object with a DataFrame.
    pandas.Series.min : Similar method for Series.
    pandas.DataFrame.min : Similar method for DataFrame.

    Examples
    --------
    Performing a rolling minimum with a window size of 3.

    >>> s = pd.Series([4, 3, 5, 2, 6])
    >>> s.rolling(3).min()
    0    NaN
    1    NaN
    2    3.0
    3    2.0
    4    2.0
    dtype: float64
    """
    )

    def min(self, *args, **kwargs):
        nv.validate_window_func("min", args, kwargs)
        window_func = window_aggregations.roll_min
        return self._apply(window_func, name="min", **kwargs)

    def mean(self, *args, **kwargs):
        nv.validate_window_func("mean", args, kwargs)
        window_func = window_aggregations.roll_mean
        return self._apply(window_func, name="mean", **kwargs)

    _shared_docs["median"] = dedent(
        """
    Calculate the %(name)s median.

    Parameters
    ----------
    **kwargs
        For compatibility with other %(name)s methods. Has no effect
        on the computed median.

    Returns
    -------
    Series or DataFrame
        Returned type is the same as the original object.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.median : Equivalent method for Series.
    pandas.DataFrame.median : Equivalent method for DataFrame.

    Examples
    --------
    Compute the rolling median of a series with a window size of 3.

    >>> s = pd.Series([0, 1, 2, 3, 4])
    >>> s.rolling(3).median()
    0    NaN
    1    NaN
    2    1.0
    3    2.0
    4    3.0
    dtype: float64
    """
    )

    def median(self, **kwargs):
        window_func = window_aggregations.roll_median_c
        # GH 32865. Move max window size calculation to
        # the median function implementation
        return self._apply(window_func, name="median", **kwargs)

    def std(self, ddof: int = 1, *args, **kwargs):
        nv.validate_window_func("std", args, kwargs)
        window_func = window_aggregations.roll_var

        def zsqrt_func(values, begin, end, min_periods):
            return zsqrt(window_func(values, begin, end, min_periods, ddof=ddof))

        return self._apply(
            zsqrt_func,
            name="std",
            **kwargs,
        )

    def var(self, ddof: int = 1, *args, **kwargs):
        nv.validate_window_func("var", args, kwargs)
        window_func = partial(window_aggregations.roll_var, ddof=ddof)
        return self._apply(
            window_func,
            name="var",
            **kwargs,
        )

    _shared_docs[
        "skew"
    ] = """
    Unbiased %(name)s skewness.

    Parameters
    ----------
    **kwargs
        Keyword arguments to be passed into func.
    """

    def skew(self, **kwargs):
        window_func = window_aggregations.roll_skew
        return self._apply(
            window_func,
            name="skew",
            **kwargs,
        )

    _shared_docs["kurt"] = dedent(
        """
    Calculate unbiased %(name)s kurtosis.

    This function uses Fisher's definition of kurtosis without bias.

    Parameters
    ----------
    **kwargs
        Under Review.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.kurt : Equivalent method for Series.
    pandas.DataFrame.kurt : Equivalent method for DataFrame.
    scipy.stats.skew : Third moment of a probability density.
    scipy.stats.kurtosis : Reference SciPy method.

    Notes
    -----
    A minimum of 4 periods is required for the %(name)s calculation.
    """
    )

    def sem(self, ddof: int = 1, *args, **kwargs):
        return self.std(*args, **kwargs) / (self.count() - ddof).pow(0.5)

    _shared_docs["sem"] = dedent(
        """
    Compute %(name)s standard error of mean.

    Parameters
    ----------

    ddof : int, default 1
        Delta Degrees of Freedom.  The divisor used in calculations
        is ``N - ddof``, where ``N`` represents the number of elements.

    *args, **kwargs
        For NumPy compatibility. No additional arguments are used.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.sem : Equivalent method for Series.
    pandas.DataFrame.sem : Equivalent method for DataFrame.

    Notes
    -----
    A minimum of one period is required for the rolling calculation.

    Examples
    --------
    >>> s = pd.Series([0, 1, 2, 3])
    >>> s.rolling(2, min_periods=1).sem()
    0         NaN
    1    0.707107
    2    0.707107
    3    0.707107
    dtype: float64

    >>> s.expanding().sem()
    0         NaN
    1    0.707107
    2    0.707107
    3    0.745356
    dtype: float64
    """
    )

    def kurt(self, **kwargs):
        window_func = window_aggregations.roll_kurt
        return self._apply(
            window_func,
            name="kurt",
            **kwargs,
        )

    _shared_docs["quantile"] = dedent(
        """
    Calculate the %(name)s quantile.

    Parameters
    ----------
    quantile : float
        Quantile to compute. 0 <= quantile <= 1.
    interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
        This optional parameter specifies the interpolation method to use,
        when the desired quantile lies between two data points `i` and `j`:

            * linear: `i + (j - i) * fraction`, where `fraction` is the
              fractional part of the index surrounded by `i` and `j`.
            * lower: `i`.
            * higher: `j`.
            * nearest: `i` or `j` whichever is nearest.
            * midpoint: (`i` + `j`) / 2.
    **kwargs
        For compatibility with other %(name)s methods. Has no effect on
        the result.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the %(name)s
        calculation.

    See Also
    --------
    pandas.Series.quantile : Computes value at the given quantile over all data
        in Series.
    pandas.DataFrame.quantile : Computes values at the given quantile over
        requested axis in DataFrame.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])
    >>> s.rolling(2).quantile(.4, interpolation='lower')
    0    NaN
    1    1.0
    2    2.0
    3    3.0
    dtype: float64

    >>> s.rolling(2).quantile(.4, interpolation='midpoint')
    0    NaN
    1    1.5
    2    2.5
    3    3.5
    dtype: float64
    """
    )

    def quantile(self, quantile: float, interpolation: str = "linear", **kwargs):
        if quantile == 1.0:
            window_func = window_aggregations.roll_max
        elif quantile == 0.0:
            window_func = window_aggregations.roll_min
        else:
            window_func = partial(
                window_aggregations.roll_quantile,
                quantile=quantile,
                interpolation=interpolation,
            )

        return self._apply(window_func, name="quantile", **kwargs)

    _shared_docs[
        "cov"
    ] = """
        Calculate the %(name)s sample covariance.

        Parameters
        ----------
        other : Series, DataFrame, or ndarray, optional
            If not supplied then will default to self and produce pairwise
            output.
        pairwise : bool, default None
            If False then only matching columns between self and other will be
            used and the output will be a DataFrame.
            If True then all pairwise combinations will be calculated and the
            output will be a MultiIndexed DataFrame in the case of DataFrame
            inputs. In the case of missing elements, only complete pairwise
            observations will be used.
        ddof : int, default 1
            Delta Degrees of Freedom.  The divisor used in calculations
            is ``N - ddof``, where ``N`` represents the number of elements.
        **kwargs
            Keyword arguments to be passed into func.
    """

    def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
        if other is None:
            other = self._selected_obj
            # only default unset
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)

        # GH 32865. We leverage rolling.mean, so we pass
        # to the rolling constructors the data used when constructing self:
        # window width, frequency data, or a BaseIndexer subclass
        # GH 16058: offset window
        window = self._get_cov_corr_window(other)

        def _get_cov(X, Y):
            # GH #12373 : rolling functions error on float32 data
            # to avoid potential overflow, cast the data to float64
            X = X.astype("float64")
            Y = Y.astype("float64")
            mean = lambda x: x.rolling(
                window, self.min_periods, center=self.center
            ).mean(**kwargs)
            count = (
                (X + Y)
                .rolling(window=window, min_periods=0, center=self.center)
                .count(**kwargs)
            )
            bias_adj = count / (count - ddof)
            return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj

        return flex_binary_moment(
            self._selected_obj, other._selected_obj, _get_cov, pairwise=bool(pairwise)
        )

    _shared_docs["corr"] = dedent(
        """
    Calculate %(name)s correlation.

    Parameters
    ----------
    other : Series, DataFrame, or ndarray, optional
        If not supplied then will default to self.
    pairwise : bool, default None
        Calculate pairwise combinations of columns within a
        DataFrame. If `other` is not specified, defaults to `True`,
        otherwise defaults to `False`.
        Not relevant for :class:`~pandas.Series`.
    **kwargs
        Unused.

    Returns
    -------
    Series or DataFrame
        Returned object type is determined by the caller of the
        %(name)s calculation.

    See Also
    --------
    pandas.Series.%(name)s : Calling object with Series data.
    pandas.DataFrame.%(name)s : Calling object with DataFrames.
    pandas.Series.corr : Equivalent method for Series.
    pandas.DataFrame.corr : Equivalent method for DataFrame.
    cov : Similar method to calculate covariance.
    numpy.corrcoef : NumPy Pearson's correlation calculation.

    Notes
    -----
    This function uses Pearson's definition of correlation
    (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).

    When `other` is not specified, the output will be self correlation (e.g.
    all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
    set to `True`.

    Function will return ``NaN`` for correlations of equal valued sequences;
    this is the result of a 0/0 division error.

    When `pairwise` is set to `False`, only matching columns between `self` and
    `other` will be used.

    When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
    with the original index on the first level, and the `other` DataFrame
    columns on the second level.

    In the case of missing elements, only complete pairwise observations
    will be used.

    Examples
    --------
    The below example shows a rolling calculation with a window size of
    four matching the equivalent function call using :meth:`numpy.corrcoef`.

    >>> v1 = [3, 3, 3, 5, 8]
    >>> v2 = [3, 4, 4, 4, 8]
    >>> # numpy returns a 2X2 array, the correlation coefficient
    >>> # is the number at entry [0][1]
    >>> print(f"{np.corrcoef(v1[:-1], v2[:-1])[0][1]:.6f}")
    0.333333
    >>> print(f"{np.corrcoef(v1[1:], v2[1:])[0][1]:.6f}")
    0.916949
    >>> s1 = pd.Series(v1)
    >>> s2 = pd.Series(v2)
    >>> s1.rolling(4).corr(s2)
    0         NaN
    1         NaN
    2         NaN
    3    0.333333
    4    0.916949
    dtype: float64

    The below example shows a similar rolling calculation on a
    DataFrame using the pairwise option.

    >>> matrix = np.array([[51., 35.], [49., 30.], [47., 32.],\
    [46., 31.], [50., 36.]])
    >>> print(np.corrcoef(matrix[:-1,0], matrix[:-1,1]).round(7))
    [[1.         0.6263001]
     [0.6263001  1.       ]]
    >>> print(np.corrcoef(matrix[1:,0], matrix[1:,1]).round(7))
    [[1.         0.5553681]
     [0.5553681  1.        ]]
    >>> df = pd.DataFrame(matrix, columns=['X','Y'])
    >>> df
          X     Y
    0  51.0  35.0
    1  49.0  30.0
    2  47.0  32.0
    3  46.0  31.0
    4  50.0  36.0
    >>> df.rolling(4).corr(pairwise=True)
                X         Y
    0 X       NaN       NaN
      Y       NaN       NaN
    1 X       NaN       NaN
      Y       NaN       NaN
    2 X       NaN       NaN
      Y       NaN       NaN
    3 X  1.000000  0.626300
      Y  0.626300  1.000000
    4 X  1.000000  0.555368
      Y  0.555368  1.000000
    """
    )

    def corr(self, other=None, pairwise=None, **kwargs):
        if other is None:
            other = self._selected_obj
            # only default unset
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)

        # GH 32865. We leverage rolling.cov and rolling.std here, so we pass
        # to the rolling constructors the data used when constructing self:
        # window width, frequency data, or a BaseIndexer subclass
        # GH 16058: offset window
        window = self._get_cov_corr_window(other)

        def _get_corr(a, b):
            a = a.rolling(
                window=window, min_periods=self.min_periods, center=self.center
            )
            b = b.rolling(
                window=window, min_periods=self.min_periods, center=self.center
            )
            # GH 31286: Through using var instead of std we can avoid numerical
            # issues when the result of var is withing floating proint precision
            # while std is not.
            return a.cov(b, **kwargs) / (a.var(**kwargs) * b.var(**kwargs)) ** 0.5

        return flex_binary_moment(
            self._selected_obj, other._selected_obj, _get_corr, pairwise=bool(pairwise)
        )


class Rolling(RollingAndExpandingMixin):
    def validate(self):
        super().validate()

        # we allow rolling on a datetimelike index
        if (
            self.obj.empty
            or isinstance(
                self._on, (ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex)
            )
        ) and isinstance(self.window, (str, BaseOffset, timedelta)):

            self._validate_monotonic()

            # we don't allow center
            if self.center:
                raise NotImplementedError(
                    "center is not implemented for "
                    "datetimelike and offset based windows"
                )

            # this will raise ValueError on non-fixed freqs
            try:
                freq = to_offset(self.window)
            except (TypeError, ValueError) as err:
                raise ValueError(
                    f"passed window {self.window} is not "
                    "compatible with a datetimelike index"
                ) from err
            if isinstance(self._on, ABCPeriodIndex):
                self._win_freq_i8 = freq.nanos / (self._on.freq.nanos / self._on.freq.n)
            else:
                self._win_freq_i8 = freq.nanos

            # min_periods must be an integer
            if self.min_periods is None:
                self.min_periods = 1

        elif isinstance(self.window, BaseIndexer):
            # Passed BaseIndexer subclass should handle all other rolling kwargs
            return
        elif not is_integer(self.window) or self.window < 0:
            raise ValueError("window must be an integer 0 or greater")

    def _validate_monotonic(self):
        """
        Validate monotonic (increasing or decreasing).
        """
        if not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing):
            self._raise_monotonic_error()

    def _raise_monotonic_error(self):
        formatted = self.on
        if self.on is None:
            formatted = "index"
        raise ValueError(f"{formatted} must be monotonic")

    _agg_see_also_doc = dedent(
        """
    See Also
    --------
    pandas.Series.rolling : Calling object with Series data.
    pandas.DataFrame.rolling : Calling object with DataFrame data.
    """
    )

    _agg_examples_doc = dedent(
        """
    Examples
    --------
    >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    >>> df
       A  B  C
    0  1  4  7
    1  2  5  8
    2  3  6  9

    >>> df.rolling(2).sum()
         A     B     C
    0  NaN   NaN   NaN
    1  3.0   9.0  15.0
    2  5.0  11.0  17.0

    >>> df.rolling(2).agg({"A": "sum", "B": "min"})
         A    B
    0  NaN  NaN
    1  3.0  4.0
    2  5.0  5.0
    """
    )

    @doc(
        _shared_docs["aggregate"],
        see_also=_agg_see_also_doc,
        examples=_agg_examples_doc,
        klass="Series/Dataframe",
        axis="",
    )
    def aggregate(self, func, *args, **kwargs):
        return super().aggregate(func, *args, **kwargs)

    agg = aggregate

    @Substitution(name="rolling")
    @Appender(_shared_docs["count"])
    def count(self):
        if self.min_periods is None:
            warnings.warn(
                (
                    "min_periods=None will default to the size of window "
                    "consistent with other methods in a future version. "
                    "Specify min_periods=0 instead."
                ),
                FutureWarning,
            )
            self.min_periods = 0
        return super().count()

    @Substitution(name="rolling")
    @Appender(_shared_docs["apply"])
    def apply(
        self, func, raw=False, engine=None, engine_kwargs=None, args=None, kwargs=None
    ):
        return super().apply(
            func,
            raw=raw,
            engine=engine,
            engine_kwargs=engine_kwargs,
            args=args,
            kwargs=kwargs,
        )

    @Substitution(name="rolling")
    @Appender(_shared_docs["sum"])
    def sum(self, *args, **kwargs):
        nv.validate_rolling_func("sum", args, kwargs)
        return super().sum(*args, **kwargs)

    @Substitution(name="rolling", func_name="max")
    @Appender(_doc_template)
    @Appender(_shared_docs["max"])
    def max(self, *args, **kwargs):
        nv.validate_rolling_func("max", args, kwargs)
        return super().max(*args, **kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["min"])
    def min(self, *args, **kwargs):
        nv.validate_rolling_func("min", args, kwargs)
        return super().min(*args, **kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["mean"])
    def mean(self, *args, **kwargs):
        nv.validate_rolling_func("mean", args, kwargs)
        return super().mean(*args, **kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["median"])
    def median(self, **kwargs):
        return super().median(**kwargs)

    @Substitution(name="rolling", versionadded="")
    @Appender(_shared_docs["std"])
    def std(self, ddof=1, *args, **kwargs):
        nv.validate_rolling_func("std", args, kwargs)
        return super().std(ddof=ddof, **kwargs)

    @Substitution(name="rolling", versionadded="")
    @Appender(_shared_docs["var"])
    def var(self, ddof=1, *args, **kwargs):
        nv.validate_rolling_func("var", args, kwargs)
        return super().var(ddof=ddof, **kwargs)

    @Substitution(name="rolling", func_name="skew")
    @Appender(_doc_template)
    @Appender(_shared_docs["skew"])
    def skew(self, **kwargs):
        return super().skew(**kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["sem"])
    def sem(self, ddof=1, *args, **kwargs):
        return self.std(*args, **kwargs) / (self.count() - ddof).pow(0.5)

    _agg_doc = dedent(
        """
    Examples
    --------

    The example below will show a rolling calculation with a window size of
    four matching the equivalent function call using `scipy.stats`.

    >>> arr = [1, 2, 3, 4, 999]
    >>> import scipy.stats
    >>> print(f"{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}")
    -1.200000
    >>> print(f"{scipy.stats.kurtosis(arr[1:], bias=False):.6f}")
    3.999946
    >>> s = pd.Series(arr)
    >>> s.rolling(4).kurt()
    0         NaN
    1         NaN
    2         NaN
    3   -1.200000
    4    3.999946
    dtype: float64
    """
    )

    @Appender(_agg_doc)
    @Substitution(name="rolling")
    @Appender(_shared_docs["kurt"])
    def kurt(self, **kwargs):
        return super().kurt(**kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["quantile"])
    def quantile(self, quantile, interpolation="linear", **kwargs):
        return super().quantile(
            quantile=quantile, interpolation=interpolation, **kwargs
        )

    @Substitution(name="rolling", func_name="cov")
    @Appender(_doc_template)
    @Appender(_shared_docs["cov"])
    def cov(self, other=None, pairwise=None, ddof=1, **kwargs):
        return super().cov(other=other, pairwise=pairwise, ddof=ddof, **kwargs)

    @Substitution(name="rolling")
    @Appender(_shared_docs["corr"])
    def corr(self, other=None, pairwise=None, **kwargs):
        return super().corr(other=other, pairwise=pairwise, **kwargs)


Rolling.__doc__ = Window.__doc__


class RollingGroupby(BaseWindowGroupby, Rolling):
    """
    Provide a rolling groupby implementation.
    """

    @property
    def _constructor(self):
        return Rolling

    def _get_window_indexer(self) -> GroupbyIndexer:
        """
        Return an indexer class that will compute the window start and end bounds

        Returns
        -------
        GroupbyIndexer
        """
        rolling_indexer: Type[BaseIndexer]
        indexer_kwargs: Optional[Dict[str, Any]] = None
        index_array = self._index_array
        window = self.window
        if isinstance(self.window, BaseIndexer):
            rolling_indexer = type(self.window)
            indexer_kwargs = self.window.__dict__
            assert isinstance(indexer_kwargs, dict)  # for mypy
            # We'll be using the index of each group later
            indexer_kwargs.pop("index_array", None)
            window = 0
        elif self._win_freq_i8 is not None:
            rolling_indexer = VariableWindowIndexer
            window = self._win_freq_i8
        else:
            rolling_indexer = FixedWindowIndexer
            index_array = None
        window_indexer = GroupbyIndexer(
            index_array=index_array,
            window_size=window,
            groupby_indicies=self._groupby.indices,
            window_indexer=rolling_indexer,
            indexer_kwargs=indexer_kwargs,
        )
        return window_indexer

    def _validate_monotonic(self):
        """
        Validate that on is monotonic;
        in this case we have to check only for nans, because
        monotonicy was already validated at a higher level.
        """
        if self._on.hasnans:
            self._raise_monotonic_error()