code: """
  Provide a generic structure to support window functions,
  similar to how we have a Groupby object.
  """
  from __future__ import annotations
  
  import copy
  from datetime import timedelta
  from functools import partial
  import inspect
  from textwrap import dedent
  from typing import (
      TYPE_CHECKING,
      Any,
      Callable,
      Hashable,
  )
  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,
      NDFrameT,
      WindowingRankType,
  )
  from pandas.compat._optional import import_optional_dependency
  from pandas.compat.numpy import function as nv
  from pandas.util._decorators import doc
  from pandas.util._exceptions import find_stack_level
  
  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,
      ABCSeries,
  )
  from pandas.core.dtypes.missing import notna
  
  from pandas.core._numba import executor
  from pandas.core.algorithms import factorize
  from pandas.core.apply import ResamplerWindowApply
  from pandas.core.arrays import ExtensionArray
  from pandas.core.base import (
      DataError,
      SelectionMixin,
  )
  import pandas.core.common as com
  from pandas.core.indexers.objects import (
      BaseIndexer,
      FixedWindowIndexer,
      GroupbyIndexer,
      VariableWindowIndexer,
  )
  from pandas.core.indexes.api import (
      DatetimeIndex,
      Index,
      MultiIndex,
      PeriodIndex,
      TimedeltaIndex,
  )
  from pandas.core.reshape.concat import concat
  from pandas.core.util.numba_ import (
      NUMBA_FUNC_CACHE,
      maybe_use_numba,
  )
  from pandas.core.window.common import (
      flex_binary_moment,
      zsqrt,
  )
  from pandas.core.window.doc import (
      _shared_docs,
      args_compat,
      create_section_header,
      kwargs_compat,
      kwargs_scipy,
      numba_notes,
      template_header,
      template_returns,
      template_see_also,
      window_agg_numba_parameters,
      window_apply_parameters,
  )
  from pandas.core.window.numba_ import (
      generate_manual_numpy_nan_agg_with_axis,
      generate_numba_apply_func,
      generate_numba_table_func,
  )
  
  if TYPE_CHECKING:
      from pandas import (
          DataFrame,
          Series,
      )
      from pandas.core.generic import NDFrame
      from pandas.core.groupby.ops import BaseGrouper
      from pandas.core.internals import Block  # noqa:F401
  
  
  class BaseWindow(SelectionMixin):
      """Provides utilities for performing windowing operations."""
  
      _attributes: list[str] = []
      exclusions: frozenset[Hashable] = frozenset()
      _on: Index
  
      def __init__(
          self,
          obj: NDFrame,
          window=None,
          min_periods: int | None = None,
          center: bool = False,
          win_type: str | None = None,
          axis: Axis = 0,
          on: str | Index | None = None,
          closed: str | None = None,
          method: str = "single",
          *,
          selection=None,
      ):
          self.obj = obj
          self.on = on
          self.closed = closed
          self.window = window
          self.min_periods = min_periods
          self.center = center
          # TODO(2.0): Change this back to self.win_type once deprecation is enforced
          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._selection = selection
          self._validate()
  
      @property
      def win_type(self):
          if self._win_freq_i8 is not None:
              warnings.warn(
                  "win_type will no longer return 'freq' in a future version. "
                  "Check the type of self.window instead.",
                  FutureWarning,
                  stacklevel=find_stack_level(),
              )
              return "freq"
          return self._win_type
  
      @property
      def is_datetimelike(self) -> bool:
          warnings.warn(
              "is_datetimelike is deprecated and will be removed in a future version.",
              FutureWarning,
              stacklevel=find_stack_level(),
          )
          return self._win_freq_i8 is not None
  
      def validate(self) -> None:
          warnings.warn(
              "validate is deprecated and will be removed in a future version.",
              FutureWarning,
              stacklevel=find_stack_level(),
          )
          return 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: NDFrameT) -> NDFrameT:
          """
          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) and 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=["number"], 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
  
          # we need to make a shallow copy of ourselves
          # with the same groupby
          with warnings.catch_warnings():
              # TODO(2.0): Remove once win_type deprecation is enforced
              warnings.filterwarnings("ignore", "win_type", FutureWarning)
              kwargs = {attr: getattr(self, attr) for attr in self._attributes}
  
          selection = None
          if subset.ndim == 2 and (
              (is_scalar(key) and key in subset) or is_list_like(key)
          ):
              selection = key
  
          new_win = type(self)(subset, selection=selection, **kwargs)
          return new_win
  
      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 __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 and attr_name[0] != "_"
          )
          attrs = ",".join(attrs_list)
          return f"{type(self).__name__} [{attrs}]"
  
      def __iter__(self):
          obj = self._selected_obj.set_axis(self._on)
          obj = self._create_data(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,
          )
  
          assert len(start) == len(
              end
          ), "these should be equal in length from get_window_bounds"
  
          for s, e in zip(start, end):
              result = obj.iloc[slice(s, e)]
              yield result
  
      def _prep_values(self, values: ArrayLike) -> np.ndarray:
          """Convert input to numpy arrays for Cython routines"""
          if needs_i8_conversion(values.dtype):
              raise NotImplementedError(
                  f"ops for {type(self).__name__} 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:
                  if isinstance(values, ExtensionArray):
                      values = values.to_numpy(np.float64, na_value=np.nan)
                  else:
                      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)
  
          # error: Incompatible return value type (got "Optional[ndarray]",
          # expected "ndarray")
          return values  # type: ignore[return-value]
  
      def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
          # 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 _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame:
          """Validate and finalize result."""
          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 _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,
                  center=self.center,
              )
          return FixedWindowIndexer(window_size=self.window)
  
      def _apply_series(
          self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None
      ) -> Series:
          """
          Series version of _apply_blockwise
          """
          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)
          try:
              values = self._prep_values(obj._values)
          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: str | None = None
      ) -> DataFrame | Series:
          """
          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()
  
          def hfunc(values: ArrayLike) -> ArrayLike:
              values = self._prep_values(values)
              return homogeneous_func(values)
  
          if self.axis == 1:
              obj = obj.T
  
          taker = []
          res_values = []
          for i, arr in enumerate(obj._iter_column_arrays()):
              # GH#42736 operate column-wise instead of block-wise
              try:
                  res = hfunc(arr)
              except (TypeError, NotImplementedError):
                  pass
              else:
                  res_values.append(res)
                  taker.append(i)
  
          df = type(obj)._from_arrays(
              res_values,
              index=obj.index,
              columns=obj.columns.take(taker),
              verify_integrity=False,
          )
  
          if self.axis == 1:
              df = df.T
  
          if 0 != len(res_values) != len(obj.columns):
              # GH#42738 ignore_failures dropped nuisance columns
              dropped = obj.columns.difference(obj.columns.take(taker))
              warnings.warn(
                  "Dropping of nuisance columns in rolling operations "
                  "is deprecated; in a future version this will raise TypeError. "
                  "Select only valid columns before calling the operation. "
                  f"Dropped columns were {dropped}",
                  FutureWarning,
                  stacklevel=find_stack_level(),
              )
  
          return self._resolve_output(df, obj)
  
      def _apply_tablewise(
          self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None
      ) -> DataFrame | Series:
          """
          Apply the given function to the DataFrame across the entire object
          """
          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)
  
          return self._resolve_output(out, obj)
  
      def _apply_pairwise(
          self,
          target: DataFrame | Series,
          other: DataFrame | Series | None,
          pairwise: bool | None,
          func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
      ) -> DataFrame | Series:
          """
          Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
          """
          if other is None:
              other = target
              # only default unset
              pairwise = True if pairwise is None else pairwise
          elif not isinstance(other, (ABCDataFrame, ABCSeries)):
              raise ValueError("other must be a DataFrame or Series")
  
          return flex_binary_moment(target, other, func, pairwise=bool(pairwise))
  
      def _apply(
          self,
          func: Callable[..., Any],
          name: str | None = None,
          numba_cache_key: tuple[Callable, str] | None = None,
          numba_args: tuple[Any, ...] = (),
          **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
          numba_args : tuple
              args to be passed when func is a 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,
                  )
                  assert len(start) == len(
                      end
                  ), "these should be equal in length from get_window_bounds"
  
                  return func(x, start, end, min_periods, *numba_args)
  
              with np.errstate(all="ignore"):
                  result = calc(values)
  
              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 _numba_apply(
          self,
          func: Callable[..., Any],
          numba_cache_key_str: str,
          engine_kwargs: dict[str, bool] | None = None,
      ):
          window_indexer = self._get_window_indexer()
          min_periods = (
              self.min_periods
              if self.min_periods is not None
              else window_indexer.window_size
          )
          obj = self._create_data(self._selected_obj)
          if self.axis == 1:
              obj = obj.T
          values = self._prep_values(obj.to_numpy())
          if values.ndim == 1:
              values = values.reshape(-1, 1)
          start, end = window_indexer.get_window_bounds(
              num_values=len(values),
              min_periods=min_periods,
              center=self.center,
              closed=self.closed,
          )
          aggregator = executor.generate_shared_aggregator(
              func, engine_kwargs, numba_cache_key_str
          )
          result = aggregator(values, start, end, min_periods)
          NUMBA_FUNC_CACHE[(func, numba_cache_key_str)] = aggregator
          result = result.T if self.axis == 1 else result
          if obj.ndim == 1:
              result = result.squeeze()
              out = obj._constructor(result, index=obj.index, name=obj.name)
              return out
          else:
              out = obj._constructor(result, index=obj.index, columns=obj.columns)
              return self._resolve_output(out, obj)
  
      def aggregate(self, func, *args, **kwargs):
          result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
          if result is None:
              return self.apply(func, raw=False, args=args, kwargs=kwargs)
          return result
  
      agg = aggregate
  
  
  class BaseWindowGroupby(BaseWindow):
      """
      Provide the groupby windowing facilities.
      """
  
      _grouper: BaseGrouper
      _as_index: bool
      _attributes: list[str] = ["_grouper"]
  
      def __init__(
          self,
          obj: DataFrame | Series,
          *args,
          _grouper: BaseGrouper,
          _as_index: bool = True,
          **kwargs,
      ):
          from pandas.core.groupby.ops import BaseGrouper
  
          if not isinstance(_grouper, BaseGrouper):
              raise ValueError("Must pass a BaseGrouper object.")
          self._grouper = _grouper
          self._as_index = _as_index
          # GH 32262: It's convention to keep the grouping column in
          # groupby.<agg_func>, but unexpected to users in
          # groupby.rolling.<agg_func>
          obj = obj.drop(columns=self._grouper.names, errors="ignore")
          super().__init__(obj, *args, **kwargs)
  
      def _apply(
          self,
          func: Callable[..., Any],
          name: str | None = None,
          numba_cache_key: tuple[Callable, str] | None = None,
          numba_args: tuple[Any, ...] = (),
          **kwargs,
      ) -> DataFrame | Series:
          result = super()._apply(
              func,
              name,
              numba_cache_key,
              numba_args,
              **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 = copy.copy(self._grouper.names)
          result_index_names = groupby_keys + grouped_index_name
  
          drop_columns = [
              key
              for key in self._grouper.names
              if key not in self.obj.index.names or key is None
          ]
  
          if len(drop_columns) != len(groupby_keys):
              # Our result will have still kept the column in the result
              result = result.drop(columns=drop_columns, errors="ignore")
  
          codes = self._grouper.codes
          levels = copy.copy(self._grouper.levels)
  
          group_indices = self._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
          if not self._as_index:
              result = result.reset_index(level=list(range(len(groupby_keys))))
          return result
  
      def _apply_pairwise(
          self,
          target: DataFrame | Series,
          other: DataFrame | Series | None,
          pairwise: bool | None,
          func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
      ) -> DataFrame | Series:
          """
          Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
          """
          # Manually drop the grouping column first
          target = target.drop(columns=self._grouper.names, errors="ignore")
          target = self._create_data(target)
          result = super()._apply_pairwise(target, other, pairwise, func)
          # 1) Determine the levels + codes of the groupby levels
          if other is not None:
              # When we have other, we must reindex (expand) the result
              # from flex_binary_moment to a "transform"-like result
              # per groupby combination
              old_result_len = len(result)
              result = concat(
                  [
                      result.take(gb_indices).reindex(result.index)
                      for gb_indices in self._grouper.indices.values()
                  ]
              )
  
              gb_pairs = (
                  com.maybe_make_list(pair) for pair in self._grouper.indices.keys()
              )
              groupby_codes = []
              groupby_levels = []
              # e.g. [[1, 2], [4, 5]] as [[1, 4], [2, 5]]
              for gb_level_pair in map(list, zip(*gb_pairs)):
                  labels = np.repeat(np.array(gb_level_pair), old_result_len)
                  codes, levels = factorize(labels)
                  groupby_codes.append(codes)
                  groupby_levels.append(levels)
  
          else:
              # When we evaluate the pairwise=True result, repeat the groupby
              # labels by the number of columns in the original object
              groupby_codes = self._grouper.codes
              # error: Incompatible types in assignment (expression has type
              # "List[Index]", variable has type "List[Union[ndarray, Index]]")
              groupby_levels = self._grouper.levels  # type: ignore[assignment]
  
              group_indices = self._grouper.indices.values()
              if group_indices:
                  indexer = np.concatenate(list(group_indices))
              else:
                  indexer = np.array([], dtype=np.intp)
  
              if target.ndim == 1:
                  repeat_by = 1
              else:
                  repeat_by = len(target.columns)
              groupby_codes = [
                  np.repeat(c.take(indexer), repeat_by) for c in groupby_codes
              ]
          # 2) Determine the levels + codes of the result from super()._apply_pairwise
          if isinstance(result.index, MultiIndex):
              result_codes = list(result.index.codes)
              result_levels = list(result.index.levels)
              result_names = list(result.index.names)
          else:
              idx_codes, idx_levels = factorize(result.index)
              result_codes = [idx_codes]
              result_levels = [idx_levels]
              result_names = [result.index.name]
  
          # 3) Create the resulting index by combining 1) + 2)
          result_codes = groupby_codes + result_codes
          result_levels = groupby_levels + result_levels
          result_names = self._grouper.names + result_names
  
          result_index = MultiIndex(
              result_levels, result_codes, names=result_names, verify_integrity=False
          )
          result.index = result_index
          return result
  
      def _create_data(self, obj: NDFrameT) -> NDFrameT:
          """
          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._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:
              # GH 43355
              subset = 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.
  
          If an integer, the fixed number of observations used for
          each window.
  
          If an offset, 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.
          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 a BaseIndexer subclass, 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 ``np.nan``.
  
          For a window that is specified by an offset, ``min_periods`` will default to 1.
  
          For a window that is specified by an integer, ``min_periods`` will default
          to the size of the window.
  
      center : bool, default False
          If False, set the window labels as the right edge of the window index.
  
          If True, set the window labels as the center of the window index.
  
      win_type : str, default None
          If ``None``, all points are evenly weighted.
  
          If a string, it must be a valid `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.
  
      on : str, optional
          For a DataFrame, a column label or Index 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
          If ``0`` or ``'index'``, roll across the rows.
  
          If ``1`` or ``'columns'``, roll across the columns.
  
      closed : str, default None
          If ``'right'``, the first point in the window is excluded from calculations.
  
          If ``'left'``, the last point in the window is excluded from calculations.
  
          If ``'both'``, the no points in the window are excluded from calculations.
  
          If ``'neither'``, the first and last points in the window are excluded
          from calculations.
  
          Default ``None`` (``'right'``)
  
          .. versionchanged:: 1.2.0
  
              The closed parameter with fixed windows is now supported.
  
      method : str {'single', 'table'}, default 'single'
  
          .. versionadded:: 1.3.0
  
          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.
  
      Returns
      -------
      ``Window`` subclass if a ``win_type`` is passed
  
      ``Rolling`` subclass if ``win_type`` is not passed
  
      See Also
      --------
      expanding : Provides expanding transformations.
      ewm : Provides exponential weighted functions.
  
      Notes
      -----
      See :ref:`Windowing Operations <window.generic>` for further usage details
      and examples.
  
      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
  
      **window**
  
      Rolling sum with a window length of 2 observations.
  
      >>> df.rolling(2).sum()
           B
      0  NaN
      1  1.0
      2  3.0
      3  NaN
      4  NaN
  
      Rolling sum with a window span of 2 seconds.
  
      >>> 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')])
  
      >>> df_time
                             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
  
      >>> df_time.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
  
      Rolling sum with forward looking windows with 2 observations.
  
      >>> 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
  
      **min_periods**
  
      Rolling sum with a window length of 2 observations, but only needs a minimum of 1
      observation to calculate a value.
  
      >>> df.rolling(2, min_periods=1).sum()
           B
      0  0.0
      1  1.0
      2  3.0
      3  2.0
      4  4.0
  
      **center**
  
      Rolling sum with the result assigned to the center of the window index.
  
      >>> df.rolling(3, min_periods=1, center=True).sum()
           B
      0  1.0
      1  3.0
      2  3.0
      3  6.0
      4  4.0
  
      >>> df.rolling(3, min_periods=1, center=False).sum()
           B
      0  0.0
      1  1.0
      2  3.0
      3  3.0
      4  6.0
  
      **win_type**
  
      Rolling sum with a window length of 2, using the Scipy ``'gaussian'``
      window type. ``std`` is required in the aggregation function.
  
      >>> df.rolling(2, win_type='gaussian').sum(std=3)
                B
      0       NaN
      1  0.986207
      2  2.958621
      3       NaN
      4       NaN
      """
  
      _attributes = [
          "window",
          "min_periods",
          "center",
          "win_type",
          "axis",
          "on",
          "closed",
          "method",
      ]
  
      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: str | None = None,
          numba_cache_key: tuple[Callable, str] | None = None,
          numba_args: tuple[Any, ...] = (),
          **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
          numba_args : 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"):
                  # Our weighted aggregations return memoryviews
                  result = np.asarray(calc(values))
  
              if self.center:
                  result = self._center_window(result, offset)
  
              return result
  
          return self._apply_blockwise(homogeneous_func, name)
  
      @doc(
          _shared_docs["aggregate"],
          see_also=dedent(
              """
          See Also
          --------
          pandas.DataFrame.aggregate : Similar DataFrame method.
          pandas.Series.aggregate : Similar Series method.
          """
          ),
          examples=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
          """
          ),
          klass="Series/DataFrame",
          axis="",
      )
      def aggregate(self, func, *args, **kwargs):
          result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
          if result is None:
  
              # these must apply directly
              result = func(self)
  
          return result
  
      agg = aggregate
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          kwargs_scipy,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="weighted window sum",
          agg_method="sum",
      )
      def sum(self, *args, **kwargs):
          nv.validate_window_func("sum", args, kwargs)
          window_func = window_aggregations.roll_weighted_sum
          # error: Argument 1 to "_apply" of "Window" has incompatible type
          # "Callable[[ndarray, ndarray, int], ndarray]"; expected
          # "Callable[[ndarray, int, int], ndarray]"
          return self._apply(window_func, name="sum", **kwargs)  # type: ignore[arg-type]
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          kwargs_scipy,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="weighted window mean",
          agg_method="mean",
      )
      def mean(self, *args, **kwargs):
          nv.validate_window_func("mean", args, kwargs)
          window_func = window_aggregations.roll_weighted_mean
          # error: Argument 1 to "_apply" of "Window" has incompatible type
          # "Callable[[ndarray, ndarray, int], ndarray]"; expected
          # "Callable[[ndarray, int, int], ndarray]"
          return self._apply(window_func, name="mean", **kwargs)  # type: ignore[arg-type]
  
      @doc(
          template_header,
          ".. versionadded:: 1.0.0 \n\n",
          create_section_header("Parameters"),
          kwargs_scipy,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="weighted window variance",
          agg_method="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)
  
      @doc(
          template_header,
          ".. versionadded:: 1.0.0 \n\n",
          create_section_header("Parameters"),
          kwargs_scipy,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="weighted window standard deviation",
          agg_method="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):
      def count(self):
          window_func = window_aggregations.roll_sum
          return self._apply(window_func, name="count")
  
      def apply(
          self,
          func: Callable[..., Any],
          raw: bool = False,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          args: tuple[Any, ...] | None = None,
          kwargs: dict[str, Any] | None = 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
          numba_args: tuple[Any, ...] = ()
          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__
              numba_args = args
              if self.method == "single":
                  apply_func = generate_numba_apply_func(
                      kwargs, func, engine_kwargs, caller_name
                  )
                  numba_cache_key = (func, f"{caller_name}_apply_single")
              else:
                  apply_func = generate_numba_table_func(
                      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,
              numba_args=numba_args,
          )
  
      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,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_window_func("sum", args, kwargs)
          if maybe_use_numba(engine):
              if self.method == "table":
                  func = generate_manual_numpy_nan_agg_with_axis(np.nansum)
                  return self.apply(
                      func,
                      raw=True,
                      engine=engine,
                      engine_kwargs=engine_kwargs,
                  )
              else:
                  from pandas.core._numba.kernels import sliding_sum
  
                  return self._numba_apply(sliding_sum, "rolling_sum", engine_kwargs)
          window_func = window_aggregations.roll_sum
          return self._apply(window_func, name="sum", **kwargs)
  
      def max(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_window_func("max", args, kwargs)
          if maybe_use_numba(engine):
              if self.method == "table":
                  func = generate_manual_numpy_nan_agg_with_axis(np.nanmax)
              else:
                  func = np.nanmax
  
              return self.apply(
                  func,
                  raw=True,
                  engine=engine,
                  engine_kwargs=engine_kwargs,
              )
          window_func = window_aggregations.roll_max
          return self._apply(window_func, name="max", **kwargs)
  
      def min(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_window_func("min", args, kwargs)
          if maybe_use_numba(engine):
              if self.method == "table":
                  func = generate_manual_numpy_nan_agg_with_axis(np.nanmin)
              else:
                  func = np.nanmin
  
              return self.apply(
                  func,
                  raw=True,
                  engine=engine,
                  engine_kwargs=engine_kwargs,
              )
          window_func = window_aggregations.roll_min
          return self._apply(window_func, name="min", **kwargs)
  
      def mean(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_window_func("mean", args, kwargs)
          if maybe_use_numba(engine):
              if self.method == "table":
                  func = generate_manual_numpy_nan_agg_with_axis(np.nanmean)
                  return self.apply(
                      func,
                      raw=True,
                      engine=engine,
                      engine_kwargs=engine_kwargs,
                  )
              else:
                  from pandas.core._numba.kernels import sliding_mean
  
                  return self._numba_apply(sliding_mean, "rolling_mean", engine_kwargs)
          window_func = window_aggregations.roll_mean
          return self._apply(window_func, name="mean", **kwargs)
  
      def median(
          self,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          if maybe_use_numba(engine):
              if self.method == "table":
                  func = generate_manual_numpy_nan_agg_with_axis(np.nanmedian)
              else:
                  func = np.nanmedian
  
              return self.apply(
                  func,
                  raw=True,
                  engine=engine,
                  engine_kwargs=engine_kwargs,
              )
          window_func = window_aggregations.roll_median_c
          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,
          )
  
      def skew(self, **kwargs):
          window_func = window_aggregations.roll_skew
          return self._apply(
              window_func,
              name="skew",
              **kwargs,
          )
  
      def sem(self, ddof: int = 1, *args, **kwargs):
          return self.std(*args, **kwargs) / (self.count() - ddof).pow(0.5)
  
      def kurt(self, **kwargs):
          window_func = window_aggregations.roll_kurt
          return self._apply(
              window_func,
              name="kurt",
              **kwargs,
          )
  
      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)
  
      def rank(
          self,
          method: WindowingRankType = "average",
          ascending: bool = True,
          pct: bool = False,
          **kwargs,
      ):
          window_func = partial(
              window_aggregations.roll_rank,
              method=method,
              ascending=ascending,
              percentile=pct,
          )
  
          return self._apply(window_func, name="rank", **kwargs)
  
      def cov(
          self,
          other: DataFrame | Series | None = None,
          pairwise: bool | None = None,
          ddof: int = 1,
          **kwargs,
      ):
          from pandas import Series
  
          def cov_func(x, y):
              x_array = self._prep_values(x)
              y_array = self._prep_values(y)
              window_indexer = self._get_window_indexer()
              min_periods = (
                  self.min_periods
                  if self.min_periods is not None
                  else window_indexer.window_size
              )
              start, end = window_indexer.get_window_bounds(
                  num_values=len(x_array),
                  min_periods=min_periods,
                  center=self.center,
                  closed=self.closed,
              )
  
              assert len(start) == len(
                  end
              ), "these should be equal in length from get_window_bounds"
  
              with np.errstate(all="ignore"):
                  mean_x_y = window_aggregations.roll_mean(
                      x_array * y_array, start, end, min_periods
                  )
                  mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
                  mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
                  count_x_y = window_aggregations.roll_sum(
                      notna(x_array + y_array).astype(np.float64), start, end, 0
                  )
                  result = (mean_x_y - mean_x * mean_y) * (count_x_y / (count_x_y - ddof))
              return Series(result, index=x.index, name=x.name)
  
          return self._apply_pairwise(self._selected_obj, other, pairwise, cov_func)
  
      def corr(
          self,
          other: DataFrame | Series | None = None,
          pairwise: bool | None = None,
          ddof: int = 1,
          **kwargs,
      ):
  
          from pandas import Series
  
          def corr_func(x, y):
              x_array = self._prep_values(x)
              y_array = self._prep_values(y)
              window_indexer = self._get_window_indexer()
              min_periods = (
                  self.min_periods
                  if self.min_periods is not None
                  else window_indexer.window_size
              )
              start, end = window_indexer.get_window_bounds(
                  num_values=len(x_array),
                  min_periods=min_periods,
                  center=self.center,
                  closed=self.closed,
              )
  
              assert len(start) == len(
                  end
              ), "these should be equal in length from get_window_bounds"
  
              with np.errstate(all="ignore"):
                  mean_x_y = window_aggregations.roll_mean(
                      x_array * y_array, start, end, min_periods
                  )
                  mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
                  mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
                  count_x_y = window_aggregations.roll_sum(
                      notna(x_array + y_array).astype(np.float64), start, end, 0
                  )
                  x_var = window_aggregations.roll_var(
                      x_array, start, end, min_periods, ddof
                  )
                  y_var = window_aggregations.roll_var(
                      y_array, start, end, min_periods, ddof
                  )
                  numerator = (mean_x_y - mean_x * mean_y) * (
                      count_x_y / (count_x_y - ddof)
                  )
                  denominator = (x_var * y_var) ** 0.5
                  result = numerator / denominator
              return Series(result, index=x.index, name=x.name)
  
          return self._apply_pairwise(self._selected_obj, other, pairwise, corr_func)
  
  
  class Rolling(RollingAndExpandingMixin):
  
      _attributes: list[str] = [
          "window",
          "min_periods",
          "center",
          "win_type",
          "axis",
          "on",
          "closed",
          "method",
      ]
  
      def _validate(self):
          super()._validate()
  
          # we allow rolling on a datetimelike index
          if (
              self.obj.empty
              or isinstance(self._on, (DatetimeIndex, TimedeltaIndex, PeriodIndex))
          ) and isinstance(self.window, (str, BaseOffset, timedelta)):
  
              self._validate_monotonic()
  
              # 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, PeriodIndex):
                  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")
  
      @doc(
          _shared_docs["aggregate"],
          see_also=dedent(
              """
          See Also
          --------
          pandas.Series.rolling : Calling object with Series data.
          pandas.DataFrame.rolling : Calling object with DataFrame data.
          """
          ),
          examples=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
          """
          ),
          klass="Series/Dataframe",
          axis="",
      )
      def aggregate(self, func, *args, **kwargs):
          return super().aggregate(func, *args, **kwargs)
  
      agg = aggregate
  
      @doc(
          template_header,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Examples"),
          dedent(
              """
          >>> 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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="count of non NaN observations",
          agg_method="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,
                  stacklevel=find_stack_level(),
              )
              self.min_periods = 0
              result = super().count()
              self.min_periods = None
          else:
              result = super().count()
          return result
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          window_apply_parameters,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="custom aggregation function",
          agg_method="apply",
      )
      def apply(
          self,
          func: Callable[..., Any],
          raw: bool = False,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          args: tuple[Any, ...] | None = None,
          kwargs: dict[str, Any] | None = None,
      ):
          return super().apply(
              func,
              raw=raw,
              engine=engine,
              engine_kwargs=engine_kwargs,
              args=args,
              kwargs=kwargs,
          )
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          args_compat,
          window_agg_numba_parameters,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          numba_notes,
          create_section_header("Examples"),
          dedent(
              """
          >>> 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.rolling(3, center=True).sum()
          0     NaN
          1     6.0
          2     9.0
          3    12.0
          4     NaN
          dtype: float64
  
          For DataFrame, each 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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="sum",
          agg_method="sum",
      )
      def sum(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_rolling_func("sum", args, kwargs)
          return super().sum(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          args_compat,
          window_agg_numba_parameters,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          numba_notes[:-1],
          window_method="rolling",
          aggregation_description="maximum",
          agg_method="max",
      )
      def max(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_rolling_func("max", args, kwargs)
          return super().max(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          args_compat,
          window_agg_numba_parameters,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          numba_notes,
          create_section_header("Examples"),
          dedent(
              """
          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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="minimum",
          agg_method="min",
      )
      def min(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_rolling_func("min", args, kwargs)
          return super().min(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          args_compat,
          window_agg_numba_parameters,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          numba_notes,
          create_section_header("Examples"),
          dedent(
              """
          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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="mean",
          agg_method="mean",
      )
      def mean(
          self,
          *args,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          nv.validate_rolling_func("mean", args, kwargs)
          return super().mean(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          window_agg_numba_parameters,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          numba_notes,
          create_section_header("Examples"),
          dedent(
              """
          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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="median",
          agg_method="median",
      )
      def median(
          self,
          engine: str | None = None,
          engine_kwargs: dict[str, bool] | None = None,
          **kwargs,
      ):
          return super().median(engine=engine, engine_kwargs=engine_kwargs, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          ddof : int, default 1
              Delta Degrees of Freedom.  The divisor used in calculations
              is ``N - ddof``, where ``N`` represents the number of elements.
          """
          ).replace("\n", "", 1),
          args_compat,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          "numpy.std : Equivalent method for NumPy array.\n",
          template_see_also,
          create_section_header("Notes"),
          dedent(
              """
          The default ``ddof`` of 1 used in :meth:`Series.std` is different
          than the default ``ddof`` of 0 in :func:`numpy.std`.
  
          A minimum of one period is required for the rolling calculation.
  
          The implementation is susceptible to floating point imprecision as
          shown in the example below.\n
          """
          ).replace("\n", "", 1),
          create_section_header("Examples"),
          dedent(
              """
          >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
          >>> s.rolling(3).std()
          0             NaN
          1             NaN
          2    5.773503e-01
          3    1.000000e+00
          4    1.000000e+00
          5    1.154701e+00
          6    2.580957e-08
          dtype: float64
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="standard deviation",
          agg_method="std",
      )
      def std(self, ddof: int = 1, *args, **kwargs):
          nv.validate_rolling_func("std", args, kwargs)
          return super().std(ddof=ddof, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          ddof : int, default 1
              Delta Degrees of Freedom.  The divisor used in calculations
              is ``N - ddof``, where ``N`` represents the number of elements.
          """
          ).replace("\n", "", 1),
          args_compat,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          "numpy.var : Equivalent method for NumPy array.\n",
          template_see_also,
          create_section_header("Notes"),
          dedent(
              """
          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 one period is required for the rolling calculation.
  
          The implementation is susceptible to floating point imprecision as
          shown in the example below.\n
          """
          ).replace("\n", "", 1),
          create_section_header("Examples"),
          dedent(
              """
          >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
          >>> s.rolling(3).var()
          0             NaN
          1             NaN
          2    3.333333e-01
          3    1.000000e+00
          4    1.000000e+00
          5    1.333333e+00
          6    6.661338e-16
          dtype: float64
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="variance",
          agg_method="var",
      )
      def var(self, ddof: int = 1, *args, **kwargs):
          nv.validate_rolling_func("var", args, kwargs)
          return super().var(ddof=ddof, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          "scipy.stats.skew : Third moment of a probability density.\n",
          template_see_also,
          create_section_header("Notes"),
          "A minimum of three periods is required for the rolling calculation.\n",
          window_method="rolling",
          aggregation_description="unbiased skewness",
          agg_method="skew",
      )
      def skew(self, **kwargs):
          return super().skew(**kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          ddof : int, default 1
              Delta Degrees of Freedom.  The divisor used in calculations
              is ``N - ddof``, where ``N`` represents the number of elements.
          """
          ).replace("\n", "", 1),
          args_compat,
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Notes"),
          "A minimum of one period is required for the calculation.\n\n",
          create_section_header("Examples"),
          dedent(
              """
          >>> 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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="standard error of mean",
          agg_method="sem",
      )
      def sem(self, ddof: int = 1, *args, **kwargs):
          return self.std(*args, **kwargs) / (self.count() - ddof).pow(0.5)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          "scipy.stats.kurtosis : Reference SciPy method.\n",
          template_see_also,
          create_section_header("Notes"),
          "A minimum of four periods is required for the calculation.\n\n",
          create_section_header("Examples"),
          dedent(
              """
          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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="Fisher's definition of kurtosis without bias",
          agg_method="kurt",
      )
      def kurt(self, **kwargs):
          return super().kurt(**kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          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.
          """
          ).replace("\n", "", 1),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Examples"),
          dedent(
              """
          >>> 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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="quantile",
          agg_method="quantile",
      )
      def quantile(self, quantile: float, interpolation: str = "linear", **kwargs):
          return super().quantile(
              quantile=quantile,
              interpolation=interpolation,
              **kwargs,
          )
  
      @doc(
          template_header,
          ".. versionadded:: 1.4.0 \n\n",
          create_section_header("Parameters"),
          dedent(
              """
          method : {{'average', 'min', 'max'}}, default 'average'
              How to rank the group of records that have the same value (i.e. ties):
  
              * average: average rank of the group
              * min: lowest rank in the group
              * max: highest rank in the group
  
          ascending : bool, default True
              Whether or not the elements should be ranked in ascending order.
          pct : bool, default False
              Whether or not to display the returned rankings in percentile
              form.
          """
          ).replace("\n", "", 1),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also,
          create_section_header("Examples"),
          dedent(
              """
          >>> s = pd.Series([1, 4, 2, 3, 5, 3])
          >>> s.rolling(3).rank()
          0    NaN
          1    NaN
          2    2.0
          3    2.0
          4    3.0
          5    1.5
          dtype: float64
  
          >>> s.rolling(3).rank(method="max")
          0    NaN
          1    NaN
          2    2.0
          3    2.0
          4    3.0
          5    2.0
          dtype: float64
  
          >>> s.rolling(3).rank(method="min")
          0    NaN
          1    NaN
          2    2.0
          3    2.0
          4    3.0
          5    1.0
          dtype: float64
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="rank",
          agg_method="rank",
      )
      def rank(
          self,
          method: WindowingRankType = "average",
          ascending: bool = True,
          pct: bool = False,
          **kwargs,
      ):
          return super().rank(
              method=method,
              ascending=ascending,
              pct=pct,
              **kwargs,
          )
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          other : Series or DataFrame, 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.
          """
          ).replace("\n", "", 1),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          template_see_also[:-1],
          window_method="rolling",
          aggregation_description="sample covariance",
          agg_method="cov",
      )
      def cov(
          self,
          other: DataFrame | Series | None = None,
          pairwise: bool | None = None,
          ddof: int = 1,
          **kwargs,
      ):
          return super().cov(other=other, pairwise=pairwise, ddof=ddof, **kwargs)
  
      @doc(
          template_header,
          create_section_header("Parameters"),
          dedent(
              """
          other : Series or DataFrame, 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.
          """
          ).replace("\n", "", 1),
          kwargs_compat,
          create_section_header("Returns"),
          template_returns,
          create_section_header("See Also"),
          dedent(
              """
          cov : Similar method to calculate covariance.
          numpy.corrcoef : NumPy Pearson's correlation calculation.
          """
          ).replace("\n", "", 1),
          template_see_also,
          create_section_header("Notes"),
          dedent(
              """
          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.\n
          """
          ).replace("\n", "", 1),
          create_section_header("Examples"),
          dedent(
              """
          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
          """
          ).replace("\n", "", 1),
          window_method="rolling",
          aggregation_description="correlation",
          agg_method="corr",
      )
      def corr(
          self,
          other: DataFrame | Series | None = None,
          pairwise: bool | None = None,
          ddof: int = 1,
          **kwargs,
      ):
          return super().corr(other=other, pairwise=pairwise, ddof=ddof, **kwargs)
  
  
  Rolling.__doc__ = Window.__doc__
  
  
  class RollingGroupby(BaseWindowGroupby, Rolling):
      """
      Provide a rolling groupby implementation.
      """
  
      _attributes = Rolling._attributes + BaseWindowGroupby._attributes
  
      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: dict[str, Any] | None = None
          index_array = self._index_array
          if isinstance(self.window, BaseIndexer):
              rolling_indexer = type(self.window)
              indexer_kwargs = self.window.__dict__.copy()
              assert isinstance(indexer_kwargs, dict)  # for mypy
              # We'll be using the index of each group later
              indexer_kwargs.pop("index_array", None)
              window = self.window
          elif self._win_freq_i8 is not None:
              rolling_indexer = VariableWindowIndexer
              window = self._win_freq_i8
          else:
              rolling_indexer = FixedWindowIndexer
              window = self.window
          window_indexer = GroupbyIndexer(
              index_array=index_array,
              window_size=window,
              groupby_indices=self._grouper.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
          monotonicity was already validated at a higher level.
          """
          if self._on.hasnans:
              self._raise_monotonic_error()
  
apis: pandas.core.window.numba_.generate_numba_apply_func pandas.Series pandas.compat.numpy.function.validate_rolling_func pandas.core.apply.ResamplerWindowApply pandas.core.dtypes.common.is_integer pandas.core.indexes.api.MultiIndex.from_arrays pandas.core.dtypes.common.is_list_like pandas._libs.window.aggregations.roll_mean pandas.core.indexers.objects.FixedWindowIndexer pandas.compat.numpy.function.validate_window_func pandas._libs.window.aggregations.roll_var pandas.core._numba.executor.generate_shared_aggregator pandas.core.window.numba_.generate_manual_numpy_nan_agg_with_axis pandas._libs.tslibs.to_offset pandas.core.window.numba_.generate_numba_table_func pandas.core.indexes.api.Index pandas.core.dtypes.missing.notna numpy.where pandas.core.dtypes.common.needs_i8_conversion pandas.util._exceptions.find_stack_level pandas.core.base.DataError pandas.core.indexers.objects.BaseIndexer pandas.core.indexers.objects.GroupbyIndexer pandas.core.indexes.api.MultiIndex pandas.core.algorithms.factorize pandas.core.dtypes.common.ensure_float64 pandas.core.dtypes.common.is_scalar pandas.core.dtypes.common.is_bool pandas.core.common.maybe_make_list numpy.isinf numpy.errstate pandas.compat._optional.import_optional_dependency pandas.core.window.doc.create_section_header pandas.core.util.numba_.maybe_use_numba numpy.array numpy.concatenate pandas.core.indexers.objects.VariableWindowIndexer