""" 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, cache_readonly, doc from pandas.core.dtypes.common import ( ensure_float64, is_bool, is_float_dtype, is_integer, is_integer_dtype, 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 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", ] 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, **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.win_freq = None self.axis = obj._get_axis_number(axis) if axis is not None else None self.validate() @property def is_datetimelike(self) -> Optional[bool]: return None @property def _on(self): return None @property def is_freq_type(self) -> bool: return self.win_type == "freq" 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" ) 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) # GH #12373 : rolling functions error on float32 data # make sure the data is coerced to float64 if is_float_dtype(values.dtype): values = ensure_float64(values) elif is_integer_dtype(values.dtype): values = ensure_float64(values) elif needs_i8_conversion(values.dtype): raise NotImplementedError( f"ops for {self._window_type} for this " f"dtype {values.dtype} are not implemented" ) else: 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 def _get_roll_func(self, func_name: str) -> Callable[..., Any]: """ Wrap rolling function to check values passed. Parameters ---------- func_name : str Cython function used to calculate rolling statistics Returns ------- func : callable """ window_func = getattr(window_aggregations, func_name, None) if window_func is None: raise ValueError( f"we do not support this function in window_aggregations.{func_name}" ) return window_func @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.is_freq_type: return VariableWindowIndexer( index_array=self._index_array, window_size=self.window ) 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( 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: 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 return self._apply_blockwise(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 from tuples # 1st set of levels = group by labels # 2nd set of levels = original index # Ignore 2nd set of levels if a group by label include an index level result_index_names = [ grouping.name for grouping in self._groupby.grouper._groupings ] grouped_object_index = None column_keys = [ key for key in result_index_names if key not in self.obj.index.names or key is None ] if len(column_keys) == len(result_index_names): grouped_object_index = self.obj.index grouped_index_name = [*grouped_object_index.names] result_index_names += grouped_index_name else: # Our result will have still kept the column in the result result = result.drop(columns=column_keys, 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) self.on = None 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. 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 `__. If ``win_type=None``, all points are evenly weighted; otherwise, ``win_type`` can accept a string of any `scipy.signal window function `__. 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 """ @property def _constructor(self): return Window def validate(self): super().validate() if isinstance(self.window, BaseIndexer): raise NotImplementedError( "BaseIndexer subclasses not implemented with win_types." ) elif is_integer(self.window): if self.window <= 0: raise ValueError("window must be > 0 ") sig = import_optional_dependency( "scipy.signal", extra="Scipy is required to generate window weight." ) if not isinstance(self.win_type, str): raise ValueError(f"Invalid win_type {self.win_type}") if getattr(sig, self.win_type, None) is None: raise ValueError(f"Invalid win_type {self.win_type}") else: raise ValueError(f"Invalid window {self.window}") 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 """ signal = import_optional_dependency( "scipy.signal", extra="Scipy is required to generate window weight." ) assert self.win_type is not None # for mypy window = getattr(signal, self.win_type)(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 = self._get_roll_func("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 = self._get_roll_func("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(self._get_roll_func("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 = self._get_roll_func("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") apply_func = generate_numba_apply_func(args, kwargs, func, engine_kwargs) numba_cache_key = (func, "rolling_apply") 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( self._get_roll_func("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 = self._get_roll_func("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 = self._get_roll_func("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 = self._get_roll_func("roll_min") return self._apply(window_func, name="min", **kwargs) def mean(self, *args, **kwargs): nv.validate_window_func("mean", args, kwargs) window_func = self._get_roll_func("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 = self._get_roll_func("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 = self._get_roll_func("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(self._get_roll_func("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 = self._get_roll_func("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 = self._get_roll_func("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 = self._get_roll_func("roll_max") elif quantile == 0.0: window_func = self._get_roll_func("roll_min") else: window_func = partial( self._get_roll_func("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) if not self.is_freq_type else self.win_freq ) 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) if not self.is_freq_type else self.win_freq ) 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): @cache_readonly def is_datetimelike(self) -> bool: return isinstance( self._on, (ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex) ) @cache_readonly def _on(self) -> Index: if self.on is None: if self.axis == 0: return self.obj.index else: # i.e. self.axis == 1 return self.obj.columns elif isinstance(self.on, Index): return self.on elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns: return 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" ) @property def _constructor(self): return Rolling def validate(self): super().validate() # we allow rolling on a datetimelike index if (self.obj.empty or self.is_datetimelike) 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 self.win_freq = self.window self.window = self._determine_window_length() self.win_type = "freq" # 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): raise ValueError("window must be an integer") elif self.window < 0: raise ValueError("window must be non-negative") def _determine_window_length(self) -> Union[int, float]: """ Calculate freq for PeriodIndexes based on Index freq. Can not use nanos, because asi8 of PeriodIndex is not in nanos """ freq = self._validate_freq() if isinstance(self._on, ABCPeriodIndex): return freq.nanos / (self._on.freq.nanos / self._on.freq.n) return freq.nanos 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") def _validate_freq(self): """ Validate & return window frequency. """ try: return 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 _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 result = super().count() self.min_periods = None else: result = super().count() return result @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. """ 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.is_freq_type: rolling_indexer = VariableWindowIndexer 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()