from __future__ import annotations from textwrap import dedent from typing import ( Any, Callable, ) from pandas._typing import ( Axis, FrameOrSeries, FrameOrSeriesUnion, ) from pandas.compat.numpy import function as nv from pandas.util._decorators import doc from pandas.core.window.doc import ( _shared_docs, args_compat, create_section_header, kwargs_compat, numba_notes, template_header, template_returns, template_see_also, window_agg_numba_parameters, window_apply_parameters, ) from pandas.core.window.indexers import ( BaseIndexer, ExpandingIndexer, GroupbyIndexer, ) from pandas.core.window.rolling import ( BaseWindowGroupby, RollingAndExpandingMixin, ) class Expanding(RollingAndExpandingMixin): """ Provide expanding transformations. Parameters ---------- min_periods : int, default 1 Minimum number of observations in window required to have a value (otherwise result is NA). center : bool, default False Set the labels at the center of the window. axis : int or str, default 0 method : str {'single', 'table'}, default 'single' Execute the rolling operation per single column or row (``'single'``) or over the entire object (``'table'``). This argument is only implemented when specifying ``engine='numba'`` in the method call. .. versionadded:: 1.3.0 Returns ------- a Window sub-classed for the particular operation See Also -------- rolling : Provides rolling window calculations. 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``. 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 >>> df.expanding(2).sum() B 0 NaN 1 1.0 2 3.0 3 3.0 4 7.0 """ _attributes = ["min_periods", "center", "axis", "method"] def __init__( self, obj: FrameOrSeries, min_periods: int = 1, center=None, axis: Axis = 0, method: str = "single", selection=None, ): super().__init__( obj=obj, min_periods=min_periods, center=center, axis=axis, method=method, selection=selection, ) def _get_window_indexer(self) -> BaseIndexer: """ Return an indexer class that will compute the window start and end bounds """ return ExpandingIndexer() @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.ewm(alpha=0.5).mean() A B C 0 1.000000 4.000000 7.000000 1 1.666667 4.666667 7.666667 2 2.428571 5.428571 8.428571 """ ), 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[:-1], window_method="expanding", aggregation_description="count of non NaN observations", agg_method="count", ) def count(self): return super().count() @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="expanding", 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[:-1], window_method="expanding", aggregation_description="sum", agg_method="sum", ) def sum( self, *args, engine: str | None = None, engine_kwargs: dict[str, bool] | None = None, **kwargs, ): nv.validate_expanding_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="expanding", aggregation_description="maximum", agg_method="max", ) def max( self, *args, engine: str | None = None, engine_kwargs: dict[str, bool] | None = None, **kwargs, ): nv.validate_expanding_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[:-1], window_method="expanding", aggregation_description="minimum", agg_method="min", ) def min( self, *args, engine: str | None = None, engine_kwargs: dict[str, bool] | None = None, **kwargs, ): nv.validate_expanding_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[:-1], window_method="expanding", aggregation_description="mean", agg_method="mean", ) def mean( self, *args, engine: str | None = None, engine_kwargs: dict[str, bool] | None = None, **kwargs, ): nv.validate_expanding_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[:-1], window_method="expanding", 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.\n """ ).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.\n """ ).replace("\n", "", 1), create_section_header("Examples"), dedent( """ >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> 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 """ ).replace("\n", "", 1), window_method="expanding", aggregation_description="standard deviation", agg_method="std", ) def std(self, ddof: int = 1, *args, **kwargs): nv.validate_expanding_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.\n """ ).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.\n """ ).replace("\n", "", 1), create_section_header("Examples"), dedent( """ >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> 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 """ ).replace("\n", "", 1), window_method="expanding", aggregation_description="variance", agg_method="var", ) def var(self, ddof: int = 1, *args, **kwargs): nv.validate_expanding_func("var", args, kwargs) return super().var(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.\n """ ).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.expanding().sem() 0 NaN 1 0.707107 2 0.707107 3 0.745356 dtype: float64 """ ).replace("\n", "", 1), window_method="expanding", aggregation_description="standard error of mean", agg_method="sem", ) def sem(self, ddof: int = 1, *args, **kwargs): return super().sem(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="expanding", aggregation_description="unbiased skewness", agg_method="skew", ) def skew(self, **kwargs): return super().skew(**kwargs) @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, bias=False):.6f}}") 4.999874 >>> s = pd.Series(arr) >>> s.expanding(4).kurt() 0 NaN 1 NaN 2 NaN 3 -1.200000 4 4.999874 dtype: float64 """ ).replace("\n", "", 1), window_method="expanding", 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[:-1], window_method="expanding", 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, 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="expanding", aggregation_description="sample covariance", agg_method="cov", ) def cov( self, other: FrameOrSeriesUnion | 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. """ ).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. """ ).replace("\n", "", 1), window_method="expanding", aggregation_description="correlation", agg_method="corr", ) def corr( self, other: FrameOrSeriesUnion | None = None, pairwise: bool | None = None, ddof: int = 1, **kwargs, ): return super().corr(other=other, pairwise=pairwise, ddof=ddof, **kwargs) class ExpandingGroupby(BaseWindowGroupby, Expanding): """ Provide a expanding groupby implementation. """ _attributes = Expanding._attributes + BaseWindowGroupby._attributes def _get_window_indexer(self) -> GroupbyIndexer: """ Return an indexer class that will compute the window start and end bounds Returns ------- GroupbyIndexer """ window_indexer = GroupbyIndexer( groupby_indices=self._grouper.indices, window_indexer=ExpandingIndexer, ) return window_indexer