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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 � min_periods�center�axis�method� Nr �singler �intr �str)�objr r r c s t � j||||||d� d S )N)r# r r r r � selection)�super�__init__)�selfr# r r r r r$ )� __class__� �R/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/window/expanding.pyr& a s zExpanding.__init__r )�returnc C s t � S )z[ Return an indexer class that will compute the window start and end bounds )r )r' r) r) r* �_get_window_indexers s z Expanding._get_window_indexer� aggregatez� See Also -------- pandas.DataFrame.aggregate : Similar DataFrame method. pandas.Series.aggregate : Similar Series method. a� 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 zSeries/Dataframe� )Zsee_alsoZexamples�klassr c s t � j|f|�|�S )N)r% r- )r' �func�args�kwargs)r( r) r* r- y s zExpanding.aggregateZReturnszSee Also���Z expandingz count of non NaN observations�count)Z window_methodZaggregation_descriptionZ agg_methodc s t � �� S )N)r% r4 )r' )r( r) r* r4 � s zExpanding.countZ Parameterszcustom aggregation function�applyFzCallable[..., Any]�boolz str | Nonezdict[str, bool] | Noneztuple[Any, ...] | Nonezdict[str, Any] | None)r0 �raw�engine� engine_kwargsr1 r2 c s t � j||||||d�S )N)r7 r8 r9 r1 r2 )r% r5 )r' r0 r7 r8 r9 r1 r2 )r( r) r* r5 � s zExpanding.applyZNotes�sum)r8 r9 c s& t �d||� t� j|||d�|��S )Nr: )r8 r9 )�nv�validate_expanding_funcr% r: )r' r8 r9 r1 r2 )r( r) r* r: � s z Expanding.sum�maximum�maxc s& t �d||� t� j|||d�|��S )Nr> )r8 r9 )r; r< r% r> )r' r8 r9 r1 r2 )r( r) r* r> � s z Expanding.max�minimum�minc s& t �d||� t� j|||d�|��S )Nr@ )r8 r9 )r; r< r% r@ )r' r8 r9 r1 r2 )r( r) r* r@ � s z Expanding.min�meanc s& t �d||� t� j|||d�|��S )NrA )r8 r9 )r; r< r% rA )r' r8 r9 r1 r2 )r( r) r* rA s zExpanding.mean�medianc s t � jf ||d�|��S )N)r8 r9 )r% rB )r' r8 r9 r2 )r( r) r* rB 0 s zExpanding.medianz� ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. � z/numpy.std : Equivalent method for NumPy array. z� 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. ZExamplesa >>> 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 zstandard deviation�std)�ddofc s$ t �d||� t� jf d|i|��S )NrD rE )r; r< r% rD )r' rE r1 r2 )r( r) r* rD G s /z Expanding.stdz/numpy.var : Equivalent method for NumPy array. z� 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. a >>> 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 Zvariance�varc s$ t �d||� t� jf d|i|��S )NrF rE )r; r< r% rF )r' rE r1 r2 )r( r) r* rF y s /z Expanding.varz:A minimum of one period is required for the calculation. z� >>> s = pd.Series([0, 1, 2, 3]) >>> s.expanding().sem() 0 NaN 1 0.707107 2 0.707107 3 0.745356 dtype: float64 zstandard error of mean�semc s t � jf d|i|��S )NrE )r% rG )r' rE r1 r2 )r( r) r* rG � s $z Expanding.semz:scipy.stats.skew : Third moment of a probability density. zDA minimum of three periods is required for the rolling calculation. zunbiased skewness�skewc s t � jf |�S )N)r% rH )r' r2 )r( r) r* rH � s zExpanding.skewz/scipy.stats.kurtosis : Reference SciPy method. z<A minimum of four periods is required for the calculation. a[ 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 z,Fisher's definition of kurtosis without bias�kurtc s t � jf |�S )N)r% rI )r' r2 )r( r) r* rI � s &zExpanding.kurta� 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. �quantile�linear�float)rJ � interpolationc s t � jf ||d�|��S )N)rJ rM )r% rJ )r' rJ rM r2 )r( r) r* rJ s "zExpanding.quantilea 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. zsample covariance�covzFrameOrSeriesUnion | Nonez bool | None)�other�pairwiserE c s t � jf |||d�|��S )N)rO rP rE )r% rN )r' rO rP rE r2 )r( r) r* rN 3 s $z Expanding.covaN other : Series or DataFrame, optional If not supplied then will default to self and produce pairwise output. pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a MultiIndexed DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used. z� cov : Similar method to calculate covariance. numpy.corrcoef : NumPy Pearson's correlation calculation. an This function uses Pearson's definition of correlation (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient). When `other` is not specified, the output will be self correlation (e.g. all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise` set to `True`. Function will return ``NaN`` for correlations of equal valued sequences; this is the result of a 0/0 division error. When `pairwise` is set to `False`, only matching columns between `self` and `other` will be used. When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame with the original index on the first level, and the `other` DataFrame columns on the second level. In the case of missing elements, only complete pairwise observations will be used. Z correlation�corrc s t � jf |||d�|��S )N)rO rP rE )r% rQ )r' rO rP rE r2 )r( r) r* rQ Y s ?zExpanding.corr)r Nr r N)FNNNN)NN)r )r )r )rK )NNr )NNr )&�__name__� __module__� __qualname__�__doc__� _attributesr& r, r r r r- Zaggr r r r r4 r r5 r r r r r: r> r@ rA rB �replacerD rF rG rH rI rJ rN rQ � __classcell__r) r) )r( r* r ( s< 5 r c @ s* e Zd ZdZejej Zdd�dd�ZdS )�ExpandingGroupbyz5 Provide a expanding groupby implementation. r )r+ c C s t | jjtd�}|S )z� Return an indexer class that will compute the window start and end bounds Returns ------- GroupbyIndexer )Zgroupby_indices�window_indexer)r �_grouper�indicesr )r' rZ r) r) r* r, � s z$ExpandingGroupby._get_window_indexerN)rR rS rT rU r rV r r, r) r) r) r* rY � s rY N)$� __future__r �textwrapr �typingr r Zpandas._typingr r r Zpandas.compat.numpyr r; Zpandas.util._decoratorsr Zpandas.core.window.docr r r r r r r r r r Zpandas.core.window.indexersr r r Zpandas.core.window.rollingr r r rY r) r) r) r* �<module> s 0 w
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