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# Window¶

Rolling objects are returned by .rolling calls: pandas.DataFrame.rolling(), pandas.Series.rolling(), etc. Expanding objects are returned by .expanding calls: pandas.DataFrame.expanding(), pandas.Series.expanding(), etc. EWM objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Series.ewm(), etc.

## Standard moving window functions¶

 Rolling.count() The rolling count of any non-NaN observations inside the window. Rolling.sum(*args, **kwargs) Calculate rolling sum of given DataFrame or Series. Rolling.mean(*args, **kwargs) Calculate the rolling mean of the values. Rolling.median(**kwargs) Calculate the rolling median. Rolling.var([ddof]) Calculate unbiased rolling variance. Rolling.std([ddof]) Calculate rolling standard deviation. Rolling.min(*args, **kwargs) Calculate the rolling minimum. Rolling.max(*args, **kwargs) Calculate the rolling maximum. Rolling.corr([other, pairwise]) Calculate rolling correlation. Rolling.cov([other, pairwise, ddof]) Calculate the rolling sample covariance. Rolling.skew(**kwargs) Unbiased rolling skewness. Rolling.kurt(**kwargs) Calculate unbiased rolling kurtosis. Rolling.apply(func[, raw, args, kwargs]) The rolling function’s apply function. Rolling.aggregate(arg, *args, **kwargs) Aggregate using one or more operations over the specified axis. Rolling.quantile(quantile[, interpolation]) Calculate the rolling quantile. Window.mean(*args, **kwargs) Calculate the window mean of the values. Window.sum(*args, **kwargs) Calculate window sum of given DataFrame or Series.

## Standard expanding window functions¶

 Expanding.count(**kwargs) The expanding count of any non-NaN observations inside the window. Expanding.sum(*args, **kwargs) Calculate expanding sum of given DataFrame or Series. Expanding.mean(*args, **kwargs) Calculate the expanding mean of the values. Expanding.median(**kwargs) Calculate the expanding median. Expanding.var([ddof]) Calculate unbiased expanding variance. Expanding.std([ddof]) Calculate expanding standard deviation. Expanding.min(*args, **kwargs) Calculate the expanding minimum. Expanding.max(*args, **kwargs) Calculate the expanding maximum. Expanding.corr([other, pairwise]) Calculate expanding correlation. Expanding.cov([other, pairwise, ddof]) Calculate the expanding sample covariance. Expanding.skew(**kwargs) Unbiased expanding skewness. Expanding.kurt(**kwargs) Calculate unbiased expanding kurtosis. Expanding.apply(func[, raw, args, kwargs]) The expanding function’s apply function. Expanding.aggregate(arg, *args, **kwargs) Aggregate using one or more operations over the specified axis. Expanding.quantile(quantile[, interpolation]) Calculate the expanding quantile.

## Exponentially-weighted moving window functions¶

 EWM.mean(*args, **kwargs) Exponential weighted moving average. EWM.std([bias]) Exponential weighted moving stddev. EWM.var([bias]) Exponential weighted moving variance. EWM.corr([other, pairwise]) Exponential weighted sample correlation. EWM.cov([other, pairwise, bias]) Exponential weighted sample covariance.
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