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

Standard expanding window functions

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

Exponentially-weighted moving window functions

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