# 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(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, engine, …]) The rolling function’s apply function. Rolling.aggregate(self, func, \*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. Window.var(self[, ddof]) Calculate unbiased window variance. Window.std(self[, ddof]) Calculate window standard deviation.

## 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, func, \*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.

## Window Indexer¶

Base class for defining custom window boundaries.

 api.indexers.BaseIndexer([index_array, …]) Base class for window bounds calculations