# Window#

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

## Rolling window functions#

 `Rolling.count`(*args, **kwargs) Calculate the rolling count of non NaN observations. `Rolling.sum`(*args, **kwargs) Calculate the rolling sum. `Rolling.mean`(*args, **kwargs) Calculate the rolling mean. `Rolling.median`(*args, **kwargs) Calculate the rolling median. `Rolling.var`([ddof]) Calculate the rolling variance. `Rolling.std`([ddof]) Calculate the rolling standard deviation. `Rolling.min`(*args, **kwargs) Calculate the rolling minimum. `Rolling.max`(*args, **kwargs) Calculate the rolling maximum. `Rolling.corr`(**kwargs) Calculate the rolling correlation. `Rolling.cov`(**kwargs) Calculate the rolling sample covariance. `Rolling.skew`(*args, **kwargs) Calculate the rolling unbiased skewness. `Rolling.kurt`(*args, **kwargs) Calculate the rolling Fisher's definition of kurtosis without bias. `Rolling.aggregate`(func, *args, **kwargs) Aggregate using one or more operations over the specified axis.

## Expanding window functions#

 Calculate the expanding count of non NaN observations. Calculate the expanding sum. Calculate the expanding mean. Calculate the expanding variance. Calculate the expanding standard deviation. Calculate the expanding minimum. Calculate the expanding maximum. `Expanding.aggregate`(func, **kwargs) Aggregate using one or more operations over the specified axis.

## Exponentially-weighted window functions#

 Calculate the ewm (exponential weighted moment) mean. Calculate the ewm (exponential weighted moment) standard deviation. Calculate the ewm (exponential weighted moment) variance.

## Window indexer#

Base class for defining custom window boundaries.