# 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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