# 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. ExponentialMovingWindow objects are returned by `.ewm` calls: `pandas.DataFrame.ewm()`, `pandas.Series.ewm()`, etc.

## Rolling window functions¶

 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, engine, …]) Apply an arbitrary function to each rolling window. `Rolling.aggregate`(func, *args, **kwargs) Aggregate using one or more operations over the specified axis. `Rolling.quantile`(quantile[, interpolation]) Calculate the rolling quantile. `Rolling.sem`([ddof]) Compute rolling standard error of mean.

## Weighted window functions¶

 `Window.mean`(*args, **kwargs) Calculate the window mean of the values. `Window.sum`(*args, **kwargs) Calculate window sum of given DataFrame or Series. `Window.var`([ddof]) Calculate unbiased window variance. `Window.std`([ddof]) Calculate window standard deviation.

## Expanding window functions¶

 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, engine, …]) Apply an arbitrary function to each expanding window. `Expanding.aggregate`(func, *args, **kwargs) Aggregate using one or more operations over the specified axis. `Expanding.quantile`(quantile[, interpolation]) Calculate the expanding quantile. `Expanding.sem`([ddof]) Compute expanding standard error of mean.

## Exponentially-weighted window functions¶

 `ExponentialMovingWindow.mean`(*args, **kwargs) Exponential weighted moving average. Exponential weighted moving stddev. Exponential weighted moving variance. `ExponentialMovingWindow.corr`([other, pairwise]) Exponential weighted sample correlation. `ExponentialMovingWindow.cov`([other, …]) Exponential weighted sample covariance.

## Window indexer¶

Base class for defining custom window boundaries.

 `api.indexers.BaseIndexer`([index_array, …]) Base class for window bounds calculations. Creates window boundaries for fixed-length windows that include the current row. Calculate window boundaries based on a non-fixed offset such as a BusinessDay