# 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