# 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¶

 Calculate the rolling count of non NaN observations. `Rolling.sum`(*args[, engine, engine_kwargs]) Calculate the rolling sum. `Rolling.mean`(*args[, engine, engine_kwargs]) Calculate the rolling mean. `Rolling.median`([engine, engine_kwargs]) Calculate the rolling median. `Rolling.var`([ddof, engine, engine_kwargs]) Calculate the rolling variance. `Rolling.std`([ddof, engine, engine_kwargs]) Calculate the rolling standard deviation. `Rolling.min`(*args[, engine, engine_kwargs]) Calculate the rolling minimum. `Rolling.max`(*args[, engine, engine_kwargs]) Calculate the rolling maximum. `Rolling.corr`([other, pairwise, ddof]) Calculate the rolling correlation. `Rolling.cov`([other, pairwise, ddof]) Calculate the rolling sample covariance. `Rolling.skew`(**kwargs) Calculate the rolling unbiased skewness. `Rolling.kurt`(**kwargs) Calculate the rolling Fisher's definition of kurtosis without bias. `Rolling.apply`(func[, raw, engine, ...]) Calculate the rolling custom aggregation function. `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]) Calculate the rolling standard error of mean. `Rolling.rank`([method, ascending, pct]) Calculate the rolling rank.

## Weighted window functions¶

 `Window.mean`(*args, **kwargs) Calculate the rolling weighted window mean. `Window.sum`(*args, **kwargs) Calculate the rolling weighted window sum. `Window.var`([ddof]) Calculate the rolling weighted window variance. `Window.std`([ddof]) Calculate the rolling weighted window standard deviation.

## Expanding window functions¶

 Calculate the expanding count of non NaN observations. `Expanding.sum`(*args[, engine, engine_kwargs]) Calculate the expanding sum. `Expanding.mean`(*args[, engine, engine_kwargs]) Calculate the expanding mean. `Expanding.median`([engine, engine_kwargs]) Calculate the expanding median. `Expanding.var`([ddof, engine, engine_kwargs]) Calculate the expanding variance. `Expanding.std`([ddof, engine, engine_kwargs]) Calculate the expanding standard deviation. `Expanding.min`(*args[, engine, engine_kwargs]) Calculate the expanding minimum. `Expanding.max`(*args[, engine, engine_kwargs]) Calculate the expanding maximum. `Expanding.corr`([other, pairwise, ddof]) Calculate the expanding correlation. `Expanding.cov`([other, pairwise, ddof]) Calculate the expanding sample covariance. `Expanding.skew`(**kwargs) Calculate the expanding unbiased skewness. `Expanding.kurt`(**kwargs) Calculate the expanding Fisher's definition of kurtosis without bias. `Expanding.apply`(func[, raw, engine, ...]) Calculate the expanding custom aggregation function. `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]) Calculate the expanding standard error of mean. `Expanding.rank`([method, ascending, pct]) Calculate the expanding rank.

## Exponentially-weighted window functions¶

 `ExponentialMovingWindow.mean`(*args[, ...]) Calculate the ewm (exponential weighted moment) mean. `ExponentialMovingWindow.sum`(*args[, engine, ...]) Calculate the ewm (exponential weighted moment) sum. Calculate the ewm (exponential weighted moment) standard deviation. Calculate the ewm (exponential weighted moment) variance. `ExponentialMovingWindow.corr`([other, pairwise]) Calculate the ewm (exponential weighted moment) sample correlation. `ExponentialMovingWindow.cov`([other, ...]) Calculate the ewm (exponential weighted moment) 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.