# 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#

 Rolling.count([numeric_only]) Calculate the rolling count of non NaN observations. Rolling.sum([numeric_only, engine, ...]) Calculate the rolling sum. Rolling.mean([numeric_only, engine, ...]) Calculate the rolling mean. Rolling.median([numeric_only, engine, ...]) Calculate the rolling median. Rolling.var([ddof, numeric_only, engine, ...]) Calculate the rolling variance. Rolling.std([ddof, numeric_only, engine, ...]) Calculate the rolling standard deviation. Rolling.min([numeric_only, engine, ...]) Calculate the rolling minimum. Rolling.max([numeric_only, engine, ...]) 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([numeric_only]) Calculate the rolling unbiased skewness. Rolling.kurt([numeric_only]) 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, numeric_only]) Calculate the rolling standard error of mean. Rolling.rank([method, ascending, pct, ...]) Calculate the rolling rank.

## Weighted window functions#

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

## Expanding window functions#

 Expanding.count([numeric_only]) Calculate the expanding count of non NaN observations. Expanding.sum([numeric_only, engine, ...]) Calculate the expanding sum. Expanding.mean([numeric_only, engine, ...]) Calculate the expanding mean. Expanding.median([numeric_only, engine, ...]) Calculate the expanding median. Expanding.var([ddof, numeric_only, engine, ...]) Calculate the expanding variance. Expanding.std([ddof, numeric_only, engine, ...]) Calculate the expanding standard deviation. Expanding.min([numeric_only, engine, ...]) Calculate the expanding minimum. Expanding.max([numeric_only, engine, ...]) 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([numeric_only]) Calculate the expanding unbiased skewness. Expanding.kurt([numeric_only]) 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[, ...]) Calculate the expanding quantile. Expanding.sem([ddof, numeric_only]) Calculate the expanding standard error of mean. Expanding.rank([method, ascending, pct, ...]) Calculate the expanding rank.

## Exponentially-weighted window functions#

 ExponentialMovingWindow.mean([numeric_only, ...]) Calculate the ewm (exponential weighted moment) mean. ExponentialMovingWindow.sum([numeric_only, ...]) Calculate the ewm (exponential weighted moment) sum. ExponentialMovingWindow.std([bias, numeric_only]) Calculate the ewm (exponential weighted moment) standard deviation. ExponentialMovingWindow.var([bias, numeric_only]) Calculate the ewm (exponential weighted moment) variance. ExponentialMovingWindow.corr([other, ...]) 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.