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() 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       
Expanding.count() 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.  
ExponentialMovingWindow.std([bias]) Calculate the ewm (exponential weighted moment) standard deviation.  
ExponentialMovingWindow.var([bias]) 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.  
api.indexers.FixedForwardWindowIndexer([...]) Creates window boundaries for fixed-length windows that include the current row.  
api.indexers.VariableOffsetWindowIndexer([...]) Calculate window boundaries based on a non-fixed offset such as a BusinessDay.