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])	Calculate the rolling variance.
Rolling.std([ddof])	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.

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])	Calculate the expanding variance.
Expanding.std([ddof])	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.

Exponentially-weighted window functions

ExponentialMovingWindow.mean(*args[, …])	Calculate the ewm (exponential weighted moment) mean.
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

pandas.tseries.frequencies.to_offset pandas.core.window.rolling.Rolling.count