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.
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.