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.

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])

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.

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