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.

Standard moving window functions

Rolling.count()

The rolling count of any non-NaN observations inside the window.

Rolling.sum(*args, **kwargs)

Calculate rolling sum of given DataFrame or Series.

Rolling.mean(*args, **kwargs)

Calculate the rolling mean of the values.

Rolling.median(**kwargs)

Calculate the rolling median.

Rolling.var([ddof])

Calculate unbiased rolling variance.

Rolling.std([ddof])

Calculate rolling standard deviation.

Rolling.min(*args, **kwargs)

Calculate the rolling minimum.

Rolling.max(*args, **kwargs)

Calculate the rolling maximum.

Rolling.corr([other, pairwise])

Calculate rolling correlation.

Rolling.cov([other, pairwise, ddof])

Calculate the rolling sample covariance.

Rolling.skew(**kwargs)

Unbiased rolling skewness.

Rolling.kurt(**kwargs)

Calculate unbiased rolling kurtosis.

Rolling.apply(func[, raw, engine, …])

Apply an arbitrary function to each rolling window.

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])

Compute rolling standard error of mean.

Window.mean(*args, **kwargs)

Calculate the window mean of the values.

Window.sum(*args, **kwargs)

Calculate window sum of given DataFrame or Series.

Window.var([ddof])

Calculate unbiased window variance.

Window.std([ddof])

Calculate window standard deviation.

Standard expanding window functions

Expanding.count()

The expanding count of any non-NaN observations inside the window.

Expanding.sum(*args, **kwargs)

Calculate expanding sum of given DataFrame or Series.

Expanding.mean(*args, **kwargs)

Calculate the expanding mean of the values.

Expanding.median(**kwargs)

Calculate the expanding median.

Expanding.var([ddof])

Calculate unbiased expanding variance.

Expanding.std([ddof])

Calculate expanding standard deviation.

Expanding.min(*args, **kwargs)

Calculate the expanding minimum.

Expanding.max(*args, **kwargs)

Calculate the expanding maximum.

Expanding.corr([other, pairwise])

Calculate expanding correlation.

Expanding.cov([other, pairwise, ddof])

Calculate the expanding sample covariance.

Expanding.skew(**kwargs)

Unbiased expanding skewness.

Expanding.kurt(**kwargs)

Calculate unbiased expanding kurtosis.

Expanding.apply(func[, raw, engine, …])

Apply an arbitrary function to each expanding window.

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])

Compute expanding standard error of mean.

Exponentially-weighted moving window functions

ExponentialMovingWindow.mean(*args, **kwargs)

Exponential weighted moving average.

ExponentialMovingWindow.std([bias])

Exponential weighted moving stddev.

ExponentialMovingWindow.var([bias])

Exponential weighted moving variance.

ExponentialMovingWindow.corr([other, pairwise])

Exponential weighted sample correlation.

ExponentialMovingWindow.cov([other, …])

Exponential weighted 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