Window#

pandas.api.typing.Rolling instances are returned by .rolling calls: pandas.DataFrame.rolling() and pandas.Series.rolling(). pandas.api.typing.Expanding instances are returned by .expanding calls: pandas.DataFrame.expanding() and pandas.Series.expanding(). pandas.api.typing.ExponentialMovingWindow instances are returned by .ewm calls: pandas.DataFrame.ewm() and pandas.Series.ewm().

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(q[, 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(q[, interpolation, ...])

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.