Window — pandas 2.2.3 documentation (original) (raw)
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 .ewmcalls: 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.