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• pandas.core.groupby.DataFrameGroupBy.rolling (Python method, in pandas.core.groupby.DataFrameGroupBy.rolling)
• pandas.core.groupby.SeriesGroupBy.rolling (Python method, in pandas.core.groupby.SeriesGroupBy.rolling)
• pandas.DataFrame.rolling (Python method, in pandas.DataFrame.rolling)
• pandas.Series.rolling (Python method, in pandas.Series.rolling)
• pandas.core.groupby.DataFrameGroupBy.rolling

  pandas.core.groupby.DataFrameGroupBy.rolling DataFrameGroupBy.rolling(*args, **kwargs)[source] Return a rolling grouper, providing rolling functionality per group. Parameters: windowint, timedelta, str, offset, or...

• pandas.core.groupby.SeriesGroupBy.rolling

  pandas.core.groupby.SeriesGroupBy.rolling SeriesGroupBy.rolling(*args, **kwargs)[source] Return a rolling grouper, providing rolling functionality per group. Parameters: windowint, timedelta, str, offset, or BaseI...

• pandas.core.window.rolling.Rolling.aggregate

  pandas.core.window.rolling.Rolling.aggregate Rolling.aggregate(func, *args, **kwargs)[source] Aggregate using one or more operations over the specified axis. Parameters: funcfunction, str, list or dictFunction to...

• pandas.core.window.rolling.Rolling.apply

  pandas.core.window.rolling.Rolling.apply Rolling.apply(func, raw=False, engine=None, engine_kwargs=None, args=None, kwargs=None)[source] Calculate the rolling custom aggregation function. Parameters: funcfunctionM...

• pandas.core.window.rolling.Rolling.corr

  pandas.core.window.rolling.Rolling.corr Rolling.corr(other=None, pairwise=None, ddof=1, numeric_only=False)[source] Calculate the rolling correlation. Parameters: otherSeries or DataFrame, optionalIf not supplied...

• pandas.core.window.rolling.Rolling.count

  pandas.core.window.rolling.Rolling.count Rolling.count(numeric_only=False)[source] Calculate the rolling count of non NaN observations. Parameters: numeric_onlybool, default FalseInclude only float, int, boolean c...

• pandas.core.window.rolling.Rolling.cov

  pandas.core.window.rolling.Rolling.cov Rolling.cov(other=None, pairwise=None, ddof=1, numeric_only=False)[source] Calculate the rolling sample covariance. Parameters: otherSeries or DataFrame, optionalIf not suppl...

• pandas.core.window.rolling.Rolling.kurt

  pandas.core.window.rolling.Rolling.kurt Rolling.kurt(numeric_only=False)[source] Calculate the rolling Fisher’s definition of kurtosis without bias. Parameters: numeric_onlybool, default FalseInclude only float, i...

• pandas.core.window.rolling.Rolling.max

  pandas.core.window.rolling.Rolling.max Rolling.max(numeric_only=False, *args, engine=None, engine_kwargs=None, **kwargs)[source] Calculate the rolling maximum. Parameters: numeric_onlybool, default FalseInclude on...

• pandas.core.window.rolling.Rolling.mean
• pandas.core.window.rolling.Rolling.median

  pandas.core.window.rolling.Rolling.median Rolling.median(numeric_only=False, engine=None, engine_kwargs=None)[source] Calculate the rolling median. Parameters: numeric_onlybool, default FalseInclude only float, in...

• pandas.core.window.rolling.Rolling.min

  pandas.core.window.rolling.Rolling.min Rolling.min(numeric_only=False, engine=None, engine_kwargs=None)[source] Calculate the rolling minimum. Parameters: numeric_onlybool, default FalseInclude only float, int, bo...

• pandas.core.window.rolling.Rolling.quantile

  pandas.core.window.rolling.Rolling.quantile Rolling.quantile(q, interpolation='linear', numeric_only=False)[source] Calculate the rolling quantile. Parameters: quantilefloatQuantile to compute. 0 <= quantile <= 1....

• pandas.core.window.rolling.Rolling.rank

  pandas.core.window.rolling.Rolling.rank Rolling.rank(method='average', ascending=True, pct=False, numeric_only=False)[source] Calculate the rolling rank. Added in version 1.4.0. Parameters: method{‘average’, ‘mi...

• pandas.core.window.rolling.Rolling.sem
• pandas.core.window.rolling.Rolling.skew
• pandas.core.window.rolling.Rolling.std
• pandas.core.window.rolling.Rolling.sum
• pandas.core.window.rolling.Rolling.var
• pandas.core.window.rolling.Window.mean
• pandas.core.window.rolling.Window.std
• pandas.core.window.rolling.Window.sum
• pandas.core.window.rolling.Window.var
• pandas.DataFrame.rolling
• pandas.Series.rolling
• Version 0.15.0 (October 18, 2014)
• Version 0.19.0 (October 2, 2016)
• Version 0.20.1 (May 5, 2017)
• Version 0.20.2 (June 4, 2017)
• Version 0.21.0 (October 27, 2017)
• Version 0.21.1 (December 12, 2017)
• Version 0.22.0 (December 29, 2017)
• What’s new in 0.23.0 (May 15, 2018)
• What’s new in 0.24.0 (January 25, 2019)
• What’s new in 0.25.0 (July 18, 2019)
• What’s new in 0.25.1 (August 21, 2019)
• What’s new in 0.25.2 (October 15, 2019)
• What’s new in 0.25.3 (October 31, 2019)
• What’s new in 1.0.0 (January 29, 2020)
• What’s new in 1.1.0 (July 28, 2020)
• What’s new in 1.2.0 (December 26, 2020)
• What’s new in 1.3.0 (July 2, 2021)
• What’s new in 1.4.0 (January 22, 2022)
• What’s new in 1.5.0 (September 19, 2022)
• What’s new in 2.0.0 (April 3, 2023)
• What’s new in 2.1.0 (Aug 30, 2023)
• What’s new in 2.2.0 (January 19, 2024)
• Window
• Windowing operations
• Windowing operations > Rolling apply
• Windowing operations > Rolling window
• API reference
• Chart visualization
• Cookbook
• Enhancing performance
• Essential basic functionality
• Group by: split-apply-combine
• pandas.api.indexers.BaseIndexer
• pandas.api.indexers.FixedForwardWindowIndexer
• pandas.api.indexers.VariableOffsetWindowIndexer
• pandas.core.groupby.DataFrameGroupBy.cov
• pandas.core.groupby.DataFrameGroupBy.max
• pandas.core.groupby.DataFrameGroupBy.min
• pandas.core.groupby.DataFrameGroupBy.sum
• pandas.core.groupby.SeriesGroupBy.max
• pandas.core.groupby.SeriesGroupBy.min
• pandas.core.groupby.SeriesGroupBy.sum
• pandas.core.window.expanding.Expanding.apply
• pandas.core.window.expanding.Expanding.kurt
• pandas.core.window.expanding.Expanding.skew
• pandas.core.window.expanding.Expanding.std
• pandas.core.window.expanding.Expanding.var
• pandas.core.window.rolling.Rolling.aggregate (Python method, in pandas.core.window.rolling.Rolling.aggregate)
• pandas.core.window.rolling.Rolling.apply (Python method, in pandas.core.window.rolling.Rolling.apply)
• pandas.core.window.rolling.Rolling.corr (Python method, in pandas.core.window.rolling.Rolling.corr)
• pandas.core.window.rolling.Rolling.count (Python method, in pandas.core.window.rolling.Rolling.count)
• pandas.core.window.rolling.Rolling.cov (Python method, in pandas.core.window.rolling.Rolling.cov)
• pandas.core.window.rolling.Rolling.kurt (Python method, in pandas.core.window.rolling.Rolling.kurt)
• pandas.core.window.rolling.Rolling.max (Python method, in pandas.core.window.rolling.Rolling.max)
• pandas.core.window.rolling.Rolling.mean (Python method, in pandas.core.window.rolling.Rolling.mean)
• pandas.core.window.rolling.Rolling.median (Python method, in pandas.core.window.rolling.Rolling.median)
• pandas.core.window.rolling.Rolling.min (Python method, in pandas.core.window.rolling.Rolling.min)
• pandas.core.window.rolling.Rolling.quantile (Python method, in pandas.core.window.rolling.Rolling.quantile)
• pandas.core.window.rolling.Rolling.rank (Python method, in pandas.core.window.rolling.Rolling.rank)
• pandas.core.window.rolling.Rolling.sem (Python method, in pandas.core.window.rolling.Rolling.sem)
• pandas.core.window.rolling.Rolling.skew (Python method, in pandas.core.window.rolling.Rolling.skew)
• pandas.core.window.rolling.Rolling.std (Python method, in pandas.core.window.rolling.Rolling.std)
• pandas.core.window.rolling.Rolling.sum (Python method, in pandas.core.window.rolling.Rolling.sum)
• pandas.core.window.rolling.Rolling.var (Python method, in pandas.core.window.rolling.Rolling.var)
• pandas.core.window.rolling.Window.mean (Python method, in pandas.core.window.rolling.Window.mean)
• pandas.core.window.rolling.Window.std (Python method, in pandas.core.window.rolling.Window.std)
• pandas.core.window.rolling.Window.sum (Python method, in pandas.core.window.rolling.Window.sum)
• pandas.core.window.rolling.Window.var (Python method, in pandas.core.window.rolling.Window.var)
• pandas.DataFrame.agg
• pandas.DataFrame.aggregate
• pandas.DataFrame.cov
• pandas.DataFrame.ewm
• pandas.DataFrame.expanding
• pandas.DataFrame.plot.hist
• pandas.DataFrame.quantile
• pandas.DataFrame.to_sql
• pandas.errors.DataError
• pandas.Series.ewm
• pandas.Series.expanding
• pandas.Series.plot.hist
• pandas.Series.quantile
• pandas.Series.to_sql
• pandas.tseries.offsets.DateOffset
• Time series / date functionality
• User Guide
• Version 0.13.0 (January 3, 2014)
• Version 0.13.1 (February 3, 2014)
• Version 0.14.0 (May 31 , 2014)
• Version 0.17.1 (November 21, 2015)
• Version 0.18.0 (March 13, 2016)
• Version 0.18.1 (May 3, 2016)
• Version 0.20.3 (July 7, 2017)
• Version 0.8.1 (July 22, 2012)
• What’s new in 0.23.1 (June 12, 2018)
• What’s new in 0.23.4 (August 3, 2018)
• What’s new in 1.0.2 (March 12, 2020)
• What’s new in 1.0.4 (May 28, 2020)
• What’s new in 1.1.1 (August 20, 2020)
• What’s new in 1.1.2 (September 8, 2020)
• What’s new in 1.1.5 (December 07, 2020)
• What’s new in 1.2.1 (January 20, 2021)
• What’s new in 1.2.2 (February 09, 2021)
• What’s new in 1.3.2 (August 15, 2021)
• What’s new in 1.3.4 (October 17, 2021)
• What’s new in 1.4.1 (February 12, 2022)
• What’s new in 1.4.2 (April 2, 2022)
• What’s new in 1.4.3 (June 23, 2022)
• What’s new in 2.1.2 (October 26, 2023)