pandas.core.window.rolling.Rolling.mean#

Rolling.mean(numeric_only=False,engine=None,engine_kwargs=None)[source]#

Calculate the rolling mean.

Parameters:

numeric_onlybool, default False

Include only float, int, boolean columns.

Added in version 1.5.0.

enginestr, default None

• 'cython' : Runs the operation through C-extensions from cython.

• 'numba' : Runs the operation through JIT compiled code from numba.

• None : Defaults to'cython' or globally settingcompute.use_numba

  Added in version 1.3.0.

engine_kwargsdict, default None

• For'cython' engine, there are no acceptedengine_kwargs

• For'numba' engine, the engine can acceptnopython,nogilandparallel dictionary keys. The values must either beTrue orFalse. The defaultengine_kwargs for the'numba' engine is{'nopython':True,'nogil':False,'parallel':False}

  Added in version 1.3.0.

Returns:

Series or DataFrame

Return type is the same as the original object withnp.float64 dtype.

See also

pandas.Series.rolling

Calling rolling with Series data.

pandas.DataFrame.rolling

Calling rolling with DataFrames.

pandas.Series.mean

Aggregating mean for Series.

pandas.DataFrame.mean

Aggregating mean for DataFrame.

Notes

SeeNumba engine andNumba (JIT compilation) for extended documentation and performance considerations for the Numba engine.

Examples

The below examples will show rolling mean calculations with window sizes oftwo and three, respectively.

>>>s=pd.Series([1,2,3,4])>>>s.rolling(2).mean()0    NaN1    1.52    2.53    3.5dtype: float64

>>>s.rolling(3).mean()0    NaN1    NaN2    2.03    3.0dtype: float64