Rolling.mean(*args, engine=None, engine_kwargs=None, **kwargs)[source]

Calculate the rolling mean.


For NumPy compatibility and will not have an effect on the result.

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 setting compute.use_numba

    New in version 1.3.0.

engine_kwargsdict, default None
  • For 'cython' engine, there are no accepted engine_kwargs

  • For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False}

    New in version 1.3.0.


For NumPy compatibility and will not have an effect on the result.

Series or DataFrame

Return type is the same as the original object.

See also


Calling rolling with Series data.


Calling rolling with DataFrames.


Aggregating mean for Series.


Aggregating mean for DataFrame.


See Numba engine for extended documentation and performance considerations for the Numba engine.


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

>>> s = pd.Series([1, 2, 3, 4])
>>> s.rolling(2).mean()
0    NaN
1    1.5
2    2.5
3    3.5
dtype: float64
>>> s.rolling(3).mean()
0    NaN
1    NaN
2    2.0
3    3.0
dtype: float64