pandas.core.window.rolling.Rolling.mean¶

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

Calculate the rolling mean.

Parameters

*args

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.

**kwargs

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

Returns

Series or DataFrame: Return type is the same as the original object with np.float64 dtype.

See also

Notes

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

Examples

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

pandas.core.window.rolling.Rolling.sum

pandas.core.window.rolling.Rolling.median