pandas.core.window.ewm.ExponentialMovingWindow.mean#

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

Calculate the ewm (exponential weighted moment) 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 setting compute.use_numba

    Added 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}

    Added in version 1.3.0.

Returns:
Series or DataFrame

Return type is the same as the original object with np.float64 dtype.

See also

Series.ewm

Calling ewm with Series data.

DataFrame.ewm

Calling ewm with DataFrames.

Series.mean

Aggregating mean for Series.

DataFrame.mean

Aggregating mean for DataFrame.

Notes

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

Examples

>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).mean()
0    1.000000
1    1.555556
2    2.147541
3    2.775068
dtype: float64