xorbits.pandas.window.ExponentialMovingWindow.mean#
- ExponentialMovingWindow.mean()#
Calculate the ewm (exponential weighted moment) mean.
- Parameters
numeric_only (bool, default False (Not supported yet)) –
Include only float, int, boolean columns.
New in version 1.5.0(pandas).
engine (str, default None (Not supported yet)) –
'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
New in version 1.3.0(pandas).
engine_kwargs (dict, default None (Not supported yet)) –
For
'cython'
engine, there are no acceptedengine_kwargs
For
'numba'
engine, the engine can acceptnopython
,nogil
andparallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_kwargs
for the'numba'
engine is{'nopython': True, 'nogil': False, 'parallel': False}
New in version 1.3.0(pandas).
- Returns
Return type is the same as the original object with
np.float64
dtype.- Return type
See also
pandas.Series.ewm
Calling ewm with Series data.
pandas.DataFrame.ewm
Calling ewm with DataFrames.
pandas.Series.mean
Aggregating mean for Series.
pandas.DataFrame.mean
Aggregating mean for DataFrame.
Notes
See window.numba_engine and enhancingperf.numba 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
This docstring was copied from pandas.core.window.ewm.ExponentialMovingWindow.