dask.dataframe.rolling.Rolling.mean
dask.dataframe.rolling.Rolling.mean¶
- Rolling.mean()[source]¶
Calculate the rolling mean.
This docstring was copied from pandas.core.window.rolling.Rolling.mean.
Some inconsistencies with the Dask version may exist.
- Parameters
- numeric_onlybool, default False (Not supported in Dask)
Include only float, int, boolean columns.
New in version 1.5.0.
- enginestr, default None (Not supported in Dask)
'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.
- engine_kwargsdict, default None (Not supported in Dask)
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.
- Returns
- Series or DataFrame
Return type is the same as the original object with
np.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
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