xorbits.pandas.window.Rolling.std#
- Rolling.std(ddof=1, *args, **kwargs)[source]#
Calculate the rolling standard deviation.
- Parameters
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is
N - ddof
, whereN
represents the number of elements.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 1.4.0(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 1.4.0(pandas).
- Returns
Return type is the same as the original object with
np.float64
dtype.- Return type
See also
numpy.std
Equivalent method for NumPy array.
pandas.Series.rolling
Calling rolling with Series data.
pandas.DataFrame.rolling
Calling rolling with DataFrames.
pandas.Series.std
Aggregating std for Series.
pandas.DataFrame.std
Aggregating std for DataFrame.
Notes
The default
ddof
of 1 used inSeries.std()
is different than the defaultddof
of 0 innumpy.std()
.A minimum of one period is required for the rolling calculation.
Examples
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> s.rolling(3).std() 0 NaN 1 NaN 2 0.577350 3 1.000000 4 1.000000 5 1.154701 6 0.000000 dtype: float64
This docstring was copied from pandas.core.window.rolling.Rolling.