pandas.core.window.rolling.Rolling.std

Rolling.std(ddof=1, *args, engine=None, engine_kwargs=None, **kwargs)[source]

Calculate the rolling standard deviation.

Parameters
ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

*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 1.4.0.

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 1.4.0.

**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

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 in Series.std() is different than the default ddof of 0 in numpy.std().

A minimum of one period is required for the rolling calculation.

The implementation is susceptible to floating point imprecision as shown in the example below.

Examples

>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.rolling(3).std()
0             NaN
1             NaN
2    5.773503e-01
3    1.000000e+00
4    1.000000e+00
5    1.154701e+00
6    2.580957e-08
dtype: float64

previous

pandas.core.window.rolling.Rolling.var

next

pandas.core.window.rolling.Rolling.min