pandas.core.window.rolling.Rolling.var — pandas 2.3.3 documentation (original) (raw)

Rolling.var(ddof=1, numeric_only=False, engine=None, engine_kwargs=None)[source]#

Calculate the rolling variance.

Parameters:

ddofint, default 1

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

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 1.4.0.

engine_kwargsdict, default None

• For 'cython' engine, there are no accepted engine_kwargs
• For 'numba' engine, the engine can accept nopython, nogiland parallel dictionary keys. The values must either be True orFalse. The default engine_kwargs for the 'numba' engine is{'nopython': True, 'nogil': False, 'parallel': False}
  Added in version 1.4.0.

Returns:

Series or DataFrame

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

Notes

The default ddof of 1 used in Series.var() is different than the default ddof of 0 in numpy.var().

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).var() 0 NaN 1 NaN 2 0.333333 3 1.000000 4 1.000000 5 1.333333 6 0.000000 dtype: float64