var(self, ddof: 'int' = 1, *args, engine: 'str | None' = None, engine_kwargs: 'dict[str, bool] | None' = None, **kwargs)
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.
The implementation is susceptible to floating point imprecision as shown in the example below.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof
, where N
represents the number of elements.
For NumPy compatibility and will not have an effect on the result.
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}
For NumPy compatibility and will not have an effect on the result.
Return type is the same as the original object with np.float64
dtype.
Calculate the rolling variance.
numpy.var
Equivalent method for NumPy array.
pandas.DataFrame.rolling
Calling rolling with DataFrames.
pandas.DataFrame.var
Aggregating var for DataFrame.
pandas.Series.rolling
Calling rolling with Series data.
pandas.Series.var
Aggregating var for Series.
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])See :
... s.rolling(3).var() 0 NaN 1 NaN 2 3.333333e-01 3 1.000000e+00 4 1.000000e+00 5 1.333333e+00 6 6.661338e-16 dtype: float64
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SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
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