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.
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 expanding variance.
numpy.var
Equivalent method for NumPy array.
pandas.DataFrame.expanding
Calling expanding with DataFrames.
pandas.DataFrame.var
Aggregating var for DataFrame.
pandas.Series.expanding
Calling expanding with Series data.
pandas.Series.var
Aggregating var for Series.
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])This example is valid syntax, but we were not able to check execution
>>> s.expanding(3).var() 0 NaN 1 NaN 2 0.333333 3 0.916667 4 0.800000 5 0.700000 6 0.619048 dtype: float64See :
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