Expanding.
var
Calculate unbiased expanding variance.
Normalized by N-1 by default. This can be changed using the ddof argument.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
N - ddof
N
For NumPy compatibility. No additional arguments are used.
Returns the same object type as the caller of the expanding calculation.
See also
pandas.Series.expanding
Calling object with Series data.
pandas.DataFrame.expanding
Calling object with DataFrames.
pandas.Series.var
Equivalent method for Series.
pandas.DataFrame.var
Equivalent method for DataFrame.
numpy.var
Equivalent method for Numpy array.
Notes
The default ddof of 1 used in Series.var() is different than the default ddof of 0 in numpy.var().
Series.var()
numpy.var()
A minimum of 1 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
>>> s.expanding(3).var() 0 NaN 1 NaN 2 0.333333 3 0.916667 4 0.800000 5 0.700000 6 0.619048 dtype: float64