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pandas.core.window.Expanding.var

Expanding.var(ddof=1, *args, **kwargs)[source]

Calculate unbiased expanding variance.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters:

ddof : int, default 1

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

*args, **kwargs

For NumPy compatibility. No additional arguments are used.

Returns:

Series or DataFrame

Returns the same object type as the caller of the expanding calculation.

See also

Series.expanding
Calling object with Series data.
DataFrame.expanding
Calling object with DataFrames.
Series.var
Equivalent method for Series.
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().

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
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