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
, whereN
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 innumpy.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