# Skew¶

Running skew using Welford's algorithm.

## Parameters¶

• bias – defaults to False

If False, then the calculations are corrected for statistical bias.

• name

## Examples¶

>>> from river import stats
>>> import scipy.stats
>>> import numpy as np

>>> np.random.seed(42)
>>> X = np.random.normal(loc=0, scale=1, size=10)

>>> skew = stats.Skew(bias=False)
>>> for x in X:
...     print(skew.update(x).get())
0.0
0.0
-1.4802398132849872
0.5127437186677888
0.7803466510704751
1.056115628922055
0.5057840774320389
0.3478402420400934
0.4536710660918704
0.4123070197493227

>>> for i in range(2, len(X)+1):
...     print(scipy.stats.skew(X[:i], bias=False))
0.0
-1.4802398132849874
0.5127437186677893
0.7803466510704746
1.056115628922055
0.5057840774320389
0.3478402420400927
0.4536710660918703
0.4123070197493223

>>> skew = stats.Skew(bias=True)
>>> for x in X:
...     print(skew.update(x).get())
0.0
0.0
-0.6043053732501439
0.2960327239981376
0.5234724473423674
0.7712778043924866
0.39022088752624845
0.278892645224261
0.37425953513864063
0.3476878073823696

>>> for i in range(2, len(X)+1):
...     print(scipy.stats.skew(X[:i], bias=True))
0.0
-0.604305373250144
0.29603272399813796
0.5234724473423671
0.7712778043924865
0.39022088752624845
0.2788926452242604
0.3742595351386406
0.34768780738236926


## Methods¶

get

Return the current value of the statistic.

update

Update and return the called instance.

Parameters

• x (numbers.Number)