# scipy.stats.skew¶

scipy.stats.skew(a, axis=0, bias=True, nan_policy='propagate')[source]

Compute the skewness of a data set.

For normally distributed data, the skewness should be about 0. For unimodal continuous distributions, a skewness value > 0 means that there is more weight in the right tail of the distribution. The function skewtest can be used to determine if the skewness value is close enough to 0, statistically speaking.

Parameters: a : ndarray data axis : int or None, optional Axis along which skewness is calculated. Default is 0. If None, compute over the whole array a. bias : bool, optional If False, then the calculations are corrected for statistical bias. nan_policy : {‘propagate’, ‘raise’, ‘omit’}, optional Defines how to handle when input contains nan. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’. skewness : ndarray The skewness of values along an axis, returning 0 where all values are equal.

References

 [R696] Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000. Section 2.2.24.1

Examples

>>> from scipy.stats import skew
>>> skew([1, 2, 3, 4, 5])
0.0
>>> skew([2, 8, 0, 4, 1, 9, 9, 0])
0.2650554122698573


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