scipy.stats.skew norm

def skew(a, axis=0, bias=True, nan_policy='propagate'):

**
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'.

from scipy.stats import skew, norm

norm: A normal continuous random variable
skew: 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