Calculate raw moments in Python

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I want to calculate the first 4 raw moments of lists of data points, for example my_list = [34.0, 35.0, 39.0, 43.0, 46.5, 48.5, 50.0, 51.5, 52.5].

I am aware of scipy.stats.moment but seems it only calculates central moments:

scipy.stats.moment(a, moment=1, axis=0, nan_policy='propagate')

Calculate the nth moment about the mean for a sample.

A moment is a specific quantitative measure of the shape of a set of points. It is often used to calculate coefficients of skewness and kurtosis due to its close relationship with them.

I also notice there are scipy.stats functions for skewness or variance:

from scipy.stats import variation, skew
my_list = [34.0, 35.0, 39.0, 43.0, 46.5, 48.5, 50.0, 51.5, 52.5]

print(np.var(my_list))
print(variation(my_list))

print(skew(my_list))

>>> 44.0246913580247
>>> 0.14928998626833617
>>> -0.39001327934054053

but as you can see numpy and scipy return very different results.

What are the differences between these approaches and what's the most efficient way to find the first 4 raw moments(mean, variance, skewness, kurtosis)?

scipy numpy python
1 Answers

Use scipy.stats.describe():

>>> x = [34.0, 35.0, 39.0, 43.0, 46.5, 48.5, 50.0, 51.5, 52.5]
>>> moments = scipy.stats.describe(x)
>>> moments.mean
44.44444444444444
>>> moments.variance
49.527777777777786
>>> moments.skewness
-0.39001327934054053
>>> moments.kurtosis
-1.3564638918570706
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