How to make a gaussian distribution in python considering mean. variance. skewness and kurtosis?

np.random.normal(mean,sigma,size) allows to create a gaussian distribution based only on mean and variance. I want to create a distribution based on function_name(mean,sigma,skew,kurtosis,size).

I tried scipy.stats.gengamma but I don't understand how to use it. It takes 2 parameters - a,c and creates a distribution. But it is difficult to interpret for what values of a c, the function will give a particular value of skewness and kurtosis.

Can anyone explain how to use gengamma or any other way to create such a distribution in python, even from scratch by writing mathematical equations?

Edit: By Gaussian, I mean that I want the distribution to be normal with some skewness or kurtosis as well. It need not be a standard normal distribution.

Topic distribution scipy python

Category Data Science


The Gaussian distribution is fully described by its mean and variance. Gaussians have fixed values for Skewness (0) and Kurtosis (3) - so you can't really change them if you have made the Gaussian assumption for your model.

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