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 &amp; 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.
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.