Python numpy.random.normal only positive values

I assume that what you mean is that you want to modify the probability density such that it is the same shape as normal in the positive range, and zero in negative. That is a pretty common practical case. In such case, you cannot simply take the absolute value of generated normal random variables. Instead, you have to generate a new independent normally distributed number until you come up with a positive one. One way to do that is recursively, see below.

import numpy as np def PosNormal(mean, sigma): x = np.random.normal(xbar,delta_xbar,1) return(x if x>=0 else PosNormal(mean,sigma))

The normal distribution, by definition, extends from -inf to +inf so what you are asking for doesn't make sense mathematically.

You can take a normal distribution and take the absolute value to "clip" to positive values, or just discard negative values, but you should understand that it will no longer be a normal distribution.