This is the first snippet:

from scipy.stats import norm from numpy import linspace from pylab import plot,show,hist,figure,title # picking 150 of from a normal distrubution # with mean 0 and standard deviation 1 samp = norm.rvs(loc=0,scale=1,size=150) param = norm.fit(samp) # distribution fitting # now, param[0] and param[1] are the mean and # the standard deviation of the fitted distribution x = linspace(-5,5,100) # fitted distribution pdf_fitted = norm.pdf(x,loc=param[0],scale=param[1]) # original distribution pdf = norm.pdf(x) title('Normal distribution') plot(x,pdf_fitted,'r-',x,pdf,'b-') hist(samp,normed=1,alpha=.3) show()The result should be as follows

In the code above a dataset of 150 samples have been created using a normal distribution with mean 0 and standar deviation 1, then a fitting procedure have been applied on the data. In the figure we can see the original distribution (blue curve) and the fitted distribution (red curve) and we can observe that they are really similar.

Let's do the same with a Rayleigh distribution:

from scipy.stats import norm,rayleigh samp = rayleigh.rvs(loc=5,scale=2,size=150) # samples generation param = rayleigh.fit(samp) # distribution fitting x = linspace(5,13,100) # fitted distribution pdf_fitted = rayleigh.pdf(x,loc=param[0],scale=param[1]) # original distribution pdf = rayleigh.pdf(x,loc=5,scale=2) title('Rayleigh distribution') plot(x,pdf_fitted,'r-',x,pdf,'b-') hist(samp,normed=1,alpha=.3) show()The resulting plot:

As expected, the two distributions are very close.