Python: How to discretize continuous probability distributions for Kullback-Leibler Divergence

I want to find out how many samples are needed at minimum to more or less correctly fit a probability distribution (In my case the Generalized Extreme Value Distribution from scipy.stats). In order to evaluate the matched function, I want to compute the KL-divergence between the original function and the fitted one. Unfortunately, all implementations I found (e.g. scipy.stats.entropy) only take discrete arrays as input. So obviously I thought of approximating the pdf by a discrete array, but I just can't seem to figure it out. Does anyone have experience with something similar? I would be thankful for hints relating directly to my question, but also for better alternatives to determine a distance between two functions in python, if there are any.