When modeling real world scenarios , data scientists are often confronted with the need to characterize them using one of the many statistical distributions. With literally hundreds of choices, this can be confusing for people like me who are not trained as hardcore statisticians.
It would be good to have a comparative image of the most commonly used statistical distributions.
Python and matplotlib to the rescue !
Here’s a graphic comparison of all distributions listed in Python’s standard random module.
Name of Graph , Python Function
Uniform Distribution , random.uniform, (0, 1)
Triangular Disribution, random.triangular, (0, 1)
Betavariate Distribution, random.betavariate, (1, 5)
Expovariate Distribution,random.expovariate, (1,)
Gammavariate Distribution, random.gammavariate, (1, 0.5)
Gaussian Distribution, random.gauss, (1, 3)
Lognormvariate Distribution, random.lognormvariate, (1, 0.5)
Normal Variate Distribution, random.normalvariate, (1, 3)
Vonmisesvariate Distribution, random.vonmisesvariate, (math.pi, 0)
Paretovariate Distribution, random.paretovariate, (15,)
Weibullvariate Distribution, random.weibullvariate, (1, 1)