sympy.stats.Pareto() in python Last Updated : 05 Jun, 2020 Comments Improve Suggest changes Like Article Like Report With the help of sympy.stats.Pareto() method, we can get the continuous random variable which represents the Pareto distribution. Syntax : sympy.stats.Pareto(name, xm, alpha) Where, xm and alpha are real number and xm, alpha > 0. Return : Return the continuous random variable. Example #1 : In this example we can see that by using sympy.stats.Pareto() method, we are able to get the continuous random variable representing pareto distribution by using this method. Python3 1=1 # Import sympy and Pareto from sympy.stats import Pareto, density from sympy import Symbol, pprint z = Symbol("z") xm = Symbol("xm", positive = True) alpha = Symbol("alpha", positive = True) # Using sympy.stats.Pareto() method X = Pareto("x", xm, alpha) gfg = density(X)(z) pprint(gfg) Output : alpha -alpha - 1 alpha*xm *z Example #2 : Python3 1=1 # Import sympy and Pareto from sympy.stats import Pareto, density from sympy import Symbol, pprint z = 2.1 xm = 1 alpha = 2.4 # Using sympy.stats.Pareto() method X = Pareto("x", xm, alpha) gfg = density(X)(z) pprint(gfg) Output : 0.192604149590925 Comment More infoAdvertise with us Next Article sympy.stats.Pareto() in python J jitender_1998 Follow Improve Article Tags : Python SymPy Python SymPy-Stats Practice Tags : python Similar Reads sympy.stats.PowerFunction() in Python With the help of sympy.stats.PowerFunction() method, we can get the continuous random variable which represents the Power Function distribution. Syntax : sympy.stats.PowerFunction(name, alpha, a, b) Where, a, b and alpha are real number. Return : Return the continuous random variable. Example #1 : I 1 min read sympy.stats.Dagum() in python With the help of sympy.stats.Dagum() method, we can get the continuous random variable representing the dagum distribution. Syntax : sympy.stats.Dagum(name, p, a, b) Where, p, a and b are real number greater than 0. Return : Return continuous random variable. Example #1 : In this example we can see 2 min read sympy.stats.Binomial() function in Python With the help of sympy.stats.Binomial() method, we can create a Finite Random Variable representing a binomial distribution. A binomial distribution is the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times. Syntax: sympy.stats.Binomial(name, n, p 1 min read scipy stats.genpareto() | Python scipy.stats.genpareto() is an generalized Pareto continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : -> q : lower and upper tail probability -> a, b : shape parameters -> x : quantiles -> loc : [optional] 2 min read Python - Pareto Distribution in Statistics scipy.stats.pareto() is a Pareto continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail probability x : quantiles loc : [ 2 min read Like