sympy.stats.PowerFunction() in Python Last Updated : 08 Jun, 2020 Comments Improve Suggest changes Like Article Like Report 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 : In this example we can see that by using sympy.stats.PowerFunction() method, we are able to get the continuous random variable representing power function distribution by using this method. Python3 1=1 # Import sympy and PowerFunction from sympy.stats import PowerFunction, density from sympy import Symbol, pprint z = Symbol("z") alpha = Symbol("alpha", positive = True) a = Symbol("a", positive = True) b = Symbol("b", positive = True) # Using sympy.stats.PowerFunction() method X = PowerFunction("x", alpha, a, b) gfg = density(X)(z) print(gfg) Output : (-2*a + 2*z)/(-a + b)**2 Example #2 : Python3 1=1 # Import sympy and PowerFunction from sympy.stats import PowerFunction, density, variance from sympy import Symbol, pprint z = Symbol("z") alpha = 2 a = 0 b = 1 # Using sympy.stats.PowerFunction() method X = PowerFunction("x", alpha, a, b) gfg = density(X)(z) pprint(variance(gfg)) Output : 1/18 Comment More infoAdvertise with us Next Article sympy.stats.PowerFunction() in Python J jitender_1998 Follow Improve Article Tags : Python SymPy Python SymPy-Stats Practice Tags : python Similar Reads sympy.stats.FiniteRV() function in Python With the help of sympy.stats.FiniteRV() method, we can create a random variable gives a dictionary of density by using sympy.stats.FiniteRV() method. Syntax : sympy.stats.FiniteRV(name, dict) Return : Return the variable having dictionary of density. Example #1 : In this example, we can see that by 1 min read sympy.stats.Poisson() in Python With the help of sympy.stats.Poisson() method, we can get the random variable representing the poisson distribution. Syntax : sympy.stats.Poisson(name, lambda)Return : Return the random variable. Example #1 :In this example we can see that by using sympy.stats.Poisson() method, we are able to get th 1 min read sympy.stats.Pareto() in python 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 thi 1 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 sympy.stats.Logistic() in python With the help of sympy.stats.Logistic() method, we can get the continuous random variable which represents the logistic distribution. Syntax : sympy.stats.Logistic(name, mu, s) Where, mu and s are real number and mu, s > 0. Return : Return the continuous random variable. Example #1 : In this exam 1 min read Like