Python - Levy Distribution in Statistics Last Updated : 10 Jan, 2020 Comments Improve Suggest changes Like Article Like Report scipy.stats.levy() is a levy 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 : [optional]location parameter. Default = 0 scale : [optional]scale parameter. Default = 1 size : [tuple of ints, optional] shape or random variates. moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’). Results : levy continuous random variable Code #1 : Creating levy continuous random variable Python3 1== # importing library from scipy.stats import levy numargs = levy.numargs a, b = 4.32, 3.18 rv = levy(a, b) print ("RV : \n", rv) Output : RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D661FF48 Code #2 : levy continuous variates and probability distribution Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = levy.rvs(a, b) print ("Random Variates : \n", R) # PDF R = levy.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) Output : Random Variates : 10.146582883442196 Probability Distribution : [0.03263232 0.10358487 0.13698141 0.15928865 0.17532664 0.18715139 0.19589569 0.2022745 0.20677902 0.20976675] Code #3 : Graphical Representation. Python3 1== import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 3)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.pdf(distribution)) Output : Distribution : [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ] Code #4 : Varying Positional Arguments Python3 1== import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = levy .pdf(x, 1, 3) y2 = levy .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article Python - Levy_stable Distribution in Statistics M mathemagic Follow Improve Article Tags : Python Python scipy-stats-functions Practice Tags : python Similar Reads Python - Levy_stable Distribution in Statistics scipy.stats.levy_stable() is a Levy-stable 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 : quantil 2 min read Python - Mielke Distribution in Statistics scipy.stats.mielke() is a Mielke Beta-Kappa / Dagum 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 2 min read Python - Laplace Distribution in Statistics scipy.stats.laplace() is a Laplace 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 Python - Moyal Distribution in Statistics scipy.stats.moyal() is a Moyal 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 : [op 2 min read Python - Lomax Distribution in Statistics scipy.stats.lomax() is a Lomax (Pareto of the second kind) 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 probabi 2 min read Python - ksone Distribution in Statistics scipy.stats.ksone() is a General Kolmogorov-Smirnov one-sided test that is defined with a standard format and some shape parameters to complete its specification. It is a statistical test for the finite sample size n. Parameters : q : lower and upper tail probability x : quantiles loc : [optional]lo 2 min read Like