Python - Maxwell Distribution in Statistics Last Updated : 31 Dec, 2019 Comments Improve Suggest changes Like Article Like Report scipy.stats.maxwell() is a Maxwell (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 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 : Maxwell continuous random variable Code #1 : Creating Maxwell continuous random variable Python3 1== # importing library from scipy.stats import maxwell numargs = maxwell.numargs a, b = 4.32, 3.18 rv = maxwell(a, b) print ("RV : \n", rv) Output : RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D66DEC88 Code #2 : Maxwell continuous variates and probability distribution Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = maxwell.rvs(a, b) print ("Random Variates : \n", R) # PDF R = maxwell.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) Output : Random Variates : 8.999401872992793 Probability Distribution : [0.00000000e+00 3.70579394e-21 4.46576264e-05 4.02803131e-02 3.15216150e-01 6.42768234e-01 7.96800760e-01 7.98281605e-01 7.24720266e-01 6.27826999e-01] 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 = maxwell .pdf(x, 1, 3) y2 = maxwell .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article Python - Maxwell Distribution in Statistics M mathemagic Follow Improve Article Tags : Python Python scipy-stats-functions Practice Tags : python Similar Reads Python - Moyal Distribution in Statistics scipy.stats.moyal() is a Moyal continuous random variable. 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