Python - Power-Function Distribution in Statistics Last Updated : 10 Jan, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report scipy.stats.powerlaw() is a power-function 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 : power-function continuous random variable Code #1 : Creating power-function continuous random variable Python3 1== # importing library from scipy.stats import powerlaw numargs = powerlaw.numargs a, b = 4.32, 3.18 rv = powerlaw(a, b) print ("RV : \n", rv) Output : RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D8295B48 Code #2 : power-function continuous variates and probability distribution Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = powerlaw.rvs(a, b) print ("Random Variates : \n", R) # PDF R = powerlaw.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) Output : Random Variates : 3.860143037448123 Probability Distribution : [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] 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. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ] 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 = powerlaw .pdf(x, 1, 3, 5) y2 = powerlaw .pdf(x, 1, 4, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article Python - Normal Distribution in Statistics M mathemagic Follow Improve Article Tags : Python Python scipy-stats-functions Practice Tags : python Similar Reads Python - Power Normal Distribution in Statistics scipy.stats.powernorm() is a power normal 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 : quantile 2 min read Python - Power Log-Normal Distribution in Statistics scipy.stats.powerlognorm() is a power log-normal 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 : q 2 min read Python - Normal Distribution in Statistics A probability distribution determines the probability of all the outcomes a random variable takes. The distribution can either be continuous or discrete distribution depending upon the values that a random variable takes. There are several types of probability distribution like Normal distribution, 6 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 Python - Inverse Gaussian Distribution in Statistics scipy.stats.invgauss() is an inverted gauss 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 : a : shape parameter c : special case of gengaus 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 Like