scipy stats.genexpon() | Python Last Updated : 27 Mar, 2019 Summarize Comments Improve Suggest changes Share Like Article Like Report scipy.stats.genexpon() is an generalized exponential 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 -> x : quantiles -> loc : [optional]location parameter. Default = 0 -> scale : [optional]scale parameter. Default = 1 -> size : [tuple of ints, optional] shape or random variates. -> a, b, c : shape parameters -> moments : [optional] composed of letters [‘mvsk’]; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv'). Results : generalized exponential continuous random variable Code #1 : Creating generalized exponential continuous random variable Python3 from scipy.stats import genexpon numargs = genexpon .numargs [a, b, c] = [0.7, ] * numargs rv = genexpon (a, b, c) print ("RV : \n", rv) Output : RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D57997F60> Code #2 : generalized exponential random variates. Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = genexpon.rvs(a, scale = 2, size = 10) print ("Random Variates : \n", R) Output : Random Variates : [0.74505484 2.02790441 2.06823675 3.96275674 1.24274054 3.71331036 0.53957521 0.37359838 2.53934153 2.36254065] Probability Distribution : [0.43109163 0.45222638 0.47102054 0.48773188 0.50258763 0.51578837 0.52751153 0.53791424 0.54713591 0.55530037] Code #3 : Graphical Representation. Python3 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 = genexpon.pdf(x, a, 1, 3) y2 = genexpon.pdf(x, a, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article scipy stats.genexpon() | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads scipy stats.exponpow() | Python scipy.stats.exponpow() is an exponential power 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 x : quantiles loc : [optional] location parameter. Default = 0 scale : [optional 2 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 scipy stats.genextreme() | Python scipy.stats.genextreme() is an generalized extreme value 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 -> x : quantiles -> loc : [optional]location parameter. De 2 min read scipy stats.genlogistic() | Python scipy.stats.genlogistic() is an generalized logistic 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 : [optio 2 min read scipy stats.gengamma() | Python scipy.stats.gengamma() is an generalized gamma 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 -> x : quantiles -> loc : [optional]location parameter. Default = 0 2 min read Like