scipy.stats.expon() | Python Last Updated : 20 Mar, 2019 Comments Improve Suggest changes Like Article Like Report scipy.stats.expon() is an 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. moments : [optional] composed of letters [‘mvsk’]; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv'). Results : exponential continuous random variable Code #1 : Creating exponential continuous random variable Python3 from scipy.stats import expon numargs = expon.numargs [ ] = [0.6, ] * numargs rv = expon( ) print ("RV : \n", rv) Output : RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D56531CC0> Code #2 : exponential random variates and probability distribution. Python3 import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = expon.rvs(scale = 2, size = 10) print ("Random Variates : \n", R) # PDF R = expon.pdf(quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) Output : Random Variates : [2.50259466e-04 4.32311862e+00 8.22833503e-01 1.63374263e+00 4.46784023e+00 3.56781485e+00 3.95381396e+00 1.17623772e+00 3.21834266e-02 4.14778445e+00] Probability Distribution : [0.99004983 0.89583414 0.81058425 0.73344696 0.66365025 0.60049558 0.54335087 0.4916442 0.44485807 0.40252422] Code #3 : Graphical Representation. Python3 import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 5)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.pdf(distribution)) Output : Distribution : [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] Code #4 : Varying Positional Arguments Python3 import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = expon.pdf(x, 2, 6) y2 = expon.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article scipy.stats.expon() | 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. 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