Python - Laplace Distribution in Statistics Last Updated : 10 Jan, 2020 Comments Improve Suggest changes Like Article Like Report 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 : [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 : laplace continuous random variable Code #1 : Creating laplace continuous random variable Python3 1== # importing library from scipy.stats import laplace numargs = laplace.numargs a, b = 4.32, 3.18 rv = laplace(a, b) print ("RV : \n", rv) Output : RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D4DAF708 Code #2 : laplace continuous variates and probability distribution Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = laplace.rvs(a, b) print ("Random Variates : \n", R) # PDF R = laplace.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) Output : Random Variates : 10.613266250400734 Probability Distribution : [1.54667501e-48 1.43452207e-04 1.04508615e-02 4.07873394e-02 7.56198196e-02 1.04863398e-01 1.26475923e-01 1.41381881e-01 1.51096956e-01 1.56988338e-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 = laplace .pdf(x, 1, 3) y2 = laplace .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article Python - Laplacian Distribution in Statistics M mathemagic Follow Improve Article Tags : Python Python scipy-stats-functions Practice Tags : python Similar Reads Python - Laplacian Distribution in Statistics scipy.stats.dlaplace() is a Laplacian discrete random variable. It is inherited from the of generic methods as an instance of the rv_discrete class. It completes the methods with details specific for this particular distribution. Parameters : x : quantiles loc : [optional]location parameter. Default 2 min read Python - Log Laplace Distribution in Statistics scipy.stats.loglaplace() is a log-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 : quantile 2 min read Python - Levy Distribution in Statistics 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 : [opti 2 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 - kappa4 Distribution in Statistics scipy.stats.kappa4() is an Kappa 4 continuous random variable that is defined with a standard format and some shape parameters to complete its specification. The probability density is defined in the standard form and the loc and scale parameters are used to shift and/or scale the distribution. Para 2 min read Python - kappa3 Distribution in Statistics scipy.stats.kappa3() is an Kappa 3 continuous random variable that is defined with a standard format and some shape parameters to complete its specification. The probability density is defined in the standard form and the loc and scale parameters are used to shift and/or scale the distribution. Para 2 min read Like