scipy stats.genpareto() | Python

Parameters : -> q : lower and upper tail probability -> a, b : shape parameters -> 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 : generalized Pareto continuous random variable

from scipy.stats import genpareto 

numargs = genpareto .numargs
[a] = [0.7, ] * numargs
rv = genpareto (a)

print ("RV : \n", rv) 

RV : 
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D579B85C0>

import numpy as np
quantile = np.arange (0.01, 1, 0.1)
 
# Random Variates
R = genpareto.rvs(a, scale = 2,  size = 10)
print ("Random Variates : \n", R)

Random Variates : 
 [ 1.55978773  0.03897083  7.68148511  0.78339525  1.1217962   0.20434352
  1.16663003  2.06115353 12.82886098  0.27780119]

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))

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.        ]

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 5, 100)

# Varying positional arguments
y1 = genpareto.pdf(x, 1, 3)
y2 = genpareto.pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")