scipy stats.arcsine() | Python

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 : arcsine continuous random variable

# importing scipy
from scipy.stats import arcsine

numargs = arcsine.numargs
[ ] = [0.6, ] * numargs
rv = arcsine()

print ("RV : \n", rv)

RV :  
<scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029484D796D8>

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

# PDF
R = arcsine.pdf(x = quantile, scale = 2)
print ("\nProbability Distribution : \n", R)

Random Variates : 
 [1.17353658 1.96350916 1.73419819 0.71255312 0.28760466 1.54410451
 1.9644408  0.35014597 0.26798525 0.24599504]

Probability Distribution : 
 [2.25643896 0.69810843 0.51917523 0.43977033 0.39423905 0.3651505
 0.34568283 0.33260295 0.32421577 0.31960693]

# libraries
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.         0.02040816 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.        ]

from scipy.stats import arcsine
import matplotlib.pyplot as plt
import numpy as np
a = 2
b = 2
x = np.linspace(0, np.minimum(rv.dist.b, 3))

# Varying location and scale
y1 = arcsine.pdf(x, -0.1, .8)
y2 = arcsine.pdf(x, -3.25, 3.25)
plt.plot(x, y1, "*", x, y2, "r--")