Python - Bernoulli Distribution in Statistics

x : quantiles loc : [optional]location parameter. Default = 0 scale : [optional]scale parameter. Default = 1 moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’). Results : Bernoulli discrete random variable

# importing library

from scipy.stats import bernoulli 
  
numargs = bernoulli .numargs 
a, b = 0.2, 0.8
rv = bernoulli (a, b) 
  
print ("RV : \n", rv)  

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C0FC108

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 

# Random Variates 
R = bernoulli .rvs(a, b, size = 10) 
print ("Random Variates : \n", R) 

# PDF 
x = np.linspace(bernoulli.ppf(0.01, a, b),
                bernoulli.ppf(0.99, a, b), 10)
R = bernoulli.ppf(x, 1, 3)
print ("\nProbability Distribution : \n", R) 

Random Variates : 
 [0 0 0 0 0 0 0 0 0 1]

Probability Distribution : 
 [ 4.  4. nan nan nan nan nan nan nan nan]

import numpy as np 
import matplotlib.pyplot as plt 
   
distribution = np.linspace(0, np.minimum(rv.dist.b, 2)) 
print("Distribution : \n", distribution) 
   
plot = plt.plot(distribution, rv.ppf(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.        ]
  

import matplotlib.pyplot as plt 
import numpy as np 

x = np.linspace(0, 5, 100) 
   
# Varying positional arguments 
y1 = bernoulli.ppf(x, a, b) 
y2 = bernoulli.pmf(x, a, b) 
plt.plot(x, y1, "*", x, y2, "r--")