Skip to content
Related Articles

Related Articles

Python – Johnson SB Distribution in Statistics
  • Last Updated : 10 Jan, 2020

scipy.stats.johnsonsb() is a Johnson SB 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 : Johnson SB continuous random variable

Code #1 : Creating Johnson SB continuous random variable






# importing library
  
from scipy.stats import johnsonsb  
    
numargs = johnsonsb.numargs 
a, b = 4.32, 3.18
rv = johnsonsb(a, b) 
    
print ("RV : \n", rv)  

Output :

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D50286C8


Code #2 : Johnson SB continuous variates and probability distribution




import numpy as np 
quantile = np.arange (0.01, 1, 0.1
  
# Random Variates 
R = johnsonsb.rvs(a, b, scale = 2, size = 10
print ("Random Variates : \n", R) 
  
# PDF 
R = johnsonsb.pdf(a, b, quantile, loc = 0, scale = 1
print ("\nProbability Distribution : \n", R)  

Output :

Random Variates : 
 [0.42212956 0.60876766 0.35494705 0.42892958 0.25316345 0.51872977
 0.2355019  0.44657975 0.54971277 0.36683771]

Probability Distribution : 
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

Code #3 : Graphical Representation.




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.         0.02040816 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.        ]
 

Code #4 : Varying Positional Arguments




import matplotlib.pyplot as plt 
import numpy as np 
     
x = np.linspace(0, 5, 100
     
# Varying positional arguments 
y1 = johnsonsb .pdf(x, 1, 3
y2 = johnsonsb .pdf(x, 1, 4
plt.plot(x, y1, "*", x, y2, "r--"

Output :

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :