Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

Python – Left-skewed Levy Distribution in Statistics

  • Last Updated : 10 Jan, 2020

scipy.stats.levy_l() is a left-skewed 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 : [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 : left-skewed Levy continuous random variable

Code #1 : Creating left-skewed Levy continuous random variable




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

Output :

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D6707508


Code #2 : left-skewed Levy continuous variates and probability distribution




import numpy as np 
quantile = np.arange (0.03, 2, 0.21
  
# Random Variates 
R = levy_l.rvs(a, b) 
print ("Random Variates : \n", R) 
  
# PDF 
R = levy_l.pdf(a, b, quantile) 
print ("\nProbability Distribution : \n", R) 

Output :

Random Variates : 
 1.1073459342251062

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.maximum(rv.dist.b, 4)) 
print("Distribution : \n", distribution) 
     
plot = plt.plot(distribution, rv.pdf(distribution))  

Output :

Distribution : 
 [0.         0.08163265 0.16326531 0.24489796 0.32653061 0.40816327
 0.48979592 0.57142857 0.65306122 0.73469388 0.81632653 0.89795918
 0.97959184 1.06122449 1.14285714 1.2244898  1.30612245 1.3877551
 1.46938776 1.55102041 1.63265306 1.71428571 1.79591837 1.87755102
 1.95918367 2.04081633 2.12244898 2.20408163 2.28571429 2.36734694
 2.44897959 2.53061224 2.6122449  2.69387755 2.7755102  2.85714286
 2.93877551 3.02040816 3.10204082 3.18367347 3.26530612 3.34693878
 3.42857143 3.51020408 3.59183673 3.67346939 3.75510204 3.83673469
 3.91836735 4.        ]
 


Code #4 : Varying Positional Arguments




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

Output :


My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!