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scipy stats.dweibull() | Python
  • Last Updated : 20 Mar, 2019

scipy.stats.dweibull() is an double weibull 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 : double weibull continuous random variable

Code #1 : Creating double weibull continuous random variable




from scipy.stats import dweibull 
  
numargs = dweibull.numargs
[a] = [0.6, ] * numargs
rv = dweibull(a)
  
print ("RV : \n", rv) 

Output :



RV : 
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001FDC8AA8E80>

Code #2 : double weibull random variates and probability distribution.




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

Output :


Random Variates : 
 [  1.49793669   2.02019269  -1.8530545   -0.79018341   0.96852783
 -14.70570461  -1.7957089    0.79819141   4.34335483  -0.96031661]

Probability Distribution : 
 [0.00306562 0.03367007 0.06402237 0.09391113 0.12314439 0.15155039
 0.17897785 0.20529592 0.23039382 0.25418014]
 

Code #3 : Graphical Representation.




import numpy as np
import matplotlib.pyplot as plt
  
distribution = np.linspace(0, np.minimum(rv.dist.b, 5))
print("Distribution : \n", distribution)
  
plot = plt.plot(distribution, rv.pdf(distribution))

Output :

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.        ]

Code #4 : Varying Positional Arguments




import matplotlib.pyplot as plt
import numpy as np
  
x = np.linspace(0, 5, 100)
  
# Varying positional arguments
y1 = dweibull.pdf(x, 1, 6)
y2 = dweibull.pdf(x, 1, 5)
plt.plot(x, y1, "*", x, y2, "r--")

Output :

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