scipy.stats.expon() | Python (original) (raw)

Last Updated : 20 Mar, 2019

scipy.stats.expon() is an exponential 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 : quantilesloc : [optional] location parameter. Default = 0 scale : [optional] scale parameter. Default = 1size : [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 : exponential continuous random variable

Code #1 : Creating exponential continuous random variable

Python3 `

from scipy.stats import expon

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

print ("RV : \n", rv)

`

Output :

RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D56531CC0>

Code #2 : exponential random variates and probability distribution.

Python3 `

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

Random Variates

R = expon.rvs(scale = 2, size = 10) print ("Random Variates : \n", R)

PDF

R = expon.pdf(quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R)

`

Output :

Random Variates : [2.50259466e-04 4.32311862e+00 8.22833503e-01 1.63374263e+00 4.46784023e+00 3.56781485e+00 3.95381396e+00 1.17623772e+00 3.21834266e-02 4.14778445e+00]

Probability Distribution : [0.99004983 0.89583414 0.81058425 0.73344696 0.66365025 0.60049558 0.54335087 0.4916442 0.44485807 0.40252422]

Code #3 : Graphical Representation.

Python3 `

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. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ]

Code #4 : Varying Positional Arguments

Python3 `

import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 5, 100)

Varying positional arguments

y1 = expon.pdf(x, 2, 6) y2 = expon.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--")

`

Output :