scipy.stats.expon () | python



scipy.stats.expon () — an exponential continuous random variable that is defined by a standard format and some form 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: exponential continuous random variable

Code # 1: Generating an exponential continuous random variable values ​​

 

from scipy.stats import expon 

 

numargs = expon.numargs

[] = [ 0.6 ,] * numargs

rv = expon ( )

  

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D56531CC0 & gt; 

Code # 2: Exponential Random Variables and Probability Distribution.

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = expon.rvs (scale = 2 , size = 10 )

pr int ( "Random Variates:" , R)

 
# PDF

R = expon.pdf (quantile, loc = 0 , scale = 1 )

print ( "Probability Distribution:" , 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 0.6636 5025 0.60049558 0.54335087 0.4916442 0.44485807 0.40252422] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv. dist.b, 5 ))

print ( "Distribution:" , distribution)

 

plot = pl t.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: Various Positional Arguments

import matplotlib. pyplot as plt

import numpy as np

 

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

 
# Various positional arguments

y1 = expon.pdf (x, 2 , 6 )

y2 = expon.pdf (x, 1 , 4 )

plt.plot (x, y1, "*" , x, y2, "r--" )

Output: