scipy stats.genpareto () | python

NumPy | Python Methods and Functions

scipy.stats.genpareto () — it is a generalized Pareto continuous random variable that is defined by a standard format and some form parameters to complete its specification.

Parameters:
- & gt; q: lower and upper tail probability
- & gt; a, b: shape parameters
- & gt; x: quantiles
- & gt; loc: [optional] location parameter. Default = 0
- & gt; scale: [optional] scale parameter. Default = 1
- & gt; size: [tuple of ints, optional] shape or random variates.
- & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: generalized Pareto continuous random variable

Code # 1: Create generalized continuous random variable Pareto random variable

from scipy.stats import genpareto 

 

numargs = genpareto .numargs

[a] = [ 0.7 ,] * numargs

rv = genpareto (a)

  

print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D579B85C0 & gt; 

Code # 2: Pareto Generalized Random Variables.

import numpy as np

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

 
# Random Variants

R = genpareto.rvs (a, scale = 2 , size = 10 )

print ( "Random Variates:" , R)

Output:

 Random Variates: [1.55978773 0.03897083 7.68148511 0.78339525 1.1217962 0.20434352 1.16663003 2.06115353 12.82886098 0.27780119] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum ( rv.dist.b, 3 ))

print ( "Distribution:" , 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: Various Positional Arguments

import matplotlib. pyplot as plt

import numpy as np

 

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

 
# Various positional arguments

y1 = genpareto.pdf (x, 1 , 3 )

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

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

Output:





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