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# scipy stats.genpareto () | python

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:
-" q: lower and upper tail probability
-" a, b: shape parameters
-" 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: 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: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D579B85C0" `

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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