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# scipy stats.gumbel_r () | python

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: Right-Skewed Gumbel continuous random variable

Code # 1: Create right-hand continuous Gumbel random variable

 ` from ` ` scipy.stats ` ` import ` ` gumbel_r `   ` numargs ` ` = ` ` gumbel_r .numargs ` ` [] ` ` = ` ` [` ` 0.7 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` gumbel_r () ` `  `` print ( "RV:" , rv) `

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A4600F0" `

Code # 2: Right-Hand Gumbel Random Variations and Probability Distribution

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` gumbel_r .rvs (scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` print ` ` (` `" Random Variates: "` `, R) `   ` # PDF ` ` R ` ` = ` ` gumbel_r .pdf (quantile, loc ` ` = ` ` 0 ` `, scale ` ` = ` ` 1 ` `) ` ` print ` ` (` ` "Probability Distribution:" ` `, R) `

Output:

` Random Variates: [0.55349097 -0.36709655 -0.25581806 -0.81730142 0.28719592 - 0.30831366 -2.69858598 -0.23586469 -1.01965346 6.44132721] Probability Distribution: [0.36786111 0.36573943 0.36038433 0.35223844 0.34175873 0.32939568 0.3 1557754 0.3006994 0.28511631 0.26913983] `

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 ` < code class = "plain"> numpy as np   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` gumbel_r .pdf (x, ` ` 1 ` `, ` ` 3 ` `) ` ` y2 ` ` = ` ` gumbel_r .pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` `, x, y2, ` `" r-- "` `) `

Output:

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