  # scipy stats.gumbel_l () | python

NumPy | Python Methods and Functions

Parameters:
- & gt; q: lower and upper tail probability
- & 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: left-Skewed Gumbel continuous random variable

Code # 1: Create continuous Gumbel random variable skewed to the left

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

Output:

` RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A283518 & gt; `

Code # 2: Left-hand Gumbel Random Variation and Probability Distribution

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 , 0.1 ) ``   # Random Variants R = gumbel_l.rvs (scale = 2 , size = 10 ) print ( " Random Variates: " , R)   # PDF R = gumbel_l.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 0.3155 7754 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 ` numpy as np   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` gumbel_l .pdf (x, 1 , 3 ) `` y2 = gumbel_l .pdf (x, 1 , 4 ) plt.plot (x, y1,  "*" , x, y2, "r--" ) `

Output: