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# scipy stats.frechet_l () | python

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Parameters:
-" q: lower and upper tail probability
-" a: 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: Frechet left continuous random variable

Code # 1: Create left continuous random variable random variable Frechet

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

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D578BC9E8" `

Code # 2: Frechet left random changes and probability distributions.

` `

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

` `

Output:

` Random Variates: [-4.66775585e-02 -3.75425255 e + 00 -2.32248407e-01 -1.20807347e-03 -6.26373883e + 00 -1.14007755e + 00 -5.09499683e + 00 -4.18191271e-01 -4.33720753e + 00 -1.05442843e + 00] Probability Distribution: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] `

Code # 3: Various Positional Arguments

 ` import ` ` matplotlib.pyplot as plt ` ` import ` ` numpy as np `   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` frechet_l.pdf (x, ` ` 1 ` `, ` ` 3 ` `) ` ` y2 ` ` = ` ` frechet_l.pdf (x, ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` ` "*" ` `, x, y2, ` ` "r--" ` `) `

Output:

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