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# scipy stats.betaprime () | python

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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: beta prime continuous random variable

Code # 1: Generating a continuous random betaprime values ​​

 ` # scipy import ` ` from ` ` scipy.stats ` ` import ` ` betaprime `   ` numargs ` ` = ` ` betaprimeprime.numargs ` ` [a, b] ` ` = ` ` [` ` 0.6 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` betaprimeprime (a, b) ``   print ( "RV:" , rv) `

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438 & gt ; `

Code # 2: Beta Simple Random Variations and Probability Distribution.

` `

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

` `

Output:

` Random Variates: [1.59603917 1.92408727 1.2120992 0.34064091 2.68681773 22.99956678 1.45523032 2.93360219 23.93717261 18.04203815] Probability Distribution: [2.58128122 0.8832351 0.61488062 0.47835546 0.39160163 0.33053737 0.2849 0363 0.24941484 0.22101038 0.1977718] `

Code # 3: Graphic representation.

` `

` import numpy as np import matplotlib.pyplot as plt   distribution = np.linspace ( 0 , np.minimum (rv.dist. b, 5 )) print ( "Distribution:" , distribution)   plot = plt.plot (distribution, rv.pdf (distribution)) `

Output:

` Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] `

Code # 4: Various Positional Arguments

Output:

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 ` from ` ` scipy. stats ` ` import ` ` arcsi ne ` ` import ` ` matplotlib.pyplot as plt ` ` import ` ` numpy as np `   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 1.0 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` betaprime.pdf (x, ` ` 2.75 ` `, ` ` 2.75 ` `) ` ` y2 ` ` = ` ` betaprime.pdf (x, ` ` 3.25 ` `, ` ` 3.25 ` `) ` ` plt.plot (x, y1, ` ` "*" ` `, x, y2, ` ` "r--" ` `) `