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scipy stats.betaprime() | Python
• Last Updated : 20 Mar, 2019

scipy.stats.betaprime() is an beta prime continuous random variable that is defined with a standard format and some shape 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 : beta prime continuous random variable

Code #1 : Creating betaprime continuous random variable

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

Output :

```RV :
<scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438>
```

Code #2 : betaprime random variates and probability distribution.

 `import` `numpy as np``quantile ``=` `np.arange (``0.01``, ``1``, ``0.1``)``  ` `# Random Variates``R ``=` `betaprime.rvs(a, b, scale ``=` `2``,  size ``=` `10``)``print` `(``"Random Variates : \n"``, R)`` ` `# PDF``R ``=` `betaprime.pdf(quantile, a, b, loc ``=` `0``, scale ``=` `1``)``print` `(``"\nProbability Distribution : \n"``, 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
0.28490363 0.24941484 0.22101038 0.1977718 ]
```

Code #3 : Graphical Representation.

 `import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `distribution ``=` `np.linspace(``0``, np.minimum(rv.dist.b, ``5``))``print``(``"Distribution : \n"``, 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 : Varying Positional Arguments

 `from` `scipy.stats ``import` `arcsine``import` `matplotlib.pyplot as plt``import` `numpy as np`` ` `x ``=` `np.linspace(``0``, ``1.0``, ``100``)`` ` `# Varying 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--"``)`

Output :

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