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

scipy.stats.beta() is an beta 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 continuous random variable

Code #1 : Creating beta continuous random variable

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

Output :

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

Code #2 : beta random variates and probability distribution function.

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

Output :

```Random Variates :
[1.47189604 1.47284574 1.84692416 1.0686604  0.32709236 1.96857076
0.00639731 1.97093898 1.34811881 0.34269426]

Probability Distribution :
[2.62281037 1.04883674 0.84934164 0.76724957 0.73040985 0.72096547
0.73529768 0.77903762 0.8752367  1.1264383 ]```

Code #3 : Graphical Representation.

 `import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `distribution ``=` `np.linspace(``0``, np.maximum(rv.dist.b, ``5``))``plot ``=` `plt.plot(distribution, rv.pdf(distribution))`

Output :

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 ``=` `beta.pdf(x, ``2.75``, ``2.75``)``y2 ``=` `beta.pdf(x, ``3.25``, ``3.25``)``plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``)`

Output :

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