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# scipy stats.genexpon() | Python

• Last Updated : 27 Mar, 2019

scipy.stats.genexpon() is an generalized exponential 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
-> x : quantiles
-> loc : [optional]location parameter. Default = 0
-> scale : [optional]scale parameter. Default = 1
-> size : [tuple of ints, optional] shape or random variates.
-> a, b, c : shape parameters
-> moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : generalized exponential continuous random variable

Code #1 : Creating generalized exponential continuous random variable

 `from` `scipy.stats ``import` `genexpon `` ` `numargs ``=` `genexpon .numargs``[a, b, c] ``=` `[``0.7``, ] ``*` `numargs``rv ``=` `genexpon (a, b, c)`` ` `print` `(``"RV : \n"``, rv) `

Output :

```RV :
<scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D57997F60>
```

Code #2 : generalized exponential random variates.

 `import` `numpy as np``quantile ``=` `np.arange (``0.01``, ``1``, ``0.1``)``  ` `# Random Variates``R ``=` `genexpon.rvs(a, scale ``=` `2``,  size ``=` `10``)``print` `(``"Random Variates : \n"``, R)`

Output :

```Random Variates :
[0.74505484 2.02790441 2.06823675 3.96275674 1.24274054 3.71331036
0.53957521 0.37359838 2.53934153 2.36254065]

Probability Distribution :
[0.43109163 0.45222638 0.47102054 0.48773188 0.50258763 0.51578837
0.52751153 0.53791424 0.54713591 0.55530037]```

Code #3 : Graphical Representation.

 `import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `distribution ``=` `np.linspace(``0``, np.minimum(rv.dist.b, ``3``))``print``(``"Distribution : \n"``, 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 : Varying Positional Arguments

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

Output :

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