# Python – Inverse Weibull Distribution in Statistics

scipy.stats.invweibull() is an inverted weibull 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.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : Inverse weibull continuous random variable

Code #1 : Creating inverted weibull continuous random variable

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

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D4EAE9C8
```

Code #2 : inverted weibull continuous variates and probability distribution

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

Output :

```Random Variates :
[ 2.46502056 32.97160826  8.65843435  1.21357636  0.22162243  1.05724138
7.5574935   0.0624836   0.83384033 17.29417907]

Probability Distribution :
[0.00613124 0.06733615 0.12799203 0.18757349 0.24553408 0.30131353
0.35434638 0.40407156 0.44994318 0.49144206]
```

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 ``=` `invweibull .pdf(x, ``1``, ``3``)  ` `y2 ``=` `invweibull .pdf(x, ``1``, ``4``)  ` `plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``)  `

Output :

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