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# Python – Discrete Hyper-geometric Distribution in Statistics

• Last Updated : 10 Jan, 2020

scipy.stats.hypergeom() is a hypergeometric discrete random variable. It is inherited from the of generic methods as an instance of the rv_discrete class. It completes the methods with details specific for this particular distribution.

Parameters :

x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : hypergeometric discrete random variable

Code #1 : Creating hypergeometric discrete random variable

 `# importing library`` ` `from` `scipy.stats ``import` `hypergeom ``   ` `numargs ``=` `hypergeom .numargs ``a, b ``=` `0.2``, ``0.8``rv ``=` `hypergeom (a, b) ``   ` `print` `(``"RV : \n"``, rv)  `

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C0DF048
```

Code #2 : hypergeometric discrete variates and probability distribution

 `import` `numpy as np ``quantile ``=` `np.arange (``0.01``, ``1``, ``0.1``) `` ` `# Random Variates ``R ``=` `hypergeom .pmf(a, b, c, ``10``) ``print` `(``"Random Variates : \n"``, R) `` ` `# PDF ``x ``=` `np.linspace(hypergeom.ppf(``0.01``, a, b, c),``                ``hypergeom.ppf(``0.99``, a, b, c), ``10``)``R ``=` `hypergeom.ppf(x, ``1``, ``3``, ``3``)``print` `(``"\nProbability Distribution : \n"``, R) `

Output :

```Random Variates :
nan

Probability Distribution :
[nan nan nan nan nan nan nan nan nan nan]

```

Code #3 : Graphical Representation.

 `import` `numpy as np ``import` `matplotlib.pyplot as plt ``    ` `distribution ``=` `np.linspace(``0``, np.minimum(rv.dist.b, ``2``)) ``print``(``"Distribution : \n"``, distribution) `

Output :

```Distribution :
[0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
1.95918367 2.
```

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