# Python – Negative Binomial Discrete Distribution in Statistics

Last Updated : 10 Jan, 2020

scipy.stats.nbinom() is a Negative binomial 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 [â€˜mvskâ€™]; â€˜mâ€™ = mean, â€˜vâ€™ = variance, â€˜sâ€™ = Fisherâ€™s skew and â€˜kâ€™ = Fisherâ€™s kurtosis. (default = â€˜mvâ€™).

Results : Negative binomial discrete random variable

Code #1 : Creating Negative binomial discrete random variable

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

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C016208
```

Code #2 : Negative binomial discrete variates and probability distribution

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

Output :

```Random Variates :
[0 0 0 0 0 0 0 0 0 0]

Probability Distribution :
[-1. 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)  ` `    `  `plot ``=` `plt.plot(distribution, rv.ppf(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.        ]
```

Code #4 : Varying Positional Arguments

 `import` `matplotlib.pyplot as plt  ` `import` `numpy as np  ` ` `  `x ``=` `np.linspace(``0``, ``5``, ``100``)  ` `    `  `# Varying positional arguments  ` `y1 ``=` `nbinom.ppf(x, a, b)  ` `y2 ``=` `nbinom.pmf(x, a, b)  ` `plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``)  `

Output :

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