  Introduction to Numpy Pareto Distribution

Numpy Pareto Distribution – Before moving ahead, let’s know a bit of Python Exponential Distribution

Pareto Distribution is distributed in the ratio of 80-20 distribution i.e., 20% factors cause 80% outcome.

It includes two parameters –

```a – parameter shape.

size - Shape of the returned array.     ```

### Example – Creating an array of random numbers of size 3×3 for Pareto distribution.

`from numpy import randomz = random.pareto(a=2, size=(3, 3))print(z)`
```Output -

[[0.80365148 0.2705212  0.56911853]
[0.00620553 0.98018188 1.63845207]
[4.58486444 0.02704608 2.06710027]]```

As shown above, it returned an array of shapes 3×3 containing random numbers.

Visualization of Pareto Distribution

Example – Visualizing the Pareto Distribution graph without histogram.

`from numpy import randomimport matplotlib.pyplot as pltimport seaborn as klkl.distplot(random.pareto(a=2, size=5000), kde=False)plt.show()`

Example – Visualizing the Pareto Distribution graph with histogram.

`from numpy import randomimport matplotlib.pyplot as pltimport seaborn as klkl.distplot(random.pareto(a=2, size=5000))plt.show()`

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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