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Introduction to Python Poisson Distribution

Python Poisson Distribution – Before moving ahead, let’s know a bit of Python Binomial Distribution

It is also known as Discrete Distribution.

It describes how many times a particular event can take place in a specified time. E.g.,

It has two parameters:

```lam - number of occurrences e.g. 2 for the above problem.

size - Shape of the returned array.```

Example – Generating a random array containing 10 elements for occurrence 3.

```from numpy import random

x = random.poisson(lam=3, size=10)

print(x)```

As shown above, it returned an array containing random numbers.

Note: Later you will learn more in our Python Poisson Distribution Graph Tutorial.

Visualization of Poisson Distribution

Example – Visualizing the Poisson Distribution graph.

```from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.poisson(lam=3, size=1000), kde=False)

plt.show()```

As shown above, it Visualized the Poisson Distribution graph.

View to Normal Distribution and Poisson Distribution

The main difference between Normal Distribution and Poisson Distribution is that Normal Distribution leads to continuous numbers,s on the other hand, Poisson Distribution leads to finite or countable events or outcomes.

But if there’s a quiet bit of data, then Normal Distribution and Poisson Distribution can be defined as the same or similar.

Example – Visualizing the Poisson Distribution graph.

```from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.normal(loc=20, scale=7, size=1000), hist=False, label='normal')
kl.distplot(random.poisson(lam=20, size=1000), hist=False, label='poisson')

plt.show()```

As shown above, it Visualized the Poisson Distribution graph.

View to Poisson Distribution and Binomial Distribution

The main difference between Poisson Distribution and Binomial Distribution is that Poisson Distribution is for the continuous number, on the other hand, Binomial Distribution leads to finite or countable events or outcomes.

But if there’s a large amount of data, then Poisson Distribution and Binomial Distribution can be defined as the same or similar.

Example – Visualizing the Poisson Distribution graph.

```from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')
kl.distplot(random.binomial(lam=10, size=1000), hist=False, label='poisson')

plt.show()```

As shown above, it Visualized the Poisson Distribution graph.

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

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