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Rukhshan Haroon

Grokking Modern System Design Interview for Engineers & Managers

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The ** random.poisson** function is part of Python’s NumPy library and is used to model probabilistic experiments with the help of the Poisson distribution.

To use the `random.poisson`

function, you must import `numpy`

at the very start of the program:

```
from numpy import random
```

The `random.poisson`

method has the following syntax:

```
numpy.random.poisson(l,n)
```

The `random.poisson`

function takes in two parameters:

`l`

:`l`

stands for Lamba and is the rate at which a Poisson process occurs.`l`

must be greater than or equal to zero and of type float.`n`

(optional): the number of samples to be drawn. The default value is 1.

The

Poisson distributionis used to model a situation or event that occurs at a constant rate, which we denote as Lambda.

The `random.poisson`

method returns an array of length `n`

. This is the bunch of samples drawn from the Poisson distribution modeled with Lambda equal to `l`

.

The code below shows how to use the `random.poisson`

function in Python.

First, we set the values of `n`

and `l`

to 3000 and 2.34, respectively. This is to denote that a particular event occurs at a constant rate of 2.34. The units of `l`

must be consistent with the units of the drawn samples.

Next, we call the `random.poisson`

function and print its return value with the `print`

function. The return value is a list of 3000 values, which corresponds to the 3000 samples drawn from the Poisson process modeled with `l`

as the parameter.

We use the

from numpy import random#declaring and assigning values to n and ln=3000l=2.34#calling random.poisson methodret_val=random.poisson(l, n)#printing return value and its lengthprint("Return value:", ret_val)print("Length of return value:", len(ret_val))

If we graph this Poisson process, our plot would resemble the following sketch:

```
import seaborn as sns
import matplotlib.pyplot as plt
#ret_val is the return value obtained from the code snippet above
sns.distplot(ret_val, kde=True)
plt.show()
```

As expected, the probability reaches the maximum when the number of occurrences is equal to Lambda.

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Rukhshan Haroon

Grokking Modern System Design Interview for Engineers & Managers

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