This is a detailed tutorial of NumPy Exponential Distribution. Learn to implement Exponential Distribution using NumPy and visualize using Seaborn In the theory of probability and statistics, this is the distribution of time between the events which will occur in the future. In this process, the events will continuously and independently. As a result, it will always have a constant average rate..

In the theory of probability and statistics, this is the distribution of time between the events which will occur in the future. In this process, the events will continuously and independently. As a result, it will always have a constant average rate.

Similarly, it helps in predicting the success and failure of an event. It takes in two parameters as input which are:

`scale`

– It is the inverse of the rate which is by default set to 1.0`size`

– this will help us in determining the shape of the array.

Let us go through an example in order to understand properly:

numpy package with random module

from numpy import random

x = random. exponential( size =( 3, 6))

#now we will print the data

print( x)

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