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# Python Data Science NumPy Random Exponential Data Distribution

Exponential Distribution: is used for describing time till next event e.g. failure/success etc.

It has the following two parameters.

1. scale - inverse of rate ( see lam in poisson distribution ) defaults to 1.0.

2. size - The shape of the returned array.

Example 1: Draw out a sample for exponential distribution with 2.0 scale with 2x4 size.

Code

from numpy import random

x = random.exponential(scale=2, size=(2, 4))

print(x)

the output will be

[[2.25692663 0.10811811 0.03751409 4.24780845]

[0.49943354 1.11501134 1.49241455 0.2187935 ]]

Note: Every time the code is run the output may vary because of random generation.

Visualization of Exponential Distribution

Example 2

Code

from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns

sns.distplot(random.
exponential(size=100000), hist=False)

plt.show()

the output will be