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Introduction to Numpy Uniform Distribution

In This Article, You Will Learn About Numpy Uniform Distribution.

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

Describe the possible chances to occur every task equal times.  

E.g., Probabilities of generating random numbers at equal times.

It includes three parameters:

a - Lower Bound. Default value is 0.0.

b - Upper Bound. Default value is 1.0.

size - The shape of the returned array.

Example – Create a 3×2 uniform distribution sample.

from numpy import random

x = random.uniform(size=(3, 2))

Output - 

[[0.27732781 0.06429024]
[0.08773632 0.58586866]
[0.38442203 0.65052188]]

As shown above, it returned an array of shape 3×2 containing random decimal numbers.

Note: You will learn in detail later about Uniform Distribution Tutorial.

Visualization of Uniform Distribution

Example – Visualizing the Uniform Distribution graph without histogram.

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

kl.distplot(random.uniform(size=1000), hist=False)

As a result, it Visualized the Uniform Distribution graph.

Example – Visualizing the Uniform Distribution graph with histogram.

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


As shown above, it Visualized the Uniform Distribution graph.

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

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