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))

print(x)
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)

plt.show()
numpy-uniform-distribution

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

kl.distplot(random.uniform(size=1000))

plt.show()
numpy-uniform-distribution

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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