# Understanding Uniform Distribution In Numpy Random

In this article, we’ll take a closer look at the NumPy random uniform distribution and how to use it in your Python programs.

## NumPy Random Uniform Distribution

The Numpy random uniform represents a distribution where each event has a fair chance of appearing.

Random number creation, for example.

There are three parameters that make up this function:

 Parameters Overview a The lower bound of the range – by default it is zero. b An upper bound – 1.0 is the default number. size This is how the array will be constructed when it is retrieved.

NumPy random uniform can be useful for many different applications, such as generating random data for testing and simulation, or for creating randomized inputs for machine learning models.

Let’s look at some examples of how to use NumPy random uniform.

Make a five-by-two uniform distribution of the data as follows:

#### Example:

from numpy import random mrx = random.uniform(size=(5, 2)) print(mrx)

In a three-by-three uniform distribution, arrange the numbers as follows:

#### Example:

from numpy import random mrx = random.uniform(size=(3, 3)) print(mrx)

## Visualization of Uniform Distribution

Visualizing the uniform distribution can help us understand how it works and how it can be used in various applications.

Uniform Distribution Implementation:

#### Example:

from numpy import random import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot(random.uniform(size=100), hist=False) pt.show()

Below example shows the histogram representation of Uniform Distribution:

#### Example:

from numpy import random import matplotlib.pyplot as pt import seaborn as sbn sbn.distplot(random.uniform(size=25), hist=True, kde = False) pt.show()
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