Uniform Distribution in NumPy

A Uniform Distribution is used when every value in a given range has an equal probability of occurring. NumPy provides the numpy.random.uniform() method to generate such values for simulations, sampling, and numerical experiments.

Example: Here’s the example demonstrating how to generate one random value from a Uniform Distribution.

0.6869717010984568

Explanation: This generates a random floating-point number between 0 and 1, the default uniform range.

Syntax

numpy.random.uniform(low=0.0, high=1.0, size=None)

Parameters:

• low: Lower bound of the range (inclusive).
• high: Upper bound of the range (exclusive).
• size: Shape of the output array.

Examples

Example 1: This example shows how to generate five random numbers between 0 and 1 multiple uniform distribution values.

[0.11403523 0.69111039 0.92330809 0.65533514 0.6227924 ]

Explanation: np.random.uniform(size=5) creates an array of 5 random numbers in the range [0, 1).

Example 2: In this example, we generate five random numbers in the range 10 to 20 using the numpy.random.uniform() method.

[15.33364215 15.62793284 19.66237254 18.56727821 11.27919983]

Explanation: This creates an array vals with 5 values sampled uniformly from the interval 10 ≤ x < 20 using np.random.uniform(10, 20, size=5).

Using NumPy Generator

NumPy now recommends using the Generator class for random number generation instead of the legacy numpy.random functions. The Generator provides better randomness, reproducibility, and performance. You can create a Generator instance using np.random.default_rng() and then use its .uniform() method to generate uniform random numbers.

Example: Here, we generate a 2×3 matrix where each value comes from the Uniform Distribution between 1 and 5 using Generator.

[[4.42554691 4.11402029 2.90202497]
 [4.74049492 3.26084455 2.00333856]]

Explanation: The rng.uniform(1, 5, size=(2, 3)) call generates a 2×3 array where each value is drawn uniformly from 1 ≤ x < 5.

Visualizing the Uniform Distribution

Visualizing the generated numbers helps in understanding their behavior. Let's see a example to plot a histogram of random numbers using numpy.random.uniform function.

Output

Explanation:

• The histogram shows values scattered uniformly between low (10) and high (20).
• Every number in this interval appears with similar frequency, which is the core property of a uniform distribution.
• The flat pattern of bars confirms equal probability for all values in the range.