random_sample() function in NumPy array

Introduction

Generating random numbers is a crucial aspect in various fields such as data science, simulations, and algorithm testing. NumPy offers several utilities to create random values, and one of these is the random_sample() function. This function helps produce random floating-point numbers within the range [0.0, 1.0), making it very useful for different programming and analytical tasks.

Understanding the random_sample() Function

The numpy.random.random_sample() method generates random floats sampled from a uniform distribution between 0.0 (inclusive) and 1.0 (exclusive). It can return a single float or an array of floats depending on the input argument specifying the desired shape.

Syntax

numpy.random.random_sample(size=None)

• size: Optional parameter that defines the output shape. If omitted, a single float value is returned.

Example 1: Generate a Single Random Float

import numpy as np

value = np.random.random_sample()
print("Random float:", value)

This outputs a single float number like:

Random float: 0.473829182

Example 2: Create a 1-D Array of Random Floats

import numpy as np

array_1d = np.random.random_sample(5)
print("1-D Array:", array_1d)

Sample output:

1-D Array: [0.12 0.89 0.37 0.66 0.43]

Example 3: Create a 2-D Random Float Array

import numpy as np

array_2d = np.random.random_sample((3, 2))
print("2-D Array:\n", array_2d)

Possible output:

2-D Array:
[[0.24 0.75]
 [0.93 0.05]
 [0.38 0.49]]

When to Use random_sample()

The random_sample() function is ideal when you need uniformly distributed random floats between 0 and 1. This is commonly needed for:

• Initializing random values in simulations
• Creating synthetic datasets
• Random sampling in statistical modeling
• Testing algorithms with randomized inputs

Additional Notes

• The function always returns floats within the range [0.0, 1.0).
• You can specify the output shape using the size parameter.
• For numbers in a different range, multiply or transform the output accordingly.

Conclusion

The random_sample() function in NumPy is a straightforward and flexible method to generate random floating-point numbers between 0 and 1. Its versatility in returning single values or arrays makes it a handy tool for anyone working with random data in Python.