How to use np.random.random() in Python

NumPy is a fundamental package that is used to perform numerical computation tasks in Python. NumPy is based on the ndarray (n-dimensional array) used to perform operations on large data sets at high speed. The reason for speedily performing operations is the pre-compiled and optimized C code which is working behind the NumPy operations.

Being a powerful library, NumPy provides the np.random module which contains functions to generate random numbers. In this Answer, we will look into one of the functions of the np.random module: np.random.random().

The np.random.random() function

The np.random.random() function is used to generate random numbers in the interval of [0.0, 1.0).

Note: When representing intervals, square bracket [ means that the number is included in the interval, and round bracket ) means that the number is not included in the interval.

Using the function, the generated random numbers will be in a range greater than or equal to 0 (>=0) and less than 1 (<1).

Syntax

The syntax to create random numbers using the .random() function is:

np.random.random(size = (d1, d2, d3, ...))

The function takes in the size parameter, which defines the shape of the generated random numbers.

  • size = (): Generates a single random number.

  • size = (d1): Generates a 1-D array of random numbers with d1 elements.

  • size = (d1, d2): Generates a 2-D array of random numbers with d1*d2 elements.

  • size = (d1, d2, ..., dn): Generates an n-D array of random numbers with d1*d2*...*dn elements.

Coding examples

Now that we have explored the syntax of the np.random.random() function, let's explore some examples for generating random numbers of different dimensions.

Single random number

To generate a single random number, no parameters are passed in the .random() function. Please click the "Run" button below to view the random number.

import numpy as np
random_number = np.random.random()
print(random_number)

Code explanation

  • Line 1: We import the numpy module, which will provide us the .random() function.

  • Line 3: We define the np.random.random() function without any parameters, and it generates the random number.

1-D array of random numbers

To generate a 1-D array of random numbers, we have to pass the size of the array in the function. Please click the "Run" button below to generate a 1-D array of random numbers.

import numpy as np
random_number = np.random.random(size = 4)
print(random_number)

Code explanation

  • Line 3: We pass in size=4 as the parameter that generates an array of random numbers with 4 elements in it.

2-D array of random numbers

To generate a 2-D array of random numbers, we have to pass size=(rows, columns) as the parameter. Please click the "Run" button below to generate a 2-D array of random numbers.

import numpy as np
random_number = np.random.random(size = (4,3))
print(random_number)

Code explanation

  • Line 3: We pass in the size=(4,3) as the parameter that generates a 2-D array of random numbers with 4 rows and 3 elements in each row.

Conclusion

np.random.random() makes it easier to generate random numbers that play a crucial role in machine learning tasks, where we have to initialize model parameters, shuffle data, and create random numbers for testing and validation.

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