NumPy: Create a 3x3x3 array with random values

Last update on March 25 2025 12:19:48 (UTC/GMT +8 hours)

3. 3x3x3 Random Array

Write a NumPy program to create a 3x3x3 array with random values.

Sample Solution:

Python Code:

# Importing the NumPy library as np

import numpy as np

# Generating a 3x3x3 NumPy array 'x' filled with random floats between 0 and 1 using np.random.random()

x = np.random.random((3, 3, 3))

# Printing the array 'x'
print(x) 

Sample Output:

[[[ 0.08372197  0.09089865  0.54581268]                                
  [ 0.62831932  0.06252404  0.1108799 ]                                
  [ 0.25040264  0.80817908  0.37027715]]                               
                                                                       
 [[ 0.44916756  0.66390614  0.83100662]                                
  [ 0.87831954  0.17075539  0.7506945 ]                                
  [ 0.56165801  0.72280907  0.22771692]]                               
                                                                       
 [[ 0.04956428  0.7143399   0.37042941]                                
  [ 0.78875282  0.27425369  0.88486919]                                
  [ 0.13395418  0.86087257  0.36234729]]]

Explanation:

x = np.random.random((3,3,3)): This line generates a 3-dimensional array (3x3x3) of random floating-point numbers using the np.random.random() function. The input tuple (3,3,3) specifies the shape of the array, which has 3 arrays, each containing 3 arrays with 3 elements each.

print(x): Finally print() function prints the generated 3-dimensional array of random floating-point numbers.

Pictorial Presentation:

For more Practice: Solve these Related Problems:

• Create a 3x3x3 array of random values and compute the mean along the first axis.
• Reshape the 3x3x3 random array into a 9x3 array and verify the new dimensions.
• Compute the standard deviation of the elements in each 3x3 sub-array along the third axis.
• Apply a threshold to the 3x3x3 array to set all values below 0.5 to zero and count the nonzero elements.

Python-Numpy Code Editor: