NumPy: Create a function cube which cubes all the elements of an array

Last update on April 26 2025 12:38:50 (UTC/GMT +8 hours)

Apply Cube Function to All Elements

Write a NumPy program to create a function cube for all array elements.

Pictorial Presentation:

Python NumPy: Create a function cube which cubes all the elements of an array

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Defining a function 'cube' that takes an input 'e'
def cube(e):
    # Creating an nditer object 'it' to iterate over 'e' along with an uninitialized 'None' array
    it = np.nditer([e, None])
    
    # Iterating through each element 'a' in 'e' and corresponding uninitialized element 'b'
    for a, b in it:
        # Setting 'b' to the cube of 'a' in each iteration
        b[...] = a * a * a
    
    # Returning the resulting array after cube operations from the operands of the iterator
    return it.operands[1]

# Calling the 'cube' function with a list [1, 2, 3] as an argument and printing the result
print(cube([1, 2, 3]))

Sample Output:

[ 1  8 27]

Explanation:

In the above code -

def cube(e):: Define a function called cube that takes a single argument e.
it = np.nditer([e, None]): Create a NumPy iterator called it that iterates over the input array e and an uninitialized output array of the same shape as e. This is done by passing None as the second element in the list.
for a, b in it:: Iterate over the elements of e and the uninitialized output array using the iterator it. a represents an element from e, and b represents the corresponding element in the output array.
b[...] = a*a*a: Cube the value of a and assign it to the corresponding element b in the output array.
return it.operands[1]: After the loop, return the output array, which is stored as the second operand in the iterator it.
Finally ‘print(cube([1,2,3]))’ prints the result.

For more Practice: Solve these Related Problems:

Write a NumPy program to apply a cube function to each element of an array using np.vectorize.
Create a function that computes the cube of array elements and compares the output with manual exponentiation.
Use np.power to raise each element of an array to the third power and verify the results.
Implement the cube operation using both list comprehension and vectorized operations, then compare performance.

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