NumPy: Extract first and third elements of the first and third rows from a given (4x4) array

Last update on March 24 2025 13:38:57 (UTC/GMT +8 hours)

Extract First & Third Elements of First/Third Rows

Write a NumPy program to extract the first and third elements of the first and third rows from a given (4x4) array.

Pictorial Presentation:

Sample Solution:

Python Code:

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

# Creating a NumPy array 'arra_data' containing integers from 0 to 15 and reshaping it into a 4x4 matrix
arra_data = np.arange(0, 16).reshape((4, 4))

# Displaying a message indicating the original array will be printed
print("Original array:")

# Printing the original 4x4 array 'arra_data'
print(arra_data)

# Displaying a message indicating the extracted data (first and third elements of the first and third rows)
print("\nExtracted data: First and third elements of the first and third rows")

# Using slicing with a step of 2 to extract every other element in both rows and columns
print(arra_data[::2, ::2])

Sample Output:

Original array:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]]

Extracted data: First and third elements of the first and third rows 
[[ 0  2]
 [ 8 10]]

Explanation:

In the above exercise -

arra_data = np.arange(0, 16).reshape((4, 4)): This line creates a 1-dimensional NumPy array with elements from 0 to 15 (excluding 16) using np.arange(0, 16) and then reshapes it into a 2-dimensional array with 4 rows and 4 columns using .reshape((4, 4)).

print(arra_data[::2, ::2]): It prints a subarray of ‘arra_data’ by selecting every second row and every second column using the slicing syntax ::2. The : in the slicing syntax indicates selecting all elements, while the 2 after the second colon represents a step of 2, meaning every second element will be selected.

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract the first and third elements from the first and third rows of a 4x4 array.
• Create a function that uses advanced indexing to select non-consecutive elements from specified rows.
• Test the extraction using both slicing and np.take along the specified axis to verify accuracy.
• Implement a solution that dynamically accepts row and column indices to extract a custom submatrix.

Python-Numpy Code Editor: