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

Last update on August 29 2025 12:46:00 (UTC/GMT +8 hours)

Extract Second & Fourth Elements of Second/Fourth Rows

Write a NumPy program to extract the second and fourth elements of the second and fourth 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 (second and fourth elements of the second and fourth rows)
print("\nExtracted data: Second and fourth elements of the second and fourth rows")

# Using slicing to extract the second and fourth elements of every other row and column starting from the second row and second column
print(arra_data[1::2, 1::2]) 

Sample Output:

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

Extracted data: Second and fourth elements of the second and fourth rows 
[[ 5  7]
 [13 15]]

Explanation:

. In the above example -

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[1::2, 1::2]): It prints specific elements of ‘arra_data’ by selecting every second row starting from the second row (index 1) and every second column starting from the second column (index 1) using the slicing syntax 1::2.

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract the second and fourth elements from the second and fourth rows of a 4x4 array.
• Create a function that uses boolean masking to select specific elements from chosen rows.
• Test the extraction with different arrays to ensure that the correct elements are consistently retrieved.
• Implement an alternative approach using np.ix_ to form the index arrays and compare outputs.

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