NumPy: Extract all the elements of the first and fourth columns from a given (4x4) array

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

Extract First & Fourth Columns

Write a NumPy program to extract all the elements of the first and fourth columns 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 (all the elements of the first and fourth columns)
print("\nExtracted data: All the elements of the first and fourth columns")

# Using slicing to extract all rows and specific columns (1st and 4th columns) from 'arra_data'
print(arra_data[:, [0, 3]])

Sample Output:

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

Extracted data: All the elements of the first and fourth columns 
[[ 0  3]
 [ 4  7]
 [ 8 11]
 [12 15]]

Explanation:

In the above code -

arra_data = np.arange(0, 16).reshape((4, 4)): It 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[:, [0, 3]]): This line prints specific columns of ‘arra_data’ by selecting all rows (indicated by :) and only the columns with indices 0 and 3 (indicated by [0, 3]).

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract the first and fourth columns from a 4x4 array using slicing techniques.
• Create a function that accepts a 2D array and returns a new array composed of the first and last columns.
• Test the extraction on multiple arrays to ensure that the function correctly handles various column indices.
• Implement an alternative approach using np.hstack to combine the extracted column vectors and compare outputs.

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