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

NumPy: Array Object Exercise-138 with Solution

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

# Using slicing to extract the first two rows and columns 2 and 3 (2:4 refers to 2nd and 3rd indices)
print(arra_data[0:2, 2:4])

Sample Output:

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

Extracted data: Third and fourth elements of the first and second rows 
[[2 3]
 [6 7]]

Explanation:

In the above code -

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[0:2, 2:4]): It prints a subarray of ‘arra_data’ containing the first two rows and the last two columns. The slicing syntax 0:2 indicates elements from index 0 (inclusive) to index 2 (exclusive), and the slicing syntax 2:4 indicates elements from index 2 (inclusive) to index 4 (exclusive).

Python-Numpy Code Editor:

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