Select elements from 2D NumPy array using integer Indexing

Last update on December 21 2024 07:40:14 (UTC/GMT +8 hours)

Integer Indexing with Broadcasting:

Write a NumPy program that creates a 2D NumPy array and uses integer indexing with broadcasting to select elements from specific rows and all columns.

Sample Solution:

Python Code:

import numpy as np

# Create a 2D NumPy array of shape (5, 5) with random integers
array_2d = np.random.randint(0, 100, size=(5, 5))

# Define the row indices to select specific rows
row_indices = np.array([1, 3])

# Use integer indexing with broadcasting to select elements from specific rows and all columns
selected_elements = array_2d[row_indices[:, np.newaxis], np.arange(array_2d.shape[1])]
# Print the original array and the selected elements
print('Original 2D array:\n', array_2d)
print('Selected rows:\n', row_indices)
print('Selected elements from specific rows and all columns:\n', selected_elements)

Output:

Original 2D array:
 [[29 88 36 92 36]
 [11 23  6 57 79]
 [55 24 17 29 82]
 [96 61 85 34 75]
 [67 61 86 34 37]]
Selected rows:
 [1 3]
Selected elements from specific rows and all columns:
 [[11 23  6 57 79]
 [96 61 85 34 75]]

Explanation:

• Import libraries:
• Imported numpy as np for array creation and manipulation.
• Create 2D NumPy Array:
• Create a 2D NumPy array named array_2d with random integers ranging from 0 to 99 and a shape of (5, 5).
• Define row indices:
• Defined row_indices array to specify the rows from which to select elements.
• Integer Indexing with Broadcasting:
• Used integer indexing with broadcasting to select elements from the specified rows and all columns. This was done by combining row_indices with np.arange to select all columns.
• Print Results:
• Print the original 2D array, the row indices, and the selected elements to verify the indexing operation.

Python-Numpy Code Editor: