Select elements from 2D NumPy array with random floats using Boolean indexing

NumPy: Advanced Indexing Exercise-5 with Solution

Boolean Indexing with Conditions:

Write a NumPy program that creates a 2D NumPy array of random floats and uses boolean indexing to select elements that satisfy multiple conditions (e.g., greater than 0.5 and less than 0.8).

Sample Solution:

Python Code:

import numpy as np

# Create a 2D NumPy array of random floats between 0 and 1
array_2d = np.random.rand(5, 5)

# Define the conditions
condition1 = array_2d > 0.5
condition2 = array_2d < 0.8

# Use boolean indexing to select elements that satisfy both conditions
selected_elements = array_2d[condition1 & condition2]

# Print the original array and the selected elements
print('Original 2D array:\n', array_2d)
print('Elements greater than 0.5 and less than 0.8:\n', selected_elements)

Output:

Original 2D array:
 [[0.52972276 0.70225957 0.27440778 0.18107896 0.11912977]
 [0.38789867 0.55283666 0.92619903 0.45360212 0.30341309]
 [0.96337533 0.11772659 0.57502825 0.6581205  0.40238651]
 [0.21598097 0.58864959 0.83392972 0.26703946 0.57283874]
 [0.67218407 0.19983269 0.23111841 0.24815625 0.22444408]]
Elements greater than 0.5 and less than 0.8:
 [0.52972276 0.70225957 0.55283666 0.57502825 0.6581205  0.58864959
 0.57283874 0.67218407]

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 floats between 0 and 1 using np.random.rand.
• Define the conditions:
• Defined two conditions: condition1 to check if elements are greater than 0.5, and condition2 to check if elements are less than 0.8.
• Boolean Indexing:
• Applied boolean indexing to select elements that satisfy both conditions using the & operator to combine them.
• Print Results:
• Print the original 2D array and the elements that satisfy both conditions to verify the indexing operation.

Python-Numpy Code Editor:

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