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Select elements using Boolean Indexing with logical operators

NumPy: Advanced Indexing Exercise-13 with Solution

Boolean Indexing with Logical Operators:

Write a NumPy program that creates a 1D NumPy array and uses boolean indexing with logical operators (e.g., & for AND, | for OR) to select elements based on multiple conditions.

Sample Solution:

Python Code:

import numpy as np

# Create a 1D NumPy array with random integers
array_1d = np.random.randint(0, 100, size=20)

# Define multiple conditions using logical operators
condition = (array_1d > 30) & (array_1d < 70) | (array_1d % 10 == 0)

# Use boolean indexing to select elements that meet the conditions
selected_elements = array_1d[condition]

# Print the original array and the selected elements
print('Original 1D array:\n', array_1d)
print('Condition (elements > 30 AND < 70 OR divisible by 10):\n', condition)
print('Selected elements:\n', selected_elements)

Output:

Original 1D array:
 [62 59 48 29 54 10 10 59 11 94 64  3 99 37 52 75 16 60 97 77]
Condition (elements > 30 AND < 70 OR divisible by 10):
 [ True  True  True False  True  True  True  True False False  True False
 False  True  True False False  True False False]
Selected elements:
 [62 59 48 54 10 10 59 64 37 52 60]

Explanation:

  • Import Libraries:
    • Imported numpy as np for array creation and manipulation.
  • Create 1D NumPy Array:
    • Create a 1D NumPy array named array_1d with random integers ranging from 0 to 99 and a length of 20.
  • Define Multiple Conditions:
    • Define multiple conditions using logical operators:
      • Elements greater than 30 (array_1d > 30)
      • Elements less than 70 (array_1d < 70)
      • Elements divisible by 10 (array_1d % 10 == 0)
    • Combine these conditions using & (AND) and | (OR) logical operators.
  • Boolean Indexing:
    • Applied boolean indexing to select elements in array_1d that meet the defined conditions.
  • Print Results:
    • Print the original 1D array, the boolean condition array, and the selected elements to verify the indexing operation.

Python-Numpy Code Editor: