Mask elements in NumPy array based on condition using Logical operations

Last update on December 21 2024 07:37:00 (UTC/GMT +8 hours)

NumPy: Masked Arrays Exercise-9 with Solution

Write a NumPy program that creates a masked array and uses logical operations to mask elements based on a condition.

Sample Solution:

Python Code:

import numpy as np
import numpy.ma as ma

# Create a 2D NumPy array of shape (4, 4) with random integers
array_2d = np.random.randint(0, 100, size=(4, 4))

# Define the condition to mask elements greater than 50
condition = array_2d > 50

# Create a masked array from the 2D array using the condition
masked_array = ma.masked_array(array_2d, mask=condition)

# Print the original array and the masked array
print('Original 2D array:\n', array_2d)
print('Masked array (elements > 50 are masked):\n', masked_array)

Output:

Original 2D array:
 [[81  4 20 37]
 [76 37 38 18]
 [65 42 24 37]
 [15 62 57 27]]
Masked array (elements > 50 are masked):
 [[-- 4 20 37]
 [-- 37 38 18]
 [-- 42 24 37]
 [15 -- -- 27]]

Explanation:

• Import Libraries:
• Imported numpy as "np" for array creation and manipulation.
• Imported numpy.ma as "ma" for creating and working with masked arrays.
• Create 2D NumPy Array:
• Create a 2D NumPy array named array_2d with random integers ranging from 0 to 99 and a shape of (4, 4).
• Define Condition:
• Define a condition to mask elements in the array that are greater than 50.
• Create Masked Array:
• Create a masked array from the 2D array using ma.masked_array, applying the condition as the mask. Elements greater than 50 are masked.
• Print the original 2D array and the masked array to verify operation.

Python-Numpy Code Editor: