Perform Masked sum operation in NumPy ignoring Masked elements

NumPy: Masked Arrays Exercise-16 with Solution

Write a NumPy program to create a masked array and perform a masked sum operation, ignoring the masked elements.

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)

# Perform a masked sum operation, ignoring the masked elements
masked_sum = masked_array.sum()

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

Output:

Original 2D array:
 [[56 18 40 43]
 [61 87 68 74]
 [51 40  3 14]
 [51 63 49 90]]
Masked array (elements > 50 are masked):
 [[-- 18 40 43]
 [-- -- -- --]
 [-- 40 3 14]
 [-- -- 49 --]]
Sum of unmasked elements: 207

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.
• Perform Masked Sum:
• Use the sum method of the masked array to compute the sum of the unmasked elements, ignoring the masked elements.
• Print the original 2D array, the masked array, and the result of the masked sum.

Python-Numpy Code Editor:

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