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Perform Masked multiplication on a Masked array in NumPy

NumPy: Masked Arrays Exercise-13 with Solution

Write a NumPy program that creates a masked array and performs a masked operation, such as masked multiplication.

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)

# Create another 2D NumPy array of the same shape with random integers
multiplier_array = np.random.randint(1, 10, size=(4, 4))

# Perform masked multiplication
masked_multiplication_result = masked_array * multiplier_array

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

Output:

Original 2D array:
 [[19 60 37 77]
 [46 57 49 41]
 [47 48 27 24]
 [13 35  2 27]]
Masked array (elements > 50 are masked):
 [[19 -- 37 --]
 [46 -- 49 41]
 [47 48 27 24]
 [13 35 2 27]]
Multiplier array:
 [[2 3 6 1]
 [6 5 8 2]
 [7 6 9 9]
 [3 8 2 3]]
Masked multiplication result:
 [[38 -- 222 --]
 [276 -- 392 82]
 [329 288 243 216]
 [39 280 4 81]]

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.
  • Create Multiplier Array:
    • Create another 2D NumPy array named multiplier_array with random integers ranging from 1 to 9 and a shape of (4, 4).
  • Perform Masked Multiplication:
    • Performed masked multiplication using the masked array and the multiplier array. The multiplication operation respects the mask, and masked elements remain masked in the result.
  • Print the original 2D array, the masked array, the multiplier array, and the result of the masked multiplication.

Python-Numpy Code Editor: