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Mask values in NumPy array based on Complex condition

NumPy: Masked Arrays Exercise-17 with Solution

Write a NumPy program that creates a masked array from a regular array and mask values based on a complex condition.

Sample Solution:

Python Code:

import numpy as np
import numpy.ma as ma

# Create a 2D NumPy array of shape (5, 5) with random integers
array_2d = np.random.randint(0, 100, size=(5, 5))

# Define a complex condition to mask elements: values less than 20 or greater than 80
condition = (array_2d < 20) | (array_2d > 80)

# Create a masked array from the 2D array using the complex 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 (values < 20 or > 80 are masked):\n', masked_array)

Output:

Original 2D array:
 [[23 56 52 73 24]
 [70 13 78 19 65]
 [41 67 51 94 93]
 [ 2 70 32 64 55]
 [37 75  1 54 35]]
Masked array (values < 20 or > 80 are masked):
 [[23 56 52 73 24]
 [70 -- 78 -- 65]
 [41 67 51 -- --]
 [-- 70 32 64 55]
 [37 75 -- 54 35]]

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:
    • Created a 2D NumPy array named 'array_2d' with random integers ranging from 0 to 99 and a shape of (5, 5).
  • Define Complex Condition:
    • Defined a complex condition to mask elements: values less than 20 or greater than 80 using logical OR (|).
  • Create Masked Array:
    • Create a masked array from the 2D array using ma.masked_array, applying the complex condition as the mask. Elements less than 20 or greater than 80 are masked.
  • Print the original 2D array and the masked array to verify the operation.

Python-Numpy Code Editor: