Extract Unmasked data from a Masked NumPy array

NumPy: Masked Arrays Exercise-15 with Solution

Write a NumPy program that creates a masked array and extracts the unmasked data as a regular NumPy array.

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)

# Extract the unmasked data as a regular NumPy array
unmasked_data = masked_array.compressed()

# Print the original array, the masked array, and the unmasked data
print('Original 2D array:\n', array_2d)
print('Masked array (elements > 50 are masked):\n', masked_array)
print('Unmasked data extracted as a regular NumPy array:\n', unmasked_data)

Output:

Original 2D array:
 [[20 68 13 60]
 [83 45 47 49]
 [75 81 19 71]
 [10 66 56 13]]
Masked array (elements > 50 are masked):
 [[20 -- 13 --]
 [-- 45 47 49]
 [-- -- 19 --]
 [10 -- -- 13]]
Unmasked data extracted as a regular NumPy array:
 [20 13 45 47 49 19 10 13]

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.
• Extract Unmasked Data:
• Used the compressed method of the masked array to extract the unmasked data as a regular NumPy array.
• Print the original 2D array, the masked array, and the unmasked data extracted as a regular NumPy array.

Python-Numpy Code Editor:

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