Apply custom function to Unmasked elements in a Masked NumPy array

NumPy: Masked Arrays Exercise-14 with Solution

Write a NumPy program that creates a masked array and applies a custom function only to the unmasked 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)

# Define a custom function to apply to the unmasked elements
def custom_function(x):
    return x * 2

# Apply the custom function only to the unmasked elements
unmasked_result = ma.apply_along_axis(custom_function, -1, masked_array)

# Print the original array, the masked array, and the result after applying the custom function
print('Original 2D array:\n', array_2d)
print('Masked array (elements > 50 are masked):\n', masked_array)
print('Result after applying custom function to unmasked elements:\n', unmasked_result)

Output:

Original 2D array:
 [[21 56 25 76]
 [10 61 56 86]
 [74 65 89 87]
 [75 94  3 79]]
Masked array (elements > 50 are masked):
 [[21 -- 25 --]
 [10 -- -- --]
 [-- -- -- --]
 [-- -- 3 --]]
Result after applying custom function to unmasked elements:
 [[42 -- 50 --]
 [20 -- -- --]
 [-- -- -- --]
 [-- -- 6 --]]

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.
• Define Custom Function:
• Define a custom function custom_function that takes an input x and returns x * 2.
• Apply Custom Function to Unmasked Elements:
• Use ma.apply_along_axis to apply the custom function only to the unmasked elements in the masked array.
• Finally print the original 2D array, the masked array, and the result after applying the custom function to the unmasked elements.

Python-Numpy Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Perform Masked multiplication on a Masked array in NumPy.
Next: Extract Unmasked data from a Masked NumPy array.