Apply custom function to Unmasked elements in a Masked NumPy array

Last update on April 30 2025 12:56:09 (UTC/GMT +8 hours)

14. Apply Custom Function to Unmasked Elements

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.

For more Practice: Solve these Related Problems:

Write a Numpy program to apply a custom lambda function only to the unmasked elements of a masked array.
Write a Numpy program to apply a custom mathematical transformation to unmasked elements in a 2D masked array.
Write a Numpy program to create a custom function and use np.ma.apply_along_axis to process only the unmasked data.
Write a Numpy program to implement a custom ufunc that operates solely on unmasked elements and then reassemble the full array.

Go to:

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

Python-Numpy Code Editor:

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