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:
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