Create a Masked array and Mask a row in NumPy

NumPy: Masked Arrays Exercise-8 with Solution

Write a NumPy program to create a masked array from a 2D NumPy array and mask all values in a specified row.

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, 10, size=(4, 4))

# Specify the row to mask
row_to_mask = 2

# Create a masked array from the 2D array and mask all values in the specified row
masked_array = ma.masked_array(array_2d, mask=np.zeros_like(array_2d, dtype=bool))
masked_array[row_to_mask] = ma.masked

# Print the original array and the masked array
print('Original 2D array:\n', array_2d)
print('Masked array (with row {} masked):\n'.format(row_to_mask), masked_array)

Output:

Original 2D array:
 [[5 4 4 0]
 [0 1 3 2]
 [6 7 5 1]
 [5 7 3 2]]
Masked array (with row 2 masked):
 [[5 4 4 0]
 [0 1 3 2]
 [-- -- -- --]
 [5 7 3 2]]

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 9 and a shape of (4, 4).
• Specify Row to Mask:
• Specify the row index to mask, which is 2 in this example.
• Create Masked Array:
• Create a masked array from the 2D array using ma.masked_array. Initially, set the mask to be an array of False values with the same shape as the original array.
• Masked all values in the specified row by setting the mask for that row to True.
• Print the original 2D array and the masked array with the specified row masked.

Python-Numpy Code Editor:

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