Create a Masked array and count Masked elements in NumPy

Last update on December 21 2024 07:36:37 (UTC/GMT +8 hours)

NumPy: Masked Arrays Exercise-6 with Solution

Write a NumPy program to create a masked array and count the number of masked elements.

Sample Solution:

Python Code:

import numpy as np  # Import NumPy library

# Create a regular NumPy array with some values
data = np.array([1, 2, np.nan, 4, 5, np.nan, 7, 8, 9, 10])

# Create a mask to specify which values to mask (e.g., NaN values)
mask = np.isnan(data)

# Create a masked array using the regular array and the mask
masked_array = np.ma.masked_array(data, mask=mask)

# Count the number of masked elements in the masked array
num_masked_elements = np.ma.count_masked(masked_array)

# Print the original array, the masked array, and the number of masked elements
print("Original Array:")
print(data)

print("\nMasked Array:")
print(masked_array)

print("\nNumber of Masked Elements:")
print(num_masked_elements)

Output:

Original Array:
[ 1.  2. nan  4.  5. nan  7.  8.  9. 10.]

Masked Array:
[1.0 2.0 -- 4.0 5.0 -- 7.0 8.0 9.0 10.0]

Number of Masked Elements:
2

Explanation:

• Import NumPy Library:
• Import the NumPy library to handle array operations.
• Create a Regular Array:
• Define a NumPy array with integer values and include some NaN values to be masked.
• Define the Mask:
• Create a Boolean mask array where True indicates the values to be masked (e.g., NaN values).
• Create the Masked Array:
• Use "np.ma.masked_array()" to create a masked array from the regular array and the mask.
• Count Masked Elements:
• Use “np.ma.count_masked()” to count the number of masked elements in the masked array.
• Finally print the original array, the masked array, and the number of masked elements to verify the operation.

Python-Numpy Code Editor: