Element-wise addition of two Masked arrays in NumPy

Last update on December 21 2024 07:36:47 (UTC/GMT +8 hours)

NumPy: Masked Arrays Exercise-7 with Solution

Write a NumPy program to perform element-wise addition of two masked arrays, maintaining the masks.

Sample Solution:

Python Code:

import numpy as np  # Import NumPy library

# Create two regular NumPy arrays with some values
data1 = np.array([1, 2, np.nan, 4, 5])
data2 = np.array([5, np.nan, 2, 3, 1])

# Create masks to specify which values to mask (e.g., NaN values)
mask1 = np.isnan(data1)
mask2 = np.isnan(data2)

# Create masked arrays using the regular arrays and the masks
masked_array1 = np.ma.masked_array(data1, mask=mask1)
masked_array2 = np.ma.masked_array(data2, mask=mask2)

# Perform element-wise addition of the two masked arrays, maintaining the masks
result_array = np.ma.add(masked_array1, masked_array2)

# Print the original masked arrays and the resulting array
print("Masked Array 1:")
print(masked_array1)

print("\nMasked Array 2:")
print(masked_array2)

print("\nResulting Array after Element-wise Addition:")
print(result_array)

Output:

Masked Array 1:
[1.0 2.0 -- 4.0 5.0]

Masked Array 2:
[5.0 -- 2.0 3.0 1.0]

Resulting Array after Element-wise Addition:
[6.0 -- -- 7.0 6.0]

Explanation:

• Import NumPy Library:
• Import the NumPy library to handle array operations.
• Create Regular Arrays:
• Define two NumPy arrays data1 and data2 with integer values, including some NaN values to be masked.
• Define the masks:
• Create Boolean mask arrays 'mask1' and 'mask2' where True indicates the values to be masked (e.g., NaN values).
• Create Masked Arrays:
• Use "np.ma.masked_array()" to create masked arrays 'masked_array1' and 'masked_array2' from the regular arrays and the masks.
• Perform element-wise addition:
• Use "np.ma.add()" to perform element-wise addition of the two masked arrays, maintaining the masks.
• Finally display the original masked arrays and the resulting array to verify the operation.

Python-Numpy Code Editor: