Compute Mean of a Masked array ignoring Masked values in NumPy

NumPy: Masked Arrays Exercise-3 with Solution

Write a NumPy program to compute the mean of a masked array, ignoring the masked values.

Sample Solution:

Python Code:

import numpy as np  # Import NumPy library

# Create a regular NumPy array
data = np.array([1, 2, 3, 4, 5, np.nan, 7, 8, 9, 10])

# Create a mask to specify which values to mask
mask = np.isnan(data)

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

# Compute the mean of the masked array, ignoring the masked values
mean_value = np.ma.mean(masked_array)

# Print the original array, the masked array, and the computed mean
print("Original Array:")
print(data)

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

print("\nMean of the Masked Array, Ignoring Masked Values:")
print(mean_value)

Output:

Original Array:
[ 1.  2.  3.  4.  5. nan  7.  8.  9. 10.]

Masked Array:
[1.0 2.0 3.0 4.0 5.0 -- 7.0 8.0 9.0 10.0]

Mean of the Masked Array, Ignoring Masked Values:
5.444444444444445

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.
• Compute the Mean:
• Use np.ma.mean() to compute the mean of the masked array, ignoring the masked values.
• Finally display the original array, the masked array, and the computed mean to verify the operation.

Python-Numpy Code Editor:

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