Replace Masked values with Mean in NumPy Masked array

NumPy: Masked Arrays Exercise-5 with Solution

Write a NumPy program to replace all masked values in a masked array with the mean of the unmasked elements.

Sample Solution:

Python Code:

import numpy as np  # Import NumPy library

# Create a regular NumPy array with some NaN values
data = np.array([1, 2, 3, np.nan, 5, 6, np.nan, 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)

# Compute the mean of the unmasked elements
mean_value = masked_array.mean()

# Replace all masked values with the mean of the unmasked elements
filled_array = masked_array.filled(mean_value)

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

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

print("\nFilled Array (masked values replaced with mean):")
print(filled_array)

Output:

Original Array:
[ 1.  2.  3. nan  5.  6. nan  8.  9. 10.]

Masked Array:
[1.0 2.0 3.0 -- 5.0 6.0 -- 8.0 9.0 10.0]

Filled Array (masked values replaced with mean):
[ 1.   2.   3.   5.5  5.   6.   5.5  8.   9.  10. ]

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 "masked_array.mean()" to compute the mean of the unmasked elements in the masked array.
• Replace Masked Values:
• Use masked_array.filled(mean_value) to replace all masked values with the computed mean of the unmasked elements.
• Finally display the original array, the masked array, and the filled array to verify the operation.

Python-Numpy Code Editor:

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