Masked arrays concept in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of masked array sum with masked elements

What is the output of this code snippet using NumPy masked arrays?

NumPy

import numpy as np
arr = np.ma.array([1, 2, 3, 4, 5], mask=[0, 1, 0, 1, 0])
result = arr.sum()
print(result)

A masked

B 15

C 9

D TypeError

💡 Hint

Remember that masked elements are ignored in calculations like sum.

The masked array has elements 2 and 4 masked. So sum adds 1 + 3 + 5 = 9.

❓ data_output

intermediate

Resulting masked array after applying mask condition

Given this code, what is the resulting masked array?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
masked_arr = np.ma.masked_greater(arr, 25)
print(masked_arr)

A [10 20 -- -- --]

B [-- -- -- -- --]

C [10 20 30 40 50]

D [-- -- 30 40 50]

💡 Hint

masked_greater masks elements greater than the given value.

Elements greater than 25 (30, 40, 50) are masked, so they appear as '--'.

🔧 Debug

advanced

Identify the error in masked array creation

What error does this code raise?

NumPy

import numpy as np
arr = np.ma.array([1, 2, 3], mask=[True, False])

A TypeError: mask must be boolean

B ValueError: mask and data must have the same shape

C IndexError: mask index out of range

D No error, runs successfully

💡 Hint

Check if the mask length matches the data length.

The mask length is 2 but data length is 3, causing a ValueError.

🚀 Application

advanced

Using masked arrays to ignore invalid data in mean calculation

You have sensor data with invalid readings marked as -999. Which code correctly calculates the mean ignoring invalid data?

A data = np.array([1, 2, -999, 4]); mean = data.mean()

B data = np.array([1, 2, -999, 4]); masked = np.ma.masked_greater(data, 100); mean = masked.mean()

C data = np.array([1, 2, -999, 4]); masked = np.ma.masked_less(data, 0); mean = masked.mean()

D data = np.array([1, 2, -999, 4]); masked = np.ma.masked_equal(data, -999); mean = masked.mean()

💡 Hint

Mask exactly the invalid value -999 to exclude it from calculations.

Option D masks all -999 values and calculates mean ignoring them. Others do not mask correctly.

🧠 Conceptual

expert

Understanding behavior of masked array fill_value

What is the purpose of the fill_value attribute in a NumPy masked array?

A It defines the value used when filling masked elements during output or conversion.

B It sets the default value for all elements in the array.

C It specifies the value to replace unmasked elements during calculations.

D It controls the data type of the masked array elements.

💡 Hint

Think about how masked elements are represented when converting to normal arrays.

fill_value is used to fill masked elements when converting masked arrays to normal arrays or printing.