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.

Practice

1. What is the main purpose of using masked arrays in NumPy?

easy

A. To speed up array computations by using GPU

B. To sort arrays in ascending order

C. To convert arrays into lists

D. To mark certain data points as invalid without removing them

Solution

1. Step 1: Understand masked arrays concept

   Masked arrays allow marking some elements as invalid or missing without deleting them.

2. Step 2: Compare options with concept

   Only To mark certain data points as invalid without removing them correctly describes this purpose; others describe unrelated operations.

3. Final Answer:

   To mark certain data points as invalid without removing them -> Option D

4. Quick Check:

   Masked arrays = mark invalid data [OK]

Hint: Masked arrays hide invalid data without deleting it [OK]

Common Mistakes:

• Thinking masked arrays delete invalid data
• Confusing masked arrays with sorting or conversion
• Assuming masked arrays speed up GPU computations

2. Which of the following is the correct way to create a masked array from a NumPy array arr where values equal to 0 are masked?

easy

A. np.ma.masked_array(arr, mask=arr == 0)

B. np.ma.masked_where(arr, arr == 0)

C. np.ma.masked_array(arr == 0)

D. np.ma.mask(arr, arr == 0)

Solution

1. Step 1: Recall masked_array syntax

   The correct syntax is np.ma.masked_array(data, mask=condition).

2. Step 2: Match options with syntax

   np.ma.masked_array(arr, mask=arr == 0) uses np.ma.masked_array with mask=arr == 0, which is correct. np.ma.masked_where(arr, arr == 0) reverses arguments, C misses data argument, D uses invalid function.

3. Final Answer:

   np.ma.masked_array(arr, mask=arr == 0) -> Option A

4. Quick Check:

   masked_array(data, mask=condition) = np.ma.masked_array(arr, mask=arr == 0) [OK]

Hint: Use np.ma.masked_array(data, mask=condition) to mask values [OK]

Common Mistakes:

• Swapping arguments in masked_where
• Using masked_array without data argument
• Calling non-existent np.ma.mask function

3. What will be the output of the following code?

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

medium

A. 9

B. 0

C. 9.0

D. MaskedArray with sum 9

Solution

1. Step 1: Identify masked elements

   Elements equal to 0 are masked, so only 1, 3, and 5 are counted.

2. Step 2: Calculate sum ignoring masked values

   Sum = 1 + 3 + 5 = 9. The sum() returns an integer scalar, so output is 9.

3. Final Answer:

   9 -> Option A

4. Quick Check:

   Sum ignoring masked zeros = 9 [OK]

Hint: Masked values are ignored in sum calculations [OK]

Common Mistakes:

• Thinking only masked values are summed (0)
• Expecting float output (9.0)
• Thinking print shows masked array instead of sum

4. The following code throws an error. What is the most likely cause?

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

medium

A. np.ma.masked_array requires a list, not a NumPy array

B. Mask array shape does not match data array shape

C. Boolean mask must be integers, not booleans

D. Masked arrays cannot be created from 1D arrays

Solution

1. Step 1: Check shapes of data and mask

   Data array shape is (3,), mask shape is (2,), which do not match.

2. Step 2: Understand mask shape requirement

   Mask must have the same shape as data to apply element-wise masking.

3. Final Answer:

   Mask array shape does not match data array shape -> Option B

4. Quick Check:

   Mask shape must match data shape [OK]

Hint: Mask shape must match data shape exactly [OK]

Common Mistakes:

• Using mask with different shape than data
• Thinking mask must be integer array
• Believing masked_array needs list input

5. You have a dataset with some invalid temperature readings marked as -999. How would you create a masked array to ignore these invalid values and then calculate the average temperature ignoring them?

hard

A. Use np.ma.masked_where(data != -999, data) and then call .mean()

B. Replace -999 with 0 and then calculate mean normally

C. Use np.ma.masked_array(data, mask=(data == -999)) and then call .mean()

D. Filter out -999 values manually and use np.array.mean()

Solution

1. Step 1: Mask invalid values correctly

   Mask where data equals -999 to mark invalid readings.

2. Step 2: Calculate mean ignoring masked values

   Calling .mean() on masked array ignores masked elements automatically.

3. Final Answer:

   Use np.ma.masked_array(data, mask=(data == -999)) and then call .mean() -> Option C

4. Quick Check:

   Mask invalids, then mean() ignores them [OK]

Hint: Mask invalids with condition, then use .mean() [OK]

Common Mistakes:

• Replacing invalids with zero changes data meaning
• Using masked_where with wrong condition
• Manually filtering loses masked array benefits