Masked arrays concept in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a masked array from a numpy array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
masked_arr = np.ma.[1](arr)

Drag options to blanks, or click blank then click option'

Amask

Barray

Cma_array

Dmasked_array

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.array instead of np.ma.masked_array

Trying to use np.mask which is not a constructor

Using an incorrect function name like ma_array

The np.ma.masked_array() function creates a masked array from a regular numpy array.

2fill in blank

medium

Complete the code to mask all elements equal to 3 in the array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 3])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 3)

Drag options to blanks, or click blank then click option'

A==

B!=

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using != instead of ==

Using > or < which mask wrong elements

The mask should be True where elements equal 3, so use arr == 3.

3fill in blank

hard

Fix the error in the code to correctly mask elements less than 0.

NumPy

import numpy as np
arr = np.array([-1, 0, 1, 2])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 0)

Drag options to blanks, or click blank then click option'

A<=

B<

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using > or == which mask wrong elements

Using <= which masks zero as well

To mask elements less than 0, use the less than operator <.

4fill in blank

hard

Fill both blanks to create a masked array masking values greater than 10 and then fill masked values with -1.

NumPy

import numpy as np
arr = np.array([5, 12, 7, 15, 3])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 10)
filled_arr = masked_arr.[2](-1)

Drag options to blanks, or click blank then click option'

A>

Bfill

Cfilled

Dmask

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill() instead of filled()

Using mask instead of filled to replace masked values

Mask elements greater than 10 using arr > 10. Use filled(-1) to replace masked values with -1.

5fill in blank

hard

Fill all three blanks to create a masked array from a list, mask negative values, and get the compressed (unmasked) data.

NumPy

import numpy as np
lst = [4, -2, 7, -5, 9]
masked_arr = np.ma.[1](lst, mask=[x [2] 0 for x in lst])
compressed_data = masked_arr.[3]()

Drag options to blanks, or click blank then click option'

Amasked_array

B<

Ccompressed

Dmask

Attempts:

3 left

💡 Hint

Common Mistakes

Using mask instead of masked_array to create the array

Using > instead of < for masking negative values

Using mask() instead of compressed() to get unmasked data

Create a masked array with np.ma.masked_array(). Mask negative values using x < 0. Use compressed() to get unmasked data.

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