Why boolean masking matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a boolean mask that selects values greater than 5.

NumPy

import numpy as np
arr = np.array([3, 7, 2, 9, 5])
mask = arr [1] 5
print(mask)

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

A<

B==

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' results in a mask selecting only values equal to 5.

Using '<' selects values less than 5, which is the opposite of what we want.

The operator > creates a boolean mask selecting values greater than 5.

2fill in blank

medium

Complete the code to use the boolean mask to select values greater than 5 from the array.

NumPy

import numpy as np
arr = np.array([3, 7, 2, 9, 5])
mask = arr > 5
selected = arr[1]mask]
print(selected)

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

A[

B+

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' or '*' tries to add or multiply, which is incorrect for masking.

Forgetting brackets causes a syntax error.

Using square brackets [] applies the boolean mask to select elements.

3fill in blank

hard

Fix the error in the code to correctly create a mask for values less than or equal to 4.

NumPy

import numpy as np
arr = np.array([1, 4, 6, 3, 8])
mask = arr [1] 4
print(mask)

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

A<

B<=

C==

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' excludes values equal to 4.

Using '>=' selects values greater than or equal to 4, which is wrong.

The operator <= selects values less than or equal to 4, matching the requirement.

4fill in blank

hard

Complete the code to create a dictionary of words and their lengths, but only for words longer than 3 letters.

NumPy

words = ['cat', 'elephant', 'dog', 'lion']
lengths = {word: len(word) for word in words if len(word) [1] 3}
print(lengths)

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

A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' causes syntax errors in dictionary comprehension.

Using '<' filters words shorter than 3, opposite of requirement.

Use : to map word to its length, and > to filter words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words with length greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = [1]: [2] for w in words if len(w) [3] 3
print(result)

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

Aw.upper()

Blen(w)

C>

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of uppercase keys.

Using '<' instead of '>' filters wrong words.

Use w.upper() for keys, len(w) for values, and filter with > for length greater than 3.

Practice

1. What is the main purpose of boolean masking in numpy?

easy

A. To sort an array in ascending order

B. To select elements from an array based on True/False conditions

C. To change the data type of an array

D. To create a new array filled with zeros

Solution

1. Step 1: Understand boolean masking concept

   Boolean masking uses a True/False array to pick elements from another array.

2. Step 2: Identify the main use

   This helps select only the elements where the mask is True, filtering data easily.

3. Final Answer:

   To select elements from an array based on True/False conditions -> Option B

4. Quick Check:

   Boolean mask = select elements [OK]

Hint: Boolean mask picks elements where condition is True [OK]

Common Mistakes:

• Thinking it sorts the array
• Confusing masking with data type change
• Assuming it fills arrays with zeros

2. Which of the following is the correct syntax to create a boolean mask for array arr to select values greater than 5?

easy

A. mask = arr > 5

B. mask = arr = 5

C. mask = arr < 5

D. mask = arr == 5

Solution

1. Step 1: Understand comparison operators

   To select values greater than 5, use the greater than operator: >.

2. Step 2: Check syntax correctness

   mask = arr > 5 creates a boolean array where True means element > 5.

3. Final Answer:

   mask = arr > 5 -> Option A

4. Quick Check:

   Use > for greater than [OK]

Hint: Use > operator to create mask for values greater than number [OK]

Common Mistakes:

• Using single equals (=) instead of comparison (>)
• Using < instead of >
• Using == which checks equality, not greater than

3. Given the code:

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
mask = arr > 4
result = arr[mask]

What is the value of result?

medium

A. [2, 7, 9]

B. [2, 4, 1]

C. [7, 4, 9]

D. [7, 9]

Solution

1. Step 1: Create boolean mask for elements > 4

   Elements greater than 4 are 7 and 9, so mask is [False, True, False, True, False].

2. Step 2: Apply mask to array

   Using arr[mask] selects elements where mask is True: [7, 9].

3. Final Answer:

   [7, 9] -> Option D

4. Quick Check:

   Mask picks elements > 4 [OK]

Hint: Mask True picks elements, False skips [OK]

Common Mistakes:

• Including elements not > 4
• Confusing mask with index positions
• Selecting elements less than or equal to 4

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([1, 3, 5, 7])
mask = arr > 4
print(arr[mask])

It raises an error. Why?

medium

A. The mask is created correctly; no error occurs

B. The mask uses assignment (=) instead of comparison (>)

C. The array contains non-numeric values causing error

D. The mask array has different length than arr

Solution

1. Step 1: Check mask creation

   The mask arr > 4 creates a boolean array of same length as arr without error.

2. Step 2: Check indexing with mask

   Using arr[mask] selects elements > 4 without error.

3. Final Answer:

   The mask is created correctly; no error occurs -> Option A

4. Quick Check:

   Correct mask syntax means no error [OK]

Hint: Correct mask syntax means no error [OK]

Common Mistakes:

• Confusing assignment (=) with comparison (>)
• Assuming mask length mismatch error
• Thinking non-numeric values cause error here

5. You have a numpy array data = np.array([10, 0, 5, -3, 8]). You want to select only positive numbers excluding zero using boolean masking. Which code correctly achieves this?

hard

A. mask = data >= 0
result = data[mask]

B. mask = data != 0
result = data[mask]

C. mask = data > 0
result = data[mask]

D. mask = data < 0
result = data[mask]

Solution

1. Step 1: Define condition for positive numbers excluding zero

   Positive numbers are greater than zero, so condition is data > 0.

2. Step 2: Apply mask and select elements

   Using data[data > 0] selects 10, 5, and 8, excluding zero and negatives.

3. Final Answer:

   mask = data > 0 result = data[mask] -> Option C

4. Quick Check:

   Use > 0 to exclude zero and negatives [OK]

Hint: Use > 0 to select positive numbers excluding zero [OK]

Common Mistakes:

• Using >= 0 includes zero
• Using != 0 includes negatives
• Using < 0 selects negatives, not positives