Boolean type in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Boolean Array Comparison

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

NumPy

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

A [True True True False False]

B [False False False True True]

C [False False False False False]

D [True True True True True]

💡 Hint

Think about which numbers in the array are greater than 3.

The expression 'arr > 3' compares each element to 3 and returns True if greater, else False.

❓ data_output

intermediate

Count True Values in Boolean Array

What is the output of this code that counts True values in a NumPy Boolean array?

NumPy

import numpy as np
bool_arr = np.array([True, False, True, True, False])
count = np.sum(bool_arr)
print(count)

A 5

B 2

C 3

D 0

💡 Hint

Sum of Boolean True values counts how many Trues are in the array.

In NumPy, True is treated as 1 and False as 0, so sum counts True values.

🔧 Debug

advanced

Identify the Error in Boolean Indexing

What error does this code raise when trying to index a NumPy array with a Boolean list?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40])
index = [True, False, True]
print(arr[index])

A ValueError: operands could not be broadcast together with shapes (4,) (3,)

B TypeError: list indices must be integers or slices, not bool

C No error, prints [10 30]

D IndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 3

💡 Hint

Check if the Boolean list length matches the array length.

Boolean indexing requires the Boolean array to be the same length as the array being indexed.

❓ visualization

advanced

Visualize Boolean Mask on Data

Which option shows the correct Boolean mask applied to filter values greater than 50 from this NumPy array?

NumPy

import numpy as np
import matplotlib.pyplot as plt
values = np.array([10, 55, 30, 70, 45, 90])
mask = values > 50
filtered = values[mask]
plt.bar(range(len(filtered)), filtered)
plt.show()

A Bar chart with bars at heights 55, 70, 90

B Bar chart with bars at heights 10, 30, 45

C Bar chart with bars at heights 10, 55, 30, 70, 45, 90

D Empty bar chart with no bars

💡 Hint

The mask selects values greater than 50 only.

The mask filters values > 50, so only 55, 70, and 90 remain and are shown in the bar chart.

🧠 Conceptual

expert

Boolean Type Memory Usage in NumPy

Which statement correctly describes the memory usage of a NumPy array with dtype=bool compared to dtype=int8?

A A bool array uses 1 byte per element, same as int8, because NumPy stores bools as bytes.

B A bool array uses 1 bit per element, so it uses 8 times less memory than int8.

C A bool array uses variable memory depending on the values stored.

D A bool array uses 4 bytes per element, more than int8 which uses 1 byte.

💡 Hint

Consider how NumPy stores Boolean values internally.

NumPy stores Boolean arrays using 1 byte per element, not 1 bit, so memory usage equals int8 arrays.

Practice

1. What does the bool type in numpy represent?

easy

A. Values that are either True or False

B. Integer numbers only

C. Floating point numbers

D. Text strings

Solution

1. Step 1: Understand the boolean type meaning

   The boolean type stores only two possible values: True or False.

2. Step 2: Compare with other types

   Integer, float, and string types store numbers or text, not boolean values.

3. Final Answer:

   Values that are either True or False -> Option A

4. Quick Check:

   Boolean type = True/False [OK]

Hint: Boolean means True or False only [OK]

Common Mistakes:

• Confusing boolean with integer or float
• Thinking boolean stores text
• Assuming boolean can store multiple values

2. Which of the following is the correct way to create a numpy boolean array?

easy

A. np.array([1, 0, 1], dtype=str)

B. np.array([True, False, True], dtype=bool)

C. np.array([True, False, True], dtype=int)

D. np.array(['True', 'False'], dtype=bool)

Solution

1. Step 1: Check dtype for boolean array

   To create a boolean array, use dtype=bool with True/False values.

2. Step 2: Validate options

   np.array([True, False, True], dtype=bool) uses correct dtype and values. np.array([1, 0, 1], dtype=str) uses string dtype, not boolean. np.array([True, False, True], dtype=int) uses int dtype. np.array(['True', 'False'], dtype=bool) uses strings 'True', 'False' which are not boolean values.

3. Final Answer:

   np.array([True, False, True], dtype=bool) -> Option B

4. Quick Check:

   Boolean array needs dtype=bool [OK]

Hint: Use dtype=bool with True/False values [OK]

Common Mistakes:

• Using string values instead of boolean
• Setting wrong dtype like int or str
• Confusing True/False strings with boolean

3. What is the output of this code?

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

medium

A. [3 4 5]

B. [True True True False False]

C. [False False False True True]

D. [1 1 1 0 0]

Solution

1. Step 1: Evaluate the condition arr > 3

   For each element in arr, check if it is greater than 3: 1>3 False, 2>3 False, 3>3 False, 4>3 True, 5>3 True.

2. Step 2: Understand the mask output

   The mask is a boolean array showing True where condition is met, False otherwise: [False False False True True].

3. Final Answer:

   [False False False True True] -> Option C

4. Quick Check:

   Condition mask = [False False False True True] [OK]

Hint: Compare array with condition to get boolean mask [OK]

Common Mistakes:

• Confusing boolean mask with integer 1/0
• Expecting filtered values instead of mask
• Mixing up True and False positions

4. Find the error in this code snippet:

import numpy as np
arr = np.array([10, 20, 30])
mask = arr = 20
print(mask)

medium

A. print statement syntax error

B. Missing import statement for numpy

C. Array elements are not boolean

D. Using '=' instead of '==' for comparison

Solution

1. Step 1: Identify the assignment vs comparison

   The code uses 'mask = arr = 20' which assigns 20 to arr and mask, not a comparison.

2. Step 2: Correct the comparison operator

   To create a boolean mask, use 'mask = arr == 20' to compare each element with 20.

3. Final Answer:

   Using '=' instead of '==' for comparison -> Option D

4. Quick Check:

   Use '==' for comparison, '=' is assignment [OK]

Hint: Use '==' to compare, not '=' assignment [OK]

Common Mistakes:

• Using single '=' instead of '=='
• Confusing assignment with comparison
• Expecting boolean mask but getting int

5. Given a numpy array data = np.array([5, 10, 15, 20, 25]), how can you use a boolean mask to select only values greater than 12 and less than 22?

hard

A. mask = (data > 12) & (data < 22); result = data[mask]

B. mask = data > 12 or data < 22; result = data[mask]

C. mask = data > 12 and data < 22; result = data[mask]

D. mask = (data > 12) | (data < 22); result = data[mask]

Solution

1. Step 1: Create correct boolean mask with logical AND

   To select values greater than 12 and less than 22, use element-wise AND: (data > 12) & (data < 22).

2. Step 2: Apply mask to data array

   Use the mask to index data: data[mask] returns values meeting both conditions.

3. Final Answer:

   mask = (data > 12) & (data < 22); result = data[mask] -> Option A

4. Quick Check:

   Use '&' for element-wise AND in numpy [OK]

Hint: Use '&' for AND, '|' for OR in numpy boolean masks [OK]

Common Mistakes:

• Using 'and' or 'or' instead of '&' or '|'
• Forgetting parentheses around conditions
• Using '|' (OR) instead of '&' (AND) for both conditions