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.