Creating boolean arrays in NumPy - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of boolean array creation with comparison

What is the output of this code snippet that creates a boolean array by comparing elements of a numpy array to a value?

NumPy

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

A [True False True False]

B [False True False True]

C [False False False False]

D [True True True True]

💡 Hint

Think about which numbers in the array are greater than 4.

The code compares each element of the array to 4. Elements 7 and 5 are greater than 4, so their positions are True; others are False.

❓ data_output

intermediate

Count of True values in a boolean array

Given a boolean array created by comparing elements of a numpy array, how many True values does it contain?

NumPy

import numpy as np
arr = np.array([10, 15, 20, 5, 0])
bool_arr = arr <= 10
count_true = np.sum(bool_arr)
print(count_true)

A 4

B 2

C 1

D 3

💡 Hint

Count how many elements are less than or equal to 10.

Elements 10, 5, and 0 satisfy the condition, so there are 3 True values.

🔧 Debug

advanced

Identify the error in boolean array creation

What error does this code raise when trying to create a boolean array by comparing elements of a numpy array?

NumPy

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

A SyntaxError

B TypeError

C NameError

D IndexError

💡 Hint

Look carefully at the comparison operator usage.

The comparison operator '>' is incomplete without a value to compare to, causing a SyntaxError.

🚀 Application

advanced

Filter array elements using a boolean array

Which option correctly filters elements greater than 50 from the numpy array using a boolean array?

NumPy

import numpy as np
arr = np.array([10, 60, 30, 80, 50])
filtered = ???
print(filtered)

A arr[arr < 50]

B arr[arr == 50]

C arr[arr > 50]

D arr[arr >= 50]

💡 Hint

You want only elements strictly greater than 50.

arr > 50 creates a boolean array True only for elements greater than 50. Using it to index arr filters those elements.

🧠 Conceptual

expert

Understanding boolean array shape and dtype

Given a 2D numpy array, what is the shape and dtype of the boolean array created by comparing each element to zero?

NumPy

import numpy as np
arr = np.array([[1, -1], [0, 2]])
bool_arr = arr > 0
print(bool_arr.shape, bool_arr.dtype)

A (2, 2) bool

B (4,) int

C (2, 2) int

D (4,) bool

💡 Hint

The boolean array keeps the same shape as the original array.

The comparison creates a boolean array with the same shape as arr, and dtype is bool.

Practice

1. What does a boolean array in numpy represent?

easy

A. An array of integers only

B. An array of True/False values based on a condition

C. An array of strings

D. An array with only zeros

Solution

1. Step 1: Understand boolean arrays

   A boolean array stores True or False for each element, usually from a condition.

2. Step 2: Identify the correct description

   The description 'An array of True/False values based on a condition' matches what a boolean array represents. Others refer to integers, strings, or zeros.

3. Final Answer:

   An array of True/False values based on a condition -> Option B

4. Quick Check:

   Boolean array = True/False values [OK]

Hint: Boolean arrays always hold True or False values [OK]

Common Mistakes:

• Thinking boolean arrays hold numbers or strings
• Confusing boolean arrays with integer arrays

2. Which of the following is the correct way to create a boolean array from a numpy array arr to check where elements are greater than 5?

easy

A. bool_arr = arr > 5

B. bool_arr = arr == 5

C. bool_arr = arr >= 5

D. bool_arr = arr < 5

Solution

1. Step 1: Understand the condition for boolean array

   We want True where elements are greater than 5, so the condition is arr > 5.

2. Step 2: Match the correct syntax

   bool_arr = arr > 5 uses arr > 5, which is correct. arr >= 5 checks greater than or equal to 5, arr == 5 checks equality to 5, and arr < 5 checks less than 5.

3. Final Answer:

   bool_arr = arr > 5 -> Option A

4. Quick Check:

   Condition for > 5 is arr > 5 [OK]

Hint: Use comparison operators directly on arrays for boolean arrays [OK]

Common Mistakes:

• Using == instead of > for greater than
• Confusing >= with >
• Using wrong comparison operator

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9])
bool_arr = arr < 5
print(bool_arr)

medium

A. [True False True False]

B. [False True False True]

C. [True True True True]

D. [False False False False]

Solution

1. Step 1: Evaluate the condition arr < 5 for each element

   Elements: 2 < 5 is True, 7 < 5 is False, 4 < 5 is True, 9 < 5 is False.

2. Step 2: Form the boolean array

   The boolean array is [True, False, True, False].

3. Final Answer:

   [True False True False] -> Option A

4. Quick Check:

   Check each element < 5 = [True False True False] [OK]

Hint: Check each element against condition to form True/False array [OK]

Common Mistakes:

• Mixing up True and False values
• Forgetting to use < operator correctly

4. Identify the error in this code that tries to create a boolean array for elements equal to 10:

import numpy as np
arr = np.array([10, 5, 10, 3])
bool_arr = arr = 10
print(bool_arr)

medium

A. Array should be a list, not np.array

B. Missing import statement for numpy

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

D. print statement syntax error

Solution

1. Step 1: Check the comparison operator

   The code uses arr = 10 which assigns 10 to arr, not compares.

2. Step 2: Correct operator for comparison

   To compare elements to 10, use arr == 10 to create a boolean array.

3. Final Answer:

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

4. Quick Check:

   Comparison needs '==' not '=' [OK]

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

Common Mistakes:

• Using single '=' instead of '=='
• Confusing assignment with comparison

5. Given a numpy array data = np.array([3, 8, 1, 6, 0]), how can you create a boolean array that is True for elements greater than 2 and less than 7?

hard

A. bool_arr = (data > 2) && (data < 7)

B. bool_arr = data > 2 or data < 7

C. bool_arr = data > 2 and data < 7

D. bool_arr = (data > 2) & (data < 7)

Solution

1. Step 1: Understand element-wise logical operations in numpy

   Use bitwise operators & and | for element-wise AND/OR, not 'and' or 'or'.

2. Step 2: Apply correct syntax for combined condition

   bool_arr = (data > 2) & (data < 7) uses (data > 2) & (data < 7), which is correct for element-wise AND.

3. Final Answer:

   bool_arr = (data > 2) & (data < 7) -> Option D

4. Quick Check:

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

Hint: Use & for element-wise AND, not 'and' [OK]

Common Mistakes:

• Using 'and' instead of '&' for element-wise AND
• Using '&&' which is invalid in Python
• Using 'or' instead of '&' for AND condition