Boolean type in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy boolean array from a list.

NumPy

import numpy as np
arr = np.array([True, False, True], dtype=[1])
print(arr)

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

Afloat

Bint

Cbool

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int' or 'float' as dtype instead of 'bool'.

Forgetting to specify dtype and getting default integer array.

The dtype 'bool' creates a boolean array in NumPy.

2fill in blank

medium

Complete the code to create a boolean mask where values are greater than 5.

NumPy

import numpy as np
arr = np.array([3, 7, 2, 9])
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 '>' causing wrong mask.

Using '<' which selects smaller values.

The '>' operator creates a boolean array where elements greater than 5 are True.

3fill in blank

hard

Fix the error in the code to convert an integer array to boolean type.

NumPy

import numpy as np
arr = np.array([0, 1, 2, 0])
bool_arr = arr.astype([1])
print(bool_arr)

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

Abool

Bfloat

Cint

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int' or 'float' in astype which does not convert to boolean.

Using 'str' which converts numbers to strings.

Using astype('bool') converts integers to boolean values where 0 is False and others True.

4fill in blank

hard

Fill both blanks to create a boolean array where elements are True if even and False if odd.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
even_mask = (arr [1] 2) [2] 0
print(even_mask)

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

A%

B==

C!=

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '//' instead of '%' for remainder.

Using '!=' instead of '==' causing odd numbers to be True.

The modulo operator '%' finds remainder; comparing with '==' 0 selects even numbers.

5fill in blank

hard

Fill all three blanks to create a dictionary with words as keys and boolean values indicating if length is greater than 3.

NumPy

words = ['cat', 'lion', 'dog', 'tiger']
length_check = {word[1]: len(word) [2] 3 for word in words if word [3] 'dog'}
print(length_check)

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

A.upper()

B>

C!=

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using '.lower()' instead of '.upper()' for keys.

Using '<' instead of '>' for length comparison.

Using '==' instead of '!=' to exclude 'dog'.

We use word.upper() as key, check length > 3, and exclude 'dog' with '!='.

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