Boolean type in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Boolean type in NumPy?

A Boolean type in NumPy represents values that can be either True or False. It is used to store truth values and is useful for filtering and conditional operations.

beginner

How do you create a NumPy array of Boolean values?

You can create a Boolean NumPy array by passing a list of True and False values to np.array(), for example: np.array([True, False, True]).

beginner

What is the data type name for Boolean arrays in NumPy?

The data type name for Boolean arrays in NumPy is 'bool' or 'bool_' which stores True or False values efficiently.

intermediate

How can Boolean arrays be used in NumPy?

Boolean arrays can be used to filter data, perform element-wise logical operations, and control flow in data processing tasks.

intermediate

What happens when you perform a logical operation between two Boolean NumPy arrays?

Performing logical operations like AND (&), OR (|), and NOT (~) between Boolean arrays results in a new Boolean array with element-wise operation results.

Which NumPy data type stores Boolean values?

A bool

B int

C float

D str

The 'bool' data type in NumPy stores Boolean values True or False.

What is the output type when you compare two NumPy arrays element-wise?

A Integer array

B String array

C Float array

D Boolean array

Element-wise comparison returns a Boolean array indicating True or False for each comparison.

Which operator is used for element-wise logical AND in NumPy Boolean arrays?

A &

B &&

C and

D |

The '&' operator performs element-wise logical AND on Boolean NumPy arrays.

How do you create a Boolean array from a condition in NumPy?

A Using np.bool_()

B Using a comparison like arr > 5

C Using np.array([True, False])

D Using np.int32()

A condition like arr > 5 returns a Boolean array where each element shows if the condition is True or False.

What does the ~ operator do on a Boolean NumPy array?

A Logical AND

B Logical OR

C Logical NOT (negation)

D No operation

The '~' operator performs element-wise logical NOT, flipping True to False and vice versa.

Explain what a Boolean type is in NumPy and how it can be used in data analysis.

Think about how True/False values help select or filter data.

Describe how to create and manipulate Boolean arrays in NumPy with examples.

Consider how conditions and logical operators work on 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