Boolean type in NumPy - Mini Project: Build & Apply

Boolean Type with NumPy Arrays

📖 Scenario: You are working with a small dataset of temperatures recorded over a week. You want to find out which days were warmer than 20 degrees Celsius.

🎯 Goal: Create a NumPy array of temperatures, set a threshold temperature, create a Boolean array showing which days are warmer than the threshold, and print the Boolean array.

📋 What You'll Learn

Create a NumPy array called temperatures with the exact values: 18, 22, 19, 24, 20, 21, 17

Create a variable called threshold and set it to 20

Create a Boolean NumPy array called warm_days that is True where temperatures is greater than threshold

Print the warm_days array

💡 Why This Matters

🌍 Real World

Checking temperature data to find days warmer than a certain value is common in weather analysis and climate studies.

💼 Career

Data scientists often use Boolean arrays to filter data quickly and make decisions based on conditions.

1

Create the temperatures array

Create a NumPy array called temperatures with these exact values: 18, 22, 19, 24, 20, 21, 17

NumPy

import numpy as np
# Create the temperatures array with the given values
# Your code here

Hint

Use np.array([...]) to create the array with the exact values.

2

Set the threshold temperature

Create a variable called threshold and set it to the integer value 20

NumPy

import numpy as np

temperatures = np.array([18, 22, 19, 24, 20, 21, 17])
# Create the threshold variable and set it to 20
# Your code here

Hint

Just assign the number 20 to the variable threshold.

3

Create the Boolean array for warm days

Create a Boolean NumPy array called warm_days that is True where temperatures is greater than threshold using the expression temperatures > threshold

NumPy

import numpy as np

temperatures = np.array([18, 22, 19, 24, 20, 21, 17])

threshold = 20
# Create the warm_days Boolean array using temperatures > threshold
# Your code here

Hint

Use the comparison operator > between temperatures and threshold to get a Boolean array.

4

Print the Boolean array

Print the warm_days array using print(warm_days)

NumPy

import numpy as np

temperatures = np.array([18, 22, 19, 24, 20, 21, 17])

threshold = 20

warm_days = temperatures > threshold
# Print the warm_days array
# Your code here

Hint

Use print(warm_days) to display the Boolean array.

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