Boolean indexing in NumPy - Mini Project: Build & Apply

Boolean Indexing with NumPy Arrays

📖 Scenario: You are working with a list of daily temperatures recorded over a week. You want to find which days were warmer than a certain temperature to plan outdoor activities.

🎯 Goal: Build a program that uses NumPy boolean indexing to select temperatures above a threshold.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Create a threshold variable for temperature

Use boolean indexing to select temperatures above the threshold

Print the selected temperatures

💡 Why This Matters

🌍 Real World

Filtering data based on conditions is common in weather analysis, finance, and health monitoring to focus on important values.

💼 Career

Data scientists often use boolean indexing to quickly select relevant data points for analysis and visualization.

1

Create the temperature data array

Import NumPy as np and create a NumPy array called temps with these exact values: 22, 19, 25, 20, 18, 24, 21.

NumPy

import numpy as np
# Create the NumPy array called temps with the given temperature values
# Your code here

Hint

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

2

Set the temperature threshold

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

NumPy

import numpy as np
temps = np.array([22, 19, 25, 20, 18, 24, 21])
# Create the threshold variable and set it to 20
# Your code here

Hint

Just assign the number 20 to a variable named threshold.

3

Select temperatures above the threshold using boolean indexing

Create a variable called warm_days that uses boolean indexing on temps to select only the temperatures greater than threshold.

NumPy

import numpy as np
temps = np.array([22, 19, 25, 20, 18, 24, 21])
threshold = 20
# Use boolean indexing to create warm_days with temps greater than threshold
# Your code here

Hint

Use temps > threshold inside the brackets to filter the array.

4

Print the warm temperatures

Print the variable warm_days to display the temperatures above the threshold.

NumPy

import numpy as np
temps = np.array([22, 19, 25, 20, 18, 24, 21])
threshold = 20
warm_days = temps[temps > threshold]
# Print warm_days
# Your code here

Hint

Use print(warm_days) to show the filtered temperatures.

Practice

1. What does boolean indexing in numpy allow you to do?

easy

A. Create a new array filled with zeros

B. Change the data type of an array

C. Sort the array elements in ascending order

D. Select elements from an array using a True/False mask

Solution

1. Step 1: Understand boolean indexing concept

   Boolean indexing uses a True/False array to pick elements from another array.

2. Step 2: Identify the correct operation

   Only Select elements from an array using a True/False mask describes selecting elements based on True/False values.

3. Final Answer:

   Select elements from an array using a True/False mask -> Option D

4. Quick Check:

   Boolean indexing = Select elements [OK]

Hint: Boolean mask picks elements where True appears [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it creates new arrays with zeros

2. Which of the following is the correct syntax to select elements greater than 5 from a numpy array arr using boolean indexing?

easy

A. arr[arr < 5]

B. arr > 5[arr]

C. arr[arr > 5]

D. arr.select(arr > 5)

Solution

1. Step 1: Understand boolean indexing syntax

   The correct syntax is to use the array with a boolean mask inside square brackets: arr[condition].

2. Step 2: Check each option

   arr[arr > 5] uses arr[arr > 5], which is correct. Others have wrong order or invalid methods.

3. Final Answer:

   arr[arr > 5] -> Option C

4. Quick Check:

   Correct syntax = arr[condition] [OK]

Hint: Put condition inside brackets after array name [OK]

Common Mistakes:

• Placing condition before array name
• Using invalid methods like select()
• Using wrong comparison operators

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
result = arr[arr % 2 == 1]

medium

A. [7 9 1]

B. [2 4]

C. [7 4 9]

D. [2 7 4 9 1]

Solution

1. Step 1: Understand the condition arr % 2 == 1

   This condition selects elements where the remainder when divided by 2 is 1, i.e., odd numbers.

2. Step 2: Apply condition to array elements

   Elements 7, 9, and 1 are odd, so they are selected.

3. Final Answer:

   [7 9 1] -> Option A

4. Quick Check:

   Odd elements = [7 9 1] [OK]

Hint: Filter with modulo to get odd numbers [OK]

Common Mistakes:

• Selecting even numbers instead
• Confusing modulo operator
• Returning full array instead of filtered

4. The following code has a problem. What is the problem?

import numpy as np
arr = np.array([1, 2, 3, 4])
mask = arr > 2
print(arr[mask == True])

medium

A. mask == True is unnecessary and redundant

B. arr should be converted to list first

C. Comparison operator > is invalid for numpy arrays

D. Square brackets cannot be used for indexing

Solution

1. Step 1: Analyze the boolean mask creation

   mask = arr > 2 creates a boolean array correctly.

2. Step 2: Check the indexing expression

   Using mask == True is redundant since mask is already boolean. It's unnecessary and bad practice.

3. Step 3: Correct usage

   Use arr[mask] directly without comparing mask to True.

4. Final Answer:

   mask == True is unnecessary and redundant -> Option A

5. Quick Check:

   Use boolean mask directly [OK]

Hint: Use mask directly, no need for '== True' [OK]

Common Mistakes:

• Adding '== True' to boolean mask
• Trying to convert numpy array to list for indexing
• Misunderstanding comparison operators

5. Given a numpy array data = np.array([10, 15, 20, 25, 30]), how can you select elements greater than 12 and less than 28 using boolean indexing?

hard

A. data[data > 12 and data < 28]

B. data[(data > 12) & (data < 28)]

C. data[data > 12 | data < 28]

D. data[(data > 12) or (data < 28)]

Solution

1. Step 1: Understand combining conditions with & and parentheses

   In numpy, use & for element-wise AND and wrap each condition in parentheses.

2. Step 2: Check each option

   data[(data > 12) & (data < 28)] correctly uses & (element-wise AND) with parentheses around each condition. Using Python keywords and/or cause errors because they don't work element-wise on arrays. Using | (bitwise OR) selects elements greater than 12 or less than 28, which is wrong here.

3. Final Answer:

   data[(data > 12) & (data < 28)] -> Option B

4. Quick Check:

   Use & with parentheses for AND conditions [OK]

Hint: Use & with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of & or |
• Not using parentheses around conditions
• Using | (OR) instead of & (AND) for filtering