Why boolean masking matters in NumPy - See It in Action

Why Boolean Masking Matters

📖 Scenario: Imagine you have a list of temperatures recorded every day for a week. You want to find which days were hot, meaning the temperature was above 30 degrees. Instead of checking each day one by one, you can use a smart way called boolean masking to quickly find those hot days.

🎯 Goal: You will create a list of temperatures, set a threshold for hot days, use boolean masking to find which days are hot, and then print the hot days' temperatures.

📋 What You'll Learn

Create a numpy array called temperatures with values [22, 35, 27, 40, 30, 33, 25]

Create a variable called hot_threshold and set it to 30

Use boolean masking to create a variable called hot_days that contains temperatures above hot_threshold

Print the hot_days array

💡 Why This Matters

🌍 Real World

Boolean masking helps quickly filter data based on conditions, like finding hot days in weather data or selecting customers with high sales.

💼 Career

Data scientists use boolean masking to clean and analyze data efficiently, making it easier to find important patterns and insights.

1

Create the temperatures array

Create a numpy array called temperatures with these exact values: [22, 35, 27, 40, 30, 33, 25]

NumPy

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

Need a hint?

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

2

Set the hot day threshold

Create a variable called hot_threshold and set it to 30

NumPy

import numpy as np

temperatures = np.array([22, 35, 27, 40, 30, 33, 25])
# Create the variable hot_threshold and set it to 30
# Your code here

Need a hint?

Just assign the number 30 to the variable hot_threshold.

3

Apply boolean masking to find hot days

Use boolean masking to create a variable called hot_days that contains only the temperatures from temperatures that are greater than hot_threshold

NumPy

import numpy as np

temperatures = np.array([22, 35, 27, 40, 30, 33, 25])
hot_threshold = 30
# Use boolean masking to create hot_days with temperatures > hot_threshold
# Your code here

Need a hint?

Use temperatures > hot_threshold inside the brackets to select only hot days.

4

Print the hot days temperatures

Print the variable hot_days to show the temperatures above the threshold

NumPy

import numpy as np

temperatures = np.array([22, 35, 27, 40, 30, 33, 25])
hot_threshold = 30

hot_days = temperatures[temperatures > hot_threshold]
# Print the hot_days array
# Your code here

Need a hint?

Use print(hot_days) to display the result.