Why indexing matters in NumPy - See It in Action

Why indexing matters

📖 Scenario: Imagine you have a list of daily temperatures for a week. You want to find the temperature on a specific day and also change the temperature for a day if needed.

🎯 Goal: You will create a NumPy array of temperatures, select a specific day's temperature using indexing, update a temperature, and then print the updated array.

📋 What You'll Learn

Create a NumPy array called temps with the exact values: 22, 24, 19, 23, 25, 20, 21

Create a variable called day_index and set it to 3

Use day_index to get the temperature for that day from temps and store it in selected_temp

Update the temperature at day_index in temps to 26

Print the updated temps array

💡 Why This Matters

🌍 Real World

Indexing is important when working with data like daily temperatures, sales numbers, or any list of values where you want to access or change specific items.

💼 Career

Data scientists often need to select and update data points efficiently using indexing in arrays or tables.

1

Create the temperature array

Create a NumPy array called temps with these exact values: 22, 24, 19, 23, 25, 20, 21

NumPy

import numpy as np
# Create the temps array with the exact values
# Your code here

Need a hint?

Use np.array([...]) to create the array with the exact numbers inside the brackets.

2

Set the day index

Create a variable called day_index and set it to 3

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
# Create day_index and set it to 3
# Your code here

Need a hint?

Just assign the number 3 to the variable day_index.

3

Select and update the temperature

Use day_index to get the temperature from temps and store it in selected_temp. Then update the temperature at day_index in temps to 26

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
day_index = 3
# Get the temperature at day_index and store in selected_temp
# Update temps at day_index to 26
# Your code here

Need a hint?

Use temps[day_index] to get and set the value at that position.

4

Print the updated temperatures

Print the updated temps array

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
day_index = 3
selected_temp = temps[day_index]
temps[day_index] = 26
# Print the temps array
# Your code here

Need a hint?

Use print(temps) to show the updated array.