Sliding window technique in Data Structures Theory - Mini Project: Build & Apply

Sliding Window Technique

📖 Scenario: Imagine you are analyzing daily temperatures over a week to find the hottest 3-day period.

🎯 Goal: Build a step-by-step understanding of the sliding window technique by creating a list of temperatures, setting a window size, calculating sums of each window, and identifying the hottest 3-day period.

📋 What You'll Learn

Create a list called temperatures with exact values: 70, 72, 68, 75, 74, 73, 69

Create a variable called window_size and set it to 3

Use a for loop with variable i to iterate over the list and calculate the sum of each 3-day window

Create a variable called max_sum to track the highest sum and a variable called max_start_index to track the start of the hottest window

💡 Why This Matters

🌍 Real World

Sliding window technique is used in weather analysis, stock market trends, and network traffic monitoring to analyze data in fixed-size chunks.

💼 Career

Understanding sliding windows helps in roles like data analysis, software development, and system monitoring where efficient data processing is needed.

1

Create the temperature list

Create a list called temperatures with these exact values: 70, 72, 68, 75, 74, 73, 69.

Data Structures Theory

# Create the list of temperatures
# Your code here

Need a hint?

Use square brackets to create a list and separate numbers with commas.

2

Set the window size

Create a variable called window_size and set it to 3.

Data Structures Theory

temperatures = [70, 72, 68, 75, 74, 73, 69]
# Set the window size to 3
# Your code here

Need a hint?

Assign the number 3 to the variable window_size.

3

Calculate sums of each window

Use a for loop with variable i to iterate over temperatures and calculate the sum of each 3-day window using temperatures[i:i+window_size]. Store each sum in a list called window_sums.

Data Structures Theory

temperatures = [70, 72, 68, 75, 74, 73, 69]
window_size = 3
window_sums = []
# Use a for loop to calculate sums of each window
# Your code here

Need a hint?

Use range(len(temperatures) - window_size + 1) to avoid going past the list end.

4

Find the hottest 3-day period

Create variables max_sum and max_start_index. Use a for loop with variable i to find the maximum sum in window_sums and store its index in max_start_index.

Data Structures Theory

temperatures = [70, 72, 68, 75, 74, 73, 69]
window_size = 3
window_sums = []
for i in range(len(temperatures) - window_size + 1):
    current_sum = sum(temperatures[i:i+window_size])
    window_sums.append(current_sum)

# Find the maximum sum and its start index
# Your code here

Need a hint?

Initialize max_sum with the first window sum and update it when a larger sum is found.