Day 72: Python Sliding Window Maximum - Deque O(n) Solution for Efficient Max Tracking (LeetCode #239 Guide)

Day 72 of the coding challenge focused on solving the sliding window maximum problem using Python. The core solution employs a deque to efficiently find the maximum value within each sliding window of size *k* in an array. This approach achieves O(n) time complexity, surpassing the naive O(nk) methods. The function, named `sliding_window_max`, takes the array and window size as input, returning a list of maximums. A deque is used to store indices, ensuring the front always holds the index of the maximum value within the current window. The algorithm iterates through the array, removing outdated indices and smaller values from the deque before adding the current index. Indices are added to window only after window size is reached. When the window is complete, the maximum value is appended to the result list. This implementation is suitable for streaming data, stock analysis, and real-time maximum queries. The script also includes a main section for user input and output. Index storage proves crucial for valid window checks within the deque. Monotonic decreasing property ensures the front always holds the maximum. The solution highlights index tracking and amortized O(1) processing.