LeetCode 739 – Daily Temperatures Explained | Monotonic Stack Intuition in Python

LeetCode 739 – Daily Temperatures is a classic problem that tests your understanding of monotonic stacks and future-dependent traversal. While attempting this problem, I experienced an interesting shift in intuition — instead of following a tutorial step by step, I paused after reading the problem statement and tried solving it independently.

Without consciously realizing it, I started iterating from the end of the array. At first, it felt random, but later it became clear that this was the key insight needed to build an optimal solution.

Problem Understanding

The problem asks: for each day, how many days must you wait until a warmer temperature? This means the result at any index depends entirely on future values, not past ones. When a problem depends on future elements, traversing from right to left often simplifies the logic.

Key Insight: Reverse Traversal + Monotonic Stack

By iterating from the back, we can maintain a monotonic decreasing stack that stores indices of temperatures. Any temperature less than or equal to the current day becomes irrelevant and is removed from the stack.

• Traverse the temperature list from right to left
• Use a stack to keep track of potential warmer days
• Ensure the stack remains strictly decreasing
• The top of the stack gives the next warmer day

Python Solution (Optimized O(n))

class Solution:
    def dailyTemperatures(self, temperatures: List[int]) -> List[int]:
        stk = []
        result = [0] * len(temperatures)

        for i in range(len(temperatures) - 1, -1, -1):
            while stk and temperatures[stk[-1]] <= temperatures[i]:
                stk.pop()

            if stk:
                result[i] = stk[-1] - i

            stk.append(i)

        return result
  

Why This Approach Works

Each index is pushed and popped from the stack at most once, resulting in a time complexity of O(n) and space complexity of O(n). This avoids brute-force comparisons and efficiently finds the next warmer day.

What initially felt like an instinctive decision — starting from the end — turned out to be a natural application of pattern recognition. This problem reinforced an important lesson: when answers depend on future data, reverse traversal combined with a monotonic stack is often the optimal strategy.

GitHub Repository

The complete Python implementation for this problem is available on GitHub:
Daily Temperatures – LC739.py