Leetcode #2110: Number of Smooth Descent Periods of a Stock
In this guide, we solve Leetcode #2110 Number of Smooth Descent Periods of a Stock in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given an integer array prices representing the daily price history of a stock, where prices[i] is the stock price on the ith day. A smooth descent period of a stock consists of one or more contiguous days such that the price on each day is lower than the price on the preceding day by exactly 1.
Quick Facts
- Difficulty: Medium
- Premium: No
- Tags: Array, Math, Two Pointers, Dynamic Programming, Sliding Window
Intuition
The constraints hint that we can reason about two ends of the data at once, which is perfect for a two-pointer scan.
Moving one pointer at a time keeps the invariant intact and avoids nested loops.
Approach
Place pointers at the left and right ends and move them based on the comparison or target condition.
This yields a clean linear pass after any required sorting.
Steps:
- Set left and right pointers.
- Move a pointer based on the condition.
- Update the best answer while scanning.
Example
Input: prices = [3,2,1,4]
Output: 7
Explanation: There are 7 smooth descent periods:
[3], [2], [1], [4], [3,2], [2,1], and [3,2,1]
Note that a period with one day is a smooth descent period by the definition.
Python Solution
class Solution:
def getDescentPeriods(self, prices: List[int]) -> int:
ans = 0
i, n = 0, len(prices)
while i < n:
j = i + 1
while j < n and prices[j - 1] - prices[j] == 1:
j += 1
cnt = j - i
ans += (1 + cnt) * cnt // 2
i = j
return ans
Complexity
The time complexity is , where is the length of the array . The space complexity is .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.