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Leetcode #1493: Longest Subarray of 1's After Deleting One Element

In this guide, we solve Leetcode #1493 Longest Subarray of 1's After Deleting One Element 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.

Leetcode

Problem Statement

Given a binary array nums, you should delete one element from it. Return the size of the longest non-empty subarray containing only 1's in the resulting array.

Quick Facts

  • Difficulty: Medium
  • Premium: No
  • Tags: Array, Dynamic Programming, Sliding Window

Intuition

We are looking for a contiguous region that satisfies a constraint, which is a classic sliding-window signal.

Expanding and shrinking the window lets us maintain validity without restarting the scan.

Approach

Grow the window with a right pointer, and shrink from the left only when the constraint is violated.

Track the best window as you go to keep the solution linear.

Steps:

  • Expand the right end of the window.
  • While invalid, move the left end to restore constraints.
  • Update the best window found.

Example

Input: nums = [1,1,0,1] Output: 3 Explanation: After deleting the number in position 2, [1,1,1] contains 3 numbers with value of 1's.

Python Solution

class Solution: def longestSubarray(self, nums: List[int]) -> int: n = len(nums) left = [0] * (n + 1) right = [0] * (n + 1) for i, x in enumerate(nums, 1): if x: left[i] = left[i - 1] + 1 for i in range(n - 1, -1, -1): if nums[i]: right[i] = right[i + 1] + 1 return max(left[i] + right[i + 1] for i in range(n))

Complexity

The time complexity is O(n)O(n)O(n), and the space complexity is O(n)O(n)O(n). The space complexity is O(n)O(n)O(n).

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.


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