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Leetcode #88: Merge Sorted Array

In this guide, we solve Leetcode #88 Merge Sorted Array 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

You are given two integer arrays nums1 and nums2, sorted in non-decreasing order, and two integers m and n, representing the number of elements in nums1 and nums2 respectively. Merge nums1 and nums2 into a single array sorted in non-decreasing order.

Quick Facts

  • Difficulty: Easy
  • Premium: No
  • Tags: Array, Two Pointers, Sorting

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: nums1 = [1,2,3,0,0,0], m = 3, nums2 = [2,5,6], n = 3 Output: [1,2,2,3,5,6] Explanation: The arrays we are merging are [1,2,3] and [2,5,6]. The result of the merge is [1,2,2,3,5,6] with the underlined elements coming from nums1.

Python Solution

class Solution: def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None: k = m + n - 1 i, j = m - 1, n - 1 while j >= 0: if i >= 0 and nums1[i] > nums2[j]: nums1[k] = nums1[i] i -= 1 else: nums1[k] = nums2[j] j -= 1 k -= 1

Complexity

The time complexity is O(m+n)O(m + n)O(m+n), where mmm and nnn are the lengths of two arrays. The space complexity is O(1)O(1)O(1).

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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