Two Sum - Leetcode 1 - HashMap - Python

About this video

• Video Title: Two Sum - Leetcode 1 - HashMap - Python
• Channel: NeetCode
• Speakers: [Not specified in transcript]
• Duration: 00:08:26

Overview

This video explains how to solve the "Two Sum" problem, a popular LeetCode question. It covers a brute-force approach with O(n^2) time complexity and then introduces a more efficient solution using a hash map, achieving O(n) time complexity. The video walks through the logic of both approaches and demonstrates how to code the optimized hash map solution in Python.

Key takeaways

• Problem Definition: The goal is to find two numbers in an array that sum up to a given target and return their indices.
• Brute-Force Approach: This involves checking every possible pair of numbers in the array, leading to a time complexity of O(n^2).
• Hash Map Optimization: By using a hash map, we can store previously seen numbers and their indices. For each number, we calculate the "complement" (target - current number) and check if the complement exists in the hash map. This reduces the time complexity to O(n).
• One-Pass Solution: A clever optimization involves iterating through the array once. For each number, we first check if its complement is already in the hash map. If it is, we've found the pair. If not, we add the current number and its index to the hash map. This ensures we don't use the same element twice and still achieves O(n) time complexity.
• Space-Time Tradeoff: The hash map solution improves time complexity from O(n^2) to O(n) but increases space complexity to O(n) due to the storage required for the hash map.