Introduction Coding interviews can be daunting, especially when you’re faced with algorithmic problems under time pressure. However, the good news is that most coding interview questions fall into predictable patterns that can be solved using a core set of algorithms and data structures. In this comprehensive guide, we’ll explore the top 10 algorithms that frequently appear in Python coding interviews, from tech giants like Google and Meta to startups. Each algorithm is explained with: Core principles and intuition Time and space complexity analysis Complete Python implementations with detailed comments Step-by-step execution examples Common pitfalls and edge cases Problem variations and practical tips Whether you’re preparing for your first technical interview or brushing up on fundamentals, this guide will help you build a solid algorithmic foundation. 1. Breadth-First Search (BFS) Core Principle BFS explores a graph level by level, visiting all neighbors of a node before moving to the next level. It uses a queue (FIFO) data structure to maintain the order of exploration. When to use BFS: – Finding the shortest path in unweighted graphs –
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