INTRODUCTION TO ALGORITHMS FOR LEETCODE IN PYTHON

This book provides a structured introduction to algorithms and data structures through practical examples in Python, with a focus on LeetCode-style problems commonly used for technical preparation and the development of algorithmic thinking. The exposition is designed to help readers master not only a set of solutions, but also the logic of choosing an approach: from formulating the problem and identifying key constraints to selecting an appropriate data structure, designing an algorithm, and evaluating its efficiency. Throughout the text, a clear connection is maintained between theoretical foundations and engineering implementation, explaining why some solutions remain robust as input sizes grow while others degrade, and how this affects runtime, memory consumption, and the predictability of program behavior. In terms of content, the book covers fundamental elements of asymptotic analysis, basic linear data representations, and traversal techniques, including pointer-based strategies and common iterative processing patterns. It then discusses key algorithmic classes and tools that are regularly used in practice: sorting, hashing methods, hash tables, and linked lists, with attention to their properties, applicability limits, and performance implications. Within each topic, the emphasis is placed on understanding how a data structure works and how specific implementation details in Python relate to general principles of algorithm design and complexity analysis. The final part is oriented toward problems typical of technical interviews: the reader follows a path from problem formalization and decomposition into subproblems to comparing naive and optimized approaches, step-by-step implementation, and correctness checks on edge cases. An essential element is the systematic evaluation of time and space complexity, which allows readers not only to produce working code but also to justify its efficiency and scalability. The practical significance of the book lies in developing a reproducible skill set for designing algorithmic solutions in Python – from selecting data structures and building optimal approaches to confidently preparing for interviews and applying these methods to engineering tasks where performance, reliability, and transparent reasoning are critical.