Reflections on ‘Python Algorithms You Can Understand’
by Li Ming, Lianjiang No. 5 Middle School
Fujian Province Lianjiang No. 5 Middle School
Li Ming
The new curriculum reform has introduced Python into high school information technology classes, which is undoubtedly a response to the times. However, limited class hours often leave students only able to scratch the surface, making it difficult to grasp its essence. It is encouraging that this has not dampened students’ fervent desire to learn programming languages—students continuously seek my recommendations for self-study books. This enthusiasm deeply touches me and motivates me to continue learning, striving to provide them with more effective guidance both inside and outside the classroom. I am well aware that hastily searching for a few book titles to recommend would betray the trust students place in me. Only through personal experience and study can I select truly suitable books for them. ‘Python Algorithms You Can Understand’ is an excellent reading material that I believe is very suitable for high school students to self-learn algorithms after careful reading. Here are a few of my thoughts:
1. Algorithms: The ‘Core Skill’ of the Programming World
If programming is likened to martial arts, then algorithms are the ‘internal skills’ that determine the depth of one’s cultivation. They are like the CPU of a computer, where their quality directly determines the efficiency of a program. Many people chase after the ever-evolving programming languages, frameworks, and technologies (the ‘techniques’), while neglecting the more fundamental practice of algorithms. It is essential to understand that ‘the fundamentals remain unchanged amidst change’; regardless of how technology iterates, the core ideas of algorithms remain timeless. Only by mastering the ‘internal skills’ of algorithms can one navigate the future waves of technology with ease, using a solid foundation to master the endless new ‘techniques’.
2. How to Break Through? The ‘Reading Code’ of This Book
Learning algorithms often daunts beginners due to their obscurity and complexity, and many books on the market are overly academic, raising the barrier to entry. The unique value of this book lies in its successful resolution of this issue:
1. Simplified Expression: It breaks down complex algorithm concepts using clear and straightforward language, stripping away the difficult exterior.
2. Python-Based: It directly uses Python, the language from high school textbooks, as a medium, seamlessly connecting classroom knowledge and avoiding the burden of learning a new language, making after-class expansion more efficient.
3. Context-Driven: It uses real problems encountered by ‘Little Lang’ as a starting point, making learning enjoyable and greatly enhancing readability and motivation.
4. Layered Design: It serves as a friendly entry guide for beginners; for those with a foundation, it encourages thinking before verification, deepening understanding and enhancing learning efficiency through selective reading of modules and cases. This design allows students to take what they need and progress step by step.
3. Nine ‘Milestones’: Constructing an Algorithm Knowledge Map
This book is meticulously crafted in its knowledge system construction, selecting nine core ‘milestones’ on the path of algorithm learning. It starts with the basic two-pointer problem, explaining how to efficiently traverse and search in arrays or linked lists using two pointers, which is a powerful tool for solving many linear structure problems. It then delves into hash techniques, revealing how they achieve near-instant data retrieval through key-value pairs, forming the cornerstone for handling rapid search problems. In exploring non-linear structures, it provides a detailed comparison of depth-first search and breadth-first search strategies for traversing trees and graphs; the former is like an explorer delving deep into caves, while the latter spreads out like ripples, each having its applicable scenarios. When faced with complex decision-making problems, the book introduces backtracking algorithms, which systematically attempt all possible paths and backtrack when hitting a wall (such as solving the eight queens or Sudoku problems), as well as greedy algorithms, which make the best choice at each step (such as making change or scheduling activities), though they may not be globally optimal, they are often efficient. For more complex optimization problems, it emphasizes the essence of dynamic programming—’breaking big problems into small ones’—by storing solutions to subproblems to avoid redundant calculations (such as climbing stairs, mining, or the knapsack problem). Additionally, it covers the shortest path problem for finding optimal paths (such as the idea of Dijkstra’s algorithm) and the divide-and-conquer strategy for breaking down large problems into independent smaller problems and merging solutions (such as the classic application of merge sort). These nine algorithm modules, from simple to complex, cover the core areas of algorithm learning, constructing a solid and comprehensive algorithm knowledge map for students.
‘Python Algorithms You Can Understand’ is far more than just an algorithm textbook. It uses Python, a language familiar to high school students, as a vessel, clear logic as a paddle, and vivid examples as a sail, successfully opening the seemingly towering door of algorithms to young learners. It is not only a powerful extension of the classroom but also an excellent companion for self-study. More importantly, by mastering these core algorithms, students hone not only their programming skills but also their ‘meta-skills’ in logical thinking and problem-solving—this is the true ‘internal skill’ for navigating the rapidly changing digital world. When students understand how to break down large problems (divide and conquer), how to weigh choices (greedy), how to plan optimal paths (dynamic programming, shortest path), and how to explore systematically (depth-first search, breadth-first search, backtracking), they gain a universal key to tackle future complex challenges.
