趣学Python算法100例（详解100个趣味编程算法实例，为Python初学者打造，培养编程兴趣，拓宽编程思维,提高编程能力和算法设计能力） (刘河飞 闫凯峰)（Z-Library）

【One-Line Pitch】 A hands-on collection of 100 classic algorithm problems solved in Python, each walked through from problem statement to code and output—ideal for beginners who want to build confidence, sharpen logic, and learn by doing rather than by theory alone. 【Book Arc】 - **Opening (~0%–9%)**: Introduces core algorithm concepts through simple, classic problems—like the hit-and-run license plate puzzle and Newton's method for finding equation roots—teaching loop design, iteration, and the importance of correct termination conditions. - **Early (~9%–28%)**: Expands into search and math modeling, covering binary search (with a clear low/high/mid pointer walkthrough) and real-world math problems like the book-lending permutation count and progressive tax calculation using lists of tuples. - **Middle (~28%–47%)**: Dives into array-based simulations and number theory, including the candy-sharing circle problem, the magician's card trick (a clever circular-array puzzle), and integer properties like Armstrong numbers, amicable numbers, and modular arithmetic for high-power tails. - **Late (~47%–53%)**: Continues with number patterns and classic theorems, such as generating Pythagorean triples via two distinct formulas, emphasizing coprime conditions and the elegance of mathematical shortcuts over brute force. - **Ending (~53%–100%)**: The excerpts taper off here; the full book extends into fractions, primes, logic puzzles, games, arrays, recursion, theorems, graphics, and mixed problems, each following the same structured "problem→analysis→design→code→output→extension" flow. 【Key Takeaways】 - **Structured problem-solving framework** (Opening): Every example follows a consistent pipeline—problem description, analysis, algorithm design, framework, code, results, and extension—making it easy to replicate the method on new problems. - **Loop design is the backbone** (Opening): The hit-and-run example shows how to use flag variables to break nested loops efficiently, a practical trick for reducing unnecessary iterations. - **Iteration requires careful condition logic** (Opening): Newton's method highlights a common beginner trap—using `abs(x - x0) >= 1e-5` instead of `< 1e-5`—because the loop must continue while the error is still large, not when it's small. - **Binary search is a divide-and-conquer classic** (Early): The low/high/mid pointer technique is explained step-by-step with a concrete array, and the code distinguishes success (via `break`) from failure (via a sentinel `k = -1`), reinforcing control-flow mastery. - **Data structures simplify real-world math** (Early): The tax calculator uses a list of tuples to encode tax brackets and rates, then iterates to accumulate owed tax—a clean pattern for table-driven logic. - **Array simulation models physical processes** (Middle): The candy-sharing problem uses a list to track each child's sweets, with a while loop checking uniformity and nested for loops for halving and passing—showing how to translate a real-world process into code. - **Mathematical shortcuts beat brute force** (Middle): The high-power tail problem (13^13's last three digits) demonstrates that only the last three digits matter in multiplication, so `last = last * x % 1000` avoids overflow and is far more efficient. - **Variable initialization placement is critical** (Middle): The amicable numbers example warns that resetting accumulators inside vs. outside a loop changes results—a subtle bug that teaches disciplined state management. 【Reading Tips】 - **Skim the "problem analysis" sections** if you're comfortable with math; the real value is in the "algorithm design" and "code" parts, where the loop and data-structure choices are explained. - **Deep-read the binary search and Newton's method chapters** (Early): They're foundational for understanding pointers, iteration, and termination conditions—master these before moving on. - **Watch for the "problem extension" sections**: They often introduce alternative methods (e.g., sequential search vs. binary search) or variations (e.g., different digit-splitting orders), which are great for testing your understanding. - **Type out and run the code yourself**: The book emphasizes complete, tested programs; hands-on execution in PyCharm or any IDE will solidify the patterns far better than reading alone. - **Pay attention to the "notes" and "common mistakes"**: The amicable numbers bug and the Newton's method condition are the kind of pitfalls that will trip you up in real projects—internalize them. 【Coverage Limits】 This guide synthesizes only the first ~53% of the book (chapters 1–3 and part of later chapters); the remaining content—fractions, primes, logic puzzles, games, recursion, theorems, graphics, and mixed problems—is not covered in the excerpts.