A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 1 word 网盘 高速 下载地址大全 免费

If you thought data structures and algorithms were all just theory, you're missing out on what they can do for your Python code. Learn to use Big O notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work. Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. The Python edition uses Python exclusively for all code examples, exercise, and solutions. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your Python code faster and more scalable.

Table of Contents Preface Who Is This Book For? The Python Edition A Note About the Code What’s in This Book? How to Read This Book Online Resources Connecting Acknowledgments 1. Why Data Structures Matter Data Structures The Array: The Foundational Data Structure Measuring Speed Reading Searching Insertion Deletion Sets: How a Single Rule Can Affect Efficiency Wrapping Up Exercises 2. Why Algorithms Matter Ordered Arrays Searching an Ordered Array Binary Search Binary Search vs. Linear Search Wrapping Up Exercises 3. O Yes! Big O Notation Big O: How Many Steps Relative to N Elements? The Soul of Big O An Algorithm of the Third Kind Logarithms O(log N) Explained Practical Examples Wrapping Up Exercises 4. Speeding Up Your Code with Big O Bubble Sort Bubble Sort in Action The Efficiency of Bubble Sort A Quadratic Problem A Linear Solution Wrapping Up Exercises 5. Optimizing Code With and Without Big O Selection Sort Selection Sort in Action The Efficiency of Selection Sort Ignoring Constants Big O Categories Wrapping Up Exercises 6. Optimizing for Optimistic Scenarios Insertion Sort Insertion Sort in Action The Efficiency of Insertion Sort The Average Case A Practical Example Wrapping Up Exercises 7. Big O in Everyday Code Mean Average of Even Numbers Word Builder Array Sample Average Celsius Reading Clothing Labels Count the Ones Palindrome Checker Get All the Products Password Cracker Wrapping Up Exercises 8. Blazing Fast Lookup with Hash Tables Hash Tables Hashing with Hash Functions Building a Thesaurus for Fun and Profit, but Mainly Profit Hash Table Lookups Dealing with Collisions Making an Efficient Hash Table Hash Tables for Organization Hash Tables for Speed Wrapping Up Exercises 9. Crafting Elegant Code with Stacks and Queues Stacks Abstract Data Types Stacks in Action The Importance of Constrained Data Structures Queues Queues in Action Wrapping Up Exercises 10. Recursively Recurse with Recursion Recurse Instead of Loop The Base Case Reading Recursive Code Recursion in the Eyes of the Computer Filesystem Traversal Wrapping Up Exercises 11. Learning to Write in Recursive Recursive Category: Repeatedly Execute Recursive Category: Calculations Top-Down Recursion: A New Way of Thinking The Staircase Problem Anagram Generation Wrapping Up Exercises 12. Dynamic Programming Unnecessary Recursive Calls The Little Fix for Big O The Efficiency of Recursion Overlapping Subproblems Dynamic Programming Through Memoization Dynamic Programming Through Going Bottom-Up Wrapping Up Exercises 13. Recursive Algorithms for Speed Partitioning Quicksort The Efficiency of Quicksort Quicksort in the Worst-Case Scenario Quickselect Sorting as a Key to Other Algorithms Wrapping Up Exercises 14. Node-Based Data Structures Linked Lists Implementing a Linked List Reading Searching Insertion Deletion Efficiency of Linked List Operations Linked Lists in Action Doubly Linked Lists Queues as Doubly Linked Lists Wrapping Up Exercises 15. Speeding Up All the Things with Binary Search Trees Trees Binary Search Trees Searching Insertion Deletion Binary Search Trees in Action Binary Search Tree Traversal Wrapping Up Exercises 16. Keeping Your Priorities Straight with Heaps Priority Queues Heaps Heap Properties Heap Insertion Looking for the Last Node Heap Deletion Heaps vs. Ordered Arrays The Problem of the Last Node…Again Arrays as Heaps Heaps as Priority Queues Wrapping Up Exercises 17. It Doesn't Hurt to Trie Tries Storing Words Trie Search The Efficiency of Trie Search Trie Insertion Building Autocomplete Completing Autocomplete Tries with Values: A Better Autocomplete Wrapping Up Exercises 18. Connecting Everything with Graphs Graphs Directed Graphs Object-Oriented Graph Implementation Graph Search Depth-First Search Breadth-First Search The Efficiency of Graph Search Weighted Graphs Dijkstra’s Algorithm Wrapping Up Exercises 19. Dealing with Space Constraints Big O of Space Complexity Trade-Offs Between Time and Space The Hidden Cost of Recursion Wrapping Up Exercises 20. Techniques for Code Optimization Prerequisite: Determine Your Current Big O Start Here: The Best-Imaginable Big O Magical Lookups Recognizing Patterns Greedy Algorithms Change the Data Structure Wrapping Up Parting Thoughts Exercises A1. Exercise Solutions Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Index – SYMBOLS – – A – – B – – C – – D – – E – – F – – G – – H – – I – – J – – K – – L – – M – – N – – O – – P – – Q – – R – – S – – T – – U – – V – – W –

Jay Wengrow, an experienced educator and software engineer, is the founder of Actualize, an award-winning US coding bootcamp. He is passionate about simplifying the complex, and making software development more accessible by breaking the complex down into its simpler, easier parts.

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