{"product_id":"139997","title":"Really Easy Data Structures and Algorithms in Python ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXOZ3O4olgs3rni0sBGQ7W9zsof0U.png?v=1765077699\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Really Easy Data Structures and Algorithms in Python \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/148876430\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eData structures and algorithms are mostly common sense.\u003c\/b\u003e \u003cbr\u003eIf you take away the explanations that pretend to be difficult, anyone can understand!\u003cbr\u003e\u003cbr\u003e Data structures and algorithms are explained using common sense rather than mathematical concepts.\u003cbr\u003e It explains why structures like arrays, lists, hash tables, trees, and graphs are important, and which algorithms are faster in which situations, using standards that can be applied directly in practice and interviews.\u003cbr\u003e Big O notation is also explained in an easy-to-understand manner, and in the process, it provides very useful knowledge for securing the scalability and speed required in practical environments.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e Chapter 1: Why Data Structures Are Important\u003cbr\u003e 1.1 Data Structure 2\u003cbr\u003e 1.2 Arrays: Basic Data Structures 3\u003cbr\u003e 1.3 Speed ​​Measurement 5\u003cbr\u003e 1.4 Reading 6\u003cbr\u003e 1.5 Search 9\u003cbr\u003e 1.6 Insert 11\u003cbr\u003e 1.7 Delete 14\u003cbr\u003e 1.8 Sets: The Impact of a Single Rule on Efficiency 15\u003cbr\u003e 1.9 Final 19\u003cbr\u003e 1.10 Practice Problem 19\u003cbr\u003e\u003cbr\u003e Chapter 2: Why Algorithms Matter 21\u003cbr\u003e 2.1 Ordered Arrays 22 \u003cbr\u003e2.2 Searching an ordered array 25\u003cbr\u003e 2.3 Binary Search 27\u003cbr\u003e 2.4 Binary Search vs. Linear Search 31\u003cbr\u003e 2.5 Finish 34\u003cbr\u003e 2.6 Practice Problem 34\u003cbr\u003e\u003cbr\u003e Chapter 3 Big O Notation 35\u003cbr\u003e 3.1 Big O: How many steps does an algorithm need when there are N data elements? 36\u003cbr\u003e 3.2 The Essence of Big O 37\u003cbr\u003e 3.3 The third type of algorithm 40\u003cbr\u003e 3.4 log 42\u003cbr\u003e 3.5 Learn about O(log N) 43\u003cbr\u003e 3.6 Real-World Example 44\u003cbr\u003e 3.7 Final 46\u003cbr\u003e 3.8 Practice Problem 46\u003cbr\u003e\u003cbr\u003e Chapter 4: Speeding Up Big O Code 49\u003cbr\u003e 4.1 Bubble Sort 49\u003cbr\u003e 4.2 Practical Uses of Bubble Sort 51\u003cbr\u003e 4.3 Efficiency of Bubble Sort 58\u003cbr\u003e 4.4 Quadratic Problem 60\u003cbr\u003e 4.5 Linear Solution 62\u003cbr\u003e 4.6 Finish 65\u003cbr\u003e 4.7 Practice Problem 65\u003cbr\u003e\u003cbr\u003e Chapter 5 Optimization with and without Big O 67\u003cbr\u003e 5.1 Selection Sort 67\u003cbr\u003e 5.2 Practical Uses of Selection Sort 68\u003cbr\u003e 5.3 Efficiency of Selection Sort 75\u003cbr\u003e 5.4 Ignoring Constants 76\u003cbr\u003e 5.5 Big O Category 78\u003cbr\u003e 5.6 Finish 81\u003cbr\u003e 5.7 Practice Problem 82\u003cbr\u003e\u003cbr\u003e Chapter 6 Optimization for Optimistic Scenarios 85\u003cbr\u003e 6.1 Insertion Sort 85\u003cbr\u003e 6.2 Practical Uses of Insertion Sort 87\u003cbr\u003e 6.3 Efficiency of Insertion Sort 94 \u003cbr\u003e6.4 Average case 96\u003cbr\u003e 6.5 Real-World Example 98\u003cbr\u003e 6.6 Wrapping Up 101\u003cbr\u003e 6.7 Practice Problem 101\u003cbr\u003e\u003cbr\u003e Chapter 7 Big O's in Everyday Code 103\u003cbr\u003e 7.1 Even average 104\u003cbr\u003e 7.2 Word Generator 105\u003cbr\u003e 7.3 Array Sample 107\u003cbr\u003e 7.4 Average temperature 108 degrees Celsius\u003cbr\u003e 7.5 Clothing Brands 109\u003cbr\u003e 7.6 Counting the number of 1s 110\u003cbr\u003e 7.7 Palindrome Checker 111\u003cbr\u003e 7.8 Finding All Products 112\u003cbr\u003e 7.9 Handling Multiple Datasets 114\u003cbr\u003e 7.10 Password Cracker 115\u003cbr\u003e 7.11 Final 118\u003cbr\u003e 7.12 Practice Problem 118\u003cbr\u003e\u003cbr\u003e Chapter 8: Super-Fast Lookups Using Hash Tables 123\u003cbr\u003e 8.1 Hash Table 124\u003cbr\u003e 8.2 Hashing with a Hash Function 125\u003cbr\u003e 8.3 Creating a Thesaurus for Fun and Profit, Especially Profit 126\u003cbr\u003e 8.4 Hash Table Lookup 128\u003cbr\u003e 8.5 Handling Collisions 130\u003cbr\u003e 8.6 Building an Efficient Hash Table 133\u003cbr\u003e 8.7 Hash Tables for Data Organization 135\u003cbr\u003e 8.8 Hash Tables for Speed ​​Improvement 137\u003cbr\u003e 8.9 Finish 142\u003cbr\u003e 8.10 Practice Problem 142\u003cbr\u003e\u003cbr\u003e Chapter 9: Writing Concise Code with Stacks and Queues 145\u003cbr\u003e 9.1 Stack 145\u003cbr\u003e 9.2 Abstract Data Types 148\u003cbr\u003e 9.3 Practical Use of Stacks 150 \u003cbr\u003e9.4 Code Implementation: Stack-Based Code Linter 153\u003cbr\u003e 9.5 The Importance of Constrained Data Structures 156\u003cbr\u003e 9.6 Q 157\u003cbr\u003e 9.7 Practical Use of Queues 159\u003cbr\u003e 9.8 Finish 161\u003cbr\u003e 9.9 Practice Problem 161\u003cbr\u003e\u003cbr\u003e Chapter 10 Recursive Iteration Using Recursion 163\u003cbr\u003e 10.1 Recursion Instead of Loops 163\u003cbr\u003e 10.2 Base Conditions 165\u003cbr\u003e 10.3 Reading Recursive Code 166\u003cbr\u003e 10.4 Recursion Through the Eyes of a Computer 169\u003cbr\u003e 10.5 Filesystem Traversal 172\u003cbr\u003e 10.6 Finish 174\u003cbr\u003e 10.7 Practice Problem 174\u003cbr\u003e\u003cbr\u003e Chapter 11: Writing Recursively 177\u003cbr\u003e 11.1 Recursive Category: Repeated Execution 177\u003cbr\u003e 11.2 Recursive Categories: Computation 182\u003cbr\u003e 11.3 Top-Down Recursion: A New Way of Thinking 185\u003cbr\u003e 11.4 Staircase Problem 191\u003cbr\u003e 11.5 Creating Anagrams 195\u003cbr\u003e 11.6 Finish 199\u003cbr\u003e 11.7 Practice Problem 200\u003cbr\u003e\u003cbr\u003e Chapter 12 Dynamic Programming 203\u003cbr\u003e 12.1 Unnecessary recursive calls 203\u003cbr\u003e 12.2 Small Improvements for Big O 207\u003cbr\u003e 12.3 Efficiency of Recursion 208\u003cbr\u003e 12.4 Duplicate Subproblem 209\u003cbr\u003e 12.5 Dynamic Programming with Memoization 211\u003cbr\u003e 12.6 Dynamic Programming via a Bottom-Up Approach 214\u003cbr\u003e 12.7 Finish 217\u003cbr\u003e 12.8 Practice Problem 217\u003cbr\u003e \u003cbr\u003eChapter 13: Speeding Up Recursive Algorithms 219\u003cbr\u003e 13.1 Split 220\u003cbr\u003e 13.2 Quick Sort 225\u003cbr\u003e 13.3 Efficiency of Quick Sort 232\u003cbr\u003e 13.4 Worst-case scenario for quick sort 237\u003cbr\u003e 13.5 Quick Select 238\u003cbr\u003e 13.6 Sorting, the Core of Other Algorithms 242\u003cbr\u003e 13.7 Finish 244\u003cbr\u003e 13.8 Practice Problem 244\u003cbr\u003e\u003cbr\u003e Chapter 14 Node-Based Data Structures 247\u003cbr\u003e 14.1 Linked Lists 247\u003cbr\u003e 14.2 Implementing a Linked List 249\u003cbr\u003e 14.3 Reading 251\u003cbr\u003e 14.4 Search 254\u003cbr\u003e 14.5 Insert 255\u003cbr\u003e 14.6 Delete 259\u003cbr\u003e 14.7 Efficiency of Linked List Operations 262\u003cbr\u003e 14.8 Practical Uses of Linked Lists 262\u003cbr\u003e 14.9 Doubly Linked Lists 263\u003cbr\u003e 14.10 Doubly Linked List-Based Queues 266\u003cbr\u003e 14.11 Final 268\u003cbr\u003e 14.12 Practice Problem 268\u003cbr\u003e\u003cbr\u003e Chapter 15: Speeding Up with Binary Search Trees 271\u003cbr\u003e 15.1 Tree 272\u003cbr\u003e 15.2 Binary Search Trees 274\u003cbr\u003e 15.3 Search 275\u003cbr\u003e 15.4 Insert 280\u003cbr\u003e 15.5 Delete 285\u003cbr\u003e 15.6 Practical Uses of Binary Search Trees 296\u003cbr\u003e 15.7 Binary Search Tree Traversal 296\u003cbr\u003e 15.8 Finish 301\u003cbr\u003e 15.9 Practice Problem 301\u003cbr\u003e\u003cbr\u003e Chapter 16: Managing Priorities with Heaps 303\u003cbr\u003e 16.1 Priority Queue 303\u003cbr\u003e 16.2 Heap 305\u003cbr\u003e 16.3 Heap Properties 308\u003cbr\u003e 16.4 Heap Insertion 309 \u003cbr\u003e16.5 Finding the Last Node 311\u003cbr\u003e 16.6 Heap Deletion 312\u003cbr\u003e 16.7 Heaps vs. Ordered Arrays 316\u003cbr\u003e 16.8 Revisiting the Last Node Problem 317\u003cbr\u003e 16.9 Implementing a Heap with Arrays 319\u003cbr\u003e 16.10 Priority Queues Implemented with Heaps 326\u003cbr\u003e 16.11 Final 326\u003cbr\u003e 16.12 Practice Problem 327\u003cbr\u003e\u003cbr\u003e Chapter 17: It Doesn't Hurt to Know Try 329\u003cbr\u003e 17.1 Try 330\u003cbr\u003e 17.2 Saving Words 332\u003cbr\u003e 17.3 Try Search 335\u003cbr\u003e 17.4 Efficiency of Trie Search 339\u003cbr\u003e 17.5 Try Insert 339\u003cbr\u003e 17.6 Developing an Auto-Complete Feature 344\u003cbr\u003e 17.7 Completing the Auto-Complete Feature 350\u003cbr\u003e Trie with values ​​17.8: Improved auto-completion 350\u003cbr\u003e 17.9 Finish 352\u003cbr\u003e 17.10 Practice Problem 352\u003cbr\u003e\u003cbr\u003e Chapter 18 Connecting Everything into One Graph 355\u003cbr\u003e 18.1 Graph 356\u003cbr\u003e 18.2 Directed Graphs 358\u003cbr\u003e 18.3 Object-Oriented Graph Implementation 359\u003cbr\u003e 18.4 Graph Exploration 361\u003cbr\u003e 18.5 Depth-First Search 363\u003cbr\u003e 18.6 Breadth-First Search 373\u003cbr\u003e 18.7 Efficiency of Graph Search 386\u003cbr\u003e 18.8 Weighted Graph 389\u003cbr\u003e 18.9 Dijkstra's Algorithm 393\u003cbr\u003e 18.10 Finish 410\u003cbr\u003e 18.11 Practice Problem 411\u003cbr\u003e \u003cbr\u003eChapter 19: Handling Space Constraints 415\u003cbr\u003e 19.1 The Big O of Space Complexity 415\u003cbr\u003e 19.2 Time and Space Tradeoffs 418\u003cbr\u003e 19.3 The Hidden Cost of Recursion 421\u003cbr\u003e 19.4 Finish 423\u003cbr\u003e 19.5 Practice Problem 424\u003cbr\u003e\u003cbr\u003e Chapter 20 Code Optimization Techniques 427\u003cbr\u003e 20.1 Prerequisite: Understanding the Current Big O 427\u003cbr\u003e 20.2 Getting Started: The Biggest Big O You Can Imagine 428\u003cbr\u003e 20.3 Magic Views 429\u003cbr\u003e 20.4 Pattern Recognition 437\u003cbr\u003e 20.5 Greedy Algorithm 445\u003cbr\u003e 20.6 Changing Data Structures 457\u003cbr\u003e 20.7 Finish 464\u003cbr\u003e 20.8 Farewell 464\u003cbr\u003e 20.9 Practice Problem 465\u003cbr\u003e\u003cbr\u003e Appendix A Practice Problems Answers 469\u003cbr\u003e Search 505\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5441\/MidCate002\/544015975.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eIt's not the algorithm that's difficult, it's the explanation.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Books explaining data structures and algorithms often contain repetitive technical terms and mathematical concepts, making them difficult for non-specialists or beginners.\u003cbr\u003e However, most data structures and algorithms can be understood with common sense. \u003cbr\u003eMathematical notation is just a language, and everything dealt with in mathematics can be explained using common sense.\u003cbr\u003e Now, let's understand data structures and algorithms simply with explanations that make sense and are similar to everyday language.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eUnderstanding begins with words, learning begins with your fingertips.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Another reason why data structures and algorithms are difficult to learn is because of the theory-oriented explanations.\u003cbr\u003e We've all had the experience of thinking we've read and understood something, only to find ourselves unable to code.\u003cbr\u003e Conceptual understanding alone is not enough; true learning comes from learning while writing code.\u003cbr\u003e Don't be lazy and just write code line by line to get a feel for the real world.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAn introductory book for non-majors, but also useful for job seekers.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e In a job interview, it's not just about getting the right answer. \u003cbr\u003eYou should be able to explain why you used this data structure, what its time complexity is, and why your chosen data structure is more efficient than other approaches.\u003cbr\u003e With practical, real-world examples like password crackers, finding friends on social networks, finding the cheapest airline tickets, and library software, you can practice choosing data structures to maximize efficiency and improving existing algorithms to improve performance.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat this book covers\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Why data structures and algorithms are important\u003cbr\u003e ○ Understanding algorithm efficiency using Big O notation\u003cbr\u003e ○Data structures that improve code efficiency\u003cbr\u003e Recursive algorithms for elegant code\u003cbr\u003e Node-based data structures boasting tremendous performance\u003cbr\u003e ○Space complexity to determine memory efficiency\u003cbr\u003e ○Some code optimization techniques\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ○College students who find algorithms difficult to read \u003cbr\u003e○Interviewers who are busy preparing for employment\u003cbr\u003e ○A practitioner who can code but lacks algorithms \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e July 21, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 536 pages | 1,014g | 188*240*26mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9788966264803 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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