A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 1

1. A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 1 2. About the Pragmatic Bookshelf 3. Table of Contents 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 4. Early Praise for A Common-Sense Guide to Data Struc tures and Algorithms in Python, Volume 1 5. Preface 6. Who Is This Book For? 7. The Python Edition 8. A Note About the Code 9. What’s in This Book? 10. How to Read This Book 11. Online Resources 12. Connecting 13. Acknowledgments 14. Chapter 1: Why Data Structures Matter 15. Data Structures 16. The Array: The Foundational Data Structure 17. Measuring Speed 18. Reading 19. Searching 20. Insertion 21. Deletion 22. Sets: How a Single Rule Can Affect Efficiency 23. Wrapping Up 24. Exercises 25. Chapter 2: Why Algorithms Matter 26. Ordered Arrays 27. Searching an Ordered Array 28. Binary Search 29. Binary Search vs. Linear Search 30. Wrapping Up 31. Exercises 32. Chapter 3: O Yes! Big O Notation 33. Big O: How Many Steps Relative to N Elements? 34. The Soul of Big O 35. An Algorithm of the Third Kind 36. Logarithms 37. O(log N) Explained 38. Practical Examples 39. Wrapping Up 40. Exercises 41. Chapter 4: Speeding Up Your Code with Big O 42. Bubble Sort 43. Bubble Sort in Action 44. The Efficiency of Bubble Sort 45. A Quadratic Problem 46. A Linear Solution 47. Wrapping Up 48. Exercises 49. Chapter 5: Optimizing Code With and Without Big O 50. Selection Sort 51. Selection Sort in Action 52. The Efficiency of Selection Sort 53. Ignoring Constants 54. Big O Categories 55. Wrapping Up 56. Exerci ses 57. Chapter 6: Optimizing for Optimistic Scenarios 58. Insertion Sort 59. Insertion Sort in Action 60. The Efficiency of Insertion Sort 61. The Average Case 62. A Practical Example 63. Wrapping Up 64. Exercises 65. Chapter 7: Big O in Everyday Code 66. Mean Average of Even Numbers 67. Word Builder 68. Array Sample 69. Average Celsius Reading 70. Clothing Labels 71. Count the Ones 72. Palindrome Checker 73. Get All the Products 74. Password Cracker 75. Wrapping Up 76. Exercises 77. Chapter 8: Blazing Fast Lookup with Hash Tables 78. Hash Tables 79. Hashing with Hash Functions 80. Building a Thesaurus for Fun and Profit, but M ainly Profit 81. Hash Table Lookups 82. Dealing with Collisions 83. Making an Efficient Hash Table 84. Hash Tables for Organization 85. Hash Tables for Speed 86. Wrapping Up 87. Exercises 88. Chapter 9: Crafting Elegant Code with Stacks and Queues 89. Stacks 90. Abstract Data Types 91. Stacks in Action 92. The Importance of Constrained Data Structures 93. Queues 94. Queues in Action 95. Wrapping Up 96. Exercises 97. Chapter 10: Recursively Recurse with Recursion 98. Recurse Instead of Loop 99. The Base Case 100. Reading Recursive Code 101. Recursion in the Eyes of the Computer 102. Filesystem Traversal 103. Wrapping Up 104. Exercises 105. Chapter 11: Learning to Write in Recursive 106. Recursive Category: Repeatedly Execute 107. Recursive Category: Calculations 108. Top-Down Recursion: A New Way of Thinking 109. The Staircase Problem 110. Anagram Generation 111. Wrapping Up 112. Exercises 113. Chapter 12: Dynamic Programming 114. Unnecessary Recursive Calls 115. The Little Fix for Big O 116. The Efficiency of Recursion 117. Overlapping Subproblems 118. Dynamic Programming Through Memoization 119. Dynamic Programming Through Going Bottom-Up 120. Wrapping Up 121. Exercises 122. Chapter 13: Recursive Algorithms for Speed 123. Partitioning 124. Quicksort 125. The Efficiency of Quicksort 126. Quicksort in the Worst-Case Scenario 127. Quickselect 128. Sorting as a Key to Other Algorithms 129. Wrapping Up 130. Exercises 131. Chapter 14: Node-Based Data Structures 132. Linked Lists 133. Implementing a Linked List 134. Reading 135. Searching 136. Insertion 137. Deletion 138. Efficiency of Linked List Operations 139. Linked Lists in Action 140. Doubly Linked Lists 141. Queues as Doubly Linked Lists 142. Wrapping Up 143. Exercises 144. Chapter 15: Speeding Up All the Things with Binary Search Trees 145. Trees 146. Binary Search Trees 147. Searching 148. Insertion 149. Deletion 150. Binary Search Trees in Action 151. Binary Search Tree Traversal 152. Wrapping Up 153. Exercises 154. Chapter 16: Keeping Your Priorities Straight with Heaps 155. Priority Queues 156. Heaps 157. Heap Properties 158. Heap Insertion 159. Looking for the Last Node 160. Heap Deletion 161. Heaps vs. Ordered Arrays 162. The Problem of the Last Node…Again 163. Arrays as Heaps 164. Heaps as Priority Queues 165. Wrapping Up 166. Exercises 167. Chapter 17: It Doesn’t Hurt to Trie 168. Tries 169. Storing Words 170. Trie Search 171. The Efficiency of Trie Search 172. Trie Insertion 173. Building Autocomplete 174. Completing Autocomplete 175. Tries with Values: A Better Autocomplete 176. Wrapping Up 177. Exercises 178. Chapter 18: Connecting Everything with Graphs 179. Graphs 180. Directed Graphs 181. Object-Oriented Graph Implementation 182. Graph Search 183. Depth-First Search 184. Breadth-First Search 185. The Efficiency of Graph Search 186. Weighted Graphs 187. Dijkstra’s Algorithm 188. Wrapping Up 189. Exercises 190. Chapter 19: Dealing with Space Constraints 191. Big O of Space Complexity 192. Trade-Offs Between Time and Space 193. The Hidden Cost of Recursion 194. Wrapping Up 195. Exercises 196. Chapter 20: Techniques for Code Optimization 197. Prerequisite: Determine Your Current Big O 198. Start Here: The Best-Imaginable Big O 199. Magical Lookups 200. Recognizing Patterns 201. Greedy Algorithms 202. Change the Data Structure 203. Wrapping Up 204. Parting Thoughts 205. Exercises 206. Appendix 1: Exercise Solutions 207. 1: 208. 2: 209. 3: 210. 4: 211. Chapte r 5 212. 6: 213. 7: 214. 8: 215. 9: 216. 10: 217. 11: 218. 12: 219. 13: 220. 1: 4 221. 15: 222. 16: 223. 17: 224. 18: 225. 19: 226. 20: 227. You May Be Interested In…