A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 2 (for True Epub)

1. A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 2 2. Level Up Your Core Programming Skills 3. by Jay Wengrow 4. About the Pragmatic Bookshelf 5. Table of Contents 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 6. Early Praise for A Common-Sense Guide to Data Struc tures and Algorithms in Python, Volume 2 7. Acknowledgments 8. Preface 9. Who Is This Book For? 10. What’s in This Book? 11. How to Read This Book 12. A Note About the Code 13. Online Resources 14. Connecting 15. Chapter 1: Getting Things in Order with Mergesort 16. Merging Arrays 17. Merging in Action 18. The Efficiency of Merging 19. Mergesort 20. Mergesort in Action 21. The Efficiency of Mergesort 22. Comparing Mergesort and Quicksort: Lessons Learned 23. Wrapping Up 24. Exercises 25. Chapter 2: Benchmarking Code 26. Benchmarking 27. Using the timeit Module 28. Benchmarking Gotchas 29. Benchmarking Sorting Al gorithms 30. Mergesort vs. Insertion Sort 31. Mergesort vs. Quicksort 32. Using Python’s Built-In Sorting Algorithm 33. Quicksorting a Sorted Array 34. Wrapping Up 35. Exercises 36. Chapter 3: How Random Is That? 37. Randomized Quicksort 38. Randomized Algorithms 39. Generating Random Numbers 40. TRNGs vs. PRNGs 41. The Fisher-Yates Shuffle 42. The Fisher-Yates Shuffle in Action 43. The Efficiency of the Fisher-Yates Shuffle 44. Shuffling the Wrong Way 45. Binary Search Tree Randomization 46. Randomization for Distribution 47. Load Balancing 48. Wrapping Up 49. Exercises 50. Chapter 4: Cache Is King 51. Caching 52. Eviction Policies 53. LRU Cache 54. The LRU Cache Data Structure 55. Fixing the LRU Worst-Case Scenario with Random ization 56. The Memory Hierarchy 57. Writing Cache-Friendly Code 58. Spatial Locality 59. Wrapping Up 60. Exercises 61. Chapter 5: The Great Balancing Act of Red-Black Trees 62. Online Algorithms and Self-Balancing Trees 63. Red-Black Trees 64. The Red-Black Rules 65. Red-Black Tree Insertion 66. The Efficiency of Red-Black Trees 67. Red-Black Tree Deletion 68. Wrapping Up 69. Exercises 70. Chapter 6: Randomized Treaps: Haphazardly Achieving Equilibrium 71. Treaps 72. Treap Insertion 73. Self-Balancing Treaps in Action 74. The Power of Random Priorities 75. Treap Deletion 76. Wrapping Up 77. Exercises 78. Chapter 7: To B-Tree or Not to B-Tree: External-Memory Algorithms 79. External Memory 80. Count I/Os, Not Steps 81. External Binary Search 82. Optimizing External-Memory Algorithms 83. Binary Search Trees in External Memory 84. B-Trees 85. Implementing B-Trees 86. B-Tree Insertion 87. B-Tree Deletion 88. The Balance of B-Trees 89. B-Trees as Database Indexes 90. Wrapping Up 91. Exercises 92. Chapter 8: Wrangling Big Data with M/B-Way Mergesort 93. External-Memory Sorting 94. A First Attempt: Two-Way External Mergesort 95. M = Main Memory Size 96. A Second Attempt at External Mergesort 97. Merging K Sorted Lists 98. M/B-Way Mergesort 99. Wrapping Up 100. Exercises 101. Chapter 9: Counting on Monte Carlo Algorithms 102. Monte Carlo Algorithms 103. Monte Carlo Algorithms vs. Las Vegas Algorith ms 104. Obtaining Averages Through Random Sampling 105. Primality Testing 106. Monte Carlo Primality Testing 107. Fermat’s Little Theorem 108. Fermat’s Primality Test 109. Wrapping Up 110. Exercises 111. Chapter 10: Designing Great Hash Tables with Randomization 112. Hash Functions: A Quick Review 113. Scalable Hash Functions 114. The Division Method 115. Randomized Hashing 116. Wrapping Up 117. Exercises 118. Chapter 11: Keeping Your Text Search Sharp with a Little Rabin-Karp 119. Substring Search 120. Brute-Force Substring Search 121. The Sliding Window Technique 122. Rabin-Karp Substring Search 123. Covering All Our Bases 124. Perfecting Rabin-Karp with Base 26 125. Handling Long Needles 126. Monte-Carlo Rabin-Karp 127. Converting Monte Carlo to Las Vegas 128. Wrapping Up 129. Exercises 130. Chapter 12: Saving Space: Every Bit Helps 131. Sets 132. Boolean Arrays 133. Bit Vectors 134. Bit Manipulation 135. Bit Masks: The Key to Zeroing in on a Bit 136. Benchmarking Space 137. The Space Complexity of Sets 138. Classic Set Operations 139. Wrapping Up 140. Exercises 141. Chapter 13: Cultivating Efficiency with Bloom Filters 142. Finding Duplicates Revisited 143. Bloom Filters 144. Use Multiple Hash Functions 145. Using Bloom Filters for Detecting Duplicates 146. Bloom Filters in the Wild 147. Wrapping Up 148. Parting Thoughts 149. Exercises 150. Appendix 1: Solutions 151. 1: 152. 2: 153. 3: 154. 4: 155. 5: 156. 6: 157. 7: 158. 8: 159. 9: 160. 10: 161. 11: 162. 12: 163. 13: 164. You May Be Interested In…