Fundamentals of Python: From First Programs Through Data Structures

1. Fundamentals of Python:From First Programs Through Data Structures Chapter 18 Hierarchical Collections: Trees

2. Objectives After completing this chapter, you will be able to: • Describe the difference between trees and other types of collections using the relevant terminology • Recognize applications for which general trees and binary trees are appropriate • Describe the behavior and use of specialized trees, such as heaps, BSTs, and expression trees • Analyze the performance of operations on binary search trees and heaps • Develop recursive algorithms to process trees Fundamentals of Python: From First Programs Through Data Structures

3. An Overview of Trees • In a tree, the ideas of predecessor and successor are replaced with those of parent and child • Trees have two main characteristics: • Each item can have multiple children • All items, except a privileged item called the root, have exactly one parent Fundamentals of Python: From First Programs Through Data Structures

4. Tree Terminology Fundamentals of Python: From First Programs Through Data Structures

5. Tree Terminology (continued) Fundamentals of Python: From First Programs Through Data Structures

6. Tree Terminology (continued) Note: The height of a tree containing one node is 0 By convention, the height of an empty tree is –1 Fundamentals of Python: From First Programs Through Data Structures

7. General Trees and Binary Trees • In a binary tree, each node has at most two children: • The left child and the right child Fundamentals of Python: From First Programs Through Data Structures

8. Recursive Definitions of Trees • A general tree is either empty or consists of a finite set of nodes T • Node r is called the root • The set T – {r} is partitioned into disjoint subsets, each of which is a general tree • A binary tree is either empty or consists of a root plus a left subtree and a right subtree, each of which are binary trees Fundamentals of Python: From First Programs Through Data Structures

9. Why Use a Tree? • A parse tree describes the syntactic structure of a particular sentence in terms of its component parts Fundamentals of Python: From First Programs Through Data Structures

10. Why Use a Tree? (continued) • File system structures are also tree-like Fundamentals of Python: From First Programs Through Data Structures

11. Why Use a Tree? (continued) • Sorted collections can also be represented as tree-like structures • Called a binary search tree, or BST for short • Can support logarithmic searches and insertions Fundamentals of Python: From First Programs Through Data Structures

12. A full binary tree contains the maximum number of nodes for a given height H N nodes Height: N – 1 The Shape of Binary Trees • The shape of a binary tree can be described more formally by specifying the relationship between its height and the number of nodes contained in it Fundamentals of Python: From First Programs Through Data Structures

13. The Shape of Binary Trees (continued) • The number of nodes, N, contained in a full binary tree of height H is 2H+ 1 – 1 • The height, H, of a full binary tree with N nodes is log2(N + 1) – 1 • The maximum amount of work that it takes to access a given node in a full binary tree is O(log N) Fundamentals of Python: From First Programs Through Data Structures

14. The Shape of Binary Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

15. Three Common Applications of Binary Trees • In this section, we introduce three special uses of binary trees that impose an ordering on their data: • Heaps • Binary search trees • Expression trees Fundamentals of Python: From First Programs Through Data Structures

16. Heaps • In a min-heap each node is ≤ to both of its children • A max-heap places larger nodes nearer to the root • Heap property: Constraint on the order of nodes • Heap sort builds a heap from data and repeatedly removes the root item and adds it to the end of a list • Heaps are also used to implement priority queues Fundamentals of Python: From First Programs Through Data Structures

17. Binary Search Trees • A BST imposes a sorted ordering on its nodes • Nodes in left subtree of a node are < node • Nodes in right subtree of a node are > node • When shape approaches that of a perfectly balanced binary tree, searches and insertions are O(log n) in the worst case • Not all BSTs are perfectly balanced • In worst case, they become linear and support linear searches Fundamentals of Python: From First Programs Through Data Structures

18. Binary Search Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

19. Binary Search Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

20. Expression Trees • Another way to process expressions is to build a parse tree during parsing • Expression tree • An expression tree is never empty • An interior node represents a compound expression, consisting of an operator and its operands • Each leaf node represents a numeric operand • Operands of higher precedence usually appear near bottom of tree, unless overridden in source expression by parentheses Fundamentals of Python: From First Programs Through Data Structures

21. Expression Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

22. Binary Tree Traversals • Four standard types of traversals for binary trees: • Preorder traversal: Visits root node, and then traverses left subtree and right subtree in similar way • Inorder traversal: Traverses left subtree, visits root node, and traverses right subtree • Appropriate for visiting items in a BST in sorted order • Postorder traversal: Traverses left subtree, traverses right subtree, and visits root node • Level order traversal: Beginning with level 0, visits the nodes at each level in left-to-right order Fundamentals of Python: From First Programs Through Data Structures

23. Binary Tree Traversals (continued) Fundamentals of Python: From First Programs Through Data Structures

24. Binary Tree Traversals (continued) Fundamentals of Python: From First Programs Through Data Structures

25. Binary Tree Traversals (continued) Fundamentals of Python: From First Programs Through Data Structures

26. Binary Tree Traversals (continued) Fundamentals of Python: From First Programs Through Data Structures

27. A Binary Tree ADT • Provides many common operations required for building more specialized types of trees • Should support basic operations for creating trees, determining if a tree is empty, and traversing a tree • Remaining operations focus on accessing, replacing, or removing the component parts of a nonempty binary tree—its root, left subtree, and right subtree Fundamentals of Python: From First Programs Through Data Structures

28. The Interface for a Binary Tree ADT Fundamentals of Python: From First Programs Through Data Structures

29. The Interface for a Binary Tree ADT (continued) Fundamentals of Python: From First Programs Through Data Structures

30. Processing a Binary Tree • Many algorithms for processing binary trees follow the trees’ recursive structure • Programmers are occasionally interested in the frontier, or set of leaf nodes, of a tree • Example: Frontier of parse tree for English sentence shown earlier contains the words in the sentence Fundamentals of Python: From First Programs Through Data Structures

31. Processing a Binary Tree (continued) • frontierexpects a binary tree and returns a list • Two base cases: • Tree is empty  return an empty list • Tree is a leaf node  return a list containing root item Fundamentals of Python: From First Programs Through Data Structures

32. Implementing a Binary Tree Fundamentals of Python: From First Programs Through Data Structures

33. Implementing a Binary Tree (continued) Fundamentals of Python: From First Programs Through Data Structures

34. Implementing a Binary Tree (continued) Fundamentals of Python: From First Programs Through Data Structures

35. The String Representation of a Tree • __str__can be implemented with any of the traversals Fundamentals of Python: From First Programs Through Data Structures

36. Developing a Binary Search Tree • A BST imposes a special ordering on the nodes in a binary tree, so as to support logarithmic searches and insertions • In this section, we use the binary tree ADT to develop a binary search tree, and assess its performance Fundamentals of Python: From First Programs Through Data Structures

37. The Binary Search Tree Interface • The interface for a BST should include a constructor and basic methods to test a tree for emptiness, determine the number of items, add an item, remove an item, and search for an item • Another useful method is __iter__, which allows users to traverse the items in BST with a forloop Fundamentals of Python: From First Programs Through Data Structures

38. … Data Structures for the Implementation of BST Fundamentals of Python: From First Programs Through Data Structures

39. Searching a Binary Search Tree • findreturns the first matching item if the target item is in the tree; otherwise, it returns None • We can use a recursive strategy Fundamentals of Python: From First Programs Through Data Structures

40. Inserting an Item into a Binary Search Tree • addinserts an item in its proper place in the BST • Item’s proper place will be in one of three positions: • The root node, if the tree is already empty • A node in the current node’s left subtree, if new item is less than item in current node • A node in the current node’s right subtree, if new item is greater than or equal to item in current node • For options 2 and 3, adduses a recursive helper function named addHelper • In all cases, an item is added as a leaf node Fundamentals of Python: From First Programs Through Data Structures

41. Removing an Item from a Binary Search Tree • Save a reference to root node • Locate node to be removed, its parent, and its parent’s reference to this node • If item is not in tree, return None • Otherwise, if node has a left and right child, replace node’s value with largest value in left subtree and delete that value’s node from left subtree • Otherwise, set parent’s reference to node to node’s only child • Reset root node to saved reference • Decrement size and return item Fundamentals of Python: From First Programs Through Data Structures

42. Removing an Item from a Binary Search Tree (continued) • Fourth step is fairly complex: Can be factored out into a helper function, which takes node to be deleted as a parameter (node containing item to be removed is referred to as the top node): • Search top node’s left subtree for node containing the largest item (rightmost node of the subtree) • Replace top node’s value with the item • If top node’s left child contained the largest item, set top node’s left child to its left child’s left child • Otherwise, set parent node’s right child to that right child’s left child Fundamentals of Python: From First Programs Through Data Structures

43. Complexity Analysis of Binary Search Trees • BSTs are set up with intent of replicating O(log n) behavior for the binary search of a sorted list • A BST can also provide fast insertions • Optimal behavior depends on height of tree • A perfectly balanced tree supports logarithmic searches • Worst case (items are inserted in sorted order): tree’s height is linear, as is its search behavior • Insertions in random order result in a tree with close-to-optimal search behavior Fundamentals of Python: From First Programs Through Data Structures

44. Case Study: Parsing and Expression Trees • Request: • Write a program that uses an expression tree to evaluate expressions or convert them to alternative forms • Analysis: • Like the parser developed in Chapter 17, current program parses an input expression and prints syntax error messages if errors occur • If expression is syntactically correct, program prints its value and its prefix, infix, and postfix representations Fundamentals of Python: From First Programs Through Data Structures

45. Case Study: Parsing and Expression Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

46. Case Study: Parsing and Expression Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

47. Case Study: Parsing and Expression Trees (continued) • Design and Implementation of the Node Classes: Fundamentals of Python: From First Programs Through Data Structures

48. Case Study: Parsing and Expression Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

49. Case Study: Parsing and Expression Trees (continued) Fundamentals of Python: From First Programs Through Data Structures

50. Case Study: Parsing and Expression Trees (continued) • Design and Implementation of the Parser Class: • Easiest to build an expression tree with a parser that uses a recursive descent strategy • Borrow parser from Chapter 17 and modify it • parseshould now return an expression tree to its caller, which uses that tree to obtain information about the expression • factorprocesses either a number or an expression nested in parentheses • Calls expressionto parse nested expressions Fundamentals of Python: From First Programs Through Data Structures