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This chapter is from the book

This chapter is from the book

Data Structures & Algorithms in PythonData Structures & Algorithms in Python

Learn More Buy

This chapter is from the book

This chapter is from the book

Data Structures & Algorithms in PythonData Structures & Algorithms in Python

Learn More Buy

Experiments

Carrying out these experiments will help to provide insights into the topics covered in the chapter. No programming is involved.

8-A Use the Binary Search Tree Visualization tool to create 20 random trees using 20 as the requested number of items. What percentage would you say are seriously unbalanced?

8-B Use the BinarySearchTreeTester.py program shown in Listing 8-12 and provided with the code examples from the publisher’s website to do the following experiments:

  1. Delete a node that has no children.

  2. Delete a node that has 1 child node.

  3. Delete a node that has 2 child nodes.

  4. Pick a key for a new node to insert. Determine where you think it will be inserted in the tree, and then insert it with the program. Is it easy to determine where it will go?

  5. Repeat the previous step with another key but try to put it in the other child branch. For example, if your first node was inserted as the left child, try to put one as the right child or in the right subtree.

8-C The BinarySearchTreeTester.py program shown in Listing 8-12 prints an initial tree of 11 nodes across 7 levels, based on the insertion order of the items. A fully balanced version of the tree would have the same nodes stored on 4 levels. Use the program to clear the tree, and then determine what order to insert the same keys to make a balanced tree. Try your ordering and see whether the tree comes out balanced. If not, try another ordering. Can you describe in a few sentences the insertion ordering that will always create a balanced binary search tree from a particular set of keys?

8-D Use the Binary Search Tree Visualization tool to delete a node in every possible situation.

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