Binary Tree Operations
Explore fundamental binary tree operations including searching for values with depth-first search, inserting nodes using level-order traversal, and deleting nodes while maintaining tree structure. Understand the time and space complexities for these operations and learn how to implement them efficiently in Python.
Binary trees store data in a hierarchical structure. Unlike binary search trees (BSTs), they do not follow any ordering rules, so we cannot directly determine where to go when searching or inserting.
Because of this, most operations rely on traversing the tree.
In this lesson, we study three fundamental operations:
Searching
Insertion
Deletion
Searching in a binary tree
Searching in a binary tree means checking whether a given value exists in the tree. As there is no ordering property, we cannot skip parts of the tree. In the worst case, we may need to visit every node.
A common approach is to use depth-first search (DFS). We start at the root, check its value, and if it does not match, we recursively search the left subtree and then the right subtree.
How this algorithm works
Start at the root node.
If the current node is
None, returnFalsebecause the value is not found.Check if the current nodeās value matches the target:
If yes, return
True
If not, recursively search the left subtree.
If the value is not found in the left subtree, search the right subtree.
Return
Trueif the value is found in either subtree, otherwise returnFalse.
Python implementation
Below is the ...
def search(self, root, target):# Base case: if the current node is None, the value is not foundif root is None:return False# Check if the current node's value matches the targetif root.data == target:return True# Recursively search in the left subtree OR right subtree# If found in either, return Truereturn search(root.left, target) or search(root.right, target)
Time complexity: