What are Binary serch tree in python
What is Binary search Trees?
Binary Search Tree is a node-based binary tree data structure which has the following properties:
The left subtree of a node contains only nodes with keys lesser than the node’s key.
The right subtree of a node contains only nodes with keys greater than the node’s key.
The left and right subtree each must also be a binary search tree.
There must be no duplicate nodes.
###Pros of a BST
When balanced, a BST provides lightning-fast O(log(n)) insertions, deletions, and lookups.
Binary search trees are pretty simple. An ordinary BST, unlike a balanced tree like a red-black tree, requires very little code to get running.
###Cons of a BST
Slow for a brute-force search. If you need to iterate over each node, you might have more success with an array.
When the tree becomes unbalanced, all fast O(log(n)) operations quickly degrade to O(n).
Since pointers to whole objects are typically involved, a BST can require quite a bit more memory than an array, although this depends on the implementation.
There are many applications of binary search trees in real life, and one of the most common use cases is storing indexes and keys in a database. For example, when you create a primary key column in MySQL or PostgresQL, you create a binary tree where the keys are the values of the column and the nodes point to database rows. This allows the application to easily search for database rows by specifying a key, for example, to find a user record using the email primary key.
Code Example :
# Binary Search Tree operations in Python # Create a node class Node: def __init__(self, key): self.key = key self.left = None self.right = None # Inorder traversal def inorder(root): if root is not None: # Traverse left inorder(root.left) # Traverse root print(str(root.key) + "->", end=' ') # Traverse right inorder(root.right) # Insert a node def insert(node, key): # Return a new node if the tree is empty if node is None: return Node(key) # Traverse to the right place and insert the node if key < node.key: node.left = insert(node.left, key) else: node.right = insert(node.right, key) return node # Find the inorder successor def minValueNode(node): current = node # Find the leftmost leaf while(current.left is not None): current = current.left return current # Deleting a node def deleteNode(root, key): # Return if the tree is empty if root is None: return root # Find the node to be deleted if key < root.key: root.left = deleteNode(root.left, key) elif(key > root.key): root.right = deleteNode(root.right, key) else: # If the node is with only one child or no child if root.left is None: temp = root.right root = None return temp elif root.right is None: temp = root.left root = None return temp # If the node has two children, # place the inorder successor in position of the node to be deleted temp = minValueNode(root.right) root.key = temp.key # Delete the inorder successor root.right = deleteNode(root.right, temp.key) return root root = None root = insert(root, 8) root = insert(root, 3) root = insert(root, 1) root = insert(root, 6) root = insert(root, 7) root = insert(root, 10) root = insert(root, 14) root = insert(root, 4) print("Inorder traversal: ", end=' ') inorder(root) print("\nDelete 10") root = deleteNode(root, 10) print("Inorder traversal: ", end=' ') inorder(root)