Binary Search Tree Implementation in Python

In this source code example, we will write a code to implement the Binary Search Tree data structure in Python.

A binary search tree is a tree where each node has up to two children. In addition, all values in the left subtree of a node are less than the value at the root of the tree and all values in the right subtree of a node are greater than or equal to the value at the root of the tree. Finally, the left and right subtrees must also be binary search trees. This definition makes it possible to write a class where values may be inserted into the tree while maintaining the definition.

Binary Search Tree Implementation in Python

A binary search Tree implementation using Python

class Node:
    def __init__(self, value, parent):
        self.value = value
        self.parent = parent  # Added in order to delete a node easier
        self.left = None
        self.right = None

    def __repr__(self):
        from pprint import pformat

        if self.left is None and self.right is None:
            return str(self.value)
        return pformat({"%s" % (self.value): (self.left, self.right)}, indent=1)

class BinarySearchTree:
    def __init__(self, root=None):
        self.root = root

    def __str__(self):
        Return a string of all the Nodes using in order traversal
        return str(self.root)

    def __reassign_nodes(self, node, new_children):
        if new_children is not None:  # reset its kids
            new_children.parent = node.parent
        if node.parent is not None:  # reset its parent
            if self.is_right(node):  # If it is the right children
                node.parent.right = new_children
                node.parent.left = new_children
            self.root = new_children

    def is_right(self, node):
        return node == node.parent.right

    def empty(self):
        return self.root is None

    def __insert(self, value):
        Insert a new node in Binary Search Tree with value label
        new_node = Node(value, None)  # create a new Node
        if self.empty():  # if Tree is empty
            self.root = new_node  # set its root
        else:  # Tree is not empty
            parent_node = self.root  # from root
            while True:  # While we don't get to a leaf
                if value < parent_node.value:  # We go left
                    if parent_node.left is None:
                        parent_node.left = new_node  # We insert the new node in a leaf
                        parent_node = parent_node.left
                    if parent_node.right is None:
                        parent_node.right = new_node
                        parent_node = parent_node.right
            new_node.parent = parent_node

    def insert(self, *values):
        for value in values:
        return self

    def search(self, value):
        if self.empty():
            raise IndexError("Warning: Tree is empty! please use another.")
            node = self.root
            # use lazy evaluation here to avoid NoneType Attribute error
            while node is not None and node.value is not value:
                node = node.left if value < node.value else node.right
            return node

    def get_max(self, node=None):
        We go deep on the right branch
        if node is None:
            node = self.root
        if not self.empty():
            while node.right is not None:
                node = node.right
        return node

    def get_min(self, node=None):
        We go deep on the left branch
        if node is None:
            node = self.root
        if not self.empty():
            node = self.root
            while node.left is not None:
                node = node.left
        return node

    def remove(self, value):
        node =  # Look for the node with that label
        if node is not None:
            if node.left is None and node.right is None:  # If it has no children
                self.__reassign_nodes(node, None)
            elif node.left is None:  # Has only right children
                self.__reassign_nodes(node, node.right)
            elif node.right is None:  # Has only left children
                self.__reassign_nodes(node, node.left)
                tmp_node = self.get_max(
                )  # Gets the max value of the left branch
                node.value = (
                )  # Assigns the value to the node to delete and keep tree structure

    def preorder_traverse(self, node):
        if node is not None:
            yield node  # Preorder Traversal
            yield from self.preorder_traverse(node.left)
            yield from self.preorder_traverse(node.right)

    def traversal_tree(self, traversal_function=None):
        This function traversal the tree.
        You can pass a function to traversal the tree as needed by client code
        if traversal_function is None:
            return self.preorder_traverse(self.root)
            return traversal_function(self.root)

    def inorder(self, arr: list, node: Node):
        """Perform an inorder traversal and append values of the nodes to
        a list named arr"""
        if node:
            self.inorder(arr, node.left)
            self.inorder(arr, node.right)

    def find_kth_smallest(self, k: int, node: Node) -> int:
        """Return the kth smallest element in a binary search tree"""
        arr: list = []
        self.inorder(arr, node)  # append all values to list using inorder traversal
        return arr[k - 1]

def postorder(curr_node):
    postOrder (left, right, self)
    node_list = list()
    if curr_node is not None:
        node_list = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
    return node_list

if __name__ == "__main__":
    testlist = (8, 3, 6, 1, 10, 14, 13, 4, 7)
    t = BinarySearchTree()
    for i in testlist:

    # Prints all the elements of the list in order traversal

    if is not None:
        print("The value 6 exists")
        print("The value 6 doesn't exist")

    if is not None:
        print("The value -1 exists")
        print("The value -1 doesn't exist")

    if not t.empty():
        print("Max Value: ", t.get_max().value)
        print("Min Value: ", t.get_min().value)

    for i in testlist:


{'8': ({'3': (1, {'6': (4, 7)})}, {'10': (None, {'14': (13, None)})})}
The value 6 exists
The value -1 doesn't exist
Max Value:  14
Min Value:  1
{'7': ({'3': (1, {'6': (4, None)})}, {'10': (None, {'14': (13, None)})})}
{'7': ({'1': (None, {'6': (4, None)})}, {'10': (None, {'14': (13, None)})})}
{'7': ({'1': (None, 4)}, {'10': (None, {'14': (13, None)})})}
{'7': (4, {'10': (None, {'14': (13, None)})})}
{'7': (4, {'14': (13, None)})}
{'7': (4, 13)}
{'7': (4, None)}