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How to implement binary tree in Python

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May 03, 2023 am 09:16 AM

python

Python implements a binary tree

How to implement binary tree in Python

Python can implement a binary tree using object-oriented programming by defining a binary tree node class. Each node contains a data element, left and right child node pointers and some operation methods, such as inserting nodes, finding nodes, deleting nodes, etc.

The following is a simple binary tree implementation example:

class Node:
    def __init__(self, data):
        self.data = data
        self.left = None
        self.right = None

    def insert(self, data):
        if self.data:
            if data < self.data:
                if self.left is None:
                    self.left = Node(data)
                else:
                    self.left.insert(data)
            elif data > self.data:
                if self.right is None:
                    self.right = Node(data)
                else:
                    self.right.insert(data)
        else:
            self.data = data

    def find(self, data):
        if data < self.data:
            if self.left is None:
                return str(data) + " Not Found"
            return self.left.find(data)
        elif data > self.data:
            if self.right is None:
                return str(data) + " Not Found"
            return self.right.find(data)
        else:
            return str(self.data) + " is found"

    def inorder_traversal(self, root):
        res = []
        if root:
            res = self.inorder_traversal(root.left)
            res.append(root.data)
            res = res + self.inorder_traversal(root.right)
        return res

In the above code, the Node class defines a node, including the data element data, and the left and right child node pointers left and right. The insert method is used to insert nodes into a binary tree, the find method is used to find whether a specific node exists in the binary tree, and the inorder_traversal method is used to perform in-order traversal of the binary tree.

The following is how to use this Node class to create a binary tree:

root = Node(50)
root.insert(30)
root.insert(20)
root.insert(40)
root.insert(70)
root.insert(60)
root.insert(80)

# 查找节点

print(root.find(70)) # Output: 70 is found
print(root.find(90)) # Output: 90 Not Found

# 中序遍历
print(root.inorder_traversal(root)) # Output: [20, 30, 40, 50, 60, 70, 80]

In the above code, a root node root is first created, then the insert method is used to insert a node into the tree, and finally using The find method finds nodes and uses the inorder_traversal method to perform in-order traversal of the binary tree.

In addition to insertion, search and traversal methods, binary trees also have other operation methods, such as deleting nodes, determining whether it is a binary search tree, calculating the depth of the tree, etc. The following is a slightly more complete binary tree sample code:

class Node:
    def __init__(self, data):
        self.data = data
        self.left = None
        self.right = None

    def insert(self, data):
        if self.data:
            if data < self.data:
                if self.left is None:
                    self.left = Node(data)
                else:
                    self.left.insert(data)
            elif data > self.data:
                if self.right is None:
                    self.right = Node(data)
                else:
                    self.right.insert(data)
        else:
            self.data = data

    def find(self, data):
        if data < self.data:
            if self.left is None:
                return None
            return self.left.find(data)
        elif data > self.data:
            if self.right is None:
                return None
            return self.right.find(data)
        else:
            return self

    def delete(self, data):
        if self is None:
            return self

        if data < self.data:
            self.left = self.left.delete(data)
        elif data > self.data:
            self.right = self.right.delete(data)
        else:
            if self.left is None:
                temp = self.right
                self = None
                return temp
            elif self.right is None:
                temp = self.left
                self = None
                return temp
            temp = self.right.minimum()
            self.data = temp.data
            self.right = self.right.delete(temp.data)
        return self

    def minimum(self):
        if self.left is None:
            return self
        return self.left.minimum()

    def is_bst(self):
        if self.left:
            if self.left.data > self.data or not self.left.is_bst():
                return False

        if self.right:
            if self.right.data < self.data or not self.right.is_bst():
                return False

        return True

    def height(self, node):
        if node is None:
            return 0

        left_height = self.height(node.left)
        right_height = self.height(node.right)

        return max(left_height, right_height) + 1

    def inorder_traversal(self, root):
        res = []
        if root:
            res = self.inorder_traversal(root.left)
            res.append(root.data)
            res = res + self.inorder_traversal(root.right)
        return res

In this example, we have added the delete method to delete the specified node; the minimum method to find the smallest node in the tree; the is_bst method to determine the current Whether the tree is a binary search tree; the height method is used to calculate the depth of the tree.

We can use the following code to test the new method:

# 创建二叉树
root = Node(50)
root.insert(30)
root.insert(20)
root.insert(40)
root.insert(70)
root.insert(60)
root.insert(80)

# 删除节点
print("Deleting node 20:")
root.delete(20)
print(root.inorder_traversal(root))

# 判断是否为二叉搜索树
print("Is it a BST?:", root.is_bst())

# 计算树的深度
print("Tree height:", root.height(root))

In this way we have completed a relatively complete binary tree implementation, and also demonstrated how to use object-oriented programming in Python ideas to implement a data structure.

Finally, the complete binary tree class implementation code is attached:

class Node:
    def __init__(self, data):
        self.data = data
        self.left = None
        self.right = None

    def insert(self, data):
        if self.data:
            if data < self.data:
                if self.left is None:
                    self.left = Node(data)
                else:
                    self.left.insert(data)
            elif data > self.data:
                if self.right is None:
                    self.right = Node(data)
                else:
                    self.right.insert(data)
        else:
            self.data = data

    def find(self, data):
        if data < self.data:
            if self.left is None:
                return None
            return self.left.find(data)
        elif data > self.data:
            if self.right is None:
                return None
            return self.right.find(data)
        else:
            return self

    def delete(self, data):
        if self is None:
            return self

        if data < self.data:
            self.left = self.left.delete(data)
        elif data > self.data:
            self.right = self.right.delete(data)
        else:
            if self.left is None:
                temp = self.right
                self = None
                return temp
            elif self.right is None:
                temp = self.left
                self = None
                return temp
            temp = self.right.minimum()
            self.data = temp.data
            self.right = self.right.delete(temp.data)
        return self

    def minimum(self):
        if self.left is None:
            return self
        return self.left.minimum()

    def is_bst(self):
        if self.left:
            if self.left.data > self.data or not self.left.is_bst():
                return False

        if self.right:
            if self.right.data < self.data or not self.right.is_bst():
                return False

        return True

    def height(self, node):
        if node is None:
            return 0

        left_height = self.height(node.left)
        right_height = self.height(node.right)

        return max(left_height, right_height) + 1

    def inorder_traversal(self, root):
        res = []
        if root:
            res = self.inorder_traversal(root.left)
            res.append(root.data)
            res = res + self.inorder_traversal(root.right)
        return res

if __name__ == '__main__':
    # 创建二叉树
    root = Node(50)
    root.insert(30)
    root.insert(20)
    root.insert(40)
    root.insert(70)
    root.insert(60)
    root.insert(80)

    # 删除节点
    print("Deleting node 20:")
    root.delete(20)
    print(root.inorder_traversal(root))

    # 判断是否为二叉搜索树
    print("Is it a BST?:", root.is_bst())

    # 计算树的深度
    print("Tree height:", root.height(root))

After running the code, you can get the following output:

Deleting node 20 :
[30, 40, 50, 60, 70, 80]
Is it a BST?: True
Tree height: 3

This example includes insertion and search , delete, traverse, determine whether it is a binary search tree and calculate the depth of the tree, etc.

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