Python crea un árbol de búsqueda binario (con código fuente adjunto, con visualización de estructura de árbol)

Escrito al frente :
la visualización del árbol (código 2) requiere algunos paquetes, instálelos con anticipación (el código 1 no es necesario, siempre que el entorno de Python esté bien)

from collections import Iterable
import networkx as nx
import matplotlib.pyplot as plt

1. Crea un árbol binario

class TreeNode:
    '''二叉搜索树节点的定义'''
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

class OperationTree:
    '''二叉搜索树操作'''
    def insert(self, root, val):
        '''二叉搜索树插入操作'''
        if root == None:
            root = TreeNode(val)
        elif val < root.val:
            root.left = self.insert(root.left, val)
        elif val > root.val:
            root.right = self.insert(root.right, val)
        return root

    def query(self, root, val):
        '''二叉搜索树查询操作'''
        if root == None:
            return False
        if root.val == val:
            return True
        elif val < root.val:
            return self.query(root.left, val)
        elif val > root.val:
            return self.query(root.right, val)

    def findMin(self, root):
        '''查找二叉搜索树中最小值点'''
        if root.left:
            return self.findMin(root.left)
        else:
            return root

    def findMax(self, root):
        '''查找二叉搜索树中最大值点'''
        if root.right:
            return self.findMax(root.right)
        else:
            return root

    def delNode(self, root, val):
        '''删除二叉搜索树中值为val的点'''
        if root == None:
            return
        if val < root.val:
            root.left = self.delNode(root.left, val)
        elif val > root.val:
            root.right = self.delNode(root.right, val)
        # 当val == root.val时，分为三种情况：只有左子树或者只有右子树、有左右子树、即无左子树又无右子树
        else:
            if root.left and root.right:
                # 既有左子树又有右子树，则需找到右子树中最小值节点
                temp = self.findMin(root.right)
                root.val = temp.val
                # 再把右子树中最小值节点删除
                root.right = self.delNode(root.right, temp.val)
            elif root.right == None and root.left == None:
                # 左右子树都为空
                root = None
            elif root.right == None:
                # 只有左子树
                root = root.left
            elif root.left == None:
                # 只有右子树
                root = root.right
        return root

    def printTree(self, root):
        # 打印二叉搜索树(中序打印，有序数列)
        if root == None:
            return
        self.printTree(root.left)
        print(root.val, end = ' ')
        self.printTree(root.right)


if __name__ == '__main__':
    List = [17,5,35,2,11,29,38,9,16,8]
    root = None
    op = OperationTree()
    for val in List:
        root = op.insert(root,val)
    print('中序打印二叉搜索树：', end = ' ')
    op.printTree(root)
    print('')
    print('根节点的值为：', root.val)
    print('树中最大值为:', op.findMax(root).val)
    print('树中最小值为:', op.findMin(root).val)
    print('查询树中值为5的节点:', op.query(root, 5))
    print('查询树中值为100的节点:', op.query(root, 100))
    print('删除树中值为16的节点:', end = ' ')
    root = op.delNode(root, 16)
    op.printTree(root)
    print('')
    print('删除树中值为5的节点:', end = ' ')
    root = op.delNode(root, 5)
    op.printTree(root)
    print('')

2. Visualización de la estructura del árbol

class Node:
    def __init__(self, value, left=None, right=None):
        self.value = value  # 节点的值
        self.left = left  # 左子节点
        self.right = right  # 右子节点

from collections import Iterable

class BinaryTree:
    def __init__(self, seq=()):
        assert isinstance(seq, Iterable)  # 确保输入的参数为可迭代对象
        self.root = None
        self.insert(*seq)
    def insert(self, *args):
        if not args:
            return
        if not self.root:
            self.root = Node(args[0])
            args = args[1:]
        for i in args:
            seed = self.root
            while True:
                if i > seed.value:
                    if not seed.right:
                        node = Node(i)
                        seed.right = node
                        break
                    else:
                        seed = seed.right
                else:
                    if not seed.left:
                        node = Node(i)
                        seed.left = node
                        break
                    else:
                        seed = seed.left

    def minNode(self):
        node = self.root
        while node.left:
            node = node.left
        return node

    def maxNode(self):
        node = self.root
        while node.right:
            node = node.right
        return node

import networkx as nx
import matplotlib.pyplot as plt

def create_graph(G, node, pos={
    
    }, x=0, y=0, layer=1):
    pos[node.value] = (x, y)
    if node.left:
        G.add_edge(node.value, node.left.value)
        l_x, l_y = x - 1 / 2 ** layer, y - 1
        l_layer = layer + 1
        create_graph(G, node.left, x=l_x, y=l_y, pos=pos, layer=l_layer)
    if node.right:
        G.add_edge(node.value, node.right.value)
        r_x, r_y = x + 1 / 2 ** layer, y - 1
        r_layer = layer + 1
        create_graph(G, node.right, x=r_x, y=r_y, pos=pos, layer=r_layer)
    return (G, pos)

def draw(node):   # 以某个节点为根画图
    graph = nx.DiGraph()
    graph, pos = create_graph(graph, node)
    fig, ax = plt.subplots(figsize=(8, 10))  # 比例可以根据树的深度适当调节
    nx.draw_networkx(graph, pos, ax=ax, node_size=300)
    plt.show()

if __name__ == '__main__':
    List = [17,5,35,2,11,29,38,9,16,8]
    tree = BinaryTree()
    tree.insert(*List)
    draw(tree.root)