How to Implement a Binary Tree?

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Which is the best data structure that can be used to implement Binary Tree in Python?

Here is my simple recursive implementation of binary search tree.

#!/usr/bin/python

class Node:
    def __init__(self, val):
        self.l = None
        self.r = None
        self.v = val

class Tree:
    def __init__(self):
        self.root = None

    def getRoot(self):
        return self.root

    def add(self, val):
        if(self.root == None):
            self.root = Node(val)
        else:
            self._add(val, self.root)

    def _add(self, val, node):
        if(val < node.v):
            if(node.l != None):
                self._add(val, node.l)
            else:
                node.l = Node(val)
        else:
            if(node.r != None):
                self._add(val, node.r)
            else:
                node.r = Node(val)

    def find(self, val):
        if(self.root != None):
            return self._find(val, self.root)
        else:
            return None

    def _find(self, val, node):
        if(val == node.v):
            return node
        elif(val < node.v and node.l != None):
            self._find(val, node.l)
        elif(val > node.v and node.r != None):
            self._find(val, node.r)

    def deleteTree(self):
        # garbage collector will do this for us. 
        self.root = None

    def printTree(self):
        if(self.root != None):
            self._printTree(self.root)

    def _printTree(self, node):
        if(node != None):
            self._printTree(node.l)
            print str(node.v) + ' '
            self._printTree(node.r)

#     3
# 0     4
#   2      8
tree = Tree()
tree.add(3)
tree.add(4)
tree.add(0)
tree.add(8)
tree.add(2)
tree.printTree()
print (tree.find(3)).v
print tree.find(10)
tree.deleteTree()
tree.printTree()

Binary Tree, if the new node's value is greater than the current node's, go to the right child. A binary tree is a recursive tree data structure where each node can have 2 children at most. Binary trees have a few interesting properties when they’re perfect: Property 1: The number of total nodes on each “level” doubles as you move down the tree. Property 2: The number of nodes on the last level is equal to the sum of the number of nodes on all other levels, plus 1; Each data element stored in a tree structure called a node.

# simple binary tree
# in this implementation, a node is inserted between an existing node and the root


class BinaryTree():

    def __init__(self,rootid):
      self.left = None
      self.right = None
      self.rootid = rootid

    def getLeftChild(self):
        return self.left
    def getRightChild(self):
        return self.right
    def setNodeValue(self,value):
        self.rootid = value
    def getNodeValue(self):
        return self.rootid

    def insertRight(self,newNode):
        if self.right == None:
            self.right = BinaryTree(newNode)
        else:
            tree = BinaryTree(newNode)
            tree.right = self.right
            self.right = tree

    def insertLeft(self,newNode):
        if self.left == None:
            self.left = BinaryTree(newNode)
        else:
            tree = BinaryTree(newNode)
            tree.left = self.left
            self.left = tree


def printTree(tree):
        if tree != None:
            printTree(tree.getLeftChild())
            print(tree.getNodeValue())
            printTree(tree.getRightChild())



# test tree

def testTree():
    myTree = BinaryTree("Maud")
    myTree.insertLeft("Bob")
    myTree.insertRight("Tony")
    myTree.insertRight("Steven")
    printTree(myTree)

Read more about it Here:-This is a very simple implementation of a binary tree.

This is a nice tutorial with questions in between

Implementing a Binary Tree in Java, when the current node is null, we've reached a leaf node, we insert the new node in that position. Binary Tree (Array implementation) Talking about representation, trees can be represented in two way: 1) Dynamic Node Representation (Linked Representation). 2) Array Representation (Sequential Representation). We are going to talk about the sequential representation of the trees. To represent tree using an array, numbering of nodes can start either from 0–(n-1) or 1– n.

Simple implementation of BST in Python

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

class Tree:
    def __init__(self):
        self.root = None

    def addNode(self, node, value):
        if(node==None):
            self.root = TreeNode(value)
        else:
            if(value<node.data):
                if(node.left==None):
                    node.left = TreeNode(value)
                else:
                    self.addNode(node.left, value)
            else:
                if(node.right==None):
                    node.right = TreeNode(value)
                else:
                    self.addNode(node.right, value)

    def printInorder(self, node):
        if(node!=None):
            self.printInorder(node.left)
            print(node.data)
            self.printInorder(node.right)

def main():
    testTree = Tree()
    testTree.addNode(testTree.root, 200)
    testTree.addNode(testTree.root, 300)
    testTree.addNode(testTree.root, 100)
    testTree.addNode(testTree.root, 30)
    testTree.printInorder(testTree.root)

Binary Trees: Applications & Implementation, Binary Tree | Set 1 (Introduction). Trees: Unlike Arrays, Linked Lists, Stack and queues, which are linear data structures, trees are hierarchical data structures. Tree  A common type of binary tree is a binary search tree, in which every node has a value that is greater than or equal to the node values in the left sub-tree, and less than or equal to the node values in the right sub-tree. Here's a quick visual representation of this type of binary tree: For the implementation,

A very quick 'n dirty way of implementing a binary tree using lists. Not the most efficient, nor does it handle nil values all too well. But it's very transparent (at least to me):

def _add(node, v):
    new = [v, [], []]
    if node:
        left, right = node[1:]
        if not left:
            left.extend(new)
        elif not right:
            right.extend(new)
        else:
            _add(left, v)
    else:
        node.extend(new)

def binary_tree(s):
    root = []
    for e in s:
        _add(root, e)
    return root

def traverse(n, order):
    if n:
        v = n[0]
        if order == 'pre':
            yield v
        for left in traverse(n[1], order):
            yield left
        if order == 'in':
            yield v
        for right in traverse(n[2], order):
            yield right
        if order == 'post':
            yield v

Constructing a tree from an iterable:

 >>> tree = binary_tree('A B C D E'.split())
 >>> print tree
 ['A', ['B', ['D', [], []], ['E', [], []]], ['C', [], []]]

Traversing a tree:

 >>> list(traverse(tree, 'pre')), list(traverse(tree, 'in')), list(traverse(tree, 'post'))
 (['A', 'B', 'D', 'E', 'C'],
  ['D', 'B', 'E', 'A', 'C'],
  ['D', 'E', 'B', 'C', 'A'])

Binary Trees, Here's a quick visual representation of this type of binary tree: For the implementation, we'll use an auxiliary Node class that will store int values  Binary search tree: Every node has at most two children but there is a condition which states that the key in each node must be greater than or equal to any key stored in the left sub-tree, and less than or equal to any key stored in the right sub-tree. Binary search tree Implementation in Javascript.

Implementing a Binary Search Tree (BST) in Java, Binary trees can be implemented using pointers. A tree is represented by a pointer to the top Duration: 5:36 Posted: Oct 4, 2018 each product details are to be kept in a Binary search tree, write: 1) A binary search tree declaration that can be used to store product information. 2) A method called insert() that can be used to insert a product record in a tree. 3) A method called delete() that can be used to delete a product. 4) A method called update to update a product detail.

Binary Trees, A binary tree is made of nodes, where each node contains a "left" reference, a "​right" We implement a binary search tree using a private inner class BSTNode. Detailed Tutorial on Binary Search Tree (BST) In C++ Including Operations, C++ Implementation, Advantages, and Example Programs: A Binary Search Tree or BST as it is popularly called is a binary tree that fulfills the following conditions: The nodes that are lesser than the root node which is placed as left children of the BST.

Data Structure and Algorithms - Tree, data structure that has the following properties: The left subtree of a node contains only nodes with keys less than the node's key. The right subtree of a node contains only nodes with keys greater than the node's key. A sample binary tree: Tree Traversals (PreOrder, InOrder, PostOrder) Traversal is a process to visit all the nodes of a tree. In this example, I implemented three method which we use to traverse a tree. PreOrder Traversal; InOrder Traversal; PostOrder Traversal; PreOrder Traversal: Visit the root; Traverse the left subtree; Traverse the right subtree

5.3 Binary Tree Implementation, We will discuss binary tree or binary search tree specifically. We're going to implement tree using node object and connecting them through references. To insert data into a binary tree involves a function searching for an unused node in the proper position in the tree in which to insert the key value. The insert function is generally a recursive function that continues moving down the levels of a binary tree until there is an unused leaf in a position which follows the rules of placing nodes.

Comments
  • Lot of solutions here are implementing BST but questionsasked Biner Tree Implementation
  • Nice implementation. I'm just here to point out some style stuff. python usually does node is not None instead of your (node!=None). Also, you can use the __str__ function instead of the printTree method.
  • Also, your _find should probably be: def _find(self, val, node): if(val == node.v): return node elif(val < node.v and node.l != None): return self._find(val, node.l) elif(val > node.v and node.r != None): return self._find(val, node.r)
  • Isnt this a binary search tree whereleft<=root<=right ?
  • tree.find(0) ,tree.find(2) ,tree.find(4),tree.find(8) all return None.
  • There is a small bug, when you try to insert an existing key then it goes down the tree to create a new node with the a duplicate key.
  • The code in insertLeft is broken and will produce an infinite loop on any attempt to recursively traverse down the leftmost branch the binary tree
  • It can be easily fixed by swaping the following lines: tree.left = self.left self.left = tree
  • the last link is broken. Can you fix it.
  • You have ended some sentences with a semicolon and some without a semicolon. Could you please explain the reason for that? P.S - I am a Python beginner, that's why asking such a basic question.
  • @outlier229 All the semicolons in the code above are optional, removing them doesn't change anything. My guess is that Fox is simply used to coding a language like C++ or Java, which require the semicolon at the end of the line. Aside from that optional use, semicolons can be used to chain statements in a single line. For example a=1; b=2; c=3 would be a valid single line in python.
  • Very nice! I couldn't help but comment that the resulting tree does not hold the invariant that all elements in the left subtree are less than v. - A property that is important for binary search trees. (Yes I realize that OP did not ask for a "search tree") however, FWIW it can be done with a simple modification to the check in _add(). Then your inorder traversal gives a sorted list.
  • It is better to have two classes. That is better implementation
  • @user3022012 your comment is simply wrong. by definition, a tree is composed of data, and sub trees (for binary tree, it's two sub-trees), which are also trees. No reason, whatsoever to tree the root node differently.