Binarytree Module in Python

A binary tree is a data structure in which every node or vertex has at most two children. In Python, a binary tree can be represented in different ways with different data structures(dictionary, list) and class representations for a node. However, binarytree library helps to directly implement a binary tree. It also supports heap and binary search tree(BST). This module does not come pre-installed with Python's standard utility module. To install it type the below command in the terminal.
 

pip install binarytree 

Creating Node

The node class represents the structure of a particular node in the binary tree. The attributes of this class are values, left, right.
 

Syntax: binarytree.Node(value, left=None, right=None)
Parameters: 
value: Contains the data for a node. This value must be number. 
left: Contains the details of left node child. 
right: Contains details of the right node child. 
 

Note: If left or right child node is not an instance of binarytree.Node class then binarytree.exceptions.NodeTypeError is raised and if the node value is not a number then binarytree.exceptions.NodeValueError is raised.
Example:

from binarytree import Node
root = Node(3)
root.left = Node(6)
root.right = Node(8)

# Getting binary tree
print('Binary tree :', root)

# Getting list of nodes
print('List of nodes :', list(root))

# Getting inorder of nodes
print('Inorder of nodes :', root.inorder)

# Checking tree properties
print('Size of tree :', root.size)
print('Height of tree :', root.height)

# Get all properties at once
print('Properties of tree : \n', root.properties)

Output:

Binary tree : 
3 
/ \ 
6 8
List of nodes : [Node(3), Node(6), Node(8)]
Inorder of nodes : [Node(6), Node(3), Node(8)]
Size of tree : 3
Height of tree : 1
Properties of tree : 
{'height': 1, 'size': 3, 'is_max_heap': False, 'is_min_heap': True, 'is_perfect': True, 'is_strict': True, 'is_complete': True, 'leaf_count': 2, 'min_node_value': 3, 'max_node_value': 8, 'min_leaf_depth': 1, 'max_leaf_depth': 1, 'is_bst': False, 'is_balanced': True, 'is_symmetric': False}

Build a binary tree from the List:

Instead of using the Node method repeatedly, we can use build() method to convert a list of values into a binary tree. 
Here, a given list contains the nodes of tree such that the element at index i has its left child at index 2*i+1, the right child at index 2*i+2 and parent at (i - 1)//2. The elements at index j for j>len(list)//2 are leaf nodes. None indicates the absence of a node at that index. We can also get the list of nodes back after building a binary tree using values attribute.
 

Syntax: binarytree.build(values)
Parameters: 
values: List representation of the binary tree.
Returns: root of the binary tree. 
 

Example: 

# Creating binary tree 
# from given list
from binarytree import build


# List of nodes
nodes =[3, 6, 8, 2, 11, None, 13]

# Building the binary tree
binary_tree = build(nodes)
print('Binary tree from list :\n',
      binary_tree)

# Getting list of nodes from
# binarytree
print('\nList from binary tree :', 
      binary_tree.values)

Output:

Binary tree from list :
 
    ___3
   /    \
  6      8
 / \      \
2   11     13


List from binary tree : [3, 6, 8, 2, 11, None, 13]

Build a random binary tree:

tree() generates a random binary tree and returns its root node.
 

Syntax: binarytree.tree(height=3, is_perfect=False)
Parameters: 
height: It is the height of the tree and its value can be between the range 0-9 (inclusive) 
is_perfect: If set True a perfect binary is created.
Returns: Root node of the binary tree. 
 

Example:

from binarytree import tree


# Create a random binary 
# tree of any height
root = tree()
print("Binary tree of any height :")
print(root)

# Create a random binary 
# tree of given height
root2 = tree(height = 2)
print("Binary tree of given height :")
print(root2)

# Create a random perfect 
# binary tree of given height
root3 = tree(height = 2,
             is_perfect = True)
print("Perfect binary tree of given height :")
print(root3)

Output: 

Binary tree of any height :

      14____
     /      \
    2        5__
   /        /   \
  6        1     13
 /        /     /  \
7        9     4    8

Binary tree of given height :

  1__
 /   \
5     2
     / \
    4   3

Perfect binary tree of given height :

    __3__
   /     \
  2       4
 / \     / \
6   0   1   5

Building a BST:

The binary search tree is a special type of tree data structure whose inorder gives a sorted list of nodes or vertices. In Python, we can directly create a BST object using binarytree module. bst() generates a random binary search tree and return its root node.
 

Syntax: binarytree.bst(height=3, is_perfect=False)
Parameters: 
height: It is the height of the tree and its value can be between the range 0-9 (inclusive) 
is_perfect: If set True a perfect binary is created.
Returns: Root node of the BST. 
 

Example:

from binarytree import bst


# Create a random BST 
# of any height
root = bst()
print('BST of any height : \n',
      root)

# Create a random BST of 
# given height
root2 = bst(height = 2)
print('BST of given height : \n',
      root2)

# Create a random perfect 
# BST of given height
root3 = bst(height = 2, 
            is_perfect = True)
print('Perfect BST of given height : \n',
      root3)

Output:

BST of any height : 
 
        ____9______
       /           \
    __5__       ____12___
   /     \     /         \
  2       8   10         _14
 / \     /      \       /
1   4   7        11    13

BST of given height : 
 
    5
   / \
  4   6
 /
3

Perfect BST of given height : 
 
    __3__
   /     \
  1       5
 / \     / \
0   2   4   6

Importing heap:

Heap is a tree data structure that can be of two types - 

• max heap 
• min heap 
   

Using the heap() method of binarytree library, we can generate a random maxheap and return its root node. To generate minheap, we need to set the is_max attribute as False.
 

Syntax: binarytree.heap(height=3, is_max=True, is_perfect=False)
Parameters: 
height: It is the height of the tree and its value can be between the range 0-9 (inclusive) 
is_max: If set True generates a max heap else min heap. 
is_perfect: If set True a perfect binary is created.
Returns: Root node of the heap. 
  

from binarytree import heap


# Create a random max-heap
root = heap()
print('Max-heap of any height : \n',
      root)

# Create a random max-heap
# of given height
root2 = heap(height = 2)

print('Max-heap of given height : \n',
      root2)

# Create a random perfect 
# min-heap of given height
root3 = heap(height = 2, 
             is_max = False, 
             is_perfect = True)

print('Perfect min-heap of given height : \n',
      root3)

Output:

Max-heap of any height : 
 
         _______14______
        /               \
    ___12__            __13__
   /       \          /      \
  10        8        3        9
 /  \      / \      / \      /
1    5    4   6    0   2    7

Max-heap of given height : 
 
    __6__
   /     \
  4       5
 / \     / \
2   0   1   3

Perfect min-heap of given height : 
 
    __0__
   /     \
  1       3
 / \     / \
2   6   4   5