# Complete Binary Tree

#### In this tutorial, you will learn about a complete binary tree and its different types. Also, you will find working examples of a complete binary tree in Python.

A complete binary tree is a binary tree in which all the levels are completely filled except possibly the lowest one, which is filled from the left.

A complete binary tree is just like a full binary tree, but with two major differences

- All the leaf elements must lean towards the left.
- The last leaf element might not have a right sibling i.e. a complete binary tree doesn’t have to be a full binary tree.

## Full Binary Tree vs Complete Binary Tree

## How a Complete Binary Tree is Created?

- Select the first element of the list to be the root node. (no. of elements on level-I: 1)

- Put the second element as a left child of the root node and the third element as the right child. (no. of elements on level-II: 2)

- Put the next two elements as children of the left node of the second level. Again, put the next two elements as children of the right node of the second level (no. of elements on level-III: 4) elements).
- Keep repeating until you reach the last element.

## Python Examples

```
/* Checking if a binary tree is a complete binary tree in Python */
class Node:
def __init__(self, item):
self.item = item
self.left = None
self.right = None
/* Count the number of nodes */
def count_nodes(root):
if root is None:
return 0
return (1 + count_nodes(root.left) + count_nodes(root.right))
/* Check if the tree is complete binary tree */
def is_complete(root, index, numberNodes):
/* Check if the tree is empty */
if root is None:
return True
if index >= numberNodes:
return False
return (is_complete(root.left, 2 * index + 1, numberNodes)
and is_complete(root.right, 2 * index + 2, numberNodes))
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
root.right.left = Node(6)
node_count = count_nodes(root)
index = 0
if is_complete(root, index, node_count):
print("The tree is a complete binary tree")
else:
print("The tree is not a complete binary tree")
```

## Relationship between array indexes and tree element

A complete binary tree has an interesting property that we can use to find the children and parents of any node.

If the index of any element in the array is `i`, the element in the index `2i+1`

will become the left child and element in `2i+2`

index will become the right child. Also, the parent of any element at index `i` is given by the lower bound of `(i-1)/2`

.

Let’s test it out,

Left child of 1 (index 0) = element in (2*0+1) index = element in 1 index = 12 Right child of 1 = element in (2*0+2) index = element in 2 index = 9 Similarly, Left child of 12 (index 1) = element in (2*1+1) index = element in 3 index = 5 Right child of 12 = element in (2*1+2) index = element in 4 index = 6

Let us also confirm that the rules hold for finding parent of any node

Parent of 9 (position 2) = (2-1)/2 = ½ = 0.5 ~ 0 index = 1 Parent of 12 (position 1) = (1-1)/2 = 0 index = 1

Understanding this mapping of array indexes to tree positions is critical to understanding how the Heap Data Structure works and how it is used to implement Heap Sort.

# Python Example for Beginners

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There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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