# Python - Tree Traversal Algorithms

Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree. There are three ways which we use to traverse a tree −

• In-order Traversal
• Pre-order Traversal
• Post-order Traversal

## In-order Traversal

In this traversal method, the left subtree is visited first, then the root and later the right sub-tree. We should always remember that every node may represent a subtree itself.

In the below python program, we use the Node class to create place holders for the root node as well as the left and right nodes. Then we create a insert function to add data to the tree. Finally the Inorder traversal logic is implemented by creating an empty list and adding the left node first followed by the root or parent node. At last the left node is added to complete the Inorder traversal. Please note that this process is repeated for each sub-tree until all the nodes are traversed.

```class Node:

def __init__(self, data):

self.left = None
self.right = None
self.data = data
# Insert Node
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

# Print the Tree
def PrintTree(self):
if self.left:
self.left.PrintTree()
print( self.data),
if self.right:
self.right.PrintTree()

# Inorder traversal
# Left -> Root -> Right
def inorderTraversal(self, root):
res = []
if root:
res = self.inorderTraversal(root.left)
res.append(root.data)
res = res + self.inorderTraversal(root.right)
return res

root = Node(27)
root.insert(14)
root.insert(35)
root.insert(10)
root.insert(19)
root.insert(31)
root.insert(42)
print(root.inorderTraversal(root))

```

When the above code is executed, it produces the following result −

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```[10, 14, 19, 27, 31, 35, 42]
```

## Pre-order Traversal

In this traversal method, the root node is visited first, then the left subtree and finally the right subtree.

In the below python program, we use the Node class to create place holders for the root node as well as the left and right nodes. Then we create a insert function to add data to the tree. Finally the Pre-order traversal logic is implemented by creating an empty list and adding the root node first followed by the left node. At last the right node is added to complete the Pre-order traversal. Please note that this process is repeated for each sub-tree until all the nodes are traversed.

```class Node:

def __init__(self, data):

self.left = None
self.right = None
self.data = data
# Insert Node
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

# Print the Tree
def PrintTree(self):
if self.left:
self.left.PrintTree()
print( self.data),
if self.right:
self.right.PrintTree()

# Preorder traversal
# Root -> Left ->Right
def PreorderTraversal(self, root):
res = []
if root:
res.append(root.data)
res = res + self.PreorderTraversal(root.left)
res = res + self.PreorderTraversal(root.right)
return res

root = Node(27)
root.insert(14)
root.insert(35)
root.insert(10)
root.insert(19)
root.insert(31)
root.insert(42)
print(root.PreorderTraversal(root))
```

When the above code is executed, it produces the following result −

```[27, 14, 10, 19, 35, 31, 42]
```

## Post-order Traversal

In this traversal method, the root node is visited last, hence the name. First we traverse the left subtree, then the right subtree and finally the root node.

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In the below python program, we use the Node class to create place holders for the root node as well as the left and right nodes. Then we create a insert function to add data to the tree. Finally the Post-order traversal logic is implemented by creating an empty list and adding the left node first followed by the right node. At last the root or parent node is added to complete the Post-order traversal. Please note that this process is repeated for each sub-tree until all the nodes are traversed.

```class Node:

def __init__(self, data):

self.left = None
self.right = None
self.data = data
# Insert Node
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

# Print the Tree
def PrintTree(self):
if self.left:
self.left.PrintTree()
print( self.data),
if self.right:
self.right.PrintTree()

# Postorder traversal
# Left ->Right -> Root
def PostorderTraversal(self, root):
res = []
if root:
res = self.PostorderTraversal(root.left)
res = res + self.PostorderTraversal(root.right)
res.append(root.data)
return res

root = Node(27)
root.insert(14)
root.insert(35)
root.insert(10)
root.insert(19)
root.insert(31)
root.insert(42)
print(root.PostorderTraversal(root))
```

When the above code is executed, it produces the following result −

```[10, 19, 14, 31, 42, 35, 27]
```

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