Class lecture of Data Structure and Algorithms and Python. Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation

1. First Year BA (IT)
CT1120 Algorithms
Lecture 17
Dr. Zia Ush Shamszaman
z.shamszaman1@nuigalway.ie
1
School of Computer Science, College of Science and Engineering
14-02-2020

2. Overview
• Data Structure
– Stack implementation
– Queue implementation
– Tree
– Binary tree
• Feedback and Assessment
214-02-2020

3. • Given the following sequence of stack
operations, what is the top item on the stack
when the sequence is complete?
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m = Stack()
m.push('x')
m.push('y')
m.pop()
m.push('z')
m.peek()

4. • Given the following sequence of stack
operations, what is the top item on the stack
when the sequence is complete?
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m = Stack()
m.push('x')
m.push('y')
m.pop()
m.push('z')
m.peek()

5. What this code is doing?
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stack = []
stack.append('a')
stack.append('b')
stack.append('c')
print('Initial stack')
print(stack)
print('nElements poped from stack:')
print(stack.pop())
print(stack.pop())
print(stack.pop())
print('nStack after elements are poped:')
print(stack)

6. Stack implementation code
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stack = []
stack.append('a')
stack.append('b')
stack.append('c')
print('Initial stack')
print(stack)
print('nElements poped from stack:')
print(stack.pop())
print(stack.pop())
print(stack.pop())
print('nStack after elements are poped:')
print(stack)
OUTPUT
Initial stack
['a', 'b', 'c']
Elements poped from stack:
c
b
a
Stack after elements are poped:
[]

7. What this code is doing?
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queue = []
queue.append('a')
queue.append('b')
queue.append('c')
print("Initial queue")
print(queue)
print("nElements dequeued from queue")
print(queue.pop(0))
print(queue.pop(0))
print(queue.pop(0))
print("nQueue after removing elements")
print(queue)

8. What this code is doing?
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queue = []
queue.append('a')
queue.append('b')
queue.append('c')
print("Initial queue")
print(queue)
print("nElements dequeued from queue")
print(queue.pop(0))
print(queue.pop(0))
print(queue.pop(0))
print("nQueue after removing elements")
print(queue)
Output
Initial queue
['a', 'b', 'c']
Elements dequeued from queue
a
b
c
Queue after removing elements
[]

9. Tree
• A Tree is a collection of elements called nodes.
• One of the node is distinguished as a root, along with a
relation (“parenthood”) that places a hierarchical
structure on the nodes.
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10. Applications of Tree
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Main applications of trees include:
1. Manipulate hierarchical data.
2. Make information easy to search (see tree
traversal).
3. Manipulate sorted lists of data.
4. As a workflow for compositing digital images for
visual effects.
5. Router algorithms
6. Form of a multi-stage decision-making (see
business chess).

11. Binary Tree
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• A tree whose elements have at most 2 children is called a
binary tree.
• Since each element in a binary tree can have only 2
children, we typically name the left child and right child.

12. Binary Tree
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• A tree whose elements have at most 2 children is called a
binary tree.
• Since each element in a binary tree can have only 2
children, we typically name the left child and right child.
((7+3)∗(5−2))

13. Binary Tree
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• A tree whose elements have at most 2 children is called a
binary tree.
• Since each element in a binary tree can have only 2
children, we typically name the left child and right child.
• A Binary Tree node contains following parts.
• Data
• Pointer to left child
• Pointer to right child

14. Python code of
Binary Tree
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# A class that represents an individual node in a
# Binary Tree
class Node:
def __init__(self,key):
self.left = None
self.right = None
self.val = key
# create root
root = Node(1)
''' following is the tree after above statement
1
/
None None'''
root.left = Node(2);
root.right = Node(3);
''' 2 and 3 become left and right children of 1
1
/
2 3
/ /
None None None None''’
root.left.left = Node(4);
'''4 becomes left child of 2
1
/
2 3
/ /
4 None None None
/
None None'''
"__init__" is a reseved method in python classes. It is
called as a constructor in object oriented terminology. This
method is called when an object is created from a class
and it allows the class to initialize the attributes of the
class.

15. Tree traversal
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Following are the generally used ways for traversing trees:
• Inorder: We recursively do an inorder traversal on the
left subtree, visit the root node, and finally do a recursive
inorder traversal of the right subtree.
• Preorder: We visit the root node first, then recursively
do a preorder traversal of the left subtree, followed by a
recursive preorder traversal of the right subtree.
• Postorder: We recursively do a postorder traversal of
the left subtree and the right subtree followed by a visit
to the root node.

16. Tree traversal
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(a) Inorder (Left, Root, Right) : 4 2 5 1 3
(b) Preorder (Root, Left, Right) : 1 2 4 5 3
(c) Postorder (Left, Right, Root) : 4 5 2 3 1
Level Order Traversal : 1 2 3 4 5

17. Tree traversal
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(a) Inorder (Left, Root, Right) : 4 2 5 1 3
(b) Preorder (Root, Left, Right) : 1 2 4 5 3
(c) Postorder (Left, Right, Root) : 4 5 2 3 1
Level Order Traversal : 1 2 3 4 5

18. Tree traversal code
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# A class that represents an individual node in a
Binary Tree
class Node:
def __init__(self,key):
self.left = None
self.right = None
self.val = key
# A function to do inorder tree traversal
def printInorder(root):
if root:
# First recur on left child
printInorder(root.left)
# then print the data of node
print(root.val),
# now recur on right child
printInorder(root.right)
# A function to do postorder tree traversal
def printPostorder(root):
if root:
# First recur on left child
printPostorder(root.left)
# the recur on right child
printPostorder(root.right)
# now print the data of node
print(root.val),
# A function to do preorder tree traversal
def printPreorder(root):
if root:
# First print the data of node
print(root.val),
# Then recur on left child
printPreorder(root.left)
# Finally recur on right child
printPreorder(root.right)
# Driver code
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
print ("nPreorder traversal of binary tree is")
printPreorder(root)
print ("nInorder traversal of binary tree is")
printInorder(root)
print ("nPostorder traversal of binary tree is")
printPostorder(root)

19. Inorder traversal without recursion
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# A binary tree node
class Node:
# Constructor to create a new node
def __init__(self, data):
self.data = data
self.left = None
self.right = None
# Iterative function for inorder tree traversal
def inOrder(root):
# Set current to root of binary tree
current = root
stack = [] # initialize stack
done = 0
while True:
# Reach the left most Node of the current Node
if current is not None:
# Place pointer to a tree node on the stack
# before traversing the node's left subtree
stack.append(current)
current = current.left
# BackTrack from the empty subtree and visit the Nod
# at the top of the stack; however, if the stack is
# empty you are done
elif(stack):
current = stack.pop()
print(current.data, end=" ")
# Python 3 printing
# We have visited the node and its left
# subtree. Now, it's right subtree's turn
current = current.right
else:
break
print()
# Driver program to test above function
""" Constructed binary tree is
1
/
2 3
/
4 5 """
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
inOrder(root)

20. Priority Queue
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• A priority queue acts like a queue in that you dequeue an item by
removing it from the front.
• In a priority queue the logical order of items inside a queue is
determined by their priority.
• The highest priority items are at the front of the queue and the lowest
priority items are at the back.
• When you enqueue an item on a priority queue, the new item may move
all the way to the front.

21. Binary Heaps
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• A Heap is a special Tree-based data structure in
which the tree is a complete binary tree. Generally,
Heaps can be of two types:
• Max-Heap: The key present at the root node must be
greatest among the keys present at all of it’s children. The
same property must be recursively true for all sub-trees in
that Binary Tree.
• Min-Heap: The key present at the root node must be
minimum among the keys present at all of it’s children. The
same property must be recursively true for all sub-trees in
that Binary Tree.

22. Binary Heap Operation
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The basic operations we will implement for our
binary heap are as follows:
• BinaryHeap() creates a new, empty, binary heap.
• insert(k) adds a new item to the heap.
• findMin() returns the item with the minimum key value,
leaving item in the heap.
• delMin() returns the item with the minimum key value,
removing the item from the heap.
• isEmpty() returns true if the heap is empty, false
otherwise.
• size() returns the number of items in the heap.
• buildHeap(list) builds a new heap from a list of keys.

23. Next Lecture
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24. • Binary Heap Implementation
• Binary Search Tree
14-02-2020 24

25. Useful Links
• http://www.csanimated.com/animation.php?t=Quicksort
• http://www.hakansozer.com/category/c/
• http://www.nczonline.net/blog/2012/11/27/computer-science-in-
javascript-quicksort/
• http://www.sorting-algorithms.com/shell-sort
• https://www.tutorialspoint.com/
• https://www.hackerearth.com/
• www.khanacademy.org/computing/computer-science/algorithms
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26. Feedback & Assessment
14-02-2020 26