PythonDataStructuresAlgorithms

I will document some of my DataStructures taken notes here, Stay Tuned...

checkout the DSA MindMaps i just added :)

Mind map 1

Mind map 2

Trees

• trees are used to store information.
• tree is usually upside down.
• each circle is called a node or a vertex.
• link between 2 nodes is an edge.

• from the above tree, we deduce the following. . .
• the edge is the distance (connection) between 2 nodes, node 5 has no edge with node 4
• Tree with n nodes has n - 1 edges
• the tree has 4 levels, levels (0,1,2,3)
• Node(1) has 2 children: 2 and 3
• The parent of Node(7) is node(2)
• Nodes {5, 9} are siblings (brothers)
• height (specific to each node) represents the number of edges on the longest downward path between a node(vertex) and a leaf.
• Tree of N levels has N-1 heights, since that the tree above has 4 levels, then the height is 4-1 = 3 edges
• the height of the node 1 (root) is 3 (start from the root to the longest path downward to the farest leaf)
• node 7 has a height = 0
• Node's Depth : the number of edges from the node to the root node. Difference between Depth and Height
  • depth is specific about 2 nodes (root node and the current node only)
  • height is going down from the node to the leaves. (height is about my current node and any other node (leaf) i can reach - longest path)
• there is only 1 path between any 2 nodes. (you are now at the root node and you wanted to go to the node 4, then there is only 1 way (simple tree)
• in a tree where every node has only 1 single parent, then there is only 1 path from a node to another.

Sub Trees

• recursive nature where each tree has a subtree and each subtree has another subtree.
• Problem solving tip: we deduce that when we want to get the elements of a tree, we will do this recursively.

Binary Trees

• a tree where each node at least has at most 2 children (left and right nodes).
• a leaf node is a node with no children.

this is a simple tree (a node has many children)

this is a binary tree (each node has at most 2 children)

-- A linked List is a special case of a binary tree.

---

binary tree traversal 1

traversal is a way of walking through an elements of a data structure.

we need to create an expression tree (leaves are operands and others are operators)

can draw this tree : (2+3)*4

types of traversal

• LVR (left, visit, right) --> in order traversal

how to know the printed nodes visually ?

project down each node and read from left to right, this is how the binary tree is represented visually as shown.

---

• LRV (left, right, visit) --> post order traversal

--> left first, then right first , then printing the nodes

how to deduce the printed nodes visually ?

just in 2 steps :

step 1 for every node , if it doesnot have a right , then project it down, what if it does have a right node? --> wait for it

step 2

for the nodes that have a right node , project it down after its right node.

note that, when projecting the nodes in step 2 , we go from bottom to up level by level.

---

• VLR (visit, left, right) --> preorder traversal

how to deduce the printed nodes visually ?

will be the opposite of the post order traversal (2 steps)

step 1 for every node , if it doesnot have a left, then project it down, what if it does have a left node? --> wait for it

step 2 then we project the nodes that have a left node before its left subtrees

• note that, when projecting the nodes in step 2 , we go from bottom to up level by level.

---

BFS Tree Structure explanation for the 'whatTasteIsItLike.py' question :

initial state

                    food_items
                        │
            ┌───────────┴───────────┐
          fruits               vegetables

Queue Box: ['fruits', 'vegetables']
Current: []
Processed: []

---

step 1

                    food_items
                        │
            ┌───────────┴───────────┐
          fruits ←             vegetables

Queue Box: ['vegetables', 'tropical', 'temperate']
Current: 'fruits'
Processed: ['fruits']

---

step 2

                    food_items
                        │
            ┌───────────┴───────────┐
          fruits               vegetables ←
            │                     │
    ┌───────┴───────┐      ┌─────┴─────┐
tropical        temperate  leafy      root

Queue Box: ['tropical', 'temperate', 'leafy', 'root']
Current: 'vegetables'
Processed: ['fruits', 'vegetables']

---

step 3

                    food_items
                        │
            ┌───────────┴───────────┐
          fruits               vegetables
            │                     │
    ┌───────┴───────┐      ┌─────┴─────┐
tropical ←      temperate  leafy      root
    │
┌───┴───┐

mango pineapple

Queue Box: ['temperate', 'leafy', 'root', 'mango', 'pineapple']
Current: 'tropical'
Processed: ['fruits', 'vegetables', 'tropical']

---

and so on...

Logic :

When processing 'apple':
Queue Box: ['pear', 'spinach', 'kale', 'carrot', 'beet']
Current: 'apple' (red found!) , since apple is a dict instance and has 'color' in one of its keys...
Processed: [..., 'apple']
Result: ["The apple is sweet."]

When processing 'beet':
Queue Box: []
Current: 'beet' (red found!) , since beet is a dict instance and has 'color' in one of its keys...
Processed: [..., 'apple', ..., 'beet']
Result: ["The apple is sweet.", "The beet is earthy."]

---

The key movements:

Item moves from Queue Box → Current Box
After processing:
Item moves to Processed Box
If it has children, they're added to Queue Box
If it's red, it adds to Result

further explanation :

So when we say "red found!", we mean:

• val is a dictionary (passed isinstance check)

• val has a 'color' key (passed 'color' in val check)

• val['color'] equals 'red' (passed color comparison)

• This is different from nodes like 'fruits' or 'temperate' which:

-- Are dictionaries but don't have a 'color' key
-- Have nested dictionaries instead of direct properties

---

⚠️ Heads Up!

This ReadMe is under constant updates.

Stay tuned for new notes as I progress. . . 📚💡

---

Acknowledgments

Thanks to Dr.Moustafa Saad, Nidal Fikri