🧱Data Structures in python: Built-in and user-defined.

🔍 Introduction

A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Python provides a rich set of built-in data structures and also allows us to create user-defined ones for more complex operations.

In this guide, you’ll learn:

• The basics of built-in data structures: lists, tuples, sets, and dictionaries
• How to implement user-defined structures: stack, queue, linked list, tree, graph, and hashmap

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✅ 1. Built-in Data Structures

🧾 Lists

• Mutable
• Ordered
• Allows duplicates

fruits = ["apple", "banana", "cherry"]
fruits.append("orange")
print(fruits)

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🧊 Tuples

• Immutable
• Ordered
• Allows duplicates

coordinates = (10, 20)
print(coordinates[0])

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🔀 Sets

• Mutable
• Unordered
• No duplicates

unique_nums = {1, 2, 2, 3}
unique_nums.add(4)
print(unique_nums)

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🧭 Dictionaries

• Mutable
• Key-value pairs
• Unordered (Python 3.6+ keeps insertion order)

person = {"name": "Alice", "age": 25}
print(person["name"])

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🛠️ 2. User-defined Data Structures

📚 Stack (LIFO)

stack = []
stack.append(1)
stack.append(2)
stack.pop()  # Removes 2

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🧾 Queue (FIFO)

from collections import deque

queue = deque()
queue.append(1)
queue.append(2)
queue.popleft()  # Removes 1

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🔗 Linked List

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class LinkedList:
    def __init__(self):
        self.head = None

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🌳 Tree (Binary Tree)

class TreeNode:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

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🌐 Graph (Adjacency List)

graph = {
    "A": ["B", "C"],
    "B": ["A", "D"],
    "C": ["A"],
    "D": ["B"]
}

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🧮 HashMap (Custom Dictionary)

class HashMap:
    def __init__(self):
        self.map = [None] * 10

    def _get_hash(self, key):
        return hash(key) % len(self.map)

    def add(self, key, value):
        key_hash = self._get_hash(key)
        self.map[key_hash] = value

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📊 Summary Table

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🧠 Practice Challenges

1. Reverse a list using a stack
2. Implement a queue using two stacks
3. Traverse a binary tree (in-order, pre-order, post-order)
4. Implement Dijkstra's algorithm on a graph
5. Create a simple hash map with collision handling

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🧩 Conclusion

Choosing the right data structure is key to solving programming problems efficiently. Python gives you both the tools and flexibility to use simple built-ins or build your own from scratch. Practice is the best way to master them!