https://github.com/sarmad426/dsa-python

Data Structures and Algorithms using python from basics to adavnce featuring Leetcode problems.
https://github.com/sarmad426/dsa-python

algorithms data-structures-and-algorithms leetcode leetcode-solutions python

Last synced: 15 days ago
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Data Structures and Algorithms using python from basics to adavnce featuring Leetcode problems.

• Host: GitHub
• URL: https://github.com/sarmad426/dsa-python
• Owner: Sarmad426
• Created: 2024-02-12T03:33:04.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-10-16T18:40:48.000Z (3 months ago)
• Last Synced: 2024-11-12T04:14:25.447Z (2 months ago)
• Topics: algorithms, data-structures-and-algorithms, leetcode, leetcode-solutions, python
• Language: Python
• Homepage:
• Size: 127 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# Data Structure and Algorithms using **Python**

This repository aims to provide a comprehensive collection of data structures and algorithms implementations in Python. Following along with this repository requires fundamental knowledge of programming in python, for that checkout this Repository. [Python](https://github.com/sarmad426/Python)

## Table of Contents

- [Data Structures](#data-structures)

- [Algorithms](#algorithms)

## Data Structures

A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently.

### 1. Linked List

- Implementation of a singly linked list

- Implementation of a doubly linked list

- Operations: insertion, deletion, traversal, etc.

### 2. Stack

- Implementation of a stack using arrays and linked lists

- Operations: push, pop, peek, etc.

### 3. Queue

- Implementation of a queue using arrays and linked lists

- Operations: enqueue, dequeue, peek, etc.

### 4. Tree

- Implementation of various tree data structures: binary tree, binary search tree, AVL tree, etc.

- Tree traversal algorithms: inorder, preorder, postorder

- Operations: insertion, deletion, searching, etc.

### 5. Graph

- Implementation of graph data structure

- Graph traversal algorithms: BFS, DFS

- Shortest path algorithms: Dijkstra's algorithm, Bellman-Ford algorithm

- Minimum spanning tree algorithms: Prim's algorithm, Kruskal's algorithm

## Algorithms

An algorithm is a step-by-step procedure or formula for solving a problem.

### 1. Sorting Algorithms

- Bubble Sort

- Selection Sort

- Insertion Sort

- Merge Sort

- Quick Sort

- Heap Sort

### 2. Searching Algorithms

- Linear Search

- Binary Search

- Depth-First Search (DFS)

- Breadth-First Search (BFS)

### 3. [Greedy Algorithms](/1-Basics/2-Algorithms/2-Algo-types/1-Greedy-algo/)

- Implementation of greedy algorithms

- Examples: Minimum spanning tree, Huffman coding, etc.

### 4. [Divide and Conquer](/1-Basics/2-Algorithms/2-Algo-types/2-Divide-conquer/)

- Implementation of divide and conquer algorithms

- Examples: Binary search, merge sort, etc.

### 5. [Dynamic Programming](/1-Basics/2-Algorithms/2-Algo-types/3-Dynamic-programming/)

- Implementation of dynamic programming solutions for various problems

- Examples: Fibonacci series, knapsack problem, longest common subsequence, etc.

### How to Use This Repository

- **Learning**: If you are new to DSA, start by exploring the theoretical documents to build a strong foundation. Then, dive into the code examples and implement data structures and algorithms from scratch.

- **Leetcode Interview Problems**: Use the Leetcode interview questions section to practice and improve your problem-solving skills. Understand the logic behind each solution and try to solve them independently first.

All of the practical examples are written using **python** programming. If you wanna use **C++** we also have an amazing resource for this. Checkout this repository [DSA-C++](https://github.com/sarmad426/DSA)

### Learning Resources

**Python**: 

**Python Projects**: 

Back end **API** development using **Fast API**: 

Learn **AI** and **Data Science**: 

Learn **Generative AI**: