https://github.com/tobisamcode/dsa-python-indepth
This repository provides a comprehensive deep dive into Data Structures and Algorithms (DSA) using Python, covering fundamental concepts, advanced techniques, and optimized implementations.
https://github.com/tobisamcode/dsa-python-indepth
Last synced: about 1 year ago
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This repository provides a comprehensive deep dive into Data Structures and Algorithms (DSA) using Python, covering fundamental concepts, advanced techniques, and optimized implementations.
- Host: GitHub
- URL: https://github.com/tobisamcode/dsa-python-indepth
- Owner: tobisamcode
- Created: 2025-02-22T11:03:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-25T15:41:07.000Z (about 1 year ago)
- Last Synced: 2025-03-25T16:22:16.287Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📌 DSA-Python-InDepth
Welcome to **DSA-Python-InDepth**! 🚀 This repository provides a **comprehensive deep dive** into Data Structures and Algorithms (DSA) using Python, covering fundamental concepts, advanced techniques, and optimized implementations.
---
## 📖 **About This Repository**
This repo is designed to help programmers, students, and coding enthusiasts **master DSA in Python** with:
- **Detailed Explanations** of each data structure and algorithm
- **Python Implementations** with optimized code
- **Time & Space Complexity Analysis**
- **Practice Problems** and solutions
- **Best Practices & Interview Tips**
---
## 📂 **Folder Structure**
📌 The repository is organized as follows:
```
📦 dsa-python-indepth
├── 📁 data_structures
│ ├── arrays.py
│ ├── linked_list.py
│ ├── stack.py
│ ├── queue.py
│ ├── hash_table.py
│ ├── binary_tree.py
│ ├── graph.py
│ └── heap.py
│
├── 📁 algorithms
│ ├── sorting
│ │ ├── bubble_sort.py
│ │ ├── merge_sort.py
│ │ ├── quick_sort.py
│ │ └── heap_sort.py
│ ├── searching
│ │ ├── linear_search.py
│ │ ├── binary_search.py
│ ├── recursion
│ │ ├── fibonacci.py
│ │ ├── factorial.py
│ ├── dynamic_programming
│ │ ├── knapsack.py
│ │ ├── longest_common_subsequence.py
│ ├── graph_algorithms
│ │ ├── dijkstra.py
│ │ ├── bfs.py
│ │ ├── dfs.py
│ ├── greedy
│ │ ├── job_scheduling.py
│ └── backtracking
│ ├── n_queens.py
│ ├── sudoku_solver.py
│
├── 📁 practice_problems
│ ├── leetcode
│ ├── hackerrank
│ ├── codeforces
│
├── README.md
└── CONTRIBUTING.md
```
---
## 🚀 **Topics Covered**
### ✅ **Data Structures**
- Arrays & Strings
- Linked Lists (Singly & Doubly)
- Stacks & Queues
- Hash Tables
- Trees (Binary, BST, AVL, Trie, etc.)
- Graphs (Adjacency List, Adjacency Matrix)
- Heaps (Min Heap, Max Heap)
### ✅ **Algorithms**
- Sorting (Bubble, Merge, Quick, Heap Sort, etc.)
- Searching (Linear, Binary Search, etc.)
- Recursion & Backtracking
- Greedy Algorithms
- Dynamic Programming (DP)
- Graph Algorithms (DFS, BFS, Dijkstra, Floyd-Warshall, etc.)
- Divide & Conquer Techniques
---
## 🛠 **How to Use This Repository?**
1. **Clone the repository**
```sh
git clone https://github.com/your-username/dsa-python-indepth.git
cd dsa-python-indepth
```
2. **Explore different data structures and algorithms** in the respective folders.
3. **Run Python scripts** to test implementations.
4. **Solve practice problems** and improve problem-solving skills.
---
## 💡 **Contributing**
We welcome contributions! Feel free to:
- Fix bugs or optimize existing code.
- Add new algorithms or data structures.
- Solve and contribute practice problems.
- Improve documentation.
📜 Check the [CONTRIBUTING.md](./CONTRIBUTING.md) file for guidelines.
---
## 📚 **Resources & References**
- [GeeksforGeeks](https://www.geeksforgeeks.org/)
- [LeetCode](https://leetcode.com/)
- [HackerRank](https://www.hackerrank.com/)
- [MIT OpenCourseWare - Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/)
---
## 📩 **Contact & Support**
If you find this repository helpful, give it a ⭐ and follow for updates!
📧 Feel free to reach out via GitHub Issues for questions and suggestions.
Happy Coding! 🚀