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

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

Last synced: about 9 hours ago
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• Host: GitHub
• URL: https://github.com/ngunyigachie/dsa-python
• Owner: NgunyiGachie
• Created: 2024-06-18T12:14:12.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-04T11:26:52.000Z (6 months ago)
• Last Synced: 2024-11-11T19:06:53.600Z (about 2 months ago)
• Language: Python
• Size: 22.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Structures and Algorithms in Python

This repository contains implementations of various data structures and algorithms in Python.

## Table of Contents

- [Data Structures](#data-structures)

- [Algorithms](#algorithms)

## Data Structures

1. **Linked List**: Implementation of singly linked list, doubly linked list, etc.

2. **Stack**: Implementation of stack data structure.

3. **Queue**: Implementation of queue data structure.

4. **Binary Tree**: Implementation of binary tree data structure.

5. **Heap**: Implementation of heap data structure.

6. **Hash Table**: Implementation of hash table (hash map) data structure.

7. **Graph**: Implementation of graph data structure.

## Algorithms

1. **Sorting Algorithms**:

   - Bubble Sort

   - Selection Sort

   - Insertion Sort

   - Merge Sort

   - Quick Sort

   - Heap Sort

   - Recursion

   - Shell Sort

2. **Algorithms for Searching Strings**:

   - Linear Search: Simple approach where you check each character or substring sequentially.

   - Knuth-Morris-Pratt (KMP) Algorithm: Efficient algorithm for substring searching that avoids unnecessary character comparisons.

   - Boyer-Moore Algorithm: Another efficient algorithm for substring searching, focusing on skipping characters based on a preprocessing step.

   - Rabin-Karp Algorithm: Uses hashing to find one or more patterns in a text string.

## Contributing

Contributions are welcome! If you'd like to contribute:

- Fork the repository

- Clone the forked repository to your local machine

- Create a new branch for your feature: `git checkout -b feature-name`

- Make your changes and commit them: `git commit -m 'Add some feature'`

- Push to the branch: `git push origin feature-name`

- Submit a pull request

Please make sure to update tests as appropriate.