py_practice

Welcome to the py_practice repository! This repo contains a collection of data structures and algorithms implemented in Python. It's designed to help you practice and improve your understanding of these fundamental concepts.

Table of Contents

• Introduction
• Getting Started
• Data Structures
• Algorithms
• Contributing
• License

Introduction

This repository is a collection of Python scripts that implement various data structures and algorithms. The purpose is to provide a resource for learning, practicing, and mastering these essential programming concepts. Each implementation is designed to be as clear and concise as possible, with comments and explanations to aid understanding.

Getting Started

To get started with this repository, you'll need to have Python installed on your machine. You can download Python from the official website: python.org.

Cloning the Repository

To clone this repository, run the following command in your terminal:

git clone https://github.com/your-username/py_practice.git](https://github.com/GiddyLesGid/py_practice.git

Running the Scripts

Each script can be run individually. Navigate to the directory where you've cloned the repository and run a script using Python. For example:

cd py_practice
python path/to/script.py

Data Structures

This section includes various data structures implemented in Python. Each data structure has its own directory containing the implementation and example usage.

• Arrays: Implementation and operations on arrays.
• Linked Lists: Singly and doubly linked lists.
• tacks: Stack implementation and operations.
• Queues: Queue implementation and operations.
• Trees: Binary trees, binary search trees, and tree traversal methods.
• Graphs: Graph representation and traversal algorithms.
• Hash Tables: Hash table implementation and operations.

Algorithms

This section includes various algorithms implemented in Python, categorized by their type and purpose.

• Sorting Algorithms: Bubble sort, quicksort, mergesort, etc.
• Searching Algorithms: Linear search, binary search, etc.
• Graph Algorithms: Depth-first search (DFS), breadth-first search (BFS), Dijkstra's algorithm, etc.
• Dynamic Programming: Examples of dynamic programming problems and solutions.
• Recursion: Examples of recursive algorithms.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.