Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/aymen016/data-visualization
https://github.com/aymen016/data-visualization
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/aymen016/data-visualization
- Owner: Aymen016
- Created: 2024-03-06T18:15:59.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-10-31T18:04:46.000Z (3 months ago)
- Last Synced: 2024-10-31T18:33:06.989Z (3 months ago)
- Language: Jupyter Notebook
- Size: 1.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Data Visualization
This repository contains code and resources for Data Visualization using Python libraries like Matplotlib and Seaborn.
Table of Contents
- Introduction
- Files
- Requirements
- Usage li>
- Contributing
Introduction
Data Visualization is an essential part of data analysis. It helps in understanding the data, identifying patterns, and communicating insights effectively. This repository provides examples and tutorials on how to create visualizations using Python libraries such as Matplotlib and Seaborn.
Files
Basics of Visualization.ipynb: Notebook covering the basics of data visualization using Matplotlib.
Visualisation with Seaborn.ipynb: Notebook demonstrating various visualization techniques using Seaborn.
Visualisation with Matplotlib.ipynb: Notebook showcasing advanced visualization techniques using Matplotlib.
Requirements
To run the code in this repository, you need:
Python (>=3.6)
Jupyter Notebook
Matplotlib
Seaborn
You can install the required libraries using pip:
bash
Copy code
pip install matplotlib seaborn
Usage
You can clone this repository to your local machine using the following command:
bash
Copy code
git clone https://github.com/Aymen016/Data-Visualization.git
Then, navigate to the repository directory and run Jupyter Notebook:
bash
Copy code
cd Data-Visualization
jupyter notebook
You can then open and run the Jupyter notebooks to explore the examples and tutorials on data visualization.
Contributing
Contributions are welcome! If you find any issues or have suggestions for improvement, please feel free to open an issue or create a pull request.