python-data-visulaization

Python Data Visualization: Master Matplotlib and Seaborn

Python Data Visualization: Master Matplotlib and Seaborn

Welcome to the Python Data Visualization repository! This repository contains the source code, examples, and resources featured in the YouTube tutorial: "How to Create Stunning Data Visualizations with Python: Master Matplotlib and Seaborn".

🧑‍💻 What You'll Learn

This tutorial covers the basics of Matplotlib and Seaborn, two essential Python libraries for creating impactful data visualizations.

Key Topics:

1. Matplotlib Basics:

   • Line plots, bar charts, and scatter plots.
   • Customizing titles, axes, and legends.

2. Advanced Visualizations with Seaborn:

   • Histograms with density curves.
   • Boxplots, violin plots, and pairplots.

3. Practical Examples:

   • Real-world use cases to visualize trends, distributions, and relationships in your datasets.

🔧 Getting Started

Prerequisites:

• Python 3.7+
• Libraries:

  pip install matplotlib seaborn

📄 Code Examples

Matplotlib Example: Line Plot

   import matplotlib.pyplot as plt  

   x = [1, 2, 3, 4, 5]  
   y = [10, 20, 30, 40, 50]  

   plt.plot(x, y, label='Line Plot')  
   plt.title('Line Plot Example')  
   plt.xlabel('X-axis')  
   plt.ylabel('Y-axis')  
   plt.legend()  
   plt.show()

Seaborn Example: Boxplot

   import seaborn as sns  
   import matplotlib.pyplot as plt  

   data = [10, 20, 25, 30, 35, 40, 45]  

   sns.boxplot(x=data)  
   plt.title('Boxplot Example')  
   plt.show()  

📚 Resources and Documentation

Matplotlib Documentation Seaborn Documentation

🌟 Stay Connected

Watch the full tutorial on YouTube: Youtube Video Follow for more data science projects: Github Profile

License

This project is licensed under the MIT License - see the LICENSE file for details.

Happy coding! 🚀

Let me know if you want a specific link placeholder updated or additional sections!

💡 Contribute

Feel free to fork this repository, improve the code, or share your ideas. Pull requests are welcome!