A collection of simple, clear, and reusable plotting examples using Python and Jupyter notebooks.
This repository is maintained by the Biomed Apps group and is intended to serve as a reference for generating high-quality figures for scientific computing, data analysis, education, and documentation.
This repository contains Jupyter notebooks demonstrating:
- Basic 2D plotting (line plots, scatter plots, bar charts)
- Subplots and figure layouts
- Customizing labels, legends, ticks, and styles
- Common scientific visualizations
- Exporting figures for publications or reports
All notebooks are designed to run directly in the cloud via:
- Google Colab
- GitHub Codespaces
No local Python installation is required.
basic-plotting/
│
├── notebooks/
│ ├── line-plots.ipynb
│ ├── scatter-plots.ipynb
│ ├── bar-charts.ipynb
│ ├── subplots.ipynb
│ ├── styling-and-themes.ipynb
│ └── exporting-figures.ipynb
│
└── README.md
- Open any notebook directly in Colab:
https://colab.research.google.com/github/your-org/basic-plotting/blob/main/notebooks/line-plots.ipynb
Click Open in Colab at the top.
Run cells directly in your browser.
All dependencies (matplotlib, numpy, pandas) are installed automatically in Colab.
GitHub Codespaces
Open the repository in GitHub Codespaces .
Launch JupyterLab or VS Code interface.
Open any notebook from the notebooks/ folder and run interactively.
Codespaces provides a full Python environment, so you can run and modify notebooks without installing anything locally.
🧪 Contributions
Contributions from members of the Biomed Apps group are welcome. If adding new examples, please follow these guidelines:
Use clean, self-contained code
Annotate plots with explanations or markdown cells
Keep dependencies minimal
Name notebooks descriptively
To submit a contribution, open a pull request.
© 2025 Biomed Applications Group, Pittsburgh Supercomputing Center. All rights reserved.