birhekobo/matplotlib-training

Matplotlib Training Course: A 40-day comprehensive bootcamp on data visualization using real CHIRPS satellite rainfall data for Ethiopia (1981-2022) # Matplotlib Training Course A comprehensive **40-day Matplotlib bootcamp** taking learners from beginner to advanced level through hands-on projects using real-world climate and environmental datasets. ## Features - **40 structured lessons** covering everything from basics to publication-quality figures - **Real-world datasets** — CHIRPS rainfall data (1981–2022) for Ethiopia - **Jupyter Notebooks** + **Python scripts** for every lesson - **MyST documentation** website hosted on GitHub Pages - **Typst-generated eBook** for offline study - **Exercises, quizzes, and mini-projects** with solutions - **Capstone project** building a complete visualization portfolio - **GitHub Actions** for automated testing and deployment ## Prerequisites - Python 3.10+ - Basic programming knowledge - No prior Matplotlib experience required ## Quick Start ```bash # Clone the repository git clone github.com cd matplotlib-training # Create a virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Launch Jupyter jupyter notebook ``` Start with `notebooks/01_introduction_to_visualization.ipynb`. ## Course Outline | Week | Module | Topics | |------|--------|--------| | 1 | Introduction & Setup | Visualization principles, Python setup, Jupyter basics | | 2 | Matplotlib Fundamentals | Figure, Axes, pyplot, basic charts | | 3 | Styling & Customization | Colors, markers, themes, labels, legends | | 4 | Axes & Subplots | Axis limits, ticks, scales, GridSpec, insets | | 5 | Statistical & Scientific Viz | Box plots, heatmaps, contours, surfaces | | 6 | Time Series & Geospatial | Date axes, rolling stats, maps, rainfall data | | 7 | NumPy, Pandas & Advanced | DataFrame plotting, OOP interface, animations | | 8 | Projects & Capstone | Real-world dashboards, portfolio building | ## Dataset The course uses CHIRPS (Climate Hazards Group I …