MatplotlibMasterPro is a complete, portfolio-grade project designed to master data visualization using matplotlib.pyplot.
It’s structured to serve both as a:

• 📘 Self-paced learning notebook series
• 💼 Professional showcase project

Whether you’re revisiting fundamentals or creating complex dashboards — this project brings it all together in one place.

MatplotlibMasterPro/
├── notebooks/               # 📚 Step-by-step concept notebooks (23 total)
├── scripts/                 # 🐍 Production-ready Python scripts
├── examples/                # 🚀 Quick copy-paste examples
├── tests/                   # 🧪 Unit tests for utilities
├── utils/                   # 🛠️  Plotting utility functions
│   ├── plot_utils.py        # Line, bar, scatter, histogram helpers
│   └── theme_utils.py       # Publication-ready themes
├── docs/                    # 📖 Documentation files
│   ├── SETUP.md             # Environment setup guide
│   ├── TROUBLESHOOTING.md   # Common issues and solutions
│   ├── RESOURCES.md         # Learning materials
│   ├── RECOMMENDATIONS.md   # Project roadmap
│   ├── CONTRIBUTING.md      # Contribution guidelines
│   └── CODE_OF_CONDUCT.md   # Community standards
├── cheatsheets/             # 📋 Quick-reference syntax guides
├── datasets/                # 📊 Sample CSV datasets
├── exports/                 # 💾 Generated plots and dashboards
├── streamlit_app.py         # 🌐 Streamlit dashboard viewer
├── activate.sh              # ⚡ Quick venv activation script
├── requirements.txt         # 📦 Minimal dependencies
├── requirements_dev.txt     # 🔧 Full dev environment
└── Dockerfile               # 🐳 Dockerized Jupyter environment

This project covers comprehensive matplotlib capabilities including:

• � 23 Interactive Notebooks — From basics to advanced techniques
• 🐍 5 Production Scripts — Real-world automation examples
• 🚀 5 Quick Examples — Copy-paste ready templates
• 🧪 80+ Unit Tests — Professional testing practices
• 📦 3D Visualizations — Surface plots, wireframes, 3D scatter plots
• 📊 Statistical Analysis — Box plots, violin plots, distribution comparisons
• 📉 Error Visualization — Error bars, confidence intervals, uncertainty quantification
• 🗺️ Field Representation — Contour plots, filled contours, heatmaps
• 🔵 Polar & Circular Data — Radar charts, rose plots, wind roses
• 🔄 Multi-Scale Plots — Twin axes, layered visualizations, broken axes
• 🔍 Detail Views — Inset plots, zoomed views, magnified regions
• 🎨 8 Publication Themes — IEEE, academic, colorblind-friendly palettes
• 🎬 Animations — FuncAnimation, timeline effects, dynamic visualizations
• 📐 Complex Layouts — GridSpec, nested subplots, multi-panel dashboards

Here are two dashboards from the project:

Advanced layout using GridSpec for flexible placement

Subplots with shared axes and tight layout for cleaner visuals

Here are animated visualizations exported from the project:

• 🎬 product_revenue_bars.mp4
  Animated bar chart showing revenue distribution by product

• 📈 revenue_growth.mp4
  Revenue growth over time with animated line movement

• 📊 units_revenue_growth.mp4
  Dual-plot animation comparing units sold and revenue growth

• 🔄 revenue_vs_units_scatter.mp4
  Dynamic scatter plot showing correlation over time

All datasets are generated using pandas and numpy, and stored under datasets/.

Reusable helper functions to accelerate your matplotlib workflow:

• utils/plot_utils.py — 20+ functions for common plots (line, bar, scatter, histogram, pie, etc.)
• utils/theme_utils.py — 8 publication-ready themes (dark, minimal, corporate, IEEE, colorblind-friendly, etc.)

Ready-to-run Python scripts in scripts/:

Use Cases: Automated reporting, batch processing, production pipelines

Minimal, copy-paste ready scripts in examples/:

Use Cases: Learning, prototyping, templates for projects

Professional unit tests in tests/:

• test_plot_utils.py — Tests for plotting functions (50+ tests)
• test_theme_utils.py — Tests for theme utilities (30+ tests)

Run tests:

python -m pytest tests/ -v

With coverage:

python -m pytest tests/ --cov=utils --cov-report=html

Quick-reference syntax guides available at:

• cheatsheets/matplotlib_cheatsheet.md

This project includes comprehensive documentation to support your learning:

• 📋 SETUP.md — Complete environment setup guide with activation instructions
• 💡 RECOMMENDATIONS.md — Prioritized improvement suggestions and project roadmap
• 🔧 TROUBLESHOOTING.md — Solutions for common matplotlib issues (backends, fonts, performance, etc.)
• 📚 RESOURCES.md — Curated learning resources (courses, books, communities, datasets)
• 🤝 CONTRIBUTING.md — Contribution guidelines for the project
• 📜 CODE_OF_CONDUCT.md — Community standards and behavior expectations
• 🐍 scripts/README.md — Production scripts documentation
• 🚀 examples/README.md — Quick examples documentation
• 🧪 tests/README.md — Testing guide and best practices

1. Clone the repository:

   git clone https://github.com/SatvikPraveen/MatplotlibMasterPro.git
   cd MatplotlibMasterPro

2. Use the quick activation script:

   source activate.sh

   Or activate manually:

   source venv/bin/activate  # macOS/Linux
   venv\Scripts\activate     # Windows

3. Launch JupyterLab:

   jupyter lab

4. Or run production scripts:

   python scripts/generate_dashboard.py
   python examples/quick_start.py

5. Run tests (optional):

   python -m pytest tests/ -v

📖 For detailed setup instructions, see SETUP.md
💡 For improvement suggestions, see RECOMMENDATIONS.md
🔧 For troubleshooting help, see TROUBLESHOOTING.md
📚 For learning resources, see RESOURCES.md

See the Dockerized Setup section below.

Explore exported dashboards interactively:

streamlit run streamlit_app.py

Or via Docker:

docker build -t matplotlibmasterpro .
docker run -p 8501:8501 matplotlibmasterpro

Run a fully isolated Jupyter + Streamlit environment with ease.

# Build the container
docker build -t matplotlibmasterpro .

# Launch Jupyter
docker run -p 8888:8888 matplotlibmasterpro

Tokenless access enabled by default. Use --rm -d to run in background.

• Streamlit integration for dashboard browsing
• JupyterLab with Docker
• Advanced 3D visualizations
• Statistical plotting techniques
• Publication-ready themes
• Comprehensive troubleshooting guide
• Curated learning resources
• Production-ready Python scripts
• Quick copy-paste examples
• Unit tests with pytest
• PDF report export
• Pip-installable library version
• Interactive Plotly/Bokeh integrations
• Real-world case studies
• CI/CD pipeline

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.

Want to contribute?

• ✅ Fork the repo
• 🔧 Create a feature branch
• 🔁 Submit a PR with your improvements
• 🐛 Open issues for bugs or suggestions