🎨 SeabornMasterPro

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📌 Overview

SeabornMasterPro is an open-source, end-to-end visualization mastery project built to help you learn, recall, and reuse Seaborn effectively — from plotting basics to dashboard-level visual storytelling.

🎯 Ideal for:

• Learners who want structured notebooks and projects
• Practitioners who want reusable utilities and themes
• Professionals who need dashboards, cheatsheets, and reproducible setups

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📽️ Project Preview

Here’s a glimpse into the Seaborn visualizations and dashboards created in this project:

🧭 Streamlit EDA Dashboard

Powered by streamlit_app.py, this dashboard provides real-time interaction with synthetic datasets.

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📊 Real-World Visual Storytelling

Plots generated using notebooks/05_realworld_EDA.ipynb and reusable functions from plot_utils.py.

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🧠 Learning Outcomes

By working through this project, you'll be able to:

• 📊 Visualize structured data with Seaborn’s full plotting suite
• 🧱 Build custom dashboards using Streamlit and save visual reports
• ♻️ Reuse plot components using plot_utils.py- 🐍 Deploy production-ready scripts with proper error handling- 📦 Package everything in a container-ready environment

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🧱 Project Structure

SeabornMasterPro/
├── notebooks/               # Comprehensive coverage (10 notebooks)
│   ├── 01_setup_and_basics.ipynb
│   ├── 02_distributions_relationships.ipynb
│   ├── 03_categorical_matrixplots.ipynb
│   ├── 04_multi_custom_dashboards.ipynb
│   ├── 05_realworld_EDA.ipynb
│   ├── 06_timeseries_lineplots.ipynb
│   ├── 07_figure_level_functions.ipynb
│   ├── 08_advanced_categorical.ipynb
│   ├── 09_styling_customization.ipynb
│   └── 10_statistical_parameters.ipynb
├── examples/                # Production-ready Python scripts (7 scripts)
│   ├── basic_workflow.py
│   ├── production_dashboard.py
│   ├── custom_styling.py
│   ├── statistical_viz.py
│   ├── batch_processing.py
│   ├── publication_figures.py
│   ├── reusable_template.py
│   └── README.md
├── utils/                   # Enhanced utility functions
│   ├── __init__.py
│   └── plot_utils.py        # 12 functions: themes, export, palettes, grids, annotations
├── scripts/                 # Dataset generators and helpers
│   └── generate_datasets.py
├── datasets/                # Synthetic datasets (auto-generated)
│   ├── ecommerce_data.csv
│   ├── employee_data.csv
│   ├── marketing_campaign.csv
│   ├── sales_data.csv
│   ├── student_scores.csv
│   └── web_traffic.csv
├── exports/                 # Saved visuals for all 10 notebooks
│   ├── 01_setup/ ... 06_timeseries/
│   ├── 07_figure_level/
│   ├── 08_categorical/
│   ├── 09_styling/
│   └── 10_statistical/
├── docs/                    # Comprehensive documentation
│   ├── best_practices.md
│   ├── plot_comparison.md
│   ├── troubleshooting.md
│   └── feature_matrix.md
├── tests/                   # Testing infrastructure
│   ├── __init__.py
│   └── test_plot_utils.py
├── .github/                 # GitHub templates and workflows
│   ├── workflows/
│   │   └── test.yml
│   ├── ISSUE_TEMPLATE/
│   └── PULL_REQUEST_TEMPLATE.md
├── cheatsheets/             # Markdown cheatsheets
│   └── seaborn_cheatsheet.md
├── streamlit_app.py         # Interactive dashboard (all 10 notebooks)
├── setup.py                 # Package installation
├── pytest.ini               # Test configuration
├── requirements.txt         # Minimal dependencies
├── requirements_dev.txt     # Full dev environment
├── Dockerfile               # Container setup
├── .dockerignore
├── .gitignore
├── README.md
└── LICENSE

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🌟 Features

• 📘 10 comprehensive notebooks covering all Seaborn functionality
• � 7 production-ready Python scripts with CLI, error handling, and logging
• �📊 100% Seaborn coverage: All 30+ core functions including relplot, displot, catplot, jointplot, lmplot, FacetGrid, and more
• 🎯 Figure-level vs Axes-level paradigm explained in depth
• 📊 Advanced categorical plots: barplot with custom estimators, statistical comparisons
• 🎨 Styling mastery: Color theory, palettes (sequential/diverging/qualitative), themes
• 📐 Statistical parameters: Deep dive into estimator and errorbar options
• ♻️ Enhanced utilities: 12 reusable functions in plot_utils.py
• 📚 Comprehensive docs: Best practices, plot comparison guide, troubleshooting, feature matrix
• 🧪 Testing infrastructure: pytest with CI/CD via GitHub Actions
• 📁 Exports saved with save_fig() into logical folders
• 🗂️ Cheatsheet in Markdown for quick revision
• 🌐 Streamlit dashboard to explore all visualizations interactively
• 🐳 Docker support for full reproducibility
• 📦 Pip-installable: pip install -e .

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✅ Mastery Checklist

• Setup environment and install dependencies
• Master Seaborn basics, distributions, relationships
• Learn categorical & matrix visualizations
• Build dashboards and multi-panel plots
• Analyze real-world synthetic datasets
• Master figure-level functions (relplot, displot, catplot, lmplot, jointplot)
• Understand axes-level vs figure-level paradigm
• Learn advanced categorical plots (barplot with custom estimators)
• Master styling and customization (themes, palettes, color theory)
• Understand statistical parameters (estimator, errorbar options)
• Use .pipe() and enhanced plot_utils.py for reusability
• Run comprehensive tests with pytest
• Explore all visualizations via Streamlit dashboard
• Run everything inside Docker for reproducibility

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🔧 Setup Instructions

▶️ Install Requirements

pip install -r requirements.txt

Or using Conda:

conda create -n seabornpro python=3.10
conda activate seabornpro
pip install -r requirements.txt

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🐳 Run in Docker (JupyterLab)

docker build -t seaborn-masterpro .
docker run -p 8890:8888 -p 8501:8501 -v $(pwd):/app -d seaborn-masterpro

You can now open JupyterLab in your browser at: 🔗 http://localhost:8890

The container disables Jupyter token/password prompts for local ease.

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📊 Launch Streamlit Dashboard

streamlit run streamlit_app.py

Then visit: 🔗 http://localhost:8501

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🐍 Run Production Scripts

# Navigate to examples directory
cd examples

# Run basic workflow
python basic_workflow.py

# Create production dashboard
python production_dashboard.py --format pdf

# Generate publication figures
python publication_figures.py --size journal_double --dpi 300

# Batch process all datasets
python batch_processing.py --parallel

# See all options for any script
python reusable_template.py --help

📚 See examples/README.md for complete documentation on all 7 production scripts

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🔁 Extend This Repo

Want to take this further?

• Add Plotly/Altair interactive options
• Create animated visualizations
• Create seaborn_themes.py presets for custom themes
• Add more interactive controls to Streamlit dashboard
• Add benchmarking and performance comparisons
• Create video tutorials for each notebook

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🧠 Learn by Doing

Each notebook is carefully structured with:

• ✅ Concepts grouped by theme
• ✅ Code + plots + comments inline
• ✅ Modular reusability via plot_utils.py
• ✅ Dataset links and exports
• ✅ Time-based, categorical, and real-world examples

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📌 Topics Covered

Core Notebooks

• 01: Setup and Seaborn basics
• 02: Distributions and pairwise relationships
• 03: Categorical plots and matrix visualizations
• 04: Multi-panel layouts and custom dashboards
• 05: Real-world EDA with Titanic & Marketing Campaign
• 06: Advanced time series (multi-series, confidence intervals, seasonality)

Advanced Notebooks

• 07: Figure-level functions (relplot, lmplot, jointplot, displot)
• 08: Advanced categorical plots (barplot with custom estimators)
• 09: Styling & customization (color theory, palettes, themes)
• 10: Statistical parameters (estimator, errorbar deep dive)

Documentation

• Best practices for data visualization
• Plot comparison and decision guide
• Troubleshooting common issues
• Complete feature matrix and coverage map

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🎯 Who Is This For?

Level	Use Case
✅ Beginner	Learn Seaborn from scratch
✅ Intermediate	Build reusable pipelines
✅ Advanced	Automate dashboards with Docker

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🔗 Related Projects

• 🧠 NumPyMasterPro — Deep dive into NumPy fundamentals
• 🐼 PandasPlayground — Data cleaning and EDA workflows

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📄 License

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

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🙌 Acknowledgements

• Seaborn
• Matplotlib
• Streamlit
• Python

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✨ Author

Made with 💙 by Satvik Praveen

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