🎨 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
• 📦 Package everything in a container-ready environment

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🧱 Project Structure

SeabornMasterPro/
├── notebooks/               # Modular notebooks (merged logically)
│   ├── 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
├── utils/                   # Reusable helper functions
│   └── plot_utils.py        # Theme, save_fig, annotate, format_date_axis, etc.
├── 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 & reports
│   ├── 01_basics/
│   ├── 02_distributions/
│   ├── 03_categorical/
│   ├── 04_dashboards/
│   ├── 05_realworld/
│   └── 06_timeseries/
├── cheatsheets/             # Markdown cheatsheets
│   └── seaborn_cheatsheet.md
├── streamlit_app.py         # Interactive EDA dashboard
├── requirements.txt         # Minimal dependencies to run the project
├── requirements_dev.txt     # Full dev environment
├── Dockerfile               # Container setup
├── .dockerignore
├── .gitignore
├── README.md
└── LICENSE

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🌟 Features

• 📘 Modular notebooks for each topic
• 📊 Full Seaborn coverage: histplot, kdeplot, scatterplot, heatmap, pairplot, catplot, FacetGrid, and more
• 🎨 Unified visual style via plot_utils.py
• 📁 Exports saved with save_fig() into logical folders
• 🗂️ Cheatsheet in Markdown for quick revision
• 🌐 Streamlit dashboard to explore visualizations interactively
• 🐳 Docker support for full reproducibility

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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
• Use .pipe() and plot_utils.py for reusability
• Run everything inside Docker or via Streamlit

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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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🔁 Extend This Repo

Want to take this further?

• Add Plotly/Altair interactive options
• Create animated visualizations
• Add unit tests for plot_utils.py
• Create seaborn_themes.py presets
• Integrate other dashboards into Streamlit

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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

• Setup and Seaborn basics
• Distributions and pairwise relationships
• Categorical plots and matrix visualizations
• Multi-panel layouts and custom dashboards
• Real-world EDA with Titanic & Marketing Campaign
• Time series line plots with date formatting

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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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