https://github.com/satvikpraveen/seabornmasterpro

🎨 SeabornMasterPro is a comprehensive, modular project to master Seaborn for data visualization. Includes themed utilities, advanced plotting notebooks, dashboards, time series, Streamlit app, and Docker support — perfect for learners, analysts, and open-source enthusiasts.
https://github.com/satvikpraveen/seabornmasterpro

categorical-plots correlation-heatmap custom-theme data-visualization docker interactive-dashboard jupyter-notebook matplotlib modular-code multi-panel-layouts open-source-project pandas plot-utils project-structure python reproducible-research seaborn streamlit time-series-visualization utility-functions

Last synced: 2 months ago
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Host: GitHub
URL: https://github.com/satvikpraveen/seabornmasterpro
Owner: SatvikPraveen
License: mit
Created: 2025-07-25T09:34:59.000Z (11 months ago)
Default Branch: main
Last Pushed: 2025-07-25T10:15:27.000Z (11 months ago)
Last Synced: 2025-07-25T16:37:23.182Z (11 months ago)
Topics: categorical-plots, correlation-heatmap, custom-theme, data-visualization, docker, interactive-dashboard, jupyter-notebook, matplotlib, modular-code, multi-panel-layouts, open-source-project, pandas, plot-utils, project-structure, python, reproducible-research, seaborn, streamlit, time-series-visualization, utility-functions
Language: Jupyter Notebook
Homepage:
Size: 6.01 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

# 🎨 SeabornMasterPro

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![Python](https://img.shields.io/badge/Python-3.10%2B-darkgreen.svg)](https://www.python.org/)
[![Jupyter Notebooks](https://img.shields.io/badge/Jupyter-Notebook-orange.svg)](https://jupyter.org/)
[![Seaborn Mastery](https://img.shields.io/badge/Seaborn-100%25-brightgreen.svg)](https://seaborn.pydata.org/)
[![Docker Ready](https://img.shields.io/badge/Docker-Ready-blueviolet.svg)](https://www.docker.com/)
[![Streamlit App](https://img.shields.io/badge/Streamlit-Dashboard-red.svg)](https://streamlit.io/)
[![PRs Welcome](https://img.shields.io/badge/PRs-Welcome-lightgrey.svg)](https://github.com/SatvikPraveen/SeabornMasterPro/pulls)
[![Last Commit](https://img.shields.io/github/last-commit/SatvikPraveen/SeabornMasterPro.svg)](https://github.com/SatvikPraveen/SeabornMasterPro/commits)

---

## 📌 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

---

## 📽️ 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.

![Streamlit Dashboard Preview](exports/04_dashboards/campaign_facetgrid_scatter.png)

---

### 📊 Real-World Visual Storytelling

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

![E-Commerce EDA Preview](exports/05_eda/ecommerce_correlation_heatmap.png)

---

## 🧠 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

---

## 🧱 Project Structure

```bash
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
```

---

## 🌟 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 .`

---

## ✅ Mastery Checklist

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

---

## 🔧 Setup Instructions

### ▶️ Install Requirements

```bash
pip install -r requirements.txt
```

Or using Conda:

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

---

## 🐳 Run in Docker (JupyterLab)

```bash
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](http://localhost:8890)

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

---

## 📊 Launch Streamlit Dashboard

```bash
streamlit run streamlit_app.py
```

Then visit:
🔗 [http://localhost:8501](http://localhost:8501)

---

## 🐍 Run Production Scripts

```bash
# 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](examples/README.md) for complete documentation on all 7 production scripts**

---

## 🔁 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

---

## 🧠 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

---

## 📌 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

---

## 🎯 Who Is This For?

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

---

## 🔗 Related Projects

- 🧠 [NumPyMasterPro](https://github.com/SatvikPraveen/NumPyMasterPro) — Deep dive into NumPy fundamentals
- 🐼 [PandasPlayground](https://github.com/SatvikPraveen/PandasPlayground) — Data cleaning and EDA workflows

---

## 📄 License

This project is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0). See the [LICENSE](./LICENSE) file for more details.

---

## 🙌 Acknowledgements

- [Seaborn](https://seaborn.pydata.org/)
- [Matplotlib](https://matplotlib.org/)
- [Streamlit](https://streamlit.io/)
- [Python](https://www.python.org/)

---

## ✨ Author

Made with 💙 by [Satvik Praveen](https://github.com/SatvikPraveen)

---

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