📊 Seaborn Beginner-to-Pro — Visual EDA Guide

A practical Seaborn tutorial for exploratory data analysis: from beginner plots to cleaner comparisons, faceting, density views, heatmaps, and a compact Titanic EDA workflow.

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✅ What’s inside

• Beginner-friendly Seaborn examples with short, reusable cells
• Distribution, categorical, relationship, time-series, and faceted plots
• Version-safe snippets for Seaborn API differences such as ci vs errorbar
• Advanced visual patterns: pairplot, jointplot, regplot, lmplot, ECDF, hexbin, and masked correlation heatmaps
• A compact Titanic EDA mini-pipeline with a note on target-mirror leakage
• A final cheat sheet mapping analytical goals to Seaborn functions

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📓 Main notebook

• seaborn-beginner-to-pro.ipynb

Kaggle version:

• https://www.kaggle.com/code/tarekmasryo/seaborn-beginner-to-pro

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📂 Data

This project uses Seaborn built-in sample datasets loaded with:

sns.load_dataset(...)

Datasets used:

• tips
• penguins
• flights
• iris
• titanic

Note: sns.load_dataset() may require internet access on the first run because Seaborn downloads the sample datasets.

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🚀 Run locally

python -m venv .venv

Windows:

.venv\Scripts\activate

macOS/Linux:

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Then open the notebook in Jupyter, JupyterLab, VS Code, or Kaggle and run it top-to-bottom.

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📁 Repository layout

.
├── seaborn-beginner-to-pro.ipynb
├── CASE_STUDY.md
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore

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🧠 Why this notebook exists

The goal is not to memorize every Seaborn function. The goal is to build judgment around plot selection:

• Which plot answers the current question?
• When is a simple chart better than a complex one?
• When should hue, facets, scales, or statistical helpers be added?
• Which visual patterns can mislead if used carelessly?

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🧾 Case study

See CASE_STUDY.md for the project story, design decisions, and publishing notes.

📄 License

MIT License. See LICENSE for details.

Copyright © Tarek Masryo.