# 📊 Seaborn & Matplotlib Visual Lab

Interactive Streamlit app for exploring **Seaborn** and **Matplotlib** side by side — from quick EDA plots to code snippets you can reuse in your notebooks.

This Space runs directly in the browser.  
No file uploads are required: the app uses **Seaborn’s built-in demo datasets** for safe, fast experimentation.

[![Streamlit](https://img.shields.io/badge/Powered%20by-Streamlit-FF4B4B)](https://streamlit.io/)<br>
[![Seaborn](https://img.shields.io/badge/Seaborn-Statistical%20Visuals-0ea5e9.svg)](https://seaborn.pydata.org/)<br>
[![Matplotlib](https://img.shields.io/badge/Matplotlib-Plot%20Engine-11557c.svg)](https://matplotlib.org/)<br>
[![Made with ❤️ by Tarek Masryo](https://img.shields.io/badge/Made%20by-Tarek%20Masryo-blue)](https://github.com/tarekmasryo)

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## 📌 What this app does

The Seaborn & Matplotlib Visual Lab lets you:

- Load classic **Seaborn demo datasets** (Tips, Penguins, Flights, Iris, Diamonds, Titanic, Car Crashes)  
- Build **Seaborn plots** interactively (distribution, relationships, categories, heatmaps, pairplots)  
- Recreate the same ideas using **Matplotlib** with more low-level control  
- Compare **Seaborn vs Matplotlib** for the *same* pattern in one screen  
- Save plots into a **gallery** and download them as PNG or a ZIP archive  

Use it as a small **visual lab for plots**: learn, tweak, copy the code, and move it into your own projects.

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## 📸 Dashboard Preview

### 1️⃣ Seaborn — Distribution Builder (Tips)

<p align="center">
  <img src="assets/seaborn-tips-total-bill-hist-sex.png" alt="Seaborn histogram of total_bill by sex from the tips dataset" />
</p>

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### 2️⃣ Seaborn — Relationship Builder (Tips)

<p align="center">
  <img src="assets/seaborn-tips-total-bill-vs-tip-scatter.png" alt="Seaborn scatter plot of total_bill vs tip from the tips dataset" />
</p>

---

### 3️⃣ Matplotlib — Histogram (Iris)

<p align="center">
  <img src="assets/matplotlib-iris-sepal-length-hist.png" alt="Matplotlib histogram of sepal length from the iris dataset" />
</p>

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### 4️⃣ Matplotlib — Line Plot (Iris)

<p align="center">
  <img src="assets/matplotlib-iris-sepal-length-line.png" alt="Matplotlib line plot of sepal length over index from the iris dataset" />
</p>

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### 5️⃣ Compare — Histogram + KDE (Tips)

<p align="center">
  <img src="assets/compare-hist-kde-tips.png" alt="Compare tab showing Seaborn vs Matplotlib histogram + KDE for the tips dataset" />
</p>

---

### 6️⃣ Compare — Scatter (Flights)

<p align="center">
  <img src="assets/compare-scatter-flights.png" alt="Compare tab showing Seaborn vs Matplotlib scatter plot for the flights dataset" />
</p>

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## 🧭 How to use this Space

The app is organised into five main tabs:

### 1. Overview

High-level view of the active dataset:

- Sample preview (top rows)  
- Column types and missingness summary  
- Quick numeric distribution  
- Small correlation heatmap for a subset of numeric features  

Good starting point for any dataset before plotting.

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### 2. Seaborn builder

UI-driven Seaborn plots:

- Plot families: **Distribution, Relationship, Category, Matrix / Heatmap, Multi-variable**  
- Controls for:
  - Numeric / categorical column selection  
  - Bins, KDE, ECDF, log-scale  
  - Hue grouping and top-K categories  
- Auto-updated **Python code snippet** that you can copy into a notebook  

---

### 3. Matplotlib builder

Low-level Matplotlib plotting:

- Plot types: **Line, Scatter, Bar, Histogram, Box, Subplots overview**  
- Controls for:
  - Axes selection (X/Y)
  - Markers, point size, transparency  
  - Horizontal vs vertical bars  
  - Density vs counts, optional KDE overlay in overview  

The goal is to show how to translate visual ideas into **raw Matplotlib commands**.

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### 4. Compare

Side-by-side comparison of Seaborn and Matplotlib for:

- **Distribution pattern:** histogram + KDE  
- **Relationship pattern:** scatter plot  

Useful for teaching how high-level Seaborn APIs map to Matplotlib primitives.

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### 5. Gallery

A lightweight export hub:

- Save any Seaborn or Matplotlib plot into a session **gallery**  
- Download individual PNGs  
- Prepare and download a **ZIP** with all saved plots  

---

## 📚 Data & Datasets

All data lives inside the Space and comes from **Seaborn’s built-in datasets**.  
No uploads, no external APIs, and no personal data.

Available datasets:

- `tips`
- `penguins` (NaNs dropped)
- `flights`
- `iris`
- `diamonds` (1,000-row sample)
- `titanic`
- `car_crashes`

Switch between them from the sidebar and see the plots update instantly.

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## 🧩 Tech Stack

- **Python**  
- **Streamlit** — app framework  
- **Seaborn** — high-level statistical plotting  
- **Matplotlib** — core plotting engine  
- **NumPy** & **pandas** — data handling  

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## 🖥 Run locally (optional)

If you want to run the same app outside Hugging Face Spaces:

```bash
git clone https://github.com/tarekmasryo/seaborn-matplotlib-visual-lab.git
cd seaborn-matplotlib-visual-lab

pip install -r requirements.txt
streamlit run app.py

```

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Use this Space as a safe place to experiment with plots, learn the APIs, and copy production-ready snippets into your own notebooks and dashboards.