https://github.com/nevin100/data-visualization-with-seaborn

In this repository, I revisited core concepts of Seaborn and explored a wide range of plots and styling techniques to visualize data effectively. Each notebook demonstrates one or more visualization techniques with explanations and examples.
https://github.com/nevin100/data-visualization-with-seaborn

jupyter-notebook matplotlib-pyplot numpy pandas python seaborn

Last synced: 21 days ago
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In this repository, I revisited core concepts of Seaborn and explored a wide range of plots and styling techniques to visualize data effectively. Each notebook demonstrates one or more visualization techniques with explanations and examples.

• Host: GitHub
• URL: https://github.com/nevin100/data-visualization-with-seaborn
• Owner: Nevin100
• Created: 2025-07-15T07:14:24.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-07-16T04:51:59.000Z (11 months ago)
• Last Synced: 2025-07-16T09:54:17.777Z (11 months ago)
• Topics: jupyter-notebook, matplotlib-pyplot, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.89 MB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📊 Data Visualization with Seaborn

Welcome to the **Data Visualization with Seaborn** repository!  

This project is a comprehensive revision of Seaborn fundamentals and essential plots used in data analysis and visual storytelling.  

Seaborn is a powerful Python library built on top of Matplotlib, and it's particularly good at making statistical graphics beautiful and insightful.

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## 🔍 About This Repository

In this repository, I revisited core concepts of Seaborn and explored a wide range of plots and styling techniques to visualize data effectively. Each notebook demonstrates one or more visualization techniques with explanations and examples.

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## 📚 Topics Covered

Below is a list of concepts and visualizations revised and implemented:

- ✅ **Bar Plot**

- ✅ **Distplot**

- ✅ **Heatmaps**

- ✅ **Styling Seaborn Plots**

- ✅ **Categorical Plots**

- ✅ **Matrix Plots**

- ✅ **Color Palette Customization**

- ✅ **Violin Plot**

- ✅ **Swarm Plot**

- ✅ **Box Plot**

- ✅ **Kernel Density Estimation (KDE)**

- ✅ **Joint Plot**

- ✅ **Pair Plot**

Each of these plots includes examples and scenarios demonstrating when and how to use them.

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## 🧠 Learning Outcome

By exploring this repository, you will:

- Understand how Seaborn enhances visualization over raw Matplotlib.

- Learn different types of plots and when to use them.

- Gain hands-on experience with customizing the look and feel of your charts.

- Build intuition around statistical visualization through real examples.

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