Formation 90 min read Data Visualization

📦 Advanced Visualizations with Seaborn

Python & Data Science Chapter : Data Visualization Sub-chapter : Seaborn

Learning objectives

🎯 Objectives:\n
1Create heatmaps\n2. Create pairplots\n3. Create box plots\n4. Create violin plots

Introduction

📖 Seaborn is based on Matplotlib and offers advanced statistical plots.

Theoretical content

Seaborn:\n
PYTHON
\nimport seaborn as sns\nsns.boxplot(x="category", y="value", data=df)\n

Practical examples

💻 Example: Iris data analysis\n
PYTHON
\nimport seaborn as sns\niris = sns.load_dataset("iris")\nsns.pairplot(iris, hue="species")\n

Best practices

1Use heatmap for correlations\n✅ 2. Use pairplot for multiple relationships\n✅ 3. Use box plot for comparisons\n✅ 4. Use violin plot for distribution

Common pitfalls

Forgetting to install seaborn\n
pip install seaborn

Summary

heatmap(): correlation matrix\n✅ pairplot(): multiple relationships\n✅ boxplot(): comparisons\n✅ violinplot(): distribution\n✅ hue: color by category

Additional resources

📚 seaborn.pydata.org/tutorial.html