Seaborn: Statistical Data Visualization

Learning Objectives: After this lesson, you'll create beautiful statistical graphics with minimal code using seaborn—distributions, relationships, categorical comparisons, and styled presentations.

Why Seaborn?

Seaborn is built on matplotlib but provides a high-level interface for creating attractive statistical graphics. What takes 10 lines in matplotlib often takes 1 line in seaborn.

Setting Up Seaborn

Distribution Plots

Understanding data distributions is fundamental to data analysis.

Histograms and KDE

Box Plots and Violin Plots

Relationship Plots

Explore relationships between variables.

Scatter Plots with Regression

Pair Plots

Visualize relationships between all pairs of variables:

Heatmaps

Visualize matrices and correlations:

Categorical Plots

Compare categories effectively.

Bar Plots with Error Bars

Count Plots

Strip and Swarm Plots

Show individual data points:

Combining Plots

FacetGrid: Multiple Subplots

Create a grid of plots based on data subsets:

Complete Example: EDA Dashboard

Key Takeaways

✅ Distribution plots: histplot(), kdeplot(), boxplot(), violinplot()

✅ Relationship plots: scatterplot(), regplot(), pairplot(), heatmap()

✅ Categorical plots: barplot(), countplot(), stripplot(), swarmplot()

✅ FacetGrid: Create grids of plots by data subsets

✅ Styling: set_theme(), color palettes, context settings

✅ Integration: Works seamlessly with pandas DataFrames

Connections: Seaborn in Practice

🔗 Connection to Statistics

🔗 When to Use Which Plot

Practice Exercises

Exercise 1: Complete Visualization

Next Steps

Now you're ready for Exploratory Data Analysis (EDA)—a systematic approach to understanding data by combining all the techniques you've learned.

Ready to explore real datasets? EDA is next!