Matplotlib and Seaborn: Data Visualization

The Grammar of Graphics

Data visualization is the graphical representation of information and data. A good visualization tells a story and reveals patterns that numbers alone cannot.

Matplotlib: The Foundation

Basic Plot Structure

Essential Plot Types

Subplots and Layouts

Seaborn: Statistical Visualization

Distribution Plots

Categorical Plots

Relationship Plots

Matrix Plots

Customization and Themes

Publication-Quality Visualizations

Practical Example: Sales Dashboard

Key Takeaways

Practice Exercise

1. Create a multi-panel figure with 4 different plot types using plt.subplots(2, 2)
2. Customize colors, fonts, and layout using sns.set_theme and plt.rcParams
3. Build a Seaborn FacetGrid that facets a dataset by two categorical variables
4. Compute and visualize a correlation heatmap with annotations
5. Create a publication-quality scatter plot with regression line, removing top/right spines
6. Export your final figure in both PNG (300 DPI) and PDF formats
7. Build a mini-dashboard with at least 5 panels summarizing a dataset