Styling Line and Scatter Plots

Great visualizations aren't just about data - they're about communication. Let's learn advanced styling techniques to make your plots professional and impactful.

Color Palettes

Choose colors that work well together:

Using Colormaps

Matplotlib provides many built-in colormaps:

Custom Markers

Create distinctive data points:

Adding Annotations

Highlight important data points:

Text Styling

Customize fonts and text elements:

Error Bars

Show uncertainty in your data:

Professional Styling Template

Here's a template for publication-quality plots:

Dark Theme

Create plots with a dark background:

Practice: Style a Professional Chart

Key Takeaways

• Use professional color palettes - limit to 5-7 colors
• Colormaps (viridis, plasma, etc.) work well for continuous data
• Customize markers for distinct data series
• Annotations highlight important insights
• Remove unnecessary elements (spines, borders) for cleaner plots
• Use plt.rcParams for consistent styling across multiple plots
• Dark themes can be effective for presentations
• Always consider your audience and medium (print vs. screen)

In the next module, we'll explore bar charts and histograms for categorical data and distributions.