Chapter Summary

Chapter Summary

Key Points

• 1.

  Seaborn is the fastest path to statistical plots. Use sns.boxplot, sns.violinplot, and sns.ecdfplot with Pandas DataFrames and hue/col mappings to explore simulation results across multiple factors. Always use axes-level functions when composing with Matplotlib subplots.

• 2.

  Plotly adds interactivity for free. Use plotly.express for quick exploration with hover, zoom, and pan. Use plotly.graph_objects for dashboards and Dash apps. For papers, recreate final figures in Matplotlib for LaTeX/PDF quality.

• 3.

  Animate convergence and time-varying phenomena. FuncAnimation with blit=True is the standard approach. Save as GIF for presentations and MP4 for quality. Always provide a static "final frame" for papers.

• 4.

  Master domain-specific plot types. Smith charts for impedance matching (use scikit-rf), eye diagrams for ISI analysis (overlay symbol periods), polar plots for antenna patterns (radians!), and waterfall plots for spectrum monitoring. These are all built on Matplotlib primitives.

• 5.

  Choose the right tool for the output medium. Matplotlib for journals (vector PDF, LaTeX), Plotly for dashboards (HTML, Dash), Seaborn for statistical exploration (Pandas integration). Most real projects use all three.