Chapter 11

Plotting and Inspection

Line plots, scatter plots, histograms, array displays, labeling, saving figures, and visual debugging.

Subject
Python
Lessons
7 lessons
Practice
12 checks
Review
3 pages

What this chapter does

Plotting is not decoration. This chapter teaches readers to use plots as inspection tools for data, computed curves, distributions, and mistakes.

Lessons

Read these in order.

The chapter opening gives the main idea. Move through these lessons next; each page reuses ideas from the pages before it.

  1. 01
    Why Plot?

    Using plots to notice patterns, mistakes, outliers, distributions, and computed behavior.

  2. 02
    Line Plots

    Plotting ordered values, checking matching lengths, and labeling trends.

  3. 03
    Scatter Plots

    Inspecting paired measurements, prediction-versus-target plots, clusters, and outliers.

  4. 04
    Histograms

    Inspecting distributions, ranges, bins, concentration, and outliers.

  5. 05
    Plotting Arrays

    Displaying two-dimensional arrays as color grids and stating what axes mean.

  6. 06
    Labeling and Saving Figures

    Adding labels, titles, context, and saving figures when needed.

  7. 07
    Visual Debugging

    Comparing expected and computed curves, then writing what the plot shows.

  1. •
    Conclusion

    Turning plots into written inspection results before randomness and experiments.

  2. •
    Review

    A compact review of line plots, scatter plots, histograms, matrix plots, labels, saving, and visual debugging.

  3. •
    Exercises

    Chapter-level practice for plotting and describing numerical inspection results.

You are ready when

  • Make a simple line plot.
  • Use scatter plots for paired measurements.
  • Use histograms for distributions.
  • Display small two-dimensional arrays.
  • Label and save figures.
  • Use plots to notice and explain computational mistakes.

Where this leads

  • Randomness and Reproducibility
  • Small Numerical Experiments

Chapter progress