Chapter 12

Seeing Numerical Results

Turn small numerical questions into checked line, bar, scatter, histogram, and heatmap views, then use the same evidence to find a wrong result.

A table of numbers records a result, but its order, differences, and unusual values may still be hard to see. A plot places those numbers where we can inspect a particular relationship. The same small observation-by-sensor data from the previous chapters lets us connect every visible mark to a value we already understand.

We begin by drawing one ordered measurement line. Bars compare named sensors, a scatter plot keeps measurements paired by observation, a histogram counts values in intervals, and a heatmap preserves both axes of a numerical table. Only after using these forms do we choose among them from the question we need to answer.

The final lessons make comparisons fair, save the intended figure, and place a plot inside the debugging method. A plot is evidence, not an explanation by itself. We keep labels, units, source numbers, and numerical checks beside it so that another reader can inspect the claim.

After this chapter

  • Draw ordered measurements through an explicit figure and axes object, with checked coordinates, labels, units, markers, and a title.
  • Compare named categories with deliberately ordered bars and an honest magnitude baseline.
  • Preserve row-by-row pairs in a scatter plot and describe visible relationships without claiming unsupported causes.
  • Read histogram bins as explicit intervals and counts, and use shared edges for a fair comparison.
  • Map a numerical table to a heatmap while checking orientation, color meaning, and one known cell.
  • Choose the smallest plot from the question and the ordered, categorical, paired, distributional, or tabular relationship it must preserve.
  • Make figures comparable and reusable through labels, units, shared rules, accessible distinctions, explicit ownership, saving, and closing.
  • Use a plot with a trusted reference and numerical checks to locate, repair, and verify a wrong result.

Lessons

  1. 01
    Draw the First Measurement Plot

    Draw one ordered measurement line through an explicit Matplotlib axes object and check a plotted point against its source values.

    3 exercises
  2. 02
    Compare Categories with Bars

    Build a bar plot from named sensors and checked values, keep a meaningful order, and use an honest magnitude baseline.

    3 exercises
  3. 03
    Compare Paired Measurements

    Use a scatter plot to preserve row pairs, check one point, and add an equality reference only when equal coordinates have meaning.

    3 exercises
  4. 04
    Inspect a Distribution

    Construct a histogram with hand-checkable bin edges and compare distributions only through shared intervals.

    3 exercises
  5. 05
    Display a Numerical Table

    Display a two-dimensional array with imshow, choose a suitable color scale, and verify one known cell and both named axes.

    3 exercises
  6. 06
    Choose and Interpret a Plot

    Connect ordered, categorical, paired, distributional, and tabular questions to the smallest suitable plot and inspect its construction.

    3 exercises
  7. 07
    Make Figures Comparable and Reusable

    Use shared visual rules, accessible distinctions, explicit figure ownership, savefig, and deliberate figure closing.

    3 exercises
  8. 08
    Debug a Wrong Result with a Plot

    Use a plot inside the established debugging method to locate one wrong transformed point and preserve its repair in a check.

    3 exercises

Review and practice

  1. •
    Review

    Review preserved relationships, hand-checkable marks, cautious interpretation, fair comparison, saved evidence, and visual debugging.

  2. •
    Exercises

    Apply the complete Chapter 12 path through twelve cumulative decisions, calculations, implementations, and diagnoses.

Chapter progress