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Capstone: A Small Data Project · Lesson 165

Create a Plot

The project plot should encode one analytical message from the validated summary data rather than duplicate every available variable.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Create a Plot

The project plot should encode one analytical message from the validated summary data rather than duplicate every available variable.

Learning goal: explain why Create a Plot behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.

Deeper walkthrough

Read Create a Plot as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced. Stage 2: Keep the stage inside the same data/validation definitions used by the rest of the project. Stage 3: Save the evidence produced by this stage so the next stage can be audited.

Mechanism

Follow the transformation

Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.

Keep the stage inside the same data/validation definitions used by the rest of the project.

Save the evidence produced by this stage so the next stage can be audited.

Evidence

Know what would convince you

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
Useful distinctionInput: Objects/values supplied to the operation.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1

Choose chart type from the question

Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.

Visual focus: use the graphic to test a specific expectation, not merely to decorate the analysis.
How it works

Trace the mechanism step by step

  1. Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.
  2. Keep the stage inside the same data/validation definitions used by the rest of the project.
  3. Save the evidence produced by this stage so the next stage can be audited.
Worked demonstration

Create a Plot evidence

Create a Plot evidence
Evidence: changing a source value and rerunning updates the corresponding plotted value.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Create a Plot, trace the specific input through the mechanism above and independently verify one returned value, state change or side effect.

Distinctions & related ideas

Place the concept correctly

InputObjects/values supplied to the operation.
StateNames or mutable objects that may change during execution.
OutputReturned value, side effect, file, plot or exception to inspect.
Use deliberately

When it is appropriate

Use Create a Plot when it answers a defined question in Capstone: A Small Data Project and its inputs/assumptions match the current data or program state.

Boundary conditions

When to stop or reconsider

Reconsider Create a Plot when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.

Common mistakes

Failure modes to recognise

  • Running the operation on the wrong object/type or in the wrong environment.
  • Inferring correctness from “no exception” without checking the produced value/state.
  • Hiding a boundary case instead of making its behaviour explicit.
Verification

How to check the result

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
  • Run an edge or invalid case and confirm the exception/behaviour is deliberate.
Hands-on practice

Demonstrate understanding

Try this:

Construct a tiny example of Create a Plot. First choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced. Then keep the stage inside the same data/validation definitions used by the rest of the project. Predict the result before execution and explain one boundary or failure case.

List the stage inputs and expected artifact, rerun it from a clean state, and compare against a concrete acceptance check.
Knowledge check

Check reasoning, not memorisation

Which approach best demonstrates understanding of Create a Plot?

Quick reference

Remember the logic

Step 1Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.
Step 2Keep the stage inside the same data/validation definitions used by the rest of the project.
Step 3Save the evidence produced by this stage so the next stage can be audited.
Lesson summary

What to remember

  • The project plot should encode one analytical message from the validated summary data rather than duplicate every available variable.
  • Choose chart type from the question, label units/categories, avoid misleading scales and save the figure from code so it can be reproduced.
  • Running the operation on the wrong object/type or in the wrong environment.
  • Trace a tiny input by hand and compare the runtime result.