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Capstone: A Small Data Project · Lesson 169

Export Results

Exporting serialises final validated artifacts to stable files with an explicit format, location and overwrite/versioning rule.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Export Results

Exporting serialises final validated artifacts to stable files with an explicit format, location and overwrite/versioning rule.

Learning goal: explain why Export Results behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.

Deeper walkthrough

Read Export Results as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts. 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

Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.

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

Create the output directory

Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.

Verification focus: record the evidence you inspected and the condition that would make this stage fail.
How it works

Trace the mechanism step by step

  1. Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.
  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

Export Results evidence

Export Results evidence
Evidence: an output manifest lists generated files and basic row/size checks.
Expected / illustrative result
The worked evidence makes the output of this project stage concrete and auditable.
Interpret the result.

For Export Results, 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 Export Results 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 Export Results 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 Export Results. First create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts. 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 Export Results?

Quick reference

Remember the logic

Step 1Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.
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

  • Exporting serialises final validated artifacts to stable files with an explicit format, location and overwrite/versioning rule.
  • Create the output directory, write tables/figures with deliberate types/encoding/index policy and read back or inspect key artifacts.
  • Running the operation on the wrong object/type or in the wrong environment.
  • Trace a tiny input by hand and compare the runtime result.