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Level 1 / Project 15 - Level 1 Mini Automation

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Concept This project Walkthrough Quiz Flashcards — Browser

Estimated time: 40 minutes

Focus

  • multi-step beginner automation flow

Why this project exists

Build a multi-step data pipeline that parses, validates, transforms, filters, and summarises records. This Level 1 capstone combines everything you have learned into a realistic ETL (Extract-Transform-Load) workflow.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/level-1/15-level1-mini-automation
python project.py --input data/sample_input.txt
pytest -q

Expected terminal output

=== Automation Pipeline ===

  Step 1: Parsed 3 records
  Step 2: Validated 3 records (0 errors)
  Step 3: Transformed values
  Step 4: Filtered to 2 active records
  Step 5: Summary -- total value: $350.00

Output written to data/output.json
8 passed

Expected artifacts

  • data/output.json
  • Passing tests
  • Updated notes.md

Checkpoint: Baseline code runs and all tests pass. Commit your work before continuing.

Alter it (required) — Extension

  1. Add a Step 6: step_export_csv() that writes active records to a CSV file.
  2. Add a --verbose flag that prints the result of each pipeline step as it executes.
  3. Re-run script and tests.

Break it (required) — Core

  1. Add a line with only 2 pipe-separated values -- does step_parse_records() skip it or crash?
  2. Add a record with a non-numeric value field -- does step_transform() use the default 0.0?
  3. Use a file where all records have status "failed" -- does step_summarise() handle an empty active list?

Fix it (required) — Core

  1. Ensure step_parse_records() skips lines with fewer than 3 pipe-separated fields.
  2. Handle the all-filtered-out case in step_summarise() by returning zero counts.
  3. Add a test for the non-numeric-value fallback.

Checkpoint: All modifications done, tests still pass. Good time to review your changes.

Explain it (teach-back)

  1. Why is the pipeline split into 5 separate step_* functions instead of one big function?
  2. What is the "pipeline pattern" and why does each step take input and return output?
  3. Why does run_pipeline() track counts at each step (total_lines, parsed, active)?
  4. Where would multi-step pipelines appear in real software (ETL systems, CI/CD, data processing)?

Mastery check

You can move on when you can: - run baseline without docs, - explain one core function line-by-line, - break and recover in one session, - keep tests passing after your change.

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