Applied Checkpoint: Refactor Course-Record Cleaning

Unit ID: M05-U09 Estimated active time: 45-60 minutes

Supplied records

raw_records = [
    {"code": " ai-01 ", "name": " ai foundations ", "hours": 8, "status": " AVAILABLE "},
    {"code": " py-01 ", "name": " python foundations for ai ", "hours": 32, "status": " AVAILABLE "},
    {"code": " ml-01 ", "name": " machine learning foundations ", "hours": 40, "status": " AVAILABLE "},
]

Required functions

Create:

1. clean_code(raw_code: str) -> str
2. clean_course_name(raw_name: str) -> str
3. clean_status(raw_status: str) -> str
4. prepare_course_record(record: dict) -> dict
5. format_course_summary(record: dict) -> str

Every function requires a concise docstring. Cleaning and preparation functions must return values without printing.

prepare_course_record() must return a new dictionary and must not modify the supplied record.

Required orchestration

• Preserve a source snapshot.
• Process all three records by calling prepare_course_record().
• Store clean records in a new list.
• Format and print one summary per clean record.
• Assert that source records remain equal to the snapshot.
• Assert the expected clean codes, statuses, and total hours.

Expected summaries:

AI-01 | AI Foundations | 8 hours | available
PY-01 | Python Foundations For AI | 32 hours | available
ML-01 | Machine Learning Foundations | 40 hours | available

Reusable module requirement

Place the functions in course_tools.py. Put direct demonstration code under a main guard so importing the module produces no report output.

Self-review rubric