Free Python Data Cleaning Learning Path
The clean-room notebook

GOAL
Build a repeatable Python cleaner that normalizes fields, rejects invalid rows and produces an audit summary without overwriting its input.

START
Read workbook.html, inspect case-data.csv, complete the five stage outputs, then the capstone. Use evidence-log.csv to record only actions actually performed.

CASE
A fictional workshop export contains five registration rows. IDs should be unique, names nonblank and seats integers from 1 to 4. Keep the first valid occurrence of an ID; subsequent duplicates are rejected. Whitespace is normalized, but missing facts are never guessed.

TOOLS
Basic Python variables, loops and functions. Python 3 and its standard library are sufficient.

LOCAL COMMANDS / OPENING
python clean.py case-data.csv new-output

ASSESSMENT
Score at least 80/100, supply every required artifact, and satisfy the path-specific critical condition. Rework missing evidence before marking complete.
Critical condition: No input file is overwritten and every excluded row has an explicit reason.

SOURCE COURSES
Data Analysis with Python: Free Course — https://wikifree.com/courses/free-learn-skills-daily-data-analysis-python/

RIGHTS AND SCOPE
Original WikiFree teaching case and exercises. All case data are synthetic. Provider lessons are linked, not redistributed. No certificate, real project delivery or unperformed runtime test is claimed.
