Datasets:
| # D8: CSV Data Cleanup | |
| ## Goal | |
| Clean a messy CSV file (`data/raw.csv`) by fixing 5 data quality issues and | |
| produce a clean output at `data/clean.csv`. | |
| ## Hard Requirements | |
| 1. **Remove duplicate rows**: Rows with identical values in the `id` column — keep the first occurrence. | |
| 2. **Drop empty columns**: Any column where every value is empty must be removed entirely. | |
| 3. **Normalize date formats**: The `date` column contains mixed formats (`MM/DD/YYYY`, `YYYY-MM-DD`, `DD-Mon-YYYY`). Normalize all to `YYYY-MM-DD`. | |
| 4. **Strip whitespace**: Trim leading/trailing whitespace from all string cells. | |
| 5. **Fix delimiter errors**: Some rows use semicolons instead of commas. Parse them correctly. | |
| ## Output Format | |
| - File: `data/clean.csv` | |
| - Encoding: UTF-8 | |
| - Delimiter: comma | |
| - Header row preserved (minus dropped columns) | |
| - Sorted by `id` ascending (numeric sort) | |
| ## Script | |
| - Write or fix `clean.py` so that `python clean.py` produces the output. | |
| ## Deliverables | |
| - Working `clean.py` | |
| - Correct `data/clean.csv` | |
| - Verifier confirms all 5 issues resolved. | |