CSV CLEANUP / WORKING SAMPLE
See every change.
Keep the uncertain values visible.
Load the fictional job file or choose a file with the same nine columns. Review the output, flagged rows and full change audit before using the result.
Try record 1 for a safe change, record 3 for an ambiguous date, and records 11–13 to see why only the exact duplicate is removed. The original 16 records become 15; seven kept records still need review.
Compare a record, field by field
| Field | Original | Cleaned | Decision |
|---|
What happened to each record
| Source row | Job ID | Output date | Outcome | Changes and review reasons |
|---|
Flagged values remain in the cleaned file. “Cleaned” does not mean every field is valid. Formula-like cells are preserved and flagged; review them and import fields as text in a spreadsheet. The JSON audit is a safer way to inspect exact values.
Cleanup rules and supported format
- Trim surrounding whitespace; preserve original values in the audit.
- Convert valid YYYY/MM/DD dates to YYYY-MM-DD.
- Remove only subsequent rows with identical original values in all nine fields.
- Keep leading zeros in identifiers. Matching customer emails do not make rows duplicates.
- Flag ambiguous dates, numbers, missing required fields and formula-like values. Do not guess replacements.
Exact headers, in order:
job_id,customer_id,customer_email,job_date,service,status,hours,amount,notes
UTF-8 CSV, 1–1,000 data rows and 2 MiB (2,097,152 bytes) maximum. Quoted commas, escaped quotes and quoted newlines are supported. A malformed file is refused as a whole. Include at least one data row after the header.