I built a small data-cleaning workflow around one simple rule: bad rows should never silently dis...I built a small data-cleaning workflow around one simple rule: bad rows should never silently dis...
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Julian's avatar
Julian Vance
• Jul 21
I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.
Synthetic demo results:
• 20 raw order records • 16 clean rows • 2 duplicates removed • 2 invalid rows quarantined • $3,690 paid revenue reconciled
The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.
The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.
Portfolio: https://contra.com/p/WPLXml6R-demo-automated-sales-data-cleaning-and-excel-reporting
For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?
Data AnalysisMicrosoft ExcelPython
Post image
Geethasree's avatar
Geethasree Naguboina
pro
• Jul 23
Really like the quarantine + reasons approach — 'silently disappearing rows' is exactly the kind of thing that erodes trust in a dashboard later. In my own Excel/data cleanup work, inconsistent labels tend to cause the most manual review, especially free-text fields where the...
Back to feed
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started