CSV Cleanup with a Review ListDan Wang
I help turn an inconsistent product list or business export into a cleaned file, with uncertain records clearly marked for your decision.
The US$49 starting batch covers one CSV file, up to 1,000 rows and 20 columns, using cleanup rules agreed before work begins.
What you receive
• A cleaned CSV using the agreed rules, such as trimming selected fields and removing exact duplicate rows.
• A review list of conflicting records, missing keys and other agreed checks that need your decision.
• Short change notes explaining what changed and what remains unresolved.
• One revision within the agreed scope.
Conflicting values and legitimate product variants remain available for review. They are not silently merged or guessed.
How we start
Send a short description of the file, its approximate row and column counts, and what you need the finished file to do. A small example with customer names, amounts and other private information replaced by made-up values can help us confirm the job.
Planning estimate: one week for the starting batch after the cleanup rules, data access and start date are agreed. We confirm the complete scope, price and delivery date before work begins. Larger, more complex or recurring batches receive a quote for their complete scope.
Scope and data handling
This package covers a file cleanup task. Live store updates, marketplace import validation, ongoing integrations, external research to fill missing information and spreadsheet formula repairs need separate assessment before I can quote or commit to them.
I use AI-assisted development and check the outputs. Any use of AI with your files needs your permission. Please keep confidential information out of public messages.
Examples
The cover shows the actual CSV deliverables from a 12-record example: 11 records retained, one exact duplicate removed, and eight retained records flagged for review. Conflicting amounts are preserved for a decision rather than guessed.
See the same example and the cleanup decisions in this walkthrough:
https://dan-wang-csv-conflicts.wddanwd.chatgpt.site
This is my AI-assisted personal demonstration using made-up data.
CSV Cleanup with a Review ListDan Wang
Contact for pricing
Duration1 week
Tags
Data Analyst
I help turn an inconsistent product list or business export into a cleaned file, with uncertain records clearly marked for your decision.
The US$49 starting batch covers one CSV file, up to 1,000 rows and 20 columns, using cleanup rules agreed before work begins.
What you receive
• A cleaned CSV using the agreed rules, such as trimming selected fields and removing exact duplicate rows.
• A review list of conflicting records, missing keys and other agreed checks that need your decision.
• Short change notes explaining what changed and what remains unresolved.
• One revision within the agreed scope.
Conflicting values and legitimate product variants remain available for review. They are not silently merged or guessed.
How we start
Send a short description of the file, its approximate row and column counts, and what you need the finished file to do. A small example with customer names, amounts and other private information replaced by made-up values can help us confirm the job.
Planning estimate: one week for the starting batch after the cleanup rules, data access and start date are agreed. We confirm the complete scope, price and delivery date before work begins. Larger, more complex or recurring batches receive a quote for their complete scope.
Scope and data handling
This package covers a file cleanup task. Live store updates, marketplace import validation, ongoing integrations, external research to fill missing information and spreadsheet formula repairs need separate assessment before I can quote or commit to them.
I use AI-assisted development and check the outputs. Any use of AI with your files needs your permission. Please keep confidential information out of public messages.
Examples
The cover shows the actual CSV deliverables from a 12-record example: 11 records retained, one exact duplicate removed, and eight retained records flagged for review. Conflicting amounts are preserved for a decision rather than guessed.
See the same example and the cleanup decisions in this walkthrough:
https://dan-wang-csv-conflicts.wddanwd.chatgpt.site
This is my AI-assisted personal demonstration using made-up data.
Contact for pricing