A PRACTICAL CSV CLEANUP WORKFLOW

Review the cleanup.
Keep the original.

A tidy export should still mean what the original meant. Use this checklist before replacing a file or importing it into another system.

1. Keep an untouched copy

Work on a copy and save the cleaned result under a new name. Pick the correct delimiter: comma, tab or semicolon. Quoted commas and line breaks belong inside their cells.

2. Decide whether spaces are meaningful

Trimming helps with stray spaces around names and labels. It can also change a value whose spaces matter. Preview the result and leave trimming off when that would be inappropriate.

3. Review whole-row duplicates

CSV Rescue removes exact repeated rows after any selected trimming. It does not merge customers by ID or decide which of two different records is correct. Keep records that represent separate events, even when their values happen to match.

A small example

This synthetic file has one company cell with surrounding spaces, one repeated row after trimming, and one empty row.

id,company,amount
00123, Acme ,42
00123,Acme,42
00007,North Shop,17
,,

id,company,amount
00123,Acme,42
00007,North Shop,17

With trimming, exact deduplication and empty-row removal enabled, four data rows become two. The identifiers 00123 and 00007 remain strings in the tool.

4. Check how the destination reads the file

A spreadsheet can infer numbers from CSV text and drop leading zeros. Use its import workflow and set identifier columns to Text. CSV Rescue keeps strings, but cannot control another application's type inference.

The optional spreadsheet-safe export prefixes formula-prone values with an apostrophe. That changes the exported text, including negative numbers. Review it in the destination application before relying on it.

5. Inspect the report and a few rows

Check the counts for removed duplicates, empty rows, trimmed cells and uneven rows. Inspect important identifiers and multiline cells. Save the cleanup report alongside the new CSV or JSON.