Remove Duplicates and Fix Dates in a CSV — Without Formulas
Most spreadsheet cleanup comes down to the same two jobs: getting rid of duplicate rows, and forcing a column of messy dates into one consistent format. In Excel both are doable — but both are fiddly, easy to get subtly wrong, and worst of all, you redo them by hand every single time a new file lands.
Here's how to do both without formulas, in a way you build once and re-run on next month's export in one click.
Why duplicates are harder than they look
- "Remove Duplicates" is destructive. Excel's button deletes rows in place. Make a mistake and the original is gone.
- It's a one-off. It's not saved. Next file means clicking through the dialog again and re-picking the columns.
- Whitespace and case fool it.
"ACME "and"acme"look identical to you but Excel treats them as different rows.
The fix is to decide which columns define a duplicate (often an ID or an email, not the whole row), trim and normalize those values first, then keep the first occurrence of each.
Why mixed date formats are worse
A single date column often arrives as a mess: 3/4/2026, 2026-04-03,
Apr 3 2026, 03.04.26. Sorting it does nothing useful, and
=DATEVALUE() chokes on half the rows. The only reliable fix is to read every
format and rewrite them all one way (e.g. YYYY-MM-DD) — something Excel
won't do for you in one move.
The repeatable way, step by step
- Open your file (CSV or Excel).
- Add a Trim spaces step on the key columns so stray whitespace stops hiding duplicates.
- Add a Fix case step if your keys should match regardless of capitalization.
- Add a Remove duplicates step and pick the columns that define a duplicate.
- Add a Standardize dates step on the date column and choose your target format.
- Save the whole thing as a recipe.
Do it in one click with Kramata
Kramata is a free desktop app built for exactly this. Each cleanup is a step in a recipe; once saved, next month you load the new file and click once. It's non-destructive (your source file is never overwritten), reproducible, and your data never leaves your computer.
- Dedupe on any columns — keep the first match, review the rest.
- Date standardizer reads dozens of formats and rewrites them one way.
- Trim, fix case, fill blanks — the small fixes that make dedupe actually work.
- Reproducible: same file + same recipe = identical output, every time.
Related
How to clean a messy CSV the same way every time · Merge two spreadsheets without VLOOKUP
FAQ
Does it change my original file? No. Kramata works on a copy and exports a clean file; your source is untouched.
What date formats does it read? The common ones — slashes, dashes, dots, and textual months — including two- and four-digit years, and rewrites them all to the format you pick.
Can I see what got removed? Yes — the preview shows how many rows a step affects before you commit.