Precision-engineered normalize data formats (dates, numbers) for the modern web.
About Normalize data formats (dates, numbers)
Normalize data formats (dates, numbers) is a browser-based data-cleaning utility built for the kind of list- and column-hygiene work that comes up constantly in spreadsheet, CSV and log workflows. It runs entirely on-device, which means you can clean sensitive rosters, customer lists and internal spreadsheets without pushing them to an online cleaner that logs every paste. The tool is optimised for long inputs (hundreds of thousands of lines) and produces deterministic, auditable output — the same input always produces the same cleaned output, which is essential in data pipelines.
Problems This Tool Solves
Spreadsheet pastes routinely arrive with duplicate rows, trailing whitespace and inconsistent capitalisation. - Normalize data formats (dates, numbers) normalises, deduplicates and sorts the input in one pass, with a preview before you commit.
Pasting data into an online tool means trusting a third-party server with your content. - Normalize data formats (dates, numbers) runs 100% inside your browser — nothing you paste is transmitted, logged, or stored.
Manual cleaning loses track of what was changed and why. - Normalize data formats (dates, numbers) produces a deterministic diff so you know exactly which rows were altered and how.
Cleaning tens of thousands of rows in a spreadsheet is painfully slow. - Normalize data formats (dates, numbers) handles hundreds of thousands of rows in the browser with no lag.
How to Use
Paste or upload your list - Supports raw paste, CSV upload and drag-and-drop of .txt and .tsv files.
Pick the cleaning rules - Toggle dedup, case, whitespace, sort, special-character stripping, and normalisation as needed.
Preview the diff - Every change is shown before it commits so you can review and adjust.
Copy or export - Download cleaned output as CSV/TXT or copy straight to clipboard.
Why This Tool Is Better
Deterministic output - The same input always produces the same output — essential for reproducible data pipelines.
Scales to big lists - Handles hundreds of thousands of rows in-browser, with no slowdown and no server upload.
Preview before commit - Every change is previewed so destructive operations can be reviewed and adjusted.