Data cleanup
How to Clean a Messy CSV Before You Share It
A useful dashboard begins with a trustworthy file. Before sharing a CSV, check its shape, missing values, duplicates, numeric fields, and values that could be interpreted as spreadsheet formulas.
Upload
Bring the CSV you already use.
Understand
Start with the brief and evidence.
Act
Ask, edit, filter, and export.
Profile the file first
Count rows and columns, inspect types, and look for empty or inconsistent values. A profile tells you whether the file is ready for analysis or needs a cleanup pass.
Edit intentionally
Read-only is the safer default. When you enter edit mode, change only the cells you understand, then export a new file rather than silently replacing the source.
Protect spreadsheet users
Values beginning with =, +, -, or @ can be interpreted as formulas by spreadsheet software. Treat these as text when exporting data from an untrusted source.
Common questions
Does cleaning change my original upload?
The intended workflow keeps the original view available and exports the edited result as a separate file.
Can the AI repair every data issue?
No. It can explain visible problems and suggest checks, but critical data changes need human review.
Keep exploring
See what your own CSV is saying.
Try the browser workspace with a real file and keep the public guide open when you need help.
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