How do I combine two CSV files?

In Dotwave, upload both CSVs, choose Combine, then pick Join to match rows on a shared field or Union to stack files with the same columns. Dotwave suggests the key, shows how many rows match, and creates one clean dataset you can chart.

How does Dotwave prevent wrong results when combining data?

Before you commit, Dotwave shows the match rate between sources, blocks joins with no shared field, and warns when a join would double-count rows (fan-out), offering a one-click fix to keep one row per key.

Combine data

Merge and stack client data — without breaking it.

Join two datasets on a shared key (with a match-rate report before you commit), or stack multiple files into one unified dataset. The result updates live when either source changes.

Real analyst problem

Your client has sales data in one CSV and customer data in another. You need them joined on customer_id — but the IDs don’t match perfectly. Dotwave shows you the match rate and fan-out report before joining so you know exactly what you’re getting.

Join — merge on a shared key

Select two datasets and a shared column. Dotwave shows you: match rate (how many rows join cleanly), fan-out (whether one-to-many relationships will inflate your row count), and a preview of the first 8 matched rows. Commit only when you’re satisfied.

Union — stack multiple files

Stack multiple datasets vertically into one. Dotwave aligns columns by name — mismatched columns become nulls, not errors.

Live recalculation

Combined datasets stay linked to their sources. When you re-clean or re-import a source dataset, the combined view updates automatically.

Merge and stack client data — without breaking it.

Join — merge on a shared key

Union — stack multiple files

Live recalculation

Ready to clean your data?