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Unpivot CSV:Wide to Long
Turn columns into rows. Pick the identifier columns to keep, and every other column becomes a name/value pair — the tidy shape pivot tables, charts, and BI tools expect.
Turn columns into rows. Pick the identifier columns to keep, and every other column becomes a name/value pair — the tidy shape pivot tables, charts, and BI tools expect.
Unpivoting (also called melting) collapses many value columns into two: one holding the original column name, one holding its cell value. Here is how to do it in three steps.
Drop in a wide CSV — one where each measurement or period lives in its own column (e.g. Jan, Feb, Mar). Everything is parsed locally in your browser.
Tick the columns that identify each row and should be repeated on every output line, such as Region or Product. Leave the rest to be melted.
Each non-identifier column becomes a row with a name field and a value field. Rename those two headers if you like, then download the tidy result.
Wide spreadsheets are easy to read but hard to analyze. Long, tidy data is what charting libraries, pivot tables, and databases actually want.
Most plotting and BI tools assume one observation per row. Melting your month or metric columns into a single value column unlocks grouping, filtering, and aggregation.
Once data is long, any pivot table can reshape it back the way you want. Long format is the neutral interchange shape between tools.
Stacking period or category columns into rows makes it trivial to union files from different months or to join against a lookup table on the name column.
The reshape runs entirely in your browser. Your CSV is never uploaded, so even sensitive financial or customer data stays on your machine.