plotiq

CSV to Table Extractor

Some CSV exports store measurements as long horizontal arrays (`dflow,4.45,117.74,...`). This tool transposes them into a 2D table with proper column headers so every chart library can consume them directly.

Ideal for permeability / filtration test exports, sensor streams, and API analytics dumps.

1. Upload a vertical CSV

The extractor handles CSVs where each row is `label,value_1,value_2,...` — the shape labs and dashboards love.

2. Review the detected table

Rows with ≥ 3 numeric values become columns automatically. The right pane shows a live preview of the transposed table.

3. Export chart-ready CSV

Download a standard `header_row\ndata_row_1\ndata_row_2...` CSV that plugs into any chart tool.

What does "transposing" mean in this context?

Each array row in your source CSV (e.g. `dflow,4.45,117.74,148.83,...`) becomes a column in the output table, with the label as the header and the values running top-to-bottom down the column.

Can I add a row-index / X-axis column?

Yes. Toggle the "Index column" option to prepend a 1-based row index as the first column — handy when feeding a line or scatter tool that needs an explicit X axis.

What about the metadata rows in my CSV?

Metadata (`key,single_value`) rows are preserved as `# key: value` comment lines at the top of the output by default. Flip the Metadata toggle to Drop if your target tool can't skip comment lines.

Does the extractor lose precision when it rounds?

Only when you set a precision. Choose "Keep original" to emit the values exactly as they were in the source — no rounding, no `toFixed`, no string reformatting.