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CSV Mapping Recipe Runner

Reuse a JSON recipe to rename, reorder, select and transform CSV columns. Prepare a starting mapping, apply safe text steps, preserve text IDs and export before/after audit rows.

Local processingEditable exampleCopy + file exports

Settings

Try the example or enter your own settings.

Recipes use 1-based source column positions: verify the input headers before reuse. Values stay text; no numeric/date inference or arbitrary code/regex execution. CSV cannot force spreadsheets to preserve leading-zero IDs; import those columns as text. Formula protection changes exported risky text only.

Using CSV Mapping Recipe Runner

Reuse a JSON recipe to rename, reorder, select and transform CSV columns. Prepare a starting mapping, apply safe text steps, preserve text IDs and export before/after audit rows.

  1. Paste header-row CSV and choose its explicit delimiter. Prepare recipe from CSV columns to create an identity mapping, or try the complete transformation example.
  2. Edit the JSON recipe: reorder/remove column entries, rename output names and add supported steps. Each entry uses a source position or constant string. Switch to Transform with recipe.
  3. Review the first200 changed/constant cells, export the complete CSV and save the mapping recipe for later files. Confirm input headers/positions and spreadsheet protection before reuse.

Example

Try the included editable example.
Edit the JSON recipe: reorder/remove column entries, rename output names and add supported steps. Each entry uses a source position or constant string. Switch to Transform with recipe.

Questions & answers

How is this different from the CSV editor?

This tool runs a repeatable declarative mapping on later files and exports the recipe. The table editor handles manual cell edits, sorting and undo. Mapping source positions are explicit and are not guessed by header names.

How do I build a recipe?

Choose Prepare recipe from CSV columns and run. The recipe editor receives version1 with an ordered columns array. Each entry has a1-based source position, name and steps. Reorder/remove entries or use value for a constant output column. Duplicate input headers need unique output names before transformation.

Which transformations are supported?

Sequential trim, upper, lower, NFC Unicode normalization, default for exact empty strings, prefix, suffix and case-sensitive literal replace. Steps use op and, where needed, value; replace also needs nonempty find. No eval, expressions, regexes, date guessing or numeric conversion.

Are IDs and large numbers preserved?

Yes, cells remain strings throughout transformation. Case/trim/other explicit steps may intentionally change them. Spreadsheet applications can still interpret numeric-looking cells when opening CSV; import identifier columns as text.

What do formula protection and audit exports do?

Protection prefixes risky exported headers/cells with an apostrophe, including negative-looking text and formula/control prefixes. Working values and the before/after audit retain raw transformed strings. The on-screen audit and its CSV export contain the first200 changed/constant cells. Main CSV contains all output rows; save-recipe exports the JSON mapping.

What limits apply?

100,000 input characters,2,000 data rows,100 columns and50,000 input/output cells. Recipes up to50,000 characters,100 output columns and20 steps per column. Literal arguments up to1,000 characters; transformed cells up to20,000. Malformed/uneven CSV, unknown recipe keys, duplicate JSON keys, invalid source positions and duplicate/blank output names are rejected. No file uploads to a server.

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