By Team Hypomnema · Data
Summarize records by multiple keys with explicit aggregate measures.
Use the guide alongside the working tool.
Open Group & Aggregate Data →Explicit working sample
id,region,product,amount,units,date
001,North,Notebook,24.5,2,2026-10-01
002,South,Pen,9,3,2026-10-02
003,North,Pen,15,5,2026-10-03
004,West,Notebook,,1,2026-10-04
005,South,Notebook,49,4,2026-10-05
006,North,Pen,1200,2,2026-10-06Summarize records by multiple keys with explicit aggregate measures. Count rows, count populated values, count distinct values, sum, mean, min or max. Empty grouping keys remain a separate group. Numeric operations reject nonnumeric nonempty cells. Measures and group keys are user-selected; no hidden totals or inferred financial meaning.
Prepare the input
Summarize records by multiple keys with explicit aggregate measures.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Count rows, count populated values, count distinct values, sum, mean, min or max. Empty grouping keys remain a separate group. Numeric operations reject nonnumeric nonempty cells. Measures and group keys are user-selected; no hidden totals or inferred financial meaning.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Group & Aggregate Data →Explore a related workflow
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- Column Split & Extract: methods and data limits
Extract structured pieces from a column while keeping the original values.
- Data Value Mapper: methods and data limits
Replace codes and labels using a checked reference dictionary.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.