By Team Hypomnema · Data
Inspect missingness, uniqueness, inferred types and numeric summaries.
Use the guide alongside the working tool.
Open Data Quality Profiler →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,1200,2,2026-10-06Inspect missingness, uniqueness, inferred types and numeric summaries. Types are inferred from observed nonempty cells, not certified schemas. Leading-zero identifiers remain text. Numeric summaries ignore nonnumeric cells and use sample standard deviation and interpolated quartiles. Missing tokens are explicit.
Prepare the input
Inspect missingness, uniqueness, inferred types and numeric summaries.
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
Types are inferred from observed nonempty cells, not certified schemas. Leading-zero identifiers remain text. Numeric summaries ignore nonnumeric cells and use sample standard deviation and interpolated quartiles. Missing tokens are explicit.
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 Data Quality Profiler →Explore a related workflow
- Missing Value Cleaner: methods and data limits
Fill or remove missing values with a complete, reviewable change log.
- Outlier Detector: methods and data limits
Flag unusual numeric values using IQR fences, z-scores or median deviation.
- Data Validation Rules: methods and data limits
Check records against clear rules and separate passing and failing rows.
- Fuzzy Record Matcher: methods and data limits
Compare approximate record names and review ranked match candidates.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.