By Team Hypomnema · Data
Find spelling and formatting variants, then apply only approved replacements.
Use the guide alongside the working tool.
Open Value Cluster Cleaner →Explicit working sample
city,amount
New York,10
new-york,20
York New,15
São Paulo,8
Sao Paulo,12
London,9Find spelling and formatting variants, then apply only approved replacements. Fingerprinting lowercases, removes accents/punctuation and sorts tokens. Suggested clusters can contain values that should remain different. No suggestions are automatically merged. Approved mappings are exact original-value replacements; preserve the original file and review the change log.
Prepare the input
Find spelling and formatting variants, then apply only approved replacements.
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
Fingerprinting lowercases, removes accents/punctuation and sorts tokens. Suggested clusters can contain values that should remain different. No suggestions are automatically merged. Approved mappings are exact original-value replacements; preserve the original file and review the change log.
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 Value Cluster Cleaner →Explore a related workflow
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
- 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.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.