CSV fuzzy match
Find near-matching text records in two CSV files and review scored suggestions.
Uses Unicode code-point Levenshtein distance. Similarity is 100 times one minus distance divided by the longer normalized length. Suggestions never automatically merge records; a right-side record may match several source records. Up to 1,000,000 characters, 10,000 data records, 100 columns and 100,000 cells across the sources. Files for multi-source tools are limited to 4,000,000 bytes total. Reports retain at most 500 diagnostic findings and state any omitted count. CSV and JSON outputs are limited to 4,000,000 characters. Fuzzy matching additionally limits selected cells to 256 code points, comparisons to 25,000 pairs and estimated edit-distance work to 5,000,000 cells. Output is budgeted incrementally before retaining each row; expanded decimal output counts toward the same 4,000,000-character limit even when input uses short exponent notation. Paste CSV data with unique column headers. Quoted multiline cells are supported. Up to 1,000,000 characters. Review the settings before running.
Your result
How to use CSV fuzzy match
- Paste both CSV tables and select their text match columns.
- Choose normalization, minimum similarity and suggestions per source row.
- Review the suggested pairs before making any changes to the source data.
Useful next steps
Prepare the source
Paste both CSV tables and select their text match columns.
Choose the matching rules
Choose normalization, minimum similarity and suggestions per source row.
Review and keep the result
Review the suggested pairs before making any changes to the source data. Uses Unicode code-point Levenshtein distance. Similarity is 100 times one minus distance divided by the longer normalized length. Suggestions never automatically merge records; a right-side record may match several source records.