Find Fuzzy Duplicates in CSV Files Online
Find near-duplicate rows in CSV files using Levenshtein edit distance or Jaro-Winkler similarity — all in your browser. Set your own threshold and download the matched pairs as CSV — no upload required.
How to find Fuzzy Duplicates in CSV files
- Drop your file onto the upload area. Files with more than 5,000 rows show a notice, because only the first 5,000 rows are compared.
- Choose the column to check for near-duplicates, usually a name, company or address column. The first column is selected by default.
- Pick a similarity method. Levenshtein counts character edits, with a maximum distance of 1 to 4 (default 2). Jaro-Winkler gives a 0 to 1 score, with a minimum similarity slider from 0.70 to 0.99 (default 0.85).
- Click Find Fuzzy Duplicates. The tool lists matching pairs of rows with their values and score.
- Review the pairs and click Download Pairs CSV. The file is a list of pairs to check, not a cleaned copy of your data.
Your file is processed locally in your browser and is never uploaded. The free limit is 50 MB per file; larger files work if your device has the memory for them.
Worked example
An accounts payable team merged supplier lists from two systems and suspects some vendors were entered twice with small spelling differences.
Input (CSV)
vendor_id,vendor_name,city
V-001,Northgate Plumbing,Leeds
V-002,Northgate Plumbing Ltd,Leeds
V-003,Brightwater Electrical,York
V-004,Brightwater Electric,York
V-005,Harlow & Sons,Hull
V-006,Northgate Plumbng,LeedsSettings
- Column: vendor_name
- Method: Jaro-Winkler
- Min similarity: 0.85
Result
| row_a | row_b | val_a | val_b | similarity |
|---|---|---|---|---|
| 1 | 6 | Northgate Plumbing | Northgate Plumbng | 0.9889 |
| 3 | 4 | Brightwater Electrical | Brightwater Electric | 0.9818 |
| 1 | 2 | Northgate Plumbing | Northgate Plumbing Ltd | 0.9636 |
| 2 | 6 | Northgate Plumbing Ltd | Northgate Plumbng | 0.9545 |
row_a and row_b are 1-based row positions in the file, and the most similar pairs come first. The three Northgate rows produce three pairs, because every pair is scored separately. Harlow & Sons matches nothing. With Levenshtein at distance 2 instead, only the typo (distance 1) and Electrical/Electric (distance 2) would be listed. Adding " Ltd" is 4 edits.
Working with CSV files
CSV values reach the comparison exactly as they sit in the file, including leading and trailing spaces and letter case. Matching is case-sensitive, so "ACME LTD" and "Acme Ltd" are 5 edits apart and Jaro-Winkler scores them only 0.58. Run Trim Whitespace and Convert Case on the name column first, then run fuzzy matching on the cleaned file. This one step often finds more real duplicates than any threshold change.
Empty fields are skipped, so blank names never pair with each other. Numeric columns can be checked too. The values are compared as text, which makes Levenshtein distance 1 useful for spotting phone numbers or account codes with a single mistyped digit. Remember that a CSV column of codes like 00417 may have been read as the number 417, which changes what is compared. The pairs download is always CSV, whatever the input. Its val_a and val_b fields are quoted when a name contains a comma, such as "Smith, Jones & Co".
Frequently Asked Questions
Why does my CSV give no pairs for names that only differ in capitals?
Comparison is case-sensitive. Run Convert Case on the column first so capitals match, then check again.
Can I fuzzy-match a numeric ID column in a CSV?
Yes. Values are compared as text. Levenshtein at distance 1 finds IDs that differ by one digit. Leading zeros may have been dropped if the column was read as a number.
Does the tool remove the near-duplicates for me?
No. It lists pairs of similar rows with their row numbers and score. You decide which rows to keep, then remove the others yourself or with Filter.
Are exact duplicates included in the pairs?
No. Levenshtein pairs must be at least 1 edit apart, and Jaro-Winkler pairs must not be identical. Use Find Duplicates or Remove Duplicates for exact matches.
Which method should I choose?
Levenshtein suits short values and typos, such as codes or single words. Jaro-Winkler suits names and company names, because it rewards a shared start. For example "Robert" and "Robrt" score about 0.96.
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