Fuzzy CSV Join

About this tool

Fuzzy CSV Join merges two CSV tables when the key values are close but not exactly equal. Use it for company names, supplier lists, customer exports, city names, or any reconciliation job where Acme Ltd on one side should match Acme Ltd. or Globex Corporation should match Globex Corp.

Paste a left CSV and a right CSV. The first row of each file is treated as its header. Choose a key column for the left table and a key column for the right table by header name or 1-based position. The tool compares every left key with every right key, keeps candidates at or above the similarity threshold, sorts them by score, and emits a joined CSV plus optional match scores. It can also return unmatched rows or a JSON coverage report.

Worked example — join approximate company names:

Left CSV:

id,company
1,Acme Ltd
2,Globex Corporation
3,Initech

Right CSV:

name,city
Acme Ltd.,Berlin
Globex Corp,Cairo
Umbrella,Delhi

Settings: left key company, right key name, algorithm Jaro-Winkler, threshold 85, output Joined CSV.

Output:

id,company,name,city,match_score
1,Acme Ltd,Acme Ltd.,Berlin,97.8
2,Globex Corporation,Globex Corp,Cairo,94.4

Initech is not included because this is an inner join by default. Switch to Left join to keep it with blank right-side cells, or choose JSON match report to see both unmatched lists and coverage stats.

Worked example — keep all left rows:

Settings: same inputs as above, join type Left, output Joined CSV.

Output:

id,company,name,city,match_score
1,Acme Ltd,Acme Ltd.,Berlin,97.8
2,Globex Corporation,Globex Corp,Cairo,94.4
3,Initech,,,

Worked example — inspect unmatched rows:

Set output to JSON match report to get matched pairs, unmatched left rows, unmatched right rows, and counts:

{
  "algorithm": "jaro_winkler",
  "threshold": 85.0,
  "max_matches": 1,
  "stats": {
    "left_rows": 3,
    "right_rows": 3,
    "matched_pairs": 2,
    "matched_left_rows": 2,
    "unmatched_left_rows": 1,
    "matched_right_rows": 2,
    "unmatched_right_rows": 1
  }
}

Choosing an algorithm

• Jaro-Winkler is the default and is usually best for names, brands, and abbreviations because it rewards matching prefixes.
• Levenshtein ratio is stricter edit-distance matching. It works well for typos of similar length.
• Token sort compares sorted words, so Acme Limited and Limited Acme can match even when word order changes.
• Soundex is phonetic and useful for English-ish names that sound alike, but it is coarse; review the scores before trusting it.

Limits and edge cases

• Each side is capped at 2,000 data rows (excluding the header). Fuzzy joining compares every left key with every right key, so the worst case is 4 million comparisons in the browser.
• The threshold is 0–100 and inclusive. A row scoring exactly 85 matches when the threshold is 85.
• max_matches can keep more than one right row per left row. Candidates are sorted by score descending; ties keep the earlier right row so output stays deterministic.
• Output columns are all left columns followed by all right columns. If a right header collides with a left header, it gets a _right suffix. The right key column is kept because, in a fuzzy join, its value is the evidence for the match.
• Delimiter accepts a single character or comma, tab, semicolon, or pipe. Quoted CSV fields are handled by the CSV parser.
• Blank right_key reuses the left key reference. That only works when both tables share the same header name or compatible column index.
• ignore punctuation removes symbols before scoring, but it does not modify the output values.
• unmatched_left, unmatched_right, and json report leftovers independently of join type; join type only controls which rows appear in the joined CSV view.

FAQ