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Fuzzy Distance
Scores from 0 to 1 how alike a column is to another column — or to one fixed value — with Levenshtein, Jaro-Winkler or Soundex.
When to use it
Use it to check a match found by Join or Lookup, to rank candidate matches, or to flag names that differ only by typos.
In a pipeline
File InputFuzzy DistanceFilter Rows
Settings
| Setting | Type | Required | Default or hint |
|---|---|---|---|
| Column | select | Yes | Select column… |
| Compare with | select | — | column |
| Other column | select | — | Select column… |
| Value | text | — | e.g. Acme Ltd |
| Algorithm | select | — | levenshtein |
| Generate New Column | checkbox | — | true |
| New Column Name | text | — | Default: <column> Similarity |
| Insert After | select | — | After the column |
Example
How alike two name columns are
- Column=Name
- Other column=CRM Name
| Name | CRM Name |
|---|---|
| Jon Smith | John Smith |
| Acme Ltd | ACME LTD. |
| Ann Lee | Bob Cole |
| Name | Name Similarity | CRM Name |
|---|---|---|
| Jon Smith | 0.9 | John Smith |
| Acme Ltd | 1 | ACME LTD. |
| Ann Lee | 0.25 | Bob Cole |
Worth knowing
- Case, spaces and punctuation are ignored before comparing, so Acme Ltd and ACME LTD. score 1.
- Soundex scores are all or nothing: 1 when both sound alike, else 0.
- The same algorithms as Fuzzy Match and Duplicate Grouping, so scores agree across the three nodes.