CSV Column Mapping Suggest

What this tool does

CSV Column Mapping Suggest compares two CSV files and recommends which source columns should map to which target columns before you run a diff, join, migration, or import. It scores each possible source→target pair with two signals: a normalized fuzzy header match and an optional value-overlap check from sampled data rows.

Use it when the same field appears under different names — for example Email Address in one export and email in another, or Zip Code and postal_code. The output is a one-to-one mapping with confidence scores, brief reasons, and lists of columns that were not mapped above your threshold.

Worked example

Source CSV

First Name,Email Address,Zip Code
Ada,[email protected],02139
Bo,[email protected],94107

Target CSV

email,postal_code,first_name
[email protected],02139,Ada
[email protected],94107,Bo

With the default settings, the table output suggests:

Source column | Target column | Score | Reason
--- | --- | ---: | ---
Email Address | email | 0.720 | header 0.53, value 1.00
First Name | first_name | 1.000 | header 1.00, value 1.00
Zip Code | postal_code | 0.640 | header 0.40, value 1.00

Unmapped source columns: (none)
Unmapped target columns: (none)

Raise the threshold when you only want strong matches; lower it when you would rather see weaker candidates for manual review.

FAQ

Limits & edge cases

• Both inputs must be CSV text. The delimiter setting is shared by both inputs.
• The tool makes one-to-one suggestions: once a source and target are paired, neither is reused.
• sample_rows is capped at 500. Use 0 for header-only matching when the files contain no representative sample values.
• Header matching is heuristic, not ML. Domain-specific synonyms such as zip ↔ postal may need value overlap or manual review.
• Empty inputs and malformed CSV rows return clear parse errors instead of partial mappings.