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Choose which duplicate records to keep

Review duplicate CSV records by a chosen key or whole row. Choose first, last or most complete, then export a new result.

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or drop files · 10 MB per file

Import settings apply only to files opened next; existing datasets stay unchanged. No-header files receive column_1, column_2, and so on while keeping the first record. Exports include those column names.

Deduplicate

Choose how records match and which duplicate record to keep.

Interactive example · fictional data

Choose the duplicate to keep

Example input · duplicates.csv
skunamestock
00123Blue·mug20
00123Blue·mug18
00456Red·cap8

Dots show spaces; ∅ means empty. Swipe tables to see all columns.

Ready to play

This small example does not change your open files.

Or open your own files. Data is processed in this browser.

See the result, then try it

Deduplicate

Interactive example · fictional data

Choose the duplicate to keep

Example input · duplicates.csv
skunamestock
00123Blue·mug20
00123Blue·mug18
00456Red·cap8

Dots show spaces; ∅ means empty. Swipe tables to see all columns.

Ready to play

This small example does not change your open files.

Rules, limits and questions

Two records sharing a SKU can contain different prices or descriptions. Identify the matching rule and survivor policy before removing records, so deduplication has a business meaning you can explain.

Your result: A reviewed result with duplicate groups handled by your selected rule.

Steps

  1. Open the duplicate example, then choose sku as the matching column. Whole-row matching is a different option when only identical records should count.
  2. Start with exact matching. Enable trimming or case folding only when those differences should not distinguish two identifiers.
  3. Compare first, last and most-complete survivor policies. Preview the removed record count, retain the desired result, and export.

Download example CSVs

These files contain fictional data. You can also close help and choose “Try sample files” in the toolbar.

Expected example result

  • Exact matching on sku identifies two repeated keys: 00123 and 00456.
  • Keeping the first occurrence produces 3 records and keeps the earlier 00123 price.
  • Keeping the last input occurrence also produces 3 records: 00123 keeps price 13.25 and stock 18 rather than 12.50 and 20. This follows file order, not a date comparison.
  • The most-complete policy prefers the populated Green Hoodie record over the record with its name blank.

Scope and limits

  • The most-complete record is not necessarily the newest or most accurate record; review the policy against your task.
  • Normalization can create new collisions. Blank keys must not be treated as evidence that unrelated records represent one item.
  • This version uses one key column or whole-row equality, not fuzzy product matching.

Questions

Should I remove repeated SKUs before every join?

Only if repetition is actually an error. Separate packs or legitimate offer lines can share a code. Choose a valid identity rule before changing the data.

Does keeping the last row mean keeping the latest date?

No. Last means the later input row. If dates determine recency, establish an explicit ordering and review it first.

Related tool instructions

Close help and use the workspace tools to continue with your current files. The links below open in a new tab.

Full cell value

Review input record

No files open. Refreshing clears the session.

Session limits: 8 source files, 30 MB combined input and 16 retained datasets. Output is limited to 250,000 rows and 2,000,000 cells.

Data stays in this page's memory. Original files are never overwritten. A download action starts the browser download; confirm the saved file yourself.