Find rows that repeat, then keep one and remove the extras.

Use a different file

Preview: customers_export.csv

18,442 rows, 6 columns

	name	email	city	phone	signup_date
0	Jane Doe	jane@acme.io	Austin	512-555-0190	2024-01-04
1	jane doe	JANE@ACME.IO	austin	(512) 555-0190	01/04/2024
2	Bob Smith	bob@globex.com	Denver	720-555-7781	2024-02-11
3	R. Smith	bob@globex.com	Denver	720-555-7781	2024-02-11

Advanced options

Leave these empty to auto-detect which columns to compare. Otherwise, list the columns that must match exactly and the ones that only need to match approximately — together these are the columns used to find duplicates.

Columns that must match exactly

email ✕

Columns to match approximately

name ✕

Approximate-match algorithm

jaro_winkler

check Merge mode — fill missing fields in the surviving row

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Find Duplicates

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Results

Preview of an auto-resolved run: each group keeps its auto-picked survivor. Review the groups below to override any pending picks before the final download.

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Match Groups

Differing columns are highlighted. The survivor row is kept; uncheck a row to split it out of the group.

Decisions: 1 merged, 1 pending · Pending groups keep their auto-picked survivor unless you review them.

Apply Review Decisions & Download Final CSV

Processing Log

[00:00.01] Loaded 18,442 rows from customers_export.csv [00:00.04] Strategy: exact(email) + fuzzy(name, jaro_winkler ≥ 85) [00:00.91] Compared 18,442 rows → 147 match groups [00:01.02] Survivor rule: most-complete · merge=on [00:01.05] 312 rows flagged for removal