Find Duplicates

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

Import CSV or Excel file

upload_file Drag and drop file here Up to 1.5 GB · CSV, TSV, XLSX, XLS · encoding & delimiter auto-detected

customers_export.csv 2.1 MB

Delimiter

Comma (,)

Auto-detected on upload. Change if the preview looks wrong.

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

Options

Advanced Options

Match on columns

Leave empty to auto-detect

Strong keys

email ✕

Fuzzy columns

name ✕

Fuzzy algorithm

jaro_winkler

Similarity threshold

Survivor rule

most-complete

check Merge mode — fill missing fields in the surviving row

Results

Original rows

18,442

Duplicate rows

312

−312 removed

Match groups

147

Rows kept

18,130

Match Groups

Group 1 · 2 rows 98% match

keep	name	email	city	phone	signup_date
keep	Jane Doe	jane@acme.io	Austin	512-555-0190	2024-01-04
remove	jane doe	JANE@ACME.IO	austin	(512) 555-0190	01/04/2024

Differing columns highlighted. The survivor row is kept; uncheck rows to split the group.

Group 2 · 2 rows 87% match

keep	name	email	city	phone	signup_date
keep	Bob Smith	bob@globex.com	Denver	720-555-7781	2024-02-11
remove	R. Smith	bob@globex.com	Denver	720-555-7781	2024-02-11

Decisions: 1 merged, 1 pending

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