FileGizmo — Data & CSV tools
Remove duplicate CSV rows
Remove duplicate CSV rows using every column or selected key columns, entirely in your browser. Preview and download the cleaned file privately.
Cleaning your CSV…
Processing locally in your browser.
Cleanup complete
Your cleaned CSV is ready
Simple by design
Remove CSV duplicates in three steps
Removing CSV duplicates creates one clean record for repeated rows while preserving the original order. It is useful for customer lists, mailing exports, event registrations, inventory records, survey responses, and combined datasets that may contain the same entry more than once.
FileGizmo performs duplicate detection entirely inside your browser. Select a CSV, choose whether to compare every column or specific key columns, and download the cleaned result. The source file and its contents are never uploaded.
Complete-row mode treats two rows as duplicates only when every column value matches. Key-column mode is useful when one field, such as email, account ID, SKU, or transaction reference, identifies a record even if other values differ. You may enter several comma-separated column names to create a combined key. FileGizmo keeps the first matching row and removes later occurrences. Quoted fields, multiline values, common delimiters, and Unicode text are handled by the same tested CSV parser used across the Data and CSV cluster.
- 1
Select or drop the CSV file that contains repeated records.
- 2
Match complete rows or enter the column names that identify a duplicate.
- 3
Remove duplicates, review the result summary, and download the cleaned CSV.
Good to know
Frequently asked questions
Is this CSV duplicate remover free?
Yes. It works without an account, watermark, or artificial usage limit.
Is my CSV uploaded?
No. Duplicate detection and file generation happen locally inside your browser.
Which duplicate row is kept?
FileGizmo keeps the first occurrence and removes later rows with the same comparison values.
Can I find duplicates using only one column?
Yes. Enter one or more key column names, such as email or customer_id, instead of comparing every column.
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