CSV Data Deduplicator
Remove duplicate rows from a CSV file in seconds. Compare all columns or only the ones you name (for example email), optionally ignoring upper/lower case and extra spaces, and keep the first or last occurrence. You see how many rows were removed and which ones, and can download the clean file – or just the duplicates for review.
- Runs in your browser
- No sign-up
- Free to use
Processed entirely in your browser – your data is not uploaded. Files up to 50 MB.
How to use CSV Data Deduplicator
- Paste CSV or open a file.
- Name the columns to compare (optional).
- Choose the options.
- Download the deduplicated CSV.
CSV Data Deduplicator features
Column-based
Compare by any columns.
Fuzzy basics
Ignore case and extra spaces.
Keep first or last
Choose which row survives.
Duplicates export
Review before deleting.
Separator kept
Output uses the input separator.
Private
Runs in your browser; nothing is uploaded.
When to use CSV Data Deduplicator
- Cleaning mailing lists.
- Merging CRM exports.
- Preparing product imports.
- Removing repeated log lines.
CSV Data Deduplicator FAQ
Is the header row compared?
No, the first row is treated as the header.
Does the order change?
No, rows keep their original order.
What about near-duplicates?
Trimming and case-folding catch common variations; spelling differences are not merged.
Count once
Duplicate rows inflate counts, send the same email twice and break unique keys on import.
Private by design: your data is read and processed entirely in your browser with JavaScript. Nothing is uploaded to our server, so you can work with customer lists, exports and internal reports safely. Files up to 50 MB are supported; very large files depend on your device’s memory.
Input formats: paste or open CSV (comma, semicolon, tab or pipe – detected automatically), TSV or JSON (an array of objects, an object with a data, items or results array, or JSON Lines). The first CSV row is used as the header; JSON keys become columns, and nested values are kept as JSON text.
Missing values are recognised consistently across the data tools: empty cells and common placeholders such as NULL, N/A, NA, NaN, none, “-” and undefined. Types are inferred from the values – integer, decimal, boolean, date, email, URL, JSON or text – and columns with mixed types are flagged.
Related tools on this site cover the rest of a data workflow: the CSV cleaner, viewer, splitter and merger, converters between CSV, JSON, Excel and SQL, the JSON formatter and validator, and the other profiling, validation and statistics tools in this group.
Who it is for: analysts preparing data for a report, developers checking an API export or a database dump, marketers cleaning contact lists before an import, and students learning data quality. No account, no installation and no spreadsheet formulas are needed.