Remove duplicate rows from CSV data by column, and normalize whitespace and case. A proper RFC-4180 parser handles quoted fields. Clean marketing and data lists. 100% client-side.
Duplicate rows are the bane of any spreadsheet or data export — repeated contacts inflate email costs, double-counted records skew reports, and inconsistent formatting hides duplicates in plain sight. This tool cleans CSV data in your browser: choose the columns that identify a unique record, and it keeps the first occurrence of each while removing the rest. It shows you exactly how many rows were removed, so you can trust the result.
Just as important is normalization. Before comparing, the tool can trim stray whitespace, collapse repeated spaces, and standardise letter case, so near-duplicates like " Bob@Example.com " and "bob@example.com" are correctly recognised as the same. Together, these turn a messy export into a clean, deduplicated dataset in seconds.
Built for Real Marketing and Data Tasks
Cleaning CSVs is a constant chore for marketers, analysts, and developers: deduping email lists before an import, merging exports from multiple sources, tidying survey responses, or preparing data for a database load. This tool handles the real-world messiness that trips up simple find-and-replace approaches, including quoted fields that contain commas or newlines, thanks to a standards-compliant CSV parser.
You can dedupe on a single column like email, on several columns at once for a composite key, or on the entire row. Upload a file or paste your data, watch the cleaned result update live, and copy or download the deduplicated CSV — all without a single byte leaving your browser.
How to use this CSV Dedupe & Normalize?
1
Paste CSV
Add CSV data with a header row, or upload a .csv file.
2
Pick Key Columns
Choose the columns that identify a unique record — or none for whole-row.
3
Normalize
Trim spaces, collapse whitespace, and standardise case to catch near-duplicates.
4
Export
Copy or download the cleaned, deduplicated CSV.
Frequently Asked Questions
You pick one or more columns to treat as the "key". Rows that share the same value(s) in those columns are considered duplicates, and only the first occurrence is kept. For example, deduping a contact list on the email column removes repeat entries for the same person even if their name differs.
Normalization cleans up messy data before comparing: it can trim leading and trailing spaces, collapse multiple spaces into one, and convert text to lowercase or uppercase. This catches "near-duplicates" like " Ada@Example.com " and "ada@example.com" that would otherwise be treated as different.
Yes. If you select no key columns, the tool removes rows that are completely identical across every column. Selecting specific columns gives you finer control over what counts as a duplicate.
Yes. The tool uses a proper RFC-4180-aware CSV parser, so quoted fields, escaped quotes, and values that themselves contain commas or line breaks are parsed and re-serialised correctly.
No. All parsing, normalization, and deduplication happen in your browser. Your data never leaves your device, so it is safe to clean private customer or marketing lists.