Table cleaner workspace
Paste a spreadsheet table or open a CSV, choose a column and rule, review every changed cell, then download the cleaned data and audit log.
Calculation and conversion of the text and files you enter happen in your browser, and the tool does not send your original input or results to a server. For traffic related to visit statistics and ads, see the privacy policy.
How to use it
- Paste a table with headers from Excel or Google Sheets, or choose a UTF-8 CSV file.
- Choose the target column and cleaning rule. Check choices such as month/day order or which duplicate row to keep.
- Review highlighted before-and-after cells and warnings, then copy the result or download the cleaned CSV and change log.
Examples
Find equivalent phone numbers
Normalize 010 1234 5678 and +82 10-1234-5678 to one Korean format and flag the second as a duplicate candidate.
- Input
name,phone\nMin,010 1234 5678\nJin,+82 10-1234-5678
- Result
Both become 010-1234-5678; the second row is flagged
Keep the newest customer row
Use email as the key, keep the row with the newest date, and record every excluded row in the audit CSV.
Details
Table Cleaner combines phone, date, address, pseudonymization and conditional duplicate rules in one workspace. It uses the same quote-aware CSV parser as the CSV fixer, so delimiters and line breaks inside quoted cells remain cells and identifier-like values keep their leading zeros.
A date such as 03/04/2026 is inherently ambiguous when both numbers are at most 12. The tool follows your selected month/day order and leaves a warning for review. Address cleanup only normalizes spaces and punctuation or proposes a comma-based split; it does not verify that an address exists or is deliverable.
Pseudonymization creates consistent replacements or masks in this session, but it is not a guarantee of anonymization. People may still be identifiable from other columns or rare combinations. Review the whole dataset before sharing it. Input and output are neither uploaded nor saved to browser storage.
Frequently asked questions
Is my file uploaded?
No. Reading, transformation, preview and downloads happen only in the current browser tab.
Does address cleanup validate addresses?
No. It applies spacing and punctuation rules and can split on commas; it does not check existence, postal codes or deliverability.
Does pseudonymization fully anonymize data?
No. It masks selected direct identifiers. You must separately assess whether other columns can identify a person.
Can I audit removed duplicates?
Yes. The change log CSV contains the original row number, values and removal reason.