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Clean CSV and Excel tables free online with trimming, replacements, date normalization and key-based deduplication. Preview changes and check identifiers before export.
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Clean CSV and Excel tables free online with trimming, replacements, date normalization and key-based deduplication. Preview changes and check identifiers before export.
Sample A starts with four rows and configured trimming and ID deduplication. Cleaning should leave three rows. Confirm the result and group by channel: community has two rows totaling 400, email has one totaling 200.
Up to 50 files and 50 MB in total, 100,000 rows, 300 fields and 2 million cells. Numeric normalization changes text representation and is unsuitable for IDs with leading zeros. Dates require a recognizable year-month-day format. Selected keys define duplicates; real people or business identities are not inferred.
Review the result and choose “Continue with this result”. Generated output is not automatically adopted as the next input.
Rules store fields, operations and deduplication settings, not source tables. Reimport data and check mappings when reusing them.
Use the local functions for free without a KitNelo account. Optional Gemini generation sends the supplied text to Google and requires a configured server API key; provider fees and quotas can apply.
Review key selection and transformations before export. Cleaning can change identifiers if the wrong field type is used.
Read KitNelo data and privacy detailsImport a CSV table and choose field transformations such as trimming, text replacement or number normalization. Inspect the preview before exporting, especially columns containing identifiers where a numeric conversion could remove leading zeros or change the original value.
Select the columns that define a duplicate record, preview matching rows and export the cleaned table. Check whether your key should identify a person, transaction or another entity so genuinely different records are not removed together.
Choose date fields and review their normalized values before saving the cleaned spreadsheet data. Compare ambiguous day and month combinations with the source convention, and inspect missing or invalid dates instead of assuming every value was interpreted correctly.
Select the phone-number field and review the normalization result alongside the original value. Preserve country codes and leading zeros where needed. Consistent formatting does not confirm that a number is assigned, reachable or associated with the intended person.
Choose numeric cleanup for the relevant column and inspect decimal signs, grouping separators and empty cells. Keep an original column for comparison, especially when locale conventions could turn the same written value into a different number.
Enter the literal text to find and its replacement, then review the affected rows before export. Check identifiers and embedded words for unintended changes. Use a copy of the original data when applying replacements across multiple columns.
