STEP 01
Upload your file
Choose a supported CSV or Excel file to begin the analysis.
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Upload a CSV, Excel, TSV, or JSON dataset. We’ll analyze the full dataset, explain issues, and help you clean it.
Choose a category to find relevant sample datasets. Your uploaded file is still analyzed from its actual contents.
Understand the findings, choose your fixes, and review the output before downloading.
Choose a supported CSV or Excel file to begin the analysis.
Review missing cells, duplicate rows, affected columns and the quality estimate.
Select supported cleaning actions and inspect the available preview.
Check the preview, then download your cleaned dataset.
Select the actions you want to apply. Review each change before downloading.
| # | Name | Phone | City | Age | ||
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| Upload a file to preview your data here. | ||||||
Learn how to clean spreadsheet data, understand data-quality findings, and export a workbook you can review.
A data cleaner helps identify and fix common spreadsheet issues such as duplicate rows, missing values, extra spaces, and completely empty rows or columns.
Upload your CSV or Excel file, review the data-quality findings, select the cleaning operations you want, and export the cleaned workbook. Review the selected actions before downloading.
The tool is designed to accept CSV, TSV, TXT, JSON, JSONL or NDJSON, XLSX, and XLS files, subject to the upload size and backend configuration shown in the application.
Yes. Select the duplicate-removal option to remove exact duplicate records across the row. Review your output because rows that differ in any field are not exact duplicates.
The analysis reports missing values by column and can identify entirely empty rows and columns. Available cleaning options can remove empty rows or columns and fill missing values; filling values can change the meaning of data, so review it carefully.
The tool creates a downloadable output rather than overwriting the original file on your device. Check the selected cleaning operations and exported workbook before using it for business decisions.
The workbook can include the original data, cleaned data, summary metrics, column profiles, missing-value details, numeric relationships, findings, recommendations, a cleaning log, and a pivot summary when one is available.
The score is an estimate based on detected quality signals such as missing values, duplicate rows, empty rows or columns, and whitespace issues. It is not a guarantee that every value is accurate or valid.
No. Correlation describes a statistical relationship between numeric columns. It does not prove that one variable causes changes in another.
No automated cleaning tool can guarantee that data is correct. Review the findings, validate important values against their source, and confirm that each selected transformation is appropriate for your use case.