Choose a cleaner by data type or cleanup task
Clean Tools covers several different kinds of cleanup, so the best starting point depends on both your input and the result you need. Use a format-specific cleaner when the file structure is the problem: CSV & TSV Cleaner, Excel Cleaner, JSON Cleaner, JSONL Cleaner, or XML Cleaner.
For a broader, format-aware starting point, Data Cleaner handles shared cleanup and points you toward more focused workspaces. The remaining tools focus on a particular record, field, value, or data domain rather than every possible input format.
Match the tool to the cleanup outcome
Choose a field-level tool when the values need a defined rule. Fill Blank Cells supplies selected empty cells using a rule you choose, while Normalize Null Values turns different missing-value markers into one selected representation. For inconsistent text values, Value Clusterer & Standardizer groups possible matches and lets you approve a canonical value before replacement.
Use Remove Duplicate Rows when repeated records are the issue. For domain-specific data, choose Email List Cleaner, Phone Number List Cleaner, Address Format Standardizer, or Date Format Standardizer according to the field being normalized. These tools expose different controls, so do not assume that a rule available for one data type applies to another.
A practical cleanup workflow
- Identify the input structure and open the matching format cleaner, or start with Data Cleaner if you need a broader format-aware entry point.
- Choose the focused operation that matches the problem: missing cells, null markers, duplicate rows, inconsistent values, or a specific domain such as dates or contact details.
- Use the visible preview, report, or review controls to inspect the proposed changes. A dataset that needs several kinds of cleanup may require separate tools; there is no automatic chaining between these category pages.
- Export or continue with the result only after checking that the selected rules preserve the structure and meaning you need.
Review results before using the cleaned data
Cleanup rules can expose uncertain cases rather than settle every interpretation for you. Check the review output from Data Cleaner and inspect ambiguous or unparseable values in Date Format Standardizer and Address Format Standardizer. For grouped text values, review the proposed matches in Value Clusterer & Standardizer before approving replacements.
Finally, compare the result with the intended file format, column meanings, and downstream requirements. A format-cleaning change can be structurally correct while a domain-specific value still needs a human decision.