Data cleanup
Text Cleaning for Spreadsheets
Spreadsheet exports often contain extra spaces, blank lines, repeated values, copied URLs, or inconsistent casing. A simple text cleaner helps you prepare lists before importing them into a spreadsheet, CRM, keyword tool, or publishing workflow.
Use the Text Cleaner to normalize spacing, then use the Duplicate Line Remover for lists where each line should be unique.
Common cleanup tasks
- Remove repeated keywords from SEO research exports.
- Clean URL lists before crawling or submitting pages.
- Normalize pasted product titles before comparing them.
- Remove blank lines from contact lists or notes.
- Convert casing before importing labels or tags.
Safer workflow
Keep a copy of the original export before cleaning. Paste only the column or list you need, clean it, then copy the result back into your spreadsheet. If order matters, check the cleaned list before replacing your source data.
For example, a 500-row keyword export may contain leading spaces, empty rows, and the same phrase with different capitalization. Save the untouched file, clean one copied column, remove exact duplicate lines, and compare the row count before importing it again. Do not deduplicate records by a display label when two rows may represent different IDs, orders, or customers.
Check delimiters and formulas before import
Text cleanup does not understand a workbook's formulas, quoted CSV fields, or relationships between columns. Clean plain text before parsing structured CSV or JSON data, and inspect commas, tabs, line breaks, decimal separators, and text qualifiers afterward. A removed line break can join two records; a case conversion can also change case-sensitive codes.
For keyword research, cleaning is only the first step. After removing duplicates, paste the remaining list into the Word Frequency Counter to see repeated themes.