Start with a rectangular dataset
CSV has no equivalent of an HTML cell that spans multiple rows or columns. Every record needs the same logical set of fields. A converter must resolve rowspan and colspan relationships before writing a single line to the file.
Headers also matter. A visually empty top-left cell may still define the relationship between row labels and data columns. Review the captured grid and give every useful column a stable name before export.
CSV quoting and encoding rules that prevent broken imports
A value containing a comma, quote, or line break should be enclosed in quotes. Any quote inside that value must be doubled. Without those rules, one product description can shift every remaining field in the row.
Use UTF-8 so names, symbols, and non-English text survive the export. Spreadsheet applications can still interpret delimiters differently depending on locale, so semicolon-separated files may appear in some regional workflows. Standard CSV remains comma-separated unless the receiving system states otherwise.
A practical conversion checklist
Run these checks while the captured table is still visible next to the source.
- Confirm each row has the expected number of columns.
- Remove page controls, totals, ads, and explanatory notes that were captured as rows.
- Check values containing commas, quotes, and line breaks.
- Verify that leading zeroes and long identifiers should remain text.
- Open the exported CSV in both a text editor and the target spreadsheet or import tool.
CSV, TSV, or Excel
CSV is the most portable choice for imports and scripts. TSV is convenient for the clipboard because tabs rarely appear inside ordinary values. Excel is better when the file needs worksheets, direct human editing, or familiar formatting.
A good table capture workflow can produce all three from the same reviewed dataset. That keeps formatting decisions separate from extraction accuracy.