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Bulk-replace values in selected or all CSV columns using literal text or regular expressions.
Use this when the same correction appears over and over—changing “Tokyo” to “東京都”, replacing an old domain, or editing one specific column across hundreds of rows. You can target all columns or one header, using literal text or a regular expression.
Literal mode replaces every occurrence of the exact text inside the targeted cells. It is easy to reason about and avoids regex surprises. A blank find string is blocked because replacing an empty match can insert text between every character and produce a very different file.
Regex mode treats the find field as a JavaScript regular expression. The replacement field is returned as literal text, so strings such as $1 are not expanded as capture references. That distinction matters if you are coming from editors that support backreference replacement.
Literal and Regex modes are case-sensitive here, and there is no regex-flags input. Tokyo and TOKYO are separate cases. If your data has casing variants, normalize them first or run deliberate passes rather than assuming an ignore-case option.
All-columns replacement is convenient, but the same text may also appear in IDs, notes, or URLs. If you know the change belongs only in a category, city, or status column, target that header. A replacement count much larger than expected is a useful warning sign.
Replace an old host name only in a URL column, or standardize a category code across a catalog export. Keep the original CSV and review a few changed rows before you use the output in an import job.