Regex Code Generator for Text Data — Free & Online, No Sign-Up

This tool builds a regular expression (regex) for a common kind of text data, such as an email address, a date, or an IP address, and lets you test it on your own lines before you use it. A regex is a small search pattern that describes what a valid value looks like. You pick the pattern type, paste sample lines, and the tool shows the exact regex plus which lines match and which do not. It is template-based: every pattern here is a well-known, hand-checked template for a common format. It does not understand natural language, and it does not guess a pattern from free-form instructions. That limit is deliberate. A generated pattern you have not tested is a bug waiting to happen, so the tester is the main feature, not an extra.

How to use it

Choose the pattern type from the dropdown.
Paste or type your test lines, one value per line.
Click Generate & test and read the match table.
Copy the regex shown at the top into your code or spreadsheet tool.
Add edge cases — empty lines, extra spaces, unusual formats — and re-test.

Worked example

Suppose you are cleaning a survey export and need to check which rows hold a usable email address. Paste the email column into the test box and choose Email address. The tool shows the pattern ^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$ and marks ana@example.com as a match and not-an-email as a miss. A value like name@company passes the shape test but may still bounce in real life, because a regex checks format, not whether the mailbox exists. Use the regex to filter, then verify deliverability separately if it matters for your study.

Honest limits

Template patterns cover common formats only. Real-world data breaks them: phone numbers with extensions, dates like 15/03/2026, emails with new long domain endings, and IPv6 addresses are outside these templates. The email pattern accepts some addresses no mail server would, and rejects a few rare but valid ones. Do not use these patterns as a security check or as proof that an address belongs to a real person. If your data uses a format not listed here, write the pattern by hand and test it against at least twenty real examples, including the weird ones.

Who it is for

This tool is for students and researchers cleaning survey or scraped data, and for anyone learning regex who wants a correct starting pattern instead of a blank page. It also suits data managers who validate one column at a time before importing a file. Keep the tester habit: every pattern earns trust only after it passes your own examples, including the ones you expect to fail.

Going further

A good testing habit is to keep a small file of examples for every pattern you use: ten values that must match and ten that must not. Run the pattern against that file whenever you change it, and keep the file with your analysis code so a future collaborator can see exactly what the pattern was supposed to accept. Strictness is a trade-off worth deciding consciously. A loose email pattern accepts more real addresses but also lets junk through, while a strict pattern rejects valid edge cases such as plus-addressing (name+tag@example.com) or long new domain endings. For validation at data entry, a looser pattern plus a confirmation step usually beats a strict pattern that frustrates real users. For cleaning an existing dataset, a stricter pattern plus manual review of the rejects is usually better, because the rejects are a small pile you can inspect. Document the pattern itself in your methods: paste the exact regex into a README or code comment, note the date you validated it, and note any formats you knowingly excluded. A regex without that note is folklore; with it, it is a reproducible cleaning rule. One more practical rule: test your pattern against data from the same source it will clean, not against textbook examples. Real exports contain trailing spaces, mixed case, and legacy formats that textbooks never show, and a pattern validated only on clean examples will silently drop real rows. When a pattern rejects a value you expected to pass, resist the urge to loosen it blindly; first look at the rejected value, because it often reveals a genuinely different format that deserves its own handling. Keep a reject pile, review it weekly during a project, and promote recurring rejects into new patterns or new variant rules.

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