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A regular expression (regex) is a compact pattern for finding, checking, extracting, or replacing text. Start with literal characters, then add character classes, quantifiers, groups, and position checks. The examples below use common regex syntax; details can vary by programming language and regex engine, so test patterns in the same flavor your application runs.
What a regex pattern does
A regex describes text you want to match. The pattern cat matches those three literal characters wherever they occur in the text being searched. Other pattern elements let you describe alternatives, repetitions, and positions instead of spelling out every possible string.
Regex is useful when the text follows a recognizable pattern, such as a run of digits or a word with a particular ending. It is not automatically the clearest solution: for complicated tasks, ordinary code may be easier to read and maintain. Python’s introductory Regular Expression HOWTO recommends considering whether a normal Python function would be clearer.
Build a pattern from the basics
Match literal text and character choices
Begin with a literal pattern such as cat. A character class in square brackets matches one character from the listed choices: [ct]at matches cat or tat. A range such as [A-Z] represents one uppercase ASCII-range letter in common regex flavors. The exact behavior of ranges and character classes can depend on the engine and its flags.
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The shorthand d matches a digit in many flavors. For example, dd describes two digits in succession. Character-class shorthand and its treatment of Unicode characters can differ between engines, so check the documentation for the target runtime.
Control repetition with quantifiers
A quantifier applies to the pattern element immediately before it. These common quantifiers mean:
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+: one or more occurrences.*: zero or more occurrences.?: zero or one occurrence.{n}: exactlynoccurrences.
For instance, [A-Z]+ matches one or more uppercase ASCII-range letters in common flavors. In d{4}, the quantifier applies to d, so the pattern describes four digits in a row. Quantifiers do not apply to the whole pattern unless you group that pattern first.
Group related parts
Parentheses group elements so they can be treated as a unit, and they can capture the matching text for later use. For example, (cat)+ applies the plus quantifier to the whole string cat, rather than just its final character. If you need grouping but do not need to capture the text, some flavors provide non-capturing groups; Python supports the form (?:...).
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Check positions with anchors
Anchors match positions rather than characters. In common flavors, ^ checks the start of a line or input and $ checks the end. Thus ^d{4}$ describes an input consisting of exactly four digits, subject to the engine’s anchor and multiline rules. With multiline mode enabled, anchor behavior can refer to individual lines rather than only the entire input.
Try a pattern in Python
Python uses the re module, and a pattern written in Python must pass through two interpreters: Python first reads the string literal, then the regex engine reads the pattern. A raw string, written with an r prefix, avoids many doubled-backslash surprises.
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For example, this code searches for a four-digit sequence and prints the matched text:
import re
text = "Order 4821 is ready"
match = re.search(r"d{4}", text)
if match:
print(match.group()) # 4821The regex pattern is d{4}; its Python raw-string representation is r"d{4}". Without a raw string, backslashes often need additional escaping in the Python literal. Python’s re documentation describes its syntax, functions, flags, and behavior.
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Use flags when matching rules need to change
Flags adjust how a pattern is interpreted. In Python, for example, re.IGNORECASE makes matching case-insensitive, while re.MULTILINE changes how ^ and $ treat line boundaries. Other engines may name or implement options differently. Check the target engine’s documentation rather than assuming a flag behaves identically across languages.
Choose and test the right regex flavor
Regex syntax is shared across many tools, but there is no single universal flavor. Python and JavaScript have substantial overlap, yet features such as lookbehind, named groups, Unicode handling, flags, and replacement syntax can differ. The regex101 tester lets you select an engine and offers explanations and a debugger; its engine documentation outlines flavor differences.
- Choose the engine that matches the language or application where the pattern will run.
- Enter a few strings that should match and a few that should not.
- Inspect the highlighted match and any capture groups, then adjust the pattern if needed.
- Run those same cases in your actual application or runtime before relying on the pattern.
An online tester is a learning and debugging aid, not proof that a pattern will behave correctly in production. The selected flavor must match the real runtime, and the application’s actual inputs still need testing.
Know when not to use regex
Regex is concise for many pattern-matching tasks, but a long expression can obscure its intent. If ordinary code makes the rules easier to follow, use it. Likewise, do not assume a short pattern validates every possible email address, URL, date, or other complex format. Such validation depends on the accepted rules and target syntax; state those assumptions and choose an approach suited to them.
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