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A regular expression (regex) describes a pattern of characters. Use one when you need to find, extract, replace, or split text that follows a rule; use a plain string method for a fixed literal and a parser for structured formats such as HTML or XML. The examples below name their flavor: Python re or JavaScript RegExp. Their syntax overlaps, but their APIs and some behavior differ.
What a regex does
A regex is a compact set of instructions for recognizing text. The pattern cat finds the characters “cat” in that order. Add metacharacters—symbols that mean more than their literal character—and the pattern can describe choices, repetition, or position.
Regex is useful when the text varies but follows a recognizable rule. It is not automatically the clearest tool: Python’s Regular Expression HOWTO notes that ordinary string methods can be simpler and faster for fixed-string tasks, and recommends a parser for structured HTML or XML.
Build patterns from literal text
Match a literal sequence
Python re: re.search(r"cat", "The cat slept.") finds cat within the sentence. The letters are literal here; there is no special syntax to interpret.
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JavaScript RegExp: /cat/.test("The cat slept.") returns true because the pattern occurs in the string. MDN’s JavaScript regular expressions guide introduces patterns such as a for one “a” and abc for that sequence.
Choose one character from a set
Python re: r"gr[ae]y" matches either gray or grey. Brackets define a character class: [ae] accepts exactly one character, either “a” or “e”. Similarly, r"[0-9]" matches one digit from 0 through 9.
Character-class shorthands such as d, w, and s can have flavor-specific details. Check the documentation for the engine you use instead of assuming that every language interprets them identically.
Control how many times a token can appear
Quantifiers apply to the token immediately before them. In common regex flavors, these forms mean:
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| Quantifier | Meaning | Python re example |
|---|---|---|
? |
Zero or one occurrence | r"colou?r" matches color or colour. |
* |
Zero or more occurrences | r"ab*c" matches ac, abc, or abbbc. |
+ |
One or more occurrences | r"d+" matches a run of one or more digits. |
{m,n} |
Between m and n occurrences, inclusive |
r"d{2,4}" matches a run of two to four digits. |
For example, JavaScript RegExp /ab?c/ matches ac and abc; b? makes “b” optional. /ab*c/ permits any number of “b” characters, including none. These are JavaScript examples, not a promise that every flavor has identical syntax or API behavior.
Greedy matching
In many flavors, quantifiers such as * and + are greedy by default: they take as much text as they can while still allowing the rest of the pattern to match. For instance, a broad pattern like <.*> can span from the first “<” to the last “>” on a line. Do not use a clever-looking expression to parse nested markup; use an HTML or XML parser instead.
Combine choices and capture useful text
Alternation for a short list of choices
The vertical bar means “or.” Python re: r"cat|dog" finds either word in text. If alternatives share surrounding text, group them to make the intended scope explicit: r"b(cat|dog)s?b" finds “cat,” “cats,” “dog,” or “dogs” in Python re.
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Parentheses group part of a pattern and, in many flavors, capture the text matched by that part. Python re: re.search(r"Order #(d+)", "Order #4821").group(1) returns 4821. The parentheses capture the digit run; d+ requires one or more digits.
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To collect multiple matches in Python, use re.findall(). For example, re.findall(r"d+", "Boxes: 12 and 8", re.ASCII) returns the digit runs as strings. Python’s regular expression reference documents these functions and their behavior. In Python source, raw strings such as r"d+" are generally the clearest way to write patterns containing backslashes, because Python string escaping and regex escaping otherwise interact.
Decide whether a match can occur anywhere or must cover the input
A pattern can find a substring, require a match at the start, or require the entire string to match. Choose the operation that expresses the requirement instead of adding extra pattern syntax to imitate it. In Python re, the distinction is:
| Python operation | Where it checks | Example |
|---|---|---|
re.search() |
Anywhere in the string | re.search(r"d+", "ID 42") finds 42. |
re.match() |
At the beginning of the string | re.match(r"ID", "ID 42") matches at the start. |
re.fullmatch() |
The whole string | re.fullmatch(r"d+", "42") matches only if every character is a digit. |
Python’s HOWTO specifically cautions against emulating a search by prefixing a pattern with .*; use search() when the goal is to find a match anywhere. Anchors such as ^ and $ can represent positions in a pattern, but anchor and newline behavior can vary with flavor and flags. If you need to validate a complete input in Python, fullmatch() states that intent directly.
Apply practical patterns and test both sides
Find and extract a digit run
Python re: re.search(r"Invoice (d+)", "Paid invoice 731 today") returns no match because capitalization and wording do not fit the pattern. With "Paid Invoice 731 today", it finds the phrase and capture group 1 contains 731. This is a useful habit: test one string that should match and one plausible string that should not.
Recognize a date-shaped string
Python re: re.fullmatch(r"d{4}-d{2}-d{2}", "2026-10-07") accepts a four-digit, two-digit, two-digit shape separated by hyphens. It also accepts impossible dates such as 2026-19-44. A regex can check this format; use date-parsing or calendar validation to establish whether the value is a real date.
Find words with a prefix
Python re: re.findall(r"bprew*", "preview the preface") finds words beginning with “pre” under Python’s word-boundary and word-character rules. The b assertion marks a boundary between a word character and a non-word character; it does not mean “space,” and what counts as a word character depends on the flavor and settings. Test punctuation, underscores, and non-ASCII text if they matter to your data.
Collapse repeated whitespace—or use a string method
Python re: re.sub(r"s+", " ", "one twonthree") replaces each run of whitespace with one ordinary space. For the narrower task of replacing one exact fixed substring, a plain method such as Python’s str.replace() is often easier to read than a regex. Use the tool that best expresses the operation.
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Suppose a line is always written as a timestamp, a space, a level in square brackets, another space, and a message. Python re: re.fullmatch(r"(d{4}-d{2}-d{2}Td{2}:d{2}:d{2}) [(INFO|WARN|ERROR)] (.+)", line) captures those three fields if the whole line follows that specific layout. This example is only for the stated format; it is not a general log parser or validator for timestamps.
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Make patterns easier to read and maintain
Case-insensitive matching
Case-insensitive matching is typically enabled with an API option or flag, but the exact form depends on the flavor. In Python re, re.search(r"cat", text, re.IGNORECASE) ignores case for this search. JavaScript RegExp uses the i flag, as in /cat/i.test(text). Unicode case behavior and other details should be checked in the relevant engine documentation.
Use Python verbose mode for a longer expression
For a complex Python pattern, re.VERBOSE lets you lay out the expression with whitespace and comments. The Python HOWTO describes verbose expressions as a way to make patterns easier to read. Keep the explanation near the pattern, and prefer several small, named operations in ordinary code when a single expression becomes difficult to review.
Test positive and negative cases
Write down what should match and what should fail before relying on a pattern. Include edge cases that reflect the real input: empty text, extra punctuation, too few or too many digits, unexpected capitalization, and line breaks. A match confirms only that the engine accepted the pattern against that input; it does not prove a broader business rule unless the pattern actually checks that rule.
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Email addresses and web forms
A compact email regex is not a complete standards-compliant email validator. Python’s reference even labels its illustrative email pattern as a poor one. For a browser form, an <input type="email"> provides native constraint validation; an HTML pattern adds a regular-expression constraint. MDN notes that a value must match the pattern constraint for it to pass: HTML pattern attribute. This browser-side check can guide entry, but it cannot establish that a mailbox exists and does not replace server-side validation. See MDN’s email input documentation for the native control.
Nested or structured content
HTML and XML can contain nested structure, so a flat text pattern is the wrong tool for reliably understanding their hierarchy. Use a parser that understands the format. The same principle applies to other structured data: choose a parser when the structure, not just a local text shape, determines what a value means.
When a plain string operation is enough
If the task is to find or replace a fixed literal phrase, use the language’s string operations unless regex features add real value. They make intent obvious and avoid turning a simple operation into a pattern that others must decode.
Quick Recap
Choose the right approach for the task
| Need | Good starting point | Why |
|---|---|---|
| Find or replace one fixed phrase | Ordinary string methods | The operation is direct and does not need pattern syntax. |
| Find text that follows variable rules | A regex matched with the right scope | Character classes, repetition, groups, and alternatives express recurring shapes. |
| Require all of an input to follow a format | A whole-string API such as Python re.fullmatch(), plus semantic validation where needed |
A substring match can succeed even when other input remains. |
| Interpret nested HTML, XML, or another structured format | A parser for that format | Structure and nesting require more than local character matching. |
| Maintain a complicated transformation | Readable code, possibly several small regex operations | Clarity is more valuable than compressing the work into one opaque expression. |
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