Regular expressions (regex) in Python are implemented through the built-in re module, which provides pattern matching and text manipulation capabilities. Regex patterns define search templates that can match, extract, replace, or validate text using character classes, metacharacters, and quantifiers. Python 3.11 added atomic groups (?>...) and possessive quantifiers (*+, ++, ?+), giving finer control over backtracking behavior. Understanding regex is fundamental for data cleaning, validation, parsing, and text processing across web scraping, log analysis, and input sanitization. The key mental model: think of regex as a mini-language for describing text patterns — each symbol adds a constraint, and the engine tries to satisfy all constraints simultaneously.
What This Cheat Sheet Covers
This topic spans 16 focused tables and 114 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core Matching Functions
| Function | Example | Description |
|---|---|---|
re.search(r'\d+', 'abc123') | • Scans the entire string and returns a Match for the first match found anywhere • returns None if no match. | |
re.match(r'\d+', '123abc') | • Checks for match only at the beginning of string • returns None if pattern not at start. | |
re.fullmatch(r'\d+', '123') | • Requires entire string to match pattern • useful for strict validation where partial matches are invalid. | |
re.findall(r'\d+', 'a1b2c3') | • Returns list of all non-overlapping matches • with capturing groups, returns list of tuples. |