Regex

Python · Reference cheat sheet

📋 Overview

The re module provides regular expressions for search, match, split, and substitute. Prefer raw strings (r"..."). For complex grammars, consider parsers; for fixed substrings, prefer str methods.

🔧 Core concepts

Groups: (...) capturing, (?:...) non-capturing, (?P<name>...) named. Conditional patterns exist but stay rare.

💡 Examples

Search and groups:

import re

text = "user=ada id=42"
m = re.search(r"user=(?P<user>\w+)\s+id=(?P<id>\d+)", text)
if m:
    print(m.group("user"), int(m.group("id")))

Compile and findall:

import re

email = re.compile(r"[\w.+-]+@[\w-]+\.[\w.-]+")
print(email.findall("a@x.com b@y.org"))

sub and backrefs:

import re

raw = "2024-07-10"
print(re.sub(r"(\d{4})-(\d{2})-(\d{2})", r"\2/\3/\1", raw))

VERBOSE + flags:

import re

pattern = re.compile(
    r"""
    ^
    (?P<year>\d{4}) -
    (?P<month>\d{2}) -
    (?P<day>\d{2})
    $
    """,
    re.VERBOSE,
)
print(pattern.fullmatch("2024-07-10").groupdict())  # type: ignore[union-attr]

⚠️ Pitfalls

• Catastrophic backtracking on crafted input — avoid nested ambiguous quantifiers on untrusted data.
• re.match only checks the start — use fullmatch or search intentionally.
• Forgetting raw strings: "\n" vs r"\n" / "\\d" mistakes.
• str.split / replace is clearer for fixed delimiters.
• Bytes patterns require bytes input — don't mix str/bytes.

🔗 Related

• Strings
• F-strings
• JSON
• Examples: scrape HTML
• Files I/O
• Argparse