Lesson 18 of 34 · 0 of 2 exercises solved

Regular Expressions

A is a tiny language for describing text patterns. Learn a dozen symbols and you can validate, extract and rewrite text that would take pages of if-statements.

The re is the 's regex engine. Write patterns as raw (r"...") so backslashes survive. re.search finds the first match anywhere; re.match only at the start; re.fullmatch requires the whole string to match.

import re

text = "Track 23, Track 150 and Track 8"
m = re.search(r"Track (\d+)", text)
print(m.group(0), m.group(1), m.span())

print(re.findall(r"\d+", text))            # every number
print(re.fullmatch(r"[A-Z][a-z]+", "Maya") is not None)
print(re.fullmatch(r"[A-Z][a-z]+", "maya") is not None)

The core vocabulary: . any character, \d digit, \w word character, \s whitespace, [abc] a , [^abc] a negated set, ^ and $ anchors, and the quantifiers * (0+), + (1+), ? (0 or 1) and {m,n}. Parentheses capture; (?:...) groups without capturing.

import re

log = """
2026-09-05 08:12:01 INFO  backup started
2026-09-05 08:40:17 WARN  disk nearly full
2026-09-05 12:00:00 ERROR server not responding
"""
pattern = re.compile(r"^(\d{4}-\d{2}-\d{2}) (\d{2}:\d{2}):\d{2} (\w+)\s+(.*)$", re.MULTILINE)
for date, time, level, msg in pattern.findall(log):
    print(f"{time} [{level}] {msg}")

# Named groups read better in real code
email = re.compile(r"(?P<user>[\w.]+)@(?P<domain>[\w.]+)")
m = email.search("write to sam.carter@example.com today")
print(m["user"], m["domain"])

re.sub replaces matches, and the replacement can refer to groups with \1 or \g<name>, or be a that receives each match. re.split splits on a pattern instead of a fixed string.

import re

print(re.sub(r"\s+", " ", "too   many    spaces"))
print(re.sub(r"(\w+)@(\w+)", r"\2 at \1", "sam@home"))
print(re.sub(r"\d+", lambda m: str(int(m.group()) * 2), "3 apples, 5 pears"))
print(re.split(r"[,;]\s*", "red, green;blue,  yellow"))

Common question

Should I use regular expressions for all text matching?

A regular expression is a compact language for describing a set of strings: literal characters, character classes, anchors, quantifiers and groups. The re module uses it to search, match, extract and substitute. For what it is good at (validating formats, pulling fields out of lines, rewriting patterns) nothing is more concise.

Use raw strings so backslashes survive, compile patterns you reuse, prefer named groups for readability, and test each pattern against examples that should and should not match. Keep patterns short. When one grows past a line, split the problem up or write a small parser instead.

Source: Jamie Zawinski: "Some people, when confronted with a problem, think 'I know, I'll use regular expressions.' Now they have two problems."

Common mix-ups

Each of these sounds reasonable. Think about why it is wrong, then open the explanation.

A regex can describe any pattern, so it's the universal tool for text.
A simple check like "does this string end with .txt" needs a regex.
.* matches the shortest possible run of characters.

Practice

Write extract_dates(text) that returns every date written as YYYY-MM-DD in the text, in order, as a of strings.

Exercise

Find the dates

3 checks

import re


def extract_dates(text):
    ...


print(extract_dates("Opened 1998-03-21, renovated 2015-10-14, reopened 2026-09-05."))

Output

Press Run to try your code, then Check answer to grade it.

Write redact(text) that replaces every email address with [redacted], and mask_card(number) that keeps only the last four digits of a card number, replacing the rest with *. "4111 2222 3333 4444" becomes "**** **** **** 4444".

Exercise

Substitute with care

4 checks

import re


def redact(text):
    ...


def mask_card(number):
    ...


print(redact("mail sam@example.com or maya@shop.co.uk"))
print(mask_card("4111 2222 3333 4444"))

Output

Press Run to try your code, then Check answer to grade it.

Check yourself

Quick check

What does the pattern r"colou?r" match?

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