Is the Regular Expressions lesson free?

Yes. The first 5 lessons on PyRun are free forever — no signup, no card. Regular Expressions runs real Python in your browser via Pyodide.

How long does the Regular Expressions lesson take?

About 9 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Regular Expressions lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Regular Expressions lesson?

Intermediate level. Best after finishing the Beginner track (syntax, control flow, functions, data types).

← All lessons

Intermediate·9 min·intermediate · text

Regular Expressions

You’ll be able to

Match patterns with re.search, re.match, re.findall
Use character classes, quantifiers, and anchors
Capture groups to extract substrings
Substitute matched text with re.sub

Why this matters

re powers log parsing, Django URL routing, and every scraper. Catastrophic backtracking in nested quantifiers has taken down real services (Cloudflare 2019). Knowing re.compile, named groups, and re.VERBOSE separates hobbyists from engineers.

Common pitfalls

Recompiling the same pattern in a loop — hoist to module scope with re.compile() for a real speedup.
Using greedy .* when you want .*? — you'll match across delimiters and get one giant blob.
Parsing HTML or emails with regex — reach for BeautifulSoup or email.parser before you regret it.

A pattern language for text. Python's re module:

Three workhorses

re.findall(pattern, text) — every non-overlapping match, as a list
re.search(pattern, text) — first match anywhere, or None
re.sub(pattern, repl, text) — replace matches

Cheat sheet

\d digit · \w word char · \s whitespace
+ one or more · * zero or more · ? optional
( ) capture group · [abc] any of a/b/c

Use raw strings

Always prefix patterns with r to avoid backslash hell: r"\d+".

Try it

Extract all numbers from "order #1234, total ₹5,600".
Replace double-spaces with single in "a b c".

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
From text = "order #A12 and #B34567 arrived — see #C9", use re.findall to extract every #-prefixed code (letter + digits). Print the list. Expected: ['A12', 'B34567', 'C9'] (without the #).
Exercise 2
Use re.sub to redact every 10-digit phone number in text = "Call 9876543210 or 9123456789 today" — replace each with XXX-XXX-XXXX — then print. Expected output includes Call XXX-XXX-XXXX or XXX-XXX-XXXX today.
Exercise 3
From log = "user alice@x.com and bob.smith@company.io signed in", extract all email addresses using re.findall and print the list. Expected: ['alice@x.com', 'bob.smith@company.io'].

← PreviousJSON Next →Dates & Time

Regular Expressions

You’ll be able to

Match patterns with re.search, re.match, re.findall
Use character classes, quantifiers, and anchors
Capture groups to extract substrings
Substitute matched text with re.sub

Why this matters

Common pitfalls

Recompiling the same pattern in a loop — hoist to module scope with re.compile() for a real speedup.
Using greedy .* when you want .*? — you'll match across delimiters and get one giant blob.
Parsing HTML or emails with regex — reach for BeautifulSoup or email.parser before you regret it.

A pattern language for text. Python's re module:

Three workhorses

re.findall(pattern, text) — every non-overlapping match, as a list
re.search(pattern, text) — first match anywhere, or None
re.sub(pattern, repl, text) — replace matches

Cheat sheet

\d digit · \w word char · \s whitespace
+ one or more · * zero or more · ? optional
( ) capture group · [abc] any of a/b/c

Use raw strings

Always prefix patterns with r to avoid backslash hell: r"\d+".

Try it

Extract all numbers from "order #1234, total ₹5,600".
Replace double-spaces with single in "a b c".

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
From text = "order #A12 and #B34567 arrived — see #C9", use re.findall to extract every #-prefixed code (letter + digits). Print the list. Expected: ['A12', 'B34567', 'C9'] (without the #).
Exercise 2
Use re.sub to redact every 10-digit phone number in text = "Call 9876543210 or 9123456789 today" — replace each with XXX-XXX-XXXX — then print. Expected output includes Call XXX-XXX-XXXX or XXX-XXX-XXXX today.
Exercise 3
From log = "user alice@x.com and bob.smith@company.io signed in", extract all email addresses using re.findall and print the list. Expected: ['alice@x.com', 'bob.smith@company.io'].