Regular Expressions in Python

A regular expression (regex) is a pattern that describes a piece of text. Python's built-in re module lets you search for, extract, and replace text that matches a pattern. Here's a quick example that finds all the numbers in a string:

import re

text = "Order 12 pens and 34 books"
numbers = re.findall(r"\d+", text)   # \d+ means "one or more digits"
print(numbers)     # ['12', '34']

That \d+ is a regex pattern. Let's learn the building blocks.

The pattern alphabet

Patterns are built from ordinary characters plus special symbols. The most useful ones for beginners:

• \d — any digit (0–9). \d+ means one or more digits.
• \w — any word character (letter, digit, or underscore).
• \s — any whitespace (space, tab, newline).
• . — any single character.
• + — one or more of the previous item.
• * — zero or more of the previous item.
• ? — zero or one (optional).
• ^ and $ — start and end of the string.
• [...] — any one character from the set, e.g. [aeiou].

The r"..." prefix makes a raw string, so backslashes are taken literally. Always use raw strings for regex patterns.

Core functions in the re module

import re

text = "Contact: [email protected] or call 9876543210"

# search() finds the FIRST match anywhere, returns a match object or None
match = re.search(r"\d{10}", text)   # \d{10} = exactly 10 digits
if match:
    print(match.group())             # 9876543210

# findall() returns ALL matches as a list
words = re.findall(r"\w+", "one two three")
print(words)                         # ['one', 'two', 'three']

# sub() replaces matches with new text
clean = re.sub(r"\d", "*", "PIN 1234")
print(clean)                         # PIN ****

Matching with quantifiers

Quantifiers control how many of something to match:

import re

# {n} exactly n times, {n,m} between n and m times
print(re.findall(r"\d{4}", "Year 2026 not 26"))    # ['2026']
print(re.findall(r"\d{1,2}", "1 22 333"))          # ['1', '22', '33']

Groups: extracting parts

Parentheses (...) create groups so you can pull out specific pieces of a match:

import re

date = "Today is 30-05-2026"

# Three groups: day, month, year
match = re.search(r"(\d{2})-(\d{2})-(\d{4})", date)
if match:
    day, month, year = match.groups()
    print(f"Day: {day}, Month: {month}, Year: {year}")
    # Day: 30, Month: 05, Year: 2026

A practical example

import re

# Pull all email addresses out of a block of text
text = """
Reach us at [email protected] or [email protected].
Old address: [email protected] was here.
"""

# A simple email pattern: word chars, @, domain, dot, extension
emails = re.findall(r"[\w.]+@[\w.]+\.\w+", text)
print(emails)
# ['[email protected]', '[email protected]', '[email protected]']

This isn't a perfect email validator (those are surprisingly complex), but it works well for extracting addresses from ordinary text.

Common mistakes

• Forgetting the raw string: Without r"...", backslashes get misinterpreted. Always write patterns as raw strings: r"\d+".
• Confusing search and match: re.match() only checks the start of the string; re.search() looks anywhere. Most of the time you want search or findall.
• Forgetting to check for None: re.search() returns None when there's no match. Calling .group() on None crashes. Always check if match: first.
• Greedy matching surprises: .* matches as much as possible. If a pattern grabs too much, use the non-greedy .*?.
• Over-engineering patterns: Regex is powerful but can get unreadable fast. For very simple tasks, plain string methods like .split() or .replace() may be clearer.

FAQ

Is regex part of Python or a separate install? It's built in. Just import re — no installation needed.

Should I compile patterns? For patterns used many times, pattern = re.compile(r"\d+") then pattern.findall(text) is slightly faster. For one-off use, the module functions are fine.

How do I make matching case-insensitive? Pass the flag re.IGNORECASE, e.g. re.findall(r"python", text, re.IGNORECASE).

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Regex relies on the re module from the standard library — see Modules, Packages & pip. It's also a key tool in Web Scraping Basics with Python for cleaning extracted text. More on the Python learning hub.

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