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Regex Explained

A regular expression (regex) is a pattern that describes text. It lets you search for, match, and extract specific patterns from strings — like finding all email addresses in a document or validating that a phone number has the right format. Python's re module provides regex support.

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Why This Matters

String methods like .find() and .startswith() work for simple cases, but they fall apart when patterns are complex. "Find every word that starts with a capital letter and ends with a number" is one line of regex but dozens of lines of string manipulation. Regex is a universal skill — the same patterns work in Python, JavaScript, SQL, and most editors.

The re module basics

import re

text = "My phone number is 555-123-4567 and my zip is 97201"

# search — find the first match:
match = re.search(r"\d{3}-\d{3}-\d{4}", text)
if match:
    print(match.group())    # "555-123-4567"

# findall — find ALL matches:
numbers = re.findall(r"\d+", text)
print(numbers)    # ["555", "123", "4567", "97201"]

# sub — replace matches:
cleaned = re.sub(r"\d", "X", text)
print(cleaned)    # "My phone number is XXX-XXX-XXXX and my zip is XXXXX"

Always use raw strings (r"...") for regex patterns — this prevents Python from interpreting backslashes before the regex engine sees them.

Character classes

Pattern Matches Example
\d Any digit (0-9) \d+ matches "42"
\D Any non-digit \D+ matches "hello"
\w Word character (letter, digit, underscore) \w+ matches "hello_42"
\W Non-word character \W+ matches "!! "
\s Whitespace (space, tab, newline) \s+ matches " \t"
\S Non-whitespace \S+ matches "hello"
. Any character except newline a.c matches "abc", "a3c"
[abc] Any of a, b, or c [aeiou] matches vowels
[^abc] Any character NOT a, b, or c [^0-9] matches non-digits
[a-z] Any lowercase letter [A-Za-z] matches any letter

Quantifiers — how many?

Pattern Meaning Example
* Zero or more \d* matches "", "5", "42"
+ One or more \d+ matches "5", "42" but not ""
? Zero or one colou?r matches "color" and "colour"
{3} Exactly 3 \d{3} matches "123"
{2,4} Between 2 and 4 \d{2,4} matches "12", "123", "1234"
{2,} 2 or more \d{2,} matches "12", "123456"

Anchors — where in the string?

Pattern Meaning
^ Start of string
$ End of string
\b Word boundary 
# Only match if the entire string is digits:
re.match(r"^\d+$", "12345")    # Match
re.match(r"^\d+$", "123abc")   # No match

# Word boundaries — match whole words:
re.findall(r"\bcat\b", "the cat sat on the catalog")
# ["cat"] — does NOT match "cat" inside "catalog"

Groups — capturing parts of a match

Parentheses () create groups that capture parts of the match:

text = "2024-01-15"
match = re.search(r"(\d{4})-(\d{2})-(\d{2})", text)

if match:
    print(match.group())     # "2024-01-15" (entire match)
    print(match.group(1))    # "2024" (first group)
    print(match.group(2))    # "01" (second group)
    print(match.group(3))    # "15" (third group)

Named groups make code more readable:

match = re.search(r"(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})", text)

if match:
    print(match.group("year"))     # "2024"
    print(match.group("month"))    # "01"
    print(match.group("day"))      # "15"

match vs search vs findall

text = "hello 42 world 99"

# match — only checks the BEGINNING of the string:
re.match(r"\d+", text)         # None (string starts with "hello")
re.match(r"\d+", "42 cats")    # Match: "42"

# search — finds the FIRST match anywhere:
re.search(r"\d+", text)        # Match: "42"

# findall — finds ALL matches, returns a list:
re.findall(r"\d+", text)       # ["42", "99"]

# finditer — like findall but returns match objects:
for m in re.finditer(r"\d+", text):
    print(f"Found {m.group()} at position {m.start()}")

re.compile — precompile for performance

If you use the same pattern many times, compile it once:

pattern = re.compile(r"\d{3}-\d{3}-\d{4}")

# Now use the pattern object:
pattern.search(text1)
pattern.findall(text2)
pattern.sub("XXX-XXX-XXXX", text3)

This is faster when the pattern is used in a loop.

Common patterns

# Email (simplified):
email_pattern = r"[\w.+-]+@[\w-]+\.[\w.]+"
re.findall(email_pattern, "Contact alice@example.com or bob@test.org")
# ["alice@example.com", "bob@test.org"]

# URL:
url_pattern = r"https?://[\w./\-?=&#]+"
re.findall(url_pattern, "Visit https://example.com/page?id=1")

# Phone number (US):
phone_pattern = r"\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}"

# IP address:
ip_pattern = r"\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b"

# Extract key=value pairs:
kv_pattern = r"(\w+)=(\w+)"
re.findall(kv_pattern, "name=Alice age=30 city=Portland")
# [("name", "Alice"), ("age", "30"), ("city", "Portland")]

Flags

# Case-insensitive matching:
re.findall(r"python", "Python PYTHON python", re.IGNORECASE)
# ["Python", "PYTHON", "python"]

# Multiline — ^ and $ match start/end of each line:
re.findall(r"^\w+", "hello\nworld\nfoo", re.MULTILINE)
# ["hello", "world", "foo"]

# DOTALL — . matches newlines too:
re.search(r"hello.world", "hello\nworld", re.DOTALL)
# Match: "hello\nworld"

# VERBOSE — add comments to complex patterns:
pattern = re.compile(r"""
    (\d{4})    # year
    -
    (\d{2})    # month
    -
    (\d{2})    # day
""", re.VERBOSE)

Common Mistakes

Forgetting the raw string: 

# WRONG — Python interprets \b as backspace:
re.search("\bword\b", text)

# RIGHT — raw string:
re.search(r"\bword\b", text)

Greedy vs lazy matching: 

text = "<b>bold</b> and <b>more bold</b>"

# Greedy (default) — matches as MUCH as possible:
re.search(r"<b>.*</b>", text).group()
# "<b>bold</b> and <b>more bold</b>"

# Lazy (add ?) — matches as LITTLE as possible:
re.search(r"<b>.*?</b>", text).group()
# "<b>bold</b>"

Not checking for None from search(): 

# WRONG — crashes if no match:
result = re.search(r"\d+", "no numbers here").group()

# RIGHT — check first:
match = re.search(r"\d+", "no numbers here")
if match:
    result = match.group()

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