Python Regular Expressions - Complete Guide with Examples

What You'll Learn Here

What are regex — pattern matching in Python
re.search() — finding patterns in text
re.findall() — finding all matches
re.sub() — replacing patterns
re.split() — splitting with patterns
Groups and flags — advanced regex techniques

What Are Regular Expressions?

Regular expressions, often called "regex" or "regexp," are like a secret language for finding patterns in text. They let you search for specific patterns, extract information, and transform text in ways that would be incredibly tedious to do manually.

Think of regex like a super-powered search bar. Instead of just searching for exact words, you can search for patterns — like "any phone number," "any email address," or "any word that starts with 'p' and ends with 'n'." It's the tool professionals use for text processing.

In Python, you work with regular expressions using the re module. It gives you everything you need to harness the power of pattern matching.

💡 Key concept: Regular expressions are patterns that describe sets of strings. They're used for searching, extracting, and replacing text based on patterns rather than exact matches.

Regex Basics

Understanding the Language of Patterns

# Before we dive into the functions, let's understand the pattern language

import re

# 1. Literal characters - match exactly what you type
pattern = r"cat"
text = "The cat sat on the mat"
match = re.search(pattern, text)
print(match.group())  # cat

# 2. The dot (.) - matches any single character except newline
pattern = r"c.t"
print(re.search(r"c.t", "cat").group())  # cat
print(re.search(r"c.t", "cot").group())  # cot
print(re.search(r"c.t", "cut").group())  # cut

# 3. Character classes ([...]) - matches any character in the set
print(re.search(r"[aeiou]", "hello").group())  # e
print(re.findall(r"[aeiou]", "hello"))  # ['e', 'o']

# 4. Ranges - specify a range of characters
print(re.findall(r"[a-z]", "Hello123"))  # ['e', 'l', 'l', 'o']
print(re.findall(r"[0-9]", "Hello123"))  # ['1', '2', '3']

# 5. Quantifiers - specify how many times something appears
# * - zero or more times
print(re.search(r"go*gle", "ggle").group())  # ggle
print(re.search(r"go*gle", "google").group())  # google

# + - one or more times
print(re.search(r"go+gle", "google").group())  # google

# ? - zero or one times
print(re.search(r"colou?r", "color").group())  # color
print(re.search(r"colou?r", "colour").group())  # colour

# {n} - exactly n times
print(re.search(r"a{3}", "aaabbb").group())  # aaa

# {n,} - at least n times
print(re.search(r"a{2,}", "aaabbb").group())  # aaa

# {n,m} - between n and m times
print(re.search(r"a{2,3}", "aaabbb").group())  # aaa

# 6. Anchors - match positions, not characters
# ^ - start of string
print(re.search(r"^Hello", "Hello World").group())  # Hello

# $ - end of string
print(re.search(r"World$", "Hello World").group())  # World

# 7. Special characters (need escaping with \)
# . ^ $ * + ? { } [ ] \ | ( )
print(re.search(r"\$", "Price: $10").group())  # $

Regex basics summary:

. — any character (except newline)
[abc] — any of a, b, or c
[a-z] — any lowercase letter
* — zero or more
+ — one or more
? — zero or one
{n,m} — between n and m times
^ — start of string
$ — end of string

Quick Check: What does the dot (.) match in regex? (Answer: Any single character except newline)

Searching with re.search()

Finding the First Match

# re.search() finds the first occurrence of a pattern

import re

# 1. Basic search
text = "The quick brown fox jumps over the lazy dog"
pattern = r"fox"
match = re.search(pattern, text)
if match:
    print(f"Found: {match.group()}")  # fox
    print(f"Position: {match.start()}-{match.end()}")  # 16-19

# 2. Extracting email addresses
text = "Contact us at support@example.com or sales@company.com"
match = re.search(r"\w+@\w+\.\w+", text)
if match:
    print(f"Email: {match.group()}")  # support@example.com

# 3. Finding phone numbers
text = "Call me at 555-123-4567 or 555-987-6543"
pattern = r"\d{3}-\d{3}-\d{4}"
match = re.search(pattern, text)
print(f"Phone: {match.group()}")  # 555-123-4567

# 4. Using groups to extract specific parts
text = "Name: Alice, Age: 25"
pattern = r"Name: (\w+), Age: (\d+)"
match = re.search(pattern, text)
if match:
    name = match.group(1)
    age = match.group(2)
    print(f"Name: {name}, Age: {age}")

# 5. Checking if a pattern exists
text = "Hello World"
if re.search(r"World", text):
    print("Found 'World' in the text")

# 6. Case-insensitive search
text = "Hello World"
match = re.search(r"world", text, re.IGNORECASE)
if match:
    print(f"Found: {match.group()}")  # World

re.search() key points:

Returns match object — if found, else None
group() — get the matched text
start()/end() — get match positions
groups() — get captured groups
Case-insensitive — use re.IGNORECASE

Quick Check: What does re.search() return if no match is found? (Answer: None)

Finding All Matches with re.findall()

Getting All Occurrences

# re.findall() returns all non-overlapping matches as a list

import re

# 1. Finding all numbers
text = "I have 3 apples, 5 oranges, and 10 bananas"
numbers = re.findall(r"\d+", text)
print(f"Numbers: {numbers}")  # ['3', '5', '10']

# 2. Finding all words
text = "Hello world! How are you?"
words = re.findall(r"\w+", text)
print(f"Words: {words}")  # ['Hello', 'world', 'How', 'are', 'you']

# 3. Finding all email addresses
text = "Email: alice@example.com, bob@company.com"
emails = re.findall(r"\w+@\w+\.\w+", text)
print(f"Emails: {emails}")  # ['alice@example.com', 'bob@company.com']

# 4. Finding all phone numbers
text = "Call 555-123-4567 or 555-987-6543"
phones = re.findall(r"\d{3}-\d{3}-\d{4}", text)
print(f"Phones: {phones}")  # ['555-123-4567', '555-987-6543']

# 5. Finding all dates
text = "Dates: 2024-01-15, 2024-02-20, 2024-03-25"
dates = re.findall(r"\d{4}-\d{2}-\d{2}", text)
print(f"Dates: {dates}")  # ['2024-01-15', '2024-02-20', '2024-03-25']

# 6. Extracting specific parts with groups
text = "Name: Alice, Age: 25 | Name: Bob, Age: 30"
pairs = re.findall(r"Name: (\w+), Age: (\d+)", text)
print(f"Pairs: {pairs}")  # [('Alice', '25'), ('Bob', '30')]

# 7. Finding all vowels
text = "Hello World"
vowels = re.findall(r"[aeiou]", text, re.IGNORECASE)
print(f"Vowels: {vowels}")  # ['e', 'o', 'o']

re.findall() key points:

Returns list — all matches as strings
With groups — returns list of tuples
Non-overlapping — matches don't overlap
Great for extraction — pulling out specific data

Quick Check: What does re.findall() return? (Answer: A list of all non-overlapping matches)

Finding Matches with re.finditer()

Getting Match Objects for All Occurrences

# re.finditer() returns an iterator of match objects

import re

# 1. Finding all matches with position information
text = "The cat sat on the mat. The cat was happy."
for match in re.finditer(r"cat", text):
    print(f"Found 'cat' at position {match.start()}-{match.end()}")

# Output:
# Found 'cat' at position 4-7
# Found 'cat' at position 29-32

# 2. Finding all numbers with their positions
text = "I have 3 apples, 5 oranges, and 10 bananas"
for match in re.finditer(r"\d+", text):
    print(f"Number {match.group()} at position {match.start()}-{match.end()}")

# 3. Finding all words with their positions
text = "Hello world! How are you?"
for match in re.finditer(r"\w+", text):
    print(f"Word: {match.group()} at {match.start()}-{match.end()}")

# 4. Using with groups
text = "Name: Alice, Age: 25 | Name: Bob, Age: 30"
pattern = r"Name: (\w+), Age: (\d+)"
for match in re.finditer(pattern, text):
    name = match.group(1)
    age = match.group(2)
    print(f"Name: {name}, Age: {age}")

# 5. Counting matches
text = "Hello Hello Hello"
count = sum(1 for _ in re.finditer(r"Hello", text))
print(f"Count: {count}")  # 3

re.finditer() key points:

Returns iterator — memory efficient for large texts
Match objects — gives full info for each match
Position info — start() and end() methods
Groups — can access captured groups

Quick Check: What is the advantage of finditer() over findall()? (Answer: It returns match objects with position information)

Replacing with re.sub()

Find and Replace with Patterns

# re.sub() replaces all occurrences of a pattern

import re

# 1. Basic replacement
text = "The cat sat on the mat"
result = re.sub(r"cat", "dog", text)
print(result)  # The dog sat on the mat

# 2. Replacing all numbers
text = "I have 3 apples, 5 oranges, and 10 bananas"
result = re.sub(r"\d+", "X", text)
print(result)  # I have X apples, X oranges, and X bananas

# 3. Using a replacement function
def double_number(match):
    num = int(match.group())
    return str(num * 2)

text = "I have 3 apples and 5 oranges"
result = re.sub(r"\d+", double_number, text)
print(result)  # I have 6 apples and 10 oranges

# 4. Replacing multiple spaces with a single space
text = "Hello    World   How  are   you?"
result = re.sub(r"\s+", " ", text)
print(result)  # Hello World How are you?

# 5. Replacing with groups
text = "John Doe, Jane Smith"
result = re.sub(r"(\w+) (\w+)", r"\2, \1", text)
print(result)  # Doe, John Smith, Jane

# 6. Removing unwanted characters
text = "Hello! How are you? I'm fine."
result = re.sub(r"[!?.]", "", text)
print(result)  # Hello How are you Im fine

# 7. Count replacements
text = "The cat and the dog"
result, count = re.subn(r"the", "a", text, flags=re.IGNORECASE)
print(f"Result: {result}, Count: {count}")  # a cat and a dog, Count: 2

re.sub() key points:

Replaces all — all occurrences by default
Count parameter — limit number of replacements
Function replacement — dynamic replacement logic
Group references — use \1, \2 for captured groups
re.subn() — returns tuple with count

Quick Check: What does re.sub() do? (Answer: Replaces all occurrences of a pattern with a replacement string)

Splitting with re.split()

Split Strings Using Patterns

# re.split() splits a string by a pattern

import re

# 1. Basic splitting by pattern
text = "apple,banana,cherry"
result = re.split(r",", text)
print(result)  # ['apple', 'banana', 'cherry']

# 2. Splitting by multiple delimiters
text = "apple,banana;cherry:grape"
result = re.split(r"[,;:]", text)
print(result)  # ['apple', 'banana', 'cherry', 'grape']

# 3. Splitting by whitespace
text = "Hello   World  How are   you?"
result = re.split(r"\s+", text)
print(result)  # ['Hello', 'World', 'How', 'are', 'you?']

# 4. Splitting by numbers
text = "I have 3 apples and 5 oranges"
result = re.split(r"\d+", text)
print(result)  # ['I have ', ' apples and ', ' oranges']

# 5. Keeping the delimiters
text = "apple,banana,cherry"
result = re.split(r"(,)", text)
print(result)  # ['apple', ',', 'banana', ',', 'cherry']

# 6. Splitting with max splits
text = "a,b,c,d,e"
result = re.split(r",", text, maxsplit=2)
print(result)  # ['a', 'b', 'c,d,e']

re.split() key points:

Pattern-based — split by any pattern
Multiple delimiters — use character classes
Keep delimiters — use capturing groups
maxsplit — limit the number of splits

Quick Check: How do you split by multiple delimiters? (Answer: Use a character class like [,;:])

Using Groups

Extracting Specific Parts

# Groups let you extract specific parts of a match

import re

# 1. Basic groups
pattern = r"(\d{3})-(\d{3})-(\d{4})"
text = "Phone: 555-123-4567"
match = re.search(pattern, text)
if match:
    area_code = match.group(1)  # 555
    prefix = match.group(2)     # 123
    number = match.group(3)     # 4567
    print(f"Area: {area_code}, Prefix: {prefix}, Number: {number}")

# 2. Named groups
pattern = r"(?P\d{3})-(?P\d{3})-(?P\d{4})"
text = "Phone: 555-123-4567"
match = re.search(pattern, text)
if match:
    print(f"Area: {match.group('area')}")    # 555
    print(f"Prefix: {match.group('prefix')}") # 123
    print(f"Number: {match.group('number')}") # 4567

# 3. Non-capturing groups (?:...)
pattern = r"(?:Name: )(\w+)"
text = "Name: Alice"
match = re.search(pattern, text)
if match:
    print(f"Name: {match.group(1)}")  # Alice

# 4. Groups with findall
text = "Name: Alice, Age: 25 | Name: Bob, Age: 30"
pattern = r"Name: (\w+), Age: (\d+)"
matches = re.findall(pattern, text)
for name, age in matches:
    print(f"Name: {name}, Age: {age}")

# 5. Groups with substitution
text = "John Doe"
result = re.sub(r"(\w+) (\w+)", r"\2, \1", text)
print(result)  # Doe, John

# 6. Nesting groups
pattern = r"((\d{3})-(\d{3})-(\d{4}))"
text = "Phone: 555-123-4567"
match = re.search(pattern, text)
if match:
    print(f"Full: {match.group(1)}")      # 555-123-4567
    print(f"Area: {match.group(2)}")      # 555
    print(f"Prefix: {match.group(3)}")    # 123
    print(f"Number: {match.group(4)}")    # 4567

Groups key points:

() — capture group
group(1) — first captured group
named groups — (?P...)
(?:...) — non-capturing group
\1, \2 — backreferences in replacement

Quick Check: How do you create a named group? (Answer: (?Ppattern))

Regex Flags

Modifying Regex Behavior

# Flags change how the regex engine works

import re

# 1. re.IGNORECASE (or re.I) - case-insensitive matching
text = "Hello World"
pattern = r"world"
match = re.search(pattern, text, re.IGNORECASE)
print(match.group())  # World

# 2. re.MULTILINE (or re.M) - ^ and $ match start/end of lines
text = """Line 1
Line 2
Line 3"""
matches = re.findall(r"^Line \d", text, re.MULTILINE)
print(matches)  # ['Line 1', 'Line 2', 'Line 3']

# 3. re.DOTALL (or re.S) - . matches newline as well
text = "Hello\nWorld"
match = re.search(r"Hello.World", text, re.DOTALL)
print(match.group())  # Hello\nWorld

# 4. re.VERBOSE (or re.X) - allow whitespace and comments in pattern
pattern = r"""
    \d{3}  # Area code
    -      # Separator
    \d{3}  # Prefix
    -      # Separator
    \d{4}  # Number
"""
text = "555-123-4567"
match = re.search(pattern, text, re.VERBOSE)
print(match.group())  # 555-123-4567

# 5. Combining flags
text = "Hello\nWorld"
pattern = r"hello.world"
match = re.search(pattern, text, re.IGNORECASE | re.DOTALL)
print(match.group())  # Hello\nWorld

# 6. re.ASCII (or re.A) - make \w, \b, \s, \d match only ASCII
text = "café"
matches = re.findall(r"\w+", text)  # ['café']
matches_ascii = re.findall(r"\w+", text, re.ASCII)  # ['caf']

Common flags:

re.IGNORECASE — case-insensitive
re.MULTILINE — ^ and $ per line
re.DOTALL — . matches newline
re.VERBOSE — whitespace and comments
Combine — use | (pipe) to combine flags

Quick Check: Which flag makes the pattern case-insensitive? (Answer: re.IGNORECASE)

Real-World Examples

Practical Regex Applications

# Here are some real-world regex examples

import re

# 1. Email validation
def is_valid_email(email):
    pattern = r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    return bool(re.match(pattern, email))

print(is_valid_email("user@example.com"))   # True
print(is_valid_email("invalid-email"))      # False

# 2. URL extraction
def extract_urls(text):
    pattern = r"https?://[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}(?:/[^\s]*)?"
    return re.findall(pattern, text)

text = "Visit https://example.com and http://test.com"
print(extract_urls(text))  # ['https://example.com', 'http://test.com']

# 3. Password validation
def is_valid_password(password):
    # At least 8 chars, one uppercase, one lowercase, one digit
    pattern = r"^(?=.*[a-z])(?=.*[A-Z])(?=.*\d).{8,}$"
    return bool(re.match(pattern, password))

print(is_valid_password("Password123"))  # True
print(is_valid_password("pass"))         # False

# 4. Extracting hashtags
def extract_hashtags(text):
    return re.findall(r"#\w+", text)

text = "I love #Python and #coding"
print(extract_hashtags(text))  # ['#Python', '#coding']

# 5. Cleaning HTML tags
def remove_html_tags(text):
    return re.sub(r"<[^>]+>", "", text)

html = "Hello World"
print(remove_html_tags(html))  # Hello World

# 6. Extracting numbers with units
text = "The box is 10kg and the other is 5.5kg"
matches = re.findall(r"(\d+\.?\d*)kg", text)
print(matches)  # ['10', '5.5']

# 7. Date validation (YYYY-MM-DD)
def is_valid_date(date):
    pattern = r"^(20\d{2})-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])$"
    return bool(re.match(pattern, date))

print(is_valid_date("2024-01-15"))  # True
print(is_valid_date("2024-13-01"))  # False

# 8. Extracting Twitter handles
text = "Follow @alice and @bob on Twitter"
handles = re.findall(r"@\w+", text)
print(handles)  # ['@alice', '@bob']

Common use cases:

Email validation — check email format
URL extraction — find links in text
Password validation — enforce password rules
Data extraction — pull specific information
Text cleaning — remove unwanted tags

Quick Check: What pattern would you use to find hashtags? (Answer: #\w+)

Try It Yourself

Experiment with regular expressions in the editor below.

Loading Pyodide... 0%

Python Code Editor

# Python Regular Expressions - Practice

print("=" * 40)
print("REGULAR EXPRESSIONS PRACTICE")
print("=" * 40)

import re

# 1. Searching
print("\n1. SEARCHING")
text = "The quick brown fox jumps over the lazy dog"
match = re.search(r"fox", text)
if match:
    print(f"Found: {match.group()} at position {match.start()}")

# 2. Finding all
print("\n2. FINDING ALL")
text = "I have 3 apples, 5 oranges, and 10 bananas"
numbers = re.findall(r"\d+", text)
print(f"Numbers: {numbers}")

# 3. Finding all with positions
print("\n3. FINDING ALL WITH POSITIONS")
text = "The cat sat on the mat"
for match in re.finditer(r"at", text):
    print(f"Found 'at' at position {match.start()}")

# 4. Replacing
print("\n4. REPLACING")
text = "The cat sat on the mat"
result = re.sub(r"cat", "dog", text)
print(f"Result: {result}")

# 5. Splitting
print("\n5. SPLITTING")
text = "apple,banana;cherry:grape"
result = re.split(r"[,;:]", text)
print(f"Split result: {result}")

# 6. Email validation
print("\n6. EMAIL VALIDATION")
def is_valid_email(email):
    pattern = r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    return bool(re.match(pattern, email))

print(f"'user@example.com': {is_valid_email('user@example.com')}")
print(f"'invalid-email': {is_valid_email('invalid-email')}")

print("\nRegular expressions practice complete!")

========================================
REGULAR EXPRESSIONS PRACTICE
========================================

1. SEARCHING
Found: fox at position 16

2. FINDING ALL
Numbers: ['3', '5', '10']

3. FINDING ALL WITH POSITIONS
Found 'at' at position 4
Found 'at' at position 18

4. REPLACING
Result: The dog sat on the mat

5. SPLITTING
Split result: ['apple', 'banana', 'cherry', 'grape']

6. EMAIL VALIDATION
'user@example.com': True
'invalid-email': False

Regular expressions practice complete!

🏆

You've Got It!

You now understand regular expressions in Python. You can search, find, replace, and split text using powerful patterns. Regex is a skill that will serve you well in data processing and text analysis.

Quick Quiz

Test what you've learned:

1. Which method finds the first occurrence of a pattern?

A) re.findall() B) re.search() C) re.sub() D) re.split()

2. What does the dot (.) match in regex?

A) Any digit B) Any whitespace C) Any character except newline D) Any character including newline

3. Which method returns all matches as a list?

A) re.search() B) re.sub() C) re.findall() D) re.split()

4. What does \d+ match?

A) One or more letters B) One or more digits C) One or more spaces D) Zero or more digits

5. Which flag makes a regex case-insensitive?

A) re.MULTILINE B) re.DOTALL C) re.IGNORECASE D) re.VERBOSE

Frequently Asked Questions

What is the difference between re.search() and re.match()?

re.search() looks for the pattern anywhere in the string. re.match() only looks for the pattern at the beginning of the string. Use search() when you want to find patterns anywhere, match() when you want to check if a string starts with a pattern.

What is the difference between re.findall() and re.finditer()?

re.findall() returns a list of strings (or tuples with groups). re.finditer() returns an iterator of match objects, which gives you more information like position, groups, and the ability to access match details.

How do I make regex case-insensitive?

Use the re.IGNORECASE flag (or re.I for short). For example: re.search(r"world", text, re.IGNORECASE). This makes the pattern match "World", "world", "WORLD", etc.

What's a common interview question about regex?

Common questions include: "How do you validate an email address with regex?" "What is the difference between re.search() and re.match()?" and "How would you extract all numbers from a string?"

What is the difference between \d and \D?

\d matches any digit (0-9). \D matches any character that is not a digit. They are opposites. \d is equivalent to [0-9] and \D is equivalent to [^0-9].

Can I use regex to validate email addresses?

Yes, but email validation with regex is complex. A simple pattern like ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ works for most cases. For full compliance with the email specification, you'd need a very complex regex or use a specialized library.

Where to Go From Here

Now that you understand regular expressions, check out these related topics:

Previous: 📝 String Assignments Next: Basics of Object-Oriented Programming →

Python

Python: Regular Expressions

What Are Regular Expressions?

Regex Basics

Understanding the Language of Patterns

Searching with re.search()

Finding the First Match

Finding All Matches with re.findall()

Getting All Occurrences

Finding Matches with re.finditer()

Getting Match Objects for All Occurrences

Replacing with re.sub()

Find and Replace with Patterns

Splitting with re.split()

Split Strings Using Patterns

Using Groups

Extracting Specific Parts

Regex Flags

Modifying Regex Behavior

Real-World Examples

Practical Regex Applications

Try It Yourself

You've Got It!

Quick Quiz

Frequently Asked Questions

What is the difference between re.search() and re.match()?

What is the difference between re.findall() and re.finditer()?

How do I make regex case-insensitive?

What's a common interview question about regex?

What is the difference between \d and \D?

Can I use regex to validate email addresses?

Where to Go From Here

Object-Oriented Programming

Iterators

Generators

Python

Python: Regular Expressions

What Are Regular Expressions?

Regex Basics

Understanding the Language of Patterns

Searching with re.search()

Finding the First Match

Finding All Matches with re.findall()

Getting All Occurrences

Finding Matches with re.finditer()

Getting Match Objects for All Occurrences

Replacing with re.sub()

Find and Replace with Patterns

Splitting with re.split()

Split Strings Using Patterns

Using Groups

Extracting Specific Parts

Regex Flags

Modifying Regex Behavior

Real-World Examples

Practical Regex Applications

Try It Yourself

You've Got It!

Quick Quiz

Frequently Asked Questions

What is the difference between re.search() and re.match()? ▼

What is the difference between re.findall() and re.finditer()? ▼

How do I make regex case-insensitive? ▼

What's a common interview question about regex? ▼

What is the difference between \d and \D? ▼

Can I use regex to validate email addresses? ▼

Where to Go From Here

Object-Oriented Programming

Iterators

Generators

What is the difference between re.search() and re.match()?

What is the difference between re.findall() and re.finditer()?

How do I make regex case-insensitive?

What's a common interview question about regex?

What is the difference between \d and \D?

Can I use regex to validate email addresses?