Regular Expressions (regex) in Python are powerful tools for pattern matching and text manipulation. They allow you to search, match, and manipulate strings based on specific patterns. This tutorial will guide you through the basics of regex in Python using the re module, with examples to help you understand each step.

A regular expression is a sequence of characters that defines a search pattern. For example, you can use regex to:

• Find all email addresses in a text.
• Validate phone numbers.
• Extract specific words or patterns from strings.

In Python, the re module provides functions to work with regex.

To use regex in Python, you need to import the re module.

import re

Regex patterns use special characters to define rules. Here are some common ones:

• . : Matches any single character (except newline).
• ^ : Matches the start of a string.
• $ : Matches the end of a string.
• * : Matches 0 or more repetitions of the previous character.
• + : Matches 1 or more repetitions of the previous character.
• ? : Matches 0 or 1 repetition of the previous character.
• \d : Matches any digit (0–9).
• \w : Matches any alphanumeric character (a–z, A–Z, 0–9, _).
• \s : Matches any whitespace character (space, tab, newline).
• [abc] : Matches any single character in the set (e.g., a, b, or c).
• [^abc] : Matches any single character not in the set.

The re module provides several functions to work with regex:

• re.search(pattern, string): Searches for the first occurrence of the pattern in the string.
• re.match(pattern, string): Checks if the string starts with the pattern.
• re.findall(pattern, string): Returns a list of all non-overlapping matches.
• re.sub(pattern, replacement, string): Replaces matches with a new string.

Let’s find a word in a string using re.search().

import re

text = "Hello, my name is Alice."
pattern = r"Alice"

match = re.search(pattern, text)
if match:
    print("Found:", match.group())  # Output: Found: Alice
else:
    print("Not found")

Explanation:

• r"Alice": The r denotes a raw string, which prevents Python from interpreting backslashes.
• re.search(): Looks for "Alice" in the text.
• match.group(): Returns the matched string.

Let’s find all numbers in a string using \d.

import re

text = "My phone number is 123-456-7890."
pattern = r"\d+"

numbers = re.findall(pattern, text)
print(numbers)  # Output: ['123', '456', '7890']

Explanation:

• \d+: Matches one or more digits.
• re.findall(): Returns a list of all matches.

Let’s create a regex pattern to validate an email address.

import re

email = "example@domain.com"
pattern = r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"

if re.match(pattern, email):
    print("Valid email")
else:
    print("Invalid email")

Explanation:

• ^: Ensures the string starts with the pattern.
• [a-zA-Z0-9._%+-]+: Matches one or more letters, digits, or specific symbols for the username.
• @: Matches the @ symbol.
• [a-zA-Z0-9.-]+: Matches the domain name.
• \.: Matches a literal dot.
• [a-zA-Z]{2,}: Matches the top-level domain (e.g., com, org).
• $: Ensures the string ends with the pattern.

Let’s replace all digits in a string with a placeholder.

import re

text = "My number is 123-456-7890."
pattern = r"\d+"
result = re.sub(pattern, "XXX", text)
print(result)  # Output: My number is XXX-XXX-XXX.

Explanation:

• re.sub(): Replaces all matches of \d+ with "XXX".

Groups allow you to capture parts of a match using parentheses ().

import re

text = "Contact: alice@example.com"
pattern = r"(\w+)@([\w.]+)"

match = re.search(pattern, text)
if match:
    username = match.group(1)  # First group
    domain = match.group(2)    # Second group
    print(f"Username: {username}, Domain: {domain}")
    # Output: Username: alice, Domain: example.com

Explanation:

• (\w+): Captures one or more alphanumeric characters (username).
• @: Matches the @ symbol.
• ([\w.]+): Captures the domain name.

• Test your regex: Use online tools like regex101.com to experiment with patterns.
• Start simple: Break complex patterns into smaller parts.
• Use raw strings: Always use r"pattern" to avoid issues with backslashes.
• Practice: Try matching patterns like phone numbers, URLs, or dates.

• Explore more advanced regex patterns like lookaheads and lookbehinds.
• Practice with real-world examples, such as parsing logs or cleaning data.
• Check the official Python re module documentation for more functions and options.