The re (Regular Expressions) Module

The re module lets you search, match, and manipulate text patterns efficiently using regular expressions. They are powerful for validating input, cleaning data, and analyzing text.

RegEx in Python

Before using any regular expressions, you must import Python’s built-in re module:

import re

When you have imported the re module, you can start using regular expressions.

A regular expression describes a text pattern. In the pattern below, ^ marks the start, .* permits zero or more characters other than a newline, and $ marks the end (or the position before a final newline). re.search() returns a match object when it finds the pattern, or None otherwise.

Let’s search for text that starts with “The” and ends with “Spain”:

import re

txt = "The rain in Spain"
x = re.search("^The.*Spain$", txt)
if x:
  print("YES! We have a match!")
else:
  print("No match")

Output

YES! We have a match!

RegEx Functions and Syntax Overview

Before diving into examples, let’s review the key functions and symbols that make regular expressions so powerful in Python.

Regular expressions (RegEx) work through the re module, which provides several built-in functions for searching, splitting, and replacing text.

RegEx Functions

The re module offers a set of functions that allow you to search, extract, or modify text patterns:

These functions form the foundation of most regular expression tasks in Python.

Metacharacters

Metacharacters are special symbols with unique meanings in regular expressions. They let you define flexible patterns instead of exact text matches.

Flags

Flags modify how regular expressions are interpreted. They can be combined with functions like re.search() or re.findall() to change matching behavior.

Special Sequences

Special sequences start with a backslash (\) and represent common text patterns like digits, spaces, or word characters. When used inside raw strings (like r"\d"), Python reads them literally.

Sets

A set is a collection of characters enclosed in square brackets [ ]. It matches any single character inside the brackets.

With these tools — functions, metacharacters, flags, special sequences, and sets — you can define almost any pattern imaginable, from finding numbers in a text to validating phone numbers or emails.

Applying RegEx Functions

We will now demonstrate how useful functions such as findall(),search(),split(), and sub() can be used with re.

Using findall() Function

The findall() function returns a list of all parts of the string that match a pattern.

import re

text = "I have 2 apples, 5 bananas, and 12 oranges."
numbers = re.findall(r"\d+", text)
print(numbers)

Output

['2', '5', '12']

• \d+ means “find one or more digits.”
• The function goes through the text and extracts every number it finds.
• The result is a list of numbers written as strings.
• Note: The prefix r"..." tells Python to treat the pattern as a raw string, meaning backslashes (\) are read literally and not as escape characters.

Using search() Function

The search() function looks for the first match of a pattern and returns a match object if found.

text = "Hello world"
match = re.search(r"world", text)

if match:
    print("Found:", match.group())
else:
    print("No match")

Output

Found: world

Explanation:

• The pattern "world" is found inside the string "Hello world".
• match.group() returns the exact part of the text that matched.

Using split() Function

The split() function breaks a string into parts wherever the pattern appears.

text = "apple,banana;cherry orange"
result = re.split(r"[,; ]", text)
print(result)

Output

['apple', 'banana', 'cherry', 'orange']

Explanation:

• The pattern [,; ] tells Python to split wherever there is a comma, semicolon, or space.
• The result is a clean list of fruit names.

This handles simple delimiter-separated text. For CSV with quoted fields, use the csv module introduced in File Handling.