Regular Expressions in Python

8/16/2025
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Regular expressions in Python for pattern matching and text processing

Regular Expressions in Python

Regular Expressions in Python: A Complete Guide with Examples

Regular Expressions (Regex) in Python are a powerful tool for pattern matching, text searching, and string manipulation. They allow developers to search, extract, and replace text efficiently using specific patterns. Python provides the built-in re module for working with regular expressions.

In this article, we’ll explore what regular expressions are, their syntax, and practical examples in Python.

What are Regular Expressions?

A regular expression (regex) is a sequence of characters that defines a search pattern. It is widely used for:

  • Validating input (emails, phone numbers, passwords).

  • Searching for patterns in text.

  • Extracting useful information (like dates, URLs, or numbers).

  • Replacing or splitting strings based on patterns.

Importing the re Module

To use regular expressions in Python, first import the built-in re module:

import re

Common Regex Functions in Python

Python’s re module provides several useful functions:

Function Description
re.match() Checks if the pattern matches at the beginning of a string.
re.search() Searches for the first occurrence of a pattern.
re.findall() Returns all matches in a list.
re.sub() Replaces one or more matches with a given string.
re.split() Splits a string by the occurrences of a pattern.

Basic Regex Patterns

Pattern Meaning
\d Matches any digit (0–9).
\D Matches any non-digit.
\w Matches word characters (letters, digits, underscore).
\W Matches non-word characters.
\s Matches whitespace (spaces, tabs, newlines).
. Matches any character except newline.
^ Matches the start of a string.
$ Matches the end of a string.
* Matches 0 or more repetitions.
+ Matches 1 or more repetitions.
{n,m} Matches between n and m repetitions.

Examples of Regular Expressions in Python

Example 1: Validating an Email Address

import re

email = "test@example.com"
pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'

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

Example 2: Extracting Numbers from Text

import re

text = "Order numbers: 1234, 5678, and 91011"
numbers = re.findall(r'\d+', text)
print(numbers)  # ['1234', '5678', '91011']

Example 3: Replacing Text

import re

text = "I like Java"
new_text = re.sub(r'Java', 'Python', text)
print(new_text)  # I like Python

Example 4: Splitting a String

import re

text = "apple,banana;grape orange"
fruits = re.split(r'[;,\s]\s*', text)
print(fruits)  # ['apple', 'banana', 'grape', 'orange']

Best Practices for Using Regex in Python

  • Use raw strings (r"pattern") to avoid escape sequence conflicts.

  • Keep regex patterns simple and readable.

  • Test your regex using tools like regex101.

  • Use compiled regex (re.compile()) for repeated use to improve performance.

Final Thoughts

Regular expressions in Python are a must-know tool for text processing, validation, and data cleaning. By mastering regex patterns and Python’s re module, you can handle complex text manipulation tasks efficiently.

Whether you’re validating user input, cleaning datasets, or searching logs, regex provides the flexibility and power needed for robust Python applications.

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