Chapter 7: Regular Expressions in Python

Don't forget to explore our basket section filled with 15000+ objective type questions.

Regular expressions are powerful tools used for pattern matching and manipulating text data. They provide a concise and flexible way to search, extract, and manipulate strings based on specific patterns. This chapter explores the fundamentals of regular expressions, including syntax, metacharacters, character classes, quantifiers, and more. It also discusses how to use regular expressions in Python for tasks such as validation, searching, and data extraction.

Introduction to Regular Expressions

Regular expressions, often abbreviated as regex or regexp, are sequences of characters that define search patterns. They are used to match and manipulate strings based on specific patterns of characters. Regular expressions are widely used in text processing, data validation, data extraction, and search operations.

Basic Syntax

A regular expression is written as a sequence of characters and metacharacters. Metacharacters are special characters that have a specific meaning in regular expressions. For example, the dot (.) matches any character, and the asterisk (*) matches zero or more occurrences of the preceding character or group. Here's an example of a basic regular expression:

pattern = r"hello.*world"

In this example, the pattern matches any string that starts with "hello", followed by zero or more characters, and ends with "world". The r prefix before the string denotes a raw string literal, which is commonly used to define regular expressions in Python.

Matching Characters

Regular expressions provide various metacharacters and character classes to match specific characters or groups of characters. Some commonly used metacharacters include:

  • . (dot): Matches any character except a newline.
  • \w: Matches any alphanumeric character (letters or digits) or underscore.
  • \d: Matches any digit.
  • \s: Matches any whitespace character.

For example, the pattern r"\w+" matches one or more alphanumeric characters. The + symbol specifies that the preceding character or group should occur one or more times.


Quantifiers are metacharacters used to specify the number of occurrences of a character or group in a regular expression. Some commonly used quantifiers include:

  • *: Matches zero or more occurrences.
  • +: Matches one or more occurrences.
  • ?: Matches zero or one occurrence.
  • {n}: Matches exactly n occurrences.
  • {n,}: Matches at least n occurrences.
  • {n,m}: Matches between n and m occurrences.

For example, the pattern r"\d{3}-\d{3}-\d{4}" matches a phone number in the format of three digits, followed by a hyphen, three digits, another hyphen, and four digits.

Character Classes

Character classes are sets of characters enclosed in square brackets, used to match any one character from the set. They provide a convenient way to match specific ranges or groups of characters. For example:

  • [aeiou]: Matches any lowercase vowel.
  • [A-Z]: Matches any uppercase letter.
  • [0-9]: Matches any digit.

Character classes can also use metacharacters inside them. For example, the pattern [0-9a-fA-F] matches any hexadecimal digit.


Anchors are metacharacters used to match specific positions within a string. They do not match any characters but rather match the position before or after a character or group. Some commonly used anchors include:

  • ^: Matches the start of a string.
  • $: Matches the end of a string.
  • \b: Matches a word boundary.
  • \B: Matches a non-word boundary.

For example, the pattern ^\d+ matches any string that starts with one or more digits.

Using Regular Expressions in Python

Python provides the re module, which offers a comprehensive set of functions for working with regular expressions. Some commonly used functions include:

  •, string): Searches the string for a match to the pattern and returns the match object.
  • re.match(pattern, string): Determines if the pattern matches at the beginning of the string and returns the match object.
  • re.findall(pattern, string): Returns all non-overlapping matches of the pattern in the string as a list of strings.
  • re.sub(pattern, replacement, string): Substitutes all occurrences of the pattern in the string with the replacement string.

Here's an example of using regular expressions in Python:

import re

text = "The quick brown fox jumps over the lazy dog."

pattern = r"quick.*fox"
match =, text)

if match:
    print("Match found!")
    print("Matched text:",
    print("No match found.")

In this example, the pattern r"quick.*fox" is searched within the text string using the function. If a match is found, the matched text is printed; otherwise, a message indicating no match is displayed.


This chapter provided an in-depth exploration of regular expressions and their usage in Python. Regular expressions offer a powerful and flexible way to search, extract, and manipulate text based on specific patterns. You learned about basic syntax, metacharacters, quantifiers, character classes, and anchors. Understanding regular expressions enables you to perform advanced text processing tasks efficiently. In the next chapter, we will explore one of Python's most powerful features: handling and manipulating data using libraries such as NumPy and Pandas.

If you liked the article, please explore our basket section filled with 15000+ objective type questions.