Summary  
The chapter introduces regular expressions as a way to define text patterns and shows how to use Python’s re module (e.g., re.search and match.group()) to identify and extract substrings that match those patterns.  

General domain of usage  
Text processing

## Introduction to Regular Expressions

Regular expressions, often abbreviated as **regex**, are powerful tools for searching, matching, and manipulating text based on specific patterns. The concept of **regular expressions** dates back to the 1950s, when mathematician Stephen Cole Kleene introduced the idea as a way to describe certain sets of strings in formal language theory. Over the decades, **regular expressions** have become essential in computing, especially for tasks involving text processing.

**Common use cases for regular expressions include:**
- Validating user input, such as email addresses or phone numbers;
- Searching for patterns within large bodies of text;
- Extracting specific information, like dates or keywords, from documents;
- Replacing or reformatting segments of text according to defined rules.

**Regular expressions** are supported in many programming languages, including `Python`, where they are widely used for both simple and complex text processing tasks.

import re

text = "The price is $100."
pattern = r"\$\d+"

match = re.search(pattern, text)

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

In the code above, you first import Python's built-in **`re`** module, which provides support for regular expressions. The **`pattern`** variable defines a regex pattern that matches a dollar sign (`$`) followed by one or more digits. The **`re.search`** function scans the **`text`** string for the first location where the regex pattern produces a match. If a match is found, **`match.group()`** returns the matched substring; otherwise, the code prints **"No match found."**

This approach allows you to quickly identify and extract specific patterns from text using Python. Regular expressions help you:

- Find and extract information such as prices, dates, or keywords;
- Validate input formats, like email addresses or phone numbers;
- Automate repetitive text processing tasks.

Which Python module is used for **regular expressions**?
- re
- regexpy
- pattern
- string

What is the primary purpose of **regular expressions**?

Select the most accurate option below:

Which of the following is a valid use case for **regular expressions**?

A comprehensive course on Python regular expressions, designed for learners with basic Python knowledge. This course covers regex syntax, pattern matching, advanced features, and practical applications through theory and hands-on tasks.

Learn the fundamentals of regular expressions in Python, including basic syntax, pattern matching, and essential regex functions.

Delve deeper into regex with grouping, quantifiers, anchors, and substitution techniques.

Master advanced regex features such as lookahead/lookbehind, non-greedy matching, and apply regex to real-world text processing problems.

What are Regular Expressions?

Introduction to Regular Expressions

1. Which Python module is used for regular expressions?

2. What is the primary purpose of regular expressions?

3. Which of the following is a valid use case for regular expressions?