CS50P - Lecture 7 - Regular Expressions

Introduction

This tutorial provides an in-depth exploration of regular expressions (regex) within Python, emphasizing their utility for validating, cleaning, and extracting data. Regular expressions are essential for developers working with strings, especially when dealing with user inputs like email addresses or URLs. By the end of this guide, you'll understand the syntax and applications of regex in Python, enhancing your programming skills.

Chapter 1: Understanding Regular Expressions

• Definition: Regular expressions are patterns used to match character combinations in strings. They enable developers to validate and manipulate data efficiently.
• Common Use Cases:
  • Validating user inputs (e.g., email addresses).
  • Cleaning messy data.
  • Extracting specific information from strings.

Chapter 2: Validating Email Addresses Without Regex

1. Basic Validation Logic:

   • Start by prompting the user to enter their email address.
   • Check for the presence of an "@" symbol.
   • Implement basic logic to print "valid" or "invalid".

   email = input("What's your email? ").strip()
   if "@" in email:
       print("valid")
   else:
       print("invalid")
   

2. Improving Validation:

   • Add more conditions (e.g., check for a dot in the domain).
   • Split the email into username and domain components.
   • Check that the domain ends with ".edu".

   if "@" in email and "." in email:
       username, domain = email.split("@")
       if username and domain.endswith(".edu"):
           print("valid")
       else:
           print("invalid")
   

Chapter 3: Introducing the re Library

• Importing the Library: Use Python's built-in re library for regex operations.

  import re
  

• Basic Functions:

  • re.search(): Searches for a pattern in a string.
  • re.sub(): Replaces occurrences of a pattern with a specified string.

Chapter 4: Constructing Regular Expressions

1. Basic Pattern Syntax:

   • Use literal characters and special symbols.
   • Special symbols:
     • .: Matches any character except a newline.
     • *: Matches zero or more repetitions.
     • +: Matches one or more repetitions.
     • ?: Matches zero or one occurrence.

2. Defining an Email Regex Pattern:

   • Create a regex pattern to match typical email formats:

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

3. Using re.search():

   • Apply the regex pattern to validate email addresses.

   if re.search(pattern, email):
       print("valid")
   else:
       print("invalid")
   

Chapter 5: Advanced Regex Techniques

1. Grouping and Capturing:

   • Use parentheses () to create groups in regex patterns.
   • Capture specific parts of strings for extraction.

2. Optional Patterns:

   • Use ? to make parts of your regex optional (e.g., www. in URLs).

3. Character Classes:

   • Define specific sets of characters to match within square brackets [].

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

Chapter 6: Extracting Data from Strings

1. Cleaning Up User Input:

   • Use re.sub() to clean up unwanted prefixes in URLs or user inputs.

   username = re.sub(r"https?://(www\.)?twitter\.com/", "", url)
   

2. Finalizing Username Extraction:

   • Validate the cleaned-up result and ensure only valid usernames are processed.

   matches = re.search(r"^[a-zA-Z0-9_]+$", username)
   if matches:
       print("Username extracted:", username)
   else:
       print("Invalid username")
   

Conclusion

In this tutorial, we explored the powerful capabilities of regex in Python, from basic email validation to extracting usernames from URLs. Regular expressions provide a robust framework for handling string data, making it easier to enforce rules and clean up input. As you continue to develop your Python skills, consider integrating regex into your projects for efficient data handling and validation.