🐍 Lesson 22: Python Regular Expressions (Regex) – Pattern Matching Made Easy
Welcome to Lesson 22! Today we’ll explore regular expressions (regex) in Python. Regex is a powerful tool to search, match, and manipulate text efficiently. Whether you're working with text files, web scraping, or data validation, mastering regex will improve your productivity and simplify complex text processing tasks.
⭐ What You Will Learn in This Lesson
- How to use the
remodule for regex operations - Master regex pattern matching, searching, and replacing text
- Learn how to extract and manipulate data from strings efficiently
- Understand common regex patterns for different text processing tasks
👥 Who Is This Lesson For?
- Python developers who want to enhance their text processing skills
- Anyone working with text data, web scraping, or data validation
- Developers looking to master regex for clean, efficient, and reliable code
📦 1. Importing the Regex Module
Python provides the re module for regex operations.
import re
📦 2. Searching Text
To search for a pattern in a string, you can use the re.search() method.
text = "Hello, my number is 12345."
pattern = r"\d+" # \d+ matches one or more digits
match = re.search(pattern, text)
if match:
print("Found:", match.group())
📦 3. Finding All Matches
Use re.findall() to get all matches in a string:
text = "Call 12345 or 67890"
numbers = re.findall(r"\d+", text)
print(numbers) # ['12345', '67890']
📦 4. Replacing Text
Use re.sub() to replace parts of a string:
text = "Hello John"
new_text = re.sub(r"John", "Alice", text)
print(new_text) # Hello Alice
📦 5. Splitting Text
Use re.split() to split a string based on a regex pattern:
text = "apple,banana;orange"
fruits = re.split(r"[,;]", text)
print(fruits) # ['apple', 'banana', 'orange']
📦 6. Common Regex Patterns
\d– Digit (0-9)\w– Word character (a-z, A-Z, 0-9, _)\s– Whitespace+– One or more repetitions*– Zero or more repetitions?– Zero or one repetition[abc]– Matches any character inside brackets^– Start of string$– End of string
🧩 7. Why Regex Matters
- Search and extract text efficiently
- Validate formats like emails, phone numbers, and passwords
- Clean and process large datasets
- Essential for web scraping, automation, and data analysis
🧪 Practice
- Extract all numbers from a given string using
re.findall. - Replace all spaces in a text with underscores using
re.sub. - Split a comma and semicolon-separated string using
re.split. - Write a regex to validate a simple email format (e.g.,
example@example.com).
❌ Common Mistakes
- Incorrectly using special characters in regex patterns
- Not escaping special characters (e.g.,
\,*,?) - Overcomplicating simple text search with too complex patterns
❓ Frequently Asked Questions (FAQ)
1. What is the difference between re.search() and re.findall()?
re.search() returns the first match found, while re.findall() returns a list of all non-overlapping matches in the string.
2. Can I use regex to validate email addresses?
Yes! Regex is often used for basic email validation by matching patterns like example@domain.com.
3. How do I use regex to match multiple options?
You can use the | operator to match multiple alternatives, for example, cat|dog to match either "cat" or "dog".
🚀 What’s Next?
In the next lesson, you’ll learn about:
- Exception handling in Python to catch and manage errors gracefully
- Using
try,except, andfinallyto handle exceptions - Best practices for debugging and managing errors
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