Python

🔍 Python Tutorial — Regular Expressions (re Module)

Introduction 🌟

Regular Expressions (RegEx or regex) are powerful patterns used for searching, matching, extracting, and manipulating text. Python provides the re module to work with regex.

Note

💡 Perfect for validation (email, phone, password)
💡 Excellent for text extraction and cleaning
💡 Used heavily in web scraping, NLP, log analysis

1. Importing the re Module 🧱

import_re.py

import re

2. Basic Functions in re Module 📘

Function	Purpose
re.search()	Find first match anywhere in string
re.match()	Match only at the beginning
re.findall()	Find all occurrences
re.finditer()	Iterator of match objects
re.sub()	Replace using regex
re.split()	Split using regex

3. re.search() 🕵️

search_example.py

result = re.search("cat", "A black cat is here")
print(result.group())

✔ Searches anywhere in the string

4. re.match() 🎯

match_example.py

result = re.match("Hello", "Hello World")
print(result.group())

✔ Must match at the very beginning

5. re.findall() 📚

findall_example.py

print(re.findall("\d+", "My numbers: 10, 20, 30"))

✔ Extracts all numbers

6. re.sub() — Replacing Text 🛠️

sub_example.py

print(re.sub("cat", "dog", "I love cat and another cat"))

✔ Useful for data cleaning

7. re.split() — Splitting with Regex ✂️

split_example.py

print(re.split("[,; ]+", "a,b; c d"))

✔ Splits by multiple separators

8. Special Metacharacters 🎨

Symbol	Meaning
.	Any character
^	Start of string
$	End of string
*	0 or more
+	1 or more
?	0 or 1
[]	Character set
()	Group
\d	Digit
\w	Word (letters, digits, _ )
\s	Whitespace

9. Character Sets & Ranges 🔤

charset_example.py

re.findall("[A-Z]", "Hello PYTHON")  # ['H', 'P', 'Y', 'T', 'H', 'O', 'N']

10. Quantifiers 🔢

quantifier_example.py

re.findall("a{2,4}", "aaa aaaaa a")

✔ Matches "aa", "aaa", "aaaa"

11. Groups & Capturing 🎯

groups_example.py

result = re.search("(\w+)@(\w+)", "email@mail.com")
print(result.group(1))  # email
print(result.group(2))  # mail

✔ Useful for extracting structured data

12. Named Groups 🏷️

named_groups.py

pattern = r"(?P<user>\w+)@(?P<domain>\w+\.com)"
m = re.search(pattern, "sathish@gmail.com")

print(m.group("user"))
print(m.group("domain"))

13. Using Flags for Extra Power 🏴

Flag	Description
re.I / IGNORECASE	Case-insensitive
re.M / MULTILINE	^ and $ match each line
re.S / DOTALL	Dot matches newline

flag_example.py

re.search("hello", "HELLO", re.I)

14. Real-World Example — Validate Email 📧

email_validation.py

email = "user123@mail.com"
pattern = r"^[\w.-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$"

print(bool(re.match(pattern, email)))

15. Real-World Example — Extract Phone Numbers 📱

phone_extract.py

text = "Call me at 98765-43210 or 87654-32109"
print(re.findall("\d{5}-\d{5}", text))

16. Real-World Example — Remove Extra Spaces 🧹

clean_spaces.py

text = "Python     is   awesome"
clean = re.sub("\s+", " ", text)
print(clean)

17. finditer() — Detailed Matches 🎯

finditer_example.py

for m in re.finditer("\d+", "A1 B22 C333"):
    print(m.group(), "at", m.span())

Regex Cheat Sheet 📘

Pattern	Meaning
\d	Digit
\D	Non-digit
\w	Word char
\W	Non-word char
\s	Whitespace
[abc]	a or b or c
a{2,}	2 or more a's
^start	Starts with "start"
end$	Ends with "end"

Best Practices 💡

✔ Use raw strings → r"pattern"
✔ Pre-compile patterns for speed (re.compile())
✔ Keep regex simple and readable
✔ Use named groups for clarity
✔ Test patterns using online regex tools

Conclusion 🎉

>>“Regex turns messy text into structured data — mastering it unlocks powerful text-processing superpowers.” ✨

You now fully understand Regular Expressions (re module) in Python! Want the next topic? Try Lambda, OOP, File Handling, Generators, or Threading. Just tell me! 😊