Lesson 21: Regular Expressions in Python

🎯 Lesson Objective

To understand and apply Regular Expressions (RegEx) in Python for searching, validating, and manipulating text patterns efficiently.

---

🧩 1. What Are Regular Expressions?

Regular Expressions (RegEx) are special patterns used to match strings or parts of strings.
Python provides the re module to work with these expressions.

✅ Importing RegEx Module:

import re

---

⚙️ 2. Common RegEx Functions

Function	Description
re.search()	Finds the first match in a string
re.findall()	Returns all matches as a list
re.match()	Checks if string starts with the pattern
re.split()	Splits string by the matched pattern
re.sub()	Replaces pattern with a new string

---

🔍 3. Basic Examples

Example 1 – search()

import re
text = "My phone number is 9876543210"
pattern = r"\d{10}"
match = re.search(pattern, text)
print(match.group())

Output:

9876543210

---

Example 2 – findall()

text = "Emails: user1@gmail.com, user2@yahoo.com"
emails = re.findall(r'\S+@\S+', text)
print(emails)

Output:

['user1@gmail.com', 'user2@yahoo.com']

---

Example 3 – match()

text = "Python is powerful"
result = re.match(r"Python", text)
print(result)

Output:

<re.Match object; span=(0, 6), match='Python'>

---

Example 4 – split()

text = "apple,banana;orange grape"
words = re.split(r'[;,\s]+', text)
print(words)

Output:

['apple', 'banana', 'orange', 'grape']

---

Example 5 – sub()

text = "My number is 9876543210"
new_text = re.sub(r'\d+', '**********', text)
print(new_text)

Output:

My number is **********

---

🧱 4. Common RegEx Patterns

Pattern	Meaning	Example Match
\d	Digit (0–9)	3
\D	Non-digit	A
\w	Word character (a–z, 0–9, _)	a
\W	Non-word character	@
\s	Whitespace	(space, tab)
^	Start of string	^Python
$	End of string	end$
.	Any character (except newline)	c.t → cat, cot
*	0 or more repetitions	lo*l → ll, lol, loool
+	1 or more repetitions	go+ → go, goo, gooo
{n}	Exactly n times	\d{4} → 2025
`	`	OR condition
[]	Character set	[A-Z] matches A–Z

---

🧠 5. Real-Life Use Cases

• Validate email, phone number, or passwords
• Extract data from text (e.g., IDs, numbers, dates)
• Find or replace text in logs or documents
• Split structured text (CSV, JSON strings)

---

🌍 6. Real-Life Example – Email Validation

import re

email = "sameer123@gmail.com"
pattern = r'^[a-zA-Z0-9._]+@[a-z]+\.[a-z]+$'

if re.match(pattern, email):
    print("Valid email address!")
else:
    print("Invalid email address!")

Output:

Valid email address!

---

💬 7. Password Strength Validation

password = "Pa$$word123"
pattern = r'^(?=.*[A-Z])(?=.*[a-z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{8,}$'

if re.match(pattern, password):
    print("Strong password!")
else:
    print("Weak password!")

Output:

Strong password!