Python Regular Expressions (Regex) Tutorial – Complete Guide with Examples

Python Regular Expressions (Regex)

Regular Expressions, commonly known as Regex, are powerful tools used for searching, matching, extracting, and manipulating text.

Python provides built-in support for regular expressions through the re module.

Regex is widely used for:

• Data validation
• Text searching
• Email verification
• Log analysis
• Data extraction
• Web scraping

Understanding regular expressions can significantly improve your ability to process text efficiently.

---

What is a Regular Expression?

A Regular Expression is a sequence of characters that defines a search pattern.

For example:

r"python"

This pattern matches the word:

python

Regex can be simple or extremely complex depending on the requirements.

---

Importing the re Module

Before using regular expressions, import Python's built-in re module:

import re

---

The re.search() Function

The search() function searches a string for a match.

Example

import re

text = "Welcome to Python Programming"

result = re.search("Python", text)

if result:
    print("Match Found")

Output

Match Found

---

The re.match() Function

The match() function checks only the beginning of a string.

Example

import re

text = "Python Tutorial"

result = re.match("Python", text)

print(result)

Output

<re.Match object>

---

The re.findall() Function

Returns all matching occurrences.

Example

import re

text = "Python Java Python C++ Python"

matches = re.findall("Python", text)

print(matches)

Output

['Python', 'Python', 'Python']

---

The re.finditer() Function

Returns an iterator containing match objects.

Example

import re

text = "Python Python Python"

for match in re.finditer("Python", text):
    print(match.start())

Output

0
7
14

---

The re.sub() Function

Used to replace matching text.

Example

import re

text = "I love Java"

new_text = re.sub("Java", "Python", text)

print(new_text)

Output

I love Python

---

The re.split() Function

Splits a string based on a pattern.

Example

import re

text = "Apple,Banana,Orange"

result = re.split(",", text)

print(result)

Output

['Apple', 'Banana', 'Orange']

---

Common Regex Metacharacters

Symbol	Meaning
.	Any character
^	Start of string
$	End of string
*	Zero or more occurrences
+	One or more occurrences
?	Optional occurrence
[]	Character set
|	OR operator
()	Grouping

---

Character Classes

Digits

\d

Matches:

0-9

Example:

import re

text = "Order 123"

print(re.findall(r"\d", text))

Output:

['1', '2', '3']

---

Non-Digits

\D

Matches any non-numeric character.

---

Word Characters

\w

Matches:

Letters, numbers, underscore

Example:

import re

text = "Python_3"

print(re.findall(r"\w", text))

---

Whitespace

\s

Matches spaces, tabs, and line breaks.

---

Quantifiers

Zero or More (*)

ab*

Matches:

a
ab
abb
abbb

---

One or More (+)

ab+

Matches:

ab
abb
abbb

---

Optional (?)

colou?r

Matches:

color
colour

---

Character Sets

Example

import re

text = "cat bat rat"

matches = re.findall("[cbr]at", text)

print(matches)

Output

['cat', 'bat', 'rat']

---

Range Matching

Example

import re

text = "ABC123"

matches = re.findall("[A-Z]", text)

print(matches)

Output

['A', 'B', 'C']

---

Email Validation Example

import re

email = "user@example.com"

pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'

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

Output

Valid Email

---

Phone Number Validation

import re

phone = "1234567890"

if re.match(r'^\d{10}$', phone):
    print("Valid Number")

Output

Valid Number

---

Extracting Numbers from Text

import re

text = "Price: $250, Discount: 15%"

numbers = re.findall(r'\d+', text)

print(numbers)

Output

['250', '15']

---

Extracting URLs

import re

text = "Visit https://example.com"

urls = re.findall(r'https?://\S+', text)

print(urls)

Output

['https://example.com']

---

Real-World Applications of Regex

Regular expressions are commonly used for:

1. Email validation
2. Phone number validation
3. Password checking
4. Data extraction
5. Log file analysis
6. Search engines
7. Web scraping
8. Form validation
9. Text processing
10. Data cleaning

---

Best Practices

• Keep regex patterns simple.
• Use raw strings (r"") whenever possible.
• Test patterns thoroughly.
• Add comments for complex regex.
• Avoid overly complicated expressions.

---

Common Mistakes

Forgetting Raw Strings

Wrong:

"\d+"

Correct:

r"\d+"

Using Match Instead of Search

match() checks only the beginning of a string.

search() scans the entire string.

---

Summary

Python Regular Expressions provide a flexible and powerful way to work with text. Using the re module, developers can search, validate, replace, split, and extract data efficiently.

Mastering regex is essential for handling real-world text processing tasks and is a valuable skill for every Python developer.

---

Conclusion

Regular Expressions are among the most useful text-processing tools available in Python. Whether you are validating user input, extracting information from files, cleaning datasets, or building web applications, regex can save time and simplify complex tasks.

Learning regex may seem challenging at first, but with practice, it becomes an indispensable part of your Python toolkit.