Python Regular Expressions (Regex) Complete Notes

1. What is a Regular Expression (Regex)?

A Regular Expression (Regex) is a sequence of characters that forms a search pattern. It is used to check if a string contains a specified search pattern, validate form inputs, or extract data.

Easy English Explanation

Pattern Matching: Technique to find, extract, or validate text data.
Built-in Module: Python includes the re module for regex operations.
Versatility: Used across web forms, security logs, data science, and text processing.

Hinglish Explanation

Regex Ka Matlab: Text ke andar pattern dhundhne, validate karne ya extract karne ka tarika.
Python Module: Python mein Regex use karne ke liye hum built-in re module import karte hain.
Fayde: Validation (email, phone, password), data extraction aur string cleaning dynamic ho jati hai.

import re

2. Core Regex Functions: re.search(), re.match(), re.findall()

Function Differences

re.search(): Searches anywhere in the string for a match.
re.match(): Matches ONLY at the beginning/start of the string.
re.findall(): Finds ALL occurrences and returns them as a List of strings.
re.fullmatch(): Returns match object ONLY if the ENTIRE string matches the pattern.

Functions ka Difference

re.search(): Poori string me kahi bhi pattern dhundhta hai.
re.match(): Sirf string ke START me match check karta hai.
re.findall(): Saare matches ki ek List return karta hai.
re.fullmatch(): Jab poori string pattern ke a-to-z according ho tabhi True deta hai.

Python Code - search, match, findall, fullmatch

import re

text = "My name is Gagan"

search_res = re.search("Gagan", text)
print("Search Result:", search_res)

match_res = re.match("Gagan", text)
print("Match Result (Starts with Gagan?):", match_res)

text2 = "apple mango apple banana apple"
all_apples = re.findall("apple", text2)
print("Findall Result:", all_apples)

Output

Search Result: <re.Match object; span=(11, 16), match='Gagan'>
Match Result (Starts with Gagan?): None
Findall Result: ['apple', 'apple', 'apple']

3. Text Modification: re.sub() and re.split()

Python Code - sub and split

import re

text1 = "I like Java"
replaced = re.sub("Java", "Python", text1)
print("Replaced Text:", replaced)

text2 = "apple,mango,banana"
split_list = re.split(",", text2)
print("Split List:", split_list)

Output

Replaced Text: I like Python
Split List: ['apple', 'mango', 'banana']

4. Basic Metacharacters (. * + ?)

Symbol	Meaning	Example Pattern	Matches
.	Any single character (except newline)	c.t	cat, cut, cot, c1t
*	Zero or more times	co*l	cl, col, cool, coooool
+	One or more times (at least once)	co+l	col, cool, coooool
?	Zero or one time (Optional)	colou?r	color, colour

Python Code - Metacharacters in Action

import re

print("Dot match:", re.search("c.t", "cat").group())
print("Star match:", re.search("co*l", "cooooool").group())
print("Plus match:", re.search("co+l", "coool").group())
print("Question match:", re.search("colou?r", "color").group())

Output

Dot match: cat
Star match: cooooool
Plus match: coool
Question match: color

5. Character Classes & Sets ([abc], [0-9], [a-z], [A-Z])

Square brackets [] define a set of characters to match.

Python Code - Character Sets

import re

text1 = "My age is 22"
digits = re.findall("[0-9]", text1)

text2 = "hello123"
lowers = re.findall("[a-z]", text2)

text3 = "Hello GAGAN"
uppers = re.findall("[A-Z]", text3)

print("Digits:", digits)
print("Lowercase Letters:", lowers)
print("Uppercase Letters:", uppers)

Output

Digits: ['2', '2']
Lowercase Letters: ['h', 'e', 'l', 'l', 'o']
Uppercase Letters: ['H', 'G', 'A', 'G', 'A', 'N']

6. Special Sequences (\d, \D, \w, \W, \s, \S)

Sequence	Description	Matches
\d	Digits (0-9)	Any single digit
\D	Non-digits	Anything except digits
\w	Word character	Letters, numbers, and underscore (_)
\W	Non-word character	Symbols (@, !, %, spaces)
\s	Whitespace	Space, tab, newline
\S	Non-whitespace	Any non-space character

7. Anchors (^ and $)

Anchor Meaning

^: Asserts position at the START of the string.
$: Asserts position at the END of the string.

Anchor ka Matlab

^: Batata hai ki string is text se SHURU honi chahiye.
$: Batata hai ki string is text par KHATAM honi chahiye.

Python Code - Anchors

import re

text = "Hello Gagan"
starts_hello = re.search(r"^Hello", text)
ends_gagan = re.search(r"Gagan$", text)

print("Starts with Hello?:", bool(starts_hello))
print("Ends with Gagan?:", bool(ends_gagan))

Output

Starts with Hello?: True
Ends with Gagan?: True

8. Quantifiers ({n}, {n,m})

Python Code - Quantifiers

import re

text = "My number is 12345"
exact_5 = re.findall(r"\d{5}", text)

text2 = "123456789"
range_2_4 = re.findall(r"\d{2,4}", text2)

print("Exact 5 digits:", exact_5)
print("Range 2 to 4 digits:", range_2_4)

Output

Exact 5 digits: ['12345']
Range 2 to 4 digits: ['1234', '5678', '9']

9. Form Validation: Phone, Email & Password

Python Code - Validation Patterns

import re

phone = "9876543210"
phone_pattern = r"^\d{10}$"
is_valid_phone = bool(re.fullmatch(phone_pattern, phone))

email = "gagan@gmail.com"
email_pattern = r"^[\w.-]+@[\w.-]+\.[a-zA-Z]{2,}$"
is_valid_email = bool(re.fullmatch(email_pattern, email))

password = "Gagan123"
pass_pattern = r"^(?=.*[A-Za-z])(?=.*\d).{6,}$"
is_valid_pass = bool(re.fullmatch(pass_pattern, password))

print("Valid Phone?:", is_valid_phone)
print("Valid Email?:", is_valid_email)
print("Valid Password?:", is_valid_pass)

Output

Valid Phone?: True
Valid Email?: True
Valid Password?: True

Email Pattern Breakdown

Part	Pattern Snippet	Explanation
Start	^	Beginning of email string
Username	[\w.-]+	Letters, digits, underscores, dots, hyphens
Symbol	@	Mandatory @ symbol
Domain	[\w.-]+	Domain name (e.g. gmail, yahoo)
Dot	\.	Literal dot (.)
Extension	[a-zA-Z]{2,}	Minimum 2 letters (.com, .in, .org)
End	$	End of email string

10. Practical Data Extraction & Text Cleaning

Python Code - Extracting Phones, Emails, Mentions, Cleaning

import re

text = "Invoice INV1001 for Gagan
Contact: 9876543210 or email gagan@gmail.com
Mentions: @gagan and @rajat"

inv_num = re.findall(r"INV\d+", text)
phones = re.findall(r"\d{10}", text)
emails = re.findall(r"[\w.-]+@[\w.-]+\.[a-zA-Z]{2,}", text)
mentions = re.findall(r"@\w+", text)

raw_messy = "Hello     Gagan    How   are  you"
clean_spaces = re.sub(r"\s+", " ", raw_messy)

print("Invoice:", inv_num)
print("Phones:", phones)
print("Emails:", emails)
print("Mentions:", mentions)
print("Clean Spaces:", clean_spaces)

Output

Invoice: ['INV1001']
Phones: ['9876543210']
Emails: ['gagan@gmail.com']
Mentions: ['@gagan', '@rajat']
Clean Spaces: Hello Gagan How are you

11. 20 Real-World Use Cases of Regex

1. Email Validation: Signup & login forms.
2. Phone Number Validation: WhatsApp/Banking registrations.
3. Password Strength Check: Security enforcement in apps.
4. Extracting Phone Numbers: Scraped documents & customer text.
5. Extracting Emails: Customer lead generation.
6. Extracting Numbers from Invoices: Invoice prices and IDs.
7. Data Cleaning: Replacing extra spaces (\s+) in datasets.
8. Special Character Removal: Sanitizing usernames.
9. Pincode/Zip Code Check: Indian 6-digit address verification.
10. Date Format Validation: Checking DD-MM-YYYY format.
11. Username Validation: Alphanumeric + underscore checks.
12. URL Protocol Check: Ensuring http:// or https://.
13. Log File Analysis: Parsing server error codes & IP addresses.
14. Global Text Search: Search engine indexing.
15. Web Scraping Pattern Matching: Extracting price tags from HTML.
16. Mentions Extraction: Social media @mentions parsing.
17. Price Extraction: Extracting numeric prices from product titles.
18. Invoice Data Extraction: Pulling Invoice ID, phone, email, and amount.
19. Banking Reference Validation: Checking transaction IDs.
20. Document Processing: Parsing PDF contents into database fields.

12. The 5 Pillars of Regex & Summary

The 5 Core Pillars

SEARCH: Finding patterns inside text (re.search).
VALIDATE: Checking format compliance (re.fullmatch).
EXTRACT: Pulling specific pieces of data (re.findall).
REPLACE: Swapping target text (re.sub).
CLEAN: Removing unwanted symbols/spaces.

5 Pillars Breakdown

Search: Text me kuch dhundhna.
Validate: Format sahi hai ya nahi check karna.
Extract: Specific data bahar nikalna.
Replace: Text ko change/swap karna.
Clean: Unwanted symbols/spaces hatana.

Python Regular Expressions (REGEX) Notes

1. What is a Regular Expression (Regex)?

Easy English Explanation

Hinglish Explanation

2. Core Regex Functions: re.search(), re.match(), re.findall()

Function Differences

Functions ka Difference

3. Text Modification: re.sub() and re.split()

4. Basic Metacharacters (. * + ?)

5. Character Classes & Sets ([abc], [0-9], [a-z], [A-Z])

6. Special Sequences (\d, \D, \w, \W, \s, \S)

7. Anchors (^ and $)

Anchor Meaning

Anchor ka Matlab

8. Quantifiers ({n}, {n,m})

9. Form Validation: Phone, Email & Password

Email Pattern Breakdown

10. Practical Data Extraction & Text Cleaning

11. 20 Real-World Use Cases of Regex

12. The 5 Pillars of Regex & Summary

The 5 Core Pillars

5 Pillars Breakdown