Blog 10: Regular Expressions in Python

Regular Expressions in Python – Pattern Matching & Data Extraction

Regular expressions (regex) allow hackers and developers to search, match, and manipulate strings based on specific patterns. Python’s built-in re module is a powerful tool that can help in CTFs, log analysis, web scraping, and custom data filters.

Getting Started with the re Module

import re

# Basic match
pattern = r"hacker"
text = "python for hackers"
match = re.search(pattern, text)
if match:
    print("Match found!")

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Common Regex Functions

• re.search() – searches for first match
• re.findall() – returns all matching substrings
• re.match() – checks for a match at the beginning
• re.sub() – replaces matched substrings

Extracting All Matches

text = "Email me at vaibhav@example.com or test123@hackmail.com"
pattern = r"[\w.-]+@[\w.-]+"
emails = re.findall(pattern, text)
print(emails)

Replacing Sensitive Data

log = "User: root, Password: 123456"
pattern = r"Password: \w+"
clean_log = re.sub(pattern, "Password: ****", log)
print(clean_log)

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Regex Meta-Characters

• . – any character
• \d – digit
• \w – word character (a-z, A-Z, 0-9, _)
• \s – whitespace
• + – one or more
• * – zero or more
• ? – optional
• [] – character set
• () – capture group

Capture Groups Example

text = "IP: 192.168.1.1"
pattern = r"IP: (\d+\.\d+\.\d+\.\d+)"
match = re.search(pattern, text)
if match:
    print("Captured IP:", match.group(1))

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Use Cases for Hackers

• Log parsing for credentials/IPs
• Extracting tokens or secrets from web content
• Brute-force automation for credential leaks
• Scraping specific patterns (emails, phone numbers, IPs)

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Conclusion

Regex is an essential skill in any hacker’s toolbox. Mastering the re module helps you automate detection, extraction, and redaction of data — making your scripts powerful and efficient. Keep experimenting and building!