B.Tech • Chapter 10

Regular Expressions & Parsing

B.Tech level module on Regular Expressions & Parsing.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate time complexity of related algorithms.Understand memory management and background architecture.Analyze code snippets for bugs or inefficiencies.Explain theoretical concepts with real-world technical examples.

Regular Expressions (Regex) are powerful strings of characters that define search patterns. They are essential for data validation, parsing log files, and text processing in backend systems.

Advanced Engineering Concepts

The 're' module provides full support for Perl-like regular expressions. Compiling a regex pattern using re.compile() improves performance if the pattern is used repeatedly in a loop. Regex utilizes greedy (matching as much text as possible) and non-greedy (matching as little as possible) quantifiers, lookaheads, and lookbehinds for complex string assertions without consuming characters.

1Understand the Big O time and space complexities involved.
2Ensure memory leaks and race conditions are mitigated.
3Adhere to PEP 8 standards for clean, readable code.
4Utilize unit testing to verify algorithmic correctness.

Code Implementation

import re

log_data = "ERROR 404 at 10:45:01, IP: 192.168.1.1. INFO 200 at 10:46:00, IP: 10.0.0.5"

# Extract all IP addresses using a raw string pattern
# Note: r"..." is crucial to prevent Python from interpreting escape sequences
ip_pattern = re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b")

ips = ip_pattern.findall(log_data)
print("Found IPs:", ips)

# Named capturing groups
auth_pattern = re.compile(r"(?P<status>ERROR|INFO)\s(?P<code>\d{3})")
for match in auth_pattern.finditer(log_data):
    print(match.groupdict())

📝 Quick Revision Points

1Review the CPython implementation details.
2Practice the code snippets until syntax is committed to memory.
3Test edge cases and exception handling.
4Understand trade-offs between different data structures.

← PreviousAdvanced OOP: Inheritance, Polymorphism, & MRO Next →Database Connectivity & Data Science Foundations

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import re log_data = "ERROR 404 at 10:45:01, IP: 192.168.1.1. INFO 200 at 10:46:00, IP: 10.0.0.5" # Extract all IP addresses using a raw string pattern # Note: r"..." is crucial to prevent Python from interpreting escape sequences ip_pattern = re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b") ips = ip_pattern.findall(log_data) print("Found IPs:", ips) # Named capturing groups auth_pattern = re.compile(r"(?P<status>ERROR|INFO)\s(?P<code>\d{3})") for match in auth_pattern.finditer(log_data): print(match.groupdict())