Python Log File Analysis: Extract timestamp and log severity levels
Write a custom process_log function in Python for efficient log file analysis. You'll learn to parse log files, extract timestamps and log levels using regular expressions (regex), handle file errors gracefully, and integrate it into a command-line script like our word_counter.py from the previous video • Python Search Specific Word Frequencies in...
Github code: https://github.com/lephubui/command-c...
What you'll learn:
Reading and processing log files line by line.
Using "re.match" with a regex pattern like r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3})\s+(\w+)' to capture timestamps (e.g., 2025-07-30 21:15:01,234) and log levels (e.g., ERROR, INFO).
Filtering entries by a specified log level (case-insensitive) and returning a list of tuples for easy output.
Integrating the function into a driver code for command-line usage, such as --level ERROR to display filtered log entries.
This function is perfect for building tools to analyze application logs, debug issues, or summarize events by severity.
We build on a real-world script that also includes word frequency counting and specific word searches.
0:00 Introduction: The application of the word counter problem
0:56 Understand log severity levels
2:10 Recap the previous video • Python Search Specific Word Frequencies in...
3:11 Modify search specific words function
14:26 Implement log level CLI in driver code
22:25 Implement process log function
39:47 Debug code
Prerequisites:
Python 3.x installed (check with python --version in your terminal).
A sample text file (download one quickly: logs.txt with the content of your choice).
If you found this helpful, like, subscribe, and hit the bell for more videos.
Touch base links:
https://x.com/lephubui
https://github.com/lephubui
/ lephubui
/ lephubui
AI Contents:
/ commandncode
/ commandncode
/ commandncode
DISCLAIMER: This video was produced by an engineer for whom English is a second language. Please refrain from criticizing any pronunciation or typographical errors. Thank you!