The Complete Guide to Logging in Python

📋 Still using print() to debug in Python? It’s time to upgrade.

print() statements work during development, but in production they’re a trap: no levels, no source tracking, and no automatic persistence. Python’s logging module is the right solution.

📊 The 5 log levels and when to use them:

🔧 Basic example:

import logging
logger = logging.getLogger('my_app')
logger.setLevel(logging.DEBUG)

# Console handler (INFO+) and file handler (DEBUG+)
console_handler = logging.StreamHandler()
file_handler = logging.FileHandler('app.log')
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')

⚠️ The most common mistake when logging exceptions:

# BAD: only captures the error message
logger.error(f'Error: {e}')

# GOOD: captures the full stack trace
logger.error(f'Error processing user {user_id}', exc_info=True)

🔍 Explanation in a nutshell

A “stack trace” is the history of function calls that led to the error — basically the crime scene map. With exc_info=True, the log includes exactly which line failed and why, without needing to reproduce the error locally.

More information at the link 👇

The Complete Guide to Logging for Python Developers

Stop using print statements and start logging like a pro. This guide shows Python developers how to log smarter and debug faster.

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