Python Logging Tutorial: Stop Using Print() for Debugging (The Professional Guide)

Python Logging Tutorial: Stop Using Print() for Debugging

Every beginner Python developer uses `print("here")` to find bugs. Every senior developer uses `logging`. Why? Because `print` is temporary, but logs are forever.

Imagine you have a script running on a server at 3:00 AM. It crashes. If you used print(), that error message is gone forever, lost in the console void. If you used logging, that error is saved to a file with a timestamp, the module name, and the severity level.

The logging module is built into Python. It requires zero installation. It allows you to switch on/off messages without deleting code, and it lets you write to files and consoles simultaneously.

The Problem with Print: You have to manually delete every print statement before deploying. If you miss one, it clutters your user's screen. With logging, you just change one setting (Level) and the debug messages vanish.

Step 1: The Basic Setup

To start using logging, you need to import it and configure it once at the start of your script.

import logging # Configure the logger logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s' ) # Use it instead of print logging.debug("This is a debug message") logging.info("This is an info message") logging.warning("This is a warning") logging.error("This is an error")

Step 2: Understanding Levels

The magic of logging is the Level system. You categorize messages by importance. When you run your code, you set a "Threshold." Only messages at or above that threshold get shown.

• DEBUG (10): Detailed info for diagnosing problems (e.g., "Variable x is 5").
• INFO (20): Confirmation that things are working (e.g., "Connected to database").
• WARNING (30): Something unexpected happened, but code is still running (e.g., "Disk space low").
• ERROR (40): A function failed (e.g., "File not found").
• CRITICAL (50): The application crashed (e.g., "Out of Memory").

How to use this: While coding, set level to DEBUG to see everything. When you deploy, change level to WARNING. All your debug and info messages disappear automatically. No code deletion required.

Step 3: Writing to a File

This is the killer feature. You can save your logs to a file so you can read them later.

logging.basicConfig( filename='app.log', # Log to this file filemode='a', # Append mode (don't overwrite) level=logging.INFO, format='%(asctime)s - %(message)s' )

Now, every time your script runs, it appends the history to `app.log`. You have a permanent record of what your code did.

Logging vs. Print: A Comparison

Feature	Print()	Logging
Destination	Console only	Console, File, Email, Server
Timestamps	Manual	Automatic
Categorization	None	Levels (Debug, Error, etc.)
Production Ready	No	Yes

Conclusion

Transitioning from `print` to `logging` is a rite of passage for Python developers. It separates the hobbyists from the professionals. It gives you control, history, and peace of mind.

Stop printing. Start logging.

Download January Skills: Python Logging Config Template