When developing software, simply printing messages to the console using print() is not enough for debugging and monitoring applications. Professional developers use Logging to record application events, errors, warnings, and system information.

Python’s built-in logging module provides a flexible and scalable way to track program execution. Logging helps developers troubleshoot issues, monitor application performance, maintain audit trails, and diagnose problems in production environments.

Logging is widely used in Artificial Intelligence, Machine Learning, Web Development, Backend Engineering, DevOps, Cloud Computing, Cybersecurity, and Enterprise Applications.

In this lesson, you’ll learn how to configure logging, work with different log levels, save logs to files, format log messages, log exceptions, and implement production-ready logging practices.

By the end of this lesson, you’ll be able to replace print() statements with professional logging techniques suitable for real-world software development.

Learning Objectives

After completing this lesson, you will be able to:

• Understand Python Logging.
• Configure the logging module.
• Use different log levels.
• Write logs to files.
• Format log messages.
• Log exceptions.
• Rotate log files.
• Apply logging in production applications.

Topics Covered

• Introduction to Logging
• Why Logging is Important
• Logging Module
• Log Levels
• Log Formatting
• Logging to Files
• Exception Logging
• Rotating Log Files
• Best Practices

Detailed Lesson Content

Introduction to Logging

Imagine you’re developing an AI-Based Student Portal.

Thousands of users log in every day.

Sometimes users report:

• Login failures
• Payment errors
• Server crashes
• API failures
• Database issues

Using:

print("Error")

is not enough because the message disappears once the program ends.

Instead, professional developers save events inside Log Files.

Example:

2026-07-10 09:45:11

INFO

Student Login Successful

2026-07-10 09:46:22

ERROR

Database Connection Failed

This process is called Logging.

What is Logging?

Logging is the process of recording events that occur while a program is running.

Logs help developers:

• Debug programs
• Monitor applications
• Identify errors
• Analyze system performance
• Maintain security audits

Importing the Logging Module

import logging

Basic Logging

import logging

logging.warning("This is a warning message")

Output

WARNING:root:This is a warning message

Log Levels

Python provides several logging levels.

Logging Different Levels

import logging

logging.debug("Debug Message")

logging.info("Information")

logging.warning("Warning")

logging.error("Error")

logging.critical("Critical Error")

Configuring Logging

import logging

logging.basicConfig(

level=logging.DEBUG

)

logging.debug("Application Started")

Now all messages from DEBUG level and above will be displayed.

Logging to a File

Instead of displaying messages on the screen, save them to a file.

import logging

logging.basicConfig(

filename="application.log",

level=logging.INFO

)

logging.info("Application Started")

A file named:

application.log

is created automatically.

Formatting Log Messages

import logging

logging.basicConfig(

filename="application.log",

level=logging.INFO,

format="%(asctime)s %(levelname)s %(message)s"

)

logging.info("Student Login Successful")

Output inside log file

2026-07-10 09:40:22

INFO

Student Login Successful

Useful Format Specifiers

Logging Exceptions

import logging

try:

    result = 10 / 0

except Exception as error:

    logging.error(error)

Output

ERROR

division by zero

Logging Complete Exception Details

import logging

try:

    number = 10/0

except Exception:

    logging.exception(

        "Exception Occurred"

    )

This records:

• Error message
• Stack trace
• Line number

making debugging much easier.

Rotating Log Files

Large applications generate huge log files.

Python provides:

from logging.handlers import RotatingFileHandler

Example

import logging

from logging.handlers import RotatingFileHandler

handler = RotatingFileHandler(

"app.log",

maxBytes=10000,

backupCount=3

)

logger = logging.getLogger()

logger.addHandler(handler)

logger.warning("Application Started")

When the file reaches the specified size, a new backup log file is created automatically.

Real-World Example 1: Student Login

import logging

logging.basicConfig(

filename="student.log",

level=logging.INFO

)

logging.info(

"Student Rahul Logged In"

)

Real-World Example 2: Payment Gateway

import logging

try:

    payment = 1000

    print(payment)

except Exception:

    logging.exception(

        "Payment Failed"

    )

Real-World Example 3: API Monitoring

import logging

logging.info(

"API Request Sent"

)

logging.info(

"Response Received"

)

Real-World Example 4: AI Model Training

import logging

logging.info(

"Training Started"

)

logging.info(

"Epoch 1 Completed"

)

logging.info(

"Training Finished"

)

Logs help monitor long-running AI training processes.

Logging vs print()

Professional software should use logging instead of print() for application monitoring.

Common Mistakes

Using print() Instead of Logging

Incorrect

print("Error")

Correct

logging.error("Error")

Logging Sensitive Information

Avoid logging:

• Passwords
• OTPs
• Credit Card Numbers
• API Keys
• Personal Information

Using the Wrong Log Level

Use:

• DEBUG → Development
• INFO → Normal events
• WARNING → Potential issues
• ERROR → Failures
• CRITICAL → System crashes