What is the difference between logging and print() in Python?

The Python logging module records timestamped, level-tagged messages (DEBUG, INFO, WARNING, ERROR, CRITICAL) that can be routed to files, the console, or remote systems and filtered by severity. print() writes only to stdout with no level, timestamp, or destination control, so logging is the correct choice for production debugging and monitoring.

How do I write Python log messages to a file?

Call logging.basicConfig(filename=’app.log’, filemode=’w’, level=logging.DEBUG) before logging any messages. The filename parameter sets the target file, filemode=’w’ overwrites it each run (use ‘a’ to append), and level=logging.DEBUG captures all severities from DEBUG upward.

What are the five Python logging levels and their numeric values?

Python defines DEBUG (10), INFO (20), WARNING (30), ERROR (40), and CRITICAL (50). The logger emits any message at or above the configured level, so setting level=logging.WARNING suppresses DEBUG and INFO messages.

Why should I use logging.getLogger(__name__) in each module?

logging.getLogger( name ) creates a module-level logger named after the module, so every log line identifies its source module. This lets larger applications assign different levels and handlers per module and makes tracing the origin of a message straightforward.

In this post, I cover everything from logging to configuring logging to output messages to different destinations.

I also included some examples of logging levels and how to log messages at different levels based on the severity of the issue.

I hope my post will help anyone understand how to use logging effectively in their Python programs. If you have any thoughts or suggestions, feel free to share them with me.

What Is the Python logging Module

The Python logging module is a standard-library component that records timestamped, severity-tagged messages from a running program and routes them to destinations such as the console, files, or remote services. Logging is essential for developers to track events, debug issues, and understand how their programs work, and it offers a flexible alternative to print() for production diagnostics.

Logging allows us to:

Track the flow of your program
Debug and diagnose issues
Monitor applications in production
Gain insights into user behaviour

Python logging Examples

Python’s logging module is simple and can be configured to suit different needs. Let’s start with a basic example.

Configuring Python Logging: Levels, Handlers, and Formatters

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Advanced Logging Configuration

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Conclusion: When to Use the Python logging Module

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References

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Logging in Python

📚 This post is part of the "Python Projects & Utilities" series

What Is the Python logging Module

Python logging Examples

Configuring Python Logging: Levels, Handlers, and Formatters

Advanced Logging Configuration

Conclusion: When to Use the Python logging Module

References

References

Citation

Logging in Python

📚 This post is part of the "Python Projects & Utilities" series

What Is the Python logging Module

Python logging Examples

Configuring Python Logging: Levels, Handlers, and Formatters

Advanced Logging Configuration

Conclusion: When to Use the Python logging Module

References

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References

Citation