Logging in Python

Logging in Python lets you record important information about your program’s execution. You use the built-in logging module to capture logs, which provide insights into application flow, errors, and usage patterns. With Python logging, you can create and configure loggers, set log levels, and format log messages without installing additional packages. You can also generate log files to store records for later analysis.

By the end of this tutorial, you’ll understand that:

• Logging involves recording program execution information for later analysis.
• You can use logging to debug, perform analysis, and monitor usage patterns.
• Logging in Python works by configuring loggers and setting log levels.
• Using a logging library provides structured logging and control over log output.
• You should prefer logging over print() because it decreases the maintenance burden and allows you to manage log levels.

You’ll do the coding for this tutorial in the Python standard REPL. If you prefer Python files, then you’ll find a full logging example as a script in the materials of this tutorial. You can download this script by clicking the link below:

Take the Quiz: Test your knowledge with our interactive “Logging in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Logging in Python

In this quiz, you'll test your understanding of Python's logging module. With this knowledge, you'll be able to add logging to your applications, which can help you debug errors and analyze performance.

If you’re curious about an alternative to Python’s built-in logging module, then check out How to Use Loguru for Simpler Python Logging. While the standard library’s logging requires explicit configuration of handlers, formatters, and log levels, Loguru comes pre-configured after installing it with pip.

Starting With Python’s Logging Module

The logging module in Python’s standard library is a ready-to-use, powerful module that’s designed to meet the needs of beginners as well as enterprise teams.

The main component of the logging module is something called the logger. You can think of the logger as a reporter in your code that decides what to record, at what level of detail, and where to store or send these records.

Exploring the Root Logger

To get a first impression of how the logging module and a logger work, open the Python standard REPL and enter the code below:

>>> import logging >>> logging.warning("Remain calm!") WARNING:root:Remain calm! 

The output shows the severity level before each message along with root, which is the name the logging module gives to its default logger. This output shows the default format that can be configured to include things like a timestamp or other details.

In the example above, you’re sending a message on the root logger. The log level of the message is WARNING. Log levels are an important aspect of logging. By default, there are five standard severity levels for logging events. Each has a corresponding function that can be used to log events at that level of severity.

Here are the five default log levels, in order of increasing severity:

The logging module provides you with a default logger that allows you to get started with logging without needing to do much configuration. However, the logging functions listed in the table above reveal a quirk that you may not expect:

>>> logging.debug("This is a debug message") >>> logging.info("This is an info message") >>> logging.warning("This is a warning message") WARNING:root:This is a warning message >>> logging.error("This is an error message") ERROR:root:This is an error message >>> logging.critical("This is a critical message") CRITICAL:root:This is a critical message 

Notice that the debug() and info() messages didn’t get logged. This is because, by default, the logging module logs the messages with a severity level of WARNING or above. You can change that by configuring the logging module to log events of all levels.

Adjusting the Log Level

To set up your basic logging configuration and adjust the log level, the logging module comes with a basicConfig() function. As a Python developer, this camel-cased function name may look unusual to you, as it doesn’t follow the PEP 8 naming conventions:

That’s because it was adopted from Log4j, a logging utility in Java. It’s a known issue in the package, but by the time it was decided to add it to the standard library, it had already been adopted by users, and changing it to meet PEP 8 requirements would cause backwards compatibility issues.