8 Basic Debugging Techniques in Python

. Assert and Check

Another useful debugging technique in Python is the use of the assert statement.

Similar to the print and check technique, the assert statement allows for conditional checking of variables.

If the condition is False, an AssertionError is raised, indicating that there is an issue.

The advantage of using assert over print is that it provides a more structured way of checking variables and can be enabled or disabled globally with the -0 parameter.

However, it is important to use assert statements judiciously to avoid cluttering the code and making it harder to read.

Here is an example of using assert in Python:

x = 5

assert x > 0, “x should be a positive number”

In this example, if the value of x is not greater than 0, an AssertionError will be raised with the message “x should be a positive number”.

This allows developers to quickly identify and fix issues in their code by explicitly stating the expected conditions.

By incorporating the assert statement into their debugging workflow, Python developers can ensure the correctness of their code and catch errors early in the development process.
3. Using the logging Module

In addition to the print and check technique and the assert and check technique, another powerful debugging technique in Python is using the logging module.

The logging module provides a professional and structured way to debug code, offering various functionalities and options for outputting debug messages.

Instead of using print statements or assert statements, developers can use the logging functions to log messages to different locations, such as the console or a file.

One advantage of using the logging module is the flexibility it provides in organizing and managing debug messages.

Developers can set different logging levels, such as DEBUG, INFO, WARNING, etc., to filter and control the level of detail for the debug messages.











This helps in focusing on specific areas of code or narrowing down the scope of the debug information.

Additionally, logging messages can be saved in a file, making it easier to review and analyze the history of debug logs.

By utilizing the logging module, developers can enhance their debugging process and gain valuable insights into the behavior of their code.

The logging module is especially useful when dealing with large projects or complex systems where print statements may not be practical or sufficient.

It provides a professional and standardized approach to debugging, improving code quality and maintainability.
Logging Levels
Level Description
DEBUG Detailed information, typically useful only for diagnostic purposes.
INFO Confirmation that things are working as expected.
WARNING An indication that something unexpected happened or potential issues that do not prevent the program from functioning.
ERROR Due to a more serious problem, the software has not been able to perform some function.
CRITICAL A very serious error, indicating that the program itself may be unable to continue running.
4. pdb Debugger

In the realm of Python debugging techniques, the pdb debugger stands as a powerful tool that can greatly assist in the identification and resolution of complex bugs.

With its step-by-step execution mode, pdb allows you to closely monitor the running status of your code at any given point, providing real-time inspection of variables.