How can you effectively debug a Python program that is crashing or freezing?

1 Use print statements

One of the simplest and most widely used techniques to debug a Python program is to use print statements to display the values of variables, expressions, or messages at different points of the code. This can help you to check the flow of the code, locate the source of the error, and verify the expected output. However, print statements can also clutter the code, slow down the execution, and be difficult to remove. Therefore, you should use them sparingly and only for quick and simple debugging.

2 Use a debugger

A debugger is a tool that allows you to execute a Python program step by step, inspect the values and types of variables, set breakpoints, watch expressions, and modify the code on the fly. A debugger can help you to find and fix errors more efficiently and accurately than print statements, as well as to explore and understand the code better. There are many debuggers available for Python, such as pdb, ipdb, PyCharm, VS Code, and PyDev. You can choose the one that suits your preferences and needs, and learn how to use its features and commands.

4 Use unit tests

Unit tests are pieces of code that test the functionality and correctness of a specific part of a Python program, such as a function, a class, or a module. Unit tests can help you to debug a Python program by verifying that each part works as expected, detecting any errors or bugs before they affect the whole program, and ensuring that any changes or modifications do not break the existing code. You can use the built-in unittest module in Python or other frameworks, such as pytest, nose, or doctest, to write and run unit tests for your code.

6 Use cross-platform and cross-language tools

Sometimes, a Python program may crash or freeze due to compatibility issues with the operating system, the interpreter, the libraries, or the other languages that it interacts with. To debug such issues, you need to use tools that can work across different platforms and languages, and that can provide you with detailed and relevant information about the error. Some examples of such tools are gdb, valgrind, pyflame, and py-spy. You can use these tools to inspect the stack traces, memory dumps, performance metrics, and other aspects of the Python program and its environment.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?