The methods of debugging Python code mainly include: 1. Use pdb for command line debugging; 2. Use the graphical debugging function of the IDE; 3. Record logs through the logging module; 4. Use third-party debugging tools. pdb is a debugger built into Python. You can insert pdb.set_trace() into the code or start it through the command line to perform single-step execution, view variables, etc.; IDEs such as PyCharm and VS Code provide graphical interface debugging functions such as breakpoints and monitoring expressions, which are suitable for complex logic problems; the logging module can replace print output, support multi-level control and diversified output targets, which are convenient for log management at different stages; third-party tools such as ipdb, Py-Spy, and cProfile enhance the interactive experience and performance analysis capabilities, and can be flexibly selected according to specific needs. Mastering these methods can help to efficiently locate and solve problems.

Debugging Python code is an inevitable part of the development process, especially after the project complexity increases, it is difficult to quickly locate problems by relying on print output alone. Fortunately, there are many practical debugging tools and methods in the Python ecosystem that can help you find out where the bug is more efficient.

Use pdb: Python's own debugger

pdb is a debugging tool in the Python standard library. It is very convenient to use and does not require additional installation. You just need to insert import pdb; pdb.set_trace() into the code, and the program will be paused when it runs here and enters interactive debugging mode.

In this mode, you can:

• View the current variable value
• Step-by-step (using n)
• Jump into the function (using s)
• View the call stack (using w)

Although inserting set_trace() when writing code is the most direct way, debugging can also be enabled through command line startup, such as python -m pdb script.py , which is more suitable for debugging at the entire script level.

Utilize the graphical debugging function of the IDE

For many people, debugging with the IDE graphical interface will be more intuitive. PyCharm and VS Code all provide complete debugging support.

Taking VS Code as an example, after configuring the launch.json file, you can break points in the code, view variables, execute them in a single step, and even do advanced operations such as conditional breakpoints and monitoring expressions.

These features are particularly suitable for dealing with complex logical errors or state dependencies. If you are working in a team collaboration environment, the debugging capabilities of the IDE are also easier to share and reproduce problems.

Logging: A more reliable way than print

Sometimes we want to understand the overall process of the program running, but we don’t want to interrupt the execution process. The logging module comes in handy at this time.

Compared with print, logging can set different log levels (debug, info, warning, error, critical), and can flexibly control the output format and target (console, file, remote server, etc.).

A common practice is:

• Set the development stage to DEBUG level, output detailed information
• Switch to INFO or WARNING after going online to avoid excessive logs affecting performance

For example:

 import logging
logging.basicConfig(level=logging.DEBUG)
logging.debug('This is a debugging message')

This will not only retain debugging information, but will not mess up the output.

Third-party debugging tools: the choice of enhanced capabilities

In addition to built-in tools, there are also some third-party libraries that can help you debug better. for example:

• ipdb : Combined with IPython, it provides a more friendly debugging experience, supports automatic completion and syntax highlighting
• Py-Spy : used to analyze performance bottlenecks of Python programs, suitable for troubleshooting CPU and memory problems
• vspyder : Visual debugging plug-in, suitable for data display in specific scenarios

These tools can be selected and used according to specific needs. For example, if you want to see how much time a certain function takes, it is very appropriate to use cProfile snakeviz.

Basically these commonly used methods. Different tool combinations can be selected in different situations. The key is to master the basic ideas: observe the state, narrow the scope, and verify the hypothesis.

The above is the detailed content of Debugging Python Code Effectively with Tools. For more information, please follow other related articles on the PHP Chinese website!