**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Testing and Debugging Python Code **Topic:** Debugging techniques: Using `pdb` and logging for error tracking **Overview:** In this topic, we'll explore two essential debugging techniques in Python: using the `pdb` module and logging for error tracking. Debugging is an essential part of the software development process, and these techniques will help you identify and fix errors in your code more efficiently. **Why Debugging is Important:** Debugging is crucial in ensuring that your code runs smoothly and produces the expected results. A single error can lead to crashes, security vulnerabilities, or unexpected behavior. By mastering debugging techniques, you can: * Reduce the time spent on debugging * Improve code quality and reliability * Enhance your understanding of your code **Using `pdb` for Debugging:** `pdb` (Python Debugger) is a built-in module in Python that provides a powerful way to debug your code. It allows you to step through your code line by line, examine variables, and set breakpoints. Here's an example of how to use `pdb`: ```python import pdb def add_numbers(a, b): pdb.set_trace() return a + b result = add_numbers(2, 3) print(result) ``` When you run this code, the execution will stop at the `pdb.set_trace()` line, and you'll enter the `pdb` interactive shell. You can use the following commands to navigate your code: * `n(ext)`: Execute the next line of code * `s(tep)`: Step into a function call * `c(ontinue)`: Continue execution until the next breakpoint * `b(reak)`: Set a breakpoint at a specific line number or function * `p expression`: Evaluate an expression and print its value * `q(uit)`: Quit the debugger **Using Logging for Error Tracking:** Logging is another essential technique for debugging and error tracking. Python provides a built-in `logging` module that allows you to log events at different levels of severity. Here's an example of how to use logging: ```python import logging logging.basicConfig(level=logging.INFO) def divide_numbers(a, b): try: result = a / b except ZeroDivisionError: logging.error("Error: Division by zero") else: logging.info("Result: %s", result) divide_numbers(2, 3) divide_numbers(2, 0) ``` In this example, we're logging events at the `INFO` and `ERROR` levels. You can use the following levels of severity: * `DEBUG`: Low-level information, typically for debugging * `INFO`: Informational messages * `WARNING`: Potential errors or unexpected events * `ERROR`: Errors or unexpected events * `CRITICAL`: Critical errors or unexpected events **Best Practices for Debugging:** * Use `pdb` to step through your code and identify errors * Use logging to track events and identify potential errors * Set breakpoints at critical points in your code * Use try-except blocks to catch and handle exceptions * Keep your code organized and readable to facilitate debugging **Additional Resources:** * [Python `pdb` Module Documentation](https://docs.python.org/3/library/pdb.html) * [Python `logging` Module Documentation](https://docs.python.org/3/library/logging.html) **Practice and Discuss:** Try using `pdb` and logging in your own code to debug and track errors. If you have any questions or need further clarification, leave a comment below. **Next Topic:** In the next topic, we'll explore the functional programming paradigm in Python.