Python Q & A


How to debug Python code?

To debug Python code effectively, employ a combination of strategies:

  • Print Statements: Insert print() statements at crucial points in your code to display variable values and the program’s flow. This simple technique helps identify issues step by step.
  • Exception Handling: Wrap problematic sections with try-except blocks. This allows you to catch and handle errors gracefully, preventing program crashes.
  • Debugging Tools: Make the most of integrated development environments (IDEs) like PyCharm or Visual Studio Code, which offer built-in debuggers. These tools enable you to set breakpoints, inspect variables, and step through code execution.
  • Breakpoints: Set breakpoints in your code to pause execution at specific lines. This allows you to examine variable states and the program’s behavior during runtime.
  • Logging: Utilize Python’s logging module to record important information and errors. This approach is especially valuable for tracking issues in long-running or production code.
  • Tracebacks: Analyze error tracebacks, which provide valuable information about the location and nature of the issue. Understanding these tracebacks helps you pinpoint problems quickly.
  • Test Cases: Write unit tests using Python’s built-in unittest or third-party libraries like pytest. Well-structured tests isolate specific components and can identify problems as you make changes.
  • Code Analysis Tools: Leverage code analysis tools such as Pylint, Flake8, or MyPy. These tools automatically check your code for style, potential errors, and type issues.
  • Rubber Duck Debugging: Sometimes, explaining your code to someone else or even an inanimate object can help you gain insights. This process of verbalizing your thought process can uncover issues or lead to solutions.

By combining these techniques, you can efficiently identify and resolve issues in your Python code.

Previously at
Flag Argentina
time icon
Senior Software Engineer with 7+ yrs Python experience. Improved Kafka-S3 ingestion, GCP Pub/Sub metrics. Proficient in Flask, FastAPI, AWS, GCP, Kafka, Git