5 Secret Tips to Debugging Your Python Code Like a Pro

Every programmer needs to know how to debug their programs, and python developers are no different than others. To make sure that your code functions appropriately and effectively, you must be able to find the problems and able to fix these problems.

However debugging can be a difficult and time consuming task, especially for beginners. So if you are struggling to debug your python code so you are not alone. Debugging can be Frustrating but it plays a very important role in the development process.

In Today’s article, we will help you to become a pro debugger with 5 secret debugging tips which helps you to code like a pro in python and make the process earlier and more efficient.

5 Secret Tips to Debugging Your Python Code Like a Pro

  1. Use print statements
  2. Use a debugger
  3. Use a linter
  4. Break your code into smaller pieces
  5. Write tests

1. Use print statements.

The simplest way to debug your code is to use print statements. By adding print statements at various stages of the code, you can identify where the problem is occurring and what values are being used in the problematic line. This can help you narrow down the issue and fix it quickly.

For example, suppose you have a function that takes two arguments, x, and y, and returns their sum. If your function isn’t returning the expected value, you can add a print statement to check the values of x and y:

def sum(x, y):
    print("x:", x, "y:", y)
    return x + y

With this print statement, you can see the values of x and y and determine whether they’re what you expect them to be. This technique is simple, but it can be extremely effective.


  • Simple and easy to use
  • Provides a quick and easy way to output values of variables and messages to the console
  • Can be used to isolate issues and quickly fix them


  • Can be time-consuming to add and remove print statements in large codebases
  • Debugging through print statements alone can be less efficient for larger, more complex code
5 Secret Tips to Debugging Your Python Code Like a Pro
5 Secret Tips to Debugging Your Python Code Like a Pro

2. Use of Debugger

While print statements can be useful, they aren’t always the best way to debug more complex programs. A debugger can be a more powerful tool in these situations. Python includes a built-in debugger called pdb that allows you to step through your code line by line and see variable values at each step.

To use pdb, you’ll need to import it into your code and set a breakpoint where you want to start debugging:

import pdb

def sum(x, y):
    result = x + y
    pdb.set_trace()  # set breakpoint
    return result

When you run your code, it will stop at the breakpoint, and you can use pdb commands to step through your code and check the values of variables. For example, you can use the “next” command to go to the next line of code, or the “print” command to check the value of a variable.


  • it can pause the execution of code and inspect variables and control flow
  • Provides a detailed view of the code at each step, making it easier to identify and fix issues
  • it can be used to debug more complex code that may be difficult to debug using print statements alone


  • It may require more time and expertise to learn and use it effectively
  • It can be more complex and difficult to use for beginners

3. Use of Linters

Linters are tools that look for potential errors and issues in your code. Using a linter can assist you in detecting errors before they cause problems, making debugging easier and more efficient. Python has a number of well-known linters, including pylint and flake8.

Linters can assist you in detecting issues such as syntax errors, undefined variables, and unused imports. You can ensure that your code follows best practices and is free of common errors by running it through a linter.


  • Helps identify and prevent syntax errors and bugs before running the code
  • Enforces coding standards and best practices
  • Can help improve code quality and consistency


  • May not catch all issues or errors in the code
  • Can be strict and inflexible, causing issues when coding in a specific style or context

4. Breaking Codes into Smaller Pieces

Breaking your code into smaller, manageable pieces can make it easier to debug. By isolating specific sections of code, you can identify and fix issues more efficiently. Additionally, smaller functions are easier to test and debug than larger, more complex ones.


  • Helps isolate issues and makes it easier to identify and fix problems
  • Allows for easier testing and debugging of smaller, more manageable sections of code
  • Improves modularity and reusability of code


  • Can be more time-consuming to split large sections of code into smaller pieces
  • This can make it more difficult to understand the flow of the entire program

5. Write tests

Testing is a critical component of development. You can ensure that your code works as expected by writing tests for it and catching any problems early on. Python includes a testing framework called unittest that can be used to write and run tests.


  • Ensures code is working as expected and catches errors early on
  • Helps prevent regressions and issues when modifying or updating code
  • Improves code reliability and maintainability


  • Can be time-consuming to write and maintain tests
  • May not catch all issues or errors in the code, depending on the thoroughness of the tests
Credit: Patrick Loeber

Final Verdict

Finally, debugging your Python code like a pro requires the use of a variety of effective techniques and tools. While print statements, the Python debugger, linters, breaking code into smaller pieces, and writing tests are all useful techniques, it’s important to find the right combination of techniques for your specific codebase and programming style.

Whether you’re a beginner or an experienced Python developer, spending time will perfect your debugging skills and can save you time, frustration, and money in the long run. Debugging your code effectively allows you to quickly identify and resolve issues, improve code quality and reliability, and increase productivity and efficiency.

So, the next time you’re debugging Python code, remember these tips and experiment with different approaches to see what works best for you. You can become a debugging expert and advance your Python programming skills with practice, patience, and perseverance.

Also Read: 10 Reasons Why Python is the Ultimate Programming Language for Beginners?


What is Debugging in python?

Debugging is the process of identifying and fixing errors or bugs in your Python code.

Why is debugging important in Python?

Debugging is important because it helps ensure your code is functioning as intended and catches errors before they cause bigger problems. It also helps improve code quality and maintainability.

What are some common tools for debugging Python code?

Common tools for debugging Python code include print statements, the Python debugger (PDB), linters, and unit tests.

What is the benefit of using print statements for debugging?

Print statements can help you visualize the flow of your code and identify specific lines or sections where errors may be occurring. They are also simple and easy to use.

What is the Python debugger (PDB) and how does it work?

The Python debugger is a built-in tool for debugging Python code that allows you to step through your code line by line, examine variables, and identify errors.

What are linters and how can they help with debugging?

Linters are tools that analyze your code for potential errors, syntax issues, and best practices. They can help catch errors before your code even runs, making debugging easier and more efficient.

Why is breaking your code into smaller pieces helpful for debugging?

Breaking your code into smaller pieces can help you identify where errors are occurring more easily and narrow down potential causes. It also helps with the maintainability and readability of your code.

What are unit tests and how can they help with debugging?

Unit tests are automated tests that check the functionality of specific parts of your code. They can help catch errors early and ensure your code is functioning as intended.

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As a statistics student, I have a strong passion for data analysis and have honed my skills in Python. I am enthusiastic about sharing my knowledge and experience through blogging, where I aim to educate and inspire others in the field of data analysis.

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