Fixing Python Errors: Debugging in PyCharm, VS Code Jupyter

Posted on by Ravi Prajatpat's
Laptop screen displaying Python errors code with a bug icon, representing debugging errors in PyCharm, VS Code, and Jupyter Notebook. The workspace includes a coffee cup, books, and a plant.

Table of Contents

  1. Introduction
  2. Understanding Common Python Errors
    • SyntaxError
    • IndentationError
    • NameError
    • TypeError
    • ImportError
  3. Debugging in PyCharm
  4. Debugging in VS Code
  5. Debugging in Jupyter Notebook
  6. Best Practices for Debugging Python Code
  7. Conclusion

1. Introduction

Python errors can be frustrating, but with the right debugging techniques, you can resolve them quickly. Whether you’re coding in PyCharm, VS Code, or Jupyter Notebook, understanding how to fix errors efficiently is essential for improving productivity.

This guide will walk you through common Python errors, how to debug them in different IDEs, and best practices for writing error-free code.

2. Understanding Common Python Errors

2.1 SyntaxError

A SyntaxError occurs when Python detects incorrect syntax in the code.

Example:

print("Hello World"

Error Message:

SyntaxError: unexpected EOF while parsing

Fix:

✔ Ensure all parentheses, brackets, and quotes are properly closed.
✔ Use a code linter like Flake8 or PyLint to detect syntax issues.

2.2 IndentationError

An IndentationError happens when the indentation is incorrect.

Example:

def greet():
print("Hello")

Error Message:

IndentationError: expected an indented block

Fix:

✔ Use consistent indentation (preferably 4 spaces per level).
✔ Avoid mixing spaces and tabs.

2.3 NameError

A NameError occurs when trying to use a variable that hasn’t been defined.

Example:

print(message)

Error Message:

NameError: name 'message' is not defined

Fix:

✔ Ensure the variable is defined before use.
✔ Check for typos in variable names.

2.4 TypeError

A TypeError happens when an operation is performed on an incompatible data type.

Example:

num = 10
print(num + " is a number")

Error Message:

TypeError: unsupported operand type(s) for +: 'int' and 'str'

Fix:

✔ Convert types explicitly (str(num)).
✔ Use built-in functions like isinstance() to check variable types.

2.5 ImportError

An ImportError occurs when Python cannot find a module.

Example:

import non_existent_module

Error Message:

ImportError: No module named 'non_existent_module'

Fix:

✔ Ensure the module is installed (pip install module_name).
✔ Verify the correct module name and spelling.

3. Debugging in PyCharm

PyCharm provides built-in debugging tools that simplify error detection.

3.1 How to Debug in PyCharm

  1. Set Breakpoints: Click on the left margin to pause execution at a specific line.
  2. Run in Debug Mode: Click the bug icon (🔍) or press Shift + F9.
  3. Step Through Code: Use F8 (Step Over) and F7 (Step Into) to navigate execution.
  4. Evaluate Expressions: Use Alt + F8 to check variable values.
  5. Use the Watch Panel: Monitor variable changes during execution.

3.2 PyCharm Fixes for Common Errors

✔ Fix Import Errors: Press Alt + Enter to install missing packages.
✔ Code Completion: Use PyCharm’s autocomplete feature to avoid typos.
✔ Syntax Highlighting: PyCharm warns about potential errors before execution.

4. Debugging in VS Code

VS Code is a lightweight, flexible editor with powerful debugging features.

4.1 How to Debug in VS Code

  1. Install Python Extension: Open Extensions (Ctrl + Shift + X) and install “Python.”
  2. Set Breakpoints: Click on the left margin to create breakpoints.
  3. Start Debugging: Press F5 to launch the debugger.
  4. Use Debug Console: Execute Python commands in real time.
  5. Inspect Variables: Hover over variables to check their values.

4.2 VS Code Fixes for Common Errors

✔ IntelliSense: Provides autocomplete suggestions to avoid typos.
✔ Error Highlighting: Flags syntax issues before execution.
✔ Run Selection: Highlight and execute parts of the code (Shift + Enter).

5. Debugging in Jupyter Notebook

Jupyter Notebooks are widely used in data science and machine learning.

5.1 How to Debug in Jupyter Notebook

  1. Use %debug Magic Command: %debug This command enters interactive debugging mode after an error.
  2. Set Breakpoints with pdb: import pdb pdb.set_trace()
  3. Use print() for Quick Debugging: Inline output helps in real-time debugging.
  4. Check Variables with %whos: %whos Displays all active variables and their types.

5.2 Jupyter Notebook Fixes for Common Errors

✔ Restart Kernel: If execution gets stuck, restart the kernel (Kernel > Restart).
✔ Check Execution Order: Run cells in the correct order to avoid missing dependencies.
✔ Use Exception Handling: Wrap code in try-except blocks for better error messages.

6. Best Practices for Debugging Python Code

  • Read the Error Message Carefully
  • Use Print Statements Strategically
  • Use Logging Instead of Print
  • Leverage Exception Handling
  • Use a Debugger for Efficiency

7. Conclusion

Debugging Python errors doesn’t have to be difficult. By understanding common errors, using the right debugging tools in PyCharm, VS Code, and Jupyter Notebook, and following best practices, you can fix bugs efficiently and write better code.

Do you have a favorite debugging technique? Share your thoughts in the comments! 🚀

FAQs About Debugging Python Errors in PyCharm, VS Code, and Jupyter

1. What are common Python errors and how can I fix them?

Answer:
Common Python errors include SyntaxError, IndentationError, NameError, TypeError, and ImportError. Each error type can be resolved by:
SyntaxError:
➡ Fix by ensuring correct syntax, such as closing parentheses and quotes.
IndentationError:
➡ Consistently use spaces for indentation (preferably 4 spaces per level).
NameError:
➡ Define variables before using them and check for typos.
TypeError:
➡ Ensure you’re using the correct data types (e.g., converting strings to integers if needed).
ImportError:
➡ Ensure the module is installed and the correct module name is used.

2. How do I debug Python code in PyCharm?

Answer:
In PyCharm, debugging is easy with the built-in tools:
➡ Set breakpoints by clicking on the left margin of your code.
➡ Run in debug mode by clicking the bug icon (🔍) or pressing Shift + F9.
➡ Step through the code using F8 (Step Over) and F7 (Step Into) to navigate through the program.
➡ Inspect variables during debugging by hovering over them.
➡ Use the Evaluate Expression feature (Alt + F8) to evaluate variables or code snippets at runtime.

3. How can I fix Python errors in Visual Studio Code?

Answer:
In VS Code, you can debug Python easily with these steps:
➡ Install the Python extension from the Extensions tab (Ctrl + Shift + X).
➡ Set breakpoints by clicking the left margin next to the line number.
➡ Start debugging by pressing F5 or clicking the debug icon in the side panel.
➡ Inspect variables by hovering over them or using the Debug Console.
➡ Run specific code sections by highlighting the code and pressing Shift + Enter for quick testing.

4. What are some debugging tips for Jupyter Notebook?

Answer:
In Jupyter Notebook, debugging can be made easier with the following tips:
➡ Use %debug magic command after an error to start the interactive debugger.
➡ Set breakpoints using pdb.set_trace() for manual inspection.
➡ Check variables using the %whos command to view all active variables and their types.
➡ Restart the kernel if execution is stuck by selecting Kernel > Restart.
➡ Use print statements to track variable values and flow throughout the code.

5. How can I avoid Python errors while writing code?

Answer:
You can avoid Python errors by following these best practices:
➡ Write clean and readable code with proper indentation.
➡ Use meaningful variable names to avoid confusion and errors.
➡ Leverage IDE features like auto-completion and syntax highlighting to avoid typos.
➡ Write unit tests to check for errors early during development.
➡ Use version control to track code changes and identify where errors were introduced.

6. How can debugging improve my Python skills?

Answer:
Debugging is a vital skill for any Python developer. By debugging errors, you:
➡ Learn more about the Python interpreter and how it processes your code.
➡ Identify logical errors that might not be obvious at first glance.
➡ Improve your problem-solving skills by systematically analyzing and fixing issues.
➡ Refine your understanding of Python’s built-in functions and libraries.

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Author

  • Ravi Prajatpat's

    Ravi Prajapati is a BCA graduate with 4 years of experience in the IT field. Currently working as a Desktop Support Engineer in a private company in Noida, Ravi has strong technical knowledge and expertise in troubleshooting and support.

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