Debugging and Troubleshooting in Jupyter Notebooks: Tips and Tricks

Explore tips & tricks for debugging in Jupyter Notebooks, including print statements, built-in debugger, checking syntax errors, try-except blocks, logging, and breakpoints for efficient troubleshooting

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Jupyter Notebooks are a popular tool for data scientists and developers to work with data and code. However, debugging and troubleshooting can be a challenge, especially when dealing with complex code and data. In this blog post, we will explore some tips and tricks for debugging and troubleshooting in Jupyter Notebooks.

  1. Use print statements

One of the simplest and most effective ways to debug code in Jupyter Notebooks is to use print statements. By inserting print statements at strategic points in your code, you can see the values of variables and the output of functions in real-time. This can help you identify where errors are occurring and what values are causing the errors.

  1. Use the debugger

Jupyter Notebooks come with a built-in debugger that can help you step through your code line by line. To use the debugger, you can add the following line of code to your notebook:


This will activate the debugger when an error occurs, allowing you to step through your code and see what is happening at each step. You can also use the debugger to inspect variables and their values.

  1. Check for syntax errors

Syntax errors are a common cause of errors in Jupyter Notebooks. To check for syntax errors, you can use the following command:

%run -i

This will run your script in the Jupyter Notebook environment and show you any syntax errors that occur. You can then fix the errors and re-run your script.

  1. Use try-except blocks

Try-except blocks are a powerful tool for handling errors in Jupyter Notebooks. By wrapping your code in a try-except block, you can catch errors and handle them gracefully, instead of crashing your notebook. Here’s an example:

    # your code here
except Exception as e:
    print("An error occurred:", e)

This will catch any exceptions that occur in your code and print a message to the console. You can then handle the error in a way that makes sense for your notebook.

  1. Use logging

Logging is a useful technique for debugging and troubleshooting in Jupyter Notebooks. By adding logging statements to your code, you can track the flow of your program and see what values are being passed between functions. Here’s an example:

import logging


def my_function():
    logging.debug("Starting my_function")
    # your code here
    logging.debug("Ending my_function")

This will log messages to the console as your code runs, allowing you to see what is happening at each step.

  1. Use breakpoints

Breakpoints are a powerful tool for debugging complex code in Jupyter Notebooks. By setting a breakpoint at a specific line in your code, you can pause the execution of your code and inspect variables and their values. To set a breakpoint, you can use the following command:

import pdb; pdb.set_trace()

This will pause the execution of your code at the line where the breakpoint is set, allowing you to inspect variables and their values. You can then continue execution of your code by typing “c” and pressing Enter.

Debugging and troubleshooting in Jupyter Notebooks can be a challenge, but with the right tools and techniques, you can make the process easier and more effective. By using print statements, the debugger, try-except blocks, logging, and breakpoints, you can identify and fix errors in your code and data. With these tips and tricks, you can become a more effective data scientist or developer and make the most of your Jupyter Notebooks.