How Beginners Can Troubleshoot Common Errors in Jupyter Notebook
A friendly beginner’s guide on how to solve everyday errors in Jupyter Notebook
Prerequisites: To get the most out of this guide, you should have the following:
- Jupyter Notebook installed
- A basic understanding of Python, R, or Julia
- Familiarity with command line basics
- An internet connection
Introduction
Jupyter Notebook was created in 2014 as an open-source tool for data scientists. If you've started using it, you probably love how easy it makes coding in Python, Julia, or R.
It's a great tool for writing, running, and seeing the results of your code, all in one place. However, things don’t always go smoothly, you keep running into errors and are unsure what went wrong. This guide will help you fix some common Jupyter Notebook issues that beginners face. So relax, you’re not alone, this guide will have you confidently coding in Jupyter Notebook in no time!
Understanding Common Error Messages
It's common to get error messages while coding in Jupyter Notebook. As a beginner, you'll find yourself going from one error to another. It’s all part of the process of honing your skills. If you keep going, soon you’ll be writing long codes in Jupyter Notebook without getting a single error message.
Common Error Messages: What They Mean and How to Troubleshoot
Below I have listed some of the most common error messages in Jupyter Notebook, their meaning, and how to troubleshoot
1. ModuleNotFoundError
ModuleNotFoundError: No module named 'pandas'
This error occurs when you try to import a library that isn’t installed in your current environment.
How to troubleshoot:
Step 1: First, check if the library is installed by running:!pip show library_name
If it is installed, change library_name to the module you're looking for
Step 2: If it’s not installed, for pip users you can install it using:!pip install library_name For Conda users, use:conda install library_name
2. ImportError
ImportError: cannot import name 'function_name' from 'module_name'
You will get this error message when you try to import something that either doesn’t exist or isn’t accessible in your library version.
How to troubleshoot:
Step 1: Double-check the function or module name for typos.
Step 2: Ensure the library is updated, you can use the following code:!pip install --upgrade library_name
If it’s still broken, check the library documentation. Make sure the function/module exists in your version.
3. NameError
NameError: name 'variable_name' is not defined
This error message is a common one to encounter. Even the best programmers still get this error message occasionally.
It means Jupyter doesn’t recognize the variable or function you’re trying to use.
How to troubleshoot:
Step 1: Verify that the variable or function is defined above the cell you’re using it in.
Step 2: If it is, check if you restarted the kernel. Variables get cleared when you restart, so rerun all cells that define your variables.
Step 3: If that doesn’t work, make sure there are no typos in your variable or function names.
4. SyntaxError
SyntaxError: invalid syntax
This one is a classic - it happens to everyone!
It occurs when you make a typo in your code. Maybe you missed a colon or forgot to close a bracket. It just means that there is something wrong with the structure of your code.
How to troubleshoot:
Step 1: Carefully read the error message—it usually points to the exact line where the issue is.
Step 2: Look for missing colons (:), unmatched brackets, or misplaced quotes.
Step 3: You can use a static code analysis tool like Flake8. It will find and highlight syntax issues in your code.
5. IndentationError
IndentationError: unexpected indent
Python is very sensitive to indentation. If you see this error it means your code isn’t indented correctly.
How to troubleshoot:
Step 1: Check to be sure that all code blocks have consistent indentation.
Step 2: Avoid mixing tabs and spaces. Use your editor's "convert tabs to spaces" feature if needed.
Step 3: Check for accidental spaces at the beginning of the line.
6. TypeError
TypeError: unsupported operand type(s) for +: 'int' and 'str'
This error shows up when you try to perform an operation with incompatible data types. For example, adding a string to a number. It’s Jupyter’s way of saying, “These data types don’t mix!”.
How to troubleshoot:
Step 1: Use the type() function to check your variables:print(type(variable_name))
Step 2: Convert data types as needed. For example:num = int("123") # Converts string to integer
Step 3: Double-check the inputs to your functions or operations.
7. ValueError
ValueError: could not convert string to float: 'bye'
If you’ve ever tried to convert a string like 'bye' into a number, you’ve probably gotten this error. It’s telling you that the value doesn’t make sense for the operation you’re trying to perform.
How to troubleshoot:
Step 1: Check your inputs to ensure they match the function’s requirements. For example, converting strings to numbers:num = float("123.45") # Validnum = float("bye") # Causes ValueError
Step 2: Validate your data before using it in your code.
8. FileNotFoundError
FileNotFoundError: [Errno 2] No such file or directory: 'data.csv'
If you have ever tried to load a file and got this, it means that Jupyter can’t find your file.
How to troubleshoot:
Step 1: Confirm the file path is correct
Step 2: Check if the file exists in the specified location:
import osprint(os.path.exists("file_name.csv"))
Step 3: If the file is in the same folder as your notebook, ensure you’ve set the working directory correctly.
9. Kernel Error
Kernel Error
When the kernel refuses to start, you might see this message. It is often due to environment issues.
How to troubleshoot:
Step 1: Restart the kernel from the toolbar or close and reopen Jupyter
Step 2: Run this to check dependencies
!pip check
Step 3: If it’s still not responding, reinstall Jupyter Notebook
10. HTTP 404 Error
404 : Not Found
This one happens when you try to open a notebook file that Jupyter can’t find.
How to troubleshoot:
Step 1: Confirm the file hasn’t been moved or renamed
Step 2: Refresh your file browser in Jupyter to update the directory view
Step 3: If the file is gone, check your recycle bin or backups
Dealing with Browser-Related Problems
It’s not uncommon for Jupyter Notebook to have trouble loading on your browser when you first start it. You might notice that the browser tab doesn’t open, or the page just stays blank.
Ideally, Jupyter Notebook should open in your browser, automatically. You shouldn't need to do anything extra. But sometimes things go wrong
When Jupyter Notebook Won’t Work on Your Browser
Below is a table with some common browser-related problems and their solutions:
| Browser Problem | Solution |
| Jupyter Notebook doesn’t open in the default browser | Change your OS default web browser. Or, use the --browser flag to launch Jupyter Notebook with a specific browser. |
| “404: Not Found” Error | Ensure the server is running and use the correct URL displayed in the terminal when launching Jupyter Notebook. |
| Blank page or no response | Clear your browser cache or disable extensions like ad blockers or script blockers that might interfere. |
| Jupyter Notebook URL doesn’t work | Verify the server is running, and check that the full URL (including the token) was copied correctly. |
| Browser compatibility issues | Use an updated browser like Chrome or Firefox, as older browsers may not fully support Jupyter Notebook. |
| Kernel keeps disconnecting | Check your internet connection. Also, ensure that nothing is blocking WebSocket connections in the browser or network. |
| Login token issue | Look for the token in the terminal where you launched Jupyter Notebook and paste it into the browser when prompted. |
| "This site can’t be reached" Error | Confirm that the server is running and check for typos in the URL. |
| Security warnings | Allow the site in your browser’s security settings or use a trusted browser. These warnings are common for localhost environments. |
Troubleshooting Kernel Issues
Many beginners run into kernel problems while working in Jupyter Notebook. These issues can be frustrating because the kernel is the engine that runs all your code. Without it, Jupyter Notebook simply doesn’t work.
What to Do When the Kernel Won’t Start
Here’s a table of common kernel-related problems, their causes, and fixes:
| Problem | Cause | Solution |
| Kernel not starting | Some required tools are missing or not installed correctly. | Make sure everything is installed properly. Run pip install --upgrade jupyter. Restart Jupyter Notebook and check the error message for hints. |
| Kernel keeps disconnecting | Your internet connection is unstable, or there’s an issue with your browser. | Check your internet connection. Try using a different browser or clear your current browser’s cache. |
| Kernel dying unexpectedly | The computer runs out of memory, or the code has errors like endless loops. | Close other programs to free up memory. Fix your code to avoid endless loops. Restart the kernel if it stops working. |
| Kernel stuck as “busy” | The code you’re running is taking too long to finish. | Check your code for anything that takes a lot of time to run. Break big tasks into smaller ones. Use tools like NumPy to make the code faster. |
| Kernel and Notebook don’t match | The wrong environment is selected, or some tools are missing in the environment you’re using. | Go to the Kernel menu and choose the correct one. Install missing tools by running !pip install <library>. |
| No kernel found | Jupyter doesn’t know which Python to use, or something wasn’t set up correctly. | Install the kernel for your environment using python -m ipykernel install --user. Restart Jupyter Notebook and check again. |
| Can’t import a library (ModuleNotFoundError) | The library isn’t installed in the environment you’re using. | Install the library by running !pip install <library> in a code cell or in the command line. Use the correct environment. |
| Permission denied when starting kernel | Your computer settings don’t allow Jupyter to run properly. | Run Jupyter as an admin (right-click and choose “Run as Administrator” on Windows). On Mac/Linux, use sudo before the command. |
| Kernel won’t start and just says “Initializing” | Jupyter is out of date, or an add-on is causing problems. | Update Jupyter by running pip install --upgrade notebook. Turn off any extra extensions. Restart your computer if needed. |
| Interrupting kernel doesn’t work | The code is too heavy, or the notebook server isn’t responding. | Stop Jupyter and start it again. Avoid running tasks that use too much memory or processing power. Break large tasks into smaller steps. |
Library Import Errors
In Jupyter Notebook, libraries (or modules) are pre-written codes. They contain functions and tools that help you perform common operations or tasks. They help you avoid writing everything from scratch. Popular libraries are Pandas, Numpy, Matplotlib, and Seaborn.
Library import errors are quite common and they can happen for a couple of reasons. Jupyter Notebook may not recognize the library name you have specified in your code, or you have misspelled it.
How to Resolve Common Import Issues
Here are some common library import errors that beginners often face in Jupyter Notebook, along with how to resolve them:
| Error Message | Cause | Solution |
ModuleNotFoundError: No module named 'library_name' | The library is not installed in your environment. | Install the library using pip: |
ImportError: cannot import name 'library_name' from 'module' | The function/class name is incorrect or doesn't exist in the module. | Double-check the spelling and verify the function/class exists. Refer to the official documentation. |
AttributeError: 'module' object has no attribute 'library_name' | Trying to access an attribute or method that doesn't exist in the imported library. | Check the method or attribute name and verify you're using the correct syntax. Refer to the official documentation. |
| Version Mismatch (e.g., "This version of the library is not compatible with your environment") | The installed version of the library is incompatible with your environment or other libraries. | Install a compatible version of the library: |
NameError: name 'library_name' is not defined | The library or module was not imported before use. | Import the library correctly at the beginning: |
Getting Help from the Jupyter Discourse Forum
If you ever find yourself stuck (and trust me, it’ll happen a lot!) and you can’t figure out what’s wrong in Jupyter Notebook, the Jupyter Discourse Forum is your go-to spot. It’s filled with solutions to just about any problem you might face, especially as a beginner.
Why is it so helpful? Because everyone starts as a beginner, and they’ve probably faced the same issues you’re dealing with right now. Yep, even that Data Science guru you admire was once in your shoes.
The Discourse Forum is a place where Jupyter Notebook users connect. They share their work, ask for help, and even offer support to others. It covers everything from kernels to JupyterLab and JupyterHub discussions. It’s totally free—no subscriptions or fees required.
Here’s the link to the forum. Once you’re there, use the search icon to look up whatever you need.
Tips for Using the Community Effectively
Here are some tips to get the most out of the forum:
Use the search icon first. It’s in the top-right corner and will save you loads of time compared to scrolling through hundreds of posts.
Look for recent solutions. Some answers might be a few years old and were written for older Jupyter Notebook versions. Make sure the solution matches your current version.
Create your own discussion. If you don’t find a thread that matches your problem, don’t give up. Post your question, and you’ll likely get responses from others eager to help.
Give back to the community. Contribute to other discussions when you can. It’s a great way to share your knowledge and connect with fellow users.
Conclusion
Sweet! Now you’re equipped to troubleshoot most of the common errors in Jupyter Notebook. You now know to handle browser issues, kernel problems, and library import errors. And for any issue not covered here, you know where to turn—the Jupyter Forum is always there to help. With this guide, you can code with confidence, knowing you’re prepared to tackle any error message that comes your way.
As you continue coding, try not to see error messages as frustrating roadblocks. Instead, view them as valuable learning opportunities. Take a moment to understand what the message is telling you, and apply the solutions you’ve learned.
For your next steps, bookmark this guide for quick reference. Also, practice the troubleshooting methods on your projects. And if you’re still stuck, don’t hesitate to reach out to the community or even contribute your own solutions.
Happy coding, and keep learning!