Understanding which Python version you’re working with is crucial when developing in Jupyter Notebook. Whether you’re debugging compatibility issues, ensuring consistency across environments, or simply trying to confirm if a new feature is available, knowing how to check the Python version can save time and frustration. In this guide, we’ll explore various ways to check the Python version in Jupyter Notebook, why it matters, how to manage different versions, and how to troubleshoot version mismatches.
Why It’s Important to Check Python Version in Jupyter Notebook
Python is a dynamic and evolving language. Each version introduces new features, optimizations, and sometimes breaks backward compatibility. Some packages or code snippets might run perfectly in Python 3.10 but break in 3.6 or vice versa. This makes it essential to know your environment’s Python version, especially in collaborative settings, production systems, or when using third-party libraries.
Here are a few reasons why checking the Python version matters:
- Package Compatibility: Some Python libraries only support specific versions.
- Syntax Differences: Features like assignment expressions (
:=) were introduced in Python 3.8. - Reproducibility: Ensuring that collaborators or systems use the same Python version helps prevent unexpected behavior.
- Debugging: Errors might stem from subtle differences in the interpreter version.
Jupyter Notebooks are widely used in data science, machine learning, and academic research, which means environments can vary a lot. If you’re unsure about your Python version in a notebook, it’s always good practice to check.
How to Check Python Version in Jupyter Notebook Using Built-In Commands
There are multiple ways to check the Python version directly within a Jupyter Notebook cell. Let’s start with the simplest methods.
1. Using the sys module
import sys
print(sys.version)
This command prints out detailed version information such as:
3.9.17 (default, Jul 12 2023, 10:08:24)
[GCC 10.2.1 20210110]
If you want just the version number:
print(sys.version.split()[0])
This will give you a cleaner output like:
3.9.17
2. Using the platform module
import platform
print(platform.python_version())
This outputs the version string, such as:
3.10.12
The platform module is straightforward and gives only the version string, which is often all you need.
3. Using the !python shell command
Jupyter also allows you to run shell commands using the ! prefix.
!python --version
This will display something like:
Python 3.8.13
However, this command might return the version of the system’s default Python installation, not necessarily the one the notebook kernel is using. So it’s less reliable than the sys module for virtual environments or kernel-specific setups.
How to Check Python Kernel Version via Jupyter Interface
Jupyter Notebooks run in a specific kernel environment, and sometimes the version shown in the notebook is different from the one installed globally or in your terminal. To confirm the kernel’s Python version:
1. Check the Kernel Display
In the top-right of a Jupyter Notebook, you’ll see the kernel name—often something like Python 3 (ipykernel). If you’ve named your virtual environment or custom kernel, it might show as Python 3.9 (myenv).
2. Use %sys Magic Command
Jupyter also supports “magic” commands.
%system python --version
This works similarly to the !python --version command, but again may not reflect the exact kernel environment if you’ve misconfigured paths.
3. Using !which python or !where python
On Unix/macOS:
!which python
On Windows:
!where python
This shows the path to the Python executable that Jupyter is using. It’s helpful to ensure the correct virtual environment is active.
Managing Python Versions with Virtual Environments
Sometimes you need different Python versions for different projects. Here’s how to manage them efficiently.
1. Use venv or virtualenv
Create a virtual environment with a specific Python version:
python3.8 -m venv myenv
Activate it:
- On Unix/macOS:
source myenv/bin/activate
- On Windows:
myenv\Scripts\activate
Then, install Jupyter inside the virtual environment:
pip install jupyter
And start Jupyter:
jupyter notebook
Your notebook will now use the Python version specified in the virtual environment.
2. Add the Environment as a Jupyter Kernel
After setting up your virtual environment, add it as a kernel:
pip install ipykernel
python -m ipykernel install --user --name=myenv --display-name "Python (myenv)"
Now when you open Jupyter Notebook, you’ll be able to choose “Python (myenv)” as your kernel.
Handling Common Issues and Mismatches
Problem: Different version in terminal vs notebook
This usually happens if:
- You’re using a system Python in the terminal but a virtual environment in Jupyter
- The Jupyter kernel is not pointing to the right interpreter
To resolve:
- Run
!which pythonto see which interpreter is used by the notebook - Reinstall or update your kernel with the desired Python version
Problem: Jupyter says Python 3 but you want a specific version
You can explicitly install that version (e.g., Python 3.9), create a virtual environment with it, and register it as a separate kernel. This allows precise control.
Automating Python Version Checks in Notebooks
If you’re working in a collaborative setting, add a version check cell at the top of your notebooks:
import sys
assert sys.version_info >= (3, 7), "Please use Python 3.7 or newer."
This will throw an error if someone runs the notebook with an older Python version.
Or display it clearly for documentation:
import platform
print("Notebook running with Python", platform.python_version())
Conclusion
Checking the Python version in Jupyter Notebook is a basic but critical task for ensuring consistency, compatibility, and reproducibility in your projects. Whether you’re using built-in modules like sys and platform, shell commands, or inspecting kernel information, the tools are easy to use and widely available.
Moreover, managing Python versions through virtual environments and adding them as Jupyter kernels gives you powerful flexibility. With these techniques, you can prevent issues caused by version mismatches and maintain smoother workflows in data science, machine learning, and software development.
By adding a quick version check at the beginning of your notebooks, you future-proof your code and make collaboration more seamless. So the next time you open a notebook, take a moment to confirm: are you using the right Python version?