How to Change Conda Environment from an .ipynb or Notebook File in VS Code

If you’re working with Jupyter Notebooks in Visual Studio Code (VS Code) and need to switch between different Conda environments, you’re in the right place. This guide will walk you through the steps to seamlessly change your Conda environment in an .ipynb file. Follow along to ensure your projects are using the correct dependencies and packages.

Step-by-Step Guide to Change Conda Environment in VS Code

1. Open VS Code and the Jupyter Notebook

First, launch VS Code and open the Jupyter Notebook file (.ipynb) you want to work on. You can do this by navigating to the file through the File Explorer or by dragging and dropping the file into the VS Code window.

2. Select the Kernel

At the top right corner of the Jupyter Notebook interface in VS Code, you’ll see the current kernel name (e.g., Python 3, base, etc.). Click on this to open the kernel picker. This is where you can select from the available environments.

3. Choose or Add a Conda Environment

In the kernel picker, you’ll see a list of available kernels, including any Conda environments you’ve set up. If your desired Conda environment is listed, simply click on it to switch.

However, if the environment you want isn’t listed, you’ll need to add it manually.

4. Create a Kernel for a Conda Environment (If Needed)

If your environment isn’t in the list, follow these steps to add it:

• Open a terminal in VS Code by going to View > Terminal.
• Activate the Conda environment you want to use:

  conda activate myenv
  

• Install ipykernel in the activated environment:

  conda install ipykernel
  

• Add the environment to Jupyter:

  python -m ipykernel install --user --name=myenv --display-name "Python (myenv)"
  

• After running these commands, refresh the kernel list in the Jupyter Notebook.

5. Switch to the New Kernel

Go back to the kernel picker in your Jupyter Notebook and select the newly added environment. Your notebook will now run with the selected Conda environment, ensuring that all the dependencies and packages are correctly set up.

Why It’s Important to Manage Conda Environments

Managing Conda environments is crucial for reproducibility and dependency management. Each project can have its specific environment, ensuring that dependencies do not clash and that your code runs smoothly without any version conflicts.

By following these steps, you can easily switch between different Conda environments in VS Code, making your workflow more efficient and organized. Happy coding!

Keywords: VS Code, Jupyter Notebook, Conda environment, change Conda environment, manage Conda environments, ipynb file, switch kernel, Python environments, VS Code tutorial, Conda tutorial.