Setting Up a Jupyter Kernel in Visual Studio Code Using Conda

VScode extensions

Jupyter Notebooks have become an essential tool in data science and machine learning. Visual Studio Code (VS Code) is an incredibly powerful code editor that can enhance your Jupyter experience with extensions and integrations. In this blog, we’ll guide you through the steps to set up a Jupyter kernel in VS Code using Conda.

Prerequisites

1. Visual Studio Code — Make sure you have the latest version installed. You can download it from the official website.
2. Miniconda or Anaconda — Conda is a package management system that simplifies the process of managing software packages and environments. You can download Miniconda or Anaconda as per your preference.

Step 1: Installing Conda

If you haven’t installed Conda yet, follow these steps:

1. Go to the Miniconda or Anaconda download page.
2. Download the installer for your operating system.
3. Follow the installation instructions provided on the website.

After installation, you can verify that Conda is installed correctly by running the following command in your terminal:

conda --version

Step 2: Creating a Conda Environment

Creating a separate Conda environment helps to manage packages and dependencies effectively. Run the following commands in your terminal:

conda create --name myenv python=3.8

Replace myenv with the name you want to use for your environment. You can also specify other versions of Python by changing 3.8.

Activate your Conda environment:

conda activate myenv

Step 3: Installing Packages to Conda Environment

Once your environment is activated, you can install the necessary packages. For this example, let’s install numpy and pandas.

conda install numpy pandas

Step 4: Installing IPython Kernel and Kernel Using IPython Kernel

Jupyter uses IPython kernels to execute code in notebooks. To install the IPython kernel in your Conda environment, run:

conda install ipykernel

Next, add this Conda environment as a Jupyter kernel:

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

Here:

• --name myenv specifies the name of the kernel.
• --display-name "Python (myenv)" specifies how the kernel will appear in Jupyter Notebooks.

Step 5: Adding Relevant Extensions to Visual Studio Code

To effectively use Jupyter Notebooks in VS Code, you’ll need some extensions. Open VS Code and go to the Extensions view by clicking the Extensions icon on the Sidebar or pressing Ctrl+Shift+X.

Install the following extensions:

1. Python — Microsoft
2. Jupyter — Microsoft

To install the extensions, search for them by name and click on the “Install” button.

Step 6: Adding Kernel to VS Code

1. Open VS Code.
2. Open the Command Palette by pressing Ctrl+Shift+P.
3. Type Python: Select Interpreter and select the interpreter for your Conda environment (Python (myenv) in this case).

Next, create a new Jupyter Notebook in VS Code:

1. Open the Command Palette (Ctrl+Shift+P).
2. Type Jupyter: Create New Blank Notebook.

To switch the kernel to your newly added Conda environment:

1. Click on the kernel name displayed at the top-right of the notebook.
2. Select Python (myenv) from the dropdown list.

Now you are ready to start coding in your new Jupyter Notebook with the specified Conda environment in Visual Studio Code!

Conclusion

Setting up a Jupyter kernel in VS Code using Conda can greatly enhance your productivity by making the development process more efficient. You can install different packages, manage environments, and seamlessly switch between them. All these steps combined make for an incredibly powerful data science setup.