Introduction to Jupyter Notebooks in VS Code

• GitHub Repository

Jupyter Notebooks are a popular way to create and share documents for data analytics. They are interactive, easy to share, and support a wide variety of data science tools.

Step 1: Open The Project Folder

Open VS Code and clone your datafun-04-notebooks repository to your machine.

Step 2: Update Default Python

Open a terminal in VS Code. Use the menu View / Terminal. In Windows, use PowerShell, in Mac, use bash. Verify you've added some essential packages to your default Python environment.

python -m pip install --upgrade pip ipykernel jupyterlab
python -m pip install --upgrade black ruff

Note: If py or python3 works on your machine, use that instead of python in the commands.

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Step 3: Add Common Files

When starting a new project, there are some common files you should add to the project folder.

Add .gitignore

• The .gitignore file tells Git files to ignore when committing changes.
• Review the gitignore file, you can use it without modification.

Modify README.md

• Learn to edit and customize README.md files, which provide a quick overview of the project and instructions for running it.

Scan requirements.txt

• The requirements.txt file lists the packages used in the project.
• You may not use all them and may want to add others. Modify this list as you like.

🚀 Rocket Tip: When employers ask for years of experience with a language, it's not the syntax - that's learned in a few days. It's the experience with the tools, libraries, and frameworks that takes time.

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Step 4: Set up a Virtual Environment

Next, we'll create and activate a virtual environment specifically for this project. We'll also install additional packages required for this project.

Create a Virtual Environment

1. Open the terminal in VS Code. (View / Terminal)
2. Run the following command to create a virtual environment for this project.

python -m venv .venv

Verify that a new .venv folder was created. It may take a while for the command to complete.

🚀 Rocket Tip: When VS Code Python Extension offers to select the Environment, say Yes.

Activate the Virtual Environment

Wait for the creation to finish, then activate the virtual environment:

• For PowerShell: .venv\Scripts\Activate
• For macOS/Linux: source .venv/bin/

🚀 Rocket Tip: Notice the terminal changes to reflect the active virtual environment.

Install Dependencies to the Active Virtual Environment

Install additional project dependencies into the active virtual environment. The packages ipykernel and jupyterlab are required to run a notebook. The packages pandas, matplotlib, and seaborn are used to work with data and charts.

python -m pip install --upgrade pip ipykernel jupyterlab
python -m pip install --upgrade pandas matplotlib seaborn
python -m pip install --upgrade voila

Alternatively, you can install all the packages listed in the requirements.txt file.

python -m pip install --upgrade -r requirements.txt

Note: The --upgrade parameter gets the latest version of each package.

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Step 5: Working with Notebooks

In the active virtual environment, create a Python kernel to run our notebooks.

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

Create a new notebook

In VS Code, from the menu, select View / Command Palette. Type notebook and select Jupyter: Create New Blank Notebook. This will create a new notebook in the project folder. Save the notebook with a name like yourname-notebook.ipynb.

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If you've created the .venv virtual environment, installed the necessary packages, and selected a Kernel for your Jupyter Notebook, it should run - even if the code shows a missing package error. See the image below.