• 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.

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

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.

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

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

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

• 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.

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

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.

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 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.

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

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

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.

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.