• 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.

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.

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/

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

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

python -m pip install -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.

In VS Code, from the menu, select View > Command Palette.

Type kernel and select Notebook: Select Notebook Kernel. Choose the kernel named "Python (.venv)" from the list.

This ensures your notebook uses the packages installed in your virtual environment.

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.

Why Your Notebook May Still Not See a Package

If the Jupyter notebook kernel is not set to the same environment where you installed the package, the package won't be found. This can happen if:

• The terminal in VS Code is pointed to one virtual environment, but the Jupyter kernel is using another. This can occur if you have multiple virtual environments and accidentally select the wrong one for your Jupyter notebook.
• Even if you try to manually point the kernel to the right environment, there might be path conflicts or misconfigurations that prevent the notebook from recognizing the installed packages.

Recommendation 1: Keep Virtual Environment and Kernel Aligned

The environment where the Jupyter server runs (where a !pip install in your notebook would install packages) might differ from the environment where the notebook kernel runs. This can lead to scenarios where a package appears installed in the terminal but isn't accessible within the notebook. Whenever you create a new virtual environment and want to use it with Jupyter notebooks, you need to install ipykernel and your necessary packages in that environment.

Recommendation 2: Use Magic Commands (Jupyter commands use %)

Once your project virtual environment and kernel are aligned, if you still have issues, then use magic commands. Magic commands are interpreted by the IPython kernel and provide a way to interact with the underlying Python environment directly. Using %pip install ensures that the package is installed in the same environment as the notebook kernel. To use a magic command, in your Jupyter Notebook, add a Python cell with the following command:

%pip install scikit-learn

Note that we don't use the leading py -m that we do when running Python utility commands (pip, venv, etc.) in the terminal.

Virtual environments are recommended to ensure isolation and reproducibility of your project's dependencies. If you encounter issues with virtual environments and need to install packages directly within a notebook, using magic commands is a reliable alternative.

Avoid: Shell Commands for Installs (Shell Commands use !)

Don't use !pip install (with an exclamation instead of percent). This is a shell command, which means it runs in the shell environment of the system where the Jupyter server is running. While it can work, it might not install the package in the same environment that the notebook kernel is using, especially in environments with multiple Python installations or virtual environments.