2. Working Environment#
This chapter provides a comprehensive overview of the Jupyter Notebook environment, including instructions on installation, effective usage, and navigation of its environment.
We will be using Jupyter Notebooks, an interactive environment for writing and running Python, extensively in this course. Jupyter Notebook is a crucial component of the Python ecosystem, widely used by the data science and machine learning communities for its seamless integration of code and notes.
Alternatively, you can use Google Colab, VS Code, or Anaconda for performing similar tasks.
Tool |
What it is |
What it does |
|---|---|---|
Jupyter Notebook |
An interactive notebook interface |
Interactive execution |
VS Code |
A general-purpose code editor/IDE |
Write and manage code |
Anaconda |
A Python distribution + environment manager |
Install and manage Python, including Jupyter |
Google Colab |
A cloud-hosted Jupyter Notebook service |
Runs Jupyter notebooks in cloud/brwser |
Note
Some prefer to use Anaconda to manage Jupyter Notebook. We use the Python package installer pip for simplicity and better project control. The popular IDE VS Code can also run Jupyter Notebooks, but the user experience differs, and we will use Jupyter Notebooks in this course.
The best way to install and configure Jupyter Notebook is to create a Python virtual environment. To do that, we need to use the Command Line Interface (CLI) on the computer. To install and start using Jupyter Notebook, follow the four steps below:
Install Python
Create Project directory and virtual environment (.venv)
Start using Jupyter Notebook
Add shortcuts for launching Jupyter Notebook