Jupyter Notebooks and Setup

Jupyter Notebook is an open-source web application that lets you create and share documents containing live code, equations, visualisations, and narrative text. It is the standard interactive development environment for data science, and you will use it throughout this course.

---

What is Jupyter?

The name "Jupyter" stands for the three core programming languages it was designed to support: Julia, Python, and R. Today, Jupyter supports over 40 languages through different kernels, but Python remains the most popular.

A Jupyter notebook is a .ipynb file (IPython Notebook) that consists of an ordered list of cells. Each cell can contain:

• Code — Python code that can be executed interactively
• Markdown — Formatted text, headings, lists, links, and images
• Raw — Unformatted text passed through without processing

---

Installing Python and Jupyter

Step 1: Install Python

If you don't already have Python installed, download it from python.org or use a package manager:

# macOS (Homebrew)
brew install python

# Ubuntu/Debian
sudo apt update && sudo apt install python3 python3-pip

# Windows — download the installer from python.org

Verify your installation:

python3 --version
# Python 3.12.x (or similar)

Step 2: Create a Virtual Environment

It is best practice to create an isolated environment for your data science projects:

# Create a virtual environment
python3 -m venv datasci-env

# Activate it
# macOS/Linux:
source datasci-env/bin/activate

# Windows:
datasci-env\Scripts\activate

Step 3: Install Jupyter and Core Libraries

pip install jupyter numpy pandas matplotlib seaborn scikit-learn

Step 4: Launch Jupyter Notebook

jupyter notebook

This opens your default browser with the Jupyter dashboard at http://localhost:8888.

---

Alternative: Google Colab

If you prefer not to install anything locally, Google Colab provides free cloud-based Jupyter notebooks:

1. Go to colab.research.google.com
2. Sign in with your Google account
3. Create a new notebook — NumPy, Pandas, and Matplotlib are pre-installed
4. Free GPU and TPU access for machine learning

Google Colab is an excellent option for beginners and for working on machines where you cannot install software.

---

Jupyter Interface Tour

The Dashboard

When you launch Jupyter, you see a file browser. From here you can:

• Navigate directories
• Create new notebooks (New > Python 3)
• Open existing .ipynb files
• Upload notebooks

The Notebook Interface

A notebook has two main areas:

1. Toolbar — buttons for common actions (save, add cell, run, stop, restart)
2. Cell area — the main working area where you write and execute code

---

Working with Cells

Code Cells

Code cells contain Python code. Press Shift + Enter to execute a cell and move to the next one.

# This is a code cell
x = 42
print(f"The answer is {x}")

Output:

The answer is 42

Markdown Cells

Markdown cells contain formatted text. Change a cell to Markdown mode and press Shift + Enter to render it.

# This is a Heading

This is **bold** and this is *italic*.

- Bullet point 1
- Bullet point 2

```python
# Code can be displayed (but not executed) in Markdown
print("hello")

### Cell Operations

| Action | Shortcut |
|--------|----------|
| Run cell and move to next | `Shift + Enter` |
| Run cell and stay | `Ctrl + Enter` |
| Insert cell above | `A` (in command mode) |
| Insert cell below | `B` (in command mode) |
| Delete cell | `D, D` (press D twice in command mode) |
| Change to code cell | `Y` (in command mode) |
| Change to markdown cell | `M` (in command mode) |
| Enter command mode | `Escape` |
| Enter edit mode | `Enter` |
| Save notebook | `Ctrl + S` |

---

## Command Mode vs Edit Mode

Jupyter has two modes: