Jupyter Notebook — Introduction

What is Jupyter Notebook?

Jupyter Notebook is an open-source, browser-based interactive computing environment that lets you create documents combining live code, equations, visualizations, and narrative text. The name comes from the three core languages it originally supported: Julia, Python, and R.

It is the standard tool for:

Data exploration and analysis
Machine learning experimentation
Scientific computing
Teaching and documentation
Reproducible research

How It Works

A notebook is a .ipynb file (JSON format) containing a sequence of cells. The Jupyter server runs a kernel — a separate process that executes code. The browser communicates with the kernel via WebSockets.

Browser (UI) ←→ Jupyter Server ←→ Kernel (Python/R/Julia process)

Cell Types

Type	Purpose
Code	Executable code (Python, R, etc.)
Markdown	Formatted text, headings, lists, math
Raw	Unformatted text, not executed

Installation

# Via pip
pip install notebook          # Classic Jupyter Notebook
pip install jupyterlab        # JupyterLab (modern UI)

# Via conda
conda install -c conda-forge notebook
conda install -c conda-forge jupyterlab

# Launch
jupyter notebook              # opens at http://localhost:8888
jupyter lab                   # opens JupyterLab

JupyterLab vs Classic Notebook

Feature	Classic Notebook	JupyterLab
UI	Single document	Full IDE-like
Multiple tabs	No	Yes
File browser	Basic	Full
Extensions	Limited	Rich ecosystem
Terminal	No	Yes
Recommended	Legacy	Yes (current standard)

Key Concepts

Kernel: The computational engine. Each notebook connects to one kernel. You can restart, interrupt, or change the kernel.
Cell execution order: Cells can be run in any order — the kernel maintains state. This is a common source of bugs.
Output: Displayed inline below each cell — text, tables, plots, HTML, widgets.
.ipynb format: JSON file storing cell content, outputs, and metadata.

First Notebook

# Cell 1 — imports
import numpy as np
import matplotlib.pyplot as plt

# Cell 2 — computation
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

# Cell 3 — visualization
plt.plot(x, y)
plt.title("Sine Wave")
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.show()

Keyboard Shortcuts

Shortcut	Action
`Shift+Enter`	Run cell, move to next
`Ctrl+Enter`	Run cell, stay
`Alt+Enter`	Run cell, insert below
`Esc`	Command mode
`Enter`	Edit mode
`A` (command)	Insert cell above
`B` (command)	Insert cell below
`DD` (command)	Delete cell
`M` (command)	Change to Markdown
`Y` (command)	Change to Code
`00` (command)	Restart kernel