Jupyter Notebook

Overview

---

This guide is tailored for new users launching a Jupyter Notebook from instances. If you’ve never used a Jupyter Notebook before—we’ll walk you through every step.

What is Jupyter Notebook?

---

Jupyter Notebook is an interactive coding tool that lets you:

• Write and execute code one step at a time (in “cells”).

• Document your process with text, images, and graphs in the same file.

• Visualize results dynamically, such as charts or model outputs.

• Experiment and iterate on your projects without running a full script all at once.

• Collaborate and exchange ideas with others.

Understanding the Jupyter Interface

---

The Launch Jupyter Notebook button on your instance details page gives you instant access to the Jupyter interface hosted on your instance.

1. Navigate to My Instances and click the Launch Jupyter Notebook button.

1. Clicking the button will open the Jupyter Notebook interface in your browser.

2. Once the Jupyter Notebook interface loads, you’ll see two main sections, which are the File Browser and the Launcher.

   1. File Browser: Shows files and folders on your instance.

   2. Launcher: A dashboard to create notebooks, open consoles, or access other tools.

File Browser

Launcher

How to Use Jupyter Notebook

---

Running Your First Code

1. Open a new Python notebook by clicking Python 3 in the Launcher.

2. A new tab will open with a blank notebook.

3. Write your code in the first cell (print("Hello, world!")).

4. Press Shift + Enter to execute the cell. The output will appear below it.

Cell #	Input	Output
1	print("Hello, world!")	Hello, world!

Adding Text or Explanations

1. Create a new notebook (File > New > Notebook).

2. Add context to your code by inserting text cells:

   1. Click + in the toolbar to add a new cell.

   2. Change the cell type to Markdown from the dropdown menu.

   3. Write text or explanations using plain text or Markdown syntax.

3. Execute the cell (Shift + Enter) to render the text.

# Welcome to My Notebook
This notebook will demonstrate basic Python operations.

Visualizing Data

Jupyter Notebooks support graphs and charts using Python libraries like Matplotlib and Seaborn.

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.plot(x, y)
plt.title("Sample Plot")
plt.show()

Resources

---

For additional information, please visit the official documentation site for the Jupyter Notebook here.