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Jupyter Starter Kit

Welcome to the Jupyter Notebooks/Lab Starter Kit repository! This collection of Jupyter Notebook files is designed to introduce new users to the world of Jupyter, a powerful tool for interactive computing, data analysis, and visualization.

What is Jupyter?

Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It supports various programming languages, including Python, R, Julia, and more.

Getting Started

Jupyter should already be installed on the Virtual Machine (VM) you have logged on. To launch it double click either of the 2 following shortcuts

📝 More on the distinction between Jupyter Lab and Jupyter Notebook later on.

This will open a new browser window or tab with the Jupyter interface. Once the Jupyter interface is open, you can explore the various Jupyter Notebook files included in this repository. Each notebook provides an introduction to different aspects of Jupyter, including:

Feel free to experiment with the code in the notebooks, modify them, or create your own. Jupyter is all about interactive computing, so don't hesitate to explore and learn by doing.

For more information on Jupyter and its capabilities, check out the official Jupyter documentation.

💡 Don't forget about the [Jupyter Notebook CheatSheet.pdf](./notebooks/Jupyter Notebook CheatSheet.pdf)

Visual Python extension

Your Jupyter set up comes with the Visual Python package. It is a GUI-based Python code generator for data science. Read the documentation [here] https://visual-python.gitbook.io/docs/getting-started/welcome-to-visual-python

💡 Skip the installation steps. this is already taken care of.

Jupyter Notebook vs Jupyter Lab

In summary, Jupyter Notebook offers a simpler interface for basic interactive computing, while Jupyter Lab provides a more versatile environment with additional features and modularity.

Jupyter Notebook:

  • User Interface: Web-based environment for creating and sharing documents containing live code, equations, visualizations, and text.
  • Cells: Composition of code, Markdown, or raw text cells, allowing for independent execution.
  • Extensions: Basic functionality with the option to extend via additional extensions.

Jupyter Lab:

  • User Interface: Next-generation web-based interface offering a flexible and powerful environment.
  • Modularity: Extensible environment with support for arranging multiple documents and activities side by side.
  • IDE Features: Includes features akin to integrated development environments (IDEs) like syntax highlighting and debugging tools.
  • File Browser: Enhanced file browser for efficient file management.
  • Interactive Widgets: Supports interactive widgets for creating rich user interfaces within notebooks.

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