Create Your Own Kotlin Playground (and Get a Data Science Head Start) with Jupyter Notebook

Learn the basics of Jupyter Notebook and how to turn it into an interactive interpreter for Kotlin. You’ll also learn about Data Frames, an important data structure for data science applications. By Joey deVilla.

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Where to Go From Here?

You can download the Jupyter Notebook files containing all the code from the exercises above by clicking the Download Materials button at the top or bottom of the tutorial.

With Jupyter Notebook and the Kotlin kernel, you have a powerful new tool at your disposal. You can use it as a straightforward Markdown note-taking tool or as an interactive coding environment. But the most interesting use cases appear when you combine Markdown and code cells to mix narrative text with executable code:

  • You could take the “scientific paper” route and create a notebook that’s mostly text, interspersed with code performing calculations that prove your thesis.
  • You might use Jupyter Notebooks as design documents for applications, with design notes written in Markdown cells and prototype code written in code cells.
  • Jupyter Notebooks also make great libraries for often-used code snippets. You store the code in the code cells and annotate the code in rich text using Markdown cells.
  • And finally, you can use Jupyter Notebooks for data science, which is introduced in the follow-up tutorial, Beginning Data Science with Jupyter Notebook and Kotlin.

If you’d like to find out more about Jupyter Notebook and how people are using it, here are a few good places to start: