We will cover: Watch step-by-step machine learning tutorial videos on YouTube channel https://tinyurl.com/yx4ynhmj or blog posts at grabngoinfo.com. Markdown is a fast and easy way to take notes, create content for a website, and produce print-ready documents. there are two small typo errors where it talks about code blocks How do I import custom libraries in Databricks notebooks? There are several online Markdown editors that you can use to try writing in Markdown. Markdown in table comments renders in Data Explorer as soon as you save changes. Databricks notebook can include text documentation by changing a cell to a markdown cell using the %md magic command. In my example I created a Scala Notebook, but this could of course apply to any flavour. So cell URL adaptation would be necessary. We need to install emoji package as follows: Using pip $ pip install emoji --upgrade Using the github $ git clone https://github.com/carpedm20/emoji.git $ cd emoji $ python setup.py install Next,. Markdown Headings - including the Notebook title, who created it, why, input and output details. If youre looking for the simplest possible way to create a website with Markdown files, check out blot.im. Load the ggplot2 package (or any other that you use frequently) in a code chunk and regenerate the document. These tools reduce the effort to keep your code formatted and help to enforce the same coding standards across your notebooks. This is an important consideration when it comes to books, university theses, and other milestone documents that need to be preserved indefinitely. This is brittle. As @Saideep Arikontham mentioned there is no way to retrieve the cell's URL programmatically. But then what? Often, small things make a huge difference, hence the adage that "some of the best ideas are simple!" That is, they can "import"not literally, thoughthese classes as they would from Python modules in an IDE, except in a notebook's case, these defined classes come into the current notebook's scope via a %run auxiliary_notebook command. Markdown provides a robust set of options for documenting data, enhancing the options Databricks users have for increasing the discoverability and understanding of shared data assets. Instead, it was suggested to use the HTML