Jupyter notebooks are a useful tool for Python users of all levels. They allow us to mix together plain text (formatted as Markdown) with Python code. This is beneficial for beginners and experienced data scientists alike: Beginners that are learning Python for the first time can use Markdown cells to annotate code and record notes. By splitting up their code into chunks, developers can write and test their code in a modular manner.
Parameterised reporting is a powerful technique that allows you to create dynamic and customisable reports by incorporating user-defined parameters. These parameters act as placeholders that can be easily modified to generate tailored reports based on specific inputs or conditions, enabling seamless updates to reports without the need for manual modifications. Quarto, a modern and flexible document generation tool, provides excellent support for parameterised reporting. In this blog, we will be looking at a Quarto Reveal JS presentation as an example.
As data scientists we often need to communicate conclusions drawn from data. Additionally, as more data is collected, our reports invariably need updating. This is where automated reporting tools such as Quarto come in! In this blog post we will look at how Quarto allows us to weave together text and Python code to generate reproducible reports. What is Quarto? Quarto is a technical publishing system built on Pandoc. By combining code with plain text, it allows you to create reports that can easily be updated when the data changes.
Here at Jumping Rivers we like to keep our courses up to date so we can bring you training on the latest tools and technologies. To this end, we have recently added two new courses to our listing! Whether you want to start from scratch, or improve your skills, Jumping Rivers has a training course for you. Reporting with Quarto Do you create interactive documents that always need to be updated when the data changes?