Posts by James Dunkerley

In this post, I will build on top of my last post and go over how to parse values in the dataset, select down to just the columns we are interested in, and finally aggregate the data. The previous post covered installing Enso and then loading in some data. This post starts from the workflow created in the last post, still using the Kaggle Superstore Dataset. If you have not read that post, I recommend you do so before continuing. That workflow can be downloaded from here. The goal of this workflow will be to get a table of the total sales by category for each month (using the order date). Let’s start by taking a look at the structure of the data. We can do this using the info function on the first node. To add this node, select the first node and press Return or drag out from the bottom of the first node. Then, in the new node, type info or choose it from the Component Browser. This function returns a new table containing the metadata about the table. In this case: | Column…

In this post, I will use Enso to read and process a CSV file. Enso is being designed and built to make it easier to process, blend and analyse data. It is a new programming language designed to have a dual representation both as text and as a visual graph. The dataset used in this walkthrough is available from Kaggle here. The goal of this post is to cover reading this data and finding the five highest-value furniture sales. Let’s start by getting Enso installed. It will work on Windows, Mac, and Linux. This guide is based on Windows. You can download the installer from here. Download the appropriate Enso IDE entry for your operating system and run it. On Windows, you must have the Visual C++ Restributable installed. If you don’t have it, you can download it from here. Once the installation has been completed, you can launch Enso from the start menu or desktop shortcut. It may need to be approved by your firewall. Once it has started, you will see the following screen: The login is…

Enso is a functional programming language that lets you quickly and simply load, blend, and analyze your data. We’ve been building out the core capabilities of the product and are rapidly working on the IDE and cloud release to give a straightforward experience for users using it. To show some of the new capabilities, I have tackled the first three challenges of 2023 posted on Preppin Data. These data challenges are posted by Carl Allchin, Jonathan Allenby, Jenny Martin, and Tom Prowse. They are solvable in many data tools and make an excellent set of tasks to show how to use Enso. This blog was written using a recent nightly build; many features and functions are still maturing and subject to change as we approach our release. In addition, we are still working on adding more “widgets” to the nodes and improving data visualization capabilities to help guide you through building the workflow. These will appear over the next month or two in the nightly builds. https://preppindata…