AoM: Data Science

Over the past few days, I wanted to isolate my understanding of the relationship between the selection made in a dropdown form input and other cells which depend on that selection. This exploration started with drawing a rectangle and using code from example notebooks with dropdown selections to cause a transition in the chart to occur (see Arc Diagram ). One added complexity with Mike Bostock's Arc Diagram notebook is a timeout that's added which will automatically trigger a change in the dropdown selection. Originally this code block had a bug which caused me some confusion, but it has been a valuable learning experience: Mike Bostock commented on the bug and fixed the bug, so I was able to update my notebook accordingly. In any case, contained in the notebook are three charts and each chart behaves different based on whether I refer to the dropdown form object by `myNumber` or `viewof myNumber.value` as well as where I make such references. In the first chart, I referenc...

Today I was looking into ObservableHQ and their Quickstart page had many suggested resources. The second of these resources was titled "Learn D3." Since it's been several years since I've worked with D3, I felt it would be appropriate to go through it. In addition, at the time I thought the resource would lean heavily towards learning D3 within the context of an Observable notebook. Instead, the series was dominantly a comprehensive introduction on D3, delivered via a series of Observables, and occasionally touched on some of the benefits of using an Observable notebook. In hindsight, I should have expected the relationship between an Observable notebook and D3 to be more like that between a Jupyter notebook and a Python module: learning to use the features of Jupyter and learning to use a particular Python module are mutually exclusive activities. In any case, below you'll find my notes to each of the sections in the Observable series "Learn D3" by ...

A couple of weeks ago, I looked at a demo of ObservableHQ and the first interesting difference between an Observable notebook and a Jupyter Notebook is that the output of an Observable notebook is above the code. In any case, today I signed up for an account and was presented with a Quickstart page with 12 different recommended reads. I went through two of them, A Taste of Observable and Learn D3 . Quickstart A Taste of Observable (7 minute read) So here are some of the items I noticed after going through this notebook: - Can import and reuse object from another notebook (shown near the end of the notebook). In the example given, the imported object refers to local variable called `forecast`. However, since that object is being pulled away from it's source, we must assign ("inject") a new object to the variable name. - Variables can be changed and changes will automatically push forward and update any cells which depend on those variables. - I mentioned earlier tha...