I’ve been using Altair (and thus Vega-Lite) for most of my data visualization work since early last year. In general, I appreciate the declarative approach to visualization, in which one starts with long-form tidy data and in which each column of a data frame can define some aspect of a visualization.
If each row represents an observation, and each column represents an attribute of that observation, then the attributes can map directly to visual properties of a plotted point corresponding to that observation.
When my teammates and I have taught others how to use Altair in the past, we’ve shown them how to tidy data with Pandas (or through some other preprocessing step), but it’s possible to tidy data directly in Altair. I developed an interactive notebook that starts by showing how to tidy data (both via preprocessing and directly in Altair) and then demonstrates some other intermediate Altair features like interactive plotting and choropleths. You can check it out on GitHub or run it on Binder!