I work as a data scientist (like many others in this thread) and Altair is a great tool for exploring the data. I really like the grammar of graphics approach and the ability to do things like cross-filter and the general ease of enabling interactivity are great! Amazing work by everyone on the Altair/Vega team.
There are some drawbacks though that have been holding some of my colleagues back from switching from e.g. Seaborn to Altair:
- No zoom by selecting the x- or y-range. Almost all other libraries implement box-zoom, Altair does not have this. This is probably the biggest complaint I have heard so far.
- Annotating data inside the plot is difficult. I don't mean `mark_text`, I mean placing an annotation that you would do in Matplotlib via `ax.text(...)`.
- Along side that: No TeX support.
- It does become slow once there is a large number of points in the chart (and you don't want to aggregate).
- Missing mapping features. I know you can have maps in the background of your plot, but I'm talking OSM like mapping.
- Working with layered + faceted charts takes some time to get used to.
- It used to be that if you had a dataframe with 100 columns and you were only plotting two of them, still all 100 columns would be saved to html. I think this is addressed in VegaFusion?
Still, I want to just emphasize how much I love Altair. To understand relations in your data, it's amazing to just assign color to a column, shape to another column and so on. Really neat! With a background in mathematics, it was also helping me to think about the whole tidy approach to dataframes, so that was an added benefit!
Ph.D. in Applied Mathematics, experienced in Python (NumPy, Pandas), data visualization, and SQL. Proficient in mathematical modeling and optimization. Working remotely as an Algorithm Developer for an American company. Excellent communication skills and a proven track record in innovative tech projects.
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