awesome-vector-tiles VS seaborn

I suppose, I'd need to try parsing these via some github tool?

You will also need to figure out your source of basemap tiles. Again, OpenStreetMap is not an API not is it a basemap, despite what some here are recommending. It is an open dataset that is commonly used to create raster or vector tile basemaps. It is possible to download all or some of OpenStreetMap, generate vector tiles, and style them to look the way you want, but that does introduce quite a bit of extra technical overhead you might not want at this stage of development. Namely, you’d need to run you own vector tile server that your mapping API can fetch and render tiles from. Many open source vector tile servers exist and it’s kind of up to you to figure out which one meets your needs. Alternatively, Mapbox and MapTiler provide SaaS support for basemaps built in part or wholly on OpenStreetMap data. Check out “Awesome Vector Tiles” for resources and tools to help get going with vector tiles. (https://github.com/mapbox/awesome-vector-tiles)

If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.

I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.

I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.

For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.

Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).

The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.

It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).

While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.

https://seaborn.pydata.org/

Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.

Matplotlib Seaborn Example data sets

Seaborn - Statistical data visualization using Matplotlib.

Github: https://github.com/mwaskom/seaborn

Seaborn Documentation

You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts

I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340