seaborn VS folium

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

A good thing to try would be to look over the folium documentation on their GitHub Page, Folium. For help with code you could ask ChatGPT to explain what you're interested in using their docs.

I currently utilize Folium for a work project but would like to move it out of Folium into something that supports API requests for real-time data and vector tiles instead of raster.

If you know Python you can use Folium produce Leaflet maps without using Javascript. I'm not sure if the updating functionality you require is possible, but could be worth a look.

Try Folium, here are some examples.

For our visualizations we will be using Folium. This is a description from the authors:

I'm mapping some election results, and trying to create a diagrammatic representation of who voted for whom, and where. I'm using Python, and I can create a perfectly respectable initial map (without Voronoi diagrams) using its folium package, with OpenStreetMap tiles. See https://numbersandshapes.net/posts/post_election_swings/.

Look in the folium package. https://python-visualization.github.io/folium/

Choropleth maps are a quick and easy way to visualize a numeric variable geographically. I love the ease and interactivity of most functions from the folium module, but the choropleth method lacks interactivity and clarity in the legend.