ggplot VS folium

Seaborn is based on matplotlib and quite modern. Coming from R and used to ggplot (which is also available in python) I really like it.

If you're familiar with R, there's always ggplot.

I'd agree in that it's a well-specified language for defining graphics; it's not very good with rendering performance. There are packages which try to achieve similar goals in Python as well (ggplot / ggpy) and packages like Seaborn. Though, like you, I use R for lots of EDA. Hard to beat data.table and R graphics for speed and expressiveness. I prefer base graphics though; ggplot2 tends to render too slowly for any data sets I work with.

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.