Yeah, I have also been really impressed with AstroNVim. I tried using LazyVim for quite some time but I kept having a hard time getting used to it. Somehow I kept tripping up when trying to do things.
On one of my servers I needed to disable icons which AstroNVim handles very conveniently (https://docs.astronvim.com/recipes/icons/#disable-icons). After switching I noticed that using AstroNVim feels so much more natural to me. It's been a joy to use.
I think it might be because the defaults are less bespoke and it's just a bit leaner. The community packs have also been great for customization.
Well, there also is for example `pandas.plotting.scatter_matrix()` [1] which is built on top of matplotlib. I suppose the question is how does SAS or any other alternative compare to vega/altair when the desired output is less standard.
If we only look at the simplest example then I would agree that there is not much difference. But more complicated plots will require you to write code in a more verbose and imperative fashion when using matplotlib.
Take a faceted plot like this scatter matrix [1] and try to plot it in matplotlib. You would need to set up the grid using subplots, then define the combinations you want and finally write logic to fill each subplot. The vega/altair code is much more declarative. You just tell it what needs to be in the rows/columns and vega/altair takes care of the rest.
I am sure you are right. However, this does look very useful to me.
I am not a professional js developer but I do like to build dinky websites. I generally prefer not to bother with build steps or dependencies. For me it's preferable to have my own implementation of a function within the file or project I am working in. That way I can directly inspect what the function is doing, which is "more readable" to me.
Moreover, sometimes I need functionality that is similar but just a bit different from something that lodash does. This seems like a good resource to check for inspiration.
If by "vectorized" you mean: "able to delegate the task of performing mathematical operations on the array's contents to optimized, compiled C code." then I do not think you are correct (unless perhaps you are supplying map with a dict or Series).
Series.map is not compiling your lambda's to C and running it. If there is a built-in method available it usually will be faster. Notable exception are pandas str methods which devolve into Python code but generally with more overhead than map/apply.
On one of my servers I needed to disable icons which AstroNVim handles very conveniently (https://docs.astronvim.com/recipes/icons/#disable-icons). After switching I noticed that using AstroNVim feels so much more natural to me. It's been a joy to use.
I think it might be because the defaults are less bespoke and it's just a bit leaner. The community packs have also been great for customization.