I would agree that pyxley never came close to the functionality of Shiny, but my goal was a little different. My goal was to make a set of reusable components that were also really flexible. I wanted to be able to mix custom things with generic things without too much trouble. I think in trying to be flexible, I introduced too much complexity. Also, I never really got around to making a decent set of widgets and so it's definitely lacking in features.
I wrote a wrapper for the Plotly javascript library and I use Plotly charts in some of my apps. If I ever had some time, I could probably make a pretty decent clone. One interesting difference is how they are turning the python into javascript. I gave up on that and just ship a pre-built javascript bundle that receives props from the flask app.
I think the quote and definition from the blog is a good one: “better engineers than statisticians and better statisticians than engineers”. Perhaps that 1997 quote was influential in the decision to use the term Data Science, I think the current usage encompasses much more than statistics. When I started it required the ability to push production code, build statistical models, and communicate results effectively. Maybe I'm wrong and maybe the tools got better, but for a while, you couldn't provide value if you couldn't get to the data or create the data you needed.
What you are describing is a statistician and that's perfectly fine, but lumping them in with data scientists devalues the role for those of us doing more.
I completely disagree, data scientists who can not create the data they need are at a significant disadvantage to those who can. Our job is more than being able to analyze and interpret data. If you have someone in your organization that spends no time thinking about how they get the data, you need to fire them or reduce their salary.
Yup, that one. I planned on simply filtering a pandas dataframe using the request.args that are passed in. The javascript components can be used directly, but I wrote the python wrappers as a convenience for really simple dataframes.
The flask dependency is pretty strong because I used the requests module for the api route functions. It should be possible to override it and use django instead, but I don't know django that well. It's probably a good idea to separate that dependency so it's a little more flexible.
It shouldn't be too bad. My goal was just to make it a little simpler to do the basic things like formatting the data as json for a particular charting library. The python helpers just set up the APIs. I made an example called custom_react that shows how I mixed some custom code with some pyxley stuff.
I wrote a wrapper for the Plotly javascript library and I use Plotly charts in some of my apps. If I ever had some time, I could probably make a pretty decent clone. One interesting difference is how they are turning the python into javascript. I gave up on that and just ship a pre-built javascript bundle that receives props from the flask app.