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Comment by earthscienceman | Hacker News Reader

Was there not, at some point, a disagreement among the emacs developers about how this conference was being managed?

As an aside, emacs has so much potential, I'm so glad the community continues to thrive. I use emacs exclusively for science data analysis and consistently impress my colleagues with how quickly things can be accomplished. If rough edges were polished in some of the python corners there could easily be an emacs renaissance in academia, a la nicolas rougier et al.

For just how popular python is in science now, the workflows are abysmal at best. 2 out of 3 of my colleagues won't touch it because Anaconda makes no sense, a command line work flow is too obtuse, and Matlab for all its disgusting nature is at least "natural" to work with as an environment in some sense. Which makes me sad. I have dreams that emacs makes that more approachable.

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In any complex field, say data science or software development, any newcomer perceives the solutions to problems they still don't have as unnecessary complexity. So for example the whole idea of dependency management, environments or version control will get in their way. Because it's not yet their problem. They are solving simpler problems: how I run this simple script (or with Emacs, say, undo this change, etc...). In that regard Matlab excels at making it easy for newcomers and harder later.

We need gradual complexity, but I think it's very hard to design. It needs knowledge + didactic capability.

Honestly I've never understood Python for data analysis. R and Matlab have way better environments. R Studio is quite possibly the best environment I've ever used, in any language. Even creating C++ modules with Rcpp is easy. Julia is also getting there with a lot of natural advantages over R, Matlab or Python. I just don't get why anyone uses Python in this domain.

Python proper is terrible for analyzing data at scale.

It's the numpy, scipy, jupyter tools, as well as Anaconda, that make the sale.

Too, it's not just the analysis, but the extract/transform/load (ETL) beforehand. Python's general purpose wealth of tools had me transforming the FCC antenna coverage .zip[1] to shapefile and parking it in a spatialite file for QGIS in a relative jiffy.

Doubtless I could do all that in Julia or R, but I get to hone my python-fu at the office doing cloud stuff by day--another argument in favor of the general-purpose tool.

[1] ftp://ftp.fcc.gov/pub/Bureaus/MB/Databases/fm_service_contour_data/FM_service_contour_current.zip

I argue that what makes the sale, is that that is what so many in the field are using. Not much inherent to the technologies, but you are taking on a daunting task if you want to compete with pytorch, instead of using it.

Network effects: we got 'em.

But we can assume people got on python/numpy stack because it brought some value compared to R. I'm not a big fan of numpy but I find it a bit shallow to think it's mostly follower effect at play. It might be though.. but that would be sad. Maybe Julia will bring balance back in the universe.

Well, R is a rather awkward programming environment. And many folks learned on Mathematica and Matlab.

What python brought to the table, was that it was freely installed on most machines.

Not having access to a good R environment at work is one big reason.

At the company I work at there’s a pypi mirror with quite recent packages and it works out of the box - for R there’s only an ancient version and some hacks to get it to work with juptyer notebooks.

?? Can't use RStudio?

Matlab is awful at being a general-purpose language, anything that isn't wrangling matrices. Strings, paths, networking, etc are all really painful compared to Python or Julia.

Matlab was what came before Python. I do not ever want to go back. Sure, the Python ecosystem has its issues and Matlab has an IDE that is refined, but at least Python does have that ecosystem, all Matlab has is a shiny proprietary cage and littlr in the way of a cose sharing culture. Python being a proper language lets you break into other things, hookup completely unrelated code (and languages). All that's a giant pain in Matlab.

Just came here to plug Nicolas Rougier. He’s got some fantastic and very pretty-looking Emacs packages. He’s got good ideas of where Emacs should be in terms of usability in my opinion.

https://github.com/rougier

I have been a happy R + ESS (Emacs Speaks Statistics) user for a while now. I think ESS also supports python, I have no personal experience with that though. But I can agree that whenever I am forced to use Jupyter notebooks, it feel awfully unergonomic in comparison.

Strongly Agree! ESS is an incredible environment for statistical work, but I don't think it supports Python. There is some minimal support for Julia, but it's not quite as well supported as R.

Ah, looks like you are right and indeed ESS doesn't support python, a shame. As for Julia, I think a package that is shaping up to be interesting and along the same spirit is: https://github.com/gcv/julia-snail

This is interesting, I had the impression that notebooks were crossing this gap, are there specific command line issues that are a big bottleneck? Is it just better dependency management that's the big command line problem for example?

Notebooks are abysmal when it comes to state management. And that includes state of data access, as well as state of dependencies.

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