ScottPJones (u/ScottPJones)

ScottPJones commented on CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R juliacomputing.com/blog/2... · Posted by u/nemoniac

echelon · 5 years ago

I think there's a strong chance that Swift or Rust take a lot of Python's data science cake. Both of them are extremely fast, concrete, and have lots of investment being poured into numerics and fitting into the Python/ML ecosystem.

I don't think Julia or R are going to steal this away. In fact, I think Julia is a major turn off to engineers with some of the bizarre choices they made (eg. 1-based indexing to appease math-centric backgrounds). If you don't excite engineers, you're not going to get as many library and optimization contributions.

Julia feels like it came too late in the game and the newer entrants are making quick strides in the mathematics space.

I could be totally wrong. This is just my take, and I have no stake in this fight other than I want a good, safe, highly-compatible, and fast ecosystem to work with.

ScottPJones · 5 years ago

Complaining about 1-based indexing in Julia is so... 4 years old! Just use OffsetArrays, and you can use 0 based, or whatever base floats your boat (start underwater with -5, for example!)

ScottPJones commented on CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R juliacomputing.com/blog/2... · Posted by u/nemoniac

Datenstrom · 5 years ago

There is an immense value in most of the ecosystem converging on Python over the last decade and especially with leaving MATLAB behind in the dust. I don't think decade long transitions from old tech to new tech is a bad time scale or even close to that of the churn of web technologies. Julia offers enough of an improvement over Python to warrant a switch over the next 5-10 years and leave Python behind in the same way. I believe Julia offers the same level of improvement over Python as Python does over MATLAB.

I also believe that it has drawn too much from MATLAB only for the sake of being familiar to that group and not the computer science and software engineering communities though such as the one based indexing you mentioned. Unfortunately that ship has already sailed and we will likely have to wait another 10 years for the next one to hopefully fix those problems.

ScottPJones · 5 years ago

Back 5.5 years ago, I used to complain about the 1 based indexing and the column-major structure of matrices in Julia (both like Fortran), however, those issues have been solved by OffsetArrays and PermutedDimsArrays, giving far more flexibility that is possible in most other languages. It's silly to keep bringing up the issue of one based indexing, when you can use any integer as the base, just like Fortran 90 (so you can index by -5..5, for example). For some things, 0 based does make things easier, sure, but you can do that easily in Julia (and more!)

ScottPJones commented on Pluto.jl – a reactive, lightweight, simple notebook github.com/fonsp/Pluto.jl... · Posted by u/dunefox

ScottPJones · 6 years ago

One thing that is pretty great about Pluto.jl, is how responsive the author is (Fons van der Plas, or @fonsp on GitHub). I've been able to get great suggestions from him (as well as the fast growing community of Pluto users) on Zulip discussion group for Julia (https://julialang.zulipchat.com)

ScottPJones commented on Pluto.jl – a reactive, lightweight, simple notebook github.com/fonsp/Pluto.jl... · Posted by u/dunefox

jakobnissen · 6 years ago

I've switched from Jupyter to Pluto recently. Here's a few experiences with it.

* The fact that I can actually use the source files later because they're just Julia files is incredibly useful. I often copy-paste from them into actual REPL-code, and sometimes I just polish the notebook until its source becomes usable as a command-line tool.

* I like the reactive notebook concept. It does really help with bugs

* Pluto is still rough around the edges. Too few keyboard shortcuts. Buttons and text are tiny, afloat in an ocean of useless whitespace. pushing to LOAD_PATH doesn't work properly. Pluto is a very young project and just now gaining attention in the Julia community, so I'm confident these usability issues will improve.

ScottPJones · 6 years ago

I've also switched to using Pluto, shortly after seeing the presentation during JuliaCon. It is still rough around the edges, but I've found it a lot easier to deal with than Jupyter, quite frankly.

ScottPJones commented on Pluto.jl – a reactive, lightweight, simple notebook github.com/fonsp/Pluto.jl... · Posted by u/dunefox

levesque · 6 years ago

Now we just need to port all this over to python... or switch to Julia? Which one would take the least amount of effort?

ScottPJones · 6 years ago

You'll also save a lot of effort in future endeavors, IMO. Remember, once you've switched to Julia, Python is still only a `using PyCall` and `pyimport` away!

ScottPJones commented on JuliaCon2020: Julia Is Production Ready bkamins.github.io/juliala... · Posted by u/ViralBShah

dnautics · 6 years ago

Thanks! I'm also very excited by pluto.jl, looks fantastic. I'll probably be offering public machine learning vms with Pluto builtin by the end of the year.

ScottPJones · 6 years ago

I'm having a lot of fun with Pluto.jl and PlutoUI.jl this past week, it's so easy to use and add interactivity. There are still some rough edges, but that's what PRs are made for!

ScottPJones commented on JuliaLang Antipatterns white.ucc.asn.au/2020/04/... · Posted by u/oxinabox

ScottPJones · 6 years ago

Very good article - mentoring Julia over the last 5 years, I've run into a lot of these things.

ScottPJones commented on JuliaLang: The Ingredients for a Composable Programming Language white.ucc.asn.au/2020/02/... · Posted by u/mindB

real_eng_ · 6 years ago

Why every Julia user can't help but trash Python at every occasion?

It's getting really tiring

ScottPJones · 6 years ago

I haven't seen that at all - many Julia programmers are (or were) also Python programmers. I think there is a lot of respect in the Julia community for Python & the Python ecosystem. There have even been a number of Julia talks at various PyCons over the past few years.

ScottPJones commented on JuliaLang: The Ingredients for a Composable Programming Language white.ucc.asn.au/2020/02/... · Posted by u/mindB

FranzFerdiNaN · 6 years ago

Python will still be used 20 years from now. The clear advantage of Python is the enormous ecosystem that is available, the millions of questions on SO giving solutions to every problem you can run into, the books and learning materials etc, programmers and corporations having invested loads of time and effort in building, maintaining and battle-testing libraries.

Don't get me wrong, i think Julia is an amazing language, but being an amazing language is neither necessary or sufficient to succeed. R shows how you can succeed just fine with a kinda weird language.

ScottPJones · 6 years ago

Python's ecosystem is great - but Julia's is growing incredibly fast, and in some cases Julia has already surpassed what is available in other languages (for example, take a look at the whole differential equations ecosystem: https://github.com/JuliaDiffEq). Also, Python's ecosystem is only a 'pyimport(name)' away (using the PyCall.jl package). Same thing is true for R and a number of other languages (RCall.jl, JavaCall.jl, etc.) I've been using SymPy, QisKit, matplotlib and other Python packages with no problem in Julia.

ScottPJones commented on JuliaLang: The Ingredients for a Composable Programming Language white.ucc.asn.au/2020/02/... · Posted by u/mindB

smabie · 6 years ago

Julia is great. It’s significantly simpler than Python while also being much more expressive. It’s too bad the typing is only for dispatch, but hopefully someone will write a typechecker someday. I’ve found it surprisingly refreshing to not have to think about classes and just add functions on objects wherever I want. Some languages solve this with monkey patching (which is bad), others like Scala with an extension class (reasonable, but you still don’t get access to private properties), but the Julia approach is cleaner.

I wouldn’t use Julia for a non-scientific computing app as I don’t think it’s suitable, but for anything data science related, it’s great! And with the Python interop, I don’t really think there’s any reason not to use Julia for your next data science project. I suspect that over the next 5 years Python will no longer be used for these applications at all.

ScottPJones · 6 years ago

I've been using Julia for non-scientific computing programs for almost 5 years now, and (especially now that it is stable since the 1.0 release) have found it well suited for general programming as well. Having a language that is easy to write (like Python), runs fast (like C/C++), and incredibly flexible & expressive (like Lisp) makes programming fun again!