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Julia v1.0 has been released

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Re: Julia v1.0 has been released

#51

Post by peatmoss »

privong wrote: post #36
peatmoss wrote: post #22

In the past when I’ve poked around with Julia, I used Emacs Speaks Statistics. If you’re familiar with using R in this context, you’ll probably find developing and interacting with Julia to be quite easy. If the Julia community could manage to get Julia support into RStudio, I think we’d see a more accelerated uptake. I’ve heard rumblings that RStudio has at least thought of supporting Python in RStudio (more than th…

> All in all I’d prefer to do data things in Racket or another lisp Do you have any suggestions/resources specific to this? I almost exclusively work with python but am starting to learn Racket (mostly just for fun). But if I could do some analysis in Racket, that would be awesome as well.

Not really. I mean, there's a whole lot of libraries that exist for Python and R that you just won't find outside Python and R. Certainly you can do many data processing tasks in Racket, but you just won't find Hasselblat-Hertzgeminer method (I made that up) available off the shelf for Racket like you might for Python or R.

But, I do find Racket to be a nicer language than R or Python. I like to imagine that Racket's best-of-breed support for building DSLs could yield a more expressive data / stats environment.

People always talk about the unassailability of Python and R in data science. But I think that's sort of defeatist. Not very many years ago people would have laughed at you if you suggested doing statistics with Python rather than R. Even though Python is still a long ways behind R in statistical libraries, people have built enough that Python too is viable for many things.

So too could Racket (or Julia!) become more useful than they already are.

Re: Julia v1.0 has been released

#52

Post by Certhas »

tmalsburg2 wrote: post #24
sleavey wrote: post #9

Having never used it, I wonder, could Julia replace Python as the de-facto standard in scientific computing and data analysis? What does it do well?

Python is not the de-facto standard to my knowledge. It really depends on the field and task. R is huge in my research area, MatLab is king in some other areas, and Fortran is still being used for large-scale number crunching. Will Julia replace all these? That's the ambition but I doubt it's going to happen. For instance, R has a unique and vast ecosystem of extension packages. It would take decades to build somethi…

Where I am people are really pushing out of the MATLAB/Python world into Julia. It's tailor made for scientific computing in a way that none of the others are.

Clearly we're talking decades, but I am already realizing pay-offs from the switch to Julia (from Python) in my domain.

Rackauckas pointed out an amazing example recently [1]:

https://discourse.julialang.org/t/differentialequations-jl-a...

You have a library that implements calculating with numbers with uncertainties. You have the differential equations library. They don't know about each other but you can use the former in the latter to solve differential equations with uncertainties with a whole bunch of advanced solving algorithms.

This sort of power, having an ecosystem like Python that combines in a performant way, will be huge.

[1] http://www.stochasticlifestyle.com/why-numba-and-cython-are-...

Re: Julia v1.0 has been released

#53

Post by peatmoss »

jjoonathan wrote: post #29
peatmoss wrote: post #22

In the past when I’ve poked around with Julia, I used Emacs Speaks Statistics. If you’re familiar with using R in this context, you’ll probably find developing and interacting with Julia to be quite easy. If the Julia community could manage to get Julia support into RStudio, I think we’d see a more accelerated uptake. I’ve heard rumblings that RStudio has at least thought of supporting Python in RStudio (more than th…

What parts of RStudio do you prefer over Jupyter?

I prefer many things about RStudio over Jupyter! I have two main issues with Jupyter (Notebooks).

First, Jupyter Notebooks don't facilitate working with revision control very well. Each time a notebook is saved, a new mess of JSON is belched out. I think the path to collaboration via revision control should be as easy as possible for analysts. This model of notebook is pretty hostile to making the jump to collaboration.

By contrast, RStudio's "R Notebooks" (supports more than R) are human readable, and git revisionable, with no loss of functionality as compared to Jupyter Notebooks. This is the same model that Emacs's org-mode uses for a document with embedded, executable code snippets.

Second, while Jupyter Notebooks can be an easy path for someone with no computer experience to get started, it's very hard to scale the environment up to writing larger analytic projects. The notebook model that Jupyter provides doesn't allow you to escape the notebook without ditching the tools pretty much completely and learning something entirely new.

By contrast, RStudio makes moving between notebook computing and more traditional IDE support for R scripts very easy. A thing I have done that is easy in RStudio (or Emacs org-mode):

- start building a plain R script with just comments

- convert to an R Notebook where I essentially add rich documentation

- once things start to get unwieldy, I start to factor out code from my notebook into more reusable project modules. I then let a notebook report be the "tip of the iceberg" where I still have the write-up of my analysis and embedded supporting evidence like plots and tabular summaries.

At this last evolution, my notebook is really just a UI for the outputs of my reusable analysis code. If I want to take things further I can:

- Move away from a notebook / document presentation of my results toward an interactive application via Shiny.

I just don't see any way that Jupyter Notebooks are any easier than RStudio's tooling. I also see that RStudio's tooling facilitates moving up and down through different more or less technical interaction models. Jupyter Notebooks by contrast are a brittle environment that pretty much dictates you stay within their walls or learn something else entirely.

Re: Julia v1.0 has been released

#54

Post by cwyers »

jjoonathan wrote: post #29
peatmoss wrote: post #22

In the past when I’ve poked around with Julia, I used Emacs Speaks Statistics. If you’re familiar with using R in this context, you’ll probably find developing and interacting with Julia to be quite easy. If the Julia community could manage to get Julia support into RStudio, I think we’d see a more accelerated uptake. I’ve heard rumblings that RStudio has at least thought of supporting Python in RStudio (more than th…

What parts of RStudio do you prefer over Jupyter?

Not the person you're replying to, but: almost all of them. RStudio is useful for more than just notebooks, as opposed to having to switch between an IDE for code and something else for notebooks. It has more affordances than Jupyter -- a full console, for instance. RMarkdown notebooks play much more nicely with version control than Jupyter notebooks.

Re: Julia v1.0 has been released

#55

Post by cultus »

pjmlp wrote: post #12
sleavey wrote: post #9

Having never used it, I wonder, could Julia replace Python as the de-facto standard in scientific computing and data analysis? What does it do well?

It compiles to native code via JIT on the standard implementation, no need to re-write code in C. The object system is close to CLOS with support for multi-dispatch.

The object system is IMO the best feature of Julia. All functions are multiple dispatch, so the expression problem is more or less solved. This makes it easy to do things like allowing users to define custom sparse matrix types and use them seamlessly with other matrices and vectors. It makes it easy to abstract away implementation details in a much greater way than Python or R.

Re: Julia v1.0 has been released

#56

Post by privong »

peatmoss wrote: post #51
privong wrote: post #36

Earlier quoted context omitted.

> All in all I’d prefer to do data things in Racket or another lisp Do you have any suggestions/resources specific to this? I almost exclusively work with python but am starting to learn Racket (mostly just for fun). But if I could do some analysis in Racket, that would be awesome as well.

Not really. I mean, there's a whole lot of libraries that exist for Python and R that you just won't find outside Python and R. Certainly you can do many data processing tasks in Racket, but you just won't find Hasselblat-Hertzgeminer method (I made that up) available off the shelf for Racket like you might for Python or R. But, I do find Racket to be a nicer language than R or Python. I like to imagine that Racket's…

Okay, thanks for the clarification. I suspected you'd meant the ecosystem wasn't there (yet), but wanted to make sure.

I had seen this Racket data science repo, but am not yet proficient enough with Racket to properly assess it: https://github.com/n3mo/data-science

Re: Julia v1.0 has been released

#57

Post by geoalchimista »

collinf wrote: post #15
sleavey wrote: post #9

Having never used it, I wonder, could Julia replace Python as the de-facto standard in scientific computing and data analysis? What does it do well?

My only fleeting experience with Julia is in a Numerical Analysis Course three years ago... but it has 1-indexed arrays so its good for scaring away all people who are programmers before analysts/scientists! Really though, I think it has a better type system and a syntax that translates easier to mathematical expressions. Other than that, Python's breadth of packages will be hard to overcome.

Even among scientists there are people who don't like 1-indexed arrays (me, one data point)! However, Julia 0.7 and 1.0 have the keyword "begin" so that you can basically program arrays in an index-agnostic way.

Re: Julia v1.0 has been released

#58

Post by pjmlp »

collinf wrote: post #15
sleavey wrote: post #9

Having never used it, I wonder, could Julia replace Python as the de-facto standard in scientific computing and data analysis? What does it do well?

My only fleeting experience with Julia is in a Numerical Analysis Course three years ago... but it has 1-indexed arrays so its good for scaring away all people who are programmers before analysts/scientists! Really though, I think it has a better type system and a syntax that translates easier to mathematical expressions. Other than that, Python's breadth of packages will be hard to overcome.

When I started programming, all my languages with exception of Assembly used 1-indexing, so no, it doesn't scare all programmers.

Re: Julia v1.0 has been released

#59

Post by waldir »

ellisv wrote: post #39
KenoFischer wrote: post #38

Earlier quoted context omitted.

Thank you! People are always too closely watching our GitHub ;).

and the live feed

For context, they mean the live video feed of JuliaCon 2018. Here's where the release was announced: https://www.youtube.com/watch?v=1jN5wKvN-Uk

Re: Julia v1.0 has been released

#60

Post by oxinabox »

mindB wrote: post #47
FridgeSeal wrote: post #35

Earlier quoted context omitted.

If there were pandas, sklearm and Keras equivalents (don’t even need feature compleye, just mostly there) I would abandon python literally today.

- pandas :: your choice of DataFrames or JuliaDB - scikitlearn :: no single package since skl is a meta-package of sorts, but most of the stuff is there spread across the eco-system - Keras :: checkout Flux or Mocha Welcome to Julia! Also take a look here [1] for more package that might be to your interest. [1] https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e78...

Pandas actually has many alternatives. Including JuliaDB, DataFrames, and Pandas.jl

Keras also many. TensorFlow.jl, Flux, Mocha, KNet, MxNet.

Sklearn is all there but bring it together will take a bit. Some important parts are in JuliaML org. Also Clustering.jl, and MultiariateStats.jl (For DR) the classifiers are really scattered. I'ld love to fix that if I had time.

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