Homogenization of scientific computing – Python is eating other languages’ lunch
1–10 of 184 posts
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#2My only question mark from this is matplotlib. I tried it five or six years ago and it seemed clunky to use and install. And worst I couldn't seem to just throw up a plot, I recall there being a lot of settings required. And the plots didn't look good by default you had to fool with fonts, font sizes, etc.
Does anyone know if it's improved a lot since then? Otherwise I'm not seeing how it could hold a candle to R's plotting abilities and ease of use.
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#3I have a Python background and recently signed for the Coursera course on R that just started (https://www.coursera.org/course/compdata) because I wanted to get a small taste of R and see how it differed from Python's scientific computing stack.
So right now I'm not far enough in the learning curve to see all the benefits R provides. Is it worth investing time in R now if I'm already pretty familiar with a good amount of the Python ecosystem? Or, would it make more sense to continue on in Python?
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#4My only question mark from this is matplotlib. I tried it five or six years ago and it seemed clunky to use and install. And worst I couldn't seem to just throw up a plot, I recall there being a lot of settings required. And the plots didn't look good by default you had to fool with fonts, font sizes, etc. Does anyone know if it's improved a lot since then? Otherwise I'm not seeing how it could hold a candle to R's p…
R may still have an advantage when it comes to plotting simplicity.
On the Python side, matplotlib is still a bit of a pain, but has improved.
Also look at ggplot.py (alpha-ish?) and Bokeh from ContinuumIO
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#5Too bad the first part of the post title was edited out of the HN title. I think outside of scientific computing the picture is a little more nuanced.
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#6I have a Python background and recently signed for the Coursera course on R that just started ( https://www.coursera.org/course/compdata ) because I wanted to get a small taste of R and see how it differed from Python's scientific computing stack. So right now I'm not far enough in the learning curve to see all the benefits R provides. Is it worth investing time in R now if I'm already pretty familiar with a good amo…
If you are serious about data analysis you should probably at least read R (and maybe Matlab) as lots of algorithms were released and only exist in one of those languages.
You could get by with a more general statistics course that happened to use R.
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#7My only question mark from this is matplotlib. I tried it five or six years ago and it seemed clunky to use and install. And worst I couldn't seem to just throw up a plot, I recall there being a lot of settings required. And the plots didn't look good by default you had to fool with fonts, font sizes, etc. Does anyone know if it's improved a lot since then? Otherwise I'm not seeing how it could hold a candle to R's p…
R may still have an advantage when it comes to plotting simplicity. On the Python side, matplotlib is still a bit of a pain, but has improved. Also look at ggplot.py (alpha-ish?) and Bokeh from ContinuumIO
There is also Seaborn:
http://stanford.edu/~mwaskom/software/seaborn/index.html
and Vincent: https://github.com/wrobstory/vincent
Also the code examples given on AstroML to work well for figuring out how to make publication quality figures in Matplotlib: http://www.astroml.org/book_figures/
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#8Too bad the first part of the post title was edited out of the HN title. I think outside of scientific computing the picture is a little more nuanced.
Even inside of scientific computing, the picture is quite a bit more nuanced. This post is basically about a the author's personal migration to Python as a user of other people's scientific programming packages. In doing interviews with people inside of companies, there's fairly little actual use of Python for scientific computing – lots of Python for data preparation, but R and Matlab (not to mention Simulink) still dominate for the actual scientific part. And of course, there's the bizarre blind spot that the SciPy community has to the fact that they are really doing scientific computing in C – literally every single package you use that's scalable and performant is actually written in C. This is true of R and Matlab too, of course.
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#9I have a Python background and recently signed for the Coursera course on R that just started ( https://www.coursera.org/course/compdata ) because I wanted to get a small taste of R and see how it differed from Python's scientific computing stack. So right now I'm not far enough in the learning curve to see all the benefits R provides. Is it worth investing time in R now if I'm already pretty familiar with a good amo…
I would say it depends on precisely what scientific work you need to do. E.g. for phylogenetic statistics there are some nice R packages that bundle simulation techniques and measures that are so far not implemented as conveniently, or at all, in Python. So you need to explore what packages/libraries are out there that fulfill your needs (and also consider how much time/skill/interest you have to code your own packages/libraries where needed). R is still really popular for stats/prototyping/data viz and a useful language to have up your sleeve.
Re: Homogenization of scientific computing – Python is eating other languages’ lunch
#10In GIS at least I'm noticing a divided migration with one camp standardizing to python and the other to JavaScript.