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Why Python rocks for research

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Re: Why Python rocks for research

#41

Post by wynand »

Dn_Ab wrote: post #13
vog wrote: post #5

Earlier quoted context omitted.

> There are an awful lot of languages that provide ... Still, how many of them have a fast interactive interpreter ("command line") with a decent usability? How many of those provide good libraries for numerical as well as symbolic math? With an API that is easy to write, to understand and to extend? Python may not be the only language with those qualities, but there aren't many languages (and ecosystems around them)…

There are Ocaml, F# and Clojure with some combination of great tools, speed (clojure addressed this recently I think but i only have visual experience with clojure), light syntax, books and documentation, repl,excellent platform, wide libarary choice and or decent interop with C. Also F# is doing some really cool stuff to do with datasets awareness in the language. Haskell seems a perfect fit for mathematical use and…

I'm a long time Python programmer and I am currently using it for scientific programming (for my PhD).

I've shopped around a lot for alternatives to Python and looked at Ocaml, F#, Boo, Clojure, Common Lisp, Haskell and Scala.

There are a couple of things still keeping me with Python:

* The REPL, especially IPython,

* Numpy & Scipy,

* Networkx & igraph,

* jpype for fairly seamless JVM integration (this way, I can interact with Cytoscape), rpy for fairly seamless R integration,

* ZODB,

* IPython's parallel processing framework.

The .net environment probably comes the closest to providing everything here, but the REPLs need a lot of work and the graph libraries have more complicated interfaces which make them a pain to use on the command line.

Python is not without its warts but when I recently had to spend time with Matlab again after a few years, I was reminded of just how nice the Python ecosystem is in comparison.

Update: I should add that if you rely on one of Matlab's toolboxes, you might not find any decent alternatives outside of Matlab. You can always use a bridge like MLabWrap to access Matlab from Python.

Re: Why Python rocks for research

#42

Post by ekidd »

bad_user wrote: post #15
RodgerTheGreat wrote: post #3

This article makes some fairly convincing arguments that Python is a more flexible tool than Matlab or Perl, but I can't help but come away with the sense that the author hasn't tried many other languages. There are an awful lot of languages that provide iterators, a powerful set of data structures, extensive libraries and facilities for structuring and maintaining large codebases. .Net languages (maybe F# would be g…

SciPy, NumPy, Matplotlib, Cython, Sphinx, PIL. The article should've also mentioned NLTK. IMHO, languages matter less than the available libraries, and in my experience only Java matches the depth of the Python ecosystem. That Python is a nice language to work with, that's just a bonus.

I agree about Python's libraries. Normally, I'm a Ruby programmer, because I like Ruby's syntax and the Rack/Rails/Sinatra web stack.

But if I need to do scientific or linguistic programming, Python is absolutely amazing. SciPy, NumPy, Matplotlib and NLTK support a rich and deep ecosystem. And it's vastly nicer programming environment than Matlab and Octave.

(Of course, GNU R is also pretty useful if you're doing pure statistics.)

Re: Why Python rocks for research

#43

Post by jamii »

Dn_Ab wrote: post #13
vog wrote: post #5

Earlier quoted context omitted.

> There are an awful lot of languages that provide ... Still, how many of them have a fast interactive interpreter ("command line") with a decent usability? How many of those provide good libraries for numerical as well as symbolic math? With an API that is easy to write, to understand and to extend? Python may not be the only language with those qualities, but there aren't many languages (and ecosystems around them)…

There are Ocaml, F# and Clojure with some combination of great tools, speed (clojure addressed this recently I think but i only have visual experience with clojure), light syntax, books and documentation, repl,excellent platform, wide libarary choice and or decent interop with C. Also F# is doing some really cool stuff to do with datasets awareness in the language. Haskell seems a perfect fit for mathematical use and…

I have been using ocaml for about 5 years and earning moneys with it for the last 2 years. I love the language but for scientific work its hard to beat python. Ocaml just doesn't have the libraries or the community support to even be on the radar.

Re: Why Python rocks for research

#44

Post by adw »

ekidd wrote: post #42
bad_user wrote: post #15

Earlier quoted context omitted.

SciPy, NumPy, Matplotlib, Cython, Sphinx, PIL. The article should've also mentioned NLTK. IMHO, languages matter less than the available libraries, and in my experience only Java matches the depth of the Python ecosystem. That Python is a nice language to work with, that's just a bonus.

I agree about Python's libraries. Normally, I'm a Ruby programmer, because I like Ruby's syntax and the Rack/Rails/Sinatra web stack. But if I need to do scientific or linguistic programming, Python is absolutely amazing. SciPy, NumPy, Matplotlib and NLTK support a rich and deep ecosystem. And it's vastly nicer programming environment than Matlab and Octave. (Of course, GNU R is also pretty useful if you're doing pur…

Which is why we use Python. We're a web app (Django) doing big data aggregation/processing (Numpy).

Re: Why Python rocks for research

#45

Post by ogrisel »

Avshalom wrote: post #40
ogrisel wrote: post #36

Earlier quoted context omitted.

If your system is complicated enough (which can be the case for complex machine learning or NLP algorithms), an 8 pages paper (common limit for many conferences) cannot describe all the implementation details but those implementation details might be very important to be able to reproduce the results. Hence code should be both published, well documented and readable.

Fair enough. When I think of scientific uses of python I think astronomy, atmospheric physics, finite element analysis and linear systems using existing techniques... Existing techniques in general really. Fields where the interest is the data and the implications of the data. Fields like ML and NLP where the algorithm/technique is the thing of interest then yeah sure the code is important.

I agree publishing datasets is very very important too. Often more important than code.

Re: Why Python rocks for research

#46

Post by rdouble »

I've wasted most of my professional life tweaking various unix software to make it work. However, the typical scientific python setup proved to be too frustrating to install on OSX. The recommended solution is to just buy the Enthought distro. If I'm paying for software anyway, why is Enthought better than Matlab?

Re: Why Python rocks for research

#47

Post by aero142 »

apl wrote: post #8

In the end, it's all about the ecosystem. Perl wins for bioinformatics because there are boatloads of scientists already using it, with all the neat libraries and resources that brings. Equally, Python wins for, say, prototyping in robotics because of libraries, support and so on. There's nothing intrinsically science-apt about Python/Perl, but Ruby and friends can't compete when it comes to the programming environme…

Python is very accessible to casual programmers, so this might be one of the reasons it has been adopted by the scientific community.

Re: Why Python rocks for research

#48

Post by ogrisel »

BrandonM wrote: post #35
ogrisel wrote: post #12

And the corollary: Why do researchers never respect the PEP8 when they write python code? Yes I am a bit overreacting since the blog post is very well written and I actually agree 100% with the content. But please people: respect the PEP8 [1]. It makes your readers feel at home while reading your code. It is very important if you want to get new contributors to your project. See [2] for instance. [1] http://www.pytho…

From PEP 8: > The preferred place to break around a binary operator is after the operator, not before it. I'd be interested in hearing the justification for this rule. I think that leading a continuation line with the binary operator makes it super-clear that it is a continuation line. What is the benefit of the preferred style? Compare: if (the_result_of_this_function(on_this_arg) == 10 and this_overly_descriptive_b…

I agree on this specific case but consistency with conventions shared across projects is more important.

But I don't think anybody will complain if you use either of the them whereas 160 chars long expressions with no spacing between operators and funkyCamelCasing all over the code are just show-stoppers when I want to contribute a patch to a project.

Re: Why Python rocks for research

#49

Post by expeditious »

variety wrote: post #37
RodgerTheGreat wrote: post #3

This article makes some fairly convincing arguments that Python is a more flexible tool than Matlab or Perl, but I can't help but come away with the sense that the author hasn't tried many other languages. There are an awful lot of languages that provide iterators, a powerful set of data structures, extensive libraries and facilities for structuring and maintaining large codebases. .Net languages (maybe F# would be g…

actually, he doesn't make any assertions that Python is more flexible than Perl (which would be rather doubtful), only that it is more readable (which, as a perlista, I'm sad to say is probably true). but I also get the sense that this is the first time he's seriously delved into a dynamic programming language. much of what he's saying about Python is exactly what bioinformaticists were saying about Perl in the late…

The thing about readability of Perl vs. Python ... I dunno. I don't think it's the syntax itself that causes Perl to have some readability issues. Stuff like `$foo =~ s/bar/baz/g` or `join '-', split( q{ }, $a_str)` is very readable.

Context and corner cases maybe makes readability suffer. For example, what does `m///g` do in scalar vs. list context? I don't recall.

Re: Why Python rocks for research

#50

Post by killedbydeath »

ogrisel wrote: post #12

And the corollary: Why do researchers never respect the PEP8 when they write python code? Yes I am a bit overreacting since the blog post is very well written and I actually agree 100% with the content. But please people: respect the PEP8 [1]. It makes your readers feel at home while reading your code. It is very important if you want to get new contributors to your project. See [2] for instance. [1] http://www.pytho…

I worked in projects where different used slightly different coding styles and I did not find it getting in the way too much -- you just match the style of the code you are working with. I am surprised there are people who will not contribute to a project because of this.
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