Comments on: UPDATED: Must-Have Python Packages for Social Scientists

By: Vman

Vman — Sun, 16 May 2010 06:01:18 +0000

I do plenty of genomic data mining with python. I need the statistical functions of R but unfortunately R is a PAIN to learn and work with. I still dont understand its data structures, also its script is archaic. I will definitely look into rpy2.

I should mention that it is important to contribute as well to python libraries. My personal goal would be to bring pythons statistical capabilities to that of spss or systat.

By: Christopher

Christopher — Tue, 30 Mar 2010 20:23:46 +0000

Great article. I’ve recently embarked upon a bit of complex networks research with a former colleague of mine and I am currently surveying the tools available so this article is particularly timely for me. I was looking into using the Network Workbench and Gephi for most of my analysis, but I would love to have something like NetworkX that I could use from Python just for the ability to automate much of the drudgery. If you’ve got the time to reply, I’d love to read some of your thoughts on why you think NetworkX is superior to some of the tools you listed above with a specific emphasis on the Network Workbench since that is one of the tools that I am considering as well.

By: Mark

Mark — Tue, 30 Mar 2010 15:30:00 +0000

Thanks for this. Been relying heavily on python and R for years in and out of school in the field of quantatative/molecular genetics of livestock. Recently python has become more and more of a core component of my workflow with RPy for the plotting/graphing and modeling components.

By: Cesar Garcia Diaz

Cesar Garcia Diaz — Wed, 24 Feb 2010 13:47:48 +0000

Dear Drew,

Thanks for this compilation of Python tools. Fantastic! I have a question: do you have any experience building/executing agent-based models in Python? I’ve been always afraid that the easiness of programming in Python will not be compensate the execution speed (as usual with interpreters). Regards.

By: RR

RR — Fri, 01 May 2009 21:52:58 +0000

Thanks for the quick response, Drew.

By: Drew Conway

Drew Conway — Fri, 01 May 2009 14:15:15 +0000

RR,

I am very biased here, since Python pretty much brought me back from the brink of giving up software coding. That said, Python is an extremely versatile and easy to learn language, so I would recommend focusing and building you skills there.

If you are find that there is something you can’t do in Python, and there is an easy solution in R, just run it through RPy.

By: RR

RR — Fri, 01 May 2009 12:05:41 +0000

Very helpful list, thanks for posting!

A question: I like both R and Python a lot but would love to “standardize” on one for my modeling/number-crunching needs. The hassle of remembering two sets of details is getting to me. Any words of wisdom on making this choice? Thanks.

By: Abhijit

Abhijit — Mon, 06 Apr 2009 20:44:22 +0000

One small update to your list. Rpy has practically been replaced by Rpy2, which uses slightly different syntax but feels more robust. It is the actively developed strain of Rpy. You can find the links to Rpy2 from the Rpy page.

By: Drew Conway

Drew Conway — Fri, 15 Aug 2008 23:05:18 +0000

Perone, thanks, and I just updated my PyEvolve to the new version–looks great!

By: Perone

Perone — Thu, 31 Jul 2008 20:56:24 +0000

Hello Conway, I’m author of PyEvolve, thank you for indicating the framework. Good luck on your social studies; any help you needs, just mail.