Hacker News Reader

Comment by christopheraden | Hacker News Reader

Very well deserved!

With Anaconda, I just tell my students to download a quick installer, slap on iPython or PyCharm, and it's ready to go. It's one less thing to worry about! The installation is dead-simple, and is almost exactly the same whether on Mac, Windows, or Linux. When I do data science, I don't want to have to be doing IT, too!

View on HN

We would be absolutely stuck w/o conda's environment manager -- I can make sure we use exactly the same versions of the packages across our whole team.

It has also been helpful in aggressive desktop lock down corporations where I have to ask for each package to be approved prior to install.

Same. While I have a different, more custom install (homebrew), there is nothing simpler for students (or other people learning numeric/scientific/data-Python) than installing Anaconda.

(Plus seaborn - the only thing I recommend starting with, which is not (yet?) in Anaconda.)

I believe "conda install seaborn" will do what you need :)

Sure it does. I just wanted to say that the default set of packages is extremely well-chosen (as there is only one additional thing needed for my workflow when introducing Python).

While I do like the ease of deployment for MKL binaries, I told my students "do it from scratch and learn sysadmin skills as well".

As a data scientist, admin/dev skills are a huge plus.

Works great if you have the time in the classroom. Sysadmin skills aren't among any of my learning outcomes, so I don't have time to cover it. My students are still trying to grapple with the idea of a for loop!

Fixxer, I totally agree that it's great that one be capable of doing these things, but sometimes it's not as important as other things that could be taught. Like acbart, sometimes I want to teach why/when to use a statistical algorithm and not teach them how to grab all the dependencies, troubleshoot whether they have gfortran installed, etc. This problem is horribly compounded teaching undergrads when you have Windows, Linux, and Mac users in your class, where the procedures for getting a working scientific stack vary, and the errors are often not the same across platforms.

When I install the scientific python stack on a new machine, I almost always just use Anaconda. I already know how to install the stack (I'll always value the weekend I spent in undergrad fighting with a customized BLAS in R!), but sometimes I have more pressing/fun things to do.

Sure. That is important to do a few times. Just like writing a compiler. However, most people do just fine using GCC and the same thing applies here.

Respectfully, I think your comparison is false. There are always new libs with awkward dependencies on platform X.

Reply on news.ycombinator.com