Monday, December 2, 2019

Awesome review of Python in Proteomics!

Google is very nice about telling me when I log in about whether people care about what I've posted. It says some really interesting stuff about my audience. One thing that is very clear is that y'all don't care for R and Python and Linux. However, my increasing sense of horror about being force fed the habanero and turd sandwich that is Windows 10 is going to keep showing up here.

As such, I'm going to need to keep checking out new tools (good-bye Visual paid way too much for you over the years....) hello free Python IDEs that all about 90% work!

I love this new Python unreviewed review and I might just be leaving it here so I can find it. I only have the direct PDF download and this is the link to it.

As an aside, I've been tricking myself into getting more comfortable with Python by making it a game. I've got a pretty much 100% functional Tetris that insults the player and a side scroller that I'm even making all the ultra-professional level art for. Confused about what an Anaconda is and how it affects that Numpy thing you heard someone ramble about (arrays!) -- make it something fun and 16 hours later you might have it sorted out!  There are LOADs of free programs online to teach you this stuff. I'm just going through Tech by Tim's channel which starts with "installing Python" and by video 12 is like "using Tensorflow to build an AI"  -- some of these things are cooler than the others.....


  1. We, the ones that like your posts about R and Python and Linux and stuff, are few but we are loyal followers! =)
    Thanks for sharing this one.

  2. Python is great and fun to work with, but the reality is that R has more both mature and cutting-edge bio-specific packages. Additionally, and despite my love for matplotlib and seaborn, R's plotting capabilities surpass Python's. Finally, R treats statistics as a "first-class citizen", whereas Python does not (conversely, IMO, Python treats object-oriented programming and building pipelines as "first-class citizens").

    Python is catching up in many good ways, which is exciting. It is a great language with the same capabilities as R, with for example mature numpy and cython packages (or just plain c) for implementing performant algorithms. And, as a personal opinion, is an easier in which to work (though dplyr's "sub-language" of R is pretty great).

    Ultimately it behooves anyone doing informatics to have experience with both languages.