While I think, say, other areas also get things done, Rails developers are more likely to blog and have pretty fonts. Nothing against that, it just seems to be the case.
While metrics show there's more Python out there, it's just not exciting, it doesn't change a lot, and it just works. And that's fine too.
With respect to metrics, I think having the baseline is important, and you're right. I also found myself looking for more granular metrics then what I was able to find. I really want to be able to answer questions like
- "Within Python, how is usage shifting between web apps, scientific work (both research prototyping and in practice), open source, machine learning, etc.?" - "How are the 'frameworks on top of frameworks' like Flask-RESTful and Django Rest Framework changing in adoption over time?" - "Is anyone actually adopting Python 3 as their default yet?"
I wasn't quite able to find that data, but if anyone has ideas for first steps in getting there, I'd be happy to contribute (email in profile).
Python was at least previously big in a lot of local shops doing a lot of systems programming type stuff (management apps) -- including folks like Red Hat and HP, including my old one (Ansible). Startups are still highly likely to use it.
There are enough Django shops around, including at least one major Django consultancy-type shop, but it feels like slightly more (but not much more) rails on the web side.
I'm not plugged into the industry biotech to know how much Python is floating around, but I think it's probably pretty common, just that those folks travel in slightly different circles and we don't cross polinate enough.
"Is anyone actually adopting Python 3 as their default yet?"
Almost certaintly someone has, the real question is what percentage and what characterizes them :)
I stopped using Python for data science and use R and it really worked out great for me. R has gained a lot of momentum and I think that Pandas with Wes leaving isn't being developed as quickly. (Most of it is re-doing what R has done). I am amazed at all the FUD I was told to not use R and just use Python but after hitting so many walls I tired it and I am so glad I did. Your millage may vary.
<-- Maturity -- Fundamentals well designed -->
R Python JuliaMy main beef about R (and similar tools) is that I summarize my data I get from Hadoop and find it is still too big for R to handle and I then have to summarize it some more.
By the way i should have stated that Python is a good choice for doing Data Science it is just that R works better for me.
You assessment is pretty accurate. R is almost the opposite of Python -- you HAVE to use lots of external libraries. Whereas in Python, the core is super solid, but the libraries you get are of varying quality.
R took me the longest to learn out of any language. I currently have coworkers struggling with R, and cursing it. But the learning pays off... it's pretty much the only option out there right now (and yes I use Python for almost everything, but you need R too.)
[1] https://blog.javascripting.com/2014/07/28/fastest-growing-ne...
[2] http://www.pcworld.com/article/2480920/r-programming-languag...
[3] http://redmonk.com/sogrady/2015/01/14/language-rankings-1-15...
Yes I have read [R Inferno] http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
What walls and why did you switch? Did you find the shift in syntax and mindset difficult? Have you used rpy2?
There definitely seems to be more innovation in R world, but blaze, numba and bokeh have no R equal.
I work at a Microsoft Office Universe (They refuse to know of any tools besides Word and you are a tech god if you use Excel) So when I finish my reports in R I actually give them Office Documents. The presentation is actually Power Point and the documents are in Word.
I left Python and Pandas and the visualization was far behind what R had in its tool chain. I still feel that R does visualizations best.
RStudio (A true IDE) was better for my work flow than IPython/Juypter. I actually really missed IPython at first and than slowly I grew to love RStudio.
Not a wall, maybe a paper cut. A 1 index (R) over a 0 index (Python) is pretty smart for doing statistics, less errors.
I also liked the flexibility to use whatever I wanted to do my work. Was difficult at first than learned to love the fact that there were 3 good ways to do something.
Functional Programming. You can do some good functional programming with R. Functions are first class citizens.
Biggest reason for anyone to switch is Hadley Wickham and his work with RStudio. It really is amazing everything that he has done for R in the last 5 years.
Once again Python will get the job done but these are some of the reasons why I switched.
When you say "flexibility to use whatever I wanted to do my work", do you mean data munging and manipulation or statistical modeling packages?
Graphics I can use just about anything. I can use plottly, ggplot2, D3.js, PDF report, HTML, send to a blogging site, interactive graphics with ggvisand Shiney, even PowerPoint slides.