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Right now, a huge opportunity for Python seems to be data science. I didn't notice it in the list of places where Python shows up. Also, in that space, it more

Right now, a huge opportunity for Python seems to be data science. I didn't notice it in the list of places where Python shows up. Also, in that space, it more than just shows up, it brings unique capability to the space with its libraries and the support major tool developers have for it.

tedmiston 11y ago
I agree with you 100%. I also got a comment from a NASA engineer that other strong additions are NASA itself and the Department of Energy. If you know of any specific companies, I'd definitely welcome the suggestion.
mpdehaan2 11y ago
I think folks just don't talk about Python, they get things done. 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.
- tedmiston 11y ago
  I find myself thinking (hoping) this is the case too. In fact, writing the post to talk about Python at this level was indirectly inspired by a talk I heard at Code Genius (http://code.genius.com/ http://code.genius.com/), put on by the Ruby-based Rap Genius Engineering team. 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).
  - mpdehaan2 11y ago
    On my end (east coast US), I hear the Python/data/analytics things a lot but haven't directly encountered those companies. 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 :)
baldfat 11y ago
Python is still over shadowed by R in data science. 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.
- jules 11y ago
  <-- Maturity -- Fundamentals well designed --> R Python Julia
  - bsg75 11y ago
    The relationship between maturity and design with respect to usefulness is not linear, with respect to "usefulness in production".
    - jules 11y ago
      Right, but it does depend on how long term you view it. Problems in the fundamentals of the core language can't be fixed, a mature library ecosystem can be developed.
- PaulHoule 11y ago
  R is an awful language with great libraries. My 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.
  - Lofkin 11y ago
    Have you tried data.table?
  - baldfat 11y ago
    Have you tried the Hadley Universe? Seriously there isn't a data set that Python can do that R can't (Lots of ways to do things as opposed to Python's one correct way). Using dplyr has been a huge change in the way I do any data munging. Panda's has started using some of dplyr with new function assign. Dplyr really handles large data sets well like data.tables (data.table is fast and handles larger sets but I like dplyr's syntax 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.
realusername 11y ago
I also have the same feeling. Python is very mathematics-friendly compared to other imperative languages and is used a lot more than other mainstream languages for mathematics related projects. On Github, there is a lot of good quality machine learning and statistics projects built with Python.

tedmiston 11y ago

I agree with you 100%. I also got a comment from a NASA engineer that other strong additions are NASA itself and the Department of Energy. If you know of any specific companies, I'd definitely welcome the suggestion.

mpdehaan2 11y ago

I think folks just don't talk about Python, they get things done. 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.

tedmiston 11y ago
I find myself thinking (hoping) this is the case too. In fact, writing the post to talk about Python at this level was indirectly inspired by a talk I heard at Code Genius (http://code.genius.com/ http://code.genius.com/), put on by the Ruby-based Rap Genius Engineering team. 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).
- mpdehaan2 11y ago
  On my end (east coast US), I hear the Python/data/analytics things a lot but haven't directly encountered those companies. 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 :)

baldfat 11y ago

Python is still over shadowed by R in data science. 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.

jules 11y ago
<-- Maturity -- Fundamentals well designed --> R Python Julia
- bsg75 11y ago
  The relationship between maturity and design with respect to usefulness is not linear, with respect to "usefulness in production".
  - jules 11y ago
    Right, but it does depend on how long term you view it. Problems in the fundamentals of the core language can't be fixed, a mature library ecosystem can be developed.
PaulHoule 11y ago
R is an awful language with great libraries. My 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.
- Lofkin 11y ago
  Have you tried data.table?
- baldfat 11y ago
  Have you tried the Hadley Universe? Seriously there isn't a data set that Python can do that R can't (Lots of ways to do things as opposed to Python's one correct way). Using dplyr has been a huge change in the way I do any data munging. Panda's has started using some of dplyr with new function assign. Dplyr really handles large data sets well like data.tables (data.table is fast and handles larger sets but I like dplyr's syntax 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.

realusername 11y ago

I also have the same feeling. Python is very mathematics-friendly compared to other imperative languages and is used a lot more than other mainstream languages for mathematics related projects. On Github, there is a lot of good quality machine learning and statistics projects built with Python.