Pythons ecosystem on the other hand is rather small in comparison, but it got a nice niche in AI/Big Data it dominates handily.
I hear about this purported "niche" a lot, but in nearly a decade of Python coding, I've never observed it. Python has a fairly broad ecosystem when you factor in libraries (installable via pip or poetry in one line): it handles not only AI and data, but is excellent at GUI, networking, system interoperability, and just about anything you can throw at it. The only area Python really doesn't excel at is true multiprocessing, thanks to the Global Interpreter Lock (GIL), but there's work being done to resolve that too.
Python doesn't have as many conventions, but that freedom costs you the effort of doing most things yourself.
Uhm, this is most certainly not true. Python has a greater emphasis on convention and The One Obvious Way (as we put it) than most other languages, Java included. We have no fear of boilerplate — cargo cult programming, as Guido calls it — but we do avoid mindless boilerplate when we can (except, perhaps, in setup.py). Testing, packaging, deployment, all of these work well once you know where a couple of pieces fit, to the same degree as Java and Maven.
A Freelance DevOps doing container stuff and automating unhealthy amounts of software.
Need something automated or containerized? Feel free to hit me up :)
Interesting, I never really observed Python like that, though judging by your comments, it's absolutely certain you are far more knowledgeable with Python than I am.
With eco-system I meant more the tooling around the languages, like CI/CD, Security Scanning, Lifecycle-Management and the likes.
I would assume a big portion of that is probably not even necessary for Python. What would your take be on that?
So thank you for your input and correcting my wrong statements.
It seems I have to do more Research next time
CI/CD: There are a number of robust testing frameworks, including PyTest, Nose, and the newer Ward. Python also integrates beautifully into most CI/CD frameworks, especially given the relative ease of deploying a package. (On that note, many Python projects even automate publishing their package to the Python Package Index using GitHub Actions or the like.)
Security Scanning: Bandit is one prominent example, and I know there are others.
I don't know about Lifecycle-Management, as I haven't used it, but a casual glance through the Python Package Index, or PyPI, it looks like there are plenty of options.
It's a common myth that Python isn't suitable for large projects. In fact, there are a number of very large applications and libraries built entirely in Python. It has its flaws, but Python is often excellent for software architecture and deployment because it "just works" in most situations, and aims to be intuitive while staying out of your way.
A Freelance DevOps doing container stuff and automating unhealthy amounts of software.
Need something automated or containerized? Feel free to hit me up :)
Thank you for your take, you never stop learning!
I guess I'll spend this weekend on Python and taking a look at the tools you mentioned.
The last time I had any python on my screen was when I wrote custom ansible filters and a few shallow dips into Django, which confused the hell out of me.
It's about time for a refresher :)
I hear about this purported "niche" a lot, but in nearly a decade of Python coding, I've never observed it. Python has a fairly broad ecosystem when you factor in libraries (installable via pip or poetry in one line): it handles not only AI and data, but is excellent at GUI, networking, system interoperability, and just about anything you can throw at it. The only area Python really doesn't excel at is true multiprocessing, thanks to the Global Interpreter Lock (GIL), but there's work being done to resolve that too.
Uhm, this is most certainly not true. Python has a greater emphasis on convention and The One Obvious Way (as we put it) than most other languages, Java included. We have no fear of boilerplate — cargo cult programming, as Guido calls it — but we do avoid mindless boilerplate when we can (except, perhaps, in
setup.py). Testing, packaging, deployment, all of these work well once you know where a couple of pieces fit, to the same degree as Java and Maven.Interesting, I never really observed Python like that, though judging by your comments, it's absolutely certain you are far more knowledgeable with Python than I am.
With eco-system I meant more the tooling around the languages, like CI/CD, Security Scanning, Lifecycle-Management and the likes.
I would assume a big portion of that is probably not even necessary for Python. What would your take be on that?
So thank you for your input and correcting my wrong statements.
It seems I have to do more Research next time
CI/CD: There are a number of robust testing frameworks, including PyTest, Nose, and the newer Ward. Python also integrates beautifully into most CI/CD frameworks, especially given the relative ease of deploying a package. (On that note, many Python projects even automate publishing their package to the Python Package Index using GitHub Actions or the like.)
Security Scanning: Bandit is one prominent example, and I know there are others.
I don't know about Lifecycle-Management, as I haven't used it, but a casual glance through the Python Package Index, or PyPI, it looks like there are plenty of options.
It's a common myth that Python isn't suitable for large projects. In fact, there are a number of very large applications and libraries built entirely in Python. It has its flaws, but Python is often excellent for software architecture and deployment because it "just works" in most situations, and aims to be intuitive while staying out of your way.
Thank you for your take, you never stop learning!
I guess I'll spend this weekend on Python and taking a look at the tools you mentioned.
The last time I had any python on my screen was when I wrote custom ansible filters and a few shallow dips into Django, which confused the hell out of me.
It's about time for a refresher :)
You may appreciate this, then:
Introducing "Dead Simple Python"
Jason C. McDonald ・ Jan 13 '19 ・ 3 min read