151–160 of 369 posts
I work in scientific computing, and while me and my colleagues have all experimented with Julia at some level (and definitely welcome having more options), I don't yet know of anyone who has built any part of their permanent toolchain with it yet.
Are there any notable use cases out in the wild yet (known internal use by an organization, or major FOSS tool or framework)?
Julia for Data Science? Ha! I think Julia might maybe have a shot at the space Matlab plays in the moment. But I'd define that more as scientific computing than data science. Does Julia even have a properly supported HBase interface? People are confused about how to connect to SQL Server [1] - until some pretty basic things like that are sorted out I don't think anyone will take it seriously for data science. Also: h…
Julia is not even at 1.0 yet, and will be full of holes until at least then. It's still a wonderful language for technical/scientific computing, but you don't want it to be your only language if you have to make diverse real things.
That's almost the complete opposite of Python, which has the ugliness of real world use (eg, 2.7 vs 3), but also has those thousands or real world hardened packages.
I play around with NodeJS and front end frameworks like React for my own side projects, but all of my paying client work is made with good ol Django. It just does so many things well and is very mature. I recommend for production code using libraries that have a strong community and have been around for a while so that they don't keep changing all the time.
Don't forget that Django and React make a great combination. You probably still want Django to handle server-side rendering for most of your site (unless you're a masochist). Django Rest Framework for the API, and React (or whatever) to handle the app-like parts.
But... as I started publishing these apps on mobile app stores (cordova), I found myself also having to write a REST endpoint and client side renderer for the app versions.
Even though I'd much rather do a contact form, for example, in a server side view. But if I also need the REST API and client-rendered version for the app version, I might as well only write it once.
Not only is Python not dying, but it's stronger than it's ever been. The majority of top colleges are now teaching CS using Python. It's completely dominant in academia, finance, and startups. It has the best libraries and documentation of any language. And Django has arguably the strongest community of any open source project period. That's not to say there are zero problems, but Python is so much better than all th…
Completely dominant in finance? Do you have a source for that claim?
Not only is Python not dying, but it's stronger than it's ever been. The majority of top colleges are now teaching CS using Python. It's completely dominant in academia, finance, and startups. It has the best libraries and documentation of any language. And Django has arguably the strongest community of any open source project period. That's not to say there are zero problems, but Python is so much better than all th…
I've been a Pythonista for over a decade, and the API scene isn't quite as utopian as you describe. Date/time handling in particular is a really poorly handled area, but there's quite a few out there.
That said, in general I agree the 'batteries included' approach is good, and the libraries _tend_ to be easy to use for most tasks. Compared to most Java libraries, it's a breath of fresh air.
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It was coherent and I would not define it as snark. You don't comprehend his point yet you have the wherewithal to label it as incoherent & snarky -- that's snarky. But I digress.. Python users had a choice to stay in 2.7 land or make changes to their codebase for not much in return. Many chose to stay in 2.7 land, which you could safely say is stagnant. As a result, if you can't keep moving with Python, where will y…
Maybe you think you "comprehend" his point because you decide to interpret it in your own way. But it was not coherent (and honestly I'm not even sure if that was a snark in there or not, mostly because it was incoherent).
Perl is definitely dead, I think most people know that. The link was posted to illustrate that other links from that site were painting incorrect picture.
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Be aware that PyPI isn't a great way to get accurate statistics. You can't just ignore a bunch of statistics because they aren't convenient to you; that's not how maths works. Statistical sampling is based on the premise that your sample is randomized from the total population; I would argue that the stats from pypi represent a reasonable random sample of python users around the world. Certainly, you can argue otherw…
GP has argued otherwise. PyPI sampling is not random. It is heavily influenced by automatic downloads and such and completely ignores that most simple 2.x libs / non-3.3-tagged libs will work just fine on python 3. In other words, it is neither random nor representative of python 3 usage, failing the premise you yourself mentioned. There's no easy way to determine Python 3 usage overall. Your best bet is to go around…
(cue the downvotes)
Not in my sphere of activity. I have a friend who recently started his PhD... and is moving all of his development from C/C++/Fortran to Python. Numerical code even - he just doesn't want to bother.
Citing this example isn't very convincing. Would be more interesting to know what language he uses when he finishes his Ph.D.
Python is a beautiful language, and what I like to call programming at its purist. It removes the distraction of syntax from the task of problem solving. It's simple enough that a 7 year old can grasp it, and yet powerful enough that Google used it for its TensorFlow's API. Python syntax is so close to pseudocode, that I'll often code a solution in Python, see that it works, and then recode it in Java. It's difficult to describe if you haven't spent 7+ years using Java, but it's way more satisfying debugging Python code than rewriting solutions in Java.