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Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
> When printing tracebacks, the interpreter will now point to the exact expression that caused the error instead of just the line. For example: Traceback (most recent call last): File "distance.py", line 11, in print(manhattan_distance(p1, p2)) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "distance.py", line 6, in manhattan_distance return abs(point_1.x - point_2.x) + abs(point_1.y - point_2.y) ^^^^^^^^^ AttributeError: 'NoneType…
This is an INCREDIBLE improvement. I'm so excited for it for myself, and I think it will also do wonders for getting new programmers comfortable with the language. Seeing exactly where in the line your error is, is amazing! IMO this 100% cements Python as the single most newbie-friendly language, if there was any question before now (or at least, before the release of 3.11).
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Because of dependency management, or?
Because of having to know what WSGI and ASGI are and picking the right one. Then pick which implementation of the above you want to use. Then configuring it correctly for your app. And finally make it play nicely with nginx/apache/whatever. If on the other hand you're trying to deploy a desktop app, that is an entirely different kettle of 'fun'.
Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
> more complicated than any other programming language ever conceived of
is definitely untrue. Even if you take only mainstream languages then there are many more annoying to deploy, ones that are more complicated and so on.
Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
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It's still a mess, honestly. They tried to standardize on venv but since it doesn't deal well with Python libraries that depend on non-Python components, conda is still better for many purposes.
Can you give a specific example? I've had nothing but pain with conda in mixed linux/mac/win environment at work, and actively worked to get it deprecated. We're on plain venvs now, with the occasional setup.py Curious if I missed some useful case for it. - very slow dependency resolvers - using conda in docker is annoying - the worst thing: inconsistencies in downloading binaries and other resources between win/mac/…
Conda will package and version things like compilers and C libraries etc in a sane way. I can 'conda install' a package and start using it, but if i 'pip install' the same package I'm still left having to hunt down and install a bunch of additional dependencies. For example 'conda install cython' gives me a fully working and consistent cython on all platforms in a way that 'pip install cython' won't.
There are still several computational science and data science packages where 'pip install X' on windows simply doesn't work (and 'conda install X' does) due to missing dependencies and other weirdness.
Conda can version and manage R as well as Python if you work on projects that use both then you only need one tool.
Conda can also install dev tools like Spyder, VSCode or Jupyter(labs) making it easier to offer a single point of entry for an entire dev environment.
But basically the big one is simply that Conda will install a lot more numeric packages with a lot fewer headaches on win, linux and mac than pip. Although in pip's defense it has gotten a lot better over the past 2-3 years. And I do agree that the dependency resolver can take a ridiculously long time in some situations.
Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
Wouldn't say it better myself. Everything you gain with fast coding you lose on tackling performance issues, GIL, dependency management... Things harder to solve once you're committed to production.
Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
Can you be more specific about your claims? What is wrong with Python? > more complicated than any other programming language ever conceived of is definitely untrue. Even if you take only mainstream languages then there are many more annoying to deploy, ones that are more complicated and so on.
Certainly glad to see python 3.11 having so much performance improvements.
Python is deceptive. They lure you in with its simplicity, and then one day you want to deploy your software and you realize that it's more complicated than any other programming language ever conceived of.
What kind you issues you get during deployment? I think if running on right version then there should not be any issues.
Some of these approaches work for Windows, others for Mac, some for Linux, depending on versions of Python that are installed, etc. You can get around some of the problems with pyenv and a bunch of other tools.