11–20 of 242 posts
One pain point I've really felt recently with Python is in the deploy step. pip installing dependencies with a requirements.txt file seems to be the recommended way, but it's far from easy. Many dependencies (such as PyTables) don't install their own dependencies automatically, so you are left with a fragile one-by-one process for getting library code in place. It was all fine once I got it worked out but it would so…
I think this will happen eventually, what with some of the recent PEPs; I just wish it could happen faster. Optional typing is the best of both worlds and there is no reason not to have it.
edit: I wanted to respond to this myself, since upon rereading I no longer get the impression the proposed changes need 'break' backwards compatibility per se. For the suggestion on removing the GIL specifically, this would completely necessitate a revolution in the design of python programs such that even if, say, the libraries that had already been ported at the time of 3.2 still work in 3.9, their implementation would be senseless by 3.9 conventions.
> Newer programmers are not that impressed with either version of Python. Any evidence? If it's personal experience, then mine is exactly the opposite. I don't know if such a thing exists, but maybe a big list of the main changes would help convince people more (type annotations, yield from, the forthcoming @ operator). From what I've seen and read, of course all this is somewhere in the docs and release notes, but I…
Specifically, my experience -- teaching Python to dozens of experienced developers every month, for more than four years -- is that people from the C, C++, C#, and Java world and shocked to the core by the ease with which they can do certain things in Python.
Maybe a Ruby or JavaScript developer won't be blown over to the same degree. But there are a heckuva lot of people coming from static, compiled languages who haven't ever seen the power and flexibility of Python before, and love what they see.
One pain point I've really felt recently with Python is in the deploy step. pip installing dependencies with a requirements.txt file seems to be the recommended way, but it's far from easy. Many dependencies (such as PyTables) don't install their own dependencies automatically, so you are left with a fragile one-by-one process for getting library code in place. It was all fine once I got it worked out but it would so…
Not the best approach, but it does work.
The idea that people move from Python specifically to Go is one of those chestnuts of conventional wisdom that never receives any kind of backing in actual data. If you think that Python and Go are made for the same tasks then you're really confused.
The idea that people move from Python specifically to Go is one of those chestnuts of conventional wisdom that never receives any kind of backing in actual data. If you think that Python and Go are made for the same tasks then you're really confused.
I try to explain to them that obviously Python developers care about these things, and I show them some of the most common pitfalls. But this isn't the first, or even fifth, thing that you think about when you're coding in Python.
The key thing to remember with dynamic languages, and particularly interpreted dynamic languages, is that they're optimized for programmer speed, not execution speed. And that's fine nowadays, when programmers are expensive and computers are not.
So yeah, maybe Go will run your program 100 times faster. And there are cases when that's important. But in most cases, it's not, and the benefits that you'll get from having a happy, efficient developer will be more than worthwhile.
One pain point I've really felt recently with Python is in the deploy step. pip installing dependencies with a requirements.txt file seems to be the recommended way, but it's far from easy. Many dependencies (such as PyTables) don't install their own dependencies automatically, so you are left with a fragile one-by-one process for getting library code in place. It was all fine once I got it worked out but it would so…
I find the combination of virtual environments and pip very convenient to work with. When I run into trouble with missing dependencies I often find the project on GitHub and can send a pull request.
Regarding Numpy and the scientific Python stack, check out Anaconda https://store.continuum.io/cshop/anaconda/ it makes managing environments where you need these packages a lot less painful.
> Newer programmers are not that impressed with either version of Python. Any evidence? If it's personal experience, then mine is exactly the opposite. I don't know if such a thing exists, but maybe a big list of the main changes would help convince people more (type annotations, yield from, the forthcoming @ operator). From what I've seen and read, of course all this is somewhere in the docs and release notes, but I…
I agree; I have found that people are super-impressed with Python. Specifically, my experience -- teaching Python to dozens of experienced developers every month, for more than four years -- is that people from the C, C++, C#, and Java world and shocked to the core by the ease with which they can do certain things in Python. Maybe a Ruby or JavaScript developer won't be blown over to the same degree. But there are a…