For example:
class FooBar:
def __init__(self):
self.a: int = None
self.foo: str = None
Is this bad practice/go against PEP guidelines or is it fine?
For example:
class FooBar:
def __init__(self):
self.a: int = None
self.foo: str = None
Is this bad practice/go against PEP guidelines or is it fine?
You might like: https://curtispoe.org/articles/what-to-know-before-debating-type-systems.html
I remember looking at attrs but not getting around to using it. These days I use dataclasses. I don’t worry about this stuff too much in Python. I just notice some issues and smile. Haskell does a lot more to focus the mind, when it comes to types.
i’m in love with typing_extensions. Know why? I’m not on the latest greatest Python version. But want to use the usable latest features. As package author/maintainer, upgrading to the latest and greatest is not a viable option.
in py39, dataclasses lacked critical features that came out later. Especially for a fundamental structure like a dataclass, new features requiring a Python upgrade is a show stopper. On the level of a flaw. Either backport or in a separate package or i don’t want to touch it. That’s why use attrs and not dataclasses.dataclass
And so should we all.
There is always going to be some great features we want to use and then not being able to is not great UX. Every package that matters really must be external, except for Python features like free threading, sub-interpretors, etc.
There are always many coding languages, unfortunately jumping from one to the next is a pipe dream. So really doesn’t matter what Haskell can do. Or how Rust devs all have 10 gfs.
Before dataclasses I used namedtuple and that was good enough. attrs looked like an improvement over namedtuple but not enough of one to deal with the added complexity. If dataclasses have gotten interesting new features since 3.9 I haven’t seen them yet. I guess I should check the docs. I’m not a fancy user so it’s probably ok.
Haskell is worth learning mostly because of how it sharpens your mind. Developing useful programs in it still has obstacles.
dataclasses required py310+ for critical features like slot support. Only now that py39 is history, is dataclasses even a thing. But then a must have feature will require an even more recent python version. So stuck waiting in a never ending loop.
Fck that!
python -m pip install attrsProblem solved.
I haven’t needed slot support in dataclasses so far. It must not be that critical. Some of my machines still have python 3.9 and dataclasses work, at least to the extent I’ve needed them to.
That’s fine. But you coulda had access to latest greatest updates. Whether you need them or not.
Python advertises itself as being mature, which means changes if any are supposed to be incremental and backwards compatible. It’s not so successful at that but I still don’t particularly try to keep up with the bleeding edge. I don’t know what slots in dataclasses even means. I do like the match statement.
LOL have never used the match statement. Has been one of those features just outta reach. So close can taste it. But alas …
Now py310 is here. Still not enthusiastic about it. But over time maybe will come around.
__slots__ = (...)or frozen. Same thing. Make the data class read-only reducing memory usage by 30-40%. Like the memory usage difference between tuple and list.From experience, in stubs,
__slots__need to include"__weakref__". If frozen, then don’t include"__weakref__".How about this. Lets meet each other half way.
I’ll stop thinking of the match statement as thorium reactor toxic waste.
And maybe you can take a read through of the frozen option for dataclasses.
Sound fair?
If I need something like slots I’ll keep them in mind. Thanks for calling my attention to them.
Thanks for the article link. Worried code too much and don’t study enough.
Appreciate the reading material.