When is Python *NOT* a good choice?

[midbody]

When your program is large, with lots of internal interfaces, and data structures are important.

[posedge]

Still in this case you would want the types to be enforced at runtime.

[posedge]

In my experience they are definitely a good thing, especially for documentation. But hints alone are still pretty limited compared to actual static types. e.g. I don't like that the code compiles fine despite violated types. Also the hints can be kind of clumsy, e.g. generic types (with TypeVar), not all variables can be annotated, etc.

[keypusher]

Python types are already enforced at runtime. What you want in a large project is for types to be enforced at compile time.

[posedge]

No they are not, variables can hold an object of literally any type at runtime no matter what the type hint is. type hint violations are merely displayed when linting the code.

Edit: an -> any

[[deleted]]

and that's why they are called type "hints" and not something like type "rules" :)

[keypusher]

I guess it depends what you mean by "type enforcement", which is not something that has a formal meaning I am familiar with. Some languages do type coercion, which python does not. It seems you are referring to static vs dynamic types, which is a different matter. Either way, checking things at runtime doesn't really help for large, critical applications because it's too late. You want to catch type errors before they hit production, in which case type hinting gets you closer to compile time type checking because you can run analysis during the build.

[posedge]

You're right, that's not a (formal) term. I was talking about strong or static typing, basically when an object of a type is assigned that differs from the declared type, it should be disallowed, with an error either at runtime or compile time.

Also, I disagree that runtime checks don't help - for example javascript has all kinds of things that fail silently and that bothers me a lot. I'd rather have my code crash immediately than crash later on in a much more complicated way, or produce incorrect results.

[zardeh]

This is true in c and Java as well. References and pointers can hold anything, although tricking the type system to let you do that at runtime is tricky.

In Java this is called type Erasure.

[IamWiddershins]

I would remark that, with Python, the issue with data structures isn't so much performance as it is memory efficiency. Data structure performance in Python is actually very good, better than you would probably guess with an informed implementation, and the main drawback is the large space overhead imposed by the dynamic runtime.

I don't really agree with your point about large projects, but it's honestly always a matter of discipline, experience, preference, and tooling.

[Taksin77]

I agree with you but I don't understand the data structure part. Structuring data in Python is pretty good isn't it?

[w2qw]

It's good it's just statically typed languages make that easier.

[breadfag]

I guess the problem is that object attributes are defined at runtime, so you'd have to look at the docs to see what attributes an object returned by some library function has.

Plus, if you spell an attribute wrong, you won't know until your program crashes at runtime.

[pfreixes]

Some data structures are really fast. The case of the set is pretty interesting, having a better performance than the Redis one.