4 ms·

> For a container that needs to be able to store objects of any type (including instances of user-defined classes), a heterogeneous list is faster than a homoge

by Zababa 5y ago

> For a container that needs to be able to store objects of any type (including instances of user-defined classes), a heterogeneous list is faster than a homogeneous one, because, as above, it doesn't have to do extra work to check the types of objects. My question is about that. Is storing objects of any type a common use case, compared to storing objects of one type?

dragonwriter 5y ago
Storing objects of any size is common, because Python objects are rarely fixed size (Python integers aren't fixed size!) At that point, you need pointers anyway, so mixed type doesn't add much overhead. Especially given that you need to handle mixed concrete types even if you have single “typechecking” types because of inheritance (including via declared ABCs and union types.
sseagull 5y ago
I have found, at least in my own use of python, lists are for "homogeneous" collections of indeterminate length. But these are not strictly homogeneous - there may be None entries, for example. So I do use the heterogeneous capabilities. For heterogeneous collections with a fixed length, I tend to use a tuple. That makes more sense to me, conceptually (coming from C++).
- joconde 5y ago
  In Python’s type annotation system, “class T or None” is represented by “typing.Optional[T]”. I think C++ has something similar for STL containers. Even numpy has NaN values. So your usage still fits some reasonable definition of homogeneous :)
pdonis 5y ago
> Is storing objects of any type a common use case, compared to storing objects of one type? I'm not sure it's possible to know a general answer to that, since Python is used in such a wide variety of contexts. In my own programming, I use lists that can contain objects of different types often enough for that use case to be significant for me. One other thing to keep in mind is that Python has duck typing, so, for example, if you are iterating over a list and retrieving the same attribute or calling the same method on each object, that doesn't require each object to be of the same type, just to have an attribute with the required name, or a method with the required name and calling signature.

by Zababa 5y ago

dragonwriter 5y ago

Storing objects of any size is common, because Python objects are rarely fixed size (Python integers aren't fixed size!) At that point, you need pointers anyway, so mixed type doesn't add much overhead. Especially given that you need to handle mixed concrete types even if you have single “typechecking” types because of inheritance (including via declared ABCs and union types.

sseagull 5y ago

I have found, at least in my own use of python, lists are for "homogeneous" collections of indeterminate length. But these are not strictly homogeneous - there may be None entries, for example. So I do use the heterogeneous capabilities. For heterogeneous collections with a fixed length, I tend to use a tuple. That makes more sense to me, conceptually (coming from C++).

joconde 5y ago
In Python’s type annotation system, “class T or None” is represented by “typing.Optional[T]”. I think C++ has something similar for STL containers. Even numpy has NaN values. So your usage still fits some reasonable definition of homogeneous :)

pdonis 5y ago

> Is storing objects of any type a common use case, compared to storing objects of one type? I'm not sure it's possible to know a general answer to that, since Python is used in such a wide variety of contexts. In my own programming, I use lists that can contain objects of different types often enough for that use case to be significant for me. One other thing to keep in mind is that Python has duck typing, so, for example, if you are iterating over a list and retrieving the same attribute or calling the same method on each object, that doesn't require each object to be of the same type, just to have an attribute with the required name, or a method with the required name and calling signature.