It depends on your needs. Each of them has own benefits.
Here is a good explanation of Dataclasses on PyCon 2018 Raymond Hettinger – Dataclasses: The code generator to end all code generators
In Dataclass all implementation is written in Python, whereas in NamedTuple, all of these behaviors come for free because NamedTuple inherits from tuple. And because the tuple structure is written in C, standard methods are faster in NamedTuple (hash, comparing and etc).
Note also that Dataclass is based on dict whereas NamedTuple is based on tuple. Thus, you have advantages and disadvantages of using these structures. For example, space usage is less with a NamedTuple, but time access is faster with a Dataclass.
Please, see my experiment:
In [33]: a = PageDimensionsDC(width=10, height=10)
In [34]: sys.getsizeof(a) + sys.getsizeof(vars(a))
Out[34]: 168
In [35]: %timeit a.width
43.2 ns ± 1.05 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [36]: a = PageDimensionsNT(width=10, height=10)
In [37]: sys.getsizeof(a)
Out[37]: 64
In [38]: %timeit a.width
63.6 ns ± 1.33 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
But with increasing the number of attributes of NamedTuple access time remains the same small, because for each attribute it creates a property with the name of the attribute. For example, for our case the part of the namespace of the new class will look like:
from operator import itemgetter
class_namespace = {
...
'width': property(itemgetter(0, doc="Alias for field number 0")),
'height': property(itemgetter(0, doc="Alias for field number 1"))**
}
In which cases namedtuple is still a better choice?
When your data structure needs to/can be immutable, hashable, iterable, unpackable, comparable then you can use NamedTuple. If you need something more complicated, for example, a possibility of inheritance for your data structure then use Dataclass.