Data Classes vs typing.NamedTuple primary use cases

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.

In programming in general, anything that CAN be immutable SHOULD be immutable. We gain two things:

Easier to read the program- we don't need to worry about values changing, once it's instantiated, it'll never change (namedtuple) Less chance for weird bugs

That's why, if the data is immutable, you should use a named tuple instead of a dataclass

I wrote it in the comment, but I'll mention it here: You're definitely right that there is an overlap, especially with frozen=True in dataclasses- but there are still features such as unpacking belonging to namedtuples, and it always being immutable- I doubt they'll remove namedtuples as such

I had this same question, so ran a few tests and documented them here: https://shayallenhill.com/python-struct-options/

Summary:

NamedTuple is better for unpacking, exploding, and size.

DataClass is faster and more flexible.

The differences aren't tremendous, and I wouldn't refactor stable code to move from one to another.

NamedTuple is also great for soft typing when you'd like to be able to pass a tuple instead.

To do this, define a type inheriting from it...

class CircleArg(NamedTuple):
    x: float
    y: float
    radius: float

...then unpack it inside your functions. Don't use the .attributes, and you'll have a nice "type hint" without any PITA for the caller.

*focus, radius = circle_arg_instance  # or tuple

Another important limitation to NamedTuple is that it cannot be generic:

import typing as t
T=t.TypeVar('T')
class C(t.Generic[T], t.NamedTuple): ...

TypeError: Multiple inheritance with NamedTuple is not supported

One usecase for me is frameworks that do not support dataclasses. In particular, TensorFlow. There, a tf.function can work with a typing.NamedTuple but not with a dataclass.

class MyFancyData(typing.NamedTuple):
  some_tensor: tf.Tensor
  some_other_stuf: ...

@tf.function
def train_step(self, my_fancy_data: MyFancyData):
    ...