Data Classes vs typing.NamedTuple primary use cases

Question

Long story short

PEP-557 introduced data classes into Python standard library, that basically can fill the same role as collections.namedtuple and typing.NamedTuple. And now I'm wondering how to separate the use cases in which namedtuple is still a better solution.

Data classes advantages over NamedTuple

Of course, all the credit goes to dataclass if we need:

• mutable objects
• inheritance support
• property decorators, manageable attributes
• generated method definitions out of the box or customizable method definitions

Data classes advantages are briefly explained in the same PEP: Why not just use namedtuple.

Q: In which cases namedtuple is still a better choice?

But how about an opposite question for namedtuples: why not just use dataclass? I guess probably namedtuple is better from the performance standpoint but found no confirmation on that yet.

Example

Let's consider the following situation:

We are going to store pages dimensions in a small container with statically defined fields, type hinting and named access. No further hashing, comparing and so on are needed.

NamedTuple approach:

from typing import NamedTuple

PageDimensions = NamedTuple("PageDimensions", [('width', int), ('height', int)])

DataClass approach:

from dataclasses import dataclass

@dataclass
class PageDimensions:
    width: int
    height: int

Which solution is preferable and why?

P.S. the question isn't a duplicate of that one in any way, because here I'm asking about the cases in which namedtuple is better, not about the difference (I've checked docs and sources before asking)

Answer 1

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.