Using a class as a data container

Answer #1:

If you’re really never defining any class methods, a dict or a namedtuple make far more sense, in my opinion. Simple+builtin is good! To each his own, though.

Answer #2:

Background

A summary of alternative attribute-based, data containers was presented by R. Hettinger at the SF Python’s 2017 Holiday meetup. See his tweet and his slide deck. He also gave a talk at PyCon 2018 on dataclasses.

Other data container types are mentioned in this article and predominantly in Python 3 documentation (see links below).

Here is a discussion on the python-ideas mailing list on adding recordclass to the standard library.

Options

Alternatives in the Standard Library

• collections.namedtuple: tuple with attributes (see seminal recipe)
• typing.NamedTuple: sub-classable tuple (see this post comparing it with namedtuple)
• types.SimpleNamespace: simple class w/optional class declaration
• types.MappingProxy: read-only dict
• enum.Enum: constrained collection of related constants (does behave like a class)
• dataclasses.dataclass: mutable namedtuple with default/boilerplate-less classes

External options

• records: mutable namedtuple (see also recordclass)
• bunch: add attribute access to dicts (inspiration for SimpleNamedspace; see also munch (py3))
• box: wrap dicts with dot-style lookup functionality
• attrdict: access elements from a mapping as keys or attributes
• fields: remove boilerplate from container classes.
• namedlist: mutable, tuple-like containers with defaults by E. Smith
• misc.: posts on making your own custom struct, object, bunch, dict proxy, etc.

Which one?

Deciding which option to use depends on the situation (see Examples below). Usually an old fashioned mutable dictionary or immutable namedtuple is good enough. Data classes are the newest addition (Python 3.7a) offering both mutability and optional immutability, with promise of reduced boilerplate as inspired by the attrs project.

Examples

import typing as typ
import collections as ct
import dataclasses as dc


# Problem: You want a simple container to hold personal data.
# Solution: Try a NamedTuple.
>>> class Person(typ.NamedTuple):
...     name: str
...     age: int
>>> a = Person("bob", 30)
>>> a
Person(name='bob', age=30)

# Problem: You need to change age each year, but namedtuples are immutable. 
# Solution: Use assignable attributes of a traditional class.
>>> class Person:
...     def __init__(self, name, age):
...         self.name = name
...         self.age = age
>>> b = Person("bob", 30)
>>> b.age = 31
>>> b
<__main__.Person at 0x4e27128>

# Problem: You lost the pretty repr and want to add comparison features.
# Solution: Use included repr and eq features from the new dataclasses.
>>> @dc.dataclass(eq=True)
... class Person:
...     name: str
...     age: int
>>> c = Person("bob", 30)
>>> c.age = 31
>>> c
Person(name='bob', age=31)
>>> d = Person("dan", 31)
>>> c != d
True

Answer #3:

By the way, I think Python 3.7 implemented @dataclass is the simplest and most efficient way to implement classes as data containers.

@dataclass
class Data:
    a: list
    b: str    #default variables go after non default variables
    c: bool = False

def func():
    return A(a="hello")

print(func())

The output would be :hello

It is too similar to Scala like case class and the easiest way to use a class as a container.

Answer #4:

I prefer to follow YAGNI and use a dict.

Answer #5:

There is a new proposal that aims to implement exactly what you are looking for, called data classes. Take a look at it.

Using a class over a dict is a matter of preference. Personally I prefer using a dict when the keys are not known a priori. (As a mapping container).

Using a class to hold data means you can provide documentation to the class attributes.

Personally, perhaps the biggest reason for me to use a class is to make use of the IDEs auto-complete feature! (technically a lame reason, but very useful in practise)

Answer #6:

Your way is better. Don’t try to anticipate the future too much as you are not likely to succeed.

However, it may make sense sometimes to use something like a C struct, for example if you want to identify different types rather than use dicts for everything.

Answer #7:

You can combine advantages of dict and class together, using some wrapper class inherited from dict. You do not need to write boilerplate code, and at the same time can use dot notation.

class ObjDict(dict):
    def __getattr__(self,attr):
        return self[attr]
    def __setattr__(self,attr,value):
        self[attr]=value

self.group = ObjDict(a=1, b=2, c=3)
print self.group.a

Answer #8:

I disagree that the code is more readable using a class with no methods. You usually expect functionality from a class, not only data.

So, I’d go for a dict until the need for functionality arises, and then the constructor of the class could receive a dict 🙂