CODEWITHSUNDEEP
pythondto
Jo So
Jo So

Reputation: 26511

Python: Quick and dirty datatypes (DTO)

Very often, I find myself coding trivial datatypes like

class Pruefer:
    def __init__(self, ident, maxNum=float('inf'), name=""):
        self.ident  = ident
        self.maxNum = maxNum
        self.name   = name

While this is very useful (Clearly I don't want to replace the above with anonymous 3-tuples), it's also very boilerplate.

Now for example, when I want to use the class in a dict, I have to add more boilerplate like

    def __hash__(self):
        return hash(self.ident, self.maxNum, self.name)

I admit that it might be difficult to recognize a general pattern amongst all my boilerplate classes, but nevertheless I'd like to as this question:

Upvotes: 29

Views: 25030

Answers (6)

pylang
pylang

Reputation: 44545

Are there any popular idioms in python to derive quick ... datatypes with named accessors?

Dataclases. They accomplish this exact need.

Some answers have mentioned dataclasses, but here is an example.

Code

import dataclasses as dc


@dc.dataclass(unsafe_hash=True)
class Pruefer:
    ident : int
    maxnum : float = float("inf")
    name : str  = ""

Demo

pr = Pruefer(1, 2.0, "3")

pr
# Pruefer(ident=1, maxnum=2.0, name='3')

pr.ident
# 1

pr.maxnum
# 2.0

pr.name
# '3'

hash(pr)
# -5655986875063568239

Details

You get:

  • pretty reprs
  • default values
  • hashing
  • dotted attribute-access
  • ... much more

You don't (directly) get:

  • tuple unpacking (unlike namedtuple)

Here's a guide on the details of dataclasses.

Upvotes: 8

Enrique G
Enrique G

Reputation: 352

if using Python 3.7 you can use Data Classes; Data Classes can be thought of as "mutable namedtuples with defaults"

https://docs.python.org/3/library/dataclasses.html

https://www.python.org/dev/peps/pep-0557/

Upvotes: 1

eirenik0
eirenik0

Reputation: 2566

One of the most promising things from with Python 3.6 is variable annotations. They allow to define namedtuple as class in next way:

In [1]: from typing import NamedTuple

In [2]: class Pruefer(NamedTuple):
   ...:     ident: int
   ...:     max_num: int
   ...:     name: str
   ...:     

In [3]: Pruefer(1,4,"name")
Out[3]: Pruefer(ident=1, max_num=4, name='name')

It same as a namedtuple, but is saves annotations and allow to check type with some static type analyzer like mypy.

Update: 15.05.2018

Now, in Python 3.7 dataclasses are present so this would preferable way of defining DTO, also for backwardcompatibility you could use attrs library.

Upvotes: 14

Dietrich
Dietrich

Reputation: 5531

An alternate approach which might help you to make your boiler plate code a little more generic is the iteration over the (local) variable dicts. This enables you to put your variables in a list and the processing of these in a loop. E.g: 

class Pruefer:
     def __init__(self, ident, maxNum=float('inf'), name=""):
         for n in "ident maxNum name".split():
             v = locals()[n]  # extract value from local variables
             setattr(self, n, v)  # set member variable

     def printMemberVars(self):
         print("Member variables are:")
         for k,v in vars(self).items():
             print("  {}: '{}'".format(k, v))


P = Pruefer("Id", 100, "John")
P.printMemberVars()

gives:

Member Variables are:
  ident: 'Id'
  maxNum: '100'
  name: 'John'

From the viewpoint of efficient resource usage, this approach is of course suboptimal.

Upvotes: 1

mgilson
mgilson

Reputation: 310049

I don't have much to add to the already excellent answer by Alexey Kachayev -- However, one thing that may be useful is the following pattern:

Pruefer.__new__.func_defaults = (1,float('inf'),"")

This would allow you to create a factory function which returns a new named-tuple which can have default arguments:

def default_named_tuple(name,args,defaults=None):
    named_tuple = collections.namedtuple(name,args)
    if defaults is not None:
        named_tuple.__new__.func_defaults = defaults
    return named_tuple

This may seem like black magic -- It did to me at first, but it's all documented in the Data Model and discussed in this post.

In action:

>>> default_named_tuple("Pruefer", "ident maxNum name",(1,float('inf'),''))
<class '__main__.Pruefer'>
>>> Pruefer = default_named_tuple("Pruefer", "ident maxNum name",(1,float('inf'),''))
>>> Pruefer()
Pruefer(ident=1, maxNum=inf, name='')
>>> Pruefer(3)
Pruefer(ident=3, maxNum=inf, name='')
>>> Pruefer(3,10050)
Pruefer(ident=3, maxNum=10050, name='')
>>> Pruefer(3,10050,"cowhide")
Pruefer(ident=3, maxNum=10050, name='cowhide')
>>> Pruefer(maxNum=12)
Pruefer(ident=1, maxNum=12, name='')

And only specifying some of the arguments as defaults:

>>> Pruefer = default_named_tuple("Pruefer", "ident maxNum name",(float('inf'),''))
>>> Pruefer(maxNum=12)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: __new__() takes at least 2 arguments (2 given)
>>> Pruefer(1,maxNum=12)
Pruefer(ident=1, maxNum=12, name='')

Note that as written, It's probably only safe to pass a tuple in as defaults. However, you could easily get more fancy by ensuring you have a reasonable tuple object within the function.

Upvotes: 1

Oleksii Kachaiev
Oleksii Kachaiev

Reputation: 6232

>>> from collections import namedtuple
>>> Pruefer = namedtuple("Pruefer", "ident maxNum name")
>>> pr = Pruefer(1,2,3)
>>> pr.ident
1
>>> pr.maxNum
2
>>> pr.name
3
>>> hash(pr)
2528502973977326415

To provide default values, you need to do little bit more... Simple solution is to write subclass with redefinition for __new__ method:

>>> class Pruefer(namedtuple("Pruefer", "ident maxNum name")):
...     def __new__(cls, ident, maxNum=float('inf'), name=""):
...         return super(Pruefer, cls).__new__(cls, ident, maxNum, name)
... 
>>> Pruefer(1)
Pruefer(ident=1, maxNum=inf, name='')

Upvotes: 26

Related Questions