# Another reason to use python 3.7

## May 15, 2019

Python is amazing, python 3 even more amazing and any new version brings its batch of amazing novelties! And python 3.7 doesn’t break the rule. It is the first release implementing dataclasses . This article is a short review of the new possiblities offered by this module.

Here is a example code:

class MyClassA():
def __init__(self, param_a, param_b, param_c=0):
self.param_a = param_a
self.param_b = param_b
self.param_c = param_c

def __repr__(self):
return "param_a=%s, param_b=%s, param_c=%s" % (
self.param_a,
self.param_b,
self.param_c
)


Do you have to write this type of code a lot of time? Then the following will be REALLY intesting for you!

Indeed, dataclasses is THE tool to avoid repeating code in the __init__, __repr__ and a couple more methods. The equivalent of the above code using dataclasses is:

from dataclasses import dataclass

@dataclass
class MyClassA():
param_a: int
param_b: int
param_c: int = 0 # set a default value

if __name__ == '__main__':
mc = MyClassA(1, param_b=2)
print(mc)


And that’s all! The dataclass decorator will automatically generate the __init__ and __repr__ methods that we had to write manually above.

You have noticed the annotations style introduced in python 3.4 to make clear to the user what are the expected type of the parameters. In case you do not want to specify them, you can use typings.Any parameters, ie:

import typing
from dataclasses import dataclass

@dataclass
class MyClassA():
param_a: int
param_b: int
param_c: int = 0 # set a default value
param_d: typing.Any = None


Pretty cool? But what if I want to initialize other parameters that should not be modified through a init parameter? Easy, dataclasses automatically generated __init__ method call a __post_init__ method where you can exactly do this. For instance:

from dataclasses import dataclass

@dataclass
class MyClassA():
param_a: int
param_b: int
param_c: int = 0 # set a default value

def __post_init_(self):
self.initial_sum = self.param_a + self.param_b

if __name__ == '__main__':
mc = MyClassA(1, param_b=2, param_c=10)
print(mc.initial_sum)


Apart from the obvious gain in time, dataclasses also come with some helpful methods like:

from dataclasses import fields

fields(mc)


or:

from dataclasses import asdict

asdict(mc)

# {'param_a': 1, 'param_b': 2, 'param_c': 10}


Sooo, convinced?