Python Dataclasses

Dataclasses¶

Dataclasses help us in writing data oriented classes like a class representing a vector.
- They are very different from behaviour oriented classes that expose a number of methods.
Example of a builtin dataclass

How does dataclasses help in representing data oriented classes?

It adds convenience mechanisms like - Being able to represent the object as a string easily. Like __str__ and __repr__ methods are already implemented. - Compare the objects with other objects. - Add an easy initialisation mechanism to the data.

Dataclasses by default automatically initialise a bunch of dunder methods for us in a class such as:
- __init__ The initialisation method for the class
- __repr__ How the class is represented with print() is called
- __str__ How the class is represented as a string (called with __repr__)
- __eq__ Used when equality operators are used (eg, ==)
- __hash__ The hash for the class (called with __eq__)

Dataclasses should be preferred over normal dictionaries and typedicts if you are want type hinting and are planning to pass dictionaries around.

Dataclasses can be used for creating Data Transfer Objects in Python.
DTOs are objects that are used to pass data between functions and are not entities or basic types.

If we forget to add @dataclass decorator over the class then we will end up with a bunch of class variables instead of instance variables.

Using DataClasses¶

Default values in non mutable objects

from dataclasses import dataclass

@dataclass
class Test:
    a: str
    b: int = 10 # default value

obj1 = Test(a="abc")

Default values in mutable objects

from dataclasses import dataclass, field

def generate_id():
    return ""

@dataclass
class Person:
    name: str
    """
    email_addrsses: list[str] = [] 
    Each instance of Person will have access to same version of list.
    """
    email_addresses: list[str] = field(default_factory=list)

    """
    Having randomly generated values as default ids
    init=False means we cannot set IDs while creating an instance
    """
    id: str = field(init=False, default_factory=generate_id)

    """
    Create values from other instance variables using post init
    """
    search_string: str

    def __post_init__(self) -> None:
        self.search_string = self.name + self.id

obj1 = Test(a="abc")
obj2 = Test(a="abc", email_addresses=["xyz@gmail.com"])

The above way of initialising the default value of b will lead to problems. Read more about Mutable Default Values
We pass functions to the default_factory
- Here list is also a function, just like generate_id
For protected variables we use _ and for private variables we use __ in front of the variables.
If we don't want to print a particular field when we print the object then we can use some_variable: str = field(repr=False)
We can freeze the dataclass using @dataclass(frozen=True)
- Frozen means once the dataclass is initialised it is read only.
After python3.10 we can use the following
- @dataclass(kw_only=True) - If we only want to initialise the class by providing key word arguments.
- @dataclass(slots=True) - Faster dataclasses

Pydantic vs Dataclasses¶

We also have pydantic which can be used as dataclass and comes with validators.
- It does type checking at runtime.
Pydantic is an opinionated library built for parsing.
By default pydantic will try to convert the values in the types mentioned in the class.

All the types are checked at runtime and type conversions take place at runtime

Checking/converting things at runtime makes a lot of sense when the data we are reading might not be valid or be of the right format. This only makes sense for the specific task of parsing.

Using pydantic is a huge waste of resources and time if parsing is not the goal.
- If some data structure is internal to your application and your application is responsible for creating the data then it would be a huge waste of resources to do runtime checking.
It is better to use static type checking.
Pydantic is not an alternative to dataclasses.
- They serve a very different purpose of parsing. Like parsing JSON from a web API.
It is very good at what it does but it is not a general purpose thing.

Dataclasses comparison chart¶

attachments/Pasted image 20221022152857.jpg

References¶

Last updated: 2022-10-27