This article discusses data classes in Python 3.7 and provides an introductory guide for data classes in Python 3.7 and above.
Data Class is a new concept introduced in Python 3.7 version. You can use data classes not only as a knowledge container but also to write boiler-plate code for you and simplify the process of creating classes since it comes with some methods implemented for free. A data class could also be considered as a category typically containing data, but they aren’t limited to that.
Basic Data Classes
The following ways show how a basic data class can be created:
1. Using decorator: They are created using the new @dataclass decorator.
Example:
Python3
# import package from dataclasses import dataclass # make a dataclass with decorator @dataclassclass DataClassGFG: Job: str Salary: float # make an object with values required by dataclass DataClassObject = DataClassGFG("Author",50000.00) # view dataclass object print(DataClassObject) |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
2. Using make_dataclass():
Example:
Python3
# import package from dataclasses import make_dataclass # make a dataclass with make_dataclass DataClassGFG = make_dataclass("DataClassGFG",["Job","Salary"]) # make an object with values required by dataclass DataClassObject = DataClassGFG("Author",50000.00) # view dataclass object print(DataClassObject) |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
3. Using namedtuple: Using namedtuple() method from collections. The is just similar to make_dataclass().
Example:
Python3
# import package from collections import namedtuple # make a dataclass with namedtuple DataClassGFG = namedtuple("DataClassGFG",["Job","Salary"]) # make an object with values required by dataclass DataClassObject = DataClassGFG("Author",50000.00) # view dataclass object print(DataClassObject) |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
Immutable Data Classes
One of the defining features of the namedtuple we saw earlier is that it’s immutable i.e., the value of its fields may never change. For several sorts of data classes, this is often an excellent idea to create an immutable class and to achieve this just simply set frozen=True once you create it.
Example:
Python3
# import package from dataclasses import dataclass # make a dataclass with decorator @dataclass(frozen=True) class DataClassGFG: Job: str Salary: float # make an object with values required by dataclass DataClassObject = DataClassGFG("Author",50000.00) # view dataclass object print(DataClassObject) # check immutable nature of class DataClassObject.Job = "Writer" |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
Traceback (most recent call last):
File “main.py”, line 17, in <module>
DataClassObject.Job = “Writer”
File “<string>”, line 4, in __setattr__
dataclasses.FrozenInstanceError: cannot assign to field ‘Job’
Flexible Data Classes
We already discussed some methods to make dataclasses and now we’ll study some more advanced features like parameters to a @dataclass decorator. Adding parameters to a dataclass provides us with more control when creating a data class.
Example:
Python3
# import package from dataclasses import dataclass from typing import List # make a dataclass with decorator @dataclassclass DataClassGFG: Job: str Salary: float @dataclassclass GFGJobs: Jobs: List[DataClassGFG] # make objects with values required by dataclass DataClassObject1 = DataClassGFG("Author",50000.00) DataClassObject2 = DataClassGFG("Writer",40000.00) # view dataclass objects print(DataClassObject1) print(DataClassObject2) # make an object of another dataclass GFGJobsObject = GFGJobs([DataClassObject1,DataClassObject2]) # view dataclass object print(GFGJobsObject) |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
DataClassGFG(Job=’Writer’, Salary=40000.0)
GFGJobs(Jobs=[DataClassGFG(Job=’Author’, Salary=50000.0), DataClassGFG(Job=’Writer’, Salary=40000.0)])
Optimizing Data Classes
For optimizing the data classes we use slots. Slots are often used to make classes faster and use less memory. Data classes don’t have any explicit syntax for working with slots, but the traditional way of making slots works for data classes also.
Example:
Python3
# import package from dataclasses import dataclass # make a dataclass with decorator @dataclassclass DataClassGFG: Job: str Salary: float @dataclassclass SlotClassGFG: __slots__ = ["Job","Salary"] Job: str Salary: float # make objects with values required by dataclass DataClassObject1 = DataClassGFG("Author",50000.00) DataClassObject2 = SlotClassGFG("Writer",40000.00) # view dataclass objects print(DataClassObject1) print(DataClassObject2) # view dataclass object slot print(DataClassObject2.__slots__) |
Output:
DataClassGFG(Job=’Author’, Salary=50000.0)
SlotClassGFG(Job=’Writer’, Salary=40000.0)
[‘Job’, ‘Salary’]
Inheritance
We can simply use the concept of inheritance by creating sub data class of a data class.
Example:
Python3
# import package from dataclasses import dataclass # make a dataclass with decorator @dataclassclass DataClassGFG: Job: str Salary: float @dataclassclass SubDataClassGFG(DataClassGFG): Standard: str = "Top" Salary: float = 100000.00 # make objects with values required by dataclass DataClassObject1 = DataClassGFG("Author",100000.00) DataClassObject2 = DataClassGFG("Author",50000.00) # view dataclass objects print(DataClassObject1) print(DataClassObject2) # make object with values required by subdataclass SubDataClassObject = SubDataClassGFG("Author") # view subdataclass object print(SubDataClassObject) |
Output:
DataClassGFG(Job=’Author’, Salary=100000.0)
DataClassGFG(Job=’Author’, Salary=50000.0)
SubDataClassGFG(Job=’Author’, Salary=100000.0, Standard=’Top’)
