Understanding Python Dataclasses
Last Updated :
06 Aug, 2021
DataClasses has been added in a recent addition in python 3.7 as a utility tool for storing data. DataClasses provides a decorator and functions for automatically adding generated special methods such as __init__() , __repr__() and __eq__() to user-defined classes.
DataClass in Python
DataClasses are like normal classes in Python, but they have some basic functions like instantiation, comparing, and printing the classes already implemented.
Installing the DataClasses module:
pip install dataclasses
The syntax of the dataclass is:
Syntax: @dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
Parameters:
- init: If true __init__() method will be generated
- repr: If true __repr__() method will be generated
- eq: If true __eq__() method will be generated
- order: If true __lt__(), __le__(), __gt__(), and __ge__() methods will be generated.
- unsafe_hash: If False __hash__() method is generated according to how eq and frozen are set
- frozen: If true assigning to fields will generate an exception.
DataClass module provides a handy way to make classes less wordy. Let us see the traditional approach without using DataClass.
Python3
# creating a employee class
class employee:
# init method or constructor
def __init__(self, name, emp_id, age, city):
# Instance Variable
self.name = name
self.emp_id = emp_id
self.age = age
self.city = city
# magic function to return class object
def __repr__(self):
return ("employee (name={}, emp_id={}, age={}, city={} )"
.format(self.name, self.emp_id, self.age, self.city))
# magic function to return boolean
def __eq__(self, check):
return ((self.name, self.emp_id, self.age, self.city) ==
((check.name, check.emp_id, check.age, check.city)))
# initialization the object
emp1 = employee("Satyam", "ksatyam858", 21, 'Patna')
emp2 = employee("Anurag", "au23", 28, 'Delhi')
emp3 = employee("Satyam", "ksatyam858", 21, 'Patna')
print("employee object are :")
print(emp1)
print(emp2)
print(emp3)
# printing new line
print()
# referring two object to check equality
print("Data in emp1 and emp2 are same? ", emp1 == emp2)
print("Data in emp1 and emp3 are same? ", emp1 == emp3)
Outputemployee object are :
employee (name=Satyam, emp_id=ksatyam858, age=21, city=Patna )
employee (name=Anurag, emp_id=au23, age=28, city=Delhi )
employee (name=Satyam, emp_id=ksatyam858, age=21, city=Patna )
Data in emp1 and emp2 are same? False
Data in emp1 and emp3 are same? True
In the above code the biggest problem in passing the argument in __init__, __repr__, and __eq__. Each time it has to copy its properties and return the object. It is a good way of dealing with a small amount of data but supposes we have work with large data. It makes your code more complicated. So, that why DataClass will implement to make your code easier and handy.
Here are the same example, implemented in Python DataClasses.
Python3
# A basic Data Class
# importing dataclass module
from dataclasses import dataclass
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
emp_id: str
age: int
city: str
emp1 = employee("Satyam", "ksatyam858", 21, 'Patna')
emp2 = employee("Anurag", "au23", 28, 'Delhi')
emp3 = employee("Satyam", "ksatyam858", 21, 'Patna')
print("employee object are :")
print(emp1)
print(emp2)
print(emp3)
# printing new line
print()
# referring two object to check equality
print("Data in emp1 and emp2 are same? ", emp1 == emp2)
print("Data in emp1 and emp3 are same? ", emp1 == emp3)
Output:
employee object are :
employee(name='Satyam', emp_id='ksatyam858', age=21, city='Patna')
employee(name='Anurag', emp_id='au23', age=28, city='Delhi')
employee(name='Satyam', emp_id='ksatyam858', age=21, city='Patna')
Data in emp1 and emp2 are same? False
Data in emp1 and emp3 are same? True
In the above code, we don't need to write a code for __init__, __repr__, and __eq__ function.
dataclasses.Field()
The field() objects describe each defined field.
Syntax: dataclasses.field(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)
Parameters:
- default
- default_factory
- init
- repr
- hash
- compare
- metadata
Example: Demonstration of how to view the fields of a dataclass object.
Python3
from dataclasses import dataclass
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
emp_id: str
age: int
city: str
# object of the class
emp = employee("Satyam", "ksatyam858", 21, 'Patna')
emp.__dataclass_fields__
Output:
Explanation of the parameters :
- default: This field is used to specify default values for this field.
Python3
# default field example
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
emp_id: str
age: int
# default field set
# city : str = "patna"
city: str = field(default="patna")
emp = employee("Satyam", "ksatyam858", 21)
print(emp)
Output:
employee(name='Satyam', emp_id='ksatyam858', age=21, city='patna')
- default_factory: This field accepts a function and returns the initial value of the field, it must be a zero-argument.
Python3
# default factory example
from dataclasses import dataclass, field
def get_emp_id():
id = 2345
return id
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
# default factory field
emp_id: str = field(default_factory=get_emp_id)
city: str = field(default="patna")
# object of dataclass
emp = employee("Satyam", 21)
print(emp)
Output:
employee(name='Satyam', age=21, emp_id=2345, city='patna')
- init : If true this field is included as a parameter to the generated __init__() method.
Python3
# init field example
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
# init field
emp_id: str
city: str = field(init=False, default="patna")
# object of dataclass
emp = employee("Satyam", "ksatyam858", 21)
print(emp)
Output:
employee(name='Satyam', age='ksatyam858', emp_id=21, city='patna')
- repr: If true (the default), this field is included in the string returned by the generated __repr__() method.
Python3
# repr field
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
emp_id: str
city: str = field(init=False, default="patna", repr=True)
emp = employee("Satyam", 21, "ksatyam858"),
print(emp)
Output:
employee(name='Satyam', age=21, emp_id='ksatyam858', city='patna')
If repr is false then:
Python3
city: str = field(init=False, default="patna", repr=False)
emp = employee("Satyam", 21, "ksatyam858"),
emp
Output:
employee(name='Satyam', age=21, emp_id='ksatyam858')
- hash: If true, this field is included in the generated __hash__() method.
Python3
# hash
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass(unsafe_hash=True)
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
emp_id: str = field(default_factory=get_emp_id)
city: str = field(init=False, default="patna", repr=True, hash=True)
emp = employee("Satyam", "ksatyam858", 21)
hash(emp)
Output:
28166796391311520
If false then it will not consider these field.
Python3
city: str = field(init=False, default="patna", repr=True, hash=False)
# object of the class
emp = employee("Satyam", "ksatyam858", 21)
hash(emp)
Output:
6124029366977666702
- compare: If true (the default), this field is included in the generated equality and comparison methods (__eq__(), __gt__().
Python3
# hash
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass(unsafe_hash=True)
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
emp_id: str
city: str = field(init=False, default="patna",
repr=True, hash=False, compare=True)
emp1 = employee("Satyam", "ksatyam858", 21)
emp2 = employee("Kumar", "satyam.10151", 22)
emp1 == emp2
Output:
False
- metadata: This can be a mapping or None. None is treated as an empty dict.
Python3
# hash
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass(unsafe_hash=True)
class employee:
# Attributes Declaration
# using Type Hints
name: str
age: int
emp_id: str
city: str = field(init=False, default="patna", repr=True,
metadata={'format': 'State'})
emp = employee("Satyam", "ksatyam858", 21)
emp.__dataclass_fields__['city'].metadata['format']
Post-init processing
While creating object __post_init__() method call automatically. __init__() code will call a method named __post_init__().
Example: In an employee dataclass, if we want to check employee age, then we can define into the __post_init__() method.
Python3
# A basic Data Class
# importing dataclass module
from dataclasses import dataclass, field
# A class for holding an employees content
@dataclass
class employee:
# Attributes Declaration
# using Type Hints
name: str
emp_id: str
age: int
city: str
# post init function
def __post_init__(self):
if self.age >= 30:
self.check_age = True
else:
self.check_age = False
emp = employee("Satyam", "ksatyam858", 21, 'Patna')
emp.check_age
Output:
False
DataClass Inheritance
Inheritance enables us to define a class that takes all the functionality from a parent class.
Example: Child class inherits the properties of the parent class.
Python3
# A basic Data Class
# importing dataclass module
from dataclasses import dataclass, field
# parent class
@dataclass
class Staff:
name: str
emp_id: str
age: int
# child class
@dataclass
class employee(Staff):
salary: int
emp = employee("Satyam", "ksatyam858", 21, 60000)
emp
Output:
employee(name='Satyam', emp_id='ksatyam858', age=21, salary=60000)
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