如果您熟悉傳統的 Python Class 和繼承，在許多情況下，您最好使用 @dataclass 和傳統的 Class 來定義。
PYTHON
from dataclasses import dataclass, make_dataclass
# =========================================================
# Creating a simple dataclass
Person = make_dataclass('Person', ['name', 'age', 'city'])
p = Person('John', 30, 'New York')
print(p) # Output: Person(name='John', age=30, city='New York')
# =========================================================
# Use @dataclass
@dataclass
class Person:
name: str
age: int
city: str
p = Person('John', 30, 'New York')
print(p) # Output: Person(name='John', age=30, city='New York')
範例 – 基礎
PYTHON
from dataclasses import make_dataclass, dataclass, field
# =========================================================
fields = [
('name', str, field(default='Unknown')),
('age', int, field(default=0)),
('city', str, field(default='Unknown')),]
namespace = {
'greet': lambda self: f"Hello, I'm {self.name} from {self.city}!"}
Person = make_dataclass('Person', fields, namespace=namespace)
p = Person('John', 30, 'New York')
print(p.greet()) # Output: Hello, I'm John from New York!
p = Person()
print(p.greet()) # Output: Hello, I'm Unknown from Unknown!
# =========================================================
# Use @dataclass
@dataclass
class Person:
name: str = field(default='Unknown')
age: int = field(default=0)
city: str = field(default='Unknown')
def greet(self):
return f"Hello, I'm {self.name} from {self.city}!"
p = Person('John', 30, 'New York')
print(p.greet()) # Output: Hello, I'm John from New York!
p = Person()
print(p.greet()) # Output: Hello, I'm Unknown from Unknown!
範例 – bases, namespace (參數)
PYTHON
from dataclasses import dataclass, make_dataclass, field
# =========================================================
# Define a base class
class Mammal:
def breathe(self):
return "I can breathe!"
# Define the fields for the new dataclass
fields = [('name', str, field(default='Unknown'))]
# Define additional methods
namespace = {
'greet': lambda self: f"Hello, I'm {self.name}!",}
# Create a new dataclass that inherits from Mammal
Person = make_dataclass('Person', fields, bases=(Mammal,), namespace=namespace)
p = Person('John')
print(p.greet()) # Output: Hello, I'm John!
print(p.breathe()) # Output: I can breathe!
# =========================================================
# Use @dataclass
class Mammal:
def breathe(self):
return "I can breathe!"
@dataclass
class Person(Mammal):
name: str = field(default='Unknown')
def greet(self):
return f"Hello, I'm {self.name}!"
p = Person('John')
print(p.greet()) # Output: Hello, I'm John!
print(p.breathe()) # Output: I can breathe!
from dataclasses import dataclass, replace
@dataclass
class Container:
values: list
c = Container(values=[1, 2, 3, 4])
# Replace the 'values' list in the dataclass
c1 = replace(c, values=[10, 20, 30, 40])
print(c) # Output: Container(values=[1, 2, 3, 4])
print(c1) # Output: Container(values=[10, 20, 30, 40])
PYTHON
from dataclasses import dataclass, replace
@dataclass
class Point:
x: int
y: int
@dataclass
class Line:
start: Point
end: Point
line1 = Line(start=Point(0, 0),
end=Point(10, 10))
# Replace the 'start' point of the line
line2 = replace(line1, start=Point(5, 5))
print(line1) # Output: Line(start=Point(x=0, y=0), end=Point(x=10, y=10))
print(line2) # Output: Line(start=Point(x=5, y=5), end=Point(x=10, y=10))
from dataclasses import dataclass, field, replace
@dataclass
class Product:
name: str
category: str = "General" # This field has a default value and uses the init constructor
id: int = field(default=None, init=False) # This field is not included in the init constructor
def __post_init__(self):
if self.id is None: # Initialize the id if not already set
self.id = self.generate_id()
def generate_id(self):
# In a real scenario, this would generate a unique ID.
return hash((self.name, self.category))
product = Product("Table")
print(product) # Output: Product(name='Table', category='General', id=some_hash)
new_product = replace(product, name="Chair")
print(new_product) # Output: Product(name='Chair', category='General', id=different_hash)
在這個例子中：
id 是一個 init=False field。實例化後，呼叫 __post_init__，它設定 id。
當呼叫 replace(product, name="Chair") 時，會有效地建立一個新的 Product 實例，並將 name 設定為 Chair。 id 不是從 product 複製的，而是再次呼叫 __post_init__，根據新的 name 產生新的 id。
如果您想要不同的行為，例如明確複製 init=False 字段，您可以提供自訂替換方法。自訂 replace 方法可確保 init=False field (本例中為 id) 被轉移到新實例，除非它被明確變更。
PYTHON
from dataclasses import dataclass, field, replace
@dataclass
class Product:
name: str
category: str = "General" # This field has a default value and uses the init constructor
id: int = field(default=None, init=False) # This field is not included in the init constructor
def __post_init__(self):
if self.id is None: # Initialize the id if not already set
self.id = self.generate_id()
def generate_id(self):
# In a real scenario, this would generate a unique ID.
return hash((self.name, self.category))
def custom_replace(self, **changes):
# Manually create a copy of the object
new_obj = Product(self.name, self.category)
new_obj.id = self.id # Explicitly copy the `init=False` field
# Apply any requested changes
for attr, value in changes.items():
setattr(new_obj, attr, value)
# Manually call __post_init__ if necessary
new_obj.__post_init__()
return new_obj
product = Product("Table")
print(product) # Output: Product(name='Table', category='General', id=some_hash)
new_product = product.custom_replace(name="Chair")
print(new_product) # Output: Product(name='Chair', category='General', id=same_hash_as_product)
如果您需要知道一個 Class 是否是 Dataclass 的實例，而不是 Dataclass 本身。請進一步檢查 isinstance(obj, type)，即 is_dataclass_instance(obj)。
PYTHON
def is_dataclass_instance(obj):
return is_dataclass(obj) and not isinstance(obj, type)
範例：
PYTHON
from dataclasses import dataclass, is_dataclass
@dataclass
class Person:
name: str
age: int
def is_dataclass_instance(obj):
return is_dataclass(obj) and not isinstance(obj, type)
p = Person('John Doe', 30)
# Checking if the class is a dataclass or an instance of a dataclass
print(is_dataclass(Person)) # Output: True
print(is_dataclass(p)) # Output: True
print(is_dataclass_instance(Person)) # Output: False
print(is_dataclass_instance(p)) # Output: True
__post_init__
此方法在調用 data class 的 __init__ 方法後立即調用，為初始化後處理提供方便的 hook。
透過使用 __post_init__，您可以確保 data class 實例，在應用程式的其餘部分使用之前，始終處於有效狀態，從而使其成為強大且自我驗證的資料模型的強大功能。
PYTHON
from dataclasses import dataclass, field
from typing import List, Any
@dataclass
class InventoryItem:
"""Class for keeping track of an item in inventory."""
name: str
unit_price: float
quantity_on_hand: int = 0
serial_numbers: List[str] = field(default_factory=list)
def __post_init__(self):
if self.unit_price < 0:
raise ValueError("Unit price cannot be negative")
# Automatically calculate total price and validate
self.total_price: float = self.unit_price * self.quantity_on_hand
# Perform type checking, raise error if serial_numbers is not a list
if not isinstance(self.serial_numbers, list):
raise TypeError("serial_numbers must be a list of strings")
def add_serial_number(self, serial_number: str):
self.serial_numbers.append(serial_number)
# Example usage
try:
item = InventoryItem("Widget", 10.0, 100)
item.add_serial_number("SN001")
print(item)
except ValueError as e:
print(e)
except TypeError as e:
print(e)
# Output: InventoryItem(name='Widget', unit_price=10.0, quantity_on_hand=100, serial_numbers=['SN001'])
from dataclasses import dataclass, KW_ONLY
@dataclass
class Student:
name: str
_: KW_ONLY # Pseudo-field to indicate the start of keyword-only fields
age: int = 20
course: str = 'Computer Science'
# This is OK
student1 = Student('John Doe', age=21, course='Mathematics')
# This is OK - using defaults for keyword-only arguments
student2 = Student('Jane Doe')
student3 = Student('John Doe', 21, 'Mathematics')
# TypeError: Student.__init__() takes 2 positional arguments but 4 were given
