是否有一种方法可以方便地在Python中定义类c结构?我厌倦了写这样的东西:
class MyStruct():
def __init__(self, field1, field2, field3):
self.field1 = field1
self.field2 = field2
self.field3 = field3
是否有一种方法可以方便地在Python中定义类c结构?我厌倦了写这样的东西:
class MyStruct():
def __init__(self, field1, field2, field3):
self.field1 = field1
self.field2 = field2
self.field3 = field3
当前回答
NamedTuple很舒服。但是没有人共享性能和存储。
from typing import NamedTuple
import guppy # pip install guppy
import timeit
class User:
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserSlot:
__slots__ = ('name', 'uid')
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserTuple(NamedTuple):
# __slots__ = () # AttributeError: Cannot overwrite NamedTuple attribute __slots__
name: str
uid: int
def get_fn(obj, attr_name: str):
def get():
getattr(obj, attr_name)
return get
if 'memory test':
obj = [User('Carson', 1) for _ in range(1000000)] # Cumulative: 189138883
obj_slot = [UserSlot('Carson', 1) for _ in range(1000000)] # 77718299 <-- winner
obj_namedtuple = [UserTuple('Carson', 1) for _ in range(1000000)] # 85718297
print(guppy.hpy().heap()) # Run this function individually.
"""
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 24 112000000 34 112000000 34 dict of __main__.User
1 1000000 24 64000000 19 176000000 53 __main__.UserTuple
2 1000000 24 56000000 17 232000000 70 __main__.User
3 1000000 24 56000000 17 288000000 87 __main__.UserSlot
...
"""
if 'performance test':
obj = User('Carson', 1)
obj_slot = UserSlot('Carson', 1)
obj_tuple = UserTuple('Carson', 1)
time_normal = min(timeit.repeat(get_fn(obj, 'name'), repeat=20))
print(time_normal) # 0.12550550000000005
time_slot = min(timeit.repeat(get_fn(obj_slot, 'name'), repeat=20))
print(time_slot) # 0.1368690000000008
time_tuple = min(timeit.repeat(get_fn(obj_tuple, 'name'), repeat=20))
print(time_tuple) # 0.16006120000000124
print(time_tuple/time_slot) # 1.1694481584580898 # The slot is almost 17% faster than NamedTuple on Windows. (Python 3.7.7)
如果你的__dict__没有被使用,请在__slots__(更高的性能和存储)和NamedTuple(清晰的阅读和使用)之间选择。
您可以查看此链接(插槽的使用 )来获取更多的__slots__信息。
其他回答
一本字典怎么样?
就像这样:
myStruct = {'field1': 'some val', 'field2': 'some val'}
然后你可以使用这个来操作值:
print myStruct['field1']
myStruct['field2'] = 'some other values'
值不一定是字符串。它们可以是其他任何物体。
你可以用元组来做很多你在C语言中使用结构体的事情(比如x,y坐标或RGB颜色)。
对于其他任何东西,你可以使用字典,或像这样的实用程序类:
>>> class Bunch:
... def __init__(self, **kwds):
... self.__dict__.update(kwds)
...
>>> mystruct = Bunch(field1=value1, field2=value2)
我认为“权威”的讨论在这里,在Python Cookbook的出版版本中。
我写了一个装饰器,你可以在任何方法上使用它,这样所有传入的参数,或任何默认值,都被分配给实例。
def argumentsToAttributes(method):
argumentNames = method.func_code.co_varnames[1:]
# Generate a dictionary of default values:
defaultsDict = {}
defaults = method.func_defaults if method.func_defaults else ()
for i, default in enumerate(defaults, start = len(argumentNames) - len(defaults)):
defaultsDict[argumentNames[i]] = default
def newMethod(self, *args, **kwargs):
# Use the positional arguments.
for name, value in zip(argumentNames, args):
setattr(self, name, value)
# Add the key word arguments. If anything is missing, use the default.
for name in argumentNames[len(args):]:
setattr(self, name, kwargs.get(name, defaultsDict[name]))
# Run whatever else the method needs to do.
method(self, *args, **kwargs)
return newMethod
快速演示一下。注意,我使用一个位置参数a,使用默认值b,和一个命名参数c。然后我打印所有3个引用self,以显示它们在方法输入之前已正确分配。
class A(object):
@argumentsToAttributes
def __init__(self, a, b = 'Invisible', c = 'Hello'):
print(self.a)
print(self.b)
print(self.c)
A('Why', c = 'Nothing')
注意,我的装饰器应该适用于任何方法,而不仅仅是__init__。
我在这里没有看到这个答案,所以我想我将添加它,因为我现在正在学习Python,并且刚刚发现它。Python教程(在本例中是Python 2)给出了以下简单而有效的示例:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
也就是说,创建一个空类对象,然后实例化,动态添加字段。
这样做的好处是非常简单。缺点是它不是特别自记录的(在类“定义”中没有列出预期的成员),并且未设置字段在访问时可能会导致问题。这两个问题可以通过以下方法解决:
class Employee:
def __init__ (self):
self.name = None # or whatever
self.dept = None
self.salary = None
现在,您至少可以一目了然地看到程序将期望哪些字段。
两者都很容易打错别字,约翰。Slarly = 1000将成功。不过,它还是有效的。
我还想添加一个使用插槽的解决方案:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
Definitely check the documentation for slots but a quick explanation of slots is that it is python's way of saying: "If you can lock these attributes and only these attributes into the class such that you commit that you will not add any new attributes once the class is instantiated (yes you can add new attributes to a class instance, see example below) then I will do away with the large memory allocation that allows for adding new attributes to a class instance and use just what I need for these slotted attributes".
添加属性到类实例的例子(因此不使用插槽):
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
print(p1.z)
输出:8
尝试向使用插槽的类实例添加属性的示例:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
'Point'对象没有属性'z'
这可以有效地作为结构体工作,并且比类使用更少的内存(就像结构体一样,尽管我没有研究具体有多少内存)。如果要创建对象的大量实例且不需要添加属性,建议使用slot。点对象就是一个很好的例子,因为很可能会实例化许多点来描述一个数据集。