这里有一个解决方案，它使用一个类(从未实例化)来保存数据。我喜欢这种方式涉及很少的输入，不需要任何额外的软件包等。

class myStruct:
    field1 = "one"
    field2 = "2"

您可以稍后根据需要添加更多字段:

myStruct.field3 = 3

要获取值，可以像往常一样访问字段:

>>> myStruct.field1
'one'

还可以按位置将初始化参数传递给实例变量

# Abstract struct class       
class Struct:
    def __init__ (self, *argv, **argd):
        if len(argd):
            # Update by dictionary
            self.__dict__.update (argd)
        else:
            # Update by position
            attrs = filter (lambda x: x[0:2] != "__", dir(self))
            for n in range(len(argv)):
                setattr(self, attrs[n], argv[n])

# Specific class
class Point3dStruct (Struct):
    x = 0
    y = 0
    z = 0

pt1 = Point3dStruct()
pt1.x = 10

print pt1.x
print "-"*10

pt2 = Point3dStruct(5, 6)

print pt2.x, pt2.y
print "-"*10

pt3 = Point3dStruct (x=1, y=2, z=3)
print pt3.x, pt3.y, pt3.z
print "-"*10

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。

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。

这里有一个解决方案，它使用一个类(从未实例化)来保存数据。我喜欢这种方式涉及很少的输入，不需要任何额外的软件包等。

class myStruct:
    field1 = "one"
    field2 = "2"

您可以稍后根据需要添加更多字段:

myStruct.field3 = 3

要获取值，可以像往常一样访问字段:

>>> myStruct.field1
'one'

也许你正在寻找没有构造函数的struct:

class Sample:
  name = ''
  average = 0.0
  values = None # list cannot be initialized here!


s1 = Sample()
s1.name = "sample 1"
s1.values = []
s1.values.append(1)
s1.values.append(2)
s1.values.append(3)

s2 = Sample()
s2.name = "sample 2"
s2.values = []
s2.values.append(4)

for v in s1.values:   # prints 1,2,3 --> OK.
  print v
print "***"
for v in s2.values:   # prints 4 --> OK.
  print v