这不是代码高尔夫，但这里是我使用类型的最短技巧。SimpleNamespace作为JSON对象的容器。

与namedtuple解决方案相比，它是:

可能更快/更小，因为它没有为每个对象创建一个类 更短的 没有重命名选项，对于不是有效标识符的键可能有相同的限制(在幕后使用setattr)

例子:

from __future__ import print_function
import json

try:
    from types import SimpleNamespace as Namespace
except ImportError:
    # Python 2.x fallback
    from argparse import Namespace

data = '{"name": "John Smith", "hometown": {"name": "New York", "id": 123}}'

x = json.loads(data, object_hook=lambda d: Namespace(**d))

print (x.name, x.hometown.name, x.hometown.id)

我已经编写了一个名为any2any的小型(反)序列化框架，它可以帮助在两种Python类型之间进行复杂的转换。

在您的情况下，我猜您想从字典(通过json.loads获得)转换为复杂的对象response.education;Response.name，具有嵌套结构response.education.id，等等… 这就是这个框架的用途。文档还不是很好，但是通过使用any2any.simple。MappingToObject，你应该可以很容易地做到。如果需要帮助，请询问。

使用python 3.7，我发现下面的代码非常简单有效。在本例中，将JSON从文件加载到字典中:

class Characteristic:
    def __init__(self, characteristicName, characteristicUUID):
        self.characteristicName = characteristicName
        self.characteristicUUID = characteristicUUID


class Service:
    def __init__(self, serviceName, serviceUUID, characteristics):
        self.serviceName = serviceName
        self.serviceUUID = serviceUUID
        self.characteristics = characteristics

class Definitions:
    def __init__(self, services):
        self.services = []
        for service in services:
            self.services.append(Service(**service))


def main():
    parser = argparse.ArgumentParser(
        prog="BLEStructureGenerator",
        description="Taking in a JSON input file which lists all of the services, "
                    "characteristics and encoded properties. The encoding takes in "
                    "another optional template services and/or characteristics "
                    "file where the JSON file contents are applied to the templates.",
        epilog="Copyright Brown & Watson International"
    )

    parser.add_argument('definitionfile',
                        type=argparse.FileType('r', encoding='UTF-8'),
                        help="JSON file which contains the list of characteristics and "
                             "services in the required format")
    parser.add_argument('-s', '--services',
                        type=argparse.FileType('r', encoding='UTF-8'),
                        help="Services template file to be used for each service in the "
                             "JSON file list")
    parser.add_argument('-c', '--characteristics',
                        type=argparse.FileType('r', encoding='UTF-8'),
                        help="Characteristics template file to be used for each service in the "
                             "JSON file list")

    args = parser.parse_args()
    definition_dict = json.load(args.definitionfile)
    definitions = Definitions(**definition_dict)

这不是一个很难的事情，我看到上面的答案，他们中的大多数在“列表”中有一个性能问题

这段代码比上面的代码快得多

import json 

class jsonify:
    def __init__(self, data):
        self.jsonify = data

    def __getattr__(self, attr):
        value = self.jsonify.get(attr)
        if isinstance(value, (list, dict)):
            return jsonify(value)
        return value

    def __getitem__(self, index):
        value = self.jsonify[index]
        if isinstance(value, (list, dict)):
            return jsonify(value)
        return value

    def __setitem__(self, index, value):
        self.jsonify[index] = value

    def __delattr__(self, index):
        self.jsonify.pop(index)

    def __delitem__(self, index):
        self.jsonify.pop(index)

    def __repr__(self):
        return json.dumps(self.jsonify, indent=2, default=lambda x: str(x))

exmaple

response = jsonify(
    {
        'test': {
            'test1': [{'ok': 1}]
        }
    }
)
response.test -> jsonify({'test1': [{'ok': 1}]})
response.test.test1 -> jsonify([{'ok': 1}])
response.test.test1[0] -> jsonify({'ok': 1})
response.test.test1[0].ok -> int(1)

数据类向导是一种现代的选项，可以类似地为您工作。它支持自动键大小写转换，如camelCase或TitleCase，这两者在API响应中都很常见。

当将实例转储到dict/JSON时，默认的键转换是camelCase，但这可以很容易地使用主数据类上提供的Meta配置来覆盖。

https://pypi.org/project/dataclass-wizard/

from dataclasses import dataclass

from dataclass_wizard import fromdict, asdict


@dataclass
class User:
    name: str
    age: int
    is_active: bool


data = {
    'name': 'John',
    'age': 30,
    'isActive': True,
}

user = fromdict(User, data)
assert user == User(name='John', age=30, is_active=True)

json_dict = asdict(user)
assert json_dict == {'name': 'John', 'age': 30, 'isActive': True}

设置元配置的例子，当序列化为dict/JSON时，将字段转换为lisp-case:

DumpMeta(key_transform='LISP').bind_to(User)

class SimpleClass:
    def __init__(self, **kwargs):
        for k, v in kwargs.items():
            if type(v) is dict:
                setattr(self, k, SimpleClass(**v))
            else:
                setattr(self, k, v)


json_dict = {'name': 'jane doe', 'username': 'jane', 'test': {'foo': 1}}

class_instance = SimpleClass(**json_dict)

print(class_instance.name, class_instance.test.foo)
print(vars(class_instance))