这个类可以做到这一点，它将object转换为标准json。

import json


class Serializer(object):
    @staticmethod
    def serialize(object):
        return json.dumps(object, default=lambda o: o.__dict__.values()[0])

用法:

Serializer.serialize(my_object)

在python2.7和python3中工作。

import simplejson

class User(object):
    def __init__(self, name, mail):
        self.name = name
        self.mail = mail

    def _asdict(self):
        return self.__dict__

print(simplejson.dumps(User('alice', 'alice@mail.com')))

如果使用标准json，则需要定义一个默认函数

import json
def default(o):
    return o._asdict()

print(json.dumps(User('alice', 'alice@mail.com'), default=default))

要添加另一个选项:您可以使用attrs包和asdict方法。

class ObjectEncoder(JSONEncoder):
    def default(self, o):
        return attr.asdict(o)

json.dumps(objects, cls=ObjectEncoder)

然后再转换回去

def from_json(o):
    if '_obj_name' in o:
        type_ = o['_obj_name']
        del o['_obj_name']
        return globals()[type_](**o)
    else:
        return o

data = JSONDecoder(object_hook=from_json).decode(data)

类看起来像这样

@attr.s
class Foo(object):
    x = attr.ib()
    _obj_name = attr.ib(init=False, default='Foo')

我们经常在日志文件中转储JSON格式的复杂字典。虽然大多数字段携带重要信息，但我们不太关心内置的类对象(例如子进程)。Popen对象)。由于存在这些不可序列化的对象，对json.dumps()的调用会失败。

为了解决这个问题，我构建了一个小函数来转储对象的字符串表示形式，而不是转储对象本身。如果您正在处理的数据结构嵌套太多，您可以指定嵌套的最大级别/深度。

from time import time

def safe_serialize(obj , max_depth = 2):

    max_level = max_depth

    def _safe_serialize(obj , current_level = 0):

        nonlocal max_level

        # If it is a list
        if isinstance(obj , list):

            if current_level >= max_level:
                return "[...]"

            result = list()
            for element in obj:
                result.append(_safe_serialize(element , current_level + 1))
            return result

        # If it is a dict
        elif isinstance(obj , dict):

            if current_level >= max_level:
                return "{...}"

            result = dict()
            for key , value in obj.items():
                result[f"{_safe_serialize(key , current_level + 1)}"] = _safe_serialize(value , current_level + 1)
            return result

        # If it is an object of builtin class
        elif hasattr(obj , "__dict__"):
            if hasattr(obj , "__repr__"):
                result = f"{obj.__repr__()}_{int(time())}"
            else:
                try:
                    result = f"{obj.__class__.__name__}_object_{int(time())}"
                except:
                    result = f"object_{int(time())}"
            return result

        # If it is anything else
        else:
            return obj

    return _safe_serialize(obj)

由于字典也可以有不可序列化的键，转储它们的类名或对象表示将导致所有键都具有相同的名称，这将抛出错误，因为所有键都需要有唯一的名称，这就是为什么当前时间Since epoch被int(time())附加到对象名称。

可以使用以下具有不同级别/深度的嵌套字典来测试该函数

d = {
    "a" : {
        "a1" : {
            "a11" : {
                "a111" : "some_value" ,
                "a112" : "some_value" ,
            } ,
            "a12" : {
                "a121" : "some_value" ,
                "a122" : "some_value" ,
            } ,
        } ,
        "a2" : {
            "a21" : {
                "a211" : "some_value" ,
                "a212" : "some_value" ,
            } ,
            "a22" : {
                "a221" : "some_value" ,
                "a222" : "some_value" ,
            } ,
        } ,
    } ,
    "b" : {
        "b1" : {
            "b11" : {
                "b111" : "some_value" ,
                "b112" : "some_value" ,
            } ,
            "b12" : {
                "b121" : "some_value" ,
                "b122" : "some_value" ,
            } ,
        } ,
        "b2" : {
            "b21" : {
                "b211" : "some_value" ,
                "b212" : "some_value" ,
            } ,
            "b22" : {
                "b221" : "some_value" ,
                "b222" : "some_value" ,
            } ,
        } ,
    } ,
    "c" : subprocess.Popen("ls -l".split() , stdout = subprocess.PIPE , stderr = subprocess.PIPE) ,
}

执行以下命令将会得到-

print("LEVEL 3")
print(json.dumps(safe_serialize(d , 3) , indent = 4))

print("\n\n\nLEVEL 2")
print(json.dumps(safe_serialize(d , 2) , indent = 4))

print("\n\n\nLEVEL 1")
print(json.dumps(safe_serialize(d , 1) , indent = 4))

结果:

LEVEL 3
{
    "a": {
        "a1": {
            "a11": "{...}",
            "a12": "{...}"
        },
        "a2": {
            "a21": "{...}",
            "a22": "{...}"
        }
    },
    "b": {
        "b1": {
            "b11": "{...}",
            "b12": "{...}"
        },
        "b2": {
            "b21": "{...}",
            "b22": "{...}"
        }
    },
    "c": "<Popen: returncode: None args: ['ls', '-l']>"
}



LEVEL 2
{
    "a": {
        "a1": "{...}",
        "a2": "{...}"
    },
    "b": {
        "b1": "{...}",
        "b2": "{...}"
    },
    "c": "<Popen: returncode: None args: ['ls', '-l']>"
}



LEVEL 1
{
    "a": "{...}",
    "b": "{...}",
    "c": "<Popen: returncode: None args: ['ls', '-l']>"
}

[注意]:仅在不关心内置类对象的序列化时使用此选项。

Json在它可以打印的对象方面受到限制，而jsonpickle(你可能需要一个PIP安装jsonpickle)在它不能缩进文本方面受到限制。如果你想检查一个你不能改变类的对象的内容，我仍然找不到比:

 import json
 import jsonpickle
 ...
 print  json.dumps(json.loads(jsonpickle.encode(object)), indent=2)

注意:他们仍然不能打印对象方法。

TLDR:复制-粘贴下面的选项1或选项2

真正的/完整的答案:让Pythons json模块与你的类一起工作

AKA，求解:json。dump ({"thing": YOUR_CLASS()})

解释:

Yes, a good reliable solution exists No, there is no python "official" solution By official solution, I mean there is no way (as of 2023) to add a method to your class (like toJSON in JavaScript) and/or no way to register your class with the built-in json module. When something like json.dumps([1,2, your_obj]) is executed, python doesn't check a lookup table or object method. I'm not sure why other answers don't explain this The closest official approach is probably andyhasit's answer which is to inherit from a dictionary. However, inheriting from a dictionary doesn't work very well for many custom classes like AdvancedDateTime, or pytorch tensors. The ideal workaround is this: Mutate json.dumps (affects everywhere, even pip modules that import json) Add def __json__(self) method to your class

选项1:让一个模块来做补丁

PIP安装json-fix (扩展+包装版FancyJohn的回答，谢谢@FancyJohn)

your_class_definition.py

import json_fix

class YOUR_CLASS:
    def __json__(self):
        # YOUR CUSTOM CODE HERE
        #    you probably just want to do:
        #        return self.__dict__
        return "a built-in object that is naturally json-able"

这是它。

使用示例:

from your_class_definition import YOUR_CLASS
import json

json.dumps([1,2, YOUR_CLASS()], indent=0)
# '[\n1,\n2,\n"a built-in object that is naturally json-able"\n]'

生成json。dump适用于Numpy数组，Pandas DataFrames和其他第三方对象，请参阅模块(只有大约2行代码，但需要解释)。

它是如何工作的?嗯…

选项2:补丁json。把你自己

注意:这种方法是简化的，它在已知的edgcase上失败(例如:如果你的自定义类继承了dict或其他内置类)，并且它错过了控制外部类的json行为(numpy数组，datetime, dataframes，张量等)。

some_file_thats_imported_before_your_class_definitions.py

# Step: 1
# create the patch
from json import JSONEncoder
def wrapped_default(self, obj):
    return getattr(obj.__class__, "__json__", wrapped_default.default)(obj)
wrapped_default.default = JSONEncoder().default
   
# apply the patch
JSONEncoder.original_default = JSONEncoder.default
JSONEncoder.default = wrapped_default

your_class_definition.py

# Step 2
class YOUR_CLASS:
    def __json__(self, **options):
        # YOUR CUSTOM CODE HERE
        #    you probably just want to do:
        #        return self.__dict__
        return "a built-in object that is natually json-able"

_

其他答案似乎都是“序列化自定义对象的最佳实践/方法”

在这里的文档中已经介绍过了(搜索“complex”可以找到编码复数的例子)