只需要像这样添加to_json方法到你的类中:

def to_json(self):
  return self.message # or how you want it to be serialized

然后将这段代码(来自这个答案)添加到所有内容的顶部:

from json import JSONEncoder

def _default(self, obj):
    return getattr(obj.__class__, "to_json", _default.default)(obj)

_default.default = JSONEncoder().default
JSONEncoder.default = _default

这将会在导入json模块时monkey-patch，所以 JSONEncoder.default()自动检查特殊的to_json() 方法，并使用它对找到的对象进行编码。

就像Onur说的，但是这次你不需要更新项目中的每个json.dumps()。

如果你能够安装一个软件包，我建议你试试dill，它在我的项目中工作得很好。这个包的一个优点是它具有与pickle相同的接口，因此如果您已经在项目中使用了pickle，则可以简单地替换为dill并查看脚本是否运行，而无需更改任何代码。所以这是一个非常便宜的解决方案!

(完全反披露:我与莳萝项目没有任何关联，也从未参与过。)

安装包:

pip install dill

然后编辑你的代码导入莳萝而不是pickle:

# import pickle
import dill as pickle

运行脚本，看看它是否有效。(如果是的话，你可能想要清理你的代码，这样你就不再隐藏pickle模块的名字了!)

关于dill可以和不能序列化的数据类型的一些细节，来自项目页面:

dill can pickle the following standard types: none, type, bool, int, long, float, complex, str, unicode, tuple, list, dict, file, buffer, builtin, both old and new style classes, instances of old and new style classes, set, frozenset, array, functions, exceptions dill can also pickle more ‘exotic’ standard types: functions with yields, nested functions, lambdas, cell, method, unboundmethod, module, code, methodwrapper, dictproxy, methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor, xrange, slice, notimplemented, ellipsis, quit dill cannot yet pickle these standard types: frame, generator, traceback

这个函数使用递归迭代遍历字典的每个部分，然后调用非内置类型类的repr()方法。

def sterilize(obj):
    object_type = type(obj)
    if isinstance(obj, dict):
        return {k: sterilize(v) for k, v in obj.items()}
    elif object_type in (list, tuple):
        return [sterilize(v) for v in obj]
    elif object_type in (str, int, bool, float):
        return obj
    else:
        return obj.__repr__()

基于Quinten Cabo的回答:

def sterilize(obj):
    """Make an object more ameniable to dumping as json
    """
    if type(obj) in (str, float, int, bool, type(None)):
        return obj
    elif isinstance(obj, dict):
        return {k: sterilize(v) for k, v in obj.items()}
    list_ret = []
    dict_ret = {}
    for a in dir(obj):
        if a == '__iter__' and callable(obj.__iter__):
            list_ret.extend([sterilize(v) for v in obj])
        elif a == '__dict__':
            dict_ret.update({k: sterilize(v) for k, v in obj.__dict__.items() if k not in ['__module__', '__dict__', '__weakref__', '__doc__']})
        elif a not in ['__doc__', '__module__']:
            aval = getattr(obj, a)
            if type(aval) in (str, float, int, bool, type(None)):
                dict_ret[a] = aval
            elif a != '__class__' and a != '__objclass__' and isinstance(aval, type):
                dict_ret[a] = sterilize(aval)
    if len(list_ret) == 0:
        if len(dict_ret) == 0:
            return repr(obj)
        return dict_ret
    else:
        if len(dict_ret) == 0:
            return list_ret
    return (list_ret, dict_ret)

区别在于

Works for any iterable instead of just list and tuple (it works for NumPy arrays, etc.) Works for dynamic types (ones that contain a __dict__). Includes native types float and None so they don't get converted to string. Classes that have __dict__ and members will mostly work (if the __dict__ and member names collide, you will only get one - likely the member) Classes that are lists and have members will look like a tuple of the list and a dictionary Python3 (that isinstance() call may be the only thing that needs changing)

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')

Jsonweb似乎是我的最佳解决方案。参见http://www.jsonweb.info/en/latest/

from jsonweb.encode import to_object, dumper

@to_object()
class DataModel(object):
  def __init__(self, id, value):
   self.id = id
   self.value = value

>>> data = DataModel(5, "foo")
>>> dumper(data)
'{"__type__": "DataModel", "id": 5, "value": "foo"}'