For more complex classes you could consider the tool jsonpickle:

jsonpickle is a Python library for serialization and deserialization of complex Python objects to and from JSON.

The standard Python libraries for encoding Python into JSON, such as the stdlib’s json, simplejson, and demjson, can only handle Python primitives that have a direct JSON equivalent (e.g. dicts, lists, strings, ints, etc.). jsonpickle builds on top of these libraries and allows more complex data structures to be serialized to JSON. jsonpickle is highly configurable and extendable–allowing the user to choose the JSON backend and add additional backends.

(link to jsonpickle on PyPi)

Another option is to wrap JSON dumping in its own class:

import json

class FileItem:
    def __init__(self, fname):
        self.fname = fname

    def __repr__(self):
        return json.dumps(self.__dict__)

Or, even better, subclassing FileItem class from a JsonSerializable class:

import json

class JsonSerializable(object):
    def toJson(self):
        return json.dumps(self.__dict__)

    def __repr__(self):
        return self.toJson()


class FileItem(JsonSerializable):
    def __init__(self, fname):
        self.fname = fname

Testing:

>>> f = FileItem('/foo/bar')
>>> f.toJson()
'{"fname": "/foo/bar"}'
>>> f
'{"fname": "/foo/bar"}'
>>> str(f) # string coercion
'{"fname": "/foo/bar"}'

Here is a simple solution for a simple feature:

.toJSON() Method

Instead of a JSON serializable class, implement a serializer method:

import json

class Object:
    def toJSON(self):
        return json.dumps(self, default=lambda o: o.__dict__, 
            sort_keys=True, indent=4)

So you just call it to serialize:

me = Object()
me.name = "Onur"
me.age = 35
me.dog = Object()
me.dog.name = "Apollo"

print(me.toJSON())

will output:

{
    "age": 35,
    "dog": {
        "name": "Apollo"
    },
    "name": "Onur"
}

Here is my 3 cents …
This demonstrates explicit json serialization for a tree-like python object.
Note: If you actually wanted some code like this you could use the twisted
FilePath class.

import json, sys, os

class File:
    def __init__(self, path):
        self.path = path

    def isdir(self):
        return os.path.isdir(self.path)

    def isfile(self):
        return os.path.isfile(self.path)

    def children(self):        
        return [File(os.path.join(self.path, f)) 
                for f in os.listdir(self.path)]

    def getsize(self):        
        return os.path.getsize(self.path)

    def getModificationTime(self):
        return os.path.getmtime(self.path)

def _default(o):
    d = {}
    d['path'] = o.path
    d['isFile'] = o.isfile()
    d['isDir'] = o.isdir()
    d['mtime'] = int(o.getModificationTime())
    d['size'] = o.getsize() if o.isfile() else 0
    if o.isdir(): d['children'] = o.children()
    return d

folder = os.path.abspath('.')
json.dump(File(folder), sys.stdout, default=_default)

This is a small library that serializes an object with all its children to JSON and also parses it back:

https://github.com/tobiasholler/PyJSONSerialization/

jsonweb seems to be the best solution for me. See 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"}'

I like Onur’s answer but would expand to include an optional toJSON() method for objects to serialize themselves:

def dumper(obj):
    try:
        return obj.toJSON()
    except:
        return obj.__dict__
print json.dumps(some_big_object, default=dumper, indent=2)

I came up with my own solution. Use this method, pass any document (dict,list, ObjectId etc) to serialize.

def getSerializable(doc):
    # check if it's a list
    if isinstance(doc, list):
        for i, val in enumerate(doc):
            doc[i] = getSerializable(doc[i])
        return doc

    # check if it's a dict
    if isinstance(doc, dict):
        for key in doc.keys():
            doc[key] = getSerializable(doc[key])
        return doc

    # Process ObjectId
    if isinstance(doc, ObjectId):
        doc = str(doc)
        return doc

    # Use any other custom serializting stuff here...

    # For the rest of stuff
    return doc

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', '[email protected]')))

if using standard json, you need to define a default function

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

print(json.dumps(User('alice', '[email protected]'), default=default))

Most of the answers involve changing the call to json.dumps(), which is not always possible or desirable (it may happen inside a framework component for example).

If you want to be able to call json.dumps(obj) as is, then a simple solution is inheriting from dict:

class FileItem(dict):
    def __init__(self, fname):
        dict.__init__(self, fname=fname)

f = FileItem('tasks.txt')
json.dumps(f)  #No need to change anything here

This works if your class is just basic data representation, for trickier things you can always set keys explicitly in the call to dict.__init__().

This works because json.dumps() checks if the object is one of several known types via a rather unpythonic isinstance(value, dict) – so it would be possible to fudge this with __class__ and some other methods if you really don’t want to inherit from dict.

import json

class Foo(object):
    def __init__(self):
        self.bar = 'baz'
        self._qux = 'flub'

    def somemethod(self):
        pass

def default(instance):
    return {k: v
            for k, v in vars(instance).items()
            if not str(k).startswith('_')}

json_foo = json.dumps(Foo(), default=default)
assert '{"bar": "baz"}' == json_foo

print(json_foo)

I came across this problem the other day and implemented a more general version of an Encoder for Python objects that can handle nested objects and inherited fields:

import json
import inspect

class ObjectEncoder(json.JSONEncoder):
    def default(self, obj):
        if hasattr(obj, "to_json"):
            return self.default(obj.to_json())
        elif hasattr(obj, "__dict__"):
            d = dict(
                (key, value)
                for key, value in inspect.getmembers(obj)
                if not key.startswith("__")
                and not inspect.isabstract(value)
                and not inspect.isbuiltin(value)
                and not inspect.isfunction(value)
                and not inspect.isgenerator(value)
                and not inspect.isgeneratorfunction(value)
                and not inspect.ismethod(value)
                and not inspect.ismethoddescriptor(value)
                and not inspect.isroutine(value)
            )
            return self.default(d)
        return obj

Example:

class C(object):
    c = "NO"
    def to_json(self):
        return {"c": "YES"}

class B(object):
    b = "B"
    i = "I"
    def __init__(self, y):
        self.y = y
        
    def f(self):
        print "f"

class A(B):
    a = "A"
    def __init__(self):
        self.b = [{"ab": B("y")}]
        self.c = C()

print json.dumps(A(), cls=ObjectEncoder, indent=2, sort_keys=True)

Result:

{
  "a": "A", 
  "b": [
    {
      "ab": {
        "b": "B", 
        "i": "I", 
        "y": "y"
      }
    }
  ], 
  "c": {
    "c": "YES"
  }, 
  "i": "I"
}

json is limited in terms of objects it can print, and jsonpickle (you may need a pip install jsonpickle) is limited in terms it can’t indent text. If you would like to inspect the contents of an object whose class you can’t change, I still couldn’t find a straighter way than:

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

Note: that still they can’t print the object methods.

Just add to_json method to your class like this:

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

And add this code (from this answer), to somewhere at the top of everything:

from json import JSONEncoder

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

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

This will monkey-patch json module when it’s imported, so
JSONEncoder.default() automatically checks for a special to_json()
method and uses it to encode the object if found.

Just like Onur said, but this time you don’t have to update every json.dumps() in your project.

There are many approaches to this problem. ‘ObjDict’ (pip install objdict) is another. There is an emphasis on providing javascript like objects which can also act like dictionaries to best handle data loaded from JSON, but there are other features which can be useful as well. This provides another alternative solution to the original problem.

This class can do the trick, it converts object to standard json .

import json


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

usage:

Serializer.serialize(my_object)

working in python2.7 and python3.

If you don’t mind installing a package for it, you can use json-tricks:

pip install json-tricks

After that you just need to import dump(s) from json_tricks instead of json, and it’ll usually work:

from json_tricks import dumps
json_str = dumps(cls_instance, indent=4)

which’ll give

{
        "__instance_type__": [
                "module_name.test_class",
                "MyTestCls"
        ],
        "attributes": {
                "attr": "val",
                "dct_attr": {
                        "hello": 42
                }
        }
}

And that’s basically it!

This will work great in general. There are some exceptions, e.g. if special things happen in __new__, or more metaclass magic is going on.

Obviously loading also works (otherwise what’s the point):

from json_tricks import loads
json_str = loads(json_str)

This does assume that module_name.test_class.MyTestCls can be imported and hasn’t changed in non-compatible ways. You’ll get back an instance, not some dictionary or something, and it should be an identical copy to the one you dumped.

If you want to customize how something gets (de)serialized, you can add special methods to your class, like so:

class CustomEncodeCls:
        def __init__(self):
                self.relevant = 42
                self.irrelevant = 37

        def __json_encode__(self):
                # should return primitive, serializable types like dict, list, int, string, float...
                return {'relevant': self.relevant}

        def __json_decode__(self, **attrs):
                # should initialize all properties; note that __init__ is not called implicitly
                self.relevant = attrs['relevant']
                self.irrelevant = 12

which serializes only part of the attributes parameters, as an example.

And as a free bonus, you get (de)serialization of numpy arrays, date & times, ordered maps, as well as the ability to include comments in json.

Disclaimer: I created json_tricks, because I had the same problem as you.

I chose to use decorators to solve the datetime object serialization problem.
Here is my code:

#myjson.py
#Author: jmooremcc 7/16/2017

import json
from datetime import datetime, date, time, timedelta
"""
This module uses decorators to serialize date objects using json
The filename is myjson.py
In another module you simply add the following import statement:
    from myjson import json

json.dumps and json.dump will then correctly serialize datetime and date 
objects
"""

def json_serial(obj):
    """JSON serializer for objects not serializable by default json code"""

    if isinstance(obj, (datetime, date)):
        serial = str(obj)
        return serial
    raise TypeError ("Type %s not serializable" % type(obj))


def FixDumps(fn):
    def hook(obj):
        return fn(obj, default=json_serial)

    return hook

def FixDump(fn):
    def hook(obj, fp):
        return fn(obj,fp, default=json_serial)

    return hook


json.dumps=FixDumps(json.dumps)
json.dump=FixDump(json.dump)


if __name__=="__main__":
    today=datetime.now()
    data={'atime':today, 'greet':'Hello'}
    str=json.dumps(data)
    print str

By importing the above module, my other modules use json in a normal way (without specifying the default keyword) to serialize data that contains date time objects. The datetime serializer code is automatically called for json.dumps and json.dump.

I liked Lost Koder’s method the most. I ran into issues when trying to serialize more complex objects whos members/methods aren’t serializable. Here’s my implementation that works on more objects:

class Serializer(object):
    @staticmethod
    def serialize(obj):
        def check(o):
            for k, v in o.__dict__.items():
                try:
                    _ = json.dumps(v)
                    o.__dict__[k] = v
                except TypeError:
                    o.__dict__[k] = str(v)
            return o
        return json.dumps(check(obj).__dict__, indent=2)

jaraco gave a pretty neat answer. I needed to fix some minor things, but this works:

Code

# Your custom class
class MyCustom(object):
    def __json__(self):
        return {
            'a': self.a,
            'b': self.b,
            '__python__': 'mymodule.submodule:MyCustom.from_json',
        }

    to_json = __json__  # supported by simplejson

    @classmethod
    def from_json(cls, json):
        obj = cls()
        obj.a = json['a']
        obj.b = json['b']
        return obj

# Dumping and loading
import simplejson

obj = MyCustom()
obj.a = 3
obj.b = 4

json = simplejson.dumps(obj, for_json=True)

# Two-step loading
obj2_dict = simplejson.loads(json)
obj2 = MyCustom.from_json(obj2_dict)

# Make sure we have the correct thing
assert isinstance(obj2, MyCustom)
assert obj2.__dict__ == obj.__dict__

Note that we need two steps for loading. For now, the __python__ property
is not used.

How common is this?

Using the method of AlJohri, I check popularity of approaches:

Serialization (Python -> JSON):

• to_json: 266,595 on 2018-06-27
• toJSON: 96,307 on 2018-06-27
• __json__: 8,504 on 2018-06-27
• for_json: 6,937 on 2018-06-27

Deserialization (JSON -> Python):

• from_json: 226,101 on 2018-06-27

I ran into this problem when I tried to store Peewee’s model into PostgreSQL JSONField.

After struggling for a while, here’s the general solution.

The key to my solution is going through Python’s source code and realizing that the code documentation (described here) already explains how to extend the existing json.dumps to support other data types.

Suppose you current have a model that contains some fields that are not serializable to JSON and the model that contains the JSON field originally looks like this:

class SomeClass(Model):
    json_field = JSONField()

Just define a custom JSONEncoder like this:

class CustomJsonEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, SomeTypeUnsupportedByJsonDumps):
            return < whatever value you want >
        return json.JSONEncoder.default(self, obj)

    @staticmethod
    def json_dumper(obj):
        return json.dumps(obj, cls=CustomJsonEncoder)

And then just use it in your JSONField like below:

class SomeClass(Model):
    json_field = JSONField(dumps=CustomJsonEncoder.json_dumper)

The key is the default(self, obj) method above. For every single ... is not JSON serializable complaint you receive from Python, just add code to handle the unserializable-to-JSON type (such as Enum or datetime)

For example, here’s how I support a class inheriting from Enum:

class TransactionType(Enum):
   CURRENT = 1
   STACKED = 2

   def default(self, obj):
       if isinstance(obj, TransactionType):
           return obj.value
       return json.JSONEncoder.default(self, obj)

Finally, with the code implemented like above, you can just convert any Peewee models to be a JSON-seriazable object like below:

peewee_model = WhateverPeeweeModel()
new_model = SomeClass()
new_model.json_field = model_to_dict(peewee_model)

Though the code above was (somewhat) specific to Peewee, but I think:

1. It’s applicable to other ORMs (Django, etc) in general
2. Also, if you understood how json.dumps works, this solution also works with Python (sans ORM) in general too

Any questions, please post in the comments section. Thanks!

If you are able to install a package, I’d recommend trying dill, which worked just fine for my project. A nice thing about this package is that it has the same interface as pickle, so if you have already been using pickle in your project you can simply substitute in dill and see if the script runs, without changing any code. So it is a very cheap solution to try!

(Full anti-disclosure: I am in no way affiliated with and have never contributed to the dill project.)

Install the package:

pip install dill

Then edit your code to import dill instead of pickle:

# import pickle
import dill as pickle

Run your script and see if it works. (If it does you may want to clean up your code so that you are no longer shadowing the pickle module name!)

Some specifics on datatypes that dill can and cannot serialize, from the project page:

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

If you’re using Python3.5+, you could use jsons. (PyPi: https://pypi.org/project/jsons/) It will convert your object (and all its attributes recursively) to a dict.

import jsons

a_dict = jsons.dump(your_object)

Or if you wanted a string:

a_str = jsons.dumps(your_object)

Or if your class implemented jsons.JsonSerializable:

a_dict = your_object.json

This has worked well for me:

class JsonSerializable(object):

    def serialize(self):
        return json.dumps(self.__dict__)

    def __repr__(self):
        return self.serialize()

    @staticmethod
    def dumper(obj):
        if "serialize" in dir(obj):
            return obj.serialize()

        return obj.__dict__

and then

class FileItem(JsonSerializable):
    ...

and

log.debug(json.dumps(<my object>, default=JsonSerializable.dumper, indent=2))

I see no mention here of serial versioning or backcompat, so I will post my solution which I’ve been using for a bit. I probably have a lot more to learn from, specifically Java and Javascript are probably more mature than me here but here goes

https://gist.github.com/andy-d/b7878d0044a4242c0498ed6d67fd50fe

To add another option: You can use the attrs package and the asdict method.

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

json.dumps(objects, cls=ObjectEncoder)

and to convert back

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)

class looks like this

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

In addition to the Onur’s answer, You possibly want to deal with datetime type like below.
(in order to handle: ‘datetime.datetime’ object has no attribute ‘dict‘ exception.)

def datetime_option(value):
    if isinstance(value, datetime.date):
        return value.timestamp()
    else:
        return value.__dict__

Usage:

def toJSON(self):
    return json.dumps(self, default=datetime_option, sort_keys=True, indent=4)

First we need to make our object JSON-compliant, so we can dump it using the standard JSON module. I did it this way:

def serialize(o):
    if isinstance(o, dict):
        return {k:serialize(v) for k,v in o.items()}
    if isinstance(o, list):
        return [serialize(e) for e in o]
    if isinstance(o, bytes):
        return o.decode("utf-8")
    return o

This function uses recursion to iterate over every part of the dictionary and then calls the repr() methods of classes that are not build-in types.

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__()

Building on Quinten Cabo‘s answer:

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)

The differences are

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

class DObject(json.JSONEncoder):
    def delete_not_related_keys(self, _dict):
        for key in ["skipkeys", "ensure_ascii", "check_circular", "allow_nan", "sort_keys", "indent"]:
            try:
                del _dict[key]
            except:
                continue

    def default(self, o):
        if hasattr(o, '__dict__'):
            my_dict = o.__dict__.copy()
            self.delete_not_related_keys(my_dict)
            return my_dict
        else:
            return o

a = DObject()
a.name = 'abdul wahid'
b = DObject()
b.name = a

print(json.dumps(b, cls=DObject))

Kyle Delaney’s comment is correct so i tried to use the answer https://stackoverflow.com/a/15538391/1497139 as well as an improved version of https://stackoverflow.com/a/10254820/1497139

to create a "JSONAble" mixin.

So to make a class JSON serializeable use "JSONAble" as a super class and either call:

 instance.toJSON()

or

 instance.asJSON()

for the two offered methods. You could also extend the JSONAble class with other approaches offered here.

The test example for the Unit Test with Family and Person sample results in:

toJSOn():

{
    "members": {
        "Flintstone,Fred": {
            "firstName": "Fred",
            "lastName": "Flintstone"
        },
        "Flintstone,Wilma": {
            "firstName": "Wilma",
            "lastName": "Flintstone"
        }
    },
    "name": "The Flintstones"
}

asJSOn():

{'name': 'The Flintstones', 'members': {'Flintstone,Fred': {'firstName': 'Fred', 'lastName': 'Flintstone'}, 'Flintstone,Wilma': {'firstName': 'Wilma', 'lastName': 'Flintstone'}}}

Unit Test with Family and Person sample

def testJsonAble(self):
        family=Family("The Flintstones")
        family.add(Person("Fred","Flintstone")) 
        family.add(Person("Wilma","Flintstone"))
        json1=family.toJSON()
        json2=family.asJSON()
        print(json1)
        print(json2)

class Family(JSONAble):
    def __init__(self,name):
        self.name=name
        self.members={}
    
    def add(self,person):
        self.members[person.lastName+","+person.firstName]=person

class Person(JSONAble):
    def __init__(self,firstName,lastName):
        self.firstName=firstName;
        self.lastName=lastName;

jsonable.py defining JSONAble mixin

 '''
Created on 2020-09-03

@author: wf
'''
import json

class JSONAble(object):
    '''
    mixin to allow classes to be JSON serializable see
    https://stackoverflow.com/questions/3768895/how-to-make-a-class-json-serializable
    '''

    def __init__(self):
        '''
        Constructor
        '''
    
    def toJSON(self):
        return json.dumps(self, default=lambda o: o.__dict__, 
            sort_keys=True, indent=4)
        
    def getValue(self,v):
        if (hasattr(v, "asJSON")):
            return v.asJSON()
        elif type(v) is dict:
            return self.reprDict(v)
        elif type(v) is list:
            vlist=[]
            for vitem in v:
                vlist.append(self.getValue(vitem))
            return vlist
        else:   
            return v
    
    def reprDict(self,srcDict):
        '''
        get my dict elements
        '''
        d = dict()
        for a, v in srcDict.items():
            d[a]=self.getValue(v)
        return d
    
    def asJSON(self):
        '''
        recursively return my dict elements
        '''
        return self.reprDict(self.__dict__)   

You’ll find these approaches now integrated in the https://github.com/WolfgangFahl/pyLoDStorage project which is available at https://pypi.org/project/pylodstorage/

As mentioned in many other answers you can pass a function to json.dumps to convert objects that are not one of the types supported by default to a supported type. Surprisingly none of them mentions the simplest case, which is to use the built-in function vars to convert objects into a dict containing all their attributes:

json.dumps(obj, default=vars)

Note that this covers only basic cases, if you need more specific serialization for certain types (e.g. exluding certain attributes or for objects that don’t have a __dict__ attribute) you need to use a custom function or a JSONEncoder as desribed in the other answers.

TLDR: copy-paste Option 1 or Option 2 below

The Full Answer to:
Making Pythons json module work with Your Class

AKA, solving: json.dumps({ "thing": YOUR_CLASS() })

Explanation:

• Yes, a 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:
  • Add def __json__(self) method to your class
  • Mutate json.dumps to check for __json__ method (affects everywhere, even pip modules that import json)
  • Note: Modifing builtin stuff usually isn’t great, however this change should have no side effects, even if its applied multiple times by different codebases. It is entirely reversable durning runtime (if a module wants to undo the modification). And for better or worse, is the best that can done at the moment.

Option 1: Let a Module do the Patching

pip install json-fix
(extended + packaged version of Fancy John’s answer, thank you @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"

Thats it.

Example usage:

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]'

To make json.dumps work for Numpy arrays, Pandas DataFrames, and other 3rd party objects, see the Module (only ~2 lines of code but needs explanation).

How does it work? Well…

Option 2: Patch json.dumps yourself

Note: this approach is simplified, it fails on known edgecases (ex: if your custom class inherits from dict or another builtin), and it misses out on controlling the json behavior for external classes (numpy arrays, datetime, dataframes, tensors, etc).

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"

_

All other answers seem to be "Best practices/approaches to serializing a custom object"

Which, is alreadly covered here in the docs (search "complex" for an example of encoding complex numbers)

To throw another log on this 11 year old fire, I want a solution that meets the following criteria:

• Allows an instance of class FileItem to be serialized using only json.dumps(obj)
• Allows FileItem instances to have properties: fileItem.fname
• Allows FileItem instances to be given to any library which will serialise it using json.dumps(obj)
• Doesn’t require any other fields to be passed to json.dumps (like a custom serializer)

IE:

fileItem = FileItem('filename.ext')
assert json.dumps(fileItem) == '{"fname": "filename.ext"}'
assert fileItem.fname == 'filename.ext'

My solution is:

• Have obj’s class inherit from dict
• Map each object property to the underlying dict

class FileItem(dict):
    def __init__(self, fname):
        self['fname'] = fname

    #fname property
    fname: str = property()
    @fname.getter
    def fname(self):
        return self['fname']

    @fname.setter
    def fname(self, value: str):
        self['fname'] = value

    #Repeat for other properties

Yes, this is somewhat long winded if you have lots of properties, but it is JSONSerializable and it behaves like an object and you can give it to any library that’s going to json.dumps(obj) it.

Why are you guys making it so complicated? Here is a simple example:

#!/usr/bin/env python3

import json
from dataclasses import dataclass

@dataclass
class Person:
    first: str
    last: str
    age: int

    @property
    def __json__(self):
        return {
            "name": f"{self.first} {self.last}",
            "age": self.age
        }

john = Person("John", "Doe", 42)
print(json.dumps(john, indent=4, default=lambda x: x.__json__))

This way you could also serialize nested classes, as __json__ returns a python object and not a string. No need to use a JSONEncoder, as the default parameter with a simple lambda also works fine.

I’ve used @property instead of a simple function, as this feels more natural and modern. The @dataclass is also just an example, it works for a "normal" class as well.

To throw yet another log into a 10-year old fire, I would also offer the dataclass-wizard for this task, assuming you’re using Python 3.6+. This works well with dataclasses, which is actually a python builtin module in 3.7+ onwards.

The dataclass-wizard library will convert your object (and all its attributes recursively) to a dict, and makes the reverse (de-serialization) pretty straightforward too, with fromdict. Also, here is the PyPi link: https://pypi.org/project/dataclass-wizard/.

import dataclass_wizard
import dataclasses

@dataclasses.dataclass
class A:
    hello: str
    a_field: int

obj = A('world', 123)
a_dict = dataclass_wizard.asdict(obj)
# {'hello': 'world', 'aField': 123}

Or if you wanted a string:

a_str = jsons.dumps(dataclass_wizard.asdict(obj))

Or if your class extended from dataclass_wizard.JSONWizard:

a_str = your_object.to_json()

Finally, the library also supports dataclasses in Union types, which basically means that a dict can be de-serialized into an object of either class C1 or C2. For example:

from dataclasses import dataclass

from dataclass_wizard import JSONWizard

@dataclass
class Outer(JSONWizard):

    class _(JSONWizard.Meta):
        tag_key = 'tag'
        auto_assign_tags = True

    my_string: str
    inner: 'A | B'  # alternate syntax: `inner: typing.Union['A', 'B']`

@dataclass
class A:
    my_field: int

@dataclass
class B:
    my_field: str


my_dict = {'myString': 'test', 'inner': {'tag': 'B', 'myField': 'test'}}
obj = Outer.from_dict(my_dict)

# True
assert repr(obj) == "Outer(my_string='test', inner=B(my_field='test'))"

obj.to_json()
# {"myString": "test", "inner": {"myField": "test", "tag": "B"}}

Whomever wants to use basic conversion without an external library, it is simply how you can override __iter__ & __str__ functions of the custom class using following way.

class JSONCustomEncoder(json.JSONEncoder):
    def default(self, obj):
        return obj.__dict__


class Student:
    def __init__(self, name: str, slug: str):
        self.name = name
        self.age = age

    def __iter__(self):
        yield from {
            "name": self.name,
            "age": self.age,
        }.items()

    def __str__(self):
        return json.dumps(
            self.__dict__, cls=JSONCustomEncoder, ensure_ascii=False
        )

Use the object by wrapping in a dict(), so that data remains preserved.

s = Student("aman", 24)
dict(s)

A really simplistic one-liner solution

import json

json.dumps(your_object, default=lambda __o: __o.__dict__)

The end!

What comes below is a test.

import json
from dataclasses import dataclass


@dataclass
class Company:
    id: int
    name: str

@dataclass
class User:
    id: int
    name: str
    email: str
    company: Company


company = Company(id=1, name="Example Ltd")
user = User(id=1, name="John Doe", email="[email protected]", company=company)


json.dumps(user, default=lambda __o: __o.__dict__)

Output:

{
  "id": 1, 
  "name": "John Doe", 
  "email": "[email protected]", 
  "company": {
    "id": 1, 
    "name": "Example Ltd"
  }
}

We often dump complex dictionaries in JSON format in log files. While most of the fields carry important information, we don’t care much about the built-in class objects(for example a subprocess.Popen object). Due to presence of unserializable objects like these, call to json.dumps() fails.

To get around this, I built a small function that dumps object’s string representation instead of dumping the object itself. And if the data structure you are dealing with is too nested, you can specify the nesting maximum level/depth.

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)

Since a dictionary can also have unserializable keys, dumping their class name or object representation will lead to all keys with same name, which will throw error as all keys need to have unique name, that is why the current time since epoch is appended to object names with int(time()).

This function can be tested with the following nested dictionary with different levels/depths-

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) ,
}

Running the following will lead to-

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

print("nnnLEVEL 2")
print(json.dumps(safe_serialize(d , 2) , indent = 4))

print("nnnLEVEL 1")
print(json.dumps(safe_serialize(d , 1) , indent = 4))

Result:

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']>"
}

[NOTE]: Only use this if you don’t care about serialization of a built-in class object.

The most simple answer

class Object(dict):
    def __init__(self):
        pass

    def __getattr__(self, key):
        return self[key]

    def __setattr__(self, key, value):
        self[key] = value

# test
obj = Object()
obj.name = "John"
obj.age = 25
obj.brothers = [ Object() ]
text = json.dumps(obj)

Now it gives you the output, don’t change anything to json.dumps(…)

'{"name": "John", "age": 25, "brothers": [{}]}'

If the object can pe pickled one can use the following two functions to decode and encode an object:

def obj_to_json(obj):
    pickled = pickle.dumps(obj)
    coded = base64.b64encode(pickled).decode('utf8')
    return json.dumps(coded)

def json_to_obj(s):
    coded = base64.b64decode(s)
    return pickle.loads(coded)

This is for example usefull in combination with pytest and config.cache.