如何使一个Python类序列化?
class FileItem:
def __init__(self, fname):
self.fname = fname
尝试序列化为JSON:
>>> import json
>>> x = FileItem('/foo/bar')
>>> json.dumps(x)
TypeError: Object of type 'FileItem' is not JSON serializable
如何使一个Python类序列化?
class FileItem:
def __init__(self, fname):
self.fname = fname
尝试序列化为JSON:
>>> import json
>>> x = FileItem('/foo/bar')
>>> json.dumps(x)
TypeError: Object of type 'FileItem' is not JSON serializable
当前回答
下面是一个简单功能的简单解决方案:
.toJSON()方法
实现一个序列化器方法,而不是一个JSON可序列化类:
import json
class Object:
def toJSON(self):
return json.dumps(self, default=lambda o: o.__dict__,
sort_keys=True, indent=4)
所以你只需调用它来序列化:
me = Object()
me.name = "Onur"
me.age = 35
me.dog = Object()
me.dog.name = "Apollo"
print(me.toJSON())
将输出:
{
"age": 35,
"dog": {
"name": "Apollo"
},
"name": "Onur"
}
其他回答
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)
正如在许多其他答案中提到的,您可以将函数传递给json。转储将不是默认支持的类型之一的对象转换为受支持的类型。令人惊讶的是,他们都没有提到最简单的情况,即使用内置函数vars将对象转换为包含其所有属性的dict:
json.dumps(obj, default=vars)
注意,这只涵盖了基本的情况,如果你需要对某些类型进行更具体的序列化(例如排除某些属性或没有__dict__属性的对象),你需要使用自定义函数或JSONEncoder,如其他答案中所述。
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)
我没有看到这里提到串行版本或backcompat,所以我将发布我的解决方案,我已经使用了一点。我可能还有很多东西要学习,特别是Java和Javascript可能比我更成熟,但我要这样做
https://gist.github.com/andy-d/b7878d0044a4242c0498ed6d67fd50fe
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