Frequently Asked Questions

Do I need to use dataclasses?

No, not necessarily. The following example shows a class that jsons can serialize and deserialize without a problem:

class Person:
    def __init__(self, name: str, car: Car): = name = car

Do I need to use type hints?

No, not necessarily, (but it is recommended to do so anyway for readability). Only if your class has an attribute of a custom class type, you need to use a type hint for proper deserialization with jsons. Serialization works always, no matter the presence or absence of type hints.

Here is an example:

class Person:
    def __init__(self, name, birthday, car: Car): = name
        self.birthday = birthday = car

This class does not require type hints for name and birthday (assuming that they are meant to be of type str and datetime respectively). The Car is a custom class though, so that one needs a type hint if you want deserialization to work.

So in short: only for custom classes and only for deserialization.

How can I improve the performance of jsons?

With large data sets, it may take some time for jsons to dump or load. This is caused by several checks and scans that jsons does. You can increase the speed significantly by using “strict mode”: jsons.dump(some_obj, strict=True). Also make sure to provide type hints for your classes’ attributes. This will allow jsons to scan your class only once and use that to get the attributes of every object of that class it encounters.

On top of that, you could see if parallelization gains you anything: jsons.dump(some_obj, strict=True, tasks=4). By default a Process is spawned per task, but you can also choose to use Thread by providing task_type=Thread.

Is it possible to discard private attributes?

Yes it is. Use strip_privates for that.

jsons.dump(some_inst, strip_privates=True)

How can I write a custom serializer?

First create your serializer function. It requires at least the parameters obj and **kwargs. You may introduce additional parameters to your liking.

Here is an example:

def my_custom_datetime_serializer(obj: datetime,
                                  only_seconds: bool = False,
                                  **kwargs) -> Union[int, float]:
    if only_seconds:
        return int(obj.timestamp())
    return obj.timestamp()

Note: the type hints are not required.

Next, you need to register your serializer function with the type you want to use that serializer function for:

jsons.set_serializer(my_custom_datetime_serializer, datetime)

And you’re set:

>>> my_date =
>>> jsons.dump(my_date, only_seconds=True)

How can I write a custom deserializer?

First create your deserializer function. It requires at least the parameters obj, cls and **kwargs. You may introduce additional parameters to your liking.

Here is an example:

def my_custom_datetime_deserializer(obj: Union[int, float],
                                    cls: type = datetime,
                                    **kwargs) -> datetime:
    return datetime.fromtimestamp(ts)

Note: the type hints are not required.

Next, you need to register your deserializer function with the type you want to use that deserializer function for:

jsons.set_deserializer(my_custom_datetime_deserializer, datetime)

And you’re set:

>>> jsons.load(1552819054, datetime)
datetime.datetime(2019, 3, 17, 11, 37, 34)

Why does jsons tolerate additional attributes in my json object compared to the class?

The thoughts on this are as follows:

  • jsons was designed to be very tolerant by default.
  • jsons is in fact capable of deserializing json data into a class with fewer attributes; all required fields were provided. So it is reasonable that no error should occur.
  • jsons should be compatible with json schemas and they allow extra attributes by default as well.

You can however turn ‘strict-mode’ on:

jsons.load(some_json, cls=SomeClass, strict=True)

By doing so, any mismatch between the json object and the class results in a DeserializationError.

How can I deserialize without exactly knowing the target class?

Sometimes you do not know beforehand of which exact class you have a json instance.

There are two ways to deal with this. The first is to use a Union and define all possible types that you want to deserialize to:

jsons.load(car_json, Union[Audi, Porche, Tesla], strict=True)

The possible classes are examined from left to right and the first successful deserialization is returned.

The second option is to serialize verbose objects:

car_json = jsons.dump(car_inst, verbose=True)

When loading a verbose object, you may omit the expected class:

car_inst = jsons.load(car_json)

Why not just use __dict__?

For the following reasons:

  • The __dict__ attribute only creates a shallow dict of an instance. Any contained object is not serialized to a dict.
  • The __dict__ does not take @property methods in account.
  • Not all objects have a __dict__ attribute (e.g. datetime does not).
  • The serialization process of __dict__ cannot easily be tuned.
  • There is no means to deserialize with __dict__.

Why not use the standard json library?

For the following reasons:

  • It’s quite a hassle to (de)serialize custom types: you need to write a subclass of json.JSONEncoder with specific serialization/deserialization code per custom class.
  • You will need to provide that subclass of json.JSONEncoder to json.dumps/json.loads every single time.

Aren’t there already libraries for serialization to json?

There are.

Here is how jsonpickle serializes a datetime:

>>> jsonpickle.encode(my_date)
'{"py/object": "datetime.datetime", "__reduce__": [{"py/type": "datetime.datetime"}, ["B+MDEBUYLgVu1w=="]]}'

And this is how jsons does it:

>>> jsons.dumps(my_date)

And this is what marshmallow requires your classes to look like:

class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()
    artist = fields.Nested(ArtistSchema())

Compare that to jsons:

class AlbumSchema:
    def __init__(self, title: str, release_date: datetime, artist: Artist)
        self.title = title
        self.release_date = release_date
        self.artist = artist

# Or even better, using a dataclass:

class AlbumSchema:
        title: str
        release_date: datetime
        artist: Artist

And this is what a serpy serializer for your custom class looks like:

class FooSerializer(serpy.Serializer):
    """The serializer schema definition."""
    # Use a Field subclass like IntField if you need more validation.
    x = serpy.IntField()
    y = serpy.Field()
    z = serpy.Field()

Compared to that of jsons:

# Not Necessary at all.

So yes. There are already libraries for serializing Python to json. There may be some advantages for each library, so you should do your homework.

My json contains camelcase, how can I transform to the right case?

You can have the keys transformed by the serialization or deserialization process by providing a transformer function that takes a string and returns a string.

result = jsons.dump(some_obj, key_transformer=jsons.KEY_TRANSFORMER_CAMELCASE)
# result could be something like: {'thisIsTransformed': 123}

result = jsons.load(some_dict, SomeClass,
# result could be something like: {'this_is_transformed': 123}

The following casing styles are supported:


How do the jsons decorators work?

Use loaded to automatically apply jsons.load to the parameters and/or the return value.

Here is an example:

from datetime import datetime
from jsons.decorators import loaded

def some_func(x: datetime) -> datetime:
    # x is now of type datetime.
    return '2018-10-07T19:05:00+02:00'

result = some_func('2018-10-07T19:05:00+02:00')
# result is now of type datetime.

In the above case, the type hint could be omitted for the same result: jsons will recognize the timestamp from the string automatically. In case of a custom type, you do need a type hint. The same goes for the return type; it could be omitted in this case as well.

Similarly, you can decorate a function or method with @dumped as is done below:

from datetime import datetime
from jsons.decorators import dumped

class SomeClass:
    def some_meth(cls, x):
        # x is now of type str, cls remains untouched.

result = SomeClass.some_meth(
# result is now of type str.

In case of methods, like in the example above, the special self or cls parameters are not touched by the decorators @loaded() or @dumped(). Additionally, you can provide a type hint for any parameter (except self or cls) or the return value. Doing so will make jsons attempt to dump into that particular type, just like with jsons.dump(some_obj, cls=ParticularType).

For more info, see the api doc.

Can I just participate in discussions on the issues?

Yes, please do. Your opinion is highly valuated and appreciated.

I have an idea for a new feature, what should I do?

Please always check the API first, maybe your feature was already there. :-) Otherwise, open up an issue and describe your desired feature and why one would want this.

You can also open a pull request. It is advised to first open a discussion in an issue though.

I found a bug, what should I do?

Please report bugs by opening an issue on the Github page.

My question is not listed here!

I’m sorry for that. Please open up an issue on the Github page.