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Description

When using dataclasses, you often need to dump and load objects according to the described scheme. This framework not only adds this ability to serialize in different formats, but also makes serialization rapidly.

Programming language: Python
License: Apache License 2.0
Tags: YAML     Serialization     JSON     Python     Type Hints     Dataclasses     MessagePack    

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README

mashumaro (マシュマロ)

mashumaro is a fast and well tested serialization framework on top of dataclasses.

Build Status Coverage Status Latest Version Python Version License

When using dataclasses, you often need to dump and load objects according to the described scheme. This framework not only adds this ability to serialize in different formats, but also makes serialization rapidly.

Table of contents

Installation

Use pip to install:

$ pip install mashumaro

Changelog

This project follows the principles of Semantic Versioning.
Changelog is available on GitHub Releases page.

Supported serialization formats

This framework adds methods for dumping to and loading from the following formats:

Plain dict can be useful when you need to pass a dict object to a third-party library, such as a client for MongoDB.

Supported field types

There is support for generic types from the standard typing module:

for standard generic types on PEP 585 compatible Python (3.9+):

for special primitives from the typing module:

for standard interpreter types from types module:

for enumerations based on classes from the standard enum module:

for common built-in types:

for built-in datetime oriented types (see more details):

for pathlike types:

for other less popular built-in types:

for backported types from typing-extensions:

for arbitrary types:

Usage example

from enum import Enum
from typing import List
from dataclasses import dataclass
from mashumaro.mixins.json import DataClassJSONMixin

class Currency(Enum):
    USD = "USD"
    EUR = "EUR"

@dataclass
class CurrencyPosition(DataClassJSONMixin):
    currency: Currency
    balance: float

@dataclass
class StockPosition(DataClassJSONMixin):
    ticker: str
    name: str
    balance: int

@dataclass
class Portfolio(DataClassJSONMixin):
    currencies: List[CurrencyPosition]
    stocks: List[StockPosition]

my_portfolio = Portfolio(
    currencies=[
        CurrencyPosition(Currency.USD, 238.67),
        CurrencyPosition(Currency.EUR, 361.84),
    ],
    stocks=[
        StockPosition("AAPL", "Apple", 10),
        StockPosition("AMZN", "Amazon", 10),
    ]
)

json_string = my_portfolio.to_json()
Portfolio.from_json(json_string)  # same as my_portfolio

How does it work?

This framework works by taking the schema of the data and generating a specific parser and builder for exactly that schema. This is much faster than inspection of field types on every call of parsing or building at runtime.

Benchmark

  • macOS 11.5.2 Big Sur
  • Apple M1
  • 16GB RAM
  • Python 3.9.1

Load and dump sample data 1.000 times in 5 runs. The following figures show the best overall time in each case.

Framework From dict To dict Time Slowdown factor Time Slowdown factor mashumaro 0.04096 1x 0.02741 1x cattrs 0.07307 1.78x 0.05062 1.85x pydantic 0.24847 6.07x 0.12292 4.48x marshmallow 0.29205 7.13x 0.09310 3.4x dataclasses — — 0.22583 8.24x dacite 0.91553 22.35x — —

To run benchmark in your environment:

git clone [email protected]:Fatal1ty/mashumaro.git
cd mashumaro
python3 -m venv env && source env/bin/activate
pip install -e .
pip install -r requirements-dev.txt
python benchmark/run.py

Serialization mixins

Mashumaro provides mixins for each serialization format.

DataClassDictMixin

Can be imported in two ways:

from mashumaro import DataClassDictMixin
from mashumaro.mixins.dict import DataClassDictMixin

The core mixin that adds serialization functionality to a dataclass. This mixin is a base class for all other serialization format mixins. It adds methods from_dict and to_dict.

DataClassJSONMixin

Can be imported as:

from mashumaro.mixins.json import DataClassJSONMixin

This mixins adds json serialization functionality to a dataclass. It adds methods from_json and to_json.

DataClassMessagePackMixin

Can be imported as:

from mashumaro.mixins.msgpack import DataClassMessagePackMixin

This mixins adds MessagePack serialization functionality to a dataclass. It adds methods from_msgpack and to_msgpack.

In order to use this mixin, the msgpack package must be installed. You can install it manually or using an extra option for mashumaro:

pip install mashumaro[msgpack]

DataClassYAMLMixin

Can be imported as:

from mashumaro.mixins.yaml import DataClassYAMLMixin

This mixins adds YAML serialization functionality to a dataclass. It adds methods from_yaml and to_yaml.

In order to use this mixin, the pyyaml package must be installed. You can install it manually or using an extra option for mashumaro:

pip install mashumaro[yaml]

Customization

SerializableType interface

If you already have a separate custom class, and you want to serialize instances of it with mashumaro, you can achieve this by implementing SerializableType interface:

from typing import Dict
from datetime import datetime
from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializableType

class DateTime(datetime, SerializableType):
    def _serialize(self) -> Dict[str, int]:
        return {
            "year": self.year,
            "month": self.month,
            "day": self.day,
            "hour": self.hour,
            "minute": self.minute,
            "second": self.second,
        }

    @classmethod
    def _deserialize(cls, value: Dict[str, int]) -> 'DateTime':
        return DateTime(
            year=value['year'],
            month=value['month'],
            day=value['day'],
            hour=value['hour'],
            minute=value['minute'],
            second=value['second'],
        )


@dataclass
class Holiday(DataClassDictMixin):
    when: DateTime = DateTime.now()


new_year = Holiday(when=DateTime(2019, 1, 1, 12))
dictionary = new_year.to_dict()
# {'x': {'year': 2019, 'month': 1, 'day': 1, 'hour': 0, 'minute': 0, 'second': 0}}
assert Holiday.from_dict(dictionary) == new_year

If you have a custom generic type and are looking for a generic version of such an interface, read this.

Field options

In some cases creating a new class just for one little thing could be excessive. Moreover, you may need to deal with third party classes that you are not allowed to change. You can usedataclasses.field function as a default field value to configure some serialization aspects through its metadata parameter. Next section describes all supported options to use in metadata mapping.

serialize option

This option allows you to change the serialization method. When using this option, the serialization behaviour depends on what type of value the option has. It could be either Callable[[Any], Any] or str.

A value of type Callable[[Any], Any] is a generic way to specify any callable object like a function, a class method, a class instance method, an instance of a callable class or even a lambda function to be called for serialization.

A value of type str sets a specific engine for serialization. Keep in mind that all possible engines depend on the field type that this option is used with. At this moment there are next serialization engines to choose from:

Applicable field types Supported engines Description
NamedTuple, namedtuple as_list, as_dict How to pack named tuples. By default as_list engine is used that means your named tuple class instance will be packed into a list of its values. You can pack it into a dictionary using as_dict engine.

In addition, you can pass a field value as is without changes using pass_through.

Example:

from datetime import datetime
from dataclasses import dataclass, field
from typing import NamedTuple
from mashumaro import DataClassDictMixin

class MyNamedTuple(NamedTuple):
    x: int
    y: float

@dataclass
class A(DataClassDictMixin):
    dt: datetime = field(
        metadata={
            "serialize": lambda v: v.strftime('%Y-%m-%d %H:%M:%S')
        }
    )
    t: MyNamedTuple = field(metadata={"serialize": "as_dict"})

deserialize option

This option allows you to change the deserialization method. When using this option, the deserialization behaviour depends on what type of value the option has. It could be either Callable[[Any], Any] or str.

A value of type Callable[[Any], Any] is a generic way to specify any callable object like a function, a class method, a class instance method, an instance of a callable class or even a lambda function to be called for deserialization.

A value of type str sets a specific engine for deserialization. Keep in mind that all possible engines depend on the field type that this option is used with. At this moment there are next deserialization engines to choose from:

Applicable field types Supported engines Description
datetime, date, time ciso8601, pendulum How to parse datetime string. By default native fromisoformat of corresponding class will be used for datetime, date and time fields. It's the fastest way in most cases, but you can choose an alternative.
NamedTuple, namedtuple as_list, as_dict How to unpack named tuples. By default as_list engine is used that means your named tuple class instance will be created from a list of its values. You can unpack it from a dictionary using as_dict engine.

In addition, you can pass a field value as is without changes using pass_through.

Example:

from datetime import datetime
from dataclasses import dataclass, field
from typing import List, NamedTuple
from mashumaro import DataClassDictMixin
import ciso8601
import dateutil

class MyNamedTuple(NamedTuple):
    x: int
    y: float

@dataclass
class A(DataClassDictMixin):
    x: datetime = field(
        metadata={"deserialize": "pendulum"}
    )

class B(DataClassDictMixin):
    x: datetime = field(
        metadata={"deserialize": ciso8601.parse_datetime_as_naive}
    )

@dataclass
class C(DataClassDictMixin):
    dt: List[datetime] = field(
        metadata={
            "deserialize": lambda l: list(map(dateutil.parser.isoparse, l))
        }
    )

@dataclass
class D(DataClassDictMixin):
    x: MyNamedTuple = field(metadata={"deserialize": "as_dict"})

serialization_strategy option

This option is useful when you want to change the serialization behaviour for a class depending on some defined parameters. For this case you can create the special class implementing SerializationStrategy interface:

from dataclasses import dataclass, field
from datetime import datetime
from mashumaro import DataClassDictMixin
from mashumaro.types import SerializationStrategy

class FormattedDateTime(SerializationStrategy):
    def __init__(self, fmt):
        self.fmt = fmt

    def serialize(self, value: datetime) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> datetime:
        return datetime.strptime(value, self.fmt)

@dataclass
class DateTimeFormats(DataClassDictMixin):
    short: datetime = field(
        metadata={
            "serialization_strategy": FormattedDateTime(
                fmt="%d%m%Y%H%M%S",
            )
        }
    )
    verbose: datetime = field(
        metadata={
            "serialization_strategy": FormattedDateTime(
                fmt="%A %B %d, %Y, %H:%M:%S",
            )
        }
    )

formats = DateTimeFormats(
    short=datetime(2019, 1, 1, 12),
    verbose=datetime(2019, 1, 1, 12),
)
dictionary = formats.to_dict()
# {'short': '01012019120000', 'verbose': 'Tuesday January 01, 2019, 12:00:00'}
assert DateTimeFormats.from_dict(dictionary) == formats

In addition, you can pass a field value as is without changes using pass_through.

alias option

In some cases it's better to have different names for a field in your class and in its serialized view. For example, a third-party legacy API you are working with might operate with camel case style, but you stick to snake case style in your code base. Or even you want to load data with keys that are invalid identifiers in Python. This problem is easily solved by using aliases:

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options

@dataclass
class DataClass(DataClassDictMixin):
    a: int = field(metadata=field_options(alias="FieldA"))
    b: int = field(metadata=field_options(alias="#invalid"))

x = DataClass.from_dict({"FieldA": 1, "#invalid": 2})  # DataClass(a=1, b=2)
x.to_dict()  # {"a": 1, "b": 2}  # no aliases on serialization by default

If you want to write all the field aliases in one place there is such a config option.

If you want to serialize all the fields by aliases you have two options to do so:

It's hard to imagine when it might be necessary to serialize only specific fields by alias, but such functionality is easily added to the library. Open the issue if you need it.

If you don't want to remember the names of the options you can use field_options helper function:

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options

@dataclass
class A(DataClassDictMixin):
    x: int = field(
        metadata=field_options(
            serialize=str,
            deserialize=int,
            ...
        )
    )

More options are on the way. If you know which option would be useful for many, please don't hesitate to create an issue or pull request.

Config options

If inheritance is not an empty word for you, you'll fall in love with the Config class. You can register serialize and deserialize methods, define code generation options and other things just in one place. Or in some classes in different ways if you need flexibility. Inheritance is always on the first place.

There is a base class BaseConfig that you can inherit for the sake of convenience, but it's not mandatory.

In the following example you can see how the debug flag is changed from class to class: ModelA will have debug mode enabled but ModelB will not.

from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

class BaseModel(DataClassDictMixin):
    class Config(BaseConfig):
        debug = True

class ModelA(BaseModel):
    a: int

class ModelB(BaseModel):
    b: int

    class Config(BaseConfig):
        debug = False

Next section describes all supported options to use in the config.

debug config option

If you enable the debug option the generated code for your data class will be printed.

code_generation_options config option

Some users may need functionality that wouldn't exist without extra cost such as valuable cpu time to execute additional instructions. Since not everyone needs such instructions, they can be enabled by a constant in the list, so the fastest basic behavior of the library will always remain by default. The following table provides a brief overview of all the available constants described below.

Constant Description
TO_DICT_ADD_OMIT_NONE_FLAG Adds omit_none keyword-only argument to to_dict method.
TO_DICT_ADD_BY_ALIAS_FLAG Adds by_alias keyword-only argument to to_dict method.
ADD_DIALECT_SUPPORT Adds dialect keyword-only argument to from_dict and to_dict methods.

serialization_strategy config option

You can register custom SerializationStrategy, serialize and deserialize methods for specific types just in one place. It could be configured using a dictionary with types as keys. The value could be either a SerializationStrategy instance or a dictionary with serialize and deserialize values with the same meaning as in the field options.

from dataclasses import dataclass
from datetime import datetime, date
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig
from mashumaro.types import SerializationStrategy

class FormattedDateTime(SerializationStrategy):
    def __init__(self, fmt):
        self.fmt = fmt

    def serialize(self, value: datetime) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> datetime:
        return datetime.strptime(value, self.fmt)

@dataclass
class DataClass(DataClassDictMixin):

    datetime: datetime
    date: date

    class Config(BaseConfig):
        serialization_strategy = {
            datetime: FormattedDateTime("%Y"),
            date: {
                # you can use specific str values for datetime here as well
                "deserialize": "pendulum",
                "serialize": date.isoformat,
            },
        }

instance = DataClass.from_dict({"datetime": "2021", "date": "2021"})
# DataClass(datetime=datetime.datetime(2021, 1, 1, 0, 0), date=Date(2021, 1, 1))
dictionary = instance.to_dict()
# {'datetime': '2021', 'date': '2021-01-01'}

aliases config option

Sometimes it's better to write the field aliases in one place. You can mix aliases here with aliases in the field options, but the last ones will always take precedence.

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    a: int
    b: int

    class Config(BaseConfig):
        aliases = {
            "a": "FieldA",
            "b": "FieldB",
        }

DataClass.from_dict({"FieldA": 1, "FieldB": 2})  # DataClass(a=1, b=2)

serialize_by_alias config option

All the fields with aliases will be serialized by them by default when this option is enabled. You can mix this config option with by_alias keyword argument.

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options
from mashumaro.config import BaseConfig

@dataclass
class DataClass(DataClassDictMixin):
    field_a: int = field(metadata=field_options(alias="FieldA"))

    class Config(BaseConfig):
        serialize_by_alias = True

DataClass(field_a=1).to_dict()  # {'FieldA': 1}

namedtuple_as_dict config option

Dataclasses are a great way to declare and use data models. But it's not the only way. Python has a typed version of namedtuple called NamedTuple which looks similar to dataclasses:

from typing import NamedTuple

class Point(NamedTuple):
    x: int
    y: int

the same with a dataclass will look like this:

from dataclasses import dataclass

@dataclass
class Point:
    x: int
    y: int

At first glance, you can use both options. But imagine that you need to create a bunch of instances of the Point class. Due to how dataclasses work you will have more memory consumption compared to named tuples. In such a case it could be more appropriate to use named tuples.

By default, all named tuples are packed into lists. But with namedtuple_as_dict option you have a drop-in replacement for dataclasses:

from dataclasses import dataclass
from typing import List, NamedTuple
from mashumaro import DataClassDictMixin

class Point(NamedTuple):
    x: int
    y: int

@dataclass
class DataClass(DataClassDictMixin):
    points: List[Point]

    class Config:
        namedtuple_as_dict = True

obj = DataClass.from_dict({"points": [{"x": 0, "y": 0}, {"x": 1, "y": 1}]})
print(obj.to_dict())  # {"points": [{"x": 0, "y": 0}, {"x": 1, "y": 1}]}

If you want to serialize only certain named tuple fields as dictionaries, you can use the corresponding serialization and deserialization engines.

allow_postponed_evaluation config option

PEP 563 solved the problem of forward references by postponing the evaluation of annotations, so you can write the following code:

from __future__ import annotations
from dataclasses import dataclass
from mashumaro import DataClassDictMixin

@dataclass
class A(DataClassDictMixin):
    x: B

@dataclass
class B(DataClassDictMixin):
    y: int

obj = A.from_dict({'x': {'y': 1}})

You don't need to write anything special here, forward references work out of the box. If a field of a dataclass has a forward reference in the type annotations, building of from_dict and to_dict methods of this dataclass will be postponed until they are called once. However, if for some reason you don't want the evaluation to be possibly postponed, you can disable it using allow_postponed_evaluation option:

from __future__ import annotations
from dataclasses import dataclass
from mashumaro import DataClassDictMixin

@dataclass
class A(DataClassDictMixin):
    x: B

    class Config:
        allow_postponed_evaluation = False

# UnresolvedTypeReferenceError: Class A has unresolved type reference B
# in some of its fields

@dataclass
class B(DataClassDictMixin):
    y: int

In this case you will get UnresolvedTypeReferenceError regardless of whether class B is declared below or not.

dialect config option

This option is described below in the Dialects section.

Passing field values as is

In some cases it's needed to pass a field value as is without any changes during serialization / deserialization. There is a predefined pass_through object that can be used as serialization_strategy or serialize / deserialize options:

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, pass_through

class MyClass:
    def __init__(self, some_value):
        self.some_value = some_value

@dataclass
class A1(DataClassDictMixin):
    x: MyClass = field(
        metadata={
            "serialize": pass_through,
            "deserialize": pass_through,
        }
    )

@dataclass
class A2(DataClassDictMixin):
    x: MyClass = field(
        metadata={
            "serialization_strategy": pass_through,
        }
    )

@dataclass
class A3(DataClassDictMixin):
    x: MyClass

    class Config:
        serialization_strategy = {
            MyClass: pass_through,
        }

@dataclass
class A4(DataClassDictMixin):
    x: MyClass

    class Config:
        serialization_strategy = {
            MyClass: {
                "serialize": pass_through,
                "deserialize": pass_through,
            }
        }

my_class_instance = MyClass(42)

assert A1.from_dict({'x': my_class_instance}).x == my_class_instance
assert A2.from_dict({'x': my_class_instance}).x == my_class_instance
assert A3.from_dict({'x': my_class_instance}).x == my_class_instance
assert A4.from_dict({'x': my_class_instance}).x == my_class_instance

a1_dict = A1(my_class_instance).to_dict()
a2_dict = A2(my_class_instance).to_dict()
a3_dict = A3(my_class_instance).to_dict()
a4_dict = A4(my_class_instance).to_dict()

assert a1_dict == a2_dict == a3_dict == a4_dict == {"x": my_class_instance}

Dialects

Sometimes it's needed to have different serialization and deserialization methods depending on the data source where entities of the dataclass are stored or on the API to which the entities are being sent or received from. There is a special Dialect type that may contain all the differences from the default serialization and deserialization methods. You can create different dialects and use each of them for the same dataclass depending on the situation.

Suppose we have the following dataclass with a field of type date:

@dataclass
class Entity(DataClassDictMixin):
    dt: date

By default, a field of date type serializes to a string in ISO 8601 format, so the serialized entity will look like {'dt': '2021-12-31'}. But what if we have, for example, two sensitive legacy Ethiopian and Japanese APIs that use two different formats for dates — dd/mm/yyyy and yyyy年mm月dd日? Instead of creating two similar dataclasses we can have one dataclass and two dialects:

from dataclasses import dataclass
from datetime import date, datetime
from mashumaro import DataClassDictMixin
from mashumaro.config import ADD_DIALECT_SUPPORT
from mashumaro.dialect import Dialect
from mashumaro.types import SerializationStrategy

class DateTimeSerializationStrategy(SerializationStrategy):
    def __init__(self, fmt: str):
        self.fmt = fmt

    def serialize(self, value: date) -> str:
        return value.strftime(self.fmt)

    def deserialize(self, value: str) -> date:
        return datetime.strptime(value, self.fmt).date()

class EthiopianDialect(Dialect):
    serialization_strategy = {
        date: DateTimeSerializationStrategy("%d/%m/%Y")
    }

class JapaneseDialect(Dialect):
    serialization_strategy = {
        date: DateTimeSerializationStrategy("%Y年%m月%d日")
    }

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config:
        code_generation_options = [ADD_DIALECT_SUPPORT]

entity = Entity(date(2021, 12, 31))
entity.to_dict(dialect=EthiopianDialect)  # {'dt': '31/12/2021'}
entity.to_dict(dialect=JapaneseDialect)   # {'dt': '2021年12月31日'}
Entity.from_dict({'dt': '2021年12月31日'}, dialect=JapaneseDialect)

serialization_strategy dialect option

This dialect option has the same meaning as the similar config option but for the dialect scope. You can register custom SerializationStrategy, serialize and deserialize methods for specific types.

Changing the default dialect

You can change the default serialization and deserialization methods for a dataclass not only in the serialization_strategy config option but using the dialect config option. If you have multiple dataclasses without a common parent class the default dialect can help you to reduce the number of code lines written:

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config:
        dialect = JapaneseDialect

entity = Entity(date(2021, 12, 31))
entity.to_dict()  # {'dt': '2021年12月31日'}
assert Entity.from_dict({'dt': '2021年12月31日'}) == entity

Code generation options

Add omit_none keyword argument

If you want to have control over whether to skip None values on serialization you can add omit_none parameter to to_dict method using the code_generation_options list:

from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig, TO_DICT_ADD_OMIT_NONE_FLAG

@dataclass
class Inner(DataClassDictMixin):
    x: int = None
    # "x" won't be omitted since there is no TO_DICT_ADD_OMIT_NONE_FLAG here

@dataclass
class Model(DataClassDictMixin):
    x: Inner
    a: int = None
    b: str = None  # will be omitted

    class Config(BaseConfig):
        code_generation_options = [TO_DICT_ADD_OMIT_NONE_FLAG]

Model(x=Inner(), a=1).to_dict(omit_none=True)  # {'x': {'x': None}, 'a': 1}

Add by_alias keyword argument

If you want to have control over whether to serialize fields by their aliases you can add by_alias parameter to to_dict method using the code_generation_options list. The default value of by_alias parameter depends on whether the serialize_by_alias config option is enabled.

from dataclasses import dataclass, field
from mashumaro import DataClassDictMixin, field_options
from mashumaro.config import BaseConfig, TO_DICT_ADD_BY_ALIAS_FLAG

@dataclass
class DataClass(DataClassDictMixin):
    field_a: int = field(metadata=field_options(alias="FieldA"))

    class Config(BaseConfig):
        code_generation_options = [TO_DICT_ADD_BY_ALIAS_FLAG]

DataClass(field_a=1).to_dict()  # {'field_a': 1}
DataClass(field_a=1).to_dict(by_alias=True)  # {'FieldA': 1}

Add dialect keyword argument

Support for dialects is disabled by default for performance reasons. You can enable it using a ADD_DIALECT_SUPPORT constant:

from dataclasses import dataclass
from datetime import date
from mashumaro import DataClassDictMixin
from mashumaro.config import BaseConfig, ADD_DIALECT_SUPPORT

@dataclass
class Entity(DataClassDictMixin):
    dt: date

    class Config(BaseConfig):
        code_generation_options = [ADD_DIALECT_SUPPORT]

User-defined generic types

There is support for user-defined generic types. You can inherit generic dataclasses along with overwriting types in them, use generic dataclasses as field types, or create your own generic types with serialization under your control.

User-defined generic dataclasses

If you have a generic version of a dataclass and want to serialize and deserialize its instances depending on the concrete types, you can achieve this using inheritance:

from dataclasses import dataclass
from datetime import date
from typing import Generic, Mapping, TypeVar
from mashumaro import DataClassDictMixin

KT = TypeVar("KT")
VT = TypeVar("VT", date, str)

@dataclass
class GenericDataClass(Generic[KT, VT]):
    x: Mapping[KT, VT]

@dataclass
class ConcreteDataClass(GenericDataClass[str, date], DataClassDictMixin):
    pass

ConcreteDataClass.from_dict({"x": {"a": "2021-01-01"}})          # ok
ConcreteDataClass.from_dict({"x": {"a": "not a date but str"}})  # error

You can override TypeVar field with a concrete type or another TypeVar. Partial specification of concrete types is also allowed. If a generic dataclass is inherited without type overriding the types of its fields remain untouched.

Generic dataclasses as field types

Another approach is to specify concrete types in the field type hints. This can help to have different versions of the same generic dataclass:

from dataclasses import dataclass
from datetime import date
from typing import Generic, TypeVar
from mashumaro import DataClassDictMixin

T = TypeVar('T')

@dataclass
class GenericDataClass(Generic[T], DataClassDictMixin):
    x: T

@dataclass
class DataClass(DataClassDictMixin):
    date: GenericDataClass[date]
    str: GenericDataClass[str]

instance = DataClass(
    date=GenericDataClass(x=date(2021, 1, 1)),
    str=GenericDataClass(x='2021-01-01'),
)
dictionary = {'date': {'x': '2021-01-01'}, 'str': {'x': '2021-01-01'}}
assert DataClass.from_dict(dictionary) == instance

GenericSerializableType interface

There is a generic alternative to SerializableType called GenericSerializableType. It makes it possible to serialize and deserialize instances of generic types depending on the types provided:

from typing import Dict, TypeVar
from dataclasses import dataclass
from mashumaro import DataClassDictMixin
from mashumaro.types import GenericSerializableType

KT = TypeVar("KT", int, str)
VT = TypeVar("VT", int, str)

class GenericDict(Dict[KT, VT], GenericSerializableType):
    def _serialize(self, types) -> Dict[KT, VT]:
        k_type, v_type = types
        if k_type not in (int, str) or v_type not in (int, str):
            raise TypeError
        return {k_type(k): v_type(v) for k, v in self.items()}

    @classmethod
    def _deserialize(cls, value, types) -> 'GenericDict[KT, VT]':
        k_type, v_type = types
        if k_type not in (int, str) or v_type not in (int, str):
            raise TypeError
        return cls({k_type(k): v_type(v) for k, v in value.items()})

@dataclass
class DataClass(DataClassDictMixin):
    x: GenericDict[int, str]
    y: GenericDict[str, int]

instance = DataClass(GenericDict({1: 'a'}), GenericDict({'b': 2}))
dictionary = instance.to_dict()  # {'x': {1: 'a'}, 'y': {'b': 2}}
assert DataClass.from_dict(dictionary) == instance

The difference between SerializableType and GenericSerializableType is that the methods of GenericSerializableType have a parameter types, to which the concrete types will be passed. If you don't need this information you can still use SerializableType interface even with generic types.

Serialization hooks

In some cases you need to prepare input / output data or do some extraordinary actions at different stages of the deserialization / serialization lifecycle. You can do this with different types of hooks.

Before deserialization

For doing something with a dictionary that will be passed to deserialization you can use __pre_deserialize__ class method:

@dataclass
class A(DataClassJSONMixin):
    abc: int

    @classmethod
    def __pre_deserialize__(cls, d: Dict[Any, Any]) -> Dict[Any, Any]:
        return {k.lower(): v for k, v in d.items()}

print(DataClass.from_dict({"ABC": 123}))    # DataClass(abc=123)
print(DataClass.from_json('{"ABC": 123}'))  # DataClass(abc=123)

After deserialization

For doing something with a dataclass instance that was created as a result of deserialization you can use __post_deserialize__ class method:

@dataclass
class A(DataClassJSONMixin):
    abc: int

    @classmethod
    def __post_deserialize__(cls, obj: 'A') -> 'A':
        obj.abc = 456
        return obj

print(DataClass.from_dict({"abc": 123}))    # DataClass(abc=456)
print(DataClass.from_json('{"abc": 123}'))  # DataClass(abc=456)

Before serialization

For doing something before serialization you can use __pre_serialize__ method:

@dataclass
class A(DataClassJSONMixin):
    abc: int
    counter: ClassVar[int] = 0

    def __pre_serialize__(self) -> 'A':
        self.counter += 1
        return self

obj = DataClass(abc=123)
obj.to_dict()
obj.to_json()
print(obj.counter)  # 2

After serialization

For doing something with a dictionary that was created as a result of serialization you can use __post_serialize__ method:

@dataclass
class A(DataClassJSONMixin):
    user: str
    password: str

    def __post_serialize__(self, d: Dict[Any, Any]) -> Dict[Any, Any]:
        d.pop('password')
        return d

obj = DataClass(user="name", password="secret")
print(obj.to_dict())  # {"user": "name"}
print(obj.to_json())  # '{"user": "name"}'

TODO

  • add optional validation
  • write custom useful types such as URL, Email etc


*Note that all licence references and agreements mentioned in the mashumaro (マシュマロ) README section above are relevant to that project's source code only.