Types

Here is the list of the supported types. See the simple example for each type in the footnotes

• Primitives (int, float, str, bool) ¹
• Containers
  • list, set, tuple, dict ²
  • frozenset, ³
  • defaultdict ⁴
• typing.Optional ⁵
• typing.Union ^{6 7 8}
• User defined class with @dataclass ^{9 10}
• typing.NewType for primitive types ¹¹
• typing.Any ¹²
• typing.Literal ¹³
• typing.Generic ¹⁴
• typing.ClassVar ¹⁵
• typing.InitVar ¹⁶
• Enum and IntEnum ¹⁷
• Standard library
  • pathlib.Path ¹⁸
  • decimal.Decimal ¹⁹
  • uuid.UUID ²⁰
  • datetime.date, datetime.time, datetime.datetime ²¹
  • ipaddress ²²
• PyPI library
  • numpy types ²³
  • SQLAlchemy Declarative Dataclass Mapping (experimental) ²⁴

You can write pretty complex class like this:

@serde
class bar:
    i: int

@serde
class Foo:
    a: int
    b: list[str]
    c: tuple[int, float, str, bool]
    d: dict[str, list[tuple[str, int]]]
    e: str | None
    f: Bar

Numpy

All of the above (de)serialization methods can transparently handle most numpy types with the "numpy" extras package.

import numpy as np
import numpy.typing as npt

@serde
class NPFoo:
    i: np.int32
    j: np.int64
    f: np.float32
    g: np.float64
    h: np.bool_
    u: np.ndarray
    v: npt.NDArray
    w: npt.NDArray[np.int32]
    x: npt.NDArray[np.int64]
    y: npt.NDArray[np.float32]
    z: npt.NDArray[np.float64]

npfoo = NPFoo(
    np.int32(1),
    np.int64(2),
    np.float32(3.0),
    np.float64(4.0),
    np.bool_(False),
    np.array([1, 2]),
    np.array([3, 4]),
    np.array([np.int32(i) for i in [1, 2]]),
    np.array([np.int64(i) for i in [3, 4]]),
    np.array([np.float32(i) for i in [5.0, 6.0]]),
    np.array([np.float64(i) for i in [7.0, 8.0]]),
)

>>> from serde.json import to_json, from_json

>>> to_json(npfoo)
'{"i": 1, "j": 2, "f": 3.0, "g": 4.0, "h": false, "u": [1, 2], "v": [3, 4], "w": [1, 2], "x": [3, 4], "y": [5.0, 6.0], "z": [7.0, 8.0]}'

>>> from_json(NPFoo, to_json(npfoo))
NPFoo(i=1, j=2, f=3.0, g=4.0, h=False, u=array([1, 2]), v=array([3, 4]), w=array([1, 2], dtype=int32), x=array([3, 4]), y=array([5., 6.], dtype=float32), z=array([7., 8.]))

SQLAlchemy Declarative Dataclass Mapping (experimental)

While experimental support for SQLAlchemy Declarative Dataclass Mapping integration has been added, certain features such as @serde(type_check=strict) and serde.field() are not currently supported.

It's recommended to exercise caution when relying on experimental features in production environments. It's also advisable to thoroughly test your code and be aware of potential limitations or unexpected behavior.

Needs a new type support?

If you need to use a type which is currently not supported in the standard library, please creat a Github issue to request. For types in third party python packages, unless it's polular like numpy, we don't plan to support it to keep pyserde as simple as possible. We recommend to use custom class or field serializer.