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pyserdedonotuse4

Yet another serialization library on top of dataclasses, inspired by serde-rs.

by yukinarit

0.4.3 (see all)License:MIT
pypi i pyserdedonotuse4
Readme

pyserde

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Yet another serialization library on top of dataclasses.

TL;DR

Put additional @serialize and @deserialize decorator in your ordinary dataclass.

@deserialize
@serialize
@dataclass
class Foo:
    i: int
    s: str
    f: float
    b: bool

Now you can convert an object to JSON,

>>> to_json(Foo(i=10, s='foo', f=100.0, b=True))
{"i": 10, "s": "foo", "f": 100.0, "b": true}

Converted back from JSON to the object quite easily!

>>> from_json(Foo, '{"i": 10, "s": "foo", "f": 100.0, "b": true}')
Foo(i=10, s='foo', f=100.0, b=True)

pyserde supports other data formats (YAML, Toml, MsgPack) and offers many more features!

Benchmark

  • macOS 10.14 Mojave
  • Intel 2.3GHz 8-core Intel Core i9
  • DDR4 32GB RAM

Serialize and deserialize a struct into and from json 10,000 times.

SerializeDeserialize

Serialize the struct into tuple and dictionary.

to_tupleto_dict
  • raw: Serialize and deserialize manually. Fastest in theory.
  • dataclass: Serialize using dataclass's asdict.
  • pyserde: This library.
  • dacite: Simple creation of data classes from dictionaries.
  • mashumaro: Fast and well tested serialization framework on top of dataclasses.
  • marshallow: A lightweight library for converting complex objects to and from simple datatypes.
  • attrs: Python Classes Without Boilerplate.
  • cattrs: Complex custom class converters for attrs.

To run benchmark in your environment:

git clone git@github.com:yukinarit/pyserde.git
cd pyserde/bench
poetry install
poetry run python bench.py --full

You can check the benchmarking code for more information.

Getting started

Install pyserde from PyPI. pyserde requires Python>=3.6.

pip install pyserde

Additional data formats besides JSON need additional dependencies installed. Install msgpack, toml, or yaml extras to work with the appropriate data formats; you can skip formats that you don't plan to use. For example, if you want to use Toml and YAML:

pip install pyserde[toml,yaml]

Or all at once:

pip install pyserde[all]

Put additional @serialize and @deserialize decorator in your ordinary dataclass. Be careful that module name is serde, not pyserde. If you are new to dataclass, I would recommend to read dataclasses documentation first.

from serde import serialize, deserialize
from dataclasses import dataclass

@deserialize
@serialize
@dataclass
class Foo:
    i: int
    s: str
    f: float
    b: bool

pyserde generates methods necessary for serialization by @serialize and methods necessary for deserialization by @deserialize when a class is loaded into python interpreter. Generation occurs exactly only once (This is more like how decorator work, not pyserde) and there is no overhead when you actually use the generated methods. Now your class is serializable and deserializable in the data formats supported by pyserde.

Next, import pyserde helper methods. For JSON:

from serde.json import from_json, to_json

Similarly, you can use other data formats.

from serde.yaml import from_yaml, to_yaml
from serde.toml import from_toml, to_toml
from serde.msgpack import from_msgpack to_msgpack

Use to_json to serialize the object into JSON.

f = Foo(i=10, s='foo', f=100.0, b=True)
print(to_json(f))

Pass Foo class and JSON string in from_json to deserialize into Object.

s = '{"i": 10, "s": "foo", "f": 100.0, "b": true}'
print(from_json(Foo, s))

That's it! pyserde offers many more features. If you're interested, please read the rest of the documentation.

Supported types

You can write pretty complex class like this:

@deserialize
@serialize
@dataclass
class bar:
    i: int

@deserialize
@serialize
class Foo:
    i: int
    l: List[str]
    t: Tuple[int, float, str, bool]
    d: Dict[str, List[Tuple[str, int]]]
    o: Optional[str]
    nested: Bar

Supported data formats

JSON

from serde.json import from_json, to_json
print(to_json(f))
print(from_json(Foo, s))

Yaml

from serde.yaml import from_yaml, to_yaml
print(to_yaml(f))
print(from_yaml(Foo, s))

Toml

from serde.toml import from_toml, to_toml
print(to_toml(f))
print(from_toml(Foo, s))

MsgPack

from serde.msgpack import from_msgpack, to_msgpack
print(to_msgpack(f))
print(from_msgpack(Foo, s))

Python 3.9 type hiting

For python >= 3.9, you can use PEP585 style type annotations for standard collections.

@deserialize
@serialize
class Foo:
    i: int
    l: list[str]
    t: tuple[int, float, str, bool]
    d: dict[str, list[tuple[str, int]]]
    o: Optional[str]
    nested: Bar

For complete example, please see ./examples/collection.py

Postponed evaluation of type annotation

PEP563 Postponed evaluation of type annotation is supported.

from __future__ import annotations
from dataclasses import dataclass
from serde import deserialize, serialize

@deserialize
@serialize
@dataclass
class Foo:
    i: int
    s: str
    f: float
    b: bool

    def foo(self, cls: Foo):  # You can use "Foo" type before it's defined.
        print('foo')

For complete example, please see ./examples/lazy_type_evaluation.py

Forward reference

You can use a forward reference in annotations.

@dataclass
class Foo:
    i: int
    s: str
    bar: 'Bar'  # Specify type annotation in string.

@deserialize
@serialize
@dataclass
class Bar:
    f: float
    b: bool

# Evaluate pyserde decorators after `Bar` is defined.
deserialize(Foo)
serialize(Foo)

For complete example, please see ./examples/forward_reference.py

Case Conversion

Converting snake_case fields into supported case styles e.g. camelCase and kebab-case.

@serialize(rename_all = 'camelcase')
@dataclass
class Foo:
    int_field: int
    str_field: str

f = Foo(int_field=10, str_field='foo')
print(to_json(f))

Here, the output is all camelCase.

'{"intField": 10, "strField": "foo"}'

Rename Field

In case you want to use a keyword as field such as class, you can use serde_rename field attribute.

@serialize
@dataclass
class Foo:
    class_name: str = field(metadata={'serde_rename': 'class'})

print(to_json(Foo(class_name='Foo')))

Output json is having class instead of class_name.

{"class": "Foo"}

For complete example, please see ./examples/rename.py

Skip

You can skip serialization for a certain field, you can use serde_skip.

@serialize
@dataclass
class Resource:
    name: str
    hash: str
    metadata: Dict[str, str] = field(default_factory=dict, metadata={'serde_skip': True})

resources = [
    Resource("Stack Overflow", "hash1"),
    Resource("GitHub", "hash2", metadata={"headquarters": "San Francisco"}) ]
print(to_json(resources))

Here, metadata is not present in output json.

[{"name": "Stack Overflow", "hash": "hash1"}, {"name": "GitHub", "hash": "hash2"}]

For complete example, please see ./examples/skip.py

Conditional Skip

If you conditionally skip some fields, you can pass function or lambda in serde_skip_if.

@serialize
@dataclass
class World:
    player: str
    buddy: str = field(default='', metadata={'serde_skip_if': lambda v: v == 'Pikachu'})

world = World('satoshi', 'Pikachu')
print(to_json(world))

world = World('green', 'Charmander')
print(to_json(world))

As you can see below, field is skipped in serialization if buddy is "Pikachu".

{"player": "satoshi"}
{"player": "green", "buddy": "Charmander"}

For complete example, please see ./examples/skip.py

Custom field serializer/deserializer

If you want to provide a custom function to override the default (de)serialization behaviour of a field, you can pass your functions to serde_serializer and serde_deserializer dataclass metadata.

@deserialize
@serialize
@dataclass
class Foo:
    dt1: datetime
    dt2: datetime = field(
        metadata={
            'serde_serializer': lambda x: x.strftime('%d/%m/%y'),
            'serde_deserializer': lambda x: datetime.strptime(x, '%d/%m/%y'),
        }
    )

dt1 in the example will serialized into 2021-01-01T00:00:00 because the pyserde's default (de)serializier for datetime is ISO 8601. dt2 field in the example will be serialized into 01/01/21 by the custom field serializer.

For complete example, please see ./examples/custom_field_serializer.py

Custom class serializer/deserializer

If you want to provide (de)serializer at class level, you can pass your functions to serializer and deserializer class attributes.

def serializer(cls, o):
    if cls is datetime:
        return o.strftime('%d/%m/%y')
    else:
        raise SerdeSkip()

def deserializer(cls, o):
    if cls is datetime:
        return datetime.strptime(o, '%d/%m/%y')
    else:
        raise SerdeSkip()

@deserialize(deserializer=deserializer)
@serialize(serializer=serializer)
@dataclass
class Foo:
    i: int
    dt1: datetime
    dt2: datetime

For complete example, please see ./examples/custom_class_serializer.py

FAQ

How can I see the code generated by pyserde?

pyserde provides inspect submodule that works as commandline:

python -m serde.inspect <PATH_TO_FILE> <CLASS>

e.g. in pyserde project

cd pyserde
poetry shell
python -m serde.inspect examples/simple.py Foo

Output

Loading simple.Foo from examples.

==================================================
                       Foo
==================================================

--------------------------------------------------
          Functions generated by pyserde
--------------------------------------------------
def to_iter(obj, reuse_instances=True, convert_sets=False):
    if reuse_instances is Ellipsis:
        reuse_instances = True
    if convert_sets is Ellipsis:
        convert_sets = False
    if not is_dataclass(obj):
        return copy.deepcopy(obj)

    Foo = serde_scope.types["Foo"]
    res = []
    res.append(obj.i)
    res.append(obj.s)
    res.append(obj.f)
    res.append(obj.b)
    return tuple(res)
...

LICENSE

MIT

GitHub Stars

136

LAST COMMIT

5mos ago

MAINTAINERS

1

CONTRIBUTORS

14

OPEN ISSUES

21

OPEN PRs

1
VersionTagPublished
0.4.3
1yr ago
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