jetblack-serialization 4.0.13

Serialization for JSON, YAML and XML using typing

jetblack-serialization

Serialization of built in types for JSON, YAML and XML in Python using type annotations (read the docs).

Installation

This is a Python 3.12+ package.

The package can be installed with pip.

pip install jetblack-serialization

By default, the dependencies for YAML and XML are not installed.

To install the dependencies for XML (lxml).

pip install jetblack-serialization[xml]

To install the dependencies for YAML (PyYAML).

pip install jetblack-serialization[yaml]

To install the dependencies for all.

pip install jetblack-serialization[xml,yaml]

Overview

The package adds support for type annotations when serializing or deserializing JSON, YAML or XML.

JSON

Given a typed dictionary:

from datetime import datetime
from typing import Optional, TypedDict, Union

class Book(TypedDict, total=False):
    book_id: int
    title: str
    author: str
    publication_date: datetime
    keywords: list[str]
    phrases: list[str]
    age: Optional[Union[datetime, int]]
    pages: Optional[int]

Serializing JSON

This could be serialized to JSON as:

from stringcase import camelcase
from jetblack_serialization.json import serialize, SerializerConfig

obj: Book = {
    'author': 'Chairman Mao',
    'book_id': 42,
    'title': 'Little Red Book',
    'publication_date': datetime(1973, 1, 1, 21, 52, 13),
    'keywords': ['Revolution', 'Communism'],
    'phrases': [
        'Revolutionary wars are inevitable in class society',
        'War is the continuation of politics'
    ],
    'age': 24,
}
text = serialize(
    obj,
    Book,
    SerializerConfig(key_serializer=camelcase, pretty_print=True)
)
print(text)

giving:

{
    "bookId": 42,
    "title": "Little Red Book",
    "author": "Chairman Mao",
    "publicationDate": "1973-01-01T21:52:13.00Z",
    "keywords": ["Revolution", "Communism"],
    "phrases": ["Revolutionary wars are inevitable in class society", "War is the continuation of politics"],
    "age": 24,
    "pages": null
}

Note the fields have been camel cased, and the publication date has been turned into an ISO 8601 date.

Deserializing JSON

We can deserialize the data as follows:

from stringcase import snakecase
from jetblack_serialization.json import deserialize, SerializerConfig

dct = deserialize(
    text,
    Annotated[Book, JSONValue()],
    SerializerConfig(key_deserializer=snakecase)
)

YAML

YAML is a superset of JSON, so for serialization things are very similar.

Given a typed dictionary:

from datetime import datetime
from typing import Optional, TypedDict, Union

class Book(TypedDict, total=False):
    book_id: int
    title: str
    author: str
    publication_date: datetime
    keywords: list[str]
    phrases: list[str]
    age: Optional[Union[datetime, int]]
    pages: Optional[int]

Serializing YAML

This could be serialized to YAML as:

from stringcase import camelcase
from jetblack_serialization.yaml import serialize, SerializerConfig

obj: Book = {
    'author': 'Chairman Mao',
    'book_id': 42,
    'title': 'Little Red Book',
    'publication_date': datetime(1973, 1, 1, 21, 52, 13),
    'keywords': ['Revolution', 'Communism'],
    'phrases': [
        'Revolutionary wars are inevitable in class society',
        'War is the continuation of politics'
    ],
    'age': 24,
}
text = serialize(
    obj,
    Book,
    SerializerConfig(key_serializer=camelcase, pretty_print=True)
)
print(text)

giving:

bookId: 42
title: Little Red Book
author: Chairman Mao
publicationDate: '1973-01-01T21:52:13.00Z'
keywords:
- Revolution
- Communism
phrases:
- Revolutionary wars are inevitable in class society
- War is the continuation of politics
age: 24
pages: null

Note the fields have been camel cased, and the publication date has been turned into an ISO 8601 date.

Deserializing YAML

We can deserialize the data as follows:

from stringcase import snakecase
from jetblack_serialization.yaml import deserialize, SerializerConfig

dct = deserialize(
    text,
    Annotated[Book, JSONValue()],
    SerializerConfig(key_deserializer=snakecase)
)

XML

The XML version of the typed dictionary might look like this:

from datetime import datetime
from typing import Optional, TypedDict, Union
from typing_extensions import Annotated
from jetblack_serialization.xml import XMLEntity, XMLAttribute

class Book(TypedDict, total=False):
    book_id: Annotated[int, XMLAttribute("bookId")]
    title: str
    author: str
    publication_date: datetime
    keywords: Annotated[list[Annotated[str, XMLEntity("Keyword")]], XMLEntity("Keywords")]
    phrases: list[str]
    age: Optional[Union[datetime, int]]
    pages: Optional[int]

Note we have introduced some annotations to control the serialization. For XML we have used pascal-case to serialized the keys and snake-case for deserialization.

Serializing XML

To serialize we need to provide the containing tag Book:

from stringcase import pascalcase
from jetblack_serialization.xml import serialize, SerializerConfig

book: Book = {
    'author': 'Chairman Mao',
    'book_id': 42,
    'title': 'Little Red Book',
    'publication_date': datetime(1973, 1, 1, 21, 52, 13),
    'keywords': ['Revolution', 'Communism'],
    'phrases': [
        'Revolutionary wars are inevitable in class society',
        'War is the continuation of politics'
    ],
    'age': 24,
    'pages': None
}
text = serialize(
    book,
    Annotated[Book, XMLEntity("Book")],
    SerializerConfig(key_serializer=pascalcase)
)
print(text)

Producing:

<Book bookId="42">
    <Title>Little Red Book</Title>
    <Author>Chairman Mao</Author>
    <PublicationDate>1973-01-01T21:52:13.00Z</PublicationDate>
    <Keywords>
        <Keyword>Revolution</Keyword>
        <Keyword>Communism</Keyword>
    </Keywords>
    <Phrase>Revolutionary wars are inevitable in class society</Phrase>
    <Phrase>War is the continuation of politics</Phrase>
    <Age>24</Age>
</Book>'

The annotations are more elaborate here. However, much of the typed dictionary requires no annotation.

First we needed the outer document wrapper XMLEntity("Book").

Next we annotated the book_id to be an XMLAttribute.

Finally we annotated the two lists differently. The keywords list used a nested structure, which we indicated by giving the list a different XMLEntity tag to the list items. For the phrases we used the default in-line behaviour.

Deserializing XML

We can deserialize the XML as follows:

from stringcase import snakecase
from jetblack_serialization.xml import deserialize, SerializerConfig

dct = deserialize(
    text,
    Annotated[Book, XMLEntity("Book")],
    SerializerConfig(key_deserializer=snakecase)
)

Attributes

For JSON, attributes are typically not required. However JSONProperty(tag, JSONObject and JSONValue are provided for completeness.

Contributing

To run the tests with tox and pyenv:

VIRTUALENV_DISCOVERY=pyenv tox