| Name | apischema JSON |
| Version |
0.19.0
JSON |
| download |
| home_page | None |
| Summary | JSON (de)serialization, GraphQL and JSON schema generation using Python typing. |
| upload_time | 2024-10-01 07:49:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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No coveralls.
|
# apischema
JSON (de)serialization, GraphQL and JSON schema generation using Python typing.
*apischema* makes your life easier when dealing with API data.
## Documentation
[https://wyfo.github.io/apischema/](https://wyfo.github.io/apischema/)
## Install
```shell
pip install apischema
```
It requires only Python 3.8+. *PyPy3* is also fully supported.
## Why another library?
(If you wonder how this differs from the *pydantic* library, see the [dedicated section of the documentation](https://wyfo.github.io/apischema/dev/difference_with_pydantic) — there are many differences.)
This library fulfills the following goals:
- stay as close as possible to the standard library (dataclasses, typing, etc.) — as a consequence we do not need plugins for editors/linters/etc.;
- avoid object-oriented limitations — do not require a base class — thus handle easily every type (`Foo`, `list[Bar]`, `NewType(Id, int)`, etc.) the same way.
- be adaptable, provide tools to support any types (ORM, etc.);
- avoid dynamic things like using raw strings for attributes name - play nicely with your IDE.
No known alternative achieves all of this, and apischema is also [(a lot) faster](https://wyfo.github.io/apischema/dev/optimizations_and_benchmark#benchmark) than all of them.
On top of that, because APIs are not only JSON, *apischema* is also a complete GraphQL library
> Actually, *apischema* is even adaptable enough to enable support of competitor libraries in a few dozens of line of code ([pydantic support example](https://wyfo.github.io/apischema/dev/examples/pydantic_support) using [conversions feature](https://wyfo.github.io/apischema/dev/conversions))
## Example
```python
from collections.abc import Collection
from dataclasses import dataclass, field
from uuid import UUID, uuid4
import pytest
from graphql import print_schema
from apischema import ValidationError, deserialize, serialize
from apischema.graphql import graphql_schema
from apischema.json_schema import deserialization_schema
# Define a schema with standard dataclasses
@dataclass
class Resource:
id: UUID
name: str
tags: set[str] = field(default_factory=set)
# Get some data
uuid = uuid4()
data = {"id": str(uuid), "name": "wyfo", "tags": ["some_tag"]}
# Deserialize data
resource = deserialize(Resource, data)
assert resource == Resource(uuid, "wyfo", {"some_tag"})
# Serialize objects
assert serialize(Resource, resource) == data
# Validate during deserialization
with pytest.raises(ValidationError) as err: # pytest checks exception is raised
deserialize(Resource, {"id": "42", "name": "wyfo"})
assert err.value.errors == [
{"loc": ["id"], "err": "badly formed hexadecimal UUID string"}
]
# Generate JSON Schema
assert deserialization_schema(Resource) == {
"$schema": "http://json-schema.org/draft/2020-12/schema#",
"type": "object",
"properties": {
"id": {"type": "string", "format": "uuid"},
"name": {"type": "string"},
"tags": {
"type": "array",
"items": {"type": "string"},
"uniqueItems": True,
"default": [],
},
},
"required": ["id", "name"],
"additionalProperties": False,
}
# Define GraphQL operations
def resources(tags: Collection[str] | None = None) -> Collection[Resource] | None:
...
# Generate GraphQL schema
schema = graphql_schema(query=[resources], id_types={UUID})
schema_str = """\
type Query {
resources(tags: [String!]): [Resource!]
}
type Resource {
id: ID!
name: String!
tags: [String!]!
}"""
assert print_schema(schema) == schema_str
```
*apischema* works out of the box with your data model.
> This example and further ones are using *pytest* API because they are in fact run as tests in the library CI
### Run the documentation examples
All documentation examples are written using the last Python minor version — currently 3.10 — in order to provide up-to-date documentation. Because Python 3.10 specificities (like [PEP 585](https://www.python.org/dev/peps/pep-0604/)) are used, this version is "mandatory" to execute the examples as-is.
In addition to *pytest*, some examples use third-party libraries like *SQLAlchemy* or *attrs*. All of this dependencies can be downloaded using the `examples` extra with
```shell
pip install apischema[examples]
```
Once dependencies are installed, you can simply copy-paste examples and execute them, using the proper Python version.
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"description": "# apischema\n\nJSON (de)serialization, GraphQL and JSON schema generation using Python typing.\n\n*apischema* makes your life easier when dealing with API data.\n\n## Documentation\n\n[https://wyfo.github.io/apischema/](https://wyfo.github.io/apischema/)\n\n## Install\n```shell\npip install apischema\n```\nIt requires only Python 3.8+. *PyPy3* is also fully supported.\n\n## Why another library?\n\n(If you wonder how this differs from the *pydantic* library, see the [dedicated section of the documentation](https://wyfo.github.io/apischema/dev/difference_with_pydantic) \u2014 there are many differences.)\n\nThis library fulfills the following goals:\n\n- stay as close as possible to the standard library (dataclasses, typing, etc.) \u2014 as a consequence we do not need plugins for editors/linters/etc.;\n- avoid object-oriented limitations \u2014 do not require a base class \u2014 thus handle easily every type (`Foo`, `list[Bar]`, `NewType(Id, int)`, etc.) the same way.\n- be adaptable, provide tools to support any types (ORM, etc.);\n- avoid dynamic things like using raw strings for attributes name - play nicely with your IDE.\n\nNo known alternative achieves all of this, and apischema is also [(a lot) faster](https://wyfo.github.io/apischema/dev/optimizations_and_benchmark#benchmark) than all of them.\n\nOn top of that, because APIs are not only JSON, *apischema* is also a complete GraphQL library\n\n> Actually, *apischema* is even adaptable enough to enable support of competitor libraries in a few dozens of line of code ([pydantic support example](https://wyfo.github.io/apischema/dev/examples/pydantic_support) using [conversions feature](https://wyfo.github.io/apischema/dev/conversions))\n\n## Example\n\n```python\nfrom collections.abc import Collection\nfrom dataclasses import dataclass, field\nfrom uuid import UUID, uuid4\n\nimport pytest\nfrom graphql import print_schema\n\nfrom apischema import ValidationError, deserialize, serialize\nfrom apischema.graphql import graphql_schema\nfrom apischema.json_schema import deserialization_schema\n\n\n# Define a schema with standard dataclasses\n@dataclass\nclass Resource:\n id: UUID\n name: str\n tags: set[str] = field(default_factory=set)\n\n\n# Get some data\nuuid = uuid4()\ndata = {\"id\": str(uuid), \"name\": \"wyfo\", \"tags\": [\"some_tag\"]}\n# Deserialize data\nresource = deserialize(Resource, data)\nassert resource == Resource(uuid, \"wyfo\", {\"some_tag\"})\n# Serialize objects\nassert serialize(Resource, resource) == data\n# Validate during deserialization\nwith pytest.raises(ValidationError) as err: # pytest checks exception is raised\n deserialize(Resource, {\"id\": \"42\", \"name\": \"wyfo\"})\nassert err.value.errors == [\n {\"loc\": [\"id\"], \"err\": \"badly formed hexadecimal UUID string\"}\n]\n# Generate JSON Schema\nassert deserialization_schema(Resource) == {\n \"$schema\": \"http://json-schema.org/draft/2020-12/schema#\",\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\"type\": \"string\", \"format\": \"uuid\"},\n \"name\": {\"type\": \"string\"},\n \"tags\": {\n \"type\": \"array\",\n \"items\": {\"type\": \"string\"},\n \"uniqueItems\": True,\n \"default\": [],\n },\n },\n \"required\": [\"id\", \"name\"],\n \"additionalProperties\": False,\n}\n\n\n# Define GraphQL operations\ndef resources(tags: Collection[str] | None = None) -> Collection[Resource] | None:\n ...\n\n\n# Generate GraphQL schema\nschema = graphql_schema(query=[resources], id_types={UUID})\nschema_str = \"\"\"\\\ntype Query {\n resources(tags: [String!]): [Resource!]\n}\n\ntype Resource {\n id: ID!\n name: String!\n tags: [String!]!\n}\"\"\"\nassert print_schema(schema) == schema_str\n```\n*apischema* works out of the box with your data model.\n\n> This example and further ones are using *pytest* API because they are in fact run as tests in the library CI\n\n### Run the documentation examples\n\nAll documentation examples are written using the last Python minor version \u2014 currently 3.10 \u2014 in order to provide up-to-date documentation. Because Python 3.10 specificities (like [PEP 585](https://www.python.org/dev/peps/pep-0604/)) are used, this version is \"mandatory\" to execute the examples as-is.\n\nIn addition to *pytest*, some examples use third-party libraries like *SQLAlchemy* or *attrs*. All of this dependencies can be downloaded using the `examples` extra with\n```shell\npip install apischema[examples]\n```\n\nOnce dependencies are installed, you can simply copy-paste examples and execute them, using the proper Python version. \n\n",
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