# dataclass_factory
[](https://badge.fury.io/py/dataclass-factory)
[](https://travis-ci.org/Tishka17/dataclass_factory)
[](https://pypistats.org/packages/dataclass_factory)
[](https://github.com/Tishka17/dataclass_factory/blob/master/LICENSE)
**dataclass_factory** is a modern way to convert dataclasses or other objects to and from more common types like dicts
## Help
See [documentation](https://dataclass-factory.readthedocs.io/) for more details.
## TL;DR
Install
```bash
pip install dataclass_factory
```
Use
```python
from dataclasses import dataclass
import dataclass_factory
@dataclass
class Book:
title: str
price: int
author: str = "Unknown author"
data = {
"title": "Fahrenheit 451",
"price": 100,
}
factory = dataclass_factory.Factory()
book: Book = factory.load(data, Book) # Same as Book(title="Fahrenheit 451", price=100)
serialized = factory.dump(book)
```
## Requirements
* python >= 3.6
You can use `dataclass_factory` with python 3.6 and `dataclass` library installed from pip.
On python 3.7 it has no external dependencies outside of the Python standard library.
## Advantages
* No schemas or configuration needed for simple cases. Just create `Factory` and call `load`/`dump` methods
* Speed. It is up to 10 times faster than `marshmallow` and `dataclasses.asdict` (see [benchmarks](benchmarks))
* Automatic name style conversion (e.g. `snake_case` to `CamelCase`)
* Automatic skipping of "internal use" fields (with leading underscore)
* Enums, typed dicts, tuples and lists are supported out of the box
* Unions and Optionals are supported without need to define them in schema
* Generic dataclasses can be automatically parsed as well
* Cyclic-referenced structures (such as linked-lists or trees) also can be converted
* Validators, custom parser steps are supported.
* Multiple schemas for single type can be provided to support different ways of parsing of the same type
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"description": "# dataclass_factory\n\n[](https://badge.fury.io/py/dataclass-factory)\n[](https://travis-ci.org/Tishka17/dataclass_factory)\n[](https://pypistats.org/packages/dataclass_factory)\n[](https://github.com/Tishka17/dataclass_factory/blob/master/LICENSE)\n\n**dataclass_factory** is a modern way to convert dataclasses or other objects to and from more common types like dicts\n\n## Help\n\nSee [documentation](https://dataclass-factory.readthedocs.io/) for more details.\n\n## TL;DR\n\nInstall\n```bash\npip install dataclass_factory\n```\n\nUse\n```python\nfrom dataclasses import dataclass\nimport dataclass_factory\n\n\n@dataclass\nclass Book:\n title: str\n price: int\n author: str = \"Unknown author\"\n\n\ndata = {\n \"title\": \"Fahrenheit 451\",\n \"price\": 100,\n}\n\nfactory = dataclass_factory.Factory()\nbook: Book = factory.load(data, Book) # Same as Book(title=\"Fahrenheit 451\", price=100)\nserialized = factory.dump(book) \n``` \n\n## Requirements\n\n* python >= 3.6\n\nYou can use `dataclass_factory` with python 3.6 and `dataclass` library installed from pip. \n\nOn python 3.7 it has no external dependencies outside of the Python standard library.\n\n## Advantages\n\n* No schemas or configuration needed for simple cases. Just create `Factory` and call `load`/`dump` methods\n* Speed. It is up to 10 times faster than `marshmallow` and `dataclasses.asdict` (see [benchmarks](benchmarks))\n* Automatic name style conversion (e.g. `snake_case` to `CamelCase`)\n* Automatic skipping of \"internal use\" fields (with leading underscore)\n* Enums, typed dicts, tuples and lists are supported out of the box\n* Unions and Optionals are supported without need to define them in schema\n* Generic dataclasses can be automatically parsed as well\n* Cyclic-referenced structures (such as linked-lists or trees) also can be converted\n* Validators, custom parser steps are supported.\n* Multiple schemas for single type can be provided to support different ways of parsing of the same type\n",
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