An utility class for creating instances of dataclasses
Project description
Adaptix
An extremely flexible and configurable data model conversion library.
TL;DR
Install
pip install adaptix
Use
from dataclasses import dataclass
from adaptix import Retort
@dataclass
class Book:
title: str
price: int
author: str = "Unknown author"
data = {
"title": "Fahrenheit 451",
"price": 100,
}
retort = Retort()
book = retort.load(data, Book)
assert book == Book(title="Fahrenheit 451", price=100)
assert retort.dump(book) == data
Use cases
- Validation and transformation of received data for your API.
- Config loading/dumping via codec that produces/takes dict.
- Storing JSON in a database and representing it as a model inside the application code.
- Creating API clients that convert a model to JSON sending to the server.
- Persisting entities at cache storage.
- Implementing fast and primitive ORM.
Advantages
- Sane defaults for JSON processing, no configuration is needed for simple cases.
- Separated model definition and rules of conversion that allow preserving SRP and have different representations for one model.
- Speed. It is one of the fastest data parsing and serialization libraries.
- There is no forced model representation, adaptix can adjust to your needs.
- Support dozens of types,
including different model kinds:
@dataclass
,TypedDict
,NamedTuple
, andattrs
- Working with self-referenced data types (such as linked lists or trees).
- Saving path where an exception is raised (including unexpected errors).
- Easy integration with Sentry, Datadog, and other monitoring systems.
- Machine-readable errors that could be dumped.
- Support for user-defined generic models.
- Automatic name style conversion (e.g.
snake_case
tocamelCase
). - Predicate system that allows to concisely and precisely override some behavior.
- Disabling additional checks to speed up data loading from trusted sources.
- No auto casting by default. The loader does not try to guess value from plenty of input formats.
Project details
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