objectify 1.1.0

A package to convert a dictionary to an object to achieve similar syntax and auto-completion as in TypeScript when annotating objects with interfaces.

Objectify

Objectify is a lightweight library designed to convert a dictionary into a typed object based on a provided type argument. It allows developers to work seamlessly with dictionaries using dot notation while benefiting from type safety, type hinting, and IDE autocompletion.

The idea behind objectify was inspired by TypeScript, where you can take any object, define an interface or type for it, and "cast" the object to that type. This approach gives you immediate access to types and autocompletion, making writing code more efficient and less error-prone.

In Python, the closest existing standard solution is TypedDict, which provides type hints and key autocompletion for dictionaries. However, its main drawback is that you must still access values using square brackets and string keys (e.g., data['key']), which is less convenient and takes more time compared to dot notation (data.key).

This functionality is especially useful when working with large nested payloads (e.g., JSON responses from APIs). By leveraging objectify, you can easily map such data to Python objects while maintaining clarity and reducing errors.

Features

• Type Safety: Objectify ensures that the object's attributes match the specified types.
• Custom Classes: Objectify supports custom classes as type arguments, allowing for more complex object structures.
• Dataclasses: Objectify works seamlessly with dataclasses, providing a simple way to convert dictionaries to dataclass objects.
• Nested Objects: Objectify can handle nested dictionaries and convert them to nested objects.
• Literal Types: Objectify supports Literal types for specifying exact values that an attribute can take.
• Default Values: Objectify allows defining default values for attributes in class definitions, making it easier to handle optional fields.
• Union and Optional: Objectify supports Union and Optional types, allowing for flexible type definitions.
• Error Handling: Objectify raises TypeError for mismatched types and invalid literal values.

Installation

pip install objectify

Usage

Simple Example

from objectify import dict_to_object


class User:
    name: str
    age: int
    is_active: bool


data = {
    'name': 'Alice',
    'age': 30,
    'is_active': True
}

user = dict_to_object(data, User)
print(user.name)  # 'Alice'
print(user.age)  # 30
print(user.is_active)  # True

Complex Example

from objectify import dict_to_object


class Address:
    street: str
    city: str
    zip_code: int


class User:
    name: str
    age: int
    address: list[Address]


data = {
    'name': 'Alice',
    'age': 30,
    'address': [
        {'street': '123 Main St', 'city': 'Springfield', 'zip_code': 12345},
        {'street': '456 Elm St', 'city': 'Rivertown', 'zip_code': 54321}
    ]
}

user = dict_to_object(data, User)
print(user.name)  # 'Alice'
print(user.age)  # 30
print(user.address[0].street)  # '123 Main St'
print(user.address[1].city)  # 'Rivertown'