"Skinny ORM" - a lightweight Python package that simplifies data storage, manipulation, and retrieval from a SQLite (maybe others later) database using Python dataclasses.
It's not really a "Relational Mapper". It's just a simple way to persist data.
pip install skinny_orm
or
poetry add skinny_orm
- Create your model:
from dataclasses import dataclass
from datetime import datetime
@dataclass
class User:
id: int
name: str
age: int
birth: datetime
percentage: float
- Create a connection et an "orm" object
import sqlite3
from skinny_orm.orm import Orm
connection = sqlite3.connect('database.db')
orm = Orm(connection)
- And Voila (no need to create tables. if they don't exist, it will create them automatically)
users = [
User(id=1, name='Naruto', age=15, birth=datetime.now(), percentage=9.99),
User(id=2, name='Sasuke', age=15, birth=datetime.now(), percentage=9.89),
User(id=3, name='Sakura', age=15, birth=datetime.now(), percentage=9.79),
]
# Bulk insertions (if the table "User" does not exist, it will create it)
orm.bulk_insert(users)
# Selections (always end with .first() or .all() )
naruto: User = orm.select(User).where(User.name == 'Naruto').first()
the_boys: list[User] = orm.select(User).where((User.name == 'Naruto') | (User.name == 'Sasuke')).all()
# Update data by setting specific fields
orm.update(User).set(User.age == 30).where(User.id == 1)
# Or you can simply update the object with all the fields
naruto.age = 30
orm.update(naruto).using(User.id)
# Bulk update objects
users_20_year_later = [
User(id=1, name='Naruto', age=35, birth=datetime.now(), percentage=9.99),
User(id=2, name='Sasuke', age=35, birth=datetime.now(), percentage=9.89),
User(id=3, name='Sakura', age=35, birth=datetime.now(), percentage=9.79),
]
orm.bulk_update(users_20_year_later).using(User.id)