File-based ORM for dataclasses.
Project description
Datafiles: A file-based ORM for dataclasses
Datafiles is a bidirectional serialization library for Python dataclasses to synchronizes objects to the filesystem using type annotations. It supports a variety of file formats with round-trip preservation of formatting and comments, where possible. Object changes are automatically saved to disk and only include the minimum data needed to restore each object.
Popular use cases include:
- Coercing user-editable files into the proper Python types
- Storing program configuration and data in version control
- Loading data fixtures for demonstration or testing purposes
- Prototyping data models agnostic of persistence backends
Overview
Take an existing dataclass such as this example from the documentation:
from dataclasses import dataclass
@dataclass
class InventoryItem:
"""Class for keeping track of an item in inventory."""
name: str
unit_price: float
quantity_on_hand: int = 0
def total_cost(self) -> float:
return self.unit_price * self.quantity_on_hand
and decorate it with a directory pattern to synchronize instances:
from datafiles import datafile
@datafile("inventory/items/{self.name}.yml")
@dataclass
class InventoryItem:
...
Then, work with instances of the class as normal:
>>> item = InventoryItem("widget", 3)
# inventory/items/widget.yml
unit_price: 3.0
Changes to the object are automatically saved to the filesystem:
>>> item.quantity_on_hand += 100
# inventory/items/widget.yml
unit_price: 3.0
quantity_on_hand: 100
Changes to the filesystem are automatically reflected in the object:
# inventory/items/widget.yml
unit_price: 2.5 # <= manually changed from "3.0"
quantity_on_hand: 100
>>> item.unit_price
2.5
Objects can also be restored from the filesystem:
>>> from datafiles import Missing
>>> item = InventoryItem("widget", Missing)
>>> item.unit_price
2.5
>>> item.quantity_on_hand
100
Demo: Jupyter Notebook
Installation
Because datafiles relies on dataclasses and type annotations, Python 3.7+ is required. Install this library directly into an activated virtual environment:
$ pip install datafiles
or add it to your Poetry project:
$ poetry add datafiles
Documentation
To see additional synchronization and formatting options, please consult the full documentation.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file datafiles-0.7b3.tar.gz.
File metadata
- Download URL: datafiles-0.7b3.tar.gz
- Upload date:
- Size: 20.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.0.3 CPython/3.7.2 Darwin/19.3.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | e24531da23e3f6033ee8e583ffc90dd594a7d90854fc4a18e483932ec6c4b9ba |
|
| MD5 | 47dfb4102ee450083ebe9d5e61953ff4 |
|
| BLAKE2b-256 | fc1eff7fb4a510b2fe970b1fcd1efcc483564504c8feb28d9034d7e1e2f25a41 |
File details
Details for the file datafiles-0.7b3-py3-none-any.whl.
File metadata
- Download URL: datafiles-0.7b3-py3-none-any.whl
- Upload date:
- Size: 25.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.0.3 CPython/3.7.2 Darwin/19.3.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b6703bebb1f70b60fb9ca6b0f34530008fc70114300bdf24783737a1175dfbb2 |
|
| MD5 | 514bce0759529733849b9f8358d133cb |
|
| BLAKE2b-256 | 9be2fac706e6d4d066fa1c381d03578bb6f7cd916f21f17d3ab1fd3574891ea1 |
