Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/schematics/schematics
Python Data Structures for Humans™.
https://github.com/schematics/schematics
datastructures deserialization python schema serialization types validation
Last synced: 2 days ago
JSON representation
Python Data Structures for Humans™.
- Host: GitHub
- URL: https://github.com/schematics/schematics
- Owner: schematics
- License: other
- Created: 2010-12-27T02:25:29.000Z (almost 14 years ago)
- Default Branch: master
- Last Pushed: 2023-07-12T06:21:13.000Z (over 1 year ago)
- Last Synced: 2024-10-29T14:58:53.345Z (about 1 month ago)
- Topics: datastructures, deserialization, python, schema, serialization, types, validation
- Language: Python
- Homepage: http://schematics.readthedocs.org/
- Size: 2.46 MB
- Stars: 2,585
- Watchers: 62
- Forks: 289
- Open Issues: 109
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- License: LICENSE
- Authors: AUTHORS
Awesome Lists containing this project
- awesome-python-resources - GitHub - 27% open · ⏱️ 17.08.2021): (数据验证)
- starred-awesome - schematics - Python Data Structures for Humans™. (Python)
- my-awesome-starred - schematics - Python Data Structures for Humans™. (Python)
- awesome-python-models - schematics - Python Data Structures for Humans™. (Model, Schema)
README
==========
Schematics
==========
.. rubric:: Python Data Structures for Humans™.
.. image:: https://travis-ci.org/schematics/schematics.svg?branch=master
:target: https://travis-ci.org/schematics/schematics
:alt: Build Status
.. image:: https://coveralls.io/repos/github/schematics/schematics/badge.svg?branch=master
:target: https://coveralls.io/github/schematics/schematics?branch=master
:alt: Coverage
About
=====
**Project documentation:** https://schematics.readthedocs.io/en/latest/
Schematics is a Python library to combine types into structures, validate them, and transform the shapes of your data based on simple descriptions.
The internals are similar to ORM type systems, but there is no database layer in Schematics. Instead, we believe that building a database layer is easily made when Schematics handles everything except for writing the query.
Schematics can be used for tasks where having a database involved is unusual.
Some common use cases:
+ Design and document specific `data structures `_
+ `Convert structures `_ to and from different formats such as JSON or MsgPack
+ `Validate `_ API inputs
+ `Remove fields based on access rights `_ of some data's recipient
+ Define message formats for communications protocols, like an RPC
+ Custom `persistence layers `_
Example
=======
This is a simple Model.
.. code:: python
>>> from schematics.models import Model
>>> from schematics.types import StringType, URLType
>>> class Person(Model):
... name = StringType(required=True)
... website = URLType()
...
>>> person = Person({'name': u'Joe Strummer',
... 'website': 'http://soundcloud.com/joestrummer'})
>>> person.name
u'Joe Strummer'
Serializing the data to JSON.
.. code:: python
>>> import json
>>> json.dumps(person.to_primitive())
{"name": "Joe Strummer", "website": "http://soundcloud.com/joestrummer"}
Let's try validating without a name value, since it's required.
.. code:: python
>>> person = Person()
>>> person.website = 'http://www.amontobin.com/'
>>> person.validate()
Traceback (most recent call last):
File "", line 1, in
File "schematics/models.py", line 231, in validate
raise DataError(e.messages)
schematics.exceptions.DataError: {'name': ['This field is required.']}
Add the field and validation passes.
.. code:: python
>>> person = Person()
>>> person.name = 'Amon Tobin'
>>> person.website = 'http://www.amontobin.com/'
>>> person.validate()
>>>
.. _coverage:
Testing & Coverage support
==========================
Run coverage and check the missing statements. ::
$ coverage run --source schematics -m py.test && coverage report