Python Data Structures for Humans™.
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
This is a simple Model.
>>> 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.
>>> 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.
>>> person = Person()
>>> person.website = 'http://www.amontobin.com/'
>>> person.validate()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
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.
>>> person = Person()
>>> person.name = 'Amon Tobin'
>>> person.website = 'http://www.amontobin.com/'
>>> person.validate()
>>>Run coverage and check the missing statements.
$ coverage run --source schematics -m py.test && coverage report