Python Data Structures for Humans™.

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Please note that the documentation is currently somewhat out of date.


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 made significantly easier when Schematics handles everything but writing the query.

Further, it can be used for a range of tasks where having a database involved may not make sense.

Some common use cases:


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': ''})
u'Joe Strummer'

Serializing the data to JSON.

>>> import json
>>> json.dumps(person.to_primitive())
{"name": "Joe Strummer", "website": ""}

Let’s try validating without a name value, since it’s required.

>>> person = Person()
>>> = ''
>>> person.validate()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "schematics/", line 231, in validate
    raise DataError(e.messages)
schematics.exceptions.DataError: {'name': ['This field is required.']}

Add the field and validation passes:

>>> person = Person()
>>> = 'Amon Tobin'
>>> = ''
>>> person.validate()


Install stable releases of Schematics with pip.

$ pip install schematics

See the Install Guide for more detail.

Getting Started

New Schematics users should start with the Quickstart Guide. That is the fastest way to get a look at what Schematics does.


Schematics exists to make a few concepts easy to glue together. The types allow us to describe units of data, models let us put them together into structures with fields. We can then import data, check if it looks correct, and easily serialize the results into any format we need.

The User’s Guide provides the high-level concepts, but the API documentation and the code itself provide the most accurate reference.


We welcome ideas and code. We ask that you follow some of our guidelines though.

See the Developer’s Guide for more information.

Testing & Coverage

Run coverage and check the missing statements.

$ coverage run --source schematics -m py.test && coverage report