Skip to main content

Simple data validation library

Project description

Schema validation just got Pythonic
===============================================================================

**schema** is a library for validating Python data structures, such as those
obtained from config-files, forms, external services or command-line
parsing, converted from JSON/YAML (or something else) to Python data-types.


.. image:: https://secure.travis-ci.org/keleshev/schema.svg?branch=master
:target: https://travis-ci.org/keleshev/schema

.. image:: https://img.shields.io/codecov/c/github/keleshev/schema.svg
:target: http://codecov.io/github/keleshev/schema

Example
----------------------------------------------------------------------------

Here is a quick example to get a feeling of **schema**, validating a list of
entries with personal information:

.. code:: python

>>> from schema import Schema, And, Use, Optional

>>> schema = Schema([{'name': And(str, len),
... 'age': And(Use(int), lambda n: 18 <= n <= 99),
... Optional('gender'): And(str, Use(str.lower),
... lambda s: s in ('squid', 'kid'))}])

>>> data = [{'name': 'Sue', 'age': '28', 'gender': 'Squid'},
... {'name': 'Sam', 'age': '42'},
... {'name': 'Sacha', 'age': '20', 'gender': 'KID'}]

>>> validated = schema.validate(data)

>>> assert validated == [{'name': 'Sue', 'age': 28, 'gender': 'squid'},
... {'name': 'Sam', 'age': 42},
... {'name': 'Sacha', 'age' : 20, 'gender': 'kid'}]


If data is valid, ``Schema.validate`` will return the validated data
(optionally converted with `Use` calls, see below).

If data is invalid, ``Schema`` will raise ``SchemaError`` exception.
If you just want to check that the data is valid, ``schema.is_valid(data)`` will
return ``True`` or ``False``.


Installation
-------------------------------------------------------------------------------

Use `pip <http://pip-installer.org>`_ or easy_install::

pip install schema

Alternatively, you can just drop ``schema.py`` file into your project—it is
self-contained.

- **schema** is tested with Python 2.6, 2.7, 3.2, 3.3, 3.4, 3.5, 3.6 and PyPy.
- **schema** follows `semantic versioning <http://semver.org>`_.

How ``Schema`` validates data
-------------------------------------------------------------------------------

Types
~~~~~

If ``Schema(...)`` encounters a type (such as ``int``, ``str``, ``object``,
etc.), it will check if the corresponding piece of data is an instance of that type,
otherwise it will raise ``SchemaError``.

.. code:: python

>>> from schema import Schema

>>> Schema(int).validate(123)
123

>>> Schema(int).validate('123')
Traceback (most recent call last):
...
SchemaUnexpectedTypeError: '123' should be instance of 'int'

>>> Schema(object).validate('hai')
'hai'

Callables
~~~~~~~~~

If ``Schema(...)`` encounters a callable (function, class, or object with
``__call__`` method) it will call it, and if its return value evaluates to
``True`` it will continue validating, else—it will raise ``SchemaError``.

.. code:: python

>>> import os

>>> Schema(os.path.exists).validate('./')
'./'

>>> Schema(os.path.exists).validate('./non-existent/')
Traceback (most recent call last):
...
SchemaError: exists('./non-existent/') should evaluate to True

>>> Schema(lambda n: n > 0).validate(123)
123

>>> Schema(lambda n: n > 0).validate(-12)
Traceback (most recent call last):
...
SchemaError: <lambda>(-12) should evaluate to True

"Validatables"
~~~~~~~~~~~~~~

If ``Schema(...)`` encounters an object with method ``validate`` it will run
this method on corresponding data as ``data = obj.validate(data)``. This method
may raise ``SchemaError`` exception, which will tell ``Schema`` that that piece
of data is invalid, otherwise—it will continue validating.

An example of "validatable" is ``Regex``, that tries to match a string or a
buffer with the given regular expression (itself as a string, buffer or
compiled regex ``SRE_Pattern``):

.. code:: python

>>> from schema import Regex
>>> import re

>>> Regex(r'^foo').validate('foobar')
'foobar'

>>> Regex(r'^[A-Z]+$', flags=re.I).validate('those-dashes-dont-match')
Traceback (most recent call last):
...
SchemaError: Regex('^[A-Z]+$', flags=re.IGNORECASE) does not match 'those-dashes-dont-match'

For a more general case, you can use ``Use`` for creating such objects.
``Use`` helps to use a function or type to convert a value while validating it:

.. code:: python

>>> from schema import Use

>>> Schema(Use(int)).validate('123')
123

>>> Schema(Use(lambda f: open(f, 'a'))).validate('LICENSE-MIT')
<open file 'LICENSE-MIT', mode 'a' at 0x...>

Dropping the details, ``Use`` is basically:

.. code:: python

class Use(object):

def __init__(self, callable_):
self._callable = callable_

def validate(self, data):
try:
return self._callable(data)
except Exception as e:
raise SchemaError('%r raised %r' % (self._callable.__name__, e))


Sometimes you need to transform and validate part of data, but keep original data unchanged.
``Const`` helps to keep your data safe:

.. code:: python

>> from schema import Use, Const, And, Schema

>> from datetime import datetime

>> is_future = lambda date: datetime.now() > date

>> to_json = lambda v: {"timestamp": v}

>> Schema(And(Const(And(Use(datetime.fromtimestamp), is_future)), Use(to_json))).validate(1234567890)
{"timestamp": 1234567890}

Now you can write your own validation-aware classes and data types.

Lists, similar containers
~~~~~~~~~~~~~~~~~~~~~~~~~

If ``Schema(...)`` encounters an instance of ``list``, ``tuple``, ``set`` or
``frozenset``, it will validate contents of corresponding data container
against schemas listed inside that container:


.. code:: python

>>> Schema([1, 0]).validate([1, 1, 0, 1])
[1, 1, 0, 1]

>>> Schema((int, float)).validate((5, 7, 8, 'not int or float here'))
Traceback (most recent call last):
...
SchemaError: Or(<type 'int'>, <type 'float'>) did not validate 'not int or float here'
'not int or float here' should be instance of 'float'

Dictionaries
~~~~~~~~~~~~

If ``Schema(...)`` encounters an instance of ``dict``, it will validate data
key-value pairs:

.. code:: python

>>> d = Schema({'name': str,
... 'age': lambda n: 18 <= n <= 99}).validate({'name': 'Sue', 'age': 28})

>>> assert d == {'name': 'Sue', 'age': 28}

You can specify keys as schemas too:

.. code:: python

>>> schema = Schema({str: int, # string keys should have integer values
... int: None}) # int keys should be always None

>>> data = schema.validate({'key1': 1, 'key2': 2,
... 10: None, 20: None})

>>> schema.validate({'key1': 1,
... 10: 'not None here'})
Traceback (most recent call last):
...
SchemaError: Key '10' error:
None does not match 'not None here'

This is useful if you want to check certain key-values, but don't care
about others:

.. code:: python

>>> schema = Schema({'<id>': int,
... '<file>': Use(open),
... str: object}) # don't care about other str keys

>>> data = schema.validate({'<id>': 10,
... '<file>': 'README.rst',
... '--verbose': True})

You can mark a key as optional as follows:

.. code:: python

>>> from schema import Optional
>>> Schema({'name': str,
... Optional('occupation'): str}).validate({'name': 'Sam'})
{'name': 'Sam'}

``Optional`` keys can also carry a ``default``, to be used when no key in the
data matches:

.. code:: python

>>> from schema import Optional
>>> Schema({Optional('color', default='blue'): str,
... str: str}).validate({'texture': 'furry'}
... ) == {'color': 'blue', 'texture': 'furry'}
True

Defaults are used verbatim, not passed through any validators specified in the
value.

default can also be a callable:

.. code:: python

>>> from schema import Schema, Optional
>>> Schema({Optional('data', default=dict): {}}).validate({}) == {'data': {}}
True

Also, a caveat: If you specify types, **schema** won't validate the empty dict:

.. code:: python

>>> Schema({int:int}).is_valid({})
False

To do that, you need ``Schema(Or({int:int}, {}))``. This is unlike what happens with
lists, where ``Schema([int]).is_valid([])`` will return True.


**schema** has classes ``And`` and ``Or`` that help validating several schemas
for the same data:

.. code:: python

>>> from schema import And, Or

>>> Schema({'age': And(int, lambda n: 0 < n < 99)}).validate({'age': 7})
{'age': 7}

>>> Schema({'password': And(str, lambda s: len(s) > 6)}).validate({'password': 'hai'})
Traceback (most recent call last):
...
SchemaError: Key 'password' error:
<lambda>('hai') should evaluate to True

>>> Schema(And(Or(int, float), lambda x: x > 0)).validate(3.1415)
3.1415

In a dictionary, you can also combine two keys in a "one or the other" manner. To do
so, use the `Or` class as a key:

.. code:: python
>>> from schema import Or, Schema
>>> schema = Schema({
... Or("key1", "key2", only_one=True): str
... })

>>> schema.validate({"key1": "test"}) # Ok
{'key1': 'test'}

>>> schema.validate({"key1": "test", "key2": "test"}) # SchemaError
Traceback (most recent call last):
...
SchemaOnlyOneAllowedError: There are multiple keys present from the Or('key1', 'key2') condition

Hooks
~~~~~~~~~~
You can define hooks which are functions that are executed whenever a valid key:value is found.
The `Forbidden` class is an example of this.

You can mark a key as forbidden as follows:

.. code:: python

>>> from schema import Forbidden
>>> Schema({Forbidden('age'): object}).validate({'age': 50})
Traceback (most recent call last):
...
SchemaForbiddenKeyError: Forbidden key encountered: 'age' in {'age': 50}

A few things are worth noting. First, the value paired with the forbidden
key determines whether it will be rejected:

.. code:: python

>>> Schema({Forbidden('age'): str, 'age': int}).validate({'age': 50})
{'age': 50}

Note: if we hadn't supplied the 'age' key here, the call would have failed too, but with
SchemaWrongKeyError, not SchemaForbiddenKeyError.

Second, Forbidden has a higher priority than standard keys, and consequently than Optional.
This means we can do that:

.. code:: python

>>> Schema({Forbidden('age'): object, Optional(str): object}).validate({'age': 50})
Traceback (most recent call last):
...
SchemaForbiddenKeyError: Forbidden key encountered: 'age' in {'age': 50}

You can also define your own hooks. The following hook will call `_my_function` if `key` is encountered.

.. code:: python

from schema import Hook
def _my_function(key, scope, error):
print(key, scope, error)

Hook("key", handler=_my_function)

Here's an example where a `Deprecated` class is added to log warnings whenever a key is encountered:

.. code:: python

from schema import Hook, Schema
class Deprecated(Hook):
def __init__(self, *args, **kwargs):
kwargs["handler"] = lambda key, *args: logging.warn(f"`{key}` is deprecated. " + (self._error or ""))
super(Deprecated, self).__init__(*args, **kwargs)

Schema({Deprecated("test", "custom error message."): object}, ignore_extra_keys=True).validate({"test": "value"})
...
WARNING: `test` is deprecated. custom error message.

Extra Keys
~~~~~~~~~~

The ``Schema(...)`` parameter ``ignore_extra_keys`` causes validation to ignore extra keys in a dictionary, and also to not return them after validating.

.. code:: python

>>> schema = Schema({'name': str}, ignore_extra_keys=True)
>>> schema.validate({'name': 'Sam', 'age': '42'})
{'name': 'Sam'}

If you would like any extra keys returned, use ``object: object`` as one of the key/value pairs, which will match any key and any value.
Otherwise, extra keys will raise a ``SchemaError``.

User-friendly error reporting
-------------------------------------------------------------------------------

You can pass a keyword argument ``error`` to any of validatable classes
(such as ``Schema``, ``And``, ``Or``, ``Regex``, ``Use``) to report this error
instead of a built-in one.

.. code:: python

>>> Schema(Use(int, error='Invalid year')).validate('XVII')
Traceback (most recent call last):
...
SchemaError: Invalid year

You can see all errors that occurred by accessing exception's ``exc.autos``
for auto-generated error messages, and ``exc.errors`` for errors
which had ``error`` text passed to them.

You can exit with ``sys.exit(exc.code)`` if you want to show the messages
to the user without traceback. ``error`` messages are given precedence in that
case.

A JSON API example
-------------------------------------------------------------------------------

Here is a quick example: validation of
`create a gist <http://developer.github.com/v3/gists/>`_
request from github API.

.. code:: python

>>> gist = '''{"description": "the description for this gist",
... "public": true,
... "files": {
... "file1.txt": {"content": "String file contents"},
... "other.txt": {"content": "Another file contents"}}}'''

>>> from schema import Schema, And, Use, Optional

>>> import json

>>> gist_schema = Schema(And(Use(json.loads), # first convert from JSON
... # use basestring since json returns unicode
... {Optional('description'): basestring,
... 'public': bool,
... 'files': {basestring: {'content': basestring}}}))

>>> gist = gist_schema.validate(gist)

# gist:
{u'description': u'the description for this gist',
u'files': {u'file1.txt': {u'content': u'String file contents'},
u'other.txt': {u'content': u'Another file contents'}},
u'public': True}

Using **schema** with `docopt <http://github.com/docopt/docopt>`_
-------------------------------------------------------------------------------

Assume you are using **docopt** with the following usage-pattern:

Usage: my_program.py [--count=N] <path> <files>...

and you would like to validate that ``<files>`` are readable, and that
``<path>`` exists, and that ``--count`` is either integer from 0 to 5, or
``None``.

Assuming **docopt** returns the following dict:

.. code:: python

>>> args = {'<files>': ['LICENSE-MIT', 'setup.py'],
... '<path>': '../',
... '--count': '3'}

this is how you validate it using ``schema``:

.. code:: python

>>> from schema import Schema, And, Or, Use
>>> import os

>>> s = Schema({'<files>': [Use(open)],
... '<path>': os.path.exists,
... '--count': Or(None, And(Use(int), lambda n: 0 < n < 5))})

>>> args = s.validate(args)

>>> args['<files>']
[<open file 'LICENSE-MIT', mode 'r' at 0x...>, <open file 'setup.py', mode 'r' at 0x...>]

>>> args['<path>']
'../'

>>> args['--count']
3

As you can see, **schema** validated data successfully, opened files and
converted ``'3'`` to ``int``.

(Beta feature) Generating JSON schema
-------------------------------------------------------------------------------
You can also generate standard `draft-07 JSON schema <https://json-schema.org/>`_ from a dict `Schema`.
This can be used to add word completion and validation directly in code editors.
Here's an example:

.. code:: python

>>> from schema import Optional, Schema
>>> import json
>>> s = Schema({"test": str,
... "nested": {Optional("other"): str}
... })
>>> json_schema = json.dumps(s.json_schema("https://example.com/my-schema.json"))

# json_schema
{
"type":"object",
"properties": {
"test": {"type": "string"},
"nested": {
"type":"object",
"properties": {
"other": {"type": "string"}
},
"required": [],
"additionalProperties":false
}
},
"required":[
"test",
"nested"
],
"additionalProperties":false,
"id":"https://example.com/my-schema.json",
"$schema":"http://json-schema.org/draft-07/schema#"
}

Please note that this is a beta feature. Some JSON schema features are not implemented. Some caveats:

- There are no object references, items of type `object` are always fully rendered
- Some JSON schema types are not implemented. In those cases, an empty dict will be rendered.
This disables all validation for the item.
- Validations other than type are not implemented. This includes features such as integers'
minimum and maximum or arrays' minItems


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for schema, version 0.7.0
Filename, size & hash File type Python version Upload date
schema-0.7.0-py2.py3-none-any.whl (12.9 kB) View hashes Wheel py2.py3
schema-0.7.0.tar.gz (19.4 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page