schema 0.6.8

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.

>>> 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.

>>> 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):

>>> 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:

>>> 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:

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:

>> 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:

>>> 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:

>>> 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:

>>> 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 other:

>>> 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:

>>> 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:

>>> 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.

You can mark a key as forbidden as follows:

>>> 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:

>>> 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:

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

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

>>> 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

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.

>>> 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.