Schema validation just got Pythonic
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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.


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

>>> 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.


Use pip or easy_install:

pip install schema

Alternatively, you can just drop 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.

How Schema validates data


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)

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

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


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)

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


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')

>>> 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')

>>> 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):
            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: > 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'


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 others:

>>> 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'}

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

default can also be a callable:

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

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

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

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:

>>> 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)

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:


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:

>>> 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}

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

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:

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.

>>> 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.

>>> 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 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 request from github API.

>>> 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

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

Usage: [--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:

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

this is how you validate it using schema:

>>> 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 '', mode 'r' at 0x...>]

>>> args['<path>']

>>> args['--count']

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 from a dict Schema. This can be used to add word completion and validation directly in code editors. Here's an example:

>>> from schema import Optional, Schema
>>> import json
>>> s = Schema({"test": str,
...             "nested": {Optional("other"): str}
...             })
>>> json_schema = json.dumps(s.json_schema(""))

# json_schema
    "properties": {
        "test": {"type": "string"},
        "nested": {
            "properties": {
                "other": {"type": "string"}
            "required": [],

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