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.png?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('sex'): And(str, Use(str.lower),
...                                        lambda s: s in ('male', 'female'))}])

>>> data = [{'name': 'Sue', 'age': '28', 'sex': 'FEMALE'},
...         {'name': 'Sam', 'age': '42'},
...         {'name': 'Sacha', 'age': '20', 'sex': 'Male'}]

>>> validated = schema.validate(data)

>>> assert validated == [{'name': 'Sue', 'age': 28, 'sex': 'female'},
...                      {'name': 'Sam', 'age': 42},
...                      {'name': 'Sacha', 'age' : 20, 'sex': 'male'}]

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.

Installation

Use pip 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 and PyPy.
• schema follows semantic versioning.

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))

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 other:

.. 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.

**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

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``.