blob: 0bcb0f7fd4886b3cda8f3c538b4601b2938a5d6b [file] [log] [blame]
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::
.. image::
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.
Use `pip <>`_ or easy_install::
pip install schema
Alternatively, you can just drop ```` file into your projectit is
- **schema** is tested with Python 2.6, 2.7, 3.2, 3.3 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``.
.. code:: python
>>> 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, elseit 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)
>>> 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, otherwiseit 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')
>>> 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')
>>> 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):
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'
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'}
Defaults are used verbatim, not passed through any validators specified in the
**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)
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 ````
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
A JSON API example
Here is a quick example: validation of
`create a gist <>`_
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 <>`_
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
Assuming **docopt** returns the following dict:
.. code:: python
>>> args = {'<files>': ['LICENSE-MIT', ''],
... '<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 '', mode 'r' at 0x...>]
>>> args['<path>']
>>> args['--count']
As you can see, **schema** validated data successfully, opened files and
converted ``'3'`` to ``int``.