New in version 0.2.0:
- Allow intermix literals and other schemas in dictionary keys. I.e.
Schema({'<id>': int, str: object})will check<id>for beingint, and will disregard other keys of type<str>. See this StackOverflow question for more.
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('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==0.3.1
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.
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):
...
SchemaError: '123' should be instance of <type 'int'>
>>> Schema(object).validate('hai')
'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)
123
>>> Schema(lambda n: n > 0).validate(-12)
Traceback (most recent call last):
...
SchemaError: <lambda>(-12) should evaluate to TrueIf 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.
As example, 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))Now you can write your own validation-aware classes and data types.
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 <type '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: 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'}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:
>>> 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: <lambda>('hai') should evaluate to True
>>> Schema(And(Or(int, float), lambda x: x > 0)).validate(3.1415)
3.1415You can pass a keyword argument error to any of validatable classes
(such as Schema, And, Or, 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 yearYou can see all errors that occured 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.
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: 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:
>>> args = {'<files>': ['LICENSE-MIT', 'setup.py'],
... '<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 'setup.py', mode 'r' at 0x...>]
>>> args['<path>']
'../'
>>> args['--count']
3As you can see, schema validated data successfully, opened files and
converted '3' to int.