Validation Rules

allow_unknown

This can be used in conjunction with the schema rule when validating a mapping in order to set the allow_unknown property of the validator for the subdocument. For a full alaboration refer to this paragraph.

allowed

If the target value is an iterable, all its members must be in the list of allowed values. Other types of target values will validate if the value is in that list.

>>> v.schema = {'role': {'type': 'list', 'allowed': ['agent', 'client', 'supplier']}}
>>> v.validate({'role': ['agent', 'supplier']})
True

>>> v.validate({'role': ['intern']})
False
>>> v.errors
{'role': ["unallowed values ['intern']"]}

>>> v.schema = {'role': {'type': 'string', 'allowed': ['agent', 'client', 'supplier']}}
>>> v.validate({'role': 'supplier'})
True

>>> v.validate({'role': 'intern'})
False
>>> v.errors
{'role': ['unallowed value intern']}

>>> v.schema = {'a_restricted_integer': {'type': 'integer', 'allowed': [-1, 0, 1]}}
>>> v.validate({'a_restricted_integer': -1})
True

>>> v.validate({'a_restricted_integer': 2})
False
>>> v.errors
{'a_restricted_integer': ['unallowed value 2']}

Changed in version 0.5.1: Added support for the int type.

allof

Validates if all of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

anyof

Validates if any of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

dependencies

This rule allows to define either a single field name, a sequence of field names or a mapping of field names and a sequence of allowed values as required in the document if the field defined upon is present in the document.

>>> schema = {'field1': {'required': False}, 'field2': {'required': False, 'dependencies': 'field1'}}
>>> document = {'field1': 7}
>>> v.validate(document, schema)
True

>>> document = {'field2': 7}
>>> v.validate(document, schema)
False

>>> v.errors
{'field2': ["field 'field1' is required"]}

When multiple field names are defined as dependencies, all of these must be present in order for the target field to be validated.

>>> schema = {'field1': {'required': False}, 'field2': {'required': False},
...           'field3': {'required': False, 'dependencies': ['field1', 'field2']}}
>>> document = {'field1': 7, 'field2': 11, 'field3': 13}
>>> v.validate(document, schema)
True

>>> document = {'field2': 11, 'field3': 13}
>>> v.validate(document, schema)
False

>>> v.errors
{'field3': ["field 'field1' is required"]}

When a mapping is provided, not only all dependencies must be present, but also any of their allowed values must be matched.

>>> schema = {'field1': {'required': False},
...           'field2': {'required': True, 'dependencies': {'field1': ['one', 'two']}}}

>>> document = {'field1': 'one', 'field2': 7}
>>> v.validate(document, schema)
True

>>> document = {'field1': 'three', 'field2': 7}
>>> v.validate(document, schema)
False
>>> v.errors
{'field2': ["depends on these values: {'field1': ['one', 'two']}"]}

>>> # same as using a dependencies list
>>> document = {'field2': 7}
>>> v.validate(document, schema)
False
>>> v.errors
{'field2': ["depends on these values: {'field1': ['one', 'two']}"]}


>>> # one can also pass a single dependency value
>>> schema = {'field1': {'required': False}, 'field2': {'dependencies': {'field1': 'one'}}}
>>> document = {'field1': 'one', 'field2': 7}
>>> v.validate(document, schema)
True

>>> document = {'field1': 'two', 'field2': 7}
>>> v.validate(document, schema)
False

>>> v.errors
{'field2': ["depends on these values: {'field1': 'one'}"]}

Declaring dependencies on subdocument fields with dot-notation is also supported:

>>> schema = {
...   'test_field': {'dependencies': ['a_dict.foo', 'a_dict.bar']},
...   'a_dict': {
...     'type': 'dict',
...     'schema': {
...       'foo': {'type': 'string'},
...       'bar': {'type': 'string'}
...     }
...   }
... }

>>> document = {'test_field': 'foobar', 'a_dict': {'foo': 'foo'}}
>>> v.validate(document, schema)
False

>>> v.errors
{'test_field': ["field 'a_dict.bar' is required"]}

When a subdocument is processed the lookup for a field in question starts at the level of that document. In order to address the processed document as root level, the declaration has to start with a ^. An occurance of two initial carets (^^) is interpreted as a literal, single ^ with no special meaning.

>>> schema = {
...   'test_field': {},
...   'a_dict': {
...     'type': 'dict',
...     'schema': {
...       'foo': {'type': 'string'},
...       'bar': {'type': 'string', 'dependencies': '^test_field'}
...     }
...   }
... }

>>> document = {'a_dict': {'bar': 'bar'}}
>>> v.validate(document, schema)
False

>>> v.errors
{'a_dict': [{'bar': ["field '^test_field' is required"]}]}

Note

If you want to extend semantics of the dot-notation, you can override the _lookup_field() method.

Note

The evaluation of this rule does not consider any constraints defined with the required rule.

Changed in version 1.0.2: Support for absolute addressing with ^.

Changed in version 0.8.1: Support for sub-document fields as dependencies.

Changed in version 0.8: Support for dependencies as a dictionary.

New in version 0.7.

empty

If False validation of an iterable value will fail if it is empty. Setting it to True manually is pointless as it behaves like omitting the rule at all.

>>> schema = {'name': {'type': 'string', 'empty': False}}
>>> document = {'name': ''}
>>> v.validate(document, schema)
False

>>> v.errors
{'name': ['empty values not allowed']}

New in version 0.0.3.

excludes

You can declare fields to excludes others:

>>> v = Validator()
>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': 'that_field'},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field'}}
>>> v.validate({'this_field': {}, 'that_field': {}}, schema)
False
>>> v.validate({'this_field': {}}, schema)
True
>>> v.validate({'that_field': {}}, schema)
True
>>> v.validate({}, schema)
True

You can require both field to build an exclusive or:

>>> v = Validator()
>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': 'that_field',
...                          'required': True},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field',
...                          'required': True}}
>>> v.validate({'this_field': {}, 'that_field': {}}, schema)
False
>>> v.validate({'this_field': {}}, schema)
True
>>> v.validate({'that_field': {}}, schema)
True
>>> v.validate({}, schema)
False

You can also pass multiples fields to exclude in a list :

>>> schema = {'this_field': {'type': 'dict',
...                          'excludes': ['that_field', 'bazo_field']},
...           'that_field': {'type': 'dict',
...                          'excludes': 'this_field'},
...           'bazo_field': {'type': 'dict'}}
>>> v.validate({'this_field': {}, 'bazo_field': {}}, schema)
False

forbidden

Opposite to allowed this validates if a value is any but one of the defined values:

>>> schema = {'user': {'forbidden': ['root', 'admin']}}
>>> document = {'user': 'root'}
>>> v.validate(document, schema)
False

New in version 1.0.

items

Validates the items of any iterable against a sequence of rules that must validate each index-correspondent item. The items will only be evaluated if the given iterable’s size matches the definition’s.

>>> schema = {'list_of_values': {'type': 'list', 'items': [{'type': 'string'}, {'type': 'integer'}]}}
>>> document = {'list_of_values': ['hello', 100]}
>>> v.validate(document, schema)
True
>>> document = {'list_of_values': [100, 'hello']}
>>> v.validate(document, schema)
False

See schema (list) rule for dealing with arbitrary length list types.

keyschema

Validation schema for all keys of a mapping.

>>> schema = {'a_dict': {'type': 'dict', 'keyschema': {'type': 'string', 'regex': '[a-z]+'}}}
>>> document = {'a_dict': {'key': 'value'}}
>>> v.validate(document, schema)
True

>>> document = {'a_dict': {'KEY': 'value'}}
>>> v.validate(document, schema)
False

New in version 0.9.

Changed in version 1.0: Renamed from propertyschema to keyschema

min, max

Minimum and maximum value allowed for any types that implement comparison operators.

Changed in version 1.0: Allows any type to be compared.

Changed in version 0.7: Added support for float and number types.

minlength, maxlength

Minimum and maximum length allowed for iterables.

noneof

Validates if none of the provided constraints validates the field. See *of-rules for details.

New in version 0.9.

nullable

If True the field value can be set to None. It is essentially the functionality of the ignore_none_values property of a Validator instance, but allowing for more fine grained control down to the field level.

>>> v.schema = {'a_nullable_integer': {'nullable': True, 'type': 'integer'}, 'an_integer': {'type': 'integer'}}

>>> v.validate({'a_nullable_integer': 3})
True
>>> v.validate({'a_nullable_integer': None})
True

>>> v.validate({'an_integer': 3})
True
>>> v.validate({'an_integer': None})
False
>>> v.errors
{'an_integer': ['null value not allowed']}

Changed in version 0.7: nullable is valid on fields lacking type definition.

New in version 0.3.0.

*of-rules

These rules allow you to list multiple sets of rules to validate against. The field will be considered valid if it validates against the set in the list according to the prefixes logics all, any, one or none.

allof Validates if all of the provided constraints validates the field.
anyof Validates if any of the provided constraints validates the field.
noneof Validates if none of the provided constraints validates the field.
oneof Validates if exactly one of the provided constraints applies.

For example, to verify that a property is a number between 0 and 10 or 100 and 110, you could do the following:

>>> schema = {'prop1':
...           {'type': 'number',
...            'anyof':
...            [{'min': 0, 'max': 10}, {'min': 100, 'max': 110}]}}

>>> document = {'prop1': 5}
>>> v.validate(document, schema)
True

>>> document = {'prop1': 105}
>>> v.validate(document, schema)
True

>>> document = {'prop1': 55}
>>> v.validate(document, schema)
False
>>> v.errors   
{'prop1': {'anyof': 'no definitions validated', 'definition 1': 'min value is 100', 'definition 0': 'max value is 10'}}

The anyof rule works by creating a new instance of a schema for each item in the list. The above schema is equivalent to creating two separate schemas:

>>> schema1 = {'prop1': {'type': 'number', 'min':   0, 'max':  10}}
>>> schema2 = {'prop1': {'type': 'number', 'min': 100, 'max': 110}}

>>> document = {'prop1': 5}
>>> v.validate(document, schema1) or v.validate(document, schema2)
True

>>> document = {'prop1': 105}
>>> v.validate(document, schema1) or v.validate(document, schema2)
True

>>> document = {'prop1': 55}
>>> v.validate(document, schema1) or v.validate(document, schema2)
False

New in version 0.9.

*of-rules typesaver

You can concatenate any of-rule with an underscore and another rule with a list of rule-values to save typing:

{'foo': {'anyof_type': ['string', 'integer']}}
# is equivalent to
{'foo': {'anyof': [{'type': 'string'}, {'type': 'integer'}]}}

Thus you can use this to validate a document against several schemas without implementing your own logic:

>>> schemas = [{'department': {'required': True, 'regex': '^IT$'}, 'phone': {'nullable': True}},
...            {'department': {'required': True}, 'phone': {'required': True}}]
>>> emloyee_vldtr = Validator({'employee': {'oneof_schema': schemas, 'type': 'dict'}}, allow_unknown=True)
>>> invalid_employees_phones = []
>>> for employee in employees:
...     if not employee_vldtr.validate(employee):
...         invalid_employees_phones.append(employee)

oneof

Validates if exactly one of the provided constraints applies. See *of-rules for details.

New in version 0.9.

readonly

If True the value is readonly. Validation will fail if this field is present in the target dictionary. This is useful, for example, when receiving a payload which is to be validated before it is sent to the datastore. The field might be provided by the datastore, but should not writable.

Changed in version 1.0.2: Can be used in conjunction with default and default_setter, see Default Values.

regex

Validation will fail if field value does not match the provided regular expression. It is only tested on string values.

>>> schema = {'email': {'type': 'string', 'regex': '^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$'}}
>>> document = {'email': 'john@example.com'}
>>> v.validate(document, schema)
True

>>> document = {'email': 'john_at_example_dot_com'}
>>> v.validate(document, schema)
False

>>> v.errors
{'email': ["value does not match regex '^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$'"]}

For details on regular expression syntax, see the documentation on the standard library’s re-module. Mind that you can set flags as part of the expression, look for (?aiLmsux) in that document.

New in version 0.7.

required

If True the field is mandatory. Validation will fail when it is missing, unless validate() is called with update=True:

>>> v.schema = {'name': {'required': True, 'type': 'string'}, 'age': {'type': 'integer'}}
>>> document = {'age': 10}
>>> v.validate(document)
False
>>> v.errors
{'name': ['required field']}

>>> v.validate(document, update=True)
True

Note

String fields with empty values will still be validated, even when required is set to True. If you don’t want to accept empty values, see the empty rule.

Note

The evaluation of this rule does not consider any constraints defined with the dependencies rule.

Changed in version 0.8: Check field dependencies.

schema (dict)

If a field for which a schema-rule is defined has a mapping as value, that mapping will be validated against the schema that is provided as constraint.

>>> schema = {'a_dict': {'type': 'dict', 'schema': {'address': {'type': 'string'},
...                                                 'city': {'type': 'string', 'required': True}}}}
>>> document = {'a_dict': {'address': 'my address', 'city': 'my town'}}
>>> v.validate(document, schema)
True

Note

To validate arbitrary keys of a mapping, see keyschema, resp. valueschema for validating arbitrary values of a mapping.

schema (list)

If schema-validation encounters an arbritrary sized sequence as value, all items of the sequence will be validated against the rules provided in schema‘s constraint.

>>> schema = {'a_list': {'type': 'list', 'schema': {'type': 'integer'}}}
>>> document = {'a_list': [3, 4, 5]}
>>> v.validate(document, schema)
True

The schema rule on list types is also the preferred method for defining and validating a list of dictionaries.

Note

Using this rule should be accompanied with a type-rule explicitly restricting the field to the list-type like in the example. Otherwise false results can be expected when a mapping is validated against this rule with constraints for a sequence.

>>> schema = {'rows': {'type': 'list',
...                    'schema': {'type': 'dict', 'schema': {'sku': {'type': 'string'},
...                                                          'price': {'type': 'integer'}}}}}
>>> document = {'rows': [{'sku': 'KT123', 'price': 100}]}
>>> v.validate(document, schema)
True

Changed in version 0.0.3: Schema rule for list types of arbitrary length

type

Data type allowed for the key value. Can be one of the following names:

Type Name Python 2 Type Python 3 Type
boolean bool bool
binary bytes [1], bytearray bytes, bytearray
date datetime.date datetime.date
datetime datetime.datetime datetime.datetime
dict collections.Mapping collections.abc.Mapping
float float float
integer int, long int
list collections.Sequence, excl. string collections.abc.Sequence, excl. string
number float, int, long, excl. bool float, int, excl. bool
set set set
string basestring() str

You can extend this list and support custom types.

A list of types can be used to allow different values:

>>> v.schema = {'quotes': {'type': ['string', 'list']}}
>>> v.validate({'quotes': 'Hello world!'})
True
>>> v.validate({'quotes': ['Do not disturb my circles!', 'Heureka!']})
True
>>> v.schema = {'quotes': {'type': ['string', 'list'], 'schema': {'type': 'string'}}}
>>> v.validate({'quotes': 'Hello world!'})
True
>>> v.validate({'quotes': [1, 'Heureka!']})
False
>>> v.errors
{'quotes': [{0: ['must be of string type']}]}

Note

While the type rule is not required to be set at all, it is not encouraged to leave it unset especially when using more complex rules such as schema. If you decide you still don’t want to set an explicit type, rules such as schema are only applied to values where the rules can actually be used (such as dict and list). Also, in the case of schema, cerberus will try to decide if a list or a dict type rule is more appropriate and infer it depending on what the schema rule looks like.

Note

Please note that type validation is performed before most others which exist for the same field (only nullable and readonly are considered beforehand). In the occurrence of a type failure subsequent validation rules on the field will be skipped and validation will continue on other fields. This allows to safely assume that field type is correct when other (standard or custom) rules are invoked.

Changed in version 1.0: Added the binary data type.

Changed in version 0.9: If a list of types is given, the key value must match any of them.

Changed in version 0.7.1: dict and list typechecking are now performed with the more generic Mapping and Sequence types from the builtin collections module. This means that instances of custom types designed to the same interface as the builtin dict and list types can be validated with Cerberus. We exclude strings when type checking for list/Sequence because it in the validation situation it is almost certain the string was not the intended data type for a sequence.

Changed in version 0.7: Added the set data type.

Changed in version 0.6: Added the number data type.

Changed in version 0.4.0: Type validation is always executed first, and blocks other field validation rules on failure.

Changed in version 0.3.0: Added the float data type.

[1]This is actually an alias of str in Python 2.

validator

Validates the value by calling either a function or method.

A function must be implemented like this the following prototype:

def validationname(field, value, error):
    if value is invalid:
        error(field, 'error message')

The error argument points to the calling validator’s _error method. See Extending Cerberus on how to submit errors.

Here’s an example that tests whether an integer is odd or not:

def oddity(field, value, error):
    if not value & 1:
        error(field, "Must be an odd number")

Then, you can validate a value like this:

>>> schema = {'amount': {'validator': oddity}}
>>> v = Validator(schema)
>>> v.validate({'amount': 10})
False
>>> v.errors
{'amount': ['Must be an odd number']}

>>> v.validate({'amount': 9})
True

If the rule’s constraint is a string, the Validator instance must have a method with that name prefixed by _validator_. See Extending Cerberus for an equivalent to the function-based example above.

The constraint can also be a sequence of these that will be called consecutively.

schema = {'field': {'validator': [oddity, 'prime number']}}

valueschema

Validation schema for all values of a mapping.

>>> schema = {'numbers': {'type': 'dict', 'valueschema': {'type': 'integer', 'min': 10}}}
>>> document = {'numbers': {'an integer': 10, 'another integer': 100}}
>>> v.validate(document, schema)
True

>>> document = {'numbers': {'an integer': 9}}
>>> v.validate(document, schema)
False

>>> v.errors
{'numbers': [{'an integer': ['min value is 10']}]}

New in version 0.7.

Changed in version 0.9: renamed keyschema to valueschema