Normalization Rules

Normalization rules are applied to fields, also in schema for mappings, as well when defined as a bulk operation by schema (for sequences), allow_unknown, keysrules and valuesrules. Normalization rules in definitions for testing variants like with anyof are not processed.

The normalizations are applied as given in this document for each level in the mapping, traversing depth-first.

Renaming Of Fields

You can define a field to be renamed before any further processing.

>>> v = Validator({'foo': {'rename': 'bar'}})
>>> v.normalized({'foo': 0})
{'bar': 0}

To let a callable rename a field or arbitrary fields, you can define a handler for renaming. If the constraint is a string, it points to a custom method. If the constraint is an iterable, the value is processed through that chain.

>>> v = Validator({}, allow_unknown={'rename_handler': int})
>>> v.normalized({'0': 'foo'})
{0: 'foo'}

>>> even_digits = lambda x: '0' + x if len(x) % 2 else x
>>> v = Validator({}, allow_unknown={'rename_handler': [str, even_digits]})
>>> v.normalized({1: 'foo'})
{'01': 'foo'}

New in version 1.0.

Purging Unknown Fields

After renaming, unknown fields will be purged if the purge_unknown property of a Validator instance is True; it defaults to False. You can set the property per keyword-argument upon initialization or as rule for subdocuments like allow_unknown (see Allowing the Unknown). The default is False. If a subdocument includes an allow_unknown rule then unknown fields will not be purged on that subdocument.

>>> v = Validator({'foo': {'type': 'string'}}, purge_unknown=True)
>>> v.normalized({'bar': 'foo'})
{}

New in version 1.0.

Default Values

You can set default values for missing fields in the document by using the default rule.

>>> v.schema = {'amount': {'type': 'integer'}, 'kind': {'type': 'string', 'default': 'purchase'}}
>>> v.normalized({'amount': 1}) == {'amount': 1, 'kind': 'purchase'}
True

>>> v.normalized({'amount': 1, 'kind': None}) == {'amount': 1, 'kind': 'purchase'}
True

>>> v.normalized({'amount': 1, 'kind': 'other'}) == {'amount': 1, 'kind': 'other'}
True

You can also define a default setter callable to set the default value dynamically. The callable gets called with the current (sub)document as the only argument. Callables can even depend on one another, but normalizing will fail if there is a unresolvable/circular dependency. If the constraint is a string, it points to a custom method.

>>> v.schema = {'a': {'type': 'integer'}, 'b': {'type': 'integer', 'default_setter': lambda doc: doc['a'] + 1}}
>>> v.normalized({'a': 1}) == {'a': 1, 'b': 2}
True

>>> v.schema = {'a': {'type': 'integer', 'default_setter': lambda doc: doc['not_there']}}
>>> v.normalized({})
>>> v.errors
{'a': ["default value for 'a' cannot be set: Circular dependencies of default setters."]}

You can even use both default and readonly on the same field. This will create a field that cannot be assigned a value manually but it will be automatically supplied with a default value by Cerberus. Of course the same applies for default_setter.

Changed in version 1.0.2: Can be used in conjunction with readonly.

New in version 1.0.

Value Coercion

Coercion allows you to apply a callable (given as object or the name of a custom coercion method) to a value before the document is validated. The return value of the callable replaces the new value in the document. This can be used to convert values or sanitize data before it is validated. If the constraint is an iterable of callables and names, the value is processed through that chain of coercers.

>>> v.schema = {'amount': {'type': 'integer'}}
>>> v.validate({'amount': '1'})
False

>>> v.schema = {'amount': {'type': 'integer', 'coerce': int}}
>>> v.validate({'amount': '1'})
True
>>> v.document
{'amount': 1}

>>> to_bool = lambda v: v.lower() in ('true', '1')
>>> v.schema = {'flag': {'type': 'boolean', 'coerce': (str, to_bool)}}
>>> v.validate({'flag': 'true'})
True
>>> v.document
{'flag': True}

New in version 0.9.