Annotation based python object validator
Project description
Endorser
A lightweight data validation and converter package for Python 3.6+. It's always better to work with a structured set of data instead of just a simple dictionary. This package provides an easy way to do the conversion seamlessly while it provides a set of tools to validate the data. The main purpose of this package is to create structured data from unstructured types while validating it.
from validation.converter import DocumentConverter
from validation.schema import Schema
from validation.validator import min_size
class Address(Schema):
zip_code: str
house_number: int
addition: str = None
class User(Schema):
email: str
username: str
firstname: str = None # assigning None as default makes it optional during instantiation
address: Address = None # nest Schema classes
@min_size(5)
def validate_username(self, username):
"""Validates the username field, it has to be at least 5 chars long"""
return username
data = {
"email": "example@email.com",
"username": "krisz",
"address": {
"zip_code": "6757",
"house_number": 12,
"addition": "A1"
}
}
converter = DocumentConverter()
user = converter.convert(data, User) # converts the data dictionary to a User object
assert type(user) is User
assert user.email is "example@email.com"
assert type(user.address) is Address
assert user.address.zip_code is "6757"
Features
validation.schema.Schema
Base class for documents.
- Must not be instantiated directly
- Every attribute must be type hinted
- As of now, supported type hints are the primivites, list, dict, typing.List and subclasses of Schema
- Every subclass of
Schema
must be considered as final and immutable
class User(Schema):
email: str
username: str
firstname: str = None # assigning None as default makes it optional
age: int = 0 # assigning anything will be used as default value
address: Address = None # must be an instance of Schema
Note that it's possible for every attribute to have None
as it's value, the default None
only means that the value can be omitted from the document. If you want to make sure that the value cannot be None
, apply the @validator.not_none
decorator:
class User(Schema):
email: str
username: str = None
user = User(email="some@email.com") # valid, as username can be omitted
user = User(email=None) # valid, as email can have the value None
...
from validation.validator import not_none
@not_none
def valid_email(self, email):
return email
user = User(email=None) # not valid, as it's both mandatory and cannot be None
Validation
You can validate Schema
objects with following this convention:
from validation.validator import min_size
class SomeDocument(Schema):
some_prop: str
@min_size(5) # has to be at least 5 chars long
def validate_some_prop(self, value):
return value
Every validation method has to start with the validate_
prefix followed by the name of the property. The value argument is the value which will be set during instantiation. The method has to return the value as we set this value on the object.
You can see all validation methods in the validation.validator
package.
Custom validation
You can either create a new decorator and apply it on the validator (for examples see the validation.validator
package) or apply the validation on the validation method itself.
from validation import construct_error
class SomeDocument(Schema):
some_prop: str
@some_custom_validator # apply custom decorators
def validate_some_prop(self, value):
for c in value:
if c is " ":
self.instance_errors.append(construct_error(
"some_prop", "cannot contain spaces"))
return value # make sure to always return the value
Alter values
It's possible to alter the value of Schema
objects during validation:
import uuid
from validation.validator import valid_uuid
class User(Schema):
id: uuid.UUID
email: str
username: str = None
@valid_uuid # ensures that the ID is a valid UUID
def validate_id(self, id):
return uuid.UUID(id)
user = User(id="7b4f95e3-4fbe-4f94-838f-c34950240274",
email="some@email.com")
assert isinstance(user.id, uuid.UUID)
You can also create custom decorators to modify property values. Note that we hinted the id
property to be of type uuid.UUID
but we instantiate it with a string value. You are responsible to return the correct value type which you defined on the Schema
class.
Instantiation
You have to use keyword arguments to instantiate a Schema
object:
user = User(email="some@email.com", username="krisz")
You can set the _allow_unknown
property on any Schema
object to allow unknown properties:
user = User(_allow_unknown=True, email="some@email.com", unknown_prop="any value")
assert user.unknown_prop == "any value"
Validation happens during the instantiation of the Schema
object. Note that there aren't any exception raised, you have to check if there were any errors yourself:
import uuid
from validation.validator import valid_uuid
class User(Schema):
id: uuid.UUID
email: str
username: str = None
@valid_uuid # ensures that the ID is a valid UUID
def validate_id(self, id):
return uuid.UUID(id)
user = User(id="invalid-uuid",
email="some@email.com")
assert user.id == "invalid-uuid"
if user.instance_errors:
for error in user.instance_errors:
print("invalid value for property %s: %s" % (error["field"], error["error"]))
You can use the obj.instance_errors
property to check for errors on the instance and obj.doc_errors
to check for validation errors on the whole document. This means if you have nested Schema
objects, this property will return every error on every object from the root object:
from validation.schema import Schema
from validation.validator import min_size
class Address(Schema):
zip_code: str
house_number: int
addition: str = None
class User(Schema):
email: str
username: str
firstname: str = None
address: Address = None
@min_size(5)
def validate_username(self, username):
return username
user = User(email="some@email.com", username="Joe",
address=Address(zip_code="67ZZ",
house_number="invalid_type")) # validation fails on username and house_number
assert len(user.instance_errors) == 1
assert len(user.address.instance_errors) == 1
assert len(user.doc_errors) == 2
DocumentConverter
The DocumentConverter class is used to build structured data from a document. A document can either be a dictionary or a list of dictionaries. The DocumentConverter uses the Schema
class to validate and build the objects from the document.
from validation.converter import DocumentConverter
from validation.schema import Schema
from validation.validator import min_size
class Address(Schema):
zip_code: str
house_number: int
addition: str = None
class User(Schema):
email: str
username: str
firstname: str = None
address: Address = None
@min_size(5)
def validate_username(self, username):
"""Validates the username field, it has to be at least 5 chars long"""
return username
data = {
"email": "example@email.com",
"username": "krisz",
"address": {
"zip_code": "6757",
"house_number": 12,
"addition": "A1"
}
}
converter = DocumentConverter()
user = converter.convert(data, User)
assert type(user) is User
assert user.email is "example@email.com"
assert type(user.address) is Address
assert user.address.zip_code is "6757"
The DocumentConverter#convert
method raises a ConversionError
if validation fails. It holds the error messages in the ConversionError.errors
list.
You can pass the allow_unknown=True
property to the convert
method to allow unknown properties:
class SomeClass(Schema):
prop: str
data = {
"prop": "some property",
"unknown_prop": "not defined on the class"
}
converter = DocumentConverter()
some_obj = converter.convert(data, SomeClass, allow_unknown=True)
assert some_obj.unknown_prop == "not defined on the class"
You can also pass a list of objects to the converter:
data = [{
"prop": "a property"
}, {
"prop": "another property"
}]
list_of_objs = converter.convert(data, List[SomeClass])
assert type(list_of_objs) is list
assert type(list_of_objs[0]) is SomeClass
assert len(list_of_objs) == 2
Examples
For more examples see the test.example
package.
Limitations
- Only works on Python 3.6+
- Currently supported types are all primitives, list, dict and typing.List, and of course other Schema objects as well
- Classes which inherit from Schema are effectively final, you must not inherit from them
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.