Data handling made easy
Project description
SimpleModel offers a simple way to handle data using classes instead of a plenty of lists and dicts.
It has simple objectives:
Define models and its fields easily using class attributes, type annotations or tuples (whatever suits your needs)
Support for field validation, cleaning and type conversion
Easy model conversion to dict
Quickstart
Installing
Open your favorite shell and run the following command:
pip install pysimplemodelExample
Define your models using type annotations:
from simple_model import Model
class Person(Model):
age: int
height: float
is_active: bool = True
name: strSimple model automatically creates an initializer for your model and you all set to create instances:
>> person = Person(age=18, height=1.67, name='John Doe')
>> person.name
'John Doe'As you have noticed we haven’t informed a value for field is_active, but the model was still created. That’s becaused we’ve set a default value of True for it and the model takes care of assinging it automatically to the field:
>> person.is_active
TrueSimple model also offers model validation. Empty fields are considered invalid and will raise errors upon validation. Let’s perform some tests using the previous Person model:
>> person = Person()
>> print(person.name)
None
>> person.validate()
Traceback (most recent call last):
...
EmptyField: 'height' field cannot be emptyLet’s say we want the height and age fields to be optional, that can be achieved with the following piece of code:
from simple_model import Model
class Person(Model):
age: int = None
height: float = None
is_active: bool = True
name: strNow let’s test it:
>> person = Person(name='Jane Doe', is_active=False)
>> person.is_active
False
>> person.validate(raise_exception=False)
TrueThe last line won’t raise an exception which means the model instance is valid! In case you need the validation to return True or False instead of raising an exception that’s possible by doing the following:
>> person.validate(raise_exception=False)
TrueYou can also add custom validations by writing class methods prefixed by validate followed by the attribute name, e.g.
class Person:
age: int
height: float
name: str
def validate_age(self, age):
if age < 0 or age > 150:
raise ValidationError('Invalid value for age {!r}'.format(age))
def validate_height(self, height):
if height <= 0:
raise ValidationError('Invalid value for height {!r}'.format(age))Let’s test it:
>> person = Person(name='John Doe', age=190)
>> person.validate()
Traceback (most recent call last):
...
ValidationError: Invalid value for age 190
>> other_person = Person(name='Jane Doe', height=-1.67)
>> other_person.validate()
Traceback (most recent call last):
...
ValidationError: Invalid value for height -1.67It is important to note that models don’t validate types. Currently types are used for field value conversion. This is going to be explained later.
Simple model also supports cleaning the field values by defining custom methods named clean_ followed by the attribute name:
class Person:
age: int
name: str
def clean_name(self, name):
return name.strip()
>>> person = Person(age=18.0, name='John Doe ')
>>> person.name
'John Doe '
>> person.age
18.0
>>> person.clean()
>>> person.name
'John Doe'
>>> person.age # all attributes are converted to its type before cleaning
18 # converted from float (18.0) to int (18)Finally, simple model allows you to easily convert your model to dict type using python built-in function dict():
>>> dict(person)
{
'age': 18,
'name': 'John Doe'
}Documentation
Docs on simple-model.rtfd.io
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