Getting Started with Pydantic

Hello, are you Kakyo. This time, I would like to write an article about pydantic.

What is Pydantic

Pydantic is a library that uses Python type annotations to provide run-time type hints and easily provide error settings during data validation.

This library is useful for defining database models in SQLAlchemy.

model

First, when defining it, define it as follows.


from datetime import datetime
from typing import List
from pydantic import BaseModel

class Summer(BaseModel):
    id: int
    name: str   # (variable):(Mold)として、Moldを宣言する
    friends: List[int] = []  # "="You can also use to define a default value
    created_at: datetime


external_data={
    'id': '1',
    'name' :'Beast senior',
    'created_at': '2019-11-03 03:34',
    'friends': [114,'514']
}
summer = Summer(**external_data)

print(summer.id)

#> 1 #Even if the entered value is a string type, it will be automatically converted to an Int type.

print(repr(summer.created_at))

#> datetime.datetime(2019, 11, 3, 3, 34)

print(user.friends)

#> [114,514]

print(user.dict())

"""
{
   'id': 1,
   'name': 'Beast senior', 
   'created_at': datetime.datetime(2019, 11, 3, 3, 34),
   'friends': [114, 514]
}

"""

So, when a Validation Error occurs, it looks like this:

try:
    User(created_at="Painted in black",friends=[1,'2','luxury car'])

except ValidationError as e:
    print(e.json())

"""
[
  {
    "loc": [
      "id"
    ],
    "msg": "field required",
    "type": "value_error.missing"
  },
  {
    "loc": [
      "name"
    ],
    "msg": "field required",
    "type": "value_error.missing"
  },
  {
    "loc": [
      "created_at"
    ],
    "msg": "invalid datetime format",
    "type": "type_error.datetime"
  },
  {
    "loc": [
      "friends",
      2
    ],
    "msg": "value is not a valid integer",
    "type": "type_error.integer"
  }
]

"""

Here, the objects returned when an error occurs are as follows.

Try it in combination with SQLAlchemy

Now, using SQLAlchemy, Python's ORM Wrapper, we'll do the following: You can design an SQL model.

The code below quotes from here.

from typing import List
from sqlalchemy import Column, Integer, String
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.ext.declarative import declarative_base
from pydantic import BaseModel, constr

Base = declarative_base()

class CompanyOrm(Base):
    __tablename__ = 'companies'
    id = Column(Integer, primary_key=True, nullable=False)
    public_key = Column(String(20), index=True, nullable=False, unique=True)
    name = Column(String(63), unique=True)
    domains = Column(ARRAY(String(255)))

class CompanyModel(BaseModel):
    id: int
    public_key: constr(max_length=20)
    name: constr(max_length=63)
    domains: List[constr(max_length=255)]

    class Config:
        orm_mode = True
        
co_orm = CompanyOrm(
    id=123,
    public_key='foobar',
    name='Testing',
    domains=['example.com', 'foobar.com']
)
print(co_orm)
#> <orm_mode.CompanyOrm object at 0x7f0de1bc1cd0>
co_model = CompanyModel.from_orm(co_orm)
print(co_model)
#> id=123 public_key='foobar' name='Testing' domains=['example.com',
#> 'foobar.com']

Validators

You can use the ** Validator Decorator ** to see if the values you enter are correct and the complex relationships between the objects.

from pydantic import BaseModel, ValidationError, validator

class UserModel(BaseModel):
    name: str
    username: str
    password1: str
    password2: str

    @validator('name')
    def name_must_contain_space(cls, v):
        if ' ' not in v:
            raise ValueError('must contain a space')
        return v.title()

    @validator('password2')
    def passwords_match(cls, v, values, **kwargs):
        if 'password1' in values and v != values['password1']:
            raise ValueError('passwords do not match')
        return v

    @validator('username')
    def username_alphanumeric(cls, v):
        assert v.isalpha(), 'must be alphanumeric'
        return v

print(UserModel(name='samuel colvin', username='scolvin', password1='zxcvbn',
                password2='zxcvbn'))
#> name='Samuel Colvin' username='scolvin' password1='zxcvbn' password2='zxcvbn'

try:
    UserModel(name='samuel', username='scolvin', password1='zxcvbn',
              password2='zxcvbn2')
except ValidationError as e:
    print(e)
"""
2 validation errors for UserModel
name
  must contain a space (type=value_error)
password2
  passwords do not match (type=value_error)
"""

Pre and per-item validators To set the priority when starting the Validator, use the following arguments pre, pre_item. pre starts the Validator before any other Validator you have set. If ʻeach_item = True`, Validation will be executed for each element such as list and dictionary.

from typing import List
from pydantic import BaseModel, ValidationError, validator

class DemoModel(BaseModel):
    square_numbers: List[int] = []
    cube_numbers: List[int] = []

    # '*' is the same as 'cube_numbers', 'square_numbers' here:
    @validator('*', pre=True)
    def split_str(cls, v):
        if isinstance(v, str):
            return v.split('|')
        return v

    @validator('cube_numbers', 'square_numbers')
    def check_sum(cls, v):
        if sum(v) > 42:
            raise ValueError(f'sum of numbers greater than 42')
        return v

    @validator('square_numbers', each_item=True)
    def check_squares(cls, v):
        assert v ** 0.5 % 1 == 0, f'{v} is not a square number'
        return v

    @validator('cube_numbers', each_item=True)
    def check_cubes(cls, v):
        # 64 ** (1 / 3) == 3.9999999999999996 (!)
        # this is not a good way of checking cubes
        assert v ** (1 / 3) % 1 == 0, f'{v} is not a cubed number'
        return v

print(DemoModel(square_numbers=[1, 4, 9]))
#> square_numbers=[1, 4, 9] cube_numbers=[]
print(DemoModel(square_numbers='1|4|16'))
#> square_numbers=[1, 4, 16] cube_numbers=[]
print(DemoModel(square_numbers=[16], cube_numbers=[8, 27]))
#> square_numbers=[16] cube_numbers=[8, 27]
try:
    DemoModel(square_numbers=[1, 4, 2])
except ValidationError as e:
    print(e)
"""
1 validation error for DemoModel
square_numbers -> 2
  2 is not a square number (type=assertion_error)
"""

try:
    DemoModel(cube_numbers=[27, 27])
except ValidationError as e:
    print(e)
"""
1 validation error for DemoModel
cube_numbers
  sum of numbers greater than 42 (type=value_error)
"""

Validate Always By default, for performance reasons, this Validator will not start if no value is given. However, the argument must be set to ʻalways = True` in order to run even if no value is given.


from datetime import datetime

from pydantic import BaseModel, validator

class DemoModel(BaseModel):
    ts: datetime = None

    @validator('ts', pre=True, always=True)
    def set_ts_now(cls, v):
        return v or datetime.now()

print(DemoModel())
#> ts=datetime.datetime(2019, 10, 24, 15, 7, 51, 449261)
print(DemoModel(ts='2017-11-08T14:00'))
#> ts=datetime.datetime(2017, 11, 8, 14, 0)

Finally

The above is the basic usage of Pydantic. You can use this to set validation and types in the SQL steamer in Python. For detailed settings, refer to the official document below.

References

Official Pydanit documentation (https://pydantic-docs.helpmanual.io/)

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