How to validate data with pydantic

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Introduction

• This article guides you through data validation with pydantic. Pydantic is a Python library built in Rust.
• Before we get start, make sure you have Python and Pydantic library installed on your environment. Check the pydantic instalation command below.

pip install pydantic

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Creating a model

• First, we will need to import and make our class inherit from pydantic.BaseModel to start validating. In our example we will create a class named Person and it will have name, age and an e-mail.

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int
    email: str

• With this class, when we instatiate it, pydantic will validate if the parameters name and email are strings and age is integer.

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Using the model

• Now we have it created, we will instantiate the class. We will create a dict containing the data and unpack it to our class.

data = {
    "name": "John",
    "age": 19,
    "email": "john@gmail.com"
}
person = Person(**data)

• The above example will not raise any error since all parameters are on correct type. Now we will make a wrong data to confirm if our validation work.

data = {
    "name": "John",
    "age": "19",
    "email": "john@gmail.com"
}
person = Person(**data)

• In the above example it will raise the error ValidationError because age needs to be an integer and cannot be provided as a string.

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Creating dataclass

• You can also create dataclasses with pydantic which will be similar to standard Python dataclasses but will have validations like BaseModel.

from pydantic import dataclass

@dataclass
class Person:
    name: str
    age: int
    email: str

• If we send a string to age, it will convert to int.

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Extras

• Pydantic has some extras like e-mail validations and a fallback timezone package. To install them, you need to run the following commands:

pip install pydantic[email]

pip install pydantic[timezone]

• You can install both together by running the following command.

pip install pydantic[email,timezone]

• Pydantic[email] brings a class to handle emails, normalizing it and validanting the format user@domain.tld. For this, pydantic brings the class EmailStr to do this validation.

from pydantic import dataclass, EmailStr

@dataclass
class Person:
    name: str
    age: int
    email: EmailStr

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