The previous article explained how to validate data using a regular python class, then using dataclass, and the benefit of using it.

Now I will explain validation using pydantic; its documentation can be found at the following link

Pydantic is a data validation and settings management tool that uses type annotations in Python. It can be used with Python from version 3.6 onward.

To install pydantic, use pip:

pip install pydantic

Pydantic vs Dataclasses vs regular python class

Regular python class

As explained in the previous article, the attributes of the class are defined, in this case producto:


class Producto():
  def __init__(self, titulo, descripcion, precio, publicado, id):
    self.titulo = titulo
    self.descripcion = descripcion
    self.precio = precio
    self.publicado = publicado 
    self.id = id

Python dataclass


from dataclasses import dataclass 
@dataclass 
class Producto:
  titulo: str
  descripcion: str
  precio: int
  publicado: bool
  id: int

Pydantic

In the case of pydantic, BaseModel is imported from pydantic and Optional from typing.


from pydantic import BaseModel
from typing import Optional 
from random import  randrange 

class Producto(BaseModel):
    titulo: str
    descripcion: str
    precio: int
    publicado: Optional[bool]= True 
    id: Optional[int]= None

Unlike dataclass, with pydantic you can define whether a field is optional or not, and its default value. In the case of the field publicado it is a boolean, optional, with True as the default value; in the case of id it is an optional integer, with None as the default value.

Define an example data point:


dato = {
    "titulo": "Xiaomi Mi 10T",
    "descripcion": "Xiaomi",
    "precio": 300, 
    "id": randrange(0, 100000000) 
}

Validate the data with the Producto class and print the content:


producto = Producto(**dato)

print(producto)

titulo='Xiaomi Mi 10T' descripcion='Xiaomi' precio=300 publicado=True id=59872935

Pydantic gives more versatility in data validation; thanks to Pydantic you can validate the data inputs of an API endpoint, and the next article will explain how to do it using FastAPI.

This article is based on the following articles:

  1. Getting started with Pydantic, a Python data validation library (broken link)
  2. Pydantic — Better Data Validation for Python (broken link)
  3. Pydantic documentation

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