Basic Data Models

A guide to creating your first data models with Pydantic, understanding field types, validation, and setting default values.

By Kevin McAleer,    3 Minutes

Pydantic Data Models

Introduction to Pydantic Models

Pydantic models are the core of Pydantic, allowing for easy data validation and settings management through Python type annotations. These models define the structure of your data, enforce type constraints, and can automatically convert and validate data.

Creating a Basic Model

To define a Pydantic model, you create a class that inherits from BaseModel. Each attribute of the class represents a field in the model, with its type annotation defining the expected type.

from pydantic import BaseModel

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None

In this example, Item is a simple model representing an item in an inventory. It includes a name, description, price, and tax. The description and tax fields are optional and default to None.

Field Types and Validation

Pydantic supports a wide range of field types, including int, float, str, bool, and more. It also supports more complex types like datetime and List[T] for lists of items of type T.

When you create an instance of a Pydantic model, Pydantic validates the input data against the model’s field types. If the data doesn’t match the expected types, Pydantic raises a validation error.

item = Item(name="Laptop", price=1500.00, tax=0.15)

This code creates an instance of the Item model. Pydantic validates that name is a string, price is a float, and tax is a float (and optionally None).

Default Values and Optional Fields

You can define default values for fields by assigning values to the class attributes. If an instance is created without those fields, Pydantic uses the default values.

Fields without default values are considered required. To make a field optional, you can use typing.Optional[T] or simply assign a default value of None.

from typing import Optional

class Item(BaseModel):
    name: str
    description: Optional[str] = None
    price: float
    tax: Optional[float] = None

Lesson Assignment

Create a Pydantic model for a User that includes the following fields: username, email, signup_ts (signup timestamp, optional), and friends (list of usernames, optional). Experiment by creating instances of your model with different data to see how Pydantic handles validation and defaults.

Additional Resources

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