Pydantic, Type Hints, and Models
(Data validation and settings management using Python type hints)
FastAPI stands largely on a Python package called Pydantic. This uses models (Python object classes) to define data structures. These are heavily used in FastAPI applications and are a real advantage when writing larger applications. Python, known for its simplicity and readability, has evolved over the years to include powerful features that enhance data validation and settings management.
In this blog, we’ll explore the fundamentals of Pydantic, delve into the world of type hints, and discover how models play a crucial role in Python development.
Type Hints in Python:
In Python, variables are associated with objects, and it’s the objects that have types.
Type hints are used to declare the type of a variable, helping catch potential mistakes and improve code readability. In standard programming, a variable is usually associated with the same object. If we associate a type hint with that variable, we can avoid some programming mistakes. So Python added type hinting to the language, in the standard typing module. The Python interpreter ignores the type hint syntax and runs the program as though it isn’t there. You might treat a variable as a string in one line, and forget later and assign it an object of a different type.
Example:
thing: str = “yeti”
Here are some examples with initializations:
physics_magic_number: float = 1.0/137.03599913
hp_lovecraft_noun: str = “ichor”
exploding_sheep: tuple = “sis”, “boom”, bah!”
responses: dict = {“Marco”: “Polo”, “answer”: 42}
You can also include subtypes of collections:
name: dict[keytype, valtype] = {key1: val1, key2 : val2}
Pydantic and Data Validation:
Pydantic is a Python library that facilitates data validation and setting management using Python-type hints.
It allows you to define models (Python classes) to represent data structures.
Example:
from pydantic import BaseModel
class Creature(BaseModel):
name: str country: str
area: str
description: str
aka: str
thing = Creature(
name=”yeti”,
country=”CN”,
area=”Himalayas”,
description=”Hirsute Himalayan”,
aka=”Abominable Snowman”)
)
print(“Name is”, thing.name)
Type Validation with Pydantic:
Pydantic not only uses type hints but also provides additional validation capabilities.
Example:
class Creature(BaseModel):
name: str = Field(…, min_length=2)
country: str
area: str
description: str
aka: str
Data Grouping with Python Structures:
Python offers various data structures like tuples, lists, sets, and dictionaries to group related variables.
Example:
tuple_thing = (“yeti”, “CN”, “Himalayas”, “Hirsute Himalayan”, “Abominable Snowman”)
Pydantic Models in FastAPI:
FastAPI, a modern web framework for building APIs with Python 3.7+, heavily relies on Pydantic models.
Example:
from fastapi import FastAPI
from model import Creature
app = FastAPI()
@app.get(“/creature”)
def get_all() -> list[Creature]:
# Retrieve and return a list of Creature objects
It is essential to comprehend Pydantic, type hints, and models in order to construct reliable and manageable Python applications. Developers can improve data validation, efficiently manage settings, and expedite the development process by combining the power of type hints and Pydantic. As you embark on your Python journey, these tools will prove invaluable in creating clean, error-resistant code.