FastAPI Python Code Examples: A Quick Start Guide for Developers

What’s up, code wizards! Today, we’re diving deep into the awesome world of FastAPI , a super-modern, super-fast web framework for building APIs with Python. If you’re looking to whip up some lightning-quick web services, you’ve come to the right place, guys. We’re going to walk through some practical FastAPI Python code examples that will get you up and running in no time. Forget the old, clunky ways of doing things; FastAPI is here to make your life easier and your APIs more robust. It’s built on standard Python type hints, which means you get amazing features like automatic data validation, serialization, and interactive documentation right out of the box. Seriously, it’s a game-changer for Python developers who want to build web applications and APIs that are not only fast but also a joy to develop.

So, let’s get our hands dirty with some code! First things first, you’ll need to install FastAPI and an ASGI server like Uvicorn. You can do that with a simple pip command: pip install fastapi uvicorn[standard] . Once that’s done, you’re ready to create your first FastAPI application. Imagine you want to build a simple API that returns a greeting. It sounds basic, but it’s the perfect starting point to understand the core concepts. We’ll create a file, let’s call it main.py , and add the following code:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Hello, World!"}

See how clean that is? We import FastAPI , create an instance of it, and then define a path operation function using a decorator. The @app.get("/") decorator tells FastAPI that this function should handle GET requests to the root URL ( / ). When you run this application using Uvicorn ( uvicorn main:app --reload ), you can visit http://127.0.0.1:8000 in your browser, and you’ll see {"message": "Hello, World!"} . How cool is that? This is the most fundamental example of a FastAPI Python code example , showcasing the simplicity and elegance of the framework. It’s the bedrock upon which you’ll build much more complex and powerful APIs.

Handling Path Parameters in FastAPI

Alright, let’s level up! APIs often need to handle dynamic data, and that’s where path parameters come in. Imagine you want to greet a specific user by name. Instead of a generic greeting, you want a personalized message. FastAPI makes this incredibly easy. You can declare path parameters directly in your path string. Let’s modify our main.py file:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Hello, World!"}

@app.get("/items/{item_id}")
def read_item(item_id: int):
    return {"item_id": item_id}

In this FastAPI Python code example , we’ve added a new path operation: @app.get("/items/{item_id}") . Notice the {item_id} in the path. This signifies a path parameter named item_id . Crucially, we’ve also declared the function parameter item_id: int . This tells FastAPI that item_id should be an integer. If a user tries to access /items/abc , FastAPI will automatically return a validation error because abc is not an integer. This automatic data validation is one of the superpowers of FastAPI, thanks to its reliance on Python type hints and the Pydantic library. When you run this and visit http://127.0.0.1:8000/items/5 , you’ll get {"item_id": 5} . If you try /items/3.14 , you’ll get a clear error message. This robust handling of data types within path parameters is a significant advantage, ensuring your API is robust and predictable, saving you tons of debugging time. It’s a prime example of how FastAPI simplifies complex tasks into straightforward, readable Python code, making it a favorite among developers.

Query Parameters: Filtering and Optional Data

Beyond path parameters, you’ll often need to send additional data to your API via query parameters . These are the bits after the ? in a URL, like http://example.com/items?skip=0&limit=10 . FastAPI handles these seamlessly. Let’s extend our API to include query parameters for skipping and limiting items.

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Hello, World!"}

@app.get("/items/{item_id}")
def read_item(item_id: int):
    return {"item_id": item_id}

@app.get("/items/")
def read_items(skip: int = 0, limit: int = 10):
    return {"skip": skip, "limit": limit}

Here, in this FastAPI Python code example , we’ve defined a new path operation @app.get("/items/") . The function read_items takes skip: int = 0 and limit: int = 10 . Because these parameters are not part of the path (like {item_id} ), FastAPI recognizes them as query parameters. The = 0 and = 10 indicate default values. This means if you access /items/ without any query parameters, you’ll get {"skip": 0, "limit": 10} . If you go to /items/?skip=5&limit=20 , you’ll receive {"skip": 5, "limit": 20} . What’s even cooler is that FastAPI also handles type validation for query parameters. If you try /items/?skip=hello , you’ll get a validation error, just like with path parameters. This makes building APIs that accept optional or complex filtering criteria incredibly straightforward. You don’t need to manually parse query strings or validate types; FastAPI does it all for you, leveraging those beautiful Python type hints. This feature alone saves developers a massive amount of boilerplate code and reduces the potential for runtime errors, making your development process much smoother and your API much more reliable. It’s another reason why FastAPI is such a joy to work with, offering powerful features with minimal fuss.

Request Body: Sending Data to the API

So far, we’ve focused on getting data from the API using path and query parameters. But what about sending data to the API? This is typically done using a request body, often in JSON format, especially when creating or updating resources. FastAPI, again, shines here thanks to Pydantic models. Let’s create an endpoint to create a new item.

First, we need to define the structure of our item using a Pydantic model. Create a new file named models.py (or add it to main.py if you prefer a single file for simplicity at this stage):

from pydantic import BaseModel

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

Now, let’s update main.py to use this model:

from fastapi import FastAPI
from models import Item # Assuming Item is in models.py

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Hello, World!"}

@app.get("/items/{item_id}")
def read_item(item_id: int):
    return {"item_id": item_id}

@app.get("/items/")
def read_items(skip: int = 0, limit: int = 10):
    return {"skip": skip, "limit": limit}

@app.post("/items/")
def create_item(item: Item):
    return item

In this FastAPI Python code example , the magic happens with @app.post("/items/") and the function parameter item: Item . When a POST request comes to /items/ , FastAPI will expect a JSON body in the request. It will then validate this JSON against our Item Pydantic model. If the data matches the structure (e.g., has a name , price , and optional description and tax ), it will be converted into an Item object and passed to our create_item function. If the JSON is invalid (e.g., missing name or price , or price is not a number), FastAPI automatically returns a clear error. The function then simply returns the item object it received. This automatic request body validation and parsing is a huge time-saver. You don’t need to write any manual parsing or validation logic; FastAPI and Pydantic handle it all. This makes creating APIs that accept complex data structures incredibly easy and reliable. It truly embodies the