FastAPI Python Tutorials: A Practical Guide

Welcome, guys! Today, we’re diving into the awesome world of FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. If you’re looking to create robust, efficient, and easy-to-maintain APIs, you’ve come to the right place. This comprehensive guide will walk you through everything you need to know, from setting up your environment to deploying your first FastAPI application. Let’s get started!

What is FastAPI?

FastAPI, at its core, is a modern , high-performance web framework designed for building APIs with Python 3.7 and later versions. What sets FastAPI apart is its reliance on standard Python type hints, which not only makes your code more readable but also enables automatic data validation, serialization, and documentation generation. This means less boilerplate code and more time focusing on the actual logic of your application. Imagine writing code that is not only clean and easy to understand but also automatically validated and documented. That’s the power of FastAPI.

Key Features of FastAPI

• Speed and Performance: FastAPI is built on top of Starlette and Uvicorn, which are known for their asynchronous capabilities. This allows FastAPI to handle a large number of concurrent requests with minimal overhead, making it perfect for building high-performance applications. Think of it as a sports car for your API—fast, efficient, and ready to handle anything you throw at it.
• Automatic Data Validation: With FastAPI, you can define the expected data types for your API endpoints using Python type hints. FastAPI will automatically validate incoming data against these types, ensuring that your application only processes valid data. This reduces the risk of runtime errors and makes your code more robust. It’s like having a built-in bodyguard for your data, always ensuring that everything is in order.
• Automatic API Documentation: FastAPI automatically generates interactive API documentation using OpenAPI and Swagger UI. This makes it easy for developers to explore and test your API endpoints. You don’t have to spend hours writing documentation manually—FastAPI takes care of it for you. It’s like having a personal assistant who automatically creates and updates your API documentation.
• Dependency Injection: FastAPI has a built-in dependency injection system that makes it easy to manage and inject dependencies into your API endpoints. This promotes code reusability and makes your application more modular and testable. It’s like having a well-organized toolbox where everything is in its place and easily accessible.
• Easy to Learn and Use: FastAPI has a simple and intuitive API that is easy to learn, even for developers who are new to asynchronous programming. The framework provides clear and concise documentation, along with plenty of examples to help you get started. It’s like having a friendly tutor who guides you through every step of the way.

Setting Up Your Environment

Before we dive into coding, let’s set up our development environment. This involves installing Python, setting up a virtual environment, and installing the necessary packages. Don’t worry, it’s easier than it sounds!

Installing Python

If you don’t already have Python installed, you’ll need to download and install it from the official Python website ( https://www.python.org/downloads/ ). Make sure to download the latest version of Python 3.7 or higher. During the installation process, be sure to check the box that says “Add Python to PATH” so that you can run Python from the command line. Once Python is installed, you can verify the installation by opening a command prompt or terminal and running the following command:

python --version

This should display the version of Python that you have installed. If you see an error message, double-check that you have added Python to your PATH environment variable.

Creating a Virtual Environment

A virtual environment is a self-contained directory that contains a Python installation and all the packages that your project depends on. This allows you to isolate your project’s dependencies from other Python projects on your system, preventing conflicts and ensuring that your project always has the correct dependencies. To create a virtual environment, navigate to your project directory in the command prompt or terminal and run the following command:

python -m venv venv

This will create a new virtual environment in a directory named venv . To activate the virtual environment, run the following command:

• On Windows:

  venv\Scripts\activate
  

• On macOS and Linux:

  source venv/bin/activate
  

Once the virtual environment is activated, you should see the name of the virtual environment in parentheses at the beginning of your command prompt or terminal. This indicates that you are now working within the virtual environment.

Installing FastAPI and Uvicorn

Now that we have our virtual environment set up, we can install FastAPI and Uvicorn. FastAPI is the web framework that we will be using to build our API, and Uvicorn is an ASGI server that we will use to run our API. To install FastAPI and Uvicorn, run the following command:

pip install fastapi uvicorn

This will download and install FastAPI and Uvicorn, along with any dependencies that they require. Once the installation is complete, you are ready to start building your first FastAPI application!

Creating Your First FastAPI Application

Now comes the fun part: writing some code! We’ll start with a simple “Hello, World!” application to get you familiar with the basics of FastAPI. This will involve creating a new Python file, importing the FastAPI class, creating an instance of the FastAPI class, and defining an API endpoint.

Creating a New Python File

Create a new file named main.py in your project directory. This file will contain the code for our FastAPI application. You can use any text editor or IDE to create the file. Popular choices include VS Code, Sublime Text, and PyCharm. Just make sure to save the file with the .py extension.

Importing the FastAPI Class

In the main.py file, import the FastAPI class from the fastapi module. This class is the foundation of our FastAPI application. It provides the methods and functionality that we need to define API endpoints, handle requests, and generate documentation.

from fastapi import FastAPI

Creating an Instance of the FastAPI Class

Next, create an instance of the FastAPI class. This instance will represent our FastAPI application. You can name the instance anything you like, but it is common to name it app . This instance will be used to define our API endpoints and configure our application.

app = FastAPI()

Defining an API Endpoint

Now, let’s define our first API endpoint. We’ll create a simple endpoint that returns the message “Hello, World!”. To define an API endpoint, we use the app.get() decorator, which tells FastAPI that this function should be called when a GET request is made to the specified path. The path is specified as the first argument to the app.get() decorator.

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

In this example, we are defining an API endpoint that listens for GET requests to the root path ( / ). When a request is received, FastAPI will call the read_root() function, which returns a dictionary containing the message “Hello, World!”. FastAPI will automatically convert this dictionary to a JSON response and send it back to the client.

Running Your FastAPI Application

To run your FastAPI application, open a command prompt or terminal in your project directory and run the following command:

uvicorn main:app --reload

This command tells Uvicorn to run the FastAPI application defined in the main.py file. The --reload flag tells Uvicorn to automatically reload the application whenever you make changes to the code. This is useful for development because it allows you to see your changes in real-time without having to restart the server manually.

Once the application is running, you can access it by opening your web browser and navigating to http://localhost:8000 . You should see the JSON response {"Hello": "World"} displayed in your browser.

Working with Request Parameters

APIs often need to accept parameters from the client to perform specific tasks. FastAPI makes it easy to define and validate request parameters using Python type hints. Let’s explore how to work with path parameters, query parameters, and request body parameters.

Path Parameters

Path parameters are parameters that are part of the URL path. For example, in the URL /items/123 , 123 is a path parameter that identifies a specific item. To define a path parameter in FastAPI, you simply include it in the path of your API endpoint, surrounded by curly braces.

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

In this example, we are defining an API endpoint that accepts a path parameter named item_id . The item_id parameter is annotated with the int type hint, which tells FastAPI that the parameter should be an integer. FastAPI will automatically validate that the parameter is an integer and convert it to an integer before passing it to the read_item() function.

Query Parameters

Query parameters are parameters that are appended to the URL after a question mark ( ? ). For example, in the URL /items?name=foo&price=10 , name and price are query parameters. To define a query parameter in FastAPI, you simply define it as a function parameter with a default value.

@app.get("/items/")
async def read_item(name: str, price: float = 0.0):
    return {"name": name, "price": price}

In this example, we are defining an API endpoint that accepts two query parameters: name and price . The name parameter is annotated with the str type hint, which tells FastAPI that the parameter should be a string. The price parameter is annotated with the float type hint and has a default value of 0.0 . This means that the price parameter is optional, and if it is not provided in the URL, it will default to 0.0 .

Request Body Parameters

Request body parameters are parameters that are sent in the body of the HTTP request. These are typically used for sending data to the server, such as when creating or updating resources. To define a request body parameter in FastAPI, you define a Pydantic model that represents the structure of the request body. Pydantic is a data validation and settings management library that is tightly integrated with FastAPI.

First, you need to install Pydantic:

pip install pydantic

Then, you can define a Pydantic model like this:

from pydantic import BaseModel

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

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

In this example, we are defining a Pydantic model named Item that has four fields: name , description , price , and tax . The name field is a required string, the description field is an optional string, the price field is a required float, and the tax field is an optional float. We are also defining an API endpoint that accepts a request body parameter of type Item . FastAPI will automatically validate that the request body matches the structure of the Item model and convert it to an Item object before passing it to the create_item() function.

Conclusion

Congratulations! You’ve made it through the basics of FastAPI. You’ve learned how to set up your environment, create a simple “Hello, World!” application, and work with request parameters. With this foundation, you’re well on your way to building powerful and efficient APIs with FastAPI. Keep practicing, explore the official documentation, and don’t be afraid to experiment. Happy coding, and see you in the next tutorial!