Automatic API documentation

Oct 13, 2025 by ludoplayers7.com 83 views

Introduction

Are you a Python developer looking to create a robust API for your existing code? Do you want to connect your frontend with a backend that's built using Python? If yes, then you're in the right place. In this article, we'll explore how to create a FastAPI for your existing Python code using Jupyter Notebook. We'll cover the basics of FastAPI, its features, and how to convert your existing code into a production-ready API.

What is FastAPI?

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It's designed to be fast, scalable, and easy to use. With FastAPI, you can create high-performance APIs that are perfect for real-time applications, web services, and microservices.

Features of FastAPI

FastAPI has several features that make it an ideal choice for building APIs:

High-performance: FastAPI is built on top of standard Python type hints, which makes it extremely fast.
Async/await: FastAPI supports asynchronous programming, which makes it perfect for real-time applications.
Automatic API documentation: FastAPI generates automatic API documentation using Swagger UI.
Support for WebSockets: FastAPI supports WebSockets, which makes it perfect for real-time applications.
Support for GraphQL: FastAPI supports GraphQL, which makes it perfect for complex queries.

Converting Your Jupyter Notebook into a FastAPI

Now that we've covered the basics of FastAPI, let's dive into converting your Jupyter Notebook into a FastAPI. Here's a step-by-step guide:

Step 1: Install FastAPI

First, you need to install FastAPI using pip:

pip install fastapi

Step 2: Create a New FastAPI Project

Create a new directory for your project and navigate to it in your terminal:

mkdir myproject
cd myproject

Create a new file called main.py and add the following code:

from fastapi import FastAPI

app = FastAPI()

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

This code creates a new FastAPI instance and defines a single route / that returns a JSON response.

Step 3: Convert Your Jupyter Notebook into a FastAPI

Now that we have a basic FastAPI project set up, let's convert your Jupyter Notebook into a FastAPI. Here's how you can do it:

Save your Jupyter Notebook as a Python script: Save your Jupyter Notebook as a Python script using the .py extension.
Import the necessary libraries: Import the necessary libraries in your Python script.
Define a function for each operation: Define a function for each operation in your Jupyter Notebook.
Use the app instance to define routes: Use the app instance to define routes for each function.

Here's an example of how you can convert your Jupyter Notebook into a FastAPI:

from fastapi import FastAPI
import pandas as pd

app = FastAPI()

@app.get("/data")
def read_data():
    # Load data from a CSV file
    df = pd.read_csv("data.csv")
    return df.to_dict(orient="records")

@app.get("/summary")
def read_summary():
    # Calculate summary statistics
    df = pd.read_csv("data.csv")
    summary = df.describe()
    return summary.to_dict()

This code defines two routes: /data and /summary. The /data route loads data from a CSV file and returns it as a JSON response. The /summary route calculates summary statistics and returns it as a JSON response.

Step 4: Run Your FastAPI

Now that we have converted your Jupyter Notebook into a FastAPI, let's run it. Here's how you can do it:

Run the main.py file: Run the main.py file using the following command:

uvicorn main:app --host 0.0.0.0 --port 8000

    This command starts the FastAPI development server on port 8000.
*   **Test your API**: Test your API using a tool like `curl` or a web browser.

### Conclusion

In this article, we've covered the basics of FastAPI and how to convert your existing Python code into a production-ready API. We've also covered how to create a FastAPI project from scratch and how to convert your Jupyter Notebook into a FastAPI. With FastAPI, you can create high-performance APIs that are perfect for real-time applications, web services, and microservices.

### Future Work

In the future, we plan to cover more advanced topics related to FastAPI, such as:

*   **Authentication and authorization**: We'll cover how to implement authentication and authorization in FastAPI using libraries like OAuth2 and JWT.
*   **Database integration**: We'll cover how to integrate FastAPI with databases like MySQL and PostgreSQL.
*   **Testing and debugging**: We'll cover how to test and debug FastAPI applications using tools like Pytest and Debugpy.

### References

*   [FastAPI documentation](https://fastapi.tiangolo.com/)
*   [FastAPI GitHub repository](https://github.com/tiangolo/fastapi)

### Related Articles

*   [Creating a RESTful API with Flask](https://www.fullstackpython.com/flask-restful-api.html)
*   [Building a GraphQL API with Django](https://www.fullstackpython.com/django-graphql-api.html)

### Tags

*   FastAPI
*   API creation
*   Python
*   Jupyter Notebook
*   Web development
*   Real-time applications
*   Web services
*   Microservices<br/>
**FastAPI Q&A: Frequently Asked Questions and Answers**
=====================================================

### Introduction

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It's designed to be fast, scalable, and easy to use. With FastAPI, you can create high-performance APIs that are perfect for real-time applications, web services, and microservices. In this article, we'll answer some of the most frequently asked questions about FastAPI.

### Q1: What is FastAPI?

**A1:** FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It's designed to be fast, scalable, and easy to use.

### Q2: What are the key features of FastAPI?

**A2:** The key features of FastAPI include:

*   **High-performance**: FastAPI is built on top of standard Python type hints, which makes it extremely fast.
*   **Async/await**: FastAPI supports asynchronous programming, which makes it perfect for real-time applications.
*   **Automatic API documentation**: FastAPI generates automatic API documentation using Swagger UI.
*   **Support for WebSockets**: FastAPI supports WebSockets, which makes it perfect for real-time applications.
*   **Support for GraphQL**: FastAPI supports GraphQL, which makes it perfect for complex queries.

### Q3: How do I install FastAPI?

**A3:** You can install FastAPI using pip:
```bash
pip install fastapi

Q4: How do I create a new FastAPI project?

A4: To create a new FastAPI project, you can use the following command:

fastapi new myproject

This will create a new directory called myproject with a basic FastAPI project set up.

Q5: How do I convert my Jupyter Notebook into a FastAPI?

A5: To convert your Jupyter Notebook into a FastAPI, you can follow these steps:

Save your Jupyter Notebook as a Python script: Save your Jupyter Notebook as a Python script using the .py extension.
Import the necessary libraries: Import the necessary libraries in your Python script.
Define a function for each operation: Define a function for each operation in your Jupyter Notebook.
Use the app instance to define routes: Use the app instance to define routes for each function.

Q6: How do I run my FastAPI?

A6: To run your FastAPI, you can use the following command:

uvicorn main:app --host 0.0.0.0 --port 8000

This command starts the FastAPI development server on port 8000.

Q7: How do I test my API?

A7: To test your API, you can use a tool like curl or a web browser. You can also use a testing framework like Pytest to write unit tests for your API.

Q8: How do I deploy my API?

A8: To deploy your API, you can use a cloud platform like AWS or Google Cloud. You can also use a containerization platform like Docker to deploy your API.

Q9: How do I secure my API?

A9: To secure your API, you can use authentication and authorization mechanisms like OAuth2 and JWT. You can also use encryption to protect your API from unauthorized access.

Q10: What are some best practices for building APIs with FastAPI?

A10: Some best practices for building APIs with FastAPI include:

Use clear and concise API documentation: Use clear and concise API documentation to make it easy for developers to understand your API.
Use standard HTTP status codes: Use standard HTTP status codes to indicate the status of your API.
Use secure authentication and authorization mechanisms: Use secure authentication and authorization mechanisms to protect your API from unauthorized access.
Use encryption to protect your API: Use encryption to protect your API from unauthorized access.

Conclusion

In this article, we've answered some of the most frequently asked questions about FastAPI. We've covered topics like installation, project creation, conversion from Jupyter Notebook, running, testing, deployment, security, and best practices. With FastAPI, you can create high-performance APIs that are perfect for real-time applications, web services, and microservices.

Future Work

In the future, we plan to cover more advanced topics related to FastAPI, such as:

Authentication and authorization: We'll cover how to implement authentication and authorization in FastAPI using libraries like OAuth2 and JWT.
Database integration: We'll cover how to integrate FastAPI with databases like MySQL and PostgreSQL.
Testing and debugging: We'll cover how to test and debug FastAPI applications using tools like Pytest and Debugpy.