


How to Return JSON Data in FastAPI: Automatic vs. Manual Conversion?
How to return data in JSON format using FastAPI?
To return data in JSON format using FastAPI, you can use the jsonable_encoder encoder to convert Python data structures into JSON-compatible data. This can be achieved using either of the following options:
Option 1: Using the jsonable_encoder Automatically
Return data as usual, and FastAPI will automatically handle the JSON conversion. FastAPI internally uses the jsonable_encoder to convert the data to JSON-compatible format. The jsonable_encoder ensures that unsupported objects, like datetime objects, are converted to strings. FastAPI then wraps the data in a JSONResponse object with an application/json media type, which the client receives as a JSON response.
1 2 3 4 5 6 |
|
Option 2: Manual JSON Conversion
If you need to perform custom JSON conversion, you can directly return a Response object with the media_type set to 'application/json' and the content set to the JSON-encoded data. Remember to use the json.dumps() function with the default=str argument to ensure that unsupported objects are converted to strings before being encoded as JSON.
1 2 3 4 5 6 7 |
|
Additional Notes:
- By default, FastAPI adds a Content-Length and Content-Type header to the response.
- You can specify a custom status code for the response by setting the status_code attribute of the Response or JSONResponse object.
The above is the detailed content of How to Return JSON Data in FastAPI: Automatic vs. Manual Conversion?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.
