


Returning JSON Formatted Data with FastAPI
When developing a FastAPI application, it's essential to understand the nuances of returning JSON formatted data. This requires delving into the inner workings of FastAPI and comprehending the role played by JSON serialization.
The Dilemma with JSON Serialization
At the heart of the issue lies the use of json.dumps() to serialize objects before returning them. While this approach may seem logical, it introduces redundant serialization as FastAPI automatically JSON-encodes the return value during response generation. This leads to a seemingly incorrect string representation of JSON data rather than a neatly formatted dict.
The Solution
To rectify this, you must allow FastAPI to handle the JSON serialization process. This can be achieved by returning data objects (dicts, lists, etc.) directly. FastAPI will seamlessly convert them to JSON-compatible data using the jsonable_encoder and wrap it in a JSONResponse. The resulting response will contain application/json encoded data, ensuring the desired JSON format.
Implementation Options
Option 1: Using Return Values
Return data objects as you would expect:
@app.get('/') async def main(): return d
Behind the scenes, FastAPI will serialize the dict (d) using JSONResponse and encode it with json.dumps().
Option 2: Custom Response Objects
If you require precise control over the response, utilize the Response object directly:
@app.get('/') async def main(): return Response(content=json.dumps(d, indent=4, default=str), media_type='application/json')
This approach grants you freedom over the media_type (e.g., 'application/json'), providing customizability.
Note: The default argument to json.dumps() (str) allows for the serialization of datetime objects. By passing an indent, you can control the formatting of the JSON output.
The above is the detailed content of How Can I Efficiently Return JSON-Formatted Data in a FastAPI Application?. For more information, please follow other related articles on the PHP Chinese website!

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.


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

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Chinese version
Chinese version, very easy to use

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Atom editor mac version download
The most popular open source editor

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function
