如何使用 FastAPI 以 JSON 格式返回 a.csv 文件/Pandas DataFrame？

Question

I have a .csv file that I want to render in a FastAPI app. I only managed to render the .csv file in JSON format as follows:

def transform_question_format(csv_file_name):

    json_file_name = f"{csv_file_name[:-4]}.json"

    # transforms the csv file into json file
    pd.read_csv(csv_file_name ,sep=",").to_json(json_file_name)

    with open(json_file_name, "r") as f:
        json_data = json.load(f)

    return json_data

@app.get("/questions")
def load_questions():

    question_json = transform_question_format(question_csv_filename)

    return question_json

When I tried returning directly pd.read_csv(csv_file_name,sep=",").to_json(json_file_name) , it works, as it returns a string.

How should I proceed? I believe this is not the good way to do it.

Answer 1

The below shows four different ways of returning the data from a .csv file/Pandas DataFrame.

Option 1 is to convert the file data into JSON and then parse it into a dict . You can optionally change the orientation of the data using the orient parameter in the to_json() method.

• Update 1 : Using to_dict() method might be a better option, as there is no need for parsing the JSON string.
• Update 2 : When using to_dict() method and returning the dict , FastAPI, behind the scenes, automatically converts that return value into JSON , using the jsonable_encoder . Thus, to avoid that extra processing, you could still use to_json() method, but instead of parsing the JSON string, put it in a Response and return it directly, as shown in the example below.

Option 2 is to return the data in string format, using to_string() method.

Option 3 is to return the data as an HTML table, using to_html() method.

Option 4 is to return the file as is using FastAPI's FileResponse .

from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
from fastapi.responses import HTMLResponse
import pandas as pd
import json

df = pd.read_csv("file.csv")
app = FastAPI()

def parse_csv(df):
    res = df.to_json(orient="records")
    parsed = json.loads(res)
    return parsed
    
@app.get("/questions")
def load_questions():
    return Response(df.to_json(orient="records"), media_type="application/json")  # Option 1 (Updated 2): Return as JSON directly
    #return df.to_dict(orient="records")  # Option 1 (Updated 1): Return as dict (encoded to JSON behind the scenes)
    #return parse_csv(df)  # Option 1: Parse the JSON string and return as dict (encoded to JSON behind the scenes)
    #return df.to_string()  # Option 2: Return as string
    #return HTMLResponse(content=df.to_html(), status_code=200)  # Option 3: Return as HTML Table
    #return FileResponse(path="file.csv", filename="file.csv")   # Option 4: Return as File