将 JSON 映射到 CSV

Question

我尝试从站点 XHR 请求数据，然后将其保存为 CSV。响应是 JSON，响应的一部分是嵌套的。 我有两个问题：

• 首先：它不会迭代。它只返回第一个对象的数据，但在两行中。除了第一行有 floorPlan:AltText 和第二行有 floorPlan:url 的最后一列之外，两行上的数据相同
• 第二：我不喜欢我的某些字符“'charmap'编解码器无法在位置 147:...编码字符'\u2560'。”。似乎是一些 UTF-8 问题

响应的格式是（为了便于阅读，这里缩短了）：

[{
"id":"67d3686f-848b-e911-a971",
"name":"1302",
"url":"/site/",
"residenceType":"3",
"objectStatus":"1",
"price":570000.0,
"fee":245.0,
"apartmentNumber":"1302",
"address":"Major street 8",
"rooms":4.0,
"floor":3.0,
"primaryArea":92.0,
"inhabitDate":"2022-02-28T23:00:00Z",
"floorPlan":{"url":"/externalfiles/image/1.jpg","altText":"Drawing"}},
{"id":"69d3686f-848b-e911-a971-000d3ab795ed",
"name":"1303",
"url":"/site2/",
"residenceType":"3",
"objectStatus":"1",
"price":320000.0,
"fee":113.0,
"apartmentNumber":"1303",
"address":"Major Street 8",
"rooms":2.0,
"floor":3.0,
"primaryArea":47.0,
"inhabitDate":"2022-02-28T23:00:00Z",
"floorPlan":{"url":"/externalfiles/image/2.jpg","altText":"Drawing"}},

我的代码是：

import requests
import pandas as pd
import csv
import json

h = {
     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
     'X-Requested-With': 'XMLHttpRequest',
}

u = "https://cdn-search-standard-prod.azureedge.net/api/v1/search/getstageobjects/23d8dbc1-005a-e911-a961-000d3aba65fd"

x = requests.get(u,headers=h).json()

f = csv.writer(open("test.csv", "w+"))

f.writerow(["id", "name", "residenceType", "objectStatus", "price", "fee", "apartmentNumber"])

for x in x:
    f.writerow([x["id"],
                x["name"],
                x["residenceType"],
                x["objectStatus"],
                x["price"],
                x["fee"],
                x["apartmentNumber"],
                x["floorPlan"]["url"]])
    df=pd.DataFrame(x)
    df.to_csv(r'C:\Users\abc\Documents\Python Scripts\file_20200627.csv', index=False, sep=';',encoding='utf-8')

Answer 1

如果您想将 x 转换为 csv，请使用 pandas，您正在尝试将 x 转换为 csv，但 x 不是 csv 写入器对象，而且您正在同时写入行

import requests
import pandas as pd
import csv
import json

h = {
     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
     'X-Requested-With': 'XMLHttpRequest',
}

u = "https://cdn-search-standard-prod.azureedge.net/api/v1/search/getstageobjects/23d8dbc1-005a-e911-a961-000d3aba65fd"

x = requests.get(u,headers=h).json()

f = csv.writer(open("test.csv", "w+"))

f.writerow(["id", "name", "residenceType", "objectStatus", "price", "fee", "apartmentNumber"])
print(x)
for x in x:
    f.writerow([x["id"],
                x["name"],
                x["residenceType"],
                x["objectStatus"],
                x["price"],
                x["fee"],
                x["apartmentNumber"],
                x["floorPlan"]["url"]])
df=pd.DataFrame(x)
df.to_csv(r'C:\Users\abc\Documents\file.csv', index=False, sep=';',encoding='utf-8')

输出

id  name    url     residenceType   objectStatus    price   fee     secondaryArea   apartmentNumber     address     rooms   floor   maxFloor    concept     primaryArea     plotArea    inhabitDate     inhabitDateEnd  inhabitDateTimeStamp    inhabitDateDetermined   swanLabel   parkingSpaceStatus  floorPlan
altText     75d3686f-848b-e911-a971-000d3ab795ed    1504    /stockholm-lan/jarfalla-kommun/bolinder-strand...   3   1   3840000.0   1974.0  0.0     1504    Fabriksvägen 8  3.0     5.0     15.0    782ffe43-f9ef-40eb-8d92-cfd90aa6b147    73.0    0.0     2022-02-28T23:00:00Z    2022-04-30T22:00:00Z    1646089200000   False   True    1   Planritning
url     75d3686f-848b-e911-a971-000d3ab795ed    1504    /stockholm-lan/jarfalla-kommun/bolinder-strand...   3   1   3840000.0   1974.0  0.0     1504    Fabriksvägen 8  3.0     5.0     15.0    782ffe43-f9ef-40eb-8d92-cfd90aa6b147    73.0    0.0     2022-02-28T23:00:00Z    2022-04-30T22:00:00Z    1646089200000   False   True    1   /externalfiles/image/23d8dbc1-005a-e911-a961-0...

输出 csv 文件