全國主要城市擁堵前10名的商圈和路段爬蟲，每5分鐘跟新一次

可以看到北京的城市程式碼是110000

通過點選其他城市可以檢視其他城市程式碼

道路擁堵程式碼

import pandas as pd
import requests
import time
while True:
    t = time.strftime("%H%M%S", time.localtime())
    r =requests.get("http://report.amap.com/ajax/roadRank.do?roadType=0&timeType=0&cityCode=110000")#可以換成其他城市程式碼
    s=r.json()
    a=[]
    for i in range(len(s["tableData"])):
        for j in range(len(s["tableData"][i]["coords"])):
            a.append([s["tableData"][i]["coords"][j]["lon"],s["tableData"][i]["coords"][j]["lat"],s["tableData"][i]['name'],s["tableData"][i]['index'],s["tableData"][i]['speed']])
    c = pd.DataFrame(a)
    c.to_csv(t+'road.csv')
    time.sleep(300)
 

商圈爬蟲程式碼

import pandas as pd
import time
import requests
while True:
    t = time.strftime("%H%M%S", time.localtime())
    r =requests.get("http://report.amap.com/ajax/districtRank.do?linksType=3&cityCode=110000")#可以換成其他城市程式碼
    s=r.json()
    a=[]
    for i in range(len(s)):
        for j in range(len(s[i]['coords'][0][0])):
            a.append([s[i]['coords'][0][0][j]['lon'],s[i]['coords'][0][0][j]['lat'],s[i]['name'],s[i]['index'],s[i]['speed'],s[i]['number']])
    c = pd.DataFrame(a)
    c.to_csv(t+'new.csv')
    time.sleep(300)
 

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