我们做用户分析时离不开用户来源分析，在很多网站/交易平台，我们的用户在没有注册或产生消费时，是没有用户自行上传的来源信息的。

所以我们程序猿或者数据分析师，都会根据用户访问的IP地址，来做进一步划分判断，这样便能统计到观望我们的用户分布情况。

甚至有些黑客，恶意利用国外IP访问，恶意攻击我们平台，当我们确认IP来源后，就可以事先屏蔽一些恶意攻击行为。

接口

urls = r'https://open.onebox.so.com/dataApi?callback=jQuery18309089439851148142_1533546946966&type=ip&src=onebox&tpl=0&num=1&query=ip&ip=' + str(ips) + '&url=ip' + str(int(time.time() * 1000))

案例数据源

python解析代码

# coding: utf-8

# -*- coding: utf-8 -*-

import urllib.request

import urllib.parse

from urllib.error import  HTTPError ,URLError

import socket  #请求超时异常

from bs4 import BeautifulSoup

import requests

import time

import json

import csv

import re

import random

import datetime

import pymysql
def response(url):

    try:

        headers = { }

        headers['User-Agent']='Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36'

        #random.seed(datetime.datetime.now())

        req = urllib.request.Request(url, headers=headers)

        bsobj = urllib.request.urlopen(req, timeout=60).read().decode('utf-8')

        bsobj = str(BeautifulSoup(bsobj,'html.parser'))

        #print(bsobj)

        bsobjs = re.findall(r'\{.*\}', bsobj)

        bsobjs = ' '.join(bsobjs)

        #print(bsobjs)

        jsonobj = json.loads(bsobjs)

        #print(jsonobj)

        country = jsonobj.get('0')

        province = jsonobj.get('1')

        if (province)==None:

            province = country

        else:

            province = province

        city = jsonobj.get('2')

        if (city)==None and province != None:

            city = province

        elif (city)==None and province == None:

            city = country

        else:

            city = city

        classification = jsonobj.get('5')

        ip_id = jsonobj.get('ip')

        Local = jsonobj.get('4')

        print(ip_id, country ,province ,city,classification,Local)

        writer.writerows([[ip_id, country ,province ,city,classification,Local]])

except (HTTPError, URLError, socket.timeout, AttributeError,UnicodeEncodeError) as e: return if __name__ == '__main__': #创建存储表 file_name = r'D:\Case_data/360IP归属地查询' + '.csv' f = open(file_name, 'w+', newline='',encoding = 'gb18030') writer = csv.writer(f, dialect='excel') # 先写入columns_name writer.writerow(['ip_id', 'country' ,'province' ,'city','classification','Local']) # 获取ip数据 with open(r'D:\Case_data/ips.csv','rt',encoding='gb18030') as csvfile: reader = csv.reader(csvfile) column = [row[3] for row in reader] #print (column) for ips in column: #print(url) rand = random.randint(2, 5) time.sleep(rand)#延时提交 urls = r'https://open.onebox.so.com/dataApi?callback=jQuery18309089439851148142_1533546946966&type=ip&src=onebox&tpl=0&num=1&query=ip&ip=' + str(ips) + '&url=ip' + str(int(time.time() * 1000)) #print(urls) response(urls) f.close()

分类汇总

## 按照城市聚合并倒序import numpy as npimport pandas as pd 

df = pd.read_csv(r'D:\Case_data/360IP归属地查询.csv',engine='python')display(df.head(5))gd = df.groupby(['province']).agg({'province':['count']})gd.columns=['数量']  gd = gd.sort_values(by=['数量'],ascending=False) # 根据某些列进行排序gd['占比']=(((gd['数量']/df['province'].count())*100).round(2).astype('str'))+'%'display(gd.head(5))

批量获取IP地址来源，做分类汇总！