用urllib模块爬取京东笔记本电脑的数据、并对其做一个可视化
文章目录
一、前言
马上就要高考了,很多同学在高考完后都会考虑买一个笔记本电脑,我这里对京东上的前2页的笔记本的好评论数,价格,店铺等信息进行爬取,并做一个可视化,根据可视化的图,大家可以清晰的做出预测,方便大家购买划算的电脑。当然,我这里前2页的数据是远远不够的,如果大家想要预测的更精准一些,可以改一下数字,获取更多页面的数据,这样,预测结果会更精确。
二、知识要求
三、过程分析
1.观察主页面和每个电脑界面的网址
(1)观察具体界面的网址,我们可以猜测,具体每个界面都有一个id,通过构造网址https://item.jd.com/【id】.html
,就可以得到具体每个界面的网址。
(2)观察主界面的网址,我们发现page=
的属性值就是具体的页码数,通过构造page的值,我们可以实现自动翻页爬取信息。对主界面网址一些不必要的信息剔除,最后得到主界面翻页的网址规律https://list.jd.com/list.html?cat=670,671,672&page=【页码数】
同过以上的分析,我们可以看见,获取信息的关键就是每个电脑的具体id代号,接下来,我们的任务就是要找到每个电脑的id。
2.寻找每个电脑的id
(1)首先,看看网页源代码中是否会有每个电脑的id
我们再进入到刚刚搜索的哪个电脑名称的具体界面,发现,确实是他的id
(3)根据id附件的一些属性值,唯一确定所有电脑id
根据class="gl-i-wrap j-sku-item"
属性值定位,发现,唯一确定60个id,数了一下界面上的电脑,一页确实是60个电脑,所以,电脑的id获取到了。
(4)同理,根据<div class="p-name">
属性值获取具体每个电脑的网址和电脑名,这样我们连具体每个电脑的网址都不用构造了,直接可以获取。
3.找到存放电脑的价格和评论数的信息
(1)通过到网页源代码中去找,发现完全找不到,所以,我猜测这些信息隐藏在js包中。
(2)打开fiddler
抓包工具,进行抓包分析。
可以看见,这些信息确实是在js包里面,复制该js包的网址,然后分析。
(3)分析有如下结论:
这里,我也抓到了存放店铺的js包,但是,这个js包的地址每次有一部分是随机生成的,所以,获取不到每台的电脑的店铺名。但是,我有每台电脑的具体网址,而该界面里面有该电脑的店铺,所以,我可以访问每台电脑的具体界面去获取到店铺消息。
4.爬取信息的思路
(1)先爬每页的信息
(2)再爬每页中每台电脑的价格、电脑名和评论数,以及每台电脑的网址
(3)爬取每台电脑的页面,获取店铺信息
(4)获取完所有页面信息后,做一个可视化
四、urllib模块爬取京东笔记本电脑的数据、并对其做一个可视化实战
爬虫文件:
# -*- coding: utf-8 -*-
import random
import urllib.request
import re
import time
from lxml import etree
from pyecharts import Bar
from pyecharts import Pie
headers = [
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)",
"Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",
"Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)",
"Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)",
"Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)",
"Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0",
"Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5",
"Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20",
"Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; LBBROWSER)",
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 LBBROWSER",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E)",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; QQBrowser/7.0.3698.400)",
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; 360SE)",
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E)",
"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.89 Safari/537.1",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.89 Safari/537.1",
"Mozilla/5.0 (iPad; U; CPU OS 4_2_1 like Mac OS X; zh-cn) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8C148 Safari/6533.18.5",
"Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:2.0b13pre) Gecko/20110307 Firefox/4.0b13pre",
"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:16.0) Gecko/20100101 Firefox/16.0",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11",
"Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10"
]
def main():
# 用来存放所有的电脑数据
allNames = []
allCommentNums = {}
allPrices = {}
allShops = {}
# 爬取前2页的所有笔记本电脑
for i in range(0, 1):
# 每页地址规律:https://list.jd.com/list.html?cat=670,671,672&page=【页码】
print('正在爬取第'+str(i+1)+'页的信息...')
url = 'https://list.jd.com/list.html?cat=670,671,672&page='+str(i+1)
get_page_data(url, allNames, allCommentNums, allPrices, allShops)
# 以上为获取信息,以下为数据的可视化
names = allNames
commentNums = []
for name in names:
if allCommentNums[name] == None:
commentNums.append(0)
else:
commentNums.append(eval(allCommentNums[name]))
prices = []
for name in names:
if allPrices[name] == None:
prices.append(0)
else:
prices.append(eval(allPrices[name]))
shops = []
for name in names:
if allShops[name] != None:
shops.append(allShops[name])
for i in range(0, len(names)):
print(names[i])
print(commentNums[i])
print(prices[i])
print(shops[i])
# 将其评论数进行条形统计图可视化
tiaoxing(names, prices)
# 将其店铺进行饼图可视化
# 先需要统计每个店铺的个数
shopNames = list(set(shops))
nums = []
for i in range(0, len(shopNames)):
nums.append(0)
for shop in shops:
for i in range(0, len(shopNames)):
if shop == shopNames[i]:
nums[i] += 1
bingtu(shopNames, nums)
def get_page_data(url, allNames, allCommentNums, allPrices, allShops):
# 爬取该页内所有电脑的id、电脑名称和该电脑的具体网址
response = urllib.request.Request(url)
response.add_header('User-Agent', random.choice(headers))
data = urllib.request.urlopen(response, timeout=1).read().decode('utf-8', 'ignore')
data = etree.HTML(data)
ids = data.xpath('//a[@class="p-o-btn contrast J_contrast contrast-hide"]/@data-sku')
names = data.xpath('//div[@class="p-name"]/a/em/text()')
hrefs = data.xpath('//div[@class="p-name"]/a/@href')
# 去掉重复的网址
print(len(hrefs))
hrefs = list(set(hrefs))
print(len(hrefs))
# 将每个电脑的网址构造完全,加上'https:'
for i in range(0, len(hrefs)):
hrefs[i] = 'https:'+hrefs[i]
# 根据id构造存放每台电脑评论数的js包的地址
# 其网址格式为:https://club.jd.com/comment/productCommentSummaries.action?my=pinglun&referenceIds=100000323510,100002368328&callback=jQuery5043746
str = ''
for id in ids:
str = str + id + ','
commentJS_url = 'https://club.jd.com/comment/productCommentSummaries.action?my=pinglun&referenceIds='+str[:-1]+'&callback=jQuery5043746'
# 爬取该js包,获取每台电脑的评论数
response2 = urllib.request.Request(commentJS_url)
response2.add_header('User-Agent', random.choice(headers))
data = urllib.request.urlopen(response2, timeout=1).read().decode('utf-8', 'ignore')
pat = '{(.*?)}'
commentStr = re.compile(pat).findall(data) # commentStr用来存放每个商品的关于评论数方面的所有信息
comments = {}
for id in ids:
for str in commentStr:
if id in str:
pat2 = '"CommentCount":(.*?),'
comments[id] = re.compile(pat2).findall(str)[0]
print("ids为:", len(ids),ids)
print("name为:", len(names), names)
print("评论数为:", len(comments), comments)
# 根据id构造存放每台电脑价格的js包的地址
# 其网址格式为:https://p.3.cn/prices/mgets?callback=jQuery1702366&type=1&skuIds=J_7512626%2CJ_44354035037%2CJ_100003302532
str = ''
for i in range(0, len(ids)):
if i == 0:
str = str + 'J_' + ids[i] + '%'
else:
str = str + '2CJ_' + ids[i] + '%'
priceJS_url = 'https://p.3.cn/prices/mgets?callback=jQuery1702366&type=1&skuIds=' + str[:-1]
# 爬取该js包,获取每台电脑的价格
response3 = urllib.request.Request(priceJS_url)
response3.add_header('User-Agent', random.choice(headers))
data = urllib.request.urlopen(response3, timeout=1).read().decode('utf-8', 'ignore')
priceStr = re.compile(pat).findall(data) # priceStr用来存放每个商品关于价格方面的信息
prices = {}
for id in ids:
for str in priceStr:
if id in str:
pat3 = '"p":"(.*?)"'
prices[id] = re.compile(pat3).findall(str)[0]
print("价格为:", prices)
# 爬取每个商品的店铺,需要进入到对应的每个电脑的页面去爬取店铺信息
shops = {}
for id in ids:
for href in hrefs:
if id in href:
try:
response4 = urllib.request.Request(href)
response4.add_header('User-Agent', random.choice(headers))
data = urllib.request.urlopen(response4, timeout=1).read().decode('gbk', 'ignore')
shop = etree.HTML(data).xpath('//*[@id="crumb-wrap"]/div/div[2]/div[2]/div[1]/div/a/@title')
print(shop)
if shop == []:
shops[id] = None
else:
shops[id] = shop[0]
time.sleep(2)
except Exception as e:
print(e)
# 先去掉电脑名两边的空格和换行符
[name.strip() for name in names]
# 将数据分别添加到item中
for name in names:
allNames.append(name)
# 名字对应评论数的字典形式
for i in range(0, len(ids)):
if comments[ids[i]] == '':
allCommentNums[names[i]] = None
else:
allCommentNums[names[i]] = comments[ids[i]]
# 名字与价格对应起来
for i in range(0, len(ids)):
if prices[ids[i]] == '':
allPrices[names[i]] = None
else:
allPrices[names[i]] = prices[ids[i]]
# 名字与店铺对应起来
for i in range(0, len(ids)):
allShops[names[i]] = shops[ids[i]]
def tiaoxing(names, prices):
bar = Bar("笔记本电脑价格图", "X-电脑名,Y-价格")
bar.add("笔记本电脑", names, prices)
bar.show_config()
bar.render("D:\\scrapy\\jingdong\\prices.html")
def bingtu(shopNames, nums):
attr = shopNames
v1 = nums
pie = Pie("笔记本店铺饼图展示")
pie.add("", attr, v1, is_label_show=True)
pie.show_config()
pie.render("D:\\scrapy\\jingdong\\shops.html")
if __name__ == '__main__':
main()
五、可视化结果
1.运行结果
2.可视化结果
评论数条形统计图:
店铺扇形统计图:
可以看见联想的电脑买的最好。