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项目内容：
本项目选择 淘宝商品类目：零食
数量：一共100页，4400个零食商品
####项目环境：
系统环境：win10 64位
工具：pycharm，chrome devTools，Anaconda
####一、爬取数据
因为淘宝网是有反爬虫机制的，虽然我使用了多线程、修改headers参数，以及使用代理ip等，也考虑到我当前测试环境是使用校园网进行爬取淘宝商品信息的，学校只有一个公网ip，按照以往的经验，使用校园网做测试环境的话是不容易被封的，但仍然不能保证每次100%爬取，所以我增加了循环爬取，每次循环爬取未爬取成功的页面，直至所有的页面全部爬取成功。
淘宝商品页面上存储的商品数据是以Json格式存储的，在这里我选择用正则表达式进行解析：
代码如下：
import re
import time
import random
import requests
import pandas as pd
from retrying import retry
from concurrent.futures import ThreadPoolExecutor
'''
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'''
start = time.clock() # 开始计时
# 请求头池
user_agent = [
"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",
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 "
"Safari/537.36",
]
# 代理ip池
proxies = ['http://125.71.212.25:9000', 'http://202.109.157.47:9000', 'http://47.94.169.110:80',
'http://111.40.84.73:9999', 'http://114.245.221.21:8060', 'http://117.131.235.198:8060']
# plist 为1-100页的URL的编号num
plist = []
for i in range(1, 101):
j = 44 * (i - 1)
plist.append(j)
listno = plist
datatmsp = pd.DataFrame(columns=[])
while True:
@retry(stop_max_attempt_number=8)
def network_programming(num):
url = 'https://s.taobao.com/search?q=%E9%9B%B6%E9%A3%9F&imgfile=&js=1&stats_click=search_radio_tmall%3A1' \
'&initiative_id=staobaoz_20190508&tab=mall&ie=utf8&sort=sale-desc&filter=reserve_price%5B%2C200%5D' \
'&bcoffset=0&p4ppushleft=%2C44&s=' + str(num)
random_user_agent = random.choice(user_agent) # 从user_agent池中随机生成headers
random_proxies = random.choice(proxies) # 从代理ip池中随机生成proxies
web = requests.get(url, headers={'user-agent': random_user_agent}, proxies={'http': random_proxies})
web.encoding = 'utf-8'
return web
# 多线程
def multithreading():
number = listno # 每次爬取未成功爬取的页
event = []
with ThreadPoolExecutor(max_workers=10) as executor:
for result in executor.map(network_programming, number, chunksize=10):
event.append(result)
return event
headers = {"User-Agent": "Mozilla/5.0 (WindowsNT 10.0; WOW64);Chrome/55.0.2883.87 Safari/537.36"}
listpg = []
event = multithreading()
for i in event:
json = re.findall('"auctions":(.*?),"recommendAuctions"', i.text)
if len(json):
table = pd.read_json(json[0])
datatmsp = pd.concat([datatmsp, table], axis=0, ignore_index=True)
pg = re.findall('"pageNum":(.*?),"p4pbottom_up"', i.text)[0] # 记入每一次成功爬取的页码
listpg.append(pg)
# 将爬取成功的页码转为url中的num值
lists = []
for a in listpg:
b = 44 * (int(a) - 1)
lists.append(b)
listn = listno
listno = []
for p in listn:
if p not in lists:
listno.append(p)
# 当未爬取页数未0时，终止循环
if len(listno) == 0:
break
datatmsp.to_excel('datatmsp.xls', index=False)
end = time.clock()
print("爬取完成 用时：", end - start, 's')
爬取到商品数据我是先以Excel文件的xls格式保存存储到本地上，方便调试，以下图1.1是已经爬取到的数据。