1.中国大学排名定向爬取
爬取url:http://www.zuihaodaxue.cn/zuihaodaxuepaiming2019.html
爬取思路:
1.从网络上获取大学排名网页内容
2.提取网页内容中信息到合适的数据结构(二维数组)-排名,学校名称,总分
3.利用数据结构展示并输出结果
# 导入库
import requests
from bs4 import BeautifulSoup
import bs4
1.从网络上获取大学排名网页内容
# 用requests库获取url的信息
url = 'http://www.zuihaodaxue.cn/zuihaodaxuepaiming2019.html'
r = requests.get(url)
r.encoding = 'utf-8'
demo = r.text
2.提取网页内容中信息到合适的数据结构(二维数组)
- 查看网页源代码,观察并定位到需要爬取内容的标签;
- 使用bs4的查找方法提取所需信息-‘排名,学校名称,总分’
ulist = []
# 解析HTML页面
soup = BeautifulSoup(demo,'html.parser')
for tr in soup.find('tbody').children:
# 确定tr的类型
if isinstance(tr, bs4.element.Tag):
tds = tr('td')
# 根据实际提取需要的内容,0:排名;1:学校;2:分数
ulist.append([tds[0].string, tds[1].string, tds[3].string])
3.利用数据结构展示并输出结果
# 对中英文混排输出问题进行优化:对format(),设定宽度和添加参数chr(12288)
tplt = "{0:^10}\t{1:{3}^10}\t{2:^10}"
print(tplt.format('排名', '学校名称', '总分', chr(12288)))
for i in range(30):
u = ulist[i]
print(tplt.format(u[0], u[1], u[2], chr(12288)))
2.爬取丁香园-用户名和回复内容
爬取思路:
- 1.获取url的html
- 2.lxml解析html
- 3.利用Xpath表达式获取user和content
- 4.保存爬取的内容
1.获取url的html
# 导入库
from lxml import etree
import requests
url = "http://www.dxy.cn/bbs/thread/626626#626626"
req = requests.get(url)
html = req.text
2.lxml解析html
tree = etree.HTML(html)
tree
3.利用Xpath表达式获取user和content(重点)
user = tree.xpath('//div[@class="auth"]/a/text()')
# print(user)
content = tree.xpath('//td[@class="postbody"]')
4.保存爬取的内容
results = []
for i in range(0, len(user)):
results.append(user[i].strip() + ": " + content[i].xpath('string(.)').strip())
# 打印结果
for i,result in zip(range(0, len(user)),results):
print("user"+ str(i+1) + "-" + result)
print("*"*100)
3.淘宝商品比价定向爬虫
爬取网址:https://s.taobao.com/search?q=书包&js=1&stats_click=search_radio_all%25
爬取思路:
- 提交商品搜索请求,循环获取页面
- 对于每个页面,提取商品名称和价格信息
- 将信息输出到屏幕上
# 导入包
import requests
import re
- 提交商品搜索请求,循环获取页面
def getHTMLText(url):
"""
请求获取html,(字符串)
:param url: 爬取网址
:return: 字符串
"""
try:
# 添加头信息,
kv = {
'cookie': 'thw=cn; v=0; t=ab66dffdedcb481f77fd563809639584; cookie2=1f14e41c704ef58f8b66ff509d0d122e; _tb_token_=5e6bed8635536; cna=fGOnFZvieDECAXWIVi96eKju; unb=1864721683; sg=%E4%B8%8B3f; _l_g_=Ug%3D%3D; skt=83871ef3b7a49a0f; cookie1=BqeGegkL%2BLUif2jpoUcc6t6Ogy0RFtJuYXR4VHB7W0A%3D; csg=3f233d33; uc3=vt3=F8dBy3%2F50cpZbAursCI%3D&id2=UondEBnuqeCnfA%3D%3D&nk2=u%2F5wdRaOPk21wDx%2F&lg2=VFC%2FuZ9ayeYq2g%3D%3D; existShop=MTU2MjUyMzkyMw%3D%3D; tracknick=%5Cu4E36%5Cu541B%5Cu4E34%5Cu4E3F%5Cu5929%5Cu4E0B; lgc=%5Cu4E36%5Cu541B%5Cu4E34%5Cu4E3F%5Cu5929%5Cu4E0B; _cc_=WqG3DMC9EA%3D%3D; dnk=%5Cu4E36%5Cu541B%5Cu4E34%5Cu4E3F%5Cu5929%5Cu4E0B; _nk_=%5Cu4E36%5Cu541B%5Cu4E34%5Cu4E3F%5Cu5929%5Cu4E0B; cookie17=UondEBnuqeCnfA%3D%3D; tg=0; enc=2GbbFv3joWCJmxVZNFLPuxUUDA7QTpES2D5NF0D6T1EIvSUqKbx15CNrsn7nR9g%2Fz8gPUYbZEI95bhHG8M9pwA%3D%3D; hng=CN%7Czh-CN%7CCNY%7C156; mt=ci=32_1; alitrackid=www.taobao.com; lastalitrackid=www.taobao.com; swfstore=97213; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1%26_ato%3D0; uc1=cookie16=UtASsssmPlP%2Ff1IHDsDaPRu%2BPw%3D%3D&cookie21=UIHiLt3xThH8t7YQouiW&cookie15=URm48syIIVrSKA%3D%3D&existShop=false&pas=0&cookie14=UoTaGqj%2FcX1yKw%3D%3D&tag=8&lng=zh_CN; JSESSIONID=A502D8EDDCE7B58F15F170380A767027; isg=BMnJJFqj8FrUHowu4yKyNXcd2PXjvpa98f4aQWs-RbDvsunEs2bNGLfj8BYE6lWA; l=cBTDZx2mqxnxDRr0BOCanurza77OSIRYYuPzaNbMi_5dd6T114_OkmrjfF96VjWdO2LB4G2npwJ9-etkZ1QoqpJRWkvP.; whl=-1%260%260%261562528831082',
'user-agent': 'Mozilla/5.0'
}
r = requests.get(url, timeout=30, headers=kv)
# r = requests.get(url, timeout=30)
# print(r.status_code)
r.raise_for_status()
r.encoding = r.apparent_encoding
return r.text
except:
return "爬取失败"
- 对于每个页面,提取商品名称和价格信息
def parsePage(glist, html):
'''
解析网页,搜索需要的信息
:param glist: 列表作为存储容器
:param html: 由getHTMLText()得到的
:return: 商品信息的列表
'''
try:
# 使用正则表达式提取信息
price_list = re.findall(r'\"view_price\"\:\"[\d\.]*\"', html)
name_list = re.findall(r'\"raw_title\"\:\".*?\"', html)
for i in range(len(price_list)):
price = eval(price_list[i].split(":")[1]) #eval()在此可以去掉""
name = eval(name_list[i].split(":")[1])
glist.append([price, name])
except:
print("解析失败")
- 输出信息
def printGoodList(glist):
tplt = "{0:^4}\t{1:^6}\t{2:^10}"
print(tplt.format("序号", "商品价格", "商品名称"))
count = 0
for g in glist:
count = count + 1
print(tplt.format(count, g[0], g[1]))
# 根据页面url的变化寻找规律,构建爬取url
goods_name = "书包" # 搜索商品类型
start_url = "https://s.taobao.com/search?q=" + goods_name
info_list = []
page = 3 # 爬取页面数量
count = 0
for i in range(page):
count += 1
try:
url = start_url + "&s=" + str(44 * i)
html = getHTMLText(url) # 爬取url
parsePage(info_list, html) #解析HTML和爬取内容
print("\r爬取页面当前进度: {:.2f}%".format(count * 100 / page), end="") # 显示进度条
except:
continue
printGoodList(info_list)
本文介绍了三个Python爬虫实例:1.抓取中国大学排名,使用正则表达式处理网页内容;2.从丁香园网站通过XPath获取用户名和回复内容;3.实现淘宝商品比价爬虫,提取商品名称和价格。每个实例包含获取HTML、解析数据和输出结果的步骤。
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