爬虫解析Xpath 之(二) --- Xpath模块

1 from lxml import etree
2 html_tree = etree.parse("./test.html")
3 
4 print(html_tree)
5 输出结果： <lxml.etree._ElementTree object at 0x0000028A81E566C8>
6 
7 xpath语法中"/"代表当前节点的子节点 "//"代表当前节点的后代节点 如果以“/”代表从根节点开始查找
8 xpath函数，传入一个字符串参数，代表的是xpath路径，用于定位目标节点，返回值是一个列表，列表中定位到测那些节点
9 「注意」在xpath语法中数字都是从1开始数，没有0序号也没有负数

1 ret = html_tree.xpath("/html/body/ol/li[1]")
2 ret = html_tree.xpath("/html/body/div/div[1]/a")     # 里面用xpath路径来定位目标节点

1 ret = html_tree.xpath("/html/body/div/div[1]/a/text()")    # 提取标签的内容
2 
3 ret = html_tree.xpath("/html/body/div/div[1]/a/@href")    # 提取href属性，【注意】xpath语法中所有的节点属性要在前面加上“@ ”符号

1 层级定位
2 ret = html_tree.xpath("/html/body//li/text()")     # 获取页面上的所有的li

1 属性定位
2 
3 ret = html_tree.xpath("/html/body//li[@id]/text()")                       # 查找页面上所有带有id属性的li
4 ret = html_tree.xpath("/html/body//li[@class=‘dudu‘]/text()")             # 查找页面上所有的class属性为dudu的li
5 ret = html_tree.xpath("/html/body//li[@class=‘tanshui taohua‘]/text()")   # 属性的值一定写全

1 ret = html_tree.xpath("/html/body//li[contains(@class,‘he‘)]/text()")     #包含：查找所有class值中包含he的li
2 ret = html_tree.xpath("/html/body//li[starts-with(@class,‘h‘)]/text()")   # 开头：查找所有的class值以h开头的li

1 ret = html_tree.xpath("/html/body//li[@class and @id]/text()")     # 与：查找所有的包含id属性和class属性的那些li
2 ret = html_tree.xpath("//li[@class=‘nene‘ or @id=‘hh‘]/text()")    # 或：查找所有的id值为hh，或者class值为neme的li
3 print(ret)

1 ol = html_tree.xpath("//ol[2]")[0] print(ol)    # 查找第二个ol
2 
3 从上面查找到的ol中提取li "."代表当前 ".."代表当前的上一级
4 ret = ol.xpath("//li/text()")    # 用绝对路径来提取，无论xpath函数前面用谁调用，都是从文档的跟节来提取点
5 ret = ol.xpath("..//li/text()")  # 用相对路径来提取，从xpath前面调用对象来查找
6 
7 print(ret)

 1 from urllib import request,parse
 2 from time import sleep
 3 from lxml import etree
 4 import re
 5 import json
 6 import csv
 7 import redis
 8 
 9 # 1、【数据的获取】
10 def request_from(url,page,city):
11     page_url = url%(city,page)
12     req = request.Request(headers={‘User-Agent‘: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.80 Safari/537.36‘},url=page_url)
13     return req
14 def get_pages(url,start,end,city):
15     # 创建请求对象
16     for page in range(start,end+1):
17         req = request_from(url=url,page=page,city=city)
18         # 发起请求
19         res = request.urlopen(req)
20         sleep(1)
21         html = res.read().decode("utf-8")
22 
23         yield html
24 
25 # 2、【数据的解析】
26 def anylasis_data(pages):
27     for page in pages:
28         # 用etree将页面转成节点树
29         page_tree = etree.HTML(page)
30         house_list = page_tree.xpath("//ul[@class=‘sellListContent‘]/li")
31         # print(house_list)
32         # 迭代每一个li（每一个房屋信息内容）
33         for house in house_list:
34             # 提取内容
35             # 创建一个item字典，用于整合每一个房屋信息
36             item = {}
37             item["title"] = house.xpath(".//div[@class=‘title‘]//a/text()")[0]
38             item["houseInfo"] = "".join(house.xpath(".//div[@class=‘houseInfo‘]//text()"))
39             item["positionInfo"] = "".join(house.xpath(".//div[@class=‘positionInfo‘]//text()"))
40             item["unitPrice"] = re.findall(pattern=r‘[0-9]+‘,string=house.xpath(".//div[@class=‘unitPrice‘]//text()")[0])[0]
41             item["totalPrice"] = house.xpath(".//div[@class=‘totalPrice‘]//text()")[0]
42             item["picUrl"] = house.xpath(".//img[@class=‘lj-lazy‘]/@data-original")[0]
43 
44             yield item
45 
46 # 3、【数据的存储】
47 def write_to_json(houses):
48     # 整合json数据
49     # 创建一个字典用于整合所有的房屋数据
50     hd = {}
51     # 创建一个列表，用于存储每一个房屋的信息
52     hl = []
53     for house in houses:
54         hl.append(house)
55     hd["house"] = hl
56     # print(hd)
57     with open("house.json",‘w‘,encoding=‘utf-8‘) as fp:
58         fp.write(json.dumps(hd))
59 
60 def write_to_redis(houses):
61     # 创建redis数据库连接
62     rds = redis.StrictRedis(host="www.fanjianbo.com",port=6379,db=6)
63     for house in houses:
64         rds.lpush("ershoufang",house)
65 
66 def write_to_csv(houses):
67     # 打开一个csv文件
68     fp = open("ershoufang.csv","a+")
69     # 创建一个写对象
70     writer = csv.writer(fp)
71     # 写表头
72     writer.writerow(["title","houseInfo","positionInfo","unitPrice","totalPrice","picUrl"])
73     for house in houses:
74         # csv二维表的每一行是一个列表
75         values = []
76         for k,v in house.items():
77             values.append(v)
78         writer.writerow(values)
79     fp.close()
80 
81 if __name__ == ‘__main__‘:
82     url = "https://%s.lianjia.com/ershoufang/pg%d/"
83     city = input("请输入城市简称：")
84     start = int(input("请输入起始页："))
85     end = int(input("请输入终止页："))
86     pages = get_pages(url=url,city=city,start=start,end=end)
87     # print(pages)
88     houses = anylasis_data(pages)
89     # 存入json
90     write_to_csv(houses)