2019-07-31

学习Python第四天

爬虫

大数据 提取本地html中的数据

1. 新建html文件
2. 读取
3. 使用xpath语法进行提取

使用 lxml 中的xpath

使用lxml提取 h1标签中的内容

from lxml import html

读取html文件

with open('./index.html', 'r', encoding='utf-8') as f:
    html_data = f.read()
    print(html_data)

解析html文件，获得selector对象

 selector = html.fromstring(html_data)

selector中调用xpath方法
要获取标签中的内容，末尾要添加text()

h1 = selector.xpath('/html/body/h1/text()')
    print(h1[0])

可以代表从任意位置出发、
标签1[@属性=属性值]/标签2[@属性=属性值]..../text()

a = selector.xpath('//div[@id="container"]/a/text()')
    print(a)

requests

导入

import requests
 url = 'https://www.baidu.com'
 url = 'https://www.taobao.com/'
 url = 'http://www.dangdang.com/' 
response = requests.get(url)
print(response)

获取str类型的响应

print(response.text)

获取bytes类型的响应

print(response.content)

获取响应头

print(response.headers)

获取状态码

print(response.status_code)
print(response.encoding)

200 ok 404 500
没有添加请求头的知乎网站

 resp = requests.get('https://www.zhihu.com/')
print(resp.status_code)

使用字典定义请求头

headers = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get('https://www.zhihu.com/', headers = headers)
print(resp.status_code)

import requests
from lxml import html
def spider_dangdang(isbn):
 # 目标站点地址
url = 'http://search.dangdang.com/?key={}&act=input'.format(isbn)
print(url)
# 获取站点str类型的响应
headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get(url, headers=headers)
html_data = resp.text
# 将html页面写入本地
with open('dangdang.html', 'w', encoding='utf-8') as f:
f.write(html_data)
# 提取目标站的信息
selector = html.fromstring(html_data)
ul_list = selector.xpath('//div[@id="search_nature_rg"]/ul/li')
print('您好，共有{}家店铺售卖此图书'.format(len(ul_list)))
# 遍历 ul_list
for li in ul_list:
#  图书名称
title = li.xpath('./a/@title')[0].strip()
print(title)
#  图书购买链接
link = li.xpath('a/@href')[0]
print(link)
#  图书价格
price = li.xpath('./p[@class="price"]/span[@class="search_now_price"]/text()')[0]
price = float(price.replace('￥',''))
print(price)
# 图书卖家名称
store = li.xpath('./p[@class="search_shangjia"]/a/text()')
if len(store) == 0:
store = '当当自营'
  else:
 store = store[0]
store = '当当自营' if len(store) == 0 else store[0]
print(store)
spider_dangdang('9787115428028')

import requests
from lxml import html
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def spider_dangdang(isbn):
 book_list = []
# 目标站点地址
url = 'http://search.dangdang.com/?key={}&act=input'.format(isbn)
 print(url)
# 获取站点str类型的响应
 headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get(url, headers=headers)
 html_data = resp.text
  #  将html页面写入本地
 with open('dangdang.html', 'w', encoding='utf-8') as f:
 f.write(html_data)
 # 提取目标站的信息
selector = html.fromstring(html_data)
ul_list = selector.xpath('//div[@id="search_nature_rg"]/ul/li')
print('您好，共有{}家店铺售卖此图书'.format(len(ul_list)))
 # 遍历 ul_list
for li in ul_list:
#  图书名称
title = li.xpath('./a/@title')[0].strip()
print(title)
 #  图书购买链接
 link = li.xpath('a/@href')[0]
print(link)
#  图书价格
 price = li.xpath('./p[@class="price"]/span[@class="search_now_price"]/text()')[0]
price = float(price.replace('￥',''))
print(price)
 # 图书卖家名称
store = li.xpath('./p[@class="search_shangjia"]/a/text()')
if len(store) == 0:
store = '当当自营'
else:
store = store[0]
store = '当当自营' if len(store) == 0 else store[0]
 print(store)

# 添加每一个商家的图书信息
book_list.append({
            'title':title,
            'price':price,
            'link':link,
            'store':store
        })
# 按照价格进行排序
book_list.sort(key=lambda x:x['price'])
# 遍历booklist
    for book in book_list:
        print(book)

# 展示价格最低的前10家 柱状图
# 店铺的名称
top10_store = [book_list[i] for i in range(10)]
 x = []
for store in top10_store:
x.append(store['store'])
x = [x['store'] for x in top10_store]
print(x)
 # 图书的价格
y = [x['price'] for x in top10_store]
print(y)
# plt.bar(x, y)
plt.barh(x, y)
plt.show()
 # 存储成csv文件
df = pd.DataFrame(book_list)
df.to_csv('dangdang.csv')
spider_dangdang('9787115428028')