学习爬虫，拿豆瓣电影进行练手，无奈豆瓣电影存在反爬机制，爬完250就会重定向要求我进行登陆操作，所以我这一次只爬取前50进行相关测试，废话不多说，我们来看下源代码：
代码语言：javascript复制import requests
from bs4 import BeautifulSoup
import re
import pandas
headers = {
'Host':'movie.douban.com',
'Origin':'movie.douban.com',
'User-Agent':'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Mobile Safari/537.36',
}
base_url = 'https://movie.douban.com/top250?start={}&filter='
response = requests.get('https://movie.douban.com/top250?start=0&filter=', headers = headers)
if response.status_code == 200:
# print(response.text)
pass
pattern1 = re.compile('<div.*?class="item">.*?<div.*?class="pic">.*?<a.*?href="(.*?)">', re.S) # 去掉所有换行符，并用正则表达式去匹配每一个页面的具体电影
urls = re.findall(pattern1, response.text)
directors = [] # 导演
names = [] # 电影名
stars = [] # 主演
countrys = [] # 电影的出产地
languages = [] # 电影语言
headers_urls = {
'Host':'movie.douban.com',
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'
}
# <span property="v:itemreviewed">肖申克的救赎 The Shawshank Redemption</span>
# <a href="/celebrity/1047973/" rel="v:directedBy">弗兰克·德拉邦特</a>
# <a href="/celebrity/1054521/" rel="v:starring">蒂姆·罗宾斯</a>
def base_urls(base_url):
urls = []
# 这里我们只能前两页做测试，所以range只设置到了50
# for i in range(0, 275, 25):
# true_url = base_url.format(i)
# print(true_url)
for i in range(0, 50, 25):
true_url = base_url.format(i)
print(true_url)
response = requests.get(true_url, headers=headers)
if response.status_code == 200:
# print(response.text)
pattern1 = re.compile('<div.*?class="item">.*?<div.*?class="pic">.*?<a.*?href="(.*?)">',re.S)
# 去掉所有换行符，并用正则表达式去匹配每一个页面的具体电影
url = re.findall(pattern1, response.text)
# 因为这里是用findall，他返回的是一个列表，如果我们直接append，会导致列表嵌套，故我们这里用个for循环提取出列表的元素再append进去
for i in url:
urls.append(i)
return urls
def parse_url(urls):
# 因为只拿前两页做测试，所以range设置到50
for i in range(0, 50, 1):
res = requests.get(urls[i], headers = headers_urls)
print(res)
if res.status_code == 200:
soup = BeautifulSoup(res.text, 'lxml')
# 爬取电影名
name = (soup.find('span', property="v:itemreviewed"))
names.append(name.text)
# print(names)
# 爬取导演
director = soup.find('a', rel="v:directedBy")
directors.append(director.text)
# print(director.text)
# 爬取明星
star_save = []
for star in soup.find_all('a', rel="v:starring"):
star_save.append(star.text)
stars.append(star_save)
# print(stars)
# 爬取制片国家
#<span class="pl">制片国家/地区:</span> 美国<br>
# 学到的知识点：通过匹配文本内容找下个兄弟节点
country = soup.find('span', text='制片国家/地区:').next_sibling[1:]
countrys.append(country)
# print(countrys)
# 爬取影片语言
# <span class="pl">语言:</span>
language = soup.find('span', text='语言:').next_sibling[1:]
languages.append(language)
# print(language)
# print(directors)
# print(true_director)
# print(a)
if __name__ == '__main__':
base = base_urls(base_url)
print(base)
print(len(base))
parse_url(base)
print(countrys)
print(directors)
print(languages)
print(names)
#
# 最后我们将数据写入到一个excel表格里
info ={'Filmname':names, 'Directors':directors, 'Country':countrys, 'Languages':languages}
pdfile = pandas.DataFrame(info)
# pdlook.to_excel('链家.xlsx', sheet_name="链家二手房广州")
pdfile.to_excel('DoubanFilm.xlsx', sheet_name="豆瓣电影")这次用到的还是requests库，BeautifulSoup解析库，和re进行辅助的正则匹配库，最后老样子利用pandas的DataFrame进行excel的写入。
