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')